diff --git a/mediapipe/graphs/edge_detection/BUILD b/mediapipe/graphs/edge_detection/BUILD
deleted file mode 100644
index fac2411..0000000
--- a/mediapipe/graphs/edge_detection/BUILD
+++ /dev/null
@@ -1,36 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "mobile_calculators",
-    deps = [
-        "//mediapipe/calculators/image:luminance_calculator",
-        "//mediapipe/calculators/image:sobel_edges_calculator",
-    ],
-)
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-mediapipe_binary_graph(
-    name = "mobile_gpu_binary_graph",
-    graph = "edge_detection_mobile_gpu.pbtxt",
-    output_name = "mobile_gpu.binarypb",
-)
diff --git a/mediapipe/graphs/edge_detection/edge_detection_mobile_gpu.pbtxt b/mediapipe/graphs/edge_detection/edge_detection_mobile_gpu.pbtxt
deleted file mode 100644
index e3c572e..0000000
--- a/mediapipe/graphs/edge_detection/edge_detection_mobile_gpu.pbtxt
+++ /dev/null
@@ -1,22 +0,0 @@
-# MediaPipe graph that performs GPU Sobel edge detection on a live video stream.
-# Used in the examples in
-# mediapipe/examples/android/src/java/com/google/mediapipe/apps/basic:helloworld
-# and mediapipe/examples/ios/helloworld.
-
-# Images coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Converts RGB images into luminance images, still stored in RGB format.
-node: {
-  calculator: "LuminanceCalculator"
-  input_stream: "input_video"
-  output_stream: "luma_video"
-}
-
-# Applies the Sobel filter to luminance images stored in RGB format.
-node: {
-  calculator: "SobelEdgesCalculator"
-  input_stream: "luma_video"
-  output_stream: "output_video"
-}
diff --git a/mediapipe/graphs/face_detection/BUILD b/mediapipe/graphs/face_detection/BUILD
deleted file mode 100644
index 9e7cf25..0000000
--- a/mediapipe/graphs/face_detection/BUILD
+++ /dev/null
@@ -1,95 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "mobile_calculators",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
-        "//mediapipe/gpu:image_frame_to_gpu_buffer_calculator",
-        "//mediapipe/modules/face_detection:face_detection_short_range_cpu",
-        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
-    ],
-)
-
-cc_library(
-    name = "desktop_live_calculators",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/modules/face_detection:face_detection_short_range_cpu",
-    ],
-)
-
-cc_library(
-    name = "desktop_live_gpu_calculators",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "face_detection_mobile_cpu_binary_graph",
-    graph = "face_detection_mobile_cpu.pbtxt",
-    output_name = "face_detection_mobile_cpu.binarypb",
-    deps = [":mobile_calculators"],
-)
-
-mediapipe_binary_graph(
-    name = "face_detection_mobile_gpu_binary_graph",
-    graph = "face_detection_mobile_gpu.pbtxt",
-    output_name = "face_detection_mobile_gpu.binarypb",
-    deps = [":mobile_calculators"],
-)
-
-cc_library(
-    name = "face_detection_full_range_mobile_gpu_deps",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/modules/face_detection:face_detection_full_range_gpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "face_detection_full_range_mobile_gpu_binary_graph",
-    graph = "face_detection_full_range_mobile_gpu.pbtxt",
-    output_name = "face_detection_full_range_mobile_gpu.binarypb",
-    deps = [":face_detection_full_range_mobile_gpu_deps"],
-)
-
-cc_library(
-    name = "face_detection_full_range_desktop_live_deps",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/modules/face_detection:face_detection_full_range_cpu",
-    ],
-)
diff --git a/mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt b/mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt
deleted file mode 100644
index 023bea9..0000000
--- a/mediapipe/graphs/face_detection/face_detection_desktop_live.pbtxt
+++ /dev/null
@@ -1,58 +0,0 @@
-# MediaPipe graph that performs face mesh with TensorFlow Lite on CPU.
-
-# CPU buffer. (ImageFrame)
-input_stream: "input_video"
-
-# Output image with rendered results. (ImageFrame)
-output_stream: "output_video"
-# Detected faces. (std::vector<Detection>)
-output_stream: "face_detections"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Subgraph that detects faces.
-node {
-  calculator: "FaceDetectionShortRangeCpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "DETECTIONS:face_detections"
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:face_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/face_detection/face_detection_full_range_desktop_live.pbtxt b/mediapipe/graphs/face_detection/face_detection_full_range_desktop_live.pbtxt
deleted file mode 100644
index 0fdb6b9..0000000
--- a/mediapipe/graphs/face_detection/face_detection_full_range_desktop_live.pbtxt
+++ /dev/null
@@ -1,60 +0,0 @@
-# MediaPipe graph that performs face detection with TensorFlow Lite on CPU.
-# Used in the examples in
-# mediapipe/examples/desktop/face_detection:face_detection_cpu.
-
-# Images on GPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for
-# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
-# generating the corresponding detections before it passes through another
-# image. All images that come in while waiting are dropped, limiting the number
-# of in-flight images between this calculator and
-# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
-# from queuing up incoming images and data excessively, which leads to increased
-# latency and memory usage, unwanted in real-time mobile applications. It also
-# eliminates unnecessarily computation, e.g., a transformed image produced by
-# ImageTransformationCalculator may get dropped downstream if the subsequent
-# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
-# processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:detections"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Detects faces.
-node {
-  calculator: "FaceDetectionFullRangeCpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "DETECTIONS:detections"
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE:output_video"
-}
-
diff --git a/mediapipe/graphs/face_detection/face_detection_full_range_mobile_gpu.pbtxt b/mediapipe/graphs/face_detection/face_detection_full_range_mobile_gpu.pbtxt
deleted file mode 100644
index 8224543..0000000
--- a/mediapipe/graphs/face_detection/face_detection_full_range_mobile_gpu.pbtxt
+++ /dev/null
@@ -1,60 +0,0 @@
-# MediaPipe graph that performs face detection with TensorFlow Lite on GPU.
-# Used in the examples in
-# mediapipie/examples/android/src/java/com/mediapipe/apps/facedetectiongpu and
-# mediapipie/examples/ios/facedetectiongpu.
-
-# Images on GPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for
-# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
-# generating the corresponding detections before it passes through another
-# image. All images that come in while waiting are dropped, limiting the number
-# of in-flight images between this calculator and
-# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
-# from queuing up incoming images and data excessively, which leads to increased
-# latency and memory usage, unwanted in real-time mobile applications. It also
-# eliminates unnecessarily computation, e.g., a transformed image produced by
-# ImageTransformationCalculator may get dropped downstream if the subsequent
-# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
-# processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Detects faces.
-node {
-  calculator: "FaceDetectionFullRangeGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "DETECTIONS:detections"
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE_GPU:output_video"
-}
diff --git a/mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt b/mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt
deleted file mode 100644
index 681d2db..0000000
--- a/mediapipe/graphs/face_detection/face_detection_mobile_cpu.pbtxt
+++ /dev/null
@@ -1,76 +0,0 @@
-# MediaPipe graph that performs face mesh with TensorFlow Lite on CPU.
-
-# GPU buffer. (GpuBuffer)
-input_stream: "input_video"
-
-# Output image with rendered results. (GpuBuffer)
-output_stream: "output_video"
-# Detected faces. (std::vector<Detection>)
-output_stream: "face_detections"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Transfers the input image from GPU to CPU memory for the purpose of
-# demonstrating a CPU-based pipeline. Note that the input image on GPU has the
-# origin defined at the bottom-left corner (OpenGL convention). As a result,
-# the transferred image on CPU also shares the same representation.
-node: {
-  calculator: "GpuBufferToImageFrameCalculator"
-  input_stream: "throttled_input_video"
-  output_stream: "input_video_cpu"
-}
-
-# Subgraph that detects faces.
-node {
-  calculator: "FaceDetectionShortRangeCpu"
-  input_stream: "IMAGE:input_video_cpu"
-  output_stream: "DETECTIONS:face_detections"
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:face_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_video_cpu"
-  input_stream: "render_data"
-  output_stream: "IMAGE:output_video_cpu"
-}
-
-# Transfers the annotated image from CPU back to GPU memory, to be sent out of
-# the graph.
-node: {
-  calculator: "ImageFrameToGpuBufferCalculator"
-  input_stream: "output_video_cpu"
-  output_stream: "output_video"
-}
diff --git a/mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt b/mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt
deleted file mode 100644
index d235d1c..0000000
--- a/mediapipe/graphs/face_detection/face_detection_mobile_gpu.pbtxt
+++ /dev/null
@@ -1,58 +0,0 @@
-# MediaPipe graph that performs face mesh with TensorFlow Lite on GPU.
-
-# GPU buffer. (GpuBuffer)
-input_stream: "input_video"
-
-# Output image with rendered results. (GpuBuffer)
-output_stream: "output_video"
-# Detected faces. (std::vector<Detection>)
-output_stream: "face_detections"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Subgraph that detects faces.
-node {
-  calculator: "FaceDetectionShortRangeGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "DETECTIONS:face_detections"
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:face_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE_GPU:output_video"
-}
diff --git a/mediapipe/graphs/face_effect/BUILD b/mediapipe/graphs/face_effect/BUILD
deleted file mode 100644
index 69d648e..0000000
--- a/mediapipe/graphs/face_effect/BUILD
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "face_effect_gpu_deps",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:immediate_mux_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/framework/tool:switch_container",
-        "//mediapipe/graphs/face_effect/subgraphs:single_face_geometry_from_detection_gpu",
-        "//mediapipe/graphs/face_effect/subgraphs:single_face_geometry_from_landmarks_gpu",
-        "//mediapipe/modules/face_geometry:effect_renderer_calculator",
-        "//mediapipe/modules/face_geometry:env_generator_calculator",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "face_effect_gpu_binary_graph",
-    graph = "face_effect_gpu.pbtxt",
-    output_name = "face_effect_gpu.binarypb",
-    deps = [":face_effect_gpu_deps"],
-)
diff --git a/mediapipe/graphs/face_effect/data/BUILD b/mediapipe/graphs/face_effect/data/BUILD
deleted file mode 100644
index 9993699..0000000
--- a/mediapipe/graphs/face_effect/data/BUILD
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//mediapipe/framework:encode_binary_proto.bzl", "encode_binary_proto")
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-encode_binary_proto(
-    name = "axis",
-    input = "axis.pbtxt",
-    message_type = "mediapipe.face_geometry.Mesh3d",
-    output = "axis.binarypb",
-    deps = [
-        "//mediapipe/modules/face_geometry/protos:mesh_3d_proto",
-    ],
-)
-
-encode_binary_proto(
-    name = "glasses",
-    input = "glasses.pbtxt",
-    message_type = "mediapipe.face_geometry.Mesh3d",
-    output = "glasses.binarypb",
-    deps = [
-        "//mediapipe/modules/face_geometry/protos:mesh_3d_proto",
-    ],
-)
-
-# `.pngblob` is used instead of `.png` to prevent iOS build from preprocessing the image.
-# OpenCV is unable to read a PNG file preprocessed by the iOS build.
-exports_files([
-    "axis.pngblob",
-    "facepaint.pngblob",
-    "glasses.pngblob",
-])
diff --git a/mediapipe/graphs/face_effect/data/axis.pbtxt b/mediapipe/graphs/face_effect/data/axis.pbtxt
deleted file mode 100644
index 6a3fd52..0000000
--- a/mediapipe/graphs/face_effect/data/axis.pbtxt
+++ /dev/null
@@ -1,320 +0,0 @@
-vertex_type: VERTEX_PT
-primitive_type: TRIANGLE
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diff --git a/mediapipe/graphs/face_effect/data/glasses.pngblob b/mediapipe/graphs/face_effect/data/glasses.pngblob
deleted file mode 100644
index 865584b..0000000
Binary files a/mediapipe/graphs/face_effect/data/glasses.pngblob and /dev/null differ
diff --git a/mediapipe/graphs/face_effect/face_effect_gpu.pbtxt b/mediapipe/graphs/face_effect/face_effect_gpu.pbtxt
deleted file mode 100644
index 40888d0..0000000
--- a/mediapipe/graphs/face_effect/face_effect_gpu.pbtxt
+++ /dev/null
@@ -1,130 +0,0 @@
-# MediaPipe graph that applies a face effect to the input video stream.
-
-# GPU buffer. (GpuBuffer)
-input_stream: "input_video"
-
-# An integer, which indicate which effect is selected. (int)
-#
-# If `selected_effect_id` is `0`, the Axis effect is selected.
-# If `selected_effect_id` is `1`, the Facepaint effect is selected.
-# If `selected_effect_id` is `2`, the Glasses effect is selected.
-#
-# No other values are allowed for `selected_effect_id`.
-input_stream: "selected_effect_id"
-
-# Indicates whether to use the face detection as the input source. (bool)
-#
-# If `true`, the face detection pipeline will be used to produce landmarks.
-# If `false`, the face landmark pipeline will be used to produce landmarks.
-input_side_packet: "use_face_detection_input_source"
-
-# Output image with rendered results. (GpuBuffer)
-output_stream: "output_video"
-
-# A list of geometry data for a single detected face.
-#
-# NOTE: there will not be an output packet in this stream for this particular
-# timestamp if none of faces detected.
-#
-# (std::vector<face_geometry::FaceGeometry>)
-output_stream: "multi_face_geometry"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Generates an environment that describes the current virtual scene.
-node {
-  calculator: "FaceGeometryEnvGeneratorCalculator"
-  output_side_packet: "ENVIRONMENT:environment"
-  node_options: {
-    [type.googleapis.com/mediapipe.FaceGeometryEnvGeneratorCalculatorOptions] {
-      environment: {
-        origin_point_location: TOP_LEFT_CORNER
-        perspective_camera: {
-          vertical_fov_degrees: 63.0  # 63 degrees
-          near: 1.0  # 1cm
-          far: 10000.0  # 100m
-        }
-      }
-    }
-  }
-}
-
-# Computes the face geometry for a single face. The input source is defined
-# through `use_face_detection_input_source`.
-node {
-  calculator: "SwitchContainer"
-  input_stream: "IMAGE:throttled_input_video"
-  input_side_packet: "ENABLE:use_face_detection_input_source"
-  input_side_packet: "ENVIRONMENT:environment"
-  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-  node_options: {
-    [type.googleapis.com/mediapipe.SwitchContainerOptions] {
-      contained_node: {
-        calculator: "SingleFaceGeometryFromLandmarksGpu"
-      }
-      contained_node: {
-        calculator: "SingleFaceGeometryFromDetectionGpu"
-      }
-    }
-  }
-}
-
-# Renders the selected effect based on `selected_effect_id`.
-node {
-  calculator: "SwitchContainer"
-  input_stream: "SELECT:selected_effect_id"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  input_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-  input_side_packet: "ENVIRONMENT:environment"
-  output_stream: "IMAGE_GPU:output_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.SwitchContainerOptions] {
-      contained_node: {
-        calculator: "FaceGeometryEffectRendererCalculator"
-        node_options: {
-          [type.googleapis.com/mediapipe.FaceGeometryEffectRendererCalculatorOptions] {
-            effect_texture_path: "mediapipe/graphs/face_effect/data/axis.pngblob"
-            effect_mesh_3d_path: "mediapipe/graphs/face_effect/data/axis.binarypb"
-          }
-        }
-      }
-      contained_node: {
-        calculator: "FaceGeometryEffectRendererCalculator"
-        node_options: {
-          [type.googleapis.com/mediapipe.FaceGeometryEffectRendererCalculatorOptions] {
-            effect_texture_path: "mediapipe/graphs/face_effect/data/facepaint.pngblob"
-          }
-        }
-      }
-      contained_node: {
-        calculator: "FaceGeometryEffectRendererCalculator"
-        node_options: {
-          [type.googleapis.com/mediapipe.FaceGeometryEffectRendererCalculatorOptions] {
-            effect_texture_path: "mediapipe/graphs/face_effect/data/glasses.pngblob"
-            effect_mesh_3d_path: "mediapipe/graphs/face_effect/data/glasses.binarypb"
-          }
-        }
-      }
-    }
-  }
-}
-
diff --git a/mediapipe/graphs/face_effect/subgraphs/BUILD b/mediapipe/graphs/face_effect/subgraphs/BUILD
deleted file mode 100644
index 0b23ad5..0000000
--- a/mediapipe/graphs/face_effect/subgraphs/BUILD
+++ /dev/null
@@ -1,61 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "face_landmarks_smoothing",
-    graph = "face_landmarks_smoothing.pbtxt",
-    register_as = "FaceLandmarksSmoothing",
-    deps = [
-        "//mediapipe/calculators/util:landmarks_smoothing_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "single_face_geometry_from_detection_gpu",
-    graph = "single_face_geometry_from_detection_gpu.pbtxt",
-    register_as = "SingleFaceGeometryFromDetectionGpu",
-    deps = [
-        ":face_landmarks_smoothing",
-        "//mediapipe/calculators/core:concatenate_detection_vector_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
-        "//mediapipe/modules/face_geometry:face_geometry_from_detection",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "single_face_geometry_from_landmarks_gpu",
-    graph = "single_face_geometry_from_landmarks_gpu.pbtxt",
-    register_as = "SingleFaceGeometryFromLandmarksGpu",
-    deps = [
-        ":face_landmarks_smoothing",
-        "//mediapipe/calculators/core:concatenate_vector_calculator",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/util:landmarks_smoothing_calculator",
-        "//mediapipe/modules/face_geometry:face_geometry_from_landmarks",
-        "//mediapipe/modules/face_landmark:face_landmark_front_gpu",
-    ],
-)
diff --git a/mediapipe/graphs/face_effect/subgraphs/face_landmarks_smoothing.pbtxt b/mediapipe/graphs/face_effect/subgraphs/face_landmarks_smoothing.pbtxt
deleted file mode 100644
index 3f565f5..0000000
--- a/mediapipe/graphs/face_effect/subgraphs/face_landmarks_smoothing.pbtxt
+++ /dev/null
@@ -1,24 +0,0 @@
-# MediaPipe subgraph that smoothes face landmarks.
-
-type: "FaceLandmarksSmoothing"
-
-input_stream: "NORM_LANDMARKS:landmarks"
-input_stream: "IMAGE_SIZE:input_image_size"
-output_stream: "NORM_FILTERED_LANDMARKS:filtered_landmarks"
-
-# Applies smoothing to a face landmark list. The filter options were handpicked
-# to achieve better visual results.
-node {
-  calculator: "LandmarksSmoothingCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks"
-  input_stream: "IMAGE_SIZE:input_image_size"
-  output_stream: "NORM_FILTERED_LANDMARKS:filtered_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksSmoothingCalculatorOptions] {
-      velocity_filter: {
-        window_size: 5
-        velocity_scale: 20.0
-      }
-    }
-  }
-}
diff --git a/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_detection_gpu.pbtxt b/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_detection_gpu.pbtxt
deleted file mode 100644
index bce72c1..0000000
--- a/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_detection_gpu.pbtxt
+++ /dev/null
@@ -1,91 +0,0 @@
-# MediaPipe subgraph that extracts geometry from a single face using the face
-# landmark pipeline on an input GPU image. The face landmarks are also
-# "smoothed" to achieve better visual results.
-
-type: "SingleFaceGeometryFromDetectionGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:input_image"
-
-# Environment that describes the current virtual scene.
-# (face_geometry::Environment)
-input_side_packet: "ENVIRONMENT:environment"
-
-# A list of geometry data for a single detected face. The size of this
-# collection is at most 1 because of the single-face use in this graph.
-# (std::vector<face_geometry::FaceGeometry>)
-#
-# NOTE: if no face is detected at a particular timestamp, there will not be an
-# output packet in the `MULTI_FACE_GEOMETRY` stream for this timestamp. However,
-# the MediaPipe framework will internally inform the downstream calculators of
-# the absence of this packet so that they don't wait for it unnecessarily.
-output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-
-# Subgraph that detects faces and corresponding landmarks using the face
-# detection pipeline.
-node {
-  calculator: "FaceDetectionShortRangeGpu"
-  input_stream: "IMAGE:input_image"
-  output_stream: "DETECTIONS:multi_face_detection"
-}
-
-# Extracts the first face detection associated with the most prominent face from
-# a collection.
-node {
-  calculator: "SplitDetectionVectorCalculator"
-  input_stream: "multi_face_detection"
-  output_stream: "face_detection"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Extracts face detection keypoints as a normalized landmarks.
-node {
-  calculator: "DetectionToLandmarksCalculator"
-  input_stream: "DETECTION:face_detection"
-  output_stream: "LANDMARKS:face_landmarks"
-}
-
-# Extracts the input image frame dimensions as a separate packet.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:input_image"
-  output_stream: "SIZE:input_image_size"
-}
-
-# Applies smoothing to the face landmarks previously extracted from the face
-# detection keypoints.
-node {
-  calculator: "FaceLandmarksSmoothing"
-  input_stream: "NORM_LANDMARKS:face_landmarks"
-  input_stream: "IMAGE_SIZE:input_image_size"
-  output_stream: "NORM_FILTERED_LANDMARKS:smoothed_face_landmarks"
-}
-
-# Converts smoothed face landmarks back into the detection format.
-node {
-  calculator: "LandmarksToDetectionCalculator"
-  input_stream: "NORM_LANDMARKS:smoothed_face_landmarks"
-  output_stream: "DETECTION:smoothed_face_detection"
-}
-
-# Puts the smoothed single face detection back into a collection to simplify
-# passing the result into the `FaceGeometryFromDetection` subgraph.
-node {
-  calculator: "ConcatenateDetectionVectorCalculator"
-  input_stream: "smoothed_face_detection"
-  output_stream: "multi_smoothed_face_detection"
-}
-
-# Computes face geometry from the single face detection.
-node {
-  calculator: "FaceGeometryFromDetection"
-  input_stream: "MULTI_FACE_DETECTION:multi_smoothed_face_detection"
-  input_stream: "IMAGE_SIZE:input_image_size"
-  input_side_packet: "ENVIRONMENT:environment"
-  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-}
diff --git a/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_landmarks_gpu.pbtxt b/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_landmarks_gpu.pbtxt
deleted file mode 100644
index 364e386..0000000
--- a/mediapipe/graphs/face_effect/subgraphs/single_face_geometry_from_landmarks_gpu.pbtxt
+++ /dev/null
@@ -1,89 +0,0 @@
-# MediaPipe subgraph that extracts geometry from a single face using the face
-# landmark pipeline on an input GPU image. The face landmarks are also
-# "smoothed" to achieve better visual results.
-
-type: "SingleFaceGeometryFromLandmarksGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:input_image"
-
-# Environment that describes the current virtual scene.
-# (face_geometry::Environment)
-input_side_packet: "ENVIRONMENT:environment"
-
-# A list of geometry data for a single detected face. The size of this
-# collection is at most 1 because of the single-face use in this graph.
-# (std::vector<face_geometry::FaceGeometry>)
-#
-# NOTE: if no face is detected at a particular timestamp, there will not be an
-# output packet in the `MULTI_FACE_GEOMETRY` stream for this timestamp. However,
-# the MediaPipe framework will internally inform the downstream calculators of
-# the absence of this packet so that they don't wait for it unnecessarily.
-output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-
-# Creates a packet to inform the `FaceLandmarkFrontGpu` subgraph to detect at
-# most 1 face.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:num_faces"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 1 }
-    }
-  }
-}
-
-# Subgraph that detects faces and corresponding landmarks using the face
-# landmark pipeline.
-node {
-  calculator: "FaceLandmarkFrontGpu"
-  input_stream: "IMAGE:input_image"
-  input_side_packet: "NUM_FACES:num_faces"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-}
-
-# Extracts a single set of face landmarks associated with the most prominent
-# face detected from a collection.
-node {
-  calculator: "SplitNormalizedLandmarkListVectorCalculator"
-  input_stream: "multi_face_landmarks"
-  output_stream: "face_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Extracts the input image frame dimensions as a separate packet.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:input_image"
-  output_stream: "SIZE:input_image_size"
-}
-
-# Applies smoothing to the single set of face landmarks.
-node {
-  calculator: "FaceLandmarksSmoothing"
-  input_stream: "NORM_LANDMARKS:face_landmarks"
-  input_stream: "IMAGE_SIZE:input_image_size"
-  output_stream: "NORM_FILTERED_LANDMARKS:smoothed_face_landmarks"
-}
-
-# Puts the single set of smoothed landmarks back into a collection to simplify
-# passing the result into the `FaceGeometryFromLandmarks` subgraph.
-node {
-  calculator: "ConcatenateLandmarListVectorCalculator"
-  input_stream: "smoothed_face_landmarks"
-  output_stream: "multi_smoothed_face_landmarks"
-}
-
-# Computes face geometry from face landmarks for a single face.
-node {
-  calculator: "FaceGeometryFromLandmarks"
-  input_stream: "MULTI_FACE_LANDMARKS:multi_smoothed_face_landmarks"
-  input_stream: "IMAGE_SIZE:input_image_size"
-  input_side_packet: "ENVIRONMENT:environment"
-  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-}
diff --git a/mediapipe/graphs/face_mesh/BUILD b/mediapipe/graphs/face_mesh/BUILD
deleted file mode 100644
index 6926fda..0000000
--- a/mediapipe/graphs/face_mesh/BUILD
+++ /dev/null
@@ -1,69 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "desktop_calculators",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
-        "//mediapipe/graphs/face_mesh/subgraphs:face_renderer_cpu",
-        "//mediapipe/modules/face_landmark:face_landmark_front_cpu",
-    ],
-)
-
-cc_library(
-    name = "desktop_live_calculators",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/graphs/face_mesh/subgraphs:face_renderer_cpu",
-        "//mediapipe/modules/face_landmark:face_landmark_front_cpu",
-    ],
-)
-
-cc_library(
-    name = "desktop_live_gpu_calculators",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/graphs/face_mesh/subgraphs:face_renderer_gpu",
-        "//mediapipe/modules/face_landmark:face_landmark_front_gpu",
-    ],
-)
-
-cc_library(
-    name = "mobile_calculators",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/graphs/face_mesh/subgraphs:face_renderer_gpu",
-        "//mediapipe/modules/face_landmark:face_landmark_front_gpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "face_mesh_mobile_gpu_binary_graph",
-    graph = "face_mesh_mobile.pbtxt",
-    output_name = "face_mesh_mobile_gpu.binarypb",
-    deps = [":mobile_calculators"],
-)
diff --git a/mediapipe/graphs/face_mesh/calculators/BUILD b/mediapipe/graphs/face_mesh/calculators/BUILD
deleted file mode 100644
index 3bebfc9..0000000
--- a/mediapipe/graphs/face_mesh/calculators/BUILD
+++ /dev/null
@@ -1,37 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "face_landmarks_to_render_data_calculator",
-    srcs = ["face_landmarks_to_render_data_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework:calculator_options_cc_proto",
-        "//mediapipe/framework/formats:landmark_cc_proto",
-        "//mediapipe/framework/formats:location_data_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/util:color_cc_proto",
-        "//mediapipe/util:render_data_cc_proto",
-        "@com_google_absl//absl/memory",
-        "@com_google_absl//absl/strings",
-    ],
-    alwayslink = 1,
-)
diff --git a/mediapipe/graphs/face_mesh/calculators/face_landmarks_to_render_data_calculator.cc b/mediapipe/graphs/face_mesh/calculators/face_landmarks_to_render_data_calculator.cc
deleted file mode 100644
index 093a732..0000000
--- a/mediapipe/graphs/face_mesh/calculators/face_landmarks_to_render_data_calculator.cc
+++ /dev/null
@@ -1,104 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "absl/memory/memory.h"
-#include "absl/strings/str_cat.h"
-#include "absl/strings/str_join.h"
-#include "mediapipe/calculators/util/landmarks_to_render_data_calculator.h"
-#include "mediapipe/calculators/util/landmarks_to_render_data_calculator.pb.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/calculator_options.pb.h"
-#include "mediapipe/framework/formats/landmark.pb.h"
-#include "mediapipe/framework/formats/location_data.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/util/color.pb.h"
-#include "mediapipe/util/render_data.pb.h"
-namespace mediapipe {
-
-namespace {
-
-constexpr int kNumFaceLandmarkConnections = 132;
-// Pairs of landmark indices to be rendered with connections.
-constexpr int kFaceLandmarkConnections[] = {
-    // Lips.
-    61, 146, 146, 91, 91, 181, 181, 84, 84, 17, 17, 314, 314, 405, 405, 321,
-    321, 375, 375, 291, 61, 185, 185, 40, 40, 39, 39, 37, 37, 0, 0, 267, 267,
-    269, 269, 270, 270, 409, 409, 291, 78, 95, 95, 88, 88, 178, 178, 87, 87, 14,
-    14, 317, 317, 402, 402, 318, 318, 324, 324, 308, 78, 191, 191, 80, 80, 81,
-    81, 82, 82, 13, 13, 312, 312, 311, 311, 310, 310, 415, 415, 308,
-    // Left eye.
-    33, 7, 7, 163, 163, 144, 144, 145, 145, 153, 153, 154, 154, 155, 155, 133,
-    33, 246, 246, 161, 161, 160, 160, 159, 159, 158, 158, 157, 157, 173, 173,
-    133,
-    // Left eyebrow.
-    46, 53, 53, 52, 52, 65, 65, 55, 70, 63, 63, 105, 105, 66, 66, 107,
-    // Left iris.
-    474, 475, 475, 476, 476, 477, 477, 474,
-    // Right eye.
-    263, 249, 249, 390, 390, 373, 373, 374, 374, 380, 380, 381, 381, 382, 382,
-    362, 263, 466, 466, 388, 388, 387, 387, 386, 386, 385, 385, 384, 384, 398,
-    398, 362,
-    // Right eyebrow.
-    276, 283, 283, 282, 282, 295, 295, 285, 300, 293, 293, 334, 334, 296, 296,
-    336,
-    // Right iris.
-    469, 470, 470, 471, 471, 472, 472, 469,
-    // Face oval.
-    10, 338, 338, 297, 297, 332, 332, 284, 284, 251, 251, 389, 389, 356, 356,
-    454, 454, 323, 323, 361, 361, 288, 288, 397, 397, 365, 365, 379, 379, 378,
-    378, 400, 400, 377, 377, 152, 152, 148, 148, 176, 176, 149, 149, 150, 150,
-    136, 136, 172, 172, 58, 58, 132, 132, 93, 93, 234, 234, 127, 127, 162, 162,
-    21, 21, 54, 54, 103, 103, 67, 67, 109, 109, 10};
-
-}  // namespace
-
-// A calculator that converts face landmarks to RenderData proto for
-// visualization. Ignores landmark_connections specified in
-// LandmarksToRenderDataCalculatorOptions, if any, and always uses a fixed set
-// of landmark connections specific to face landmark (defined in
-// kFaceLandmarkConnections[] above).
-//
-// Example config:
-// node {
-//   calculator: "FaceLandmarksToRenderDataCalculator"
-//   input_stream: "NORM_LANDMARKS:landmarks"
-//   output_stream: "RENDER_DATA:render_data"
-//   options {
-//     [LandmarksToRenderDataCalculatorOptions.ext] {
-//       landmark_color { r: 0 g: 255 b: 0 }
-//       connection_color { r: 0 g: 255 b: 0 }
-//       thickness: 4.0
-//     }
-//   }
-// }
-class FaceLandmarksToRenderDataCalculator
-    : public LandmarksToRenderDataCalculator {
- public:
-  absl::Status Open(CalculatorContext* cc) override;
-};
-REGISTER_CALCULATOR(FaceLandmarksToRenderDataCalculator);
-
-absl::Status FaceLandmarksToRenderDataCalculator::Open(CalculatorContext* cc) {
-  cc->SetOffset(TimestampDiff(0));
-  options_ = cc->Options<mediapipe::LandmarksToRenderDataCalculatorOptions>();
-
-  for (int i = 0; i < kNumFaceLandmarkConnections; ++i) {
-    landmark_connections_.push_back(kFaceLandmarkConnections[i * 2]);
-    landmark_connections_.push_back(kFaceLandmarkConnections[i * 2 + 1]);
-  }
-
-  return absl::OkStatus();
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/graphs/face_mesh/face_mesh_desktop.pbtxt b/mediapipe/graphs/face_mesh/face_mesh_desktop.pbtxt
deleted file mode 100644
index 215791a..0000000
--- a/mediapipe/graphs/face_mesh/face_mesh_desktop.pbtxt
+++ /dev/null
@@ -1,70 +0,0 @@
-# MediaPipe graph that performs face mesh on desktop with TensorFlow Lite
-# on CPU.
-
-# Path to the input video file. (string)
-input_side_packet: "input_video_path"
-# Path to the output video file. (string)
-input_side_packet: "output_video_path"
-
-# max_queue_size limits the number of packets enqueued on any input stream
-# by throttling inputs to the graph. This makes the graph only process one
-# frame per time.
-max_queue_size: 1
-
-# Decodes an input video file into images and a video header.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:input_video"
-  output_stream: "VIDEO_PRESTREAM:input_video_header"
-}
-
-# Defines side packets for further use in the graph.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:0:num_faces"
-  output_side_packet: "PACKET:1:with_attention"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 1 }
-      packet { bool_value: true }
-    }
-  }
-}
-
-# Subgraph that detects faces and corresponding landmarks.
-node {
-  calculator: "FaceLandmarkFrontCpu"
-  input_stream: "IMAGE:input_video"
-  input_side_packet: "NUM_FACES:num_faces"
-  input_side_packet: "WITH_ATTENTION:with_attention"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-  output_stream: "DETECTIONS:face_detections"
-  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-}
-
-# Subgraph that renders face-landmark annotation onto the input video.
-node {
-  calculator: "FaceRendererCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "LANDMARKS:multi_face_landmarks"
-  input_stream: "NORM_RECTS:face_rects_from_landmarks"
-  input_stream: "DETECTIONS:face_detections"
-  output_stream: "IMAGE:output_video"
-}
-
-# Encodes the annotated images into a video file, adopting properties specified
-# in the input video header, e.g., video framerate.
-node {
-  calculator: "OpenCvVideoEncoderCalculator"
-  input_stream: "VIDEO:output_video"
-  input_stream: "VIDEO_PRESTREAM:input_video_header"
-  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
-      codec: "avc1"
-      video_format: "mp4"
-    }
-  }
-}
diff --git a/mediapipe/graphs/face_mesh/face_mesh_desktop_live.pbtxt b/mediapipe/graphs/face_mesh/face_mesh_desktop_live.pbtxt
deleted file mode 100644
index 2cc5634..0000000
--- a/mediapipe/graphs/face_mesh/face_mesh_desktop_live.pbtxt
+++ /dev/null
@@ -1,66 +0,0 @@
-# MediaPipe graph that performs face mesh with TensorFlow Lite on CPU.
-
-# Input image. (ImageFrame)
-input_stream: "input_video"
-
-# Output image with rendered results. (ImageFrame)
-output_stream: "output_video"
-# Collection of detected/processed faces, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-output_stream: "multi_face_landmarks"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Defines side packets for further use in the graph.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:0:num_faces"
-  output_side_packet: "PACKET:1:with_attention"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 1 }
-      packet { bool_value: true }
-    }
-  }
-}
-
-# Subgraph that detects faces and corresponding landmarks.
-node {
-  calculator: "FaceLandmarkFrontCpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_side_packet: "NUM_FACES:num_faces"
-  input_side_packet: "WITH_ATTENTION:with_attention"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-  output_stream: "DETECTIONS:face_detections"
-  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-}
-
-# Subgraph that renders face-landmark annotation onto the input image.
-node {
-  calculator: "FaceRendererCpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "LANDMARKS:multi_face_landmarks"
-  input_stream: "NORM_RECTS:face_rects_from_landmarks"
-  input_stream: "DETECTIONS:face_detections"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/face_mesh/face_mesh_desktop_live_gpu.pbtxt b/mediapipe/graphs/face_mesh/face_mesh_desktop_live_gpu.pbtxt
deleted file mode 100644
index ae03709..0000000
--- a/mediapipe/graphs/face_mesh/face_mesh_desktop_live_gpu.pbtxt
+++ /dev/null
@@ -1,66 +0,0 @@
-# MediaPipe graph that performs face mesh with TensorFlow Lite on GPU.
-
-# Input image. (GpuBuffer)
-input_stream: "input_video"
-
-# Output image with rendered results. (GpuBuffer)
-output_stream: "output_video"
-# Collection of detected/processed faces, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-output_stream: "multi_face_landmarks"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Defines side packets for further use in the graph.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:0:num_faces"
-  output_side_packet: "PACKET:1:with_attention"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 1 }
-      packet { bool_value: true }
-    }
-  }
-}
-
-# Subgraph that detects faces and corresponding landmarks.
-node {
-  calculator: "FaceLandmarkFrontGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_side_packet: "NUM_FACES:num_faces"
-  input_side_packet: "WITH_ATTENTION:with_attention"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-  output_stream: "DETECTIONS:face_detections"
-  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-}
-
-# Subgraph that renders face-landmark annotation onto the input image.
-node {
-  calculator: "FaceRendererGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "LANDMARKS:multi_face_landmarks"
-  input_stream: "NORM_RECTS:face_rects_from_landmarks"
-  input_stream: "DETECTIONS:face_detections"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/face_mesh/face_mesh_mobile.pbtxt b/mediapipe/graphs/face_mesh/face_mesh_mobile.pbtxt
deleted file mode 100644
index e9711e1..0000000
--- a/mediapipe/graphs/face_mesh/face_mesh_mobile.pbtxt
+++ /dev/null
@@ -1,67 +0,0 @@
-# MediaPipe graph that performs face mesh with TensorFlow Lite on GPU.
-
-# GPU buffer. (GpuBuffer)
-input_stream: "input_video"
-
-# Max number of faces to detect/process. (int)
-input_side_packet: "num_faces"
-
-# Output image with rendered results. (GpuBuffer)
-output_stream: "output_video"
-# Collection of detected/processed faces, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-output_stream: "multi_face_landmarks"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Defines side packets for further use in the graph.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:with_attention"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { bool_value: true }
-    }
-  }
-}
-
-# Subgraph that detects faces and corresponding landmarks.
-node {
-  calculator: "FaceLandmarkFrontGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_side_packet: "NUM_FACES:num_faces"
-  input_side_packet: "WITH_ATTENTION:with_attention"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-  output_stream: "DETECTIONS:face_detections"
-  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-}
-
-# Subgraph that renders face-landmark annotation onto the input image.
-node {
-  calculator: "FaceRendererGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "LANDMARKS:multi_face_landmarks"
-  input_stream: "NORM_RECTS:face_rects_from_landmarks"
-  input_stream: "DETECTIONS:face_detections"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/face_mesh/subgraphs/BUILD b/mediapipe/graphs/face_mesh/subgraphs/BUILD
deleted file mode 100644
index fbb946d..0000000
--- a/mediapipe/graphs/face_mesh/subgraphs/BUILD
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "renderer_calculators",
-    deps = [
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_data_calculator",
-        "//mediapipe/graphs/face_mesh/calculators:face_landmarks_to_render_data_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_renderer_gpu",
-    graph = "face_renderer_gpu.pbtxt",
-    register_as = "FaceRendererGpu",
-    deps = [
-        ":renderer_calculators",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_renderer_cpu",
-    graph = "face_renderer_cpu.pbtxt",
-    register_as = "FaceRendererCpu",
-    deps = [
-        ":renderer_calculators",
-    ],
-)
diff --git a/mediapipe/graphs/face_mesh/subgraphs/face_renderer_cpu.pbtxt b/mediapipe/graphs/face_mesh/subgraphs/face_renderer_cpu.pbtxt
deleted file mode 100644
index f5793f3..0000000
--- a/mediapipe/graphs/face_mesh/subgraphs/face_renderer_cpu.pbtxt
+++ /dev/null
@@ -1,96 +0,0 @@
-# MediaPipe face mesh rendering subgraph.
-
-type: "FaceRendererCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:input_image"
-# Collection of detected/predicted faces, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-input_stream: "LANDMARKS:multi_face_landmarks"
-# Regions of interest calculated based on palm detections.
-# (std::vector<NormalizedRect>)
-input_stream: "NORM_RECTS:rects"
-# Detected palms. (std::vector<Detection>)
-input_stream: "DETECTIONS:detections"
-
-# CPU image with rendered data. (ImageFrame)
-output_stream: "IMAGE:output_image"
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:input_image"
-  output_stream: "SIZE:image_size"
-}
-
-# Converts detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:detections"
-  output_stream: "RENDER_DATA:detections_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-# Outputs each element of multi_face_landmarks at a fake timestamp for the rest
-# of the graph to process. At the end of the loop, outputs the BATCH_END
-# timestamp for downstream calculators to inform them that all elements in the
-# vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITERABLE:multi_face_landmarks"
-  output_stream: "ITEM:face_landmarks"
-  output_stream: "BATCH_END:landmark_timestamp"
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "FaceLandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:face_landmarks"
-  output_stream: "RENDER_DATA:landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 0 g: 255 b: 0 }
-      thickness: 2
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-# Collects a RenderData object for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of RenderData at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopRenderDataCalculator"
-  input_stream: "ITEM:landmarks_render_data"
-  input_stream: "BATCH_END:landmark_timestamp"
-  output_stream: "ITERABLE:multi_face_landmarks_render_data"
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECTS:rects"
-  output_stream: "RENDER_DATA:rects_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_image"
-  input_stream: "detections_render_data"
-  input_stream: "VECTOR:0:multi_face_landmarks_render_data"
-  input_stream: "rects_render_data"
-  output_stream: "IMAGE:output_image"
-}
diff --git a/mediapipe/graphs/face_mesh/subgraphs/face_renderer_gpu.pbtxt b/mediapipe/graphs/face_mesh/subgraphs/face_renderer_gpu.pbtxt
deleted file mode 100644
index 4e2b3f2..0000000
--- a/mediapipe/graphs/face_mesh/subgraphs/face_renderer_gpu.pbtxt
+++ /dev/null
@@ -1,96 +0,0 @@
-# MediaPipe face mesh rendering subgraph.
-
-type: "FaceRendererGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:input_image"
-# Collection of detected/predicted faces, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-input_stream: "LANDMARKS:multi_face_landmarks"
-# Regions of interest calculated based on palm detections.
-# (std::vector<NormalizedRect>)
-input_stream: "NORM_RECTS:rects"
-# Detected palms. (std::vector<Detection>)
-input_stream: "DETECTIONS:detections"
-
-# GPU image with rendered data. (GpuBuffer)
-output_stream: "IMAGE:output_image"
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:input_image"
-  output_stream: "SIZE:image_size"
-}
-
-# Converts detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:detections"
-  output_stream: "RENDER_DATA:detections_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-# Outputs each element of multi_face_landmarks at a fake timestamp for the rest
-# of the graph to process. At the end of the loop, outputs the BATCH_END
-# timestamp for downstream calculators to inform them that all elements in the
-# vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITERABLE:multi_face_landmarks"
-  output_stream: "ITEM:face_landmarks"
-  output_stream: "BATCH_END:end_timestamp"
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "FaceLandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:face_landmarks"
-  output_stream: "RENDER_DATA:landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 0 g: 255 b: 0 }
-      thickness: 2
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-# Collects a RenderData object for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of RenderData at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopRenderDataCalculator"
-  input_stream: "ITEM:landmarks_render_data"
-  input_stream: "BATCH_END:end_timestamp"
-  output_stream: "ITERABLE:multi_face_landmarks_render_data"
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECTS:rects"
-  output_stream: "RENDER_DATA:rects_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:input_image"
-  input_stream: "detections_render_data"
-  input_stream: "VECTOR:0:multi_face_landmarks_render_data"
-  input_stream: "rects_render_data"
-  output_stream: "IMAGE_GPU:output_image"
-}
diff --git a/mediapipe/graphs/hair_segmentation/BUILD b/mediapipe/graphs/hair_segmentation/BUILD
deleted file mode 100644
index b177726..0000000
--- a/mediapipe/graphs/hair_segmentation/BUILD
+++ /dev/null
@@ -1,61 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "mobile_calculators",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/image:recolor_calculator",
-        "//mediapipe/calculators/image:set_alpha_calculator",
-        "//mediapipe/calculators/tflite:tflite_converter_calculator",
-        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
-        "//mediapipe/calculators/tflite:tflite_inference_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_segmentation_calculator",
-        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
-        "//mediapipe/gpu:image_frame_to_gpu_buffer_calculator",
-    ],
-)
-
-cc_library(
-    name = "desktop_calculators",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/image:recolor_calculator",
-        "//mediapipe/calculators/image:set_alpha_calculator",
-        "//mediapipe/calculators/tflite:tflite_converter_calculator",
-        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
-        "//mediapipe/calculators/tflite:tflite_inference_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_segmentation_calculator",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "mobile_gpu_binary_graph",
-    graph = "hair_segmentation_mobile_gpu.pbtxt",
-    output_name = "mobile_gpu.binarypb",
-    deps = [":mobile_calculators"],
-)
diff --git a/mediapipe/graphs/hair_segmentation/hair_segmentation_desktop_live.pbtxt b/mediapipe/graphs/hair_segmentation/hair_segmentation_desktop_live.pbtxt
deleted file mode 100644
index 36c6970..0000000
--- a/mediapipe/graphs/hair_segmentation/hair_segmentation_desktop_live.pbtxt
+++ /dev/null
@@ -1,152 +0,0 @@
-# MediaPipe graph that performs hair segmentation with TensorFlow Lite on CPU.
-# Used in the example in
-# mediapipie/examples/desktop/hair_segmentation:hair_segmentation_cpu
-
-# Images on CPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for
-# TfLiteTensorsToSegmentationCalculator downstream in the graph to finish
-# generating the corresponding hair mask before it passes through another
-# image. All images that come in while waiting are dropped, limiting the number
-# of in-flight images between this calculator and
-# TfLiteTensorsToSegmentationCalculator to 1. This prevents the nodes in between
-# from queuing up incoming images and data excessively, which leads to increased
-# latency and memory usage, unwanted in real-time mobile applications. It also
-# eliminates unnecessarily computation, e.g., a transformed image produced by
-# ImageTransformationCalculator may get dropped downstream if the subsequent
-# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
-# processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:hair_mask"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Transforms the input image on CPU to a 512x512 image. To scale the image, by
-# default it uses the STRETCH scale mode that maps the entire input image to the
-# entire transformed image. As a result, image aspect ratio may be changed and
-# objects in the image may be deformed (stretched or squeezed), but the hair
-# segmentation model used in this graph is agnostic to that deformation.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "IMAGE:transformed_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 512
-      output_height: 512
-    }
-  }
-}
-
-# Caches a mask fed back from the previous round of hair segmentation, and upon
-# the arrival of the next input image sends out the cached mask with the
-# timestamp replaced by that of the input image, essentially generating a packet
-# that carries the previous mask. Note that upon the arrival of the very first
-# input image, an empty packet is sent out to jump start the feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:throttled_input_video"
-  input_stream: "LOOP:hair_mask"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:previous_hair_mask"
-}
-
-# Embeds the hair mask generated from the previous round of hair segmentation
-# as the alpha channel of the current input image.
-node {
-  calculator: "SetAlphaCalculator"
-  input_stream: "IMAGE:transformed_input_video"
-  input_stream: "ALPHA:previous_hair_mask"
-  output_stream: "IMAGE:mask_embedded_input_video"
-}
-
-# Converts the transformed input image on CPU into an image tensor stored in
-# TfLiteTensor. The zero_center option is set to false to normalize the
-# pixel values to [0.f, 1.f] as opposed to [-1.f, 1.f]. With the
-# max_num_channels option set to 4, all 4 RGBA channels are contained in the
-# image tensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE:mask_embedded_input_video"
-  output_stream: "TENSORS:image_tensor"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteConverterCalculatorOptions] {
-      zero_center: false
-      max_num_channels: 4
-    }
-  }
-}
-
-# Generates a single side packet containing a TensorFlow Lite op resolver that
-# supports custom ops needed by the model used in this graph.
-node {
-  calculator: "TfLiteCustomOpResolverCalculator"
-  output_side_packet: "op_resolver"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteCustomOpResolverCalculatorOptions] {
-      use_gpu: false
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# tensor representing the hair segmentation, which has the same width and height
-# as the input image tensor.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  output_stream: "TENSORS:segmentation_tensor"
-  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/hair_segmentation.tflite"
-      use_gpu: false
-    }
-  }
-}
-
-# Decodes the segmentation tensor generated by the TensorFlow Lite model into a
-# mask of values in [0, 255], stored in a CPU buffer. It also
-# takes the mask generated previously as another input to improve the temporal
-# consistency.
-node {
-  calculator: "TfLiteTensorsToSegmentationCalculator"
-  input_stream: "TENSORS:segmentation_tensor"
-  input_stream: "PREV_MASK:previous_hair_mask"
-  output_stream: "MASK:hair_mask"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteTensorsToSegmentationCalculatorOptions] {
-      tensor_width: 512
-      tensor_height: 512
-      tensor_channels: 2
-      combine_with_previous_ratio: 0.9
-      output_layer_index: 1
-    }
-  }
-}
-
-# Colors the hair segmentation with the color specified in the option.
-node {
-  calculator: "RecolorCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "MASK:hair_mask"
-  output_stream: "IMAGE:output_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
-      color { r: 0 g: 0 b: 255 }
-      mask_channel: RED
-    }
-  }
-}
diff --git a/mediapipe/graphs/hair_segmentation/hair_segmentation_mobile_gpu.pbtxt b/mediapipe/graphs/hair_segmentation/hair_segmentation_mobile_gpu.pbtxt
deleted file mode 100644
index c8db44d..0000000
--- a/mediapipe/graphs/hair_segmentation/hair_segmentation_mobile_gpu.pbtxt
+++ /dev/null
@@ -1,152 +0,0 @@
-# MediaPipe graph that performs hair segmentation with TensorFlow Lite on GPU.
-# Used in the example in
-# mediapipie/examples/android/src/java/com/mediapipe/apps/hairsegmentationgpu.
-
-# Images on GPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for
-# TfLiteTensorsToSegmentationCalculator downstream in the graph to finish
-# generating the corresponding hair mask before it passes through another
-# image. All images that come in while waiting are dropped, limiting the number
-# of in-flight images between this calculator and
-# TfLiteTensorsToSegmentationCalculator to 1. This prevents the nodes in between
-# from queuing up incoming images and data excessively, which leads to increased
-# latency and memory usage, unwanted in real-time mobile applications. It also
-# eliminates unnecessarily computation, e.g., a transformed image produced by
-# ImageTransformationCalculator may get dropped downstream if the subsequent
-# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
-# processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:hair_mask"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Transforms the input image on GPU to a 512x512 image. To scale the image, by
-# default it uses the STRETCH scale mode that maps the entire input image to the
-# entire transformed image. As a result, image aspect ratio may be changed and
-# objects in the image may be deformed (stretched or squeezed), but the hair
-# segmentation model used in this graph is agnostic to that deformation.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  output_stream: "IMAGE_GPU:transformed_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 512
-      output_height: 512
-    }
-  }
-}
-
-# Caches a mask fed back from the previous round of hair segmentation, and upon
-# the arrival of the next input image sends out the cached mask with the
-# timestamp replaced by that of the input image, essentially generating a packet
-# that carries the previous mask. Note that upon the arrival of the very first
-# input image, an empty packet is sent out to jump start the feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:throttled_input_video"
-  input_stream: "LOOP:hair_mask"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:previous_hair_mask"
-}
-
-# Embeds the hair mask generated from the previous round of hair segmentation
-# as the alpha channel of the current input image.
-node {
-  calculator: "SetAlphaCalculator"
-  input_stream: "IMAGE_GPU:transformed_input_video"
-  input_stream: "ALPHA_GPU:previous_hair_mask"
-  output_stream: "IMAGE_GPU:mask_embedded_input_video"
-}
-
-# Converts the transformed input image on GPU into an image tensor stored in
-# tflite::gpu::GlBuffer. The zero_center option is set to false to normalize the
-# pixel values to [0.f, 1.f] as opposed to [-1.f, 1.f]. With the
-# max_num_channels option set to 4, all 4 RGBA channels are contained in the
-# image tensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE_GPU:mask_embedded_input_video"
-  output_stream: "TENSORS_GPU:image_tensor"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteConverterCalculatorOptions] {
-      zero_center: false
-      max_num_channels: 4
-    }
-  }
-}
-
-# Generates a single side packet containing a TensorFlow Lite op resolver that
-# supports custom ops needed by the model used in this graph.
-node {
-  calculator: "TfLiteCustomOpResolverCalculator"
-  output_side_packet: "op_resolver"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteCustomOpResolverCalculatorOptions] {
-      use_gpu: true
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# tensor representing the hair segmentation, which has the same width and height
-# as the input image tensor.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS_GPU:image_tensor"
-  output_stream: "TENSORS_GPU:segmentation_tensor"
-  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/hair_segmentation.tflite"
-      use_gpu: true
-    }
-  }
-}
-
-# Decodes the segmentation tensor generated by the TensorFlow Lite model into a
-# mask of values in [0.f, 1.f], stored in the R channel of a GPU buffer. It also
-# takes the mask generated previously as another input to improve the temporal
-# consistency.
-node {
-  calculator: "TfLiteTensorsToSegmentationCalculator"
-  input_stream: "TENSORS_GPU:segmentation_tensor"
-  input_stream: "PREV_MASK_GPU:previous_hair_mask"
-  output_stream: "MASK_GPU:hair_mask"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteTensorsToSegmentationCalculatorOptions] {
-      tensor_width: 512
-      tensor_height: 512
-      tensor_channels: 2
-      combine_with_previous_ratio: 0.9
-      output_layer_index: 1
-    }
-  }
-}
-
-# Colors the hair segmentation with the color specified in the option.
-node {
-  calculator: "RecolorCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  input_stream: "MASK_GPU:hair_mask"
-  output_stream: "IMAGE_GPU:output_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
-      color { r: 0 g: 0 b: 255 }
-      mask_channel: RED
-    }
-  }
-}
diff --git a/mediapipe/graphs/hand_tracking/BUILD b/mediapipe/graphs/hand_tracking/BUILD
deleted file mode 100644
index 71525bb..0000000
--- a/mediapipe/graphs/hand_tracking/BUILD
+++ /dev/null
@@ -1,91 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-exports_files(glob([
-    "*.pbtxt",
-]))
-
-cc_library(
-    name = "desktop_offline_calculators",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:immediate_mux_calculator",
-        "//mediapipe/calculators/core:packet_inner_join_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
-    ],
-)
-
-cc_library(
-    name = "desktop_tflite_calculators",
-    deps = [
-        ":desktop_offline_calculators",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:merge_calculator",
-        "//mediapipe/graphs/hand_tracking/subgraphs:hand_renderer_cpu",
-        "//mediapipe/modules/hand_landmark:hand_landmark_tracking_cpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "hand_tracking_desktop_live_binary_graph",
-    graph = "hand_tracking_desktop_live.pbtxt",
-    output_name = "hand_tracking_desktop_live.binarypb",
-    deps = [":desktop_tflite_calculators"],
-)
-
-cc_library(
-    name = "mobile_calculators",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/graphs/hand_tracking/subgraphs:hand_renderer_gpu",
-        "//mediapipe/modules/hand_landmark:hand_landmark_tracking_gpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "hand_tracking_mobile_gpu_binary_graph",
-    graph = "hand_tracking_mobile.pbtxt",
-    output_name = "hand_tracking_mobile_gpu.binarypb",
-    deps = [":mobile_calculators"],
-)
-
-cc_library(
-    name = "detection_mobile_calculators",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/modules/palm_detection:palm_detection_gpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "hand_detection_mobile_gpu_binary_graph",
-    graph = "hand_detection_mobile.pbtxt",
-    output_name = "hand_detection_mobile_gpu.binarypb",
-    deps = [":detection_mobile_calculators"],
-)
diff --git a/mediapipe/graphs/hand_tracking/calculators/BUILD b/mediapipe/graphs/hand_tracking/calculators/BUILD
deleted file mode 100644
index 3d15861..0000000
--- a/mediapipe/graphs/hand_tracking/calculators/BUILD
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
diff --git a/mediapipe/graphs/hand_tracking/hand_detection_desktop.pbtxt b/mediapipe/graphs/hand_tracking/hand_detection_desktop.pbtxt
deleted file mode 100644
index 3edcfe7..0000000
--- a/mediapipe/graphs/hand_tracking/hand_detection_desktop.pbtxt
+++ /dev/null
@@ -1,61 +0,0 @@
-# MediaPipe graph that performs hand detection on desktop with TensorFlow Lite
-# on CPU.
-# Used in the example in
-# mediapipie/examples/desktop/hand_tracking:hand_detection_tflite.
-
-# max_queue_size limits the number of packets enqueued on any input stream
-# by throttling inputs to the graph. This makes the graph only process one
-# frame per time.
-max_queue_size: 1
-
-# Decodes an input video file into images and a video header.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:input_video"
-  output_stream: "VIDEO_PRESTREAM:input_video_header"
-}
-
-# Detects palms.
-node {
-  calculator: "PalmDetectionCpu"
-  input_stream: "IMAGE:input_video"
-  output_stream: "DETECTIONS:output_detections"
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the original image coming into
-# the graph.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE:output_video"
-}
-
-# Encodes the annotated images into a video file, adopting properties specified
-# in the input video header, e.g., video framerate.
-node {
-  calculator: "OpenCvVideoEncoderCalculator"
-  input_stream: "VIDEO:output_video"
-  input_stream: "VIDEO_PRESTREAM:input_video_header"
-  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
-      codec: "avc1"
-      video_format: "mp4"
-    }
-  }
-}
diff --git a/mediapipe/graphs/hand_tracking/hand_detection_desktop_live.pbtxt b/mediapipe/graphs/hand_tracking/hand_detection_desktop_live.pbtxt
deleted file mode 100644
index 1bbd8bc..0000000
--- a/mediapipe/graphs/hand_tracking/hand_detection_desktop_live.pbtxt
+++ /dev/null
@@ -1,39 +0,0 @@
-# MediaPipe graph that performs hand detection on desktop with TensorFlow Lite
-# on CPU.
-# Used in the example in
-# mediapipe/examples/desktop/hand_tracking:hand_detection_cpu.
-
-# CPU image. (ImageFrame)
-input_stream: "input_video"
-
-# CPU image. (ImageFrame)
-output_stream: "output_video"
-
-# Detects palms.
-node {
-  calculator: "PalmDetectionCpu"
-  input_stream: "IMAGE:input_video"
-  output_stream: "DETECTIONS:output_detections"
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the original image coming into
-# the graph.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/hand_tracking/hand_detection_mobile.pbtxt b/mediapipe/graphs/hand_tracking/hand_detection_mobile.pbtxt
deleted file mode 100644
index 74ff5c5..0000000
--- a/mediapipe/graphs/hand_tracking/hand_detection_mobile.pbtxt
+++ /dev/null
@@ -1,59 +0,0 @@
-# MediaPipe graph that performs hand detection with TensorFlow Lite on GPU.
-# Used in the examples in
-# mediapipe/examples/android/src/java/com/mediapipe/apps/handdetectiongpu and
-# mediapipe/examples/ios/handdetectiongpu.
-
-# GPU image. (GpuBuffer)
-input_stream: "input_video"
-
-# GPU image. (GpuBuffer)
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for PalmDetectionGpu
-# downstream in the graph to finish its tasks before it passes through another
-# image. All images that come in while waiting are dropped, limiting the number
-# of in-flight images in PalmDetectionGpu to 1. This prevents the nodes in
-# PalmDetectionGpu from queuing up incoming images and data excessively, which
-# leads to increased latency and memory usage, unwanted in real-time mobile
-# applications. It also eliminates unnecessarily computation, e.g., the output
-# produced by a node in the subgraph may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Detects palms.
-node {
-  calculator: "PalmDetectionGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "DETECTIONS:palm_detections"
-}
-
-# Converts detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:palm_detections"
-  output_stream: "RENDER_DATA:detection_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  input_stream: "detection_render_data"
-  output_stream: "IMAGE_GPU:output_video"
-}
diff --git a/mediapipe/graphs/hand_tracking/hand_tracking_desktop.pbtxt b/mediapipe/graphs/hand_tracking/hand_tracking_desktop.pbtxt
deleted file mode 100644
index bc6e81c..0000000
--- a/mediapipe/graphs/hand_tracking/hand_tracking_desktop.pbtxt
+++ /dev/null
@@ -1,68 +0,0 @@
-# MediaPipe graph that performs hands tracking on desktop with TensorFlow Lite
-# on CPU.
-# Used in the example in
-# mediapipe/examples/desktop/hand_tracking:hand_tracking_tflite.
-
-# max_queue_size limits the number of packets enqueued on any input stream
-# by throttling inputs to the graph. This makes the graph only process one
-# frame per time.
-max_queue_size: 1
-
-# Decodes an input video file into images and a video header.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:input_video"
-  output_stream: "VIDEO_PRESTREAM:input_video_header"
-}
-
-# Generates side packet cotaining max number of hands to detect/track.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:num_hands"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 2 }
-    }
-  }
-}
-
-# Detects/tracks hand landmarks.
-node {
-  calculator: "HandLandmarkTrackingCpu"
-  input_stream: "IMAGE:input_video"
-  input_side_packet: "NUM_HANDS:num_hands"
-  output_stream: "LANDMARKS:landmarks"
-  output_stream: "HANDEDNESS:handedness"
-  output_stream: "PALM_DETECTIONS:multi_palm_detections"
-  output_stream: "HAND_ROIS_FROM_LANDMARKS:multi_hand_rects"
-  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:multi_palm_rects"
-}
-
-# Subgraph that renders annotations and overlays them on top of the input
-# images (see hand_renderer_cpu.pbtxt).
-node {
-  calculator: "HandRendererSubgraph"
-  input_stream: "IMAGE:input_video"
-  input_stream: "DETECTIONS:multi_palm_detections"
-  input_stream: "LANDMARKS:landmarks"
-  input_stream: "HANDEDNESS:handedness"
-  input_stream: "NORM_RECTS:0:multi_palm_rects"
-  input_stream: "NORM_RECTS:1:multi_hand_rects"
-  output_stream: "IMAGE:output_video"
-}
-
-# Encodes the annotated images into a video file, adopting properties specified
-# in the input video header, e.g., video framerate.
-node {
-  calculator: "OpenCvVideoEncoderCalculator"
-  input_stream: "VIDEO:output_video"
-  input_stream: "VIDEO_PRESTREAM:input_video_header"
-  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
-      codec: "avc1"
-      video_format: "mp4"
-    }
-  }
-}
diff --git a/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt b/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt
deleted file mode 100644
index 20de18f..0000000
--- a/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live.pbtxt
+++ /dev/null
@@ -1,46 +0,0 @@
-# MediaPipe graph that performs hands tracking on desktop with TensorFlow
-# Lite on CPU.
-# Used in the example in
-# mediapipe/examples/desktop/hand_tracking:hand_tracking_cpu.
-
-# CPU image. (ImageFrame)
-input_stream: "input_video"
-
-# CPU image. (ImageFrame)
-output_stream: "output_video"
-
-# Generates side packet cotaining max number of hands to detect/track.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:num_hands"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 2 }
-    }
-  }
-}
-
-# Detects/tracks hand landmarks.
-node {
-  calculator: "HandLandmarkTrackingCpu"
-  input_stream: "IMAGE:input_video"
-  input_side_packet: "NUM_HANDS:num_hands"
-  output_stream: "LANDMARKS:landmarks"
-  output_stream: "HANDEDNESS:handedness"
-  output_stream: "PALM_DETECTIONS:multi_palm_detections"
-  output_stream: "HAND_ROIS_FROM_LANDMARKS:multi_hand_rects"
-  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:multi_palm_rects"
-}
-
-# Subgraph that renders annotations and overlays them on top of the input
-# images (see hand_renderer_cpu.pbtxt).
-node {
-  calculator: "HandRendererSubgraph"
-  input_stream: "IMAGE:input_video"
-  input_stream: "DETECTIONS:multi_palm_detections"
-  input_stream: "LANDMARKS:landmarks"
-  input_stream: "HANDEDNESS:handedness"
-  input_stream: "NORM_RECTS:0:multi_palm_rects"
-  input_stream: "NORM_RECTS:1:multi_hand_rects"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live_gpu.pbtxt b/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live_gpu.pbtxt
deleted file mode 100644
index 4dcaac5..0000000
--- a/mediapipe/graphs/hand_tracking/hand_tracking_desktop_live_gpu.pbtxt
+++ /dev/null
@@ -1,48 +0,0 @@
-# MediaPipe graph that performs multi-hand tracking with TensorFlow Lite on GPU.
-# Used in the examples in
-# mediapipe/examples/android/src/java/com/mediapipe/apps/handtrackinggpu.
-
-# GPU image. (GpuBuffer)
-input_stream: "input_video"
-
-# GPU image. (GpuBuffer)
-output_stream: "output_video"
-# Collection of detected/predicted hands, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-output_stream: "hand_landmarks"
-
-# Generates side packet cotaining max number of hands to detect/track.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:num_hands"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 2 }
-    }
-  }
-}
-
-# Detects/tracks hand landmarks.
-node {
-  calculator: "HandLandmarkTrackingGpu"
-  input_stream: "IMAGE:input_video"
-  input_side_packet: "NUM_HANDS:num_hands"
-  output_stream: "LANDMARKS:hand_landmarks"
-  output_stream: "HANDEDNESS:handedness"
-  output_stream: "PALM_DETECTIONS:palm_detections"
-  output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects_from_landmarks"
-  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
-}
-
-# Subgraph that renders annotations and overlays them on top of the input
-# images (see hand_renderer_gpu.pbtxt).
-node {
-  calculator: "HandRendererSubgraph"
-  input_stream: "IMAGE:input_video"
-  input_stream: "DETECTIONS:palm_detections"
-  input_stream: "LANDMARKS:hand_landmarks"
-  input_stream: "HANDEDNESS:handedness"
-  input_stream: "NORM_RECTS:0:hand_rects_from_palm_detections"
-  input_stream: "NORM_RECTS:1:hand_rects_from_landmarks"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt b/mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt
deleted file mode 100644
index b47e2a4..0000000
--- a/mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt
+++ /dev/null
@@ -1,65 +0,0 @@
-# MediaPipe graph that performs multi-hand tracking with TensorFlow Lite on GPU.
-# Used in the examples in
-# mediapipe/examples/android/src/java/com/mediapipe/apps/handtrackinggpu.
-
-# GPU image. (GpuBuffer)
-input_stream: "input_video"
-
-# Max number of hands to detect/process. (int)
-input_side_packet: "num_hands"
-
-# Model complexity (0 or 1). (int)
-input_side_packet: "model_complexity"
-
-# GPU image. (GpuBuffer)
-output_stream: "output_video"
-# Collection of detected/predicted hands, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-output_stream: "hand_landmarks"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Detects/tracks hand landmarks.
-node {
-  calculator: "HandLandmarkTrackingGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_side_packet: "NUM_HANDS:num_hands"
-  output_stream: "LANDMARKS:hand_landmarks"
-  output_stream: "HANDEDNESS:handedness"
-  output_stream: "PALM_DETECTIONS:palm_detections"
-  output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects_from_landmarks"
-  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
-}
-
-# Subgraph that renders annotations and overlays them on top of the input
-# images (see hand_renderer_gpu.pbtxt).
-node {
-  calculator: "HandRendererSubgraph"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "DETECTIONS:palm_detections"
-  input_stream: "LANDMARKS:hand_landmarks"
-  input_stream: "HANDEDNESS:handedness"
-  input_stream: "NORM_RECTS:0:hand_rects_from_palm_detections"
-  input_stream: "NORM_RECTS:1:hand_rects_from_landmarks"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/hand_tracking/subgraphs/BUILD b/mediapipe/graphs/hand_tracking/subgraphs/BUILD
deleted file mode 100644
index f16a6db..0000000
--- a/mediapipe/graphs/hand_tracking/subgraphs/BUILD
+++ /dev/null
@@ -1,58 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "hand_renderer_cpu",
-    graph = "hand_renderer_cpu.pbtxt",
-    register_as = "HandRendererSubgraph",
-    deps = [
-        "//mediapipe/calculators/core:begin_loop_calculator",
-        "//mediapipe/calculators/core:end_loop_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:collection_has_min_size_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:labels_to_render_data_calculator",
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_data_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_renderer_gpu",
-    graph = "hand_renderer_gpu.pbtxt",
-    register_as = "HandRendererSubgraph",
-    deps = [
-        "//mediapipe/calculators/core:begin_loop_calculator",
-        "//mediapipe/calculators/core:end_loop_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:collection_has_min_size_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:labels_to_render_data_calculator",
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_data_calculator",
-    ],
-)
diff --git a/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_cpu.pbtxt b/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_cpu.pbtxt
deleted file mode 100644
index eed1388..0000000
--- a/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_cpu.pbtxt
+++ /dev/null
@@ -1,209 +0,0 @@
-# MediaPipe graph to render hand landmarks and some related debug information.
-
-type: "HandRendererSubgraph"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:input_image"
-# Collection of detected/predicted hands, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-input_stream: "LANDMARKS:multi_hand_landmarks"
-# Handedness of the detected hand (i.e. is hand left or right).
-# (std::vector<ClassificationList>)
-input_stream: "HANDEDNESS:multi_handedness"
-# Regions of interest calculated based on palm detections.
-# (std::vector<NormalizedRect>)
-input_stream: "NORM_RECTS:0:multi_palm_rects"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-input_stream: "NORM_RECTS:1:multi_hand_rects"
-# Detected palms. (std::vector<Detection>)
-input_stream: "DETECTIONS:palm_detections"
-
-# Updated CPU image. (ImageFrame)
-output_stream: "IMAGE:output_image"
-
-# Converts detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:palm_detections"
-  output_stream: "RENDER_DATA:detection_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECTS:multi_hand_rects"
-  output_stream: "RENDER_DATA:multi_hand_rects_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECTS:multi_palm_rects"
-  output_stream: "RENDER_DATA:multi_palm_rects_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 125 g: 0 b: 122 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Outputs each element of multi_palm_landmarks at a fake timestamp for the rest
-# of the graph to process. At the end of the loop, outputs the BATCH_END
-# timestamp for downstream calculators to inform them that all elements in the
-# vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITERABLE:multi_hand_landmarks"
-  output_stream: "ITEM:single_hand_landmarks"
-  output_stream: "BATCH_END:landmark_timestamp"
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:single_hand_landmarks"
-  output_stream: "RENDER_DATA:single_hand_landmark_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 4
-      landmark_connections: 0
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 8
-      landmark_connections: 5
-      landmark_connections: 9
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 9
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 14
-      landmark_connections: 14
-      landmark_connections: 15
-      landmark_connections: 15
-      landmark_connections: 16
-      landmark_connections: 13
-      landmark_connections: 17
-      landmark_connections: 0
-      landmark_connections: 17
-      landmark_connections: 17
-      landmark_connections: 18
-      landmark_connections: 18
-      landmark_connections: 19
-      landmark_connections: 19
-      landmark_connections: 20
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 0 g: 255 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Collects a RenderData object for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of RenderData at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopRenderDataCalculator"
-  input_stream: "ITEM:single_hand_landmark_render_data"
-  input_stream: "BATCH_END:landmark_timestamp"
-  output_stream: "ITERABLE:multi_hand_landmarks_render_data"
-}
-
-# Don't render handedness if there are more than one handedness reported.
-node {
-  calculator: "ClassificationListVectorHasMinSizeCalculator"
-  input_stream: "ITERABLE:multi_handedness"
-  output_stream: "disallow_handedness_rendering"
-  node_options: {
-    [type.googleapis.com/mediapipe.CollectionHasMinSizeCalculatorOptions] {
-      min_size: 2
-    }
-  }
-}
-
-node {
-  calculator: "GateCalculator"
-  input_stream: "multi_handedness"
-  input_stream: "DISALLOW:disallow_handedness_rendering"
-  output_stream: "allowed_multi_handedness"
-  node_options: {
-    [type.googleapis.com/mediapipe.GateCalculatorOptions] {
-      empty_packets_as_allow: false
-    }
-  }
-}
-
-node {
-  calculator: "SplitClassificationListVectorCalculator"
-  input_stream: "allowed_multi_handedness"
-  output_stream: "handedness"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Converts classification to drawing primitives for annotation overlay.
-node {
-  calculator: "LabelsToRenderDataCalculator"
-  input_stream: "CLASSIFICATIONS:handedness"
-  output_stream: "RENDER_DATA:handedness_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LabelsToRenderDataCalculatorOptions]: {
-      color { r: 255 g: 0 b: 0 }
-      thickness: 10.0
-      font_height_px: 50
-      horizontal_offset_px: 30
-      vertical_offset_px: 50
-
-      max_num_labels: 1
-      location: TOP_LEFT
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images. Consumes
-# a vector of RenderData objects and draws each of them on the input frame.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_image"
-  input_stream: "detection_render_data"
-  input_stream: "multi_hand_rects_render_data"
-  input_stream: "multi_palm_rects_render_data"
-  input_stream: "handedness_render_data"
-  input_stream: "VECTOR:0:multi_hand_landmarks_render_data"
-  output_stream: "IMAGE:output_image"
-}
diff --git a/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_gpu.pbtxt b/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_gpu.pbtxt
deleted file mode 100644
index 9f0af85..0000000
--- a/mediapipe/graphs/hand_tracking/subgraphs/hand_renderer_gpu.pbtxt
+++ /dev/null
@@ -1,209 +0,0 @@
-# MediaPipe graph to render hand landmarks and some related debug information.
-
-type: "HandRendererSubgraph"
-
-# GPU buffer. (GpuBuffer)
-input_stream: "IMAGE:input_image"
-# Collection of detected/predicted hands, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-input_stream: "LANDMARKS:multi_hand_landmarks"
-# Handedness of the detected hand (i.e. is hand left or right).
-# (std::vector<ClassificationList>)
-input_stream: "HANDEDNESS:multi_handedness"
-# Regions of interest calculated based on palm detections.
-# (std::vector<NormalizedRect>)
-input_stream: "NORM_RECTS:0:multi_palm_rects"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-input_stream: "NORM_RECTS:1:multi_hand_rects"
-# Detected palms. (std::vector<Detection>)
-input_stream: "DETECTIONS:palm_detections"
-
-# Updated GPU buffer. (GpuBuffer)
-output_stream: "IMAGE:output_image"
-
-# Converts detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:palm_detections"
-  output_stream: "RENDER_DATA:detection_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECTS:multi_hand_rects"
-  output_stream: "RENDER_DATA:multi_hand_rects_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECTS:multi_palm_rects"
-  output_stream: "RENDER_DATA:multi_palm_rects_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 125 g: 0 b: 122 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Outputs each element of multi_palm_landmarks at a fake timestamp for the rest
-# of the graph to process. At the end of the loop, outputs the BATCH_END
-# timestamp for downstream calculators to inform them that all elements in the
-# vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITERABLE:multi_hand_landmarks"
-  output_stream: "ITEM:single_hand_landmarks"
-  output_stream: "BATCH_END:landmark_timestamp"
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:single_hand_landmarks"
-  output_stream: "RENDER_DATA:single_hand_landmark_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 4
-      landmark_connections: 0
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 8
-      landmark_connections: 5
-      landmark_connections: 9
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 9
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 14
-      landmark_connections: 14
-      landmark_connections: 15
-      landmark_connections: 15
-      landmark_connections: 16
-      landmark_connections: 13
-      landmark_connections: 17
-      landmark_connections: 0
-      landmark_connections: 17
-      landmark_connections: 17
-      landmark_connections: 18
-      landmark_connections: 18
-      landmark_connections: 19
-      landmark_connections: 19
-      landmark_connections: 20
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 0 g: 255 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Collects a RenderData object for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of RenderData at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopRenderDataCalculator"
-  input_stream: "ITEM:single_hand_landmark_render_data"
-  input_stream: "BATCH_END:landmark_timestamp"
-  output_stream: "ITERABLE:multi_hand_landmarks_render_data"
-}
-
-# Don't render handedness if there are more than one handedness reported.
-node {
-  calculator: "ClassificationListVectorHasMinSizeCalculator"
-  input_stream: "ITERABLE:multi_handedness"
-  output_stream: "disallow_handedness_rendering"
-  node_options: {
-    [type.googleapis.com/mediapipe.CollectionHasMinSizeCalculatorOptions] {
-      min_size: 2
-    }
-  }
-}
-
-node {
-  calculator: "GateCalculator"
-  input_stream: "multi_handedness"
-  input_stream: "DISALLOW:disallow_handedness_rendering"
-  output_stream: "allowed_multi_handedness"
-  node_options: {
-    [type.googleapis.com/mediapipe.GateCalculatorOptions] {
-      empty_packets_as_allow: false
-    }
-  }
-}
-
-node {
-  calculator: "SplitClassificationListVectorCalculator"
-  input_stream: "allowed_multi_handedness"
-  output_stream: "handedness"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Converts classification to drawing primitives for annotation overlay.
-node {
-  calculator: "LabelsToRenderDataCalculator"
-  input_stream: "CLASSIFICATIONS:handedness"
-  output_stream: "RENDER_DATA:handedness_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LabelsToRenderDataCalculatorOptions]: {
-      color { r: 255 g: 0 b: 0 }
-      thickness: 10.0
-      font_height_px: 50
-      horizontal_offset_px: 30
-      vertical_offset_px: 50
-
-      max_num_labels: 1
-      location: TOP_LEFT
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images. Consumes
-# a vector of RenderData objects and draws each of them on the input frame.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:input_image"
-  input_stream: "detection_render_data"
-  input_stream: "multi_hand_rects_render_data"
-  input_stream: "multi_palm_rects_render_data"
-  input_stream: "handedness_render_data"
-  input_stream: "VECTOR:0:multi_hand_landmarks_render_data"
-  output_stream: "IMAGE_GPU:output_image"
-}
diff --git a/mediapipe/graphs/holistic_tracking/BUILD b/mediapipe/graphs/holistic_tracking/BUILD
deleted file mode 100644
index 986cf9f..0000000
--- a/mediapipe/graphs/holistic_tracking/BUILD
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-    "mediapipe_simple_subgraph",
-)
-
-package(default_visibility = ["//visibility:public"])
-
-licenses(["notice"])
-
-mediapipe_simple_subgraph(
-    name = "holistic_tracking_to_render_data",
-    graph = "holistic_tracking_to_render_data.pbtxt",
-    register_as = "HolisticTrackingToRenderData",
-    deps = [
-        "//mediapipe/calculators/core:concatenate_normalized_landmark_list_calculator",
-        "//mediapipe/calculators/core:concatenate_vector_calculator",
-        "//mediapipe/calculators/core:merge_calculator",
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_scale_calculator",
-        "//mediapipe/modules/holistic_landmark:hand_wrist_for_pose",
-    ],
-)
-
-cc_library(
-    name = "holistic_tracking_gpu_deps",
-    deps = [
-        ":holistic_tracking_to_render_data",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/modules/holistic_landmark:holistic_landmark_gpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "holistic_tracking_gpu",
-    graph = "holistic_tracking_gpu.pbtxt",
-    output_name = "holistic_tracking_gpu.binarypb",
-    deps = [":holistic_tracking_gpu_deps"],
-)
-
-cc_library(
-    name = "holistic_tracking_cpu_graph_deps",
-    deps = [
-        ":holistic_tracking_to_render_data",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/modules/holistic_landmark:holistic_landmark_cpu",
-    ],
-)
diff --git a/mediapipe/graphs/holistic_tracking/holistic_tracking_cpu.pbtxt b/mediapipe/graphs/holistic_tracking/holistic_tracking_cpu.pbtxt
deleted file mode 100644
index fead245..0000000
--- a/mediapipe/graphs/holistic_tracking/holistic_tracking_cpu.pbtxt
+++ /dev/null
@@ -1,75 +0,0 @@
-# Tracks and renders pose + hands + face landmarks.
-
-# CPU image. (ImageFrame)
-input_stream: "input_video"
-
-# CPU image with rendered results. (ImageFrame)
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.FlowLimiterCalculatorOptions] {
-      max_in_flight: 1
-      max_in_queue: 1
-      # Timeout is disabled (set to 0) as first frame processing can take more
-      # than 1 second.
-      in_flight_timeout: 0
-    }
-  }
-}
-
-node {
-  calculator: "HolisticLandmarkCpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "POSE_LANDMARKS:pose_landmarks"
-  output_stream: "POSE_ROI:pose_roi"
-  output_stream: "POSE_DETECTION:pose_detection"
-  output_stream: "FACE_LANDMARKS:face_landmarks"
-  output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-  output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-}
-
-# Gets image size.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "SIZE:image_size"
-}
-
-# Converts pose, hands and face landmarks to a render data vector.
-node {
-  calculator: "HolisticTrackingToRenderData"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "POSE_LANDMARKS:pose_landmarks"
-  input_stream: "POSE_ROI:pose_roi"
-  input_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-  input_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-  input_stream: "FACE_LANDMARKS:face_landmarks"
-  output_stream: "RENDER_DATA_VECTOR:render_data_vector"
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "VECTOR:render_data_vector"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt b/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt
deleted file mode 100644
index dc85be4..0000000
--- a/mediapipe/graphs/holistic_tracking/holistic_tracking_gpu.pbtxt
+++ /dev/null
@@ -1,75 +0,0 @@
-# Tracks and renders pose + hands + face landmarks.
-
-# GPU buffer. (GpuBuffer)
-input_stream: "input_video"
-
-# GPU image with rendered results. (GpuBuffer)
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.FlowLimiterCalculatorOptions] {
-      max_in_flight: 1
-      max_in_queue: 1
-      # Timeout is disabled (set to 0) as first frame processing can take more
-      # than 1 second.
-      in_flight_timeout: 0
-    }
-  }
-}
-
-node {
-  calculator: "HolisticLandmarkGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "POSE_LANDMARKS:pose_landmarks"
-  output_stream: "POSE_ROI:pose_roi"
-  output_stream: "POSE_DETECTION:pose_detection"
-  output_stream: "FACE_LANDMARKS:face_landmarks"
-  output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-  output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-}
-
-# Gets image size.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  output_stream: "SIZE:image_size"
-}
-
-# Converts pose, hands and face landmarks to a render data vector.
-node {
-  calculator: "HolisticTrackingToRenderData"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "POSE_LANDMARKS:pose_landmarks"
-  input_stream: "POSE_ROI:pose_roi"
-  input_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-  input_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-  input_stream: "FACE_LANDMARKS:face_landmarks"
-  output_stream: "RENDER_DATA_VECTOR:render_data_vector"
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  input_stream: "VECTOR:render_data_vector"
-  output_stream: "IMAGE_GPU:output_video"
-}
diff --git a/mediapipe/graphs/holistic_tracking/holistic_tracking_to_render_data.pbtxt b/mediapipe/graphs/holistic_tracking/holistic_tracking_to_render_data.pbtxt
deleted file mode 100644
index 4b05123..0000000
--- a/mediapipe/graphs/holistic_tracking/holistic_tracking_to_render_data.pbtxt
+++ /dev/null
@@ -1,757 +0,0 @@
-# Converts pose + hands + face landmarks to a render data vector.
-
-type: "HolisticTrackingToRenderData"
-
-# Image size. (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-# Pose landmarks. (NormalizedLandmarkList)
-input_stream: "POSE_LANDMARKS:landmarks"
-# Region of interest calculated based on pose landmarks. (NormalizedRect)
-input_stream: "POSE_ROI:roi"
-# Left hand landmarks. (NormalizedLandmarkList)
-input_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-# Right hand landmarks. (NormalizedLandmarkList)
-input_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-# Face landmarks. (NormalizedLandmarkList)
-input_stream: "FACE_LANDMARKS:face_landmarks"
-
-# Render data vector. (std::vector<RenderData>)
-output_stream: "RENDER_DATA_VECTOR:render_data_vector"
-
-# --------------------------------------------------------------------------- #
-# ------------------ Calculates scale for render objects -------------------- #
-# --------------------------------------------------------------------------- #
-
-# Calculates rendering scale based on the pose bounding box.
-node {
-  calculator: "RectToRenderScaleCalculator"
-  input_stream: "NORM_RECT:roi"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "RENDER_SCALE:render_scale"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderScaleCalculatorOptions] {
-      multiplier: 0.0008
-    }
-  }
-}
-
-# --------------------------------------------------------------------------- #
-# --------------- Combines pose and hands into pose skeleton ---------------- #
-# --------------------------------------------------------------------------- #
-
-# Gets pose landmarks before wrists.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "landmarks"
-  output_stream: "landmarks_before_wrist"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 11 end: 15 }
-    }
-  }
-}
-
-# Gets pose left wrist landmark.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "landmarks"
-  output_stream: "landmarks_left_wrist"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 15 end: 16 }
-    }
-  }
-}
-
-# Gets pose right wrist landmark.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "landmarks"
-  output_stream: "landmarks_right_wrist"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 16 end: 17 }
-    }
-  }
-}
-
-# Gets pose landmarks after wrists.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "landmarks"
-  output_stream: "landmarks_after_wrist"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 23 end: 33 }
-    }
-  }
-}
-
-# Gets left hand wrist landmark.
-node {
-  calculator: "HandWristForPose"
-  input_stream: "HAND_LANDMARKS:left_hand_landmarks"
-  output_stream: "WRIST_LANDMARK:left_hand_wrist_landmark"
-}
-
-# Gets left hand wrist landmark or keep pose wrist landmark if hand was not
-# predicted.
-node {
-  calculator: "MergeCalculator"
-  input_stream: "left_hand_wrist_landmark"
-  input_stream: "landmarks_left_wrist"
-  output_stream: "merged_left_hand_wrist_landmark"
-}
-
-# Gets right hand wrist landmark.
-node {
-  calculator: "HandWristForPose"
-  input_stream: "HAND_LANDMARKS:right_hand_landmarks"
-  output_stream: "WRIST_LANDMARK:right_hand_wrist_landmark"
-}
-
-# Gets right hand wrist landmark or keep pose wrist landmark if hand was not
-# predicted.
-node {
-  calculator: "MergeCalculator"
-  input_stream: "right_hand_wrist_landmark"
-  input_stream: "landmarks_right_wrist"
-  output_stream: "merged_right_hand_wrist_landmark"
-}
-
-# Combines pose landmarks all together.
-node {
-  calculator: "ConcatenateNormalizedLandmarkListCalculator"
-  input_stream: "landmarks_before_wrist"
-  input_stream: "merged_left_hand_wrist_landmark"
-  input_stream: "merged_right_hand_wrist_landmark"
-  input_stream: "landmarks_after_wrist"
-  output_stream: "landmarks_merged"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConcatenateVectorCalculatorOptions] {
-      only_emit_if_all_present: true
-    }
-  }
-}
-
-# Takes left pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "landmarks_merged"
-  output_stream: "landmarks_left_side"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      ranges: { begin: 2 end: 3 }
-      ranges: { begin: 4 end: 5 }
-      ranges: { begin: 6 end: 7 }
-      ranges: { begin: 8 end: 9 }
-      ranges: { begin: 10 end: 11 }
-      ranges: { begin: 12 end: 13 }
-      ranges: { begin: 14 end: 15 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Takes right pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "landmarks_merged"
-  output_stream: "landmarks_right_side"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 1 end: 2 }
-      ranges: { begin: 3 end: 4 }
-      ranges: { begin: 5 end: 6 }
-      ranges: { begin: 7 end: 8 }
-      ranges: { begin: 9 end: 10 }
-      ranges: { begin: 11 end: 12 }
-      ranges: { begin: 13 end: 14 }
-      ranges: { begin: 15 end: 16 }
-      combine_outputs: true
-    }
-  }
-}
-
-# --------------------------------------------------------------------------- #
-# ---------------------------------- Pose ----------------------------------- #
-# --------------------------------------------------------------------------- #
-
-# Converts pose connections to white lines.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks_merged"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 0
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 4
-      landmark_connections: 1
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 5
-      landmark_connections: 0
-      landmark_connections: 6
-      landmark_connections: 1
-      landmark_connections: 7
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 6
-      landmark_connections: 8
-      landmark_connections: 7
-      landmark_connections: 9
-      landmark_connections: 8
-      landmark_connections: 10
-      landmark_connections: 9
-      landmark_connections: 11
-      landmark_connections: 10
-      landmark_connections: 12
-      landmark_connections: 11
-      landmark_connections: 13
-      landmark_connections: 12
-      landmark_connections: 14
-      landmark_connections: 13
-      landmark_connections: 15
-      landmark_connections: 10
-      landmark_connections: 14
-      landmark_connections: 11
-      landmark_connections: 15
-
-      landmark_color { r: 255 g: 255 b: 255 }
-      connection_color { r: 255 g: 255 b: 255 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.1
-    }
-  }
-}
-
-# Converts pose joints to big white circles.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks_merged"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_background_joints_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 255 g: 255 b: 255 }
-      connection_color { r: 255 g: 255 b: 255 }
-      thickness: 5.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# Converts pose left side joints to orange circles (inside white ones).
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks_left_side"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_left_joints_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 255 g: 138 b: 0 }
-      connection_color { r: 255 g: 138 b: 0 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# Converts pose right side joints to cyan circles (inside white ones).
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks_right_side"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_right_joints_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 0 g: 217 b: 231 }
-      connection_color { r: 0 g: 217 b: 231 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# --------------------------------------------------------------------------- #
-# ------------------------------- Left hand --------------------------------- #
-# --------------------------------------------------------------------------- #
-
-# Converts left hand connections to white lines.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:left_hand_landmarks"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:left_hand_landmarks_connections_rd"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 4
-      landmark_connections: 0
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 8
-      landmark_connections: 5
-      landmark_connections: 9
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 9
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 14
-      landmark_connections: 14
-      landmark_connections: 15
-      landmark_connections: 15
-      landmark_connections: 16
-      landmark_connections: 13
-      landmark_connections: 17
-      landmark_connections: 0
-      landmark_connections: 17
-      landmark_connections: 17
-      landmark_connections: 18
-      landmark_connections: 18
-      landmark_connections: 19
-      landmark_connections: 19
-      landmark_connections: 20
-      landmark_color { r: 255 g: 255 b: 255 }
-      connection_color { r: 255 g: 255 b: 255 }
-      thickness: 4.0
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-# Converts left hand color joints.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:left_hand_landmarks"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:left_hand_landmarks_joints_rd"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 255 g: 138 b: 0 }
-      connection_color { r: 255 g: 138 b: 0 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-# --------------------------------------------------------------------------- #
-# -------------------------------- Right hand ------------------------------- #
-# --------------------------------------------------------------------------- #
-
-# Converts right hand connections to white lines.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:right_hand_landmarks"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:right_hand_landmarks_connections_rd"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 4
-      landmark_connections: 0
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 8
-      landmark_connections: 5
-      landmark_connections: 9
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 9
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 14
-      landmark_connections: 14
-      landmark_connections: 15
-      landmark_connections: 15
-      landmark_connections: 16
-      landmark_connections: 13
-      landmark_connections: 17
-      landmark_connections: 0
-      landmark_connections: 17
-      landmark_connections: 17
-      landmark_connections: 18
-      landmark_connections: 18
-      landmark_connections: 19
-      landmark_connections: 19
-      landmark_connections: 20
-      landmark_color { r: 255 g: 255 b: 255 }
-      connection_color { r: 255 g: 255 b: 255 }
-      thickness: 4.0
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-# Converts right hand color joints.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:right_hand_landmarks"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:right_hand_landmarks_joints_rd"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 0 g: 217 b: 231 }
-      connection_color { r: 0 g: 217 b: 231 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-# --------------------------------------------------------------------------- #
-# ---------------------------------- Face ----------------------------------- #
-# --------------------------------------------------------------------------- #
-
-# Converts face connections to white lines.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:face_landmarks"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:face_landmarks_connections_rd"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      # Lips.
-      landmark_connections: 61
-      landmark_connections: 146
-      landmark_connections: 146
-      landmark_connections: 91
-      landmark_connections: 91
-      landmark_connections: 181
-      landmark_connections: 181
-      landmark_connections: 84
-      landmark_connections: 84
-      landmark_connections: 17
-      landmark_connections: 17
-      landmark_connections: 314
-      landmark_connections: 314
-      landmark_connections: 405
-      landmark_connections: 405
-      landmark_connections: 321
-      landmark_connections: 321
-      landmark_connections: 375
-      landmark_connections: 375
-      landmark_connections: 291
-      landmark_connections: 61
-      landmark_connections: 185
-      landmark_connections: 185
-      landmark_connections: 40
-      landmark_connections: 40
-      landmark_connections: 39
-      landmark_connections: 39
-      landmark_connections: 37
-      landmark_connections: 37
-      landmark_connections: 0
-      landmark_connections: 0
-      landmark_connections: 267
-      landmark_connections: 267
-      landmark_connections: 269
-      landmark_connections: 269
-      landmark_connections: 270
-      landmark_connections: 270
-      landmark_connections: 409
-      landmark_connections: 409
-      landmark_connections: 291
-      landmark_connections: 78
-      landmark_connections: 95
-      landmark_connections: 95
-      landmark_connections: 88
-      landmark_connections: 88
-      landmark_connections: 178
-      landmark_connections: 178
-      landmark_connections: 87
-      landmark_connections: 87
-      landmark_connections: 14
-      landmark_connections: 14
-      landmark_connections: 317
-      landmark_connections: 317
-      landmark_connections: 402
-      landmark_connections: 402
-      landmark_connections: 318
-      landmark_connections: 318
-      landmark_connections: 324
-      landmark_connections: 324
-      landmark_connections: 308
-      landmark_connections: 78
-      landmark_connections: 191
-      landmark_connections: 191
-      landmark_connections: 80
-      landmark_connections: 80
-      landmark_connections: 81
-      landmark_connections: 81
-      landmark_connections: 82
-      landmark_connections: 82
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 312
-      landmark_connections: 312
-      landmark_connections: 311
-      landmark_connections: 311
-      landmark_connections: 310
-      landmark_connections: 310
-      landmark_connections: 415
-      landmark_connections: 415
-      landmark_connections: 308
-      # Left eye.
-      landmark_connections: 33
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 163
-      landmark_connections: 163
-      landmark_connections: 144
-      landmark_connections: 144
-      landmark_connections: 145
-      landmark_connections: 145
-      landmark_connections: 153
-      landmark_connections: 153
-      landmark_connections: 154
-      landmark_connections: 154
-      landmark_connections: 155
-      landmark_connections: 155
-      landmark_connections: 133
-      landmark_connections: 33
-      landmark_connections: 246
-      landmark_connections: 246
-      landmark_connections: 161
-      landmark_connections: 161
-      landmark_connections: 160
-      landmark_connections: 160
-      landmark_connections: 159
-      landmark_connections: 159
-      landmark_connections: 158
-      landmark_connections: 158
-      landmark_connections: 157
-      landmark_connections: 157
-      landmark_connections: 173
-      landmark_connections: 173
-      landmark_connections: 133
-      # Left eyebrow.
-      landmark_connections: 46
-      landmark_connections: 53
-      landmark_connections: 53
-      landmark_connections: 52
-      landmark_connections: 52
-      landmark_connections: 65
-      landmark_connections: 65
-      landmark_connections: 55
-      landmark_connections: 70
-      landmark_connections: 63
-      landmark_connections: 63
-      landmark_connections: 105
-      landmark_connections: 105
-      landmark_connections: 66
-      landmark_connections: 66
-      landmark_connections: 107
-      # Right eye.
-      landmark_connections: 263
-      landmark_connections: 249
-      landmark_connections: 249
-      landmark_connections: 390
-      landmark_connections: 390
-      landmark_connections: 373
-      landmark_connections: 373
-      landmark_connections: 374
-      landmark_connections: 374
-      landmark_connections: 380
-      landmark_connections: 380
-      landmark_connections: 381
-      landmark_connections: 381
-      landmark_connections: 382
-      landmark_connections: 382
-      landmark_connections: 362
-      landmark_connections: 263
-      landmark_connections: 466
-      landmark_connections: 466
-      landmark_connections: 388
-      landmark_connections: 388
-      landmark_connections: 387
-      landmark_connections: 387
-      landmark_connections: 386
-      landmark_connections: 386
-      landmark_connections: 385
-      landmark_connections: 385
-      landmark_connections: 384
-      landmark_connections: 384
-      landmark_connections: 398
-      landmark_connections: 398
-      landmark_connections: 362
-      # Right eyebrow.
-      landmark_connections: 276
-      landmark_connections: 283
-      landmark_connections: 283
-      landmark_connections: 282
-      landmark_connections: 282
-      landmark_connections: 295
-      landmark_connections: 295
-      landmark_connections: 285
-      landmark_connections: 300
-      landmark_connections: 293
-      landmark_connections: 293
-      landmark_connections: 334
-      landmark_connections: 334
-      landmark_connections: 296
-      landmark_connections: 296
-      landmark_connections: 336
-      # Face oval.
-      landmark_connections: 10
-      landmark_connections: 338
-      landmark_connections: 338
-      landmark_connections: 297
-      landmark_connections: 297
-      landmark_connections: 332
-      landmark_connections: 332
-      landmark_connections: 284
-      landmark_connections: 284
-      landmark_connections: 251
-      landmark_connections: 251
-      landmark_connections: 389
-      landmark_connections: 389
-      landmark_connections: 356
-      landmark_connections: 356
-      landmark_connections: 454
-      landmark_connections: 454
-      landmark_connections: 323
-      landmark_connections: 323
-      landmark_connections: 361
-      landmark_connections: 361
-      landmark_connections: 288
-      landmark_connections: 288
-      landmark_connections: 397
-      landmark_connections: 397
-      landmark_connections: 365
-      landmark_connections: 365
-      landmark_connections: 379
-      landmark_connections: 379
-      landmark_connections: 378
-      landmark_connections: 378
-      landmark_connections: 400
-      landmark_connections: 400
-      landmark_connections: 377
-      landmark_connections: 377
-      landmark_connections: 152
-      landmark_connections: 152
-      landmark_connections: 148
-      landmark_connections: 148
-      landmark_connections: 176
-      landmark_connections: 176
-      landmark_connections: 149
-      landmark_connections: 149
-      landmark_connections: 150
-      landmark_connections: 150
-      landmark_connections: 136
-      landmark_connections: 136
-      landmark_connections: 172
-      landmark_connections: 172
-      landmark_connections: 58
-      landmark_connections: 58
-      landmark_connections: 132
-      landmark_connections: 132
-      landmark_connections: 93
-      landmark_connections: 93
-      landmark_connections: 234
-      landmark_connections: 234
-      landmark_connections: 127
-      landmark_connections: 127
-      landmark_connections: 162
-      landmark_connections: 162
-      landmark_connections: 21
-      landmark_connections: 21
-      landmark_connections: 54
-      landmark_connections: 54
-      landmark_connections: 103
-      landmark_connections: 103
-      landmark_connections: 67
-      landmark_connections: 67
-      landmark_connections: 109
-      landmark_connections: 109
-      landmark_connections: 10
-      landmark_color { r: 255 g: 255 b: 255 }
-      connection_color { r: 255 g: 255 b: 255 }
-      thickness: 0.5
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-# Converts face joints to cyan circles.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:face_landmarks"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:face_landmarks_joints_rd"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 0 g: 217 b: 231 }
-      connection_color { r: 0 g: 217 b: 231 }
-      thickness: 0.5
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-# Concatenates all render data.
-node {
-  calculator: "ConcatenateRenderDataVectorCalculator"
-  input_stream: "landmarks_render_data"
-  input_stream: "landmarks_background_joints_render_data"
-  input_stream: "landmarks_left_joints_render_data"
-  input_stream: "landmarks_right_joints_render_data"
-
-  # Left hand.
-  input_stream: "left_hand_landmarks_connections_rd"
-  input_stream: "left_hand_landmarks_joints_rd"
-
-  # Right hand.
-  input_stream: "right_hand_landmarks_connections_rd"
-  input_stream: "right_hand_landmarks_joints_rd"
-
-  # Face.
-  input_stream: "face_landmarks_connections_rd"
-  input_stream: "face_landmarks_joints_rd"
-
-  output_stream: "render_data_vector"
-}
diff --git a/mediapipe/graphs/instant_motion_tracking/BUILD b/mediapipe/graphs/instant_motion_tracking/BUILD
deleted file mode 100644
index e9be587..0000000
--- a/mediapipe/graphs/instant_motion_tracking/BUILD
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "instant_motion_tracking_deps",
-    deps = [
-        "//mediapipe/graphs/instant_motion_tracking/calculators:matrices_manager_calculator",
-        "//mediapipe/graphs/instant_motion_tracking/calculators:sticker_manager_calculator",
-        "//mediapipe/graphs/instant_motion_tracking/subgraphs:region_tracking",
-        "//mediapipe/graphs/object_detection_3d/calculators:gl_animation_overlay_calculator",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "instant_motion_tracking_binary_graph",
-    graph = "instant_motion_tracking.pbtxt",
-    output_name = "instant_motion_tracking.binarypb",
-    deps = [":instant_motion_tracking_deps"],
-)
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/BUILD b/mediapipe/graphs/instant_motion_tracking/calculators/BUILD
deleted file mode 100644
index 93af68c..0000000
--- a/mediapipe/graphs/instant_motion_tracking/calculators/BUILD
+++ /dev/null
@@ -1,84 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-proto_library(
-    name = "sticker_buffer_proto",
-    srcs = [
-        "sticker_buffer.proto",
-    ],
-)
-
-mediapipe_cc_proto_library(
-    name = "sticker_buffer_cc_proto",
-    srcs = [
-        "sticker_buffer.proto",
-    ],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":sticker_buffer_proto",
-    ],
-)
-
-cc_library(
-    name = "sticker_manager_calculator",
-    srcs = ["sticker_manager_calculator.cc"],
-    hdrs = ["transformations.h"],
-    deps = [
-        ":sticker_buffer_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework:timestamp",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "matrices_manager_calculator",
-    srcs = ["matrices_manager_calculator.cc"],
-    hdrs = ["transformations.h"],
-    deps = [
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework:timestamp",
-        "//mediapipe/framework/formats:image_frame",
-        "//mediapipe/framework/port:opencv_imgproc",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/graphs/object_detection_3d/calculators:model_matrix_cc_proto",
-        "//mediapipe/modules/objectron/calculators:box",
-        "@com_google_absl//absl/memory",
-        "@com_google_absl//absl/strings",
-        "@eigen_archive//:eigen3",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "tracked_anchor_manager_calculator",
-    srcs = ["tracked_anchor_manager_calculator.cc"],
-    hdrs = ["transformations.h"],
-    deps = [
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/util/tracking:box_tracker_cc_proto",
-    ],
-    alwayslink = 1,
-)
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/matrices_manager_calculator.cc b/mediapipe/graphs/instant_motion_tracking/calculators/matrices_manager_calculator.cc
deleted file mode 100644
index c003135..0000000
--- a/mediapipe/graphs/instant_motion_tracking/calculators/matrices_manager_calculator.cc
+++ /dev/null
@@ -1,393 +0,0 @@
-// Copyright 2020 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <cmath>
-#include <memory>
-
-#include "Eigen/Core"
-#include "Eigen/Dense"
-#include "Eigen/Geometry"
-#include "absl/memory/memory.h"
-#include "absl/strings/str_cat.h"
-#include "absl/strings/str_join.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/graphs/instant_motion_tracking/calculators/transformations.h"
-#include "mediapipe/graphs/object_detection_3d/calculators/model_matrix.pb.h"
-#include "mediapipe/modules/objectron/calculators/box.h"
-
-namespace mediapipe {
-
-namespace {
-using Matrix4fCM = Eigen::Matrix<float, 4, 4, Eigen::ColMajor>;
-using Vector3f = Eigen::Vector3f;
-using Matrix3f = Eigen::Matrix3f;
-using DiagonalMatrix3f = Eigen::DiagonalMatrix<float, 3>;
-constexpr char kAnchorsTag[] = "ANCHORS";
-constexpr char kIMUMatrixTag[] = "IMU_ROTATION";
-constexpr char kUserRotationsTag[] = "USER_ROTATIONS";
-constexpr char kUserScalingsTag[] = "USER_SCALINGS";
-constexpr char kRendersTag[] = "RENDER_DATA";
-constexpr char kGifAspectRatioTag[] = "GIF_ASPECT_RATIO";
-constexpr char kFOVSidePacketTag[] = "FOV";
-constexpr char kAspectRatioSidePacketTag[] = "ASPECT_RATIO";
-// initial Z value (-10 is center point in visual range for OpenGL render)
-constexpr float kInitialZ = -10.0f;
-}  // namespace
-
-// Intermediary for rotation and translation data to model matrix usable by
-// gl_animation_overlay_calculator. For information on the construction of
-// OpenGL objects and transformations (including a breakdown of model matrices),
-// please visit: https://open.gl/transformations
-//
-// Input Side Packets:
-//  FOV - Vertical field of view for device [REQUIRED - Defines perspective
-//  matrix] ASPECT_RATIO - Aspect ratio of device [REQUIRED - Defines
-//  perspective matrix]
-//
-// Input streams:
-//  ANCHORS - Anchor data with x,y,z coordinates (x,y are in [0.0-1.0] range for
-//    position on the device screen, while z is the scaling factor that changes
-//    in proportion to the distance from the tracked region) [REQUIRED]
-//  IMU_ROTATION - float[9] of row-major device rotation matrix [REQUIRED]
-//  USER_ROTATIONS - UserRotations with corresponding radians of rotation
-//    [REQUIRED]
-//  USER_SCALINGS - UserScalings with corresponding scale factor [REQUIRED]
-//  GIF_ASPECT_RATIO - Aspect ratio of GIF image used to dynamically scale
-//    GIF asset defined as width / height [OPTIONAL]
-// Output:
-//  MATRICES - TimedModelMatrixProtoList of each object type to render
-//    [REQUIRED]
-//
-// Example config:
-// node{
-//  calculator: "MatricesManagerCalculator"
-//  input_stream: "ANCHORS:tracked_scaled_anchor_data"
-//  input_stream: "IMU_ROTATION:imu_rotation_matrix"
-//  input_stream: "USER_ROTATIONS:user_rotation_data"
-//  input_stream: "USER_SCALINGS:user_scaling_data"
-//  input_stream: "GIF_ASPECT_RATIO:gif_aspect_ratio"
-//  output_stream: "MATRICES:0:first_render_matrices"
-//  output_stream: "MATRICES:1:second_render_matrices" [unbounded input size]
-//  input_side_packet: "FOV:vertical_fov_radians"
-//  input_side_packet: "ASPECT_RATIO:aspect_ratio"
-// }
-
-class MatricesManagerCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc);
-  absl::Status Open(CalculatorContext* cc) override;
-  absl::Status Process(CalculatorContext* cc) override;
-
- private:
-  // Device properties that will be preset by side packets
-  float vertical_fov_radians_ = 0.0f;
-  float aspect_ratio_ = 0.0f;
-  float gif_aspect_ratio_ = 1.0f;
-
-  const Matrix3f GenerateUserRotationMatrix(const float rotation_radians) const;
-  const Matrix4fCM GenerateEigenModelMatrix(
-      const Vector3f& translation_vector,
-      const Matrix3f& rotation_submatrix) const;
-  const Vector3f GenerateAnchorVector(const Anchor& tracked_anchor) const;
-  DiagonalMatrix3f GetDefaultRenderScaleDiagonal(
-      const int render_id, const float user_scale_factor,
-      const float gif_aspect_ratio) const;
-
-  // Returns a user scaling increment associated with the sticker_id
-  // TODO: Adjust lookup function if total number of stickers is uncapped to
-  // improve performance
-  const float GetUserScaler(const std::vector<UserScaling>& scalings,
-                            const int sticker_id) const {
-    for (const UserScaling& user_scaling : scalings) {
-      if (user_scaling.sticker_id == sticker_id) {
-        return user_scaling.scale_factor;
-      }
-    }
-    LOG(WARNING) << "Cannot find sticker_id: " << sticker_id
-                 << ", returning 1.0f scaling";
-    return 1.0f;
-  }
-
-  // Returns a user rotation in radians associated with the sticker_id
-  const float GetUserRotation(const std::vector<UserRotation>& rotations,
-                              const int sticker_id) {
-    for (const UserRotation& rotation : rotations) {
-      if (rotation.sticker_id == sticker_id) {
-        return rotation.rotation_radians;
-      }
-    }
-    LOG(WARNING) << "Cannot find sticker_id: " << sticker_id
-                 << ", returning 0.0f rotation";
-    return 0.0f;
-  }
-};
-
-REGISTER_CALCULATOR(MatricesManagerCalculator);
-
-absl::Status MatricesManagerCalculator::GetContract(CalculatorContract* cc) {
-  RET_CHECK(cc->Inputs().HasTag(kAnchorsTag) &&
-            cc->Inputs().HasTag(kIMUMatrixTag) &&
-            cc->Inputs().HasTag(kUserRotationsTag) &&
-            cc->Inputs().HasTag(kUserScalingsTag) &&
-            cc->InputSidePackets().HasTag(kFOVSidePacketTag) &&
-            cc->InputSidePackets().HasTag(kAspectRatioSidePacketTag));
-
-  cc->Inputs().Tag(kAnchorsTag).Set<std::vector<Anchor>>();
-  cc->Inputs().Tag(kIMUMatrixTag).Set<float[]>();
-  cc->Inputs().Tag(kUserScalingsTag).Set<std::vector<UserScaling>>();
-  cc->Inputs().Tag(kUserRotationsTag).Set<std::vector<UserRotation>>();
-  cc->Inputs().Tag(kRendersTag).Set<std::vector<int>>();
-  if (cc->Inputs().HasTag(kGifAspectRatioTag)) {
-    cc->Inputs().Tag(kGifAspectRatioTag).Set<float>();
-  }
-
-  for (CollectionItemId id = cc->Outputs().BeginId("MATRICES");
-       id < cc->Outputs().EndId("MATRICES"); ++id) {
-    cc->Outputs().Get(id).Set<mediapipe::TimedModelMatrixProtoList>();
-  }
-  cc->InputSidePackets().Tag(kFOVSidePacketTag).Set<float>();
-  cc->InputSidePackets().Tag(kAspectRatioSidePacketTag).Set<float>();
-
-  return absl::OkStatus();
-}
-
-absl::Status MatricesManagerCalculator::Open(CalculatorContext* cc) {
-  cc->SetOffset(TimestampDiff(0));
-  // Set device properties from side packets
-  vertical_fov_radians_ =
-      cc->InputSidePackets().Tag(kFOVSidePacketTag).Get<float>();
-  aspect_ratio_ =
-      cc->InputSidePackets().Tag(kAspectRatioSidePacketTag).Get<float>();
-  return absl::OkStatus();
-}
-
-absl::Status MatricesManagerCalculator::Process(CalculatorContext* cc) {
-  // Define each object's model matrices
-  auto asset_matrices_gif =
-      std::make_unique<mediapipe::TimedModelMatrixProtoList>();
-  auto asset_matrices_1 =
-      std::make_unique<mediapipe::TimedModelMatrixProtoList>();
-  // Clear all model matrices
-  asset_matrices_gif->clear_model_matrix();
-  asset_matrices_1->clear_model_matrix();
-
-  const std::vector<UserRotation> user_rotation_data =
-      cc->Inputs().Tag(kUserRotationsTag).Get<std::vector<UserRotation>>();
-
-  const std::vector<UserScaling> user_scaling_data =
-      cc->Inputs().Tag(kUserScalingsTag).Get<std::vector<UserScaling>>();
-
-  const std::vector<int> render_data =
-      cc->Inputs().Tag(kRendersTag).Get<std::vector<int>>();
-
-  const std::vector<Anchor> anchor_data =
-      cc->Inputs().Tag(kAnchorsTag).Get<std::vector<Anchor>>();
-  if (cc->Inputs().HasTag(kGifAspectRatioTag) &&
-      !cc->Inputs().Tag(kGifAspectRatioTag).IsEmpty()) {
-    gif_aspect_ratio_ = cc->Inputs().Tag(kGifAspectRatioTag).Get<float>();
-  }
-
-  // Device IMU rotation submatrix
-  const auto imu_matrix = cc->Inputs().Tag(kIMUMatrixTag).Get<float[]>();
-  Matrix3f imu_rotation_submatrix;
-  int idx = 0;
-  for (int x = 0; x < 3; ++x) {
-    for (int y = 0; y < 3; ++y) {
-      // Input matrix is row-major matrix, it must be reformatted to
-      // column-major via transpose procedure
-      imu_rotation_submatrix(y, x) = imu_matrix[idx++];
-    }
-  }
-
-  int render_idx = 0;
-  for (const Anchor& anchor : anchor_data) {
-    const int id = anchor.sticker_id;
-    mediapipe::TimedModelMatrixProto* model_matrix;
-    // Add model matrix to matrices list for defined object render ID
-    if (render_data[render_idx] == 0) {  // GIF
-      model_matrix = asset_matrices_gif->add_model_matrix();
-    } else {  // Asset 3D
-      if (render_data[render_idx] != 1) {
-        LOG(ERROR) << "render id: " << render_data[render_idx]
-                   << " is not supported. Fall back to using render_id = 1.";
-      }
-      model_matrix = asset_matrices_1->add_model_matrix();
-    }
-
-    model_matrix->set_id(id);
-
-    // The user transformation data associated with this sticker must be defined
-    const float user_rotation_radians = GetUserRotation(user_rotation_data, id);
-    const float user_scale_factor = GetUserScaler(user_scaling_data, id);
-
-    // A vector representative of a user's sticker rotation transformation can
-    // be created
-    const Matrix3f user_rotation_submatrix =
-        GenerateUserRotationMatrix(user_rotation_radians);
-    // Next, the diagonal representative of the combined scaling data
-    const DiagonalMatrix3f scaling_diagonal = GetDefaultRenderScaleDiagonal(
-        render_data[render_idx], user_scale_factor, gif_aspect_ratio_);
-    // Increment to next render id from vector
-    render_idx++;
-
-    // The user transformation data can be concatenated into a final rotation
-    // submatrix with the device IMU rotational data
-    const Matrix3f user_transformed_rotation_submatrix =
-        imu_rotation_submatrix * user_rotation_submatrix * scaling_diagonal;
-
-    // A vector representative of the translation of the object in OpenGL
-    // coordinate space must be generated
-    const Vector3f translation_vector = GenerateAnchorVector(anchor);
-
-    // Concatenate all model matrix data
-    const Matrix4fCM final_model_matrix = GenerateEigenModelMatrix(
-        translation_vector, user_transformed_rotation_submatrix);
-
-    // The generated model matrix must be mapped to TimedModelMatrixProto
-    // (col-wise)
-    for (int x = 0; x < final_model_matrix.rows(); ++x) {
-      for (int y = 0; y < final_model_matrix.cols(); ++y) {
-        model_matrix->add_matrix_entries(final_model_matrix(x, y));
-      }
-    }
-  }
-
-  // Output all individual render matrices
-  // TODO: Perform depth ordering with gl_animation_overlay_calculator to render
-  // objects in order by depth to allow occlusion.
-  cc->Outputs()
-      .Get(cc->Outputs().GetId("MATRICES", 0))
-      .Add(asset_matrices_gif.release(), cc->InputTimestamp());
-  cc->Outputs()
-      .Get(cc->Outputs().GetId("MATRICES", 1))
-      .Add(asset_matrices_1.release(), cc->InputTimestamp());
-
-  return absl::OkStatus();
-}
-
-// Using a specified rotation value in radians, generate a rotation matrix for
-// use with base rotation submatrix
-const Matrix3f MatricesManagerCalculator::GenerateUserRotationMatrix(
-    const float rotation_radians) const {
-  Eigen::Matrix3f user_rotation_submatrix;
-  user_rotation_submatrix =
-      // The rotation in radians must be inverted to rotate the object
-      // with the direction of finger movement from the user (system dependent)
-      Eigen::AngleAxisf(-rotation_radians, Eigen::Vector3f::UnitY()) *
-      Eigen::AngleAxisf(0.0f, Eigen::Vector3f::UnitZ()) *
-      // Model orientations all assume z-axis is up, but we need y-axis upwards,
-      // therefore, a +(M_PI * 0.5f) transformation must be applied
-      // TODO: Bring default rotations, translations, and scalings into
-      // independent sticker configuration
-      Eigen::AngleAxisf(M_PI * 0.5f, Eigen::Vector3f::UnitX());
-  // Matrix must be transposed due to the method of submatrix generation in
-  // Eigen
-  return user_rotation_submatrix.transpose();
-}
-
-// TODO: Investigate possible differences in warping of tracking speed across
-// screen Using the sticker anchor data, a translation vector can be generated
-// in OpenGL coordinate space
-const Vector3f MatricesManagerCalculator::GenerateAnchorVector(
-    const Anchor& tracked_anchor) const {
-  // Using an initial z-value in OpenGL space, generate a new base z-axis value
-  // to mimic scaling by distance.
-  const float z = kInitialZ * tracked_anchor.z;
-
-  // Using triangle geometry, the minimum for a y-coordinate that will appear in
-  // the view field for the given z value above can be found.
-  const float y_half_range = z * (tan(vertical_fov_radians_ * 0.5f));
-
-  // The aspect ratio of the device and y_minimum calculated above can be used
-  // to find the minimum value for x that will appear in the view field of the
-  // device screen.
-  const float x_half_range = y_half_range * aspect_ratio_;
-
-  // Given the minimum bounds of the screen in OpenGL space, the tracked anchor
-  // coordinates can be converted to OpenGL coordinate space.
-  //
-  // (i.e: X and Y will be converted from [0.0-1.0] space to [x_minimum,
-  // -x_minimum] space and [y_minimum, -y_minimum] space respectively)
-  const float x = (-2.0f * tracked_anchor.x * x_half_range) + x_half_range;
-  const float y = (-2.0f * tracked_anchor.y * y_half_range) + y_half_range;
-
-  // A translation transformation vector can be generated via Eigen
-  const Vector3f t_vector(x, y, z);
-  return t_vector;
-}
-
-// Generates a model matrix via Eigen with appropriate transformations
-const Matrix4fCM MatricesManagerCalculator::GenerateEigenModelMatrix(
-    const Vector3f& translation_vector,
-    const Matrix3f& rotation_submatrix) const {
-  // Define basic empty model matrix
-  Matrix4fCM mvp_matrix;
-
-  // Set the translation vector
-  mvp_matrix.topRightCorner<3, 1>() = translation_vector;
-
-  // Set the rotation submatrix
-  mvp_matrix.topLeftCorner<3, 3>() = rotation_submatrix;
-
-  // Set trailing 1.0 required by OpenGL to define coordinate space
-  mvp_matrix(3, 3) = 1.0f;
-
-  return mvp_matrix;
-}
-
-// This returns a scaling matrix to alter the projection matrix for
-// the specified render id in order to ensure all objects render at a similar
-// size in the view screen upon initial placement
-DiagonalMatrix3f MatricesManagerCalculator::GetDefaultRenderScaleDiagonal(
-    const int render_id, const float user_scale_factor,
-    const float gif_aspect_ratio) const {
-  float scale_preset = 1.0f;
-  float x_scalar = 1.0f;
-  float y_scalar = 1.0f;
-
-  switch (render_id) {
-    case 0: {  // GIF
-      // 160 is the scaling preset to make the GIF asset appear relatively
-      // similar in size to all other assets
-      scale_preset = 160.0f;
-      if (gif_aspect_ratio >= 1.0f) {
-        // GIF is wider horizontally (scale on x-axis)
-        x_scalar = gif_aspect_ratio;
-      } else {
-        // GIF is wider vertically (scale on y-axis)
-        y_scalar = 1.0f / gif_aspect_ratio;
-      }
-      break;
-    }
-    case 1: {  // 3D asset
-      // 5 is the scaling preset to make the 3D asset appear relatively
-      // similar in size to all other assets
-      scale_preset = 5.0f;
-      break;
-    }
-    default: {
-      LOG(INFO) << "Unsupported render_id: " << render_id
-                << ", returning default render_scale";
-      break;
-    }
-  }
-
-  DiagonalMatrix3f scaling(scale_preset * user_scale_factor * x_scalar,
-                           scale_preset * user_scale_factor * y_scalar,
-                           scale_preset * user_scale_factor);
-  return scaling;
-}
-}  // namespace mediapipe
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/sticker_buffer.proto b/mediapipe/graphs/instant_motion_tracking/calculators/sticker_buffer.proto
deleted file mode 100644
index b73209c..0000000
--- a/mediapipe/graphs/instant_motion_tracking/calculators/sticker_buffer.proto
+++ /dev/null
@@ -1,33 +0,0 @@
-// Copyright 2020 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-option java_package = "com.google.mediapipe.graphs.instantmotiontracking";
-option java_outer_classname = "StickerBufferProto";
-
-message Sticker {
-  optional int32 id = 1;
-  optional float x = 2;
-  optional float y = 3;
-  optional float rotation = 4;
-  optional float scale = 5;
-  optional int32 render_id = 6;
-}
-
-message StickerRoll {
-  repeated Sticker sticker = 1;
-}
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/sticker_manager_calculator.cc b/mediapipe/graphs/instant_motion_tracking/calculators/sticker_manager_calculator.cc
deleted file mode 100644
index 40210c2..0000000
--- a/mediapipe/graphs/instant_motion_tracking/calculators/sticker_manager_calculator.cc
+++ /dev/null
@@ -1,150 +0,0 @@
-// Copyright 2020 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <vector>
-
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/graphs/instant_motion_tracking/calculators/sticker_buffer.pb.h"
-#include "mediapipe/graphs/instant_motion_tracking/calculators/transformations.h"
-
-namespace mediapipe {
-
-constexpr char kProtoDataString[] = "PROTO";
-constexpr char kAnchorsTag[] = "ANCHORS";
-constexpr char kUserRotationsTag[] = "USER_ROTATIONS";
-constexpr char kUserScalingsTag[] = "USER_SCALINGS";
-constexpr char kRenderDescriptorsTag[] = "RENDER_DATA";
-
-// This calculator takes in the sticker protobuffer data and parses each
-// individual sticker object into anchors, user rotations and scalings, in
-// addition to basic render data represented in integer form.
-//
-// Input:
-//  PROTO - String of sticker data in appropriate protobuf format [REQUIRED]
-// Output:
-//  ANCHORS - Anchors with initial normalized X,Y coordinates [REQUIRED]
-//  USER_ROTATIONS - UserRotations with radians of rotation from user [REQUIRED]
-//  USER_SCALINGS - UserScalings with increment of scaling from user [REQUIRED]
-//  RENDER_DATA - Descriptors of which objects/animations to render for stickers
-//  [REQUIRED]
-//
-// Example config:
-// node {
-//   calculator: "StickerManagerCalculator"
-//   input_stream: "PROTO:sticker_proto_string"
-//   output_stream: "ANCHORS:initial_anchor_data"
-//   output_stream: "USER_ROTATIONS:user_rotation_data"
-//   output_stream: "USER_SCALINGS:user_scaling_data"
-//   output_stream: "RENDER_DATA:sticker_render_data"
-// }
-
-class StickerManagerCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc) {
-    RET_CHECK(cc->Inputs().HasTag(kProtoDataString));
-    RET_CHECK(cc->Outputs().HasTag(kAnchorsTag) &&
-              cc->Outputs().HasTag(kUserRotationsTag) &&
-              cc->Outputs().HasTag(kUserScalingsTag) &&
-              cc->Outputs().HasTag(kRenderDescriptorsTag));
-
-    cc->Inputs().Tag(kProtoDataString).Set<std::string>();
-    cc->Outputs().Tag(kAnchorsTag).Set<std::vector<Anchor>>();
-    cc->Outputs().Tag(kUserRotationsTag).Set<std::vector<UserRotation>>();
-    cc->Outputs().Tag(kUserScalingsTag).Set<std::vector<UserScaling>>();
-    cc->Outputs().Tag(kRenderDescriptorsTag).Set<std::vector<int>>();
-
-    return absl::OkStatus();
-  }
-
-  absl::Status Open(CalculatorContext* cc) override {
-    cc->SetOffset(TimestampDiff(0));
-    return absl::OkStatus();
-  }
-
-  absl::Status Process(CalculatorContext* cc) override {
-    std::string sticker_proto_string =
-        cc->Inputs().Tag(kProtoDataString).Get<std::string>();
-
-    std::vector<Anchor> initial_anchor_data;
-    std::vector<UserRotation> user_rotation_data;
-    std::vector<UserScaling> user_scaling_data;
-    std::vector<int> render_data;
-
-    ::mediapipe::StickerRoll sticker_roll;
-    bool parse_success = sticker_roll.ParseFromString(sticker_proto_string);
-
-    // Ensure parsing was a success
-    RET_CHECK(parse_success) << "Error parsing sticker protobuf data";
-
-    for (int i = 0; i < sticker_roll.sticker().size(); ++i) {
-      // Declare empty structures for sticker data
-      Anchor initial_anchor;
-      UserRotation user_rotation;
-      UserScaling user_scaling;
-      // Get individual Sticker object as defined by Protobuffer
-      ::mediapipe::Sticker sticker = sticker_roll.sticker(i);
-      // Set individual data structure ids to associate with this sticker
-      initial_anchor.sticker_id = sticker.id();
-      user_rotation.sticker_id = sticker.id();
-      user_scaling.sticker_id = sticker.id();
-      initial_anchor.x = sticker.x();
-      initial_anchor.y = sticker.y();
-      initial_anchor.z = 1.0f;  // default to 1.0 in normalized 3d space
-      user_rotation.rotation_radians = sticker.rotation();
-      user_scaling.scale_factor = sticker.scale();
-      const int render_id = sticker.render_id();
-      // Set all vector data with sticker attributes
-      initial_anchor_data.emplace_back(initial_anchor);
-      user_rotation_data.emplace_back(user_rotation);
-      user_scaling_data.emplace_back(user_scaling);
-      render_data.emplace_back(render_id);
-    }
-
-    if (cc->Outputs().HasTag(kAnchorsTag)) {
-      cc->Outputs()
-          .Tag(kAnchorsTag)
-          .AddPacket(MakePacket<std::vector<Anchor>>(initial_anchor_data)
-                         .At(cc->InputTimestamp()));
-    }
-    if (cc->Outputs().HasTag(kUserRotationsTag)) {
-      cc->Outputs()
-          .Tag(kUserRotationsTag)
-          .AddPacket(MakePacket<std::vector<UserRotation>>(user_rotation_data)
-                         .At(cc->InputTimestamp()));
-    }
-    if (cc->Outputs().HasTag(kUserScalingsTag)) {
-      cc->Outputs()
-          .Tag(kUserScalingsTag)
-          .AddPacket(MakePacket<std::vector<UserScaling>>(user_scaling_data)
-                         .At(cc->InputTimestamp()));
-    }
-    if (cc->Outputs().HasTag(kRenderDescriptorsTag)) {
-      cc->Outputs()
-          .Tag(kRenderDescriptorsTag)
-          .AddPacket(MakePacket<std::vector<int>>(render_data)
-                         .At(cc->InputTimestamp()));
-    }
-
-    return absl::OkStatus();
-  }
-
-  absl::Status Close(CalculatorContext* cc) override {
-    return absl::OkStatus();
-  }
-};
-
-REGISTER_CALCULATOR(StickerManagerCalculator);
-}  // namespace mediapipe
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/tracked_anchor_manager_calculator.cc b/mediapipe/graphs/instant_motion_tracking/calculators/tracked_anchor_manager_calculator.cc
deleted file mode 100644
index 446aee7..0000000
--- a/mediapipe/graphs/instant_motion_tracking/calculators/tracked_anchor_manager_calculator.cc
+++ /dev/null
@@ -1,210 +0,0 @@
-// Copyright 2020 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/graphs/instant_motion_tracking/calculators/transformations.h"
-#include "mediapipe/util/tracking/box_tracker.pb.h"
-
-namespace mediapipe {
-
-constexpr char kSentinelTag[] = "SENTINEL";
-constexpr char kAnchorsTag[] = "ANCHORS";
-constexpr char kBoxesInputTag[] = "BOXES";
-constexpr char kBoxesOutputTag[] = "START_POS";
-constexpr char kCancelTag[] = "CANCEL_ID";
-// TODO: Find optimal Height/Width (0.1-0.3)
-constexpr float kBoxEdgeSize =
-    0.2f;  // Used to establish tracking box dimensions
-constexpr float kUsToMs =
-    1000.0f;  // Used to convert from microseconds to millis
-
-// This calculator manages the regions being tracked for each individual sticker
-// and adjusts the regions being tracked if a change is detected in a sticker's
-// initial anchor placement. Regions being tracked that have no associated
-// sticker will be automatically removed upon the next iteration of the graph to
-// optimize performance and remove all sticker artifacts
-//
-// Input:
-//  SENTINEL - ID of sticker which has an anchor that must be reset (-1 when no
-//  anchor must be reset) [REQUIRED]
-//  ANCHORS - Initial anchor data (tracks changes and where to re/position)
-//  [REQUIRED] BOXES - Used in cycle, boxes being tracked meant to update
-//  positions [OPTIONAL
-//  - provided by subgraph]
-// Output:
-//  START_POS - Positions of boxes being tracked (can be overwritten with ID)
-//  [REQUIRED] CANCEL_ID - Single integer ID of tracking box to remove from
-//  tracker subgraph [OPTIONAL] ANCHORS - Updated set of anchors with tracked
-//  and normalized X,Y,Z [REQUIRED]
-//
-// Example config:
-// node {
-//   calculator: "TrackedAnchorManagerCalculator"
-//   input_stream: "SENTINEL:sticker_sentinel"
-//   input_stream: "ANCHORS:initial_anchor_data"
-//   input_stream: "BOXES:boxes"
-//   input_stream_info: {
-//     tag_index: 'BOXES'
-//     back_edge: true
-//   }
-//   output_stream: "START_POS:start_pos"
-//   output_stream: "CANCEL_ID:cancel_object_id"
-//   output_stream: "ANCHORS:tracked_scaled_anchor_data"
-// }
-
-class TrackedAnchorManagerCalculator : public CalculatorBase {
- private:
-  // Previous graph iteration anchor data
-  std::vector<Anchor> previous_anchor_data_;
-
- public:
-  static absl::Status GetContract(CalculatorContract* cc) {
-    RET_CHECK(cc->Inputs().HasTag(kAnchorsTag) &&
-              cc->Inputs().HasTag(kSentinelTag));
-    RET_CHECK(cc->Outputs().HasTag(kAnchorsTag) &&
-              cc->Outputs().HasTag(kBoxesOutputTag));
-
-    cc->Inputs().Tag(kAnchorsTag).Set<std::vector<Anchor>>();
-    cc->Inputs().Tag(kSentinelTag).Set<int>();
-
-    if (cc->Inputs().HasTag(kBoxesInputTag)) {
-      cc->Inputs().Tag(kBoxesInputTag).Set<mediapipe::TimedBoxProtoList>();
-    }
-
-    cc->Outputs().Tag(kAnchorsTag).Set<std::vector<Anchor>>();
-    cc->Outputs().Tag(kBoxesOutputTag).Set<mediapipe::TimedBoxProtoList>();
-
-    if (cc->Outputs().HasTag(kCancelTag)) {
-      cc->Outputs().Tag(kCancelTag).Set<int>();
-    }
-
-    return absl::OkStatus();
-  }
-
-  absl::Status Open(CalculatorContext* cc) override { return absl::OkStatus(); }
-
-  absl::Status Process(CalculatorContext* cc) override;
-};
-REGISTER_CALCULATOR(TrackedAnchorManagerCalculator);
-
-absl::Status TrackedAnchorManagerCalculator::Process(CalculatorContext* cc) {
-  mediapipe::Timestamp timestamp = cc->InputTimestamp();
-  const int sticker_sentinel = cc->Inputs().Tag(kSentinelTag).Get<int>();
-  std::vector<Anchor> current_anchor_data =
-      cc->Inputs().Tag(kAnchorsTag).Get<std::vector<Anchor>>();
-  auto pos_boxes = absl::make_unique<mediapipe::TimedBoxProtoList>();
-  std::vector<Anchor> tracked_scaled_anchor_data;
-
-  // Delete any boxes being tracked without an associated anchor
-  for (const mediapipe::TimedBoxProto& box :
-       cc->Inputs()
-           .Tag(kBoxesInputTag)
-           .Get<mediapipe::TimedBoxProtoList>()
-           .box()) {
-    bool anchor_exists = false;
-    for (Anchor anchor : current_anchor_data) {
-      if (box.id() == anchor.sticker_id) {
-        anchor_exists = true;
-        break;
-      }
-    }
-    if (!anchor_exists) {
-      cc->Outputs()
-          .Tag(kCancelTag)
-          .AddPacket(MakePacket<int>(box.id()).At(timestamp++));
-    }
-  }
-
-  // Perform tracking or updating for each anchor position
-  for (const Anchor& anchor : current_anchor_data) {
-    Anchor output_anchor = anchor;
-    // Check if anchor position is being reset by user in this graph iteration
-    if (sticker_sentinel == anchor.sticker_id) {
-      // Delete associated tracking box
-      // TODO: BoxTrackingSubgraph should accept vector to avoid breaking
-      // timestamp rules
-      cc->Outputs()
-          .Tag(kCancelTag)
-          .AddPacket(MakePacket<int>(anchor.sticker_id).At(timestamp++));
-      // Add a tracking box
-      mediapipe::TimedBoxProto* box = pos_boxes->add_box();
-      box->set_left(anchor.x - kBoxEdgeSize * 0.5f);
-      box->set_right(anchor.x + kBoxEdgeSize * 0.5f);
-      box->set_top(anchor.y - kBoxEdgeSize * 0.5f);
-      box->set_bottom(anchor.y + kBoxEdgeSize * 0.5f);
-      box->set_id(anchor.sticker_id);
-      box->set_time_msec((timestamp++).Microseconds() / kUsToMs);
-      // Default value for normalized z (scale factor)
-      output_anchor.z = 1.0f;
-    } else {
-      // Anchor position was not reset by user
-      // Attempt to update anchor position from tracking subgraph
-      // (TimedBoxProto)
-      bool updated_from_tracker = false;
-      const mediapipe::TimedBoxProtoList box_list =
-          cc->Inputs().Tag(kBoxesInputTag).Get<mediapipe::TimedBoxProtoList>();
-      for (const auto& box : box_list.box()) {
-        if (box.id() == anchor.sticker_id) {
-          // Get center x normalized coordinate [0.0-1.0]
-          output_anchor.x = (box.left() + box.right()) * 0.5f;
-          // Get center y normalized coordinate [0.0-1.0]
-          output_anchor.y = (box.top() + box.bottom()) * 0.5f;
-          // Get center z coordinate [z starts at normalized 1.0 and scales
-          // inversely with box-width]
-          // TODO: Look into issues with uniform scaling on x-axis and y-axis
-          output_anchor.z = kBoxEdgeSize / (box.right() - box.left());
-          updated_from_tracker = true;
-          break;
-        }
-      }
-      // If anchor position was not updated from tracker, create new tracking
-      // box at last recorded anchor coordinates. This will allow all current
-      // stickers to be tracked at approximately last location even if
-      // re-acquisitioning in the BoxTrackingSubgraph encounters errors
-      if (!updated_from_tracker) {
-        for (const Anchor& prev_anchor : previous_anchor_data_) {
-          if (anchor.sticker_id == prev_anchor.sticker_id) {
-            mediapipe::TimedBoxProto* box = pos_boxes->add_box();
-            box->set_left(prev_anchor.x - kBoxEdgeSize * 0.5f);
-            box->set_right(prev_anchor.x + kBoxEdgeSize * 0.5f);
-            box->set_top(prev_anchor.y - kBoxEdgeSize * 0.5f);
-            box->set_bottom(prev_anchor.y + kBoxEdgeSize * 0.5f);
-            box->set_id(prev_anchor.sticker_id);
-            box->set_time_msec(cc->InputTimestamp().Microseconds() / kUsToMs);
-            output_anchor = prev_anchor;
-            // Default value for normalized z (scale factor)
-            output_anchor.z = 1.0f;
-            break;
-          }
-        }
-      }
-    }
-    tracked_scaled_anchor_data.emplace_back(output_anchor);
-  }
-  // Set anchor data for next iteration
-  previous_anchor_data_ = tracked_scaled_anchor_data;
-
-  cc->Outputs()
-      .Tag(kAnchorsTag)
-      .AddPacket(MakePacket<std::vector<Anchor>>(tracked_scaled_anchor_data)
-                     .At(cc->InputTimestamp()));
-  cc->Outputs()
-      .Tag(kBoxesOutputTag)
-      .Add(pos_boxes.release(), cc->InputTimestamp());
-
-  return absl::OkStatus();
-}
-}  // namespace mediapipe
diff --git a/mediapipe/graphs/instant_motion_tracking/calculators/transformations.h b/mediapipe/graphs/instant_motion_tracking/calculators/transformations.h
deleted file mode 100644
index cbacdb7..0000000
--- a/mediapipe/graphs/instant_motion_tracking/calculators/transformations.h
+++ /dev/null
@@ -1,42 +0,0 @@
-// Copyright 2020 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_GRAPHS_INSTANT_MOTION_TRACKING_CALCULATORS_TRANSFORMATIONS_H_
-#define MEDIAPIPE_GRAPHS_INSTANT_MOTION_TRACKING_CALCULATORS_TRANSFORMATIONS_H_
-
-namespace mediapipe {
-
-// Radians by which to rotate the object (Provided by UI input)
-struct UserRotation {
-  float rotation_radians;
-  int sticker_id;
-};
-
-// Scaling factor provided by the UI application end
-struct UserScaling {
-  float scale_factor;
-  int sticker_id;
-};
-
-// The normalized anchor coordinates of a sticker
-struct Anchor {
-  float x;  // [0.0-1.0]
-  float y;  // [0.0-1.0]
-  float z;  // Centered around 1.0 [current_scale = z * initial_scale]
-  int sticker_id;
-};
-
-}  // namespace mediapipe
-
-#endif  // MEDIAPIPE_GRAPHS_INSTANT_MOTION_TRACKING_CALCULATORS_TRANSFORMATIONS_H_
diff --git a/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt b/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt
deleted file mode 100644
index 468262b..0000000
--- a/mediapipe/graphs/instant_motion_tracking/instant_motion_tracking.pbtxt
+++ /dev/null
@@ -1,80 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# MediaPipe graph that performs region tracking and 3d object (AR sticker) rendering.
-
-# Images in/out of graph with sticker data and IMU information from device
-input_stream: "input_video"
-input_stream: "sticker_sentinel"
-input_stream: "sticker_proto_string"
-input_stream: "imu_rotation_matrix"
-input_stream: "gif_texture"
-input_stream: "gif_aspect_ratio"
-output_stream: "output_video"
-
-# Converts sticker data into user data (rotations/scalings), render data, and
-# initial anchors.
-node {
-  calculator: "StickerManagerCalculator"
-  input_stream: "PROTO:sticker_proto_string"
-  output_stream: "ANCHORS:initial_anchor_data"
-  output_stream: "USER_ROTATIONS:user_rotation_data"
-  output_stream: "USER_SCALINGS:user_scaling_data"
-  output_stream: "RENDER_DATA:sticker_render_data"
-}
-
-# Uses box tracking in order to create 'anchors' for associated 3d stickers.
-node {
-  calculator: "RegionTrackingSubgraph"
-  input_stream: "VIDEO:input_video"
-  input_stream: "SENTINEL:sticker_sentinel"
-  input_stream: "ANCHORS:initial_anchor_data"
-  output_stream: "ANCHORS:tracked_anchor_data"
-}
-
-# Concatenates all transformations to generate model matrices for the OpenGL
-# animation overlay calculator.
-node {
-  calculator: "MatricesManagerCalculator"
-  input_stream: "ANCHORS:tracked_anchor_data"
-  input_stream: "IMU_ROTATION:imu_rotation_matrix"
-  input_stream: "USER_ROTATIONS:user_rotation_data"
-  input_stream: "USER_SCALINGS:user_scaling_data"
-  input_stream: "RENDER_DATA:sticker_render_data"
-  input_stream: "GIF_ASPECT_RATIO:gif_aspect_ratio"
-  output_stream: "MATRICES:0:gif_matrices"
-  output_stream: "MATRICES:1:asset_3d_matrices"
-  input_side_packet: "FOV:vertical_fov_radians"
-  input_side_packet: "ASPECT_RATIO:aspect_ratio"
-}
-
-# Renders the final 3d stickers and overlays them on input image.
-node {
-  calculator: "GlAnimationOverlayCalculator"
-  input_stream: "VIDEO:input_video"
-  input_stream: "MODEL_MATRICES:gif_matrices"
-  input_stream: "TEXTURE:gif_texture"
-  input_side_packet: "ANIMATION_ASSET:gif_asset_name"
-  output_stream: "asset_gif_rendered"
-}
-
-# Renders the final 3d stickers and overlays them on top of the input image.
-node {
-  calculator: "GlAnimationOverlayCalculator"
-  input_stream: "VIDEO:asset_gif_rendered"
-  input_stream: "MODEL_MATRICES:asset_3d_matrices"
-  input_side_packet: "TEXTURE:texture_3d"
-  input_side_packet: "ANIMATION_ASSET:asset_3d"
-  output_stream: "output_video"
-}
diff --git a/mediapipe/graphs/instant_motion_tracking/subgraphs/BUILD b/mediapipe/graphs/instant_motion_tracking/subgraphs/BUILD
deleted file mode 100644
index cd1561b..0000000
--- a/mediapipe/graphs/instant_motion_tracking/subgraphs/BUILD
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "region_tracking",
-    graph = "region_tracking.pbtxt",
-    register_as = "RegionTrackingSubgraph",
-    deps = [
-        "//mediapipe/graphs/instant_motion_tracking/calculators:tracked_anchor_manager_calculator",
-        "//mediapipe/graphs/tracking/subgraphs:box_tracking_gpu",
-    ],
-)
diff --git a/mediapipe/graphs/instant_motion_tracking/subgraphs/region_tracking.pbtxt b/mediapipe/graphs/instant_motion_tracking/subgraphs/region_tracking.pbtxt
deleted file mode 100644
index f8ef3ad..0000000
--- a/mediapipe/graphs/instant_motion_tracking/subgraphs/region_tracking.pbtxt
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright 2020 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# MediaPipe graph that performs region tracking on initial anchor positions
-# provided by the application
-
-# Images in/out of graph with tracked and scaled normalized anchor data
-type: "RegionTrackingSubgraph"
-input_stream: "VIDEO:input_video"
-input_stream: "SENTINEL:sticker_sentinel"
-input_stream: "ANCHORS:initial_anchor_data"
-output_stream: "ANCHORS:tracked_scaled_anchor_data"
-
-# Manages the anchors and tracking if user changes/adds/deletes anchors
-node {
- calculator: "TrackedAnchorManagerCalculator"
- input_stream: "SENTINEL:sticker_sentinel"
- input_stream: "ANCHORS:initial_anchor_data"
- input_stream: "BOXES:boxes"
- input_stream_info: {
-   tag_index: 'BOXES'
-   back_edge: true
- }
- output_stream: "START_POS:start_pos"
- output_stream: "CANCEL_ID:cancel_object_id"
- output_stream: "ANCHORS:tracked_scaled_anchor_data"
-}
-
-# Subgraph performs anchor placement and tracking
-node {
-  calculator: "BoxTrackingSubgraphGpu"
-  input_stream: "VIDEO:input_video"
-  input_stream: "BOXES:start_pos"
-  input_stream: "CANCEL_ID:cancel_object_id"
-  output_stream: "BOXES:boxes"
-}
diff --git a/mediapipe/graphs/iris_tracking/BUILD b/mediapipe/graphs/iris_tracking/BUILD
deleted file mode 100644
index 86e667b..0000000
--- a/mediapipe/graphs/iris_tracking/BUILD
+++ /dev/null
@@ -1,86 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "iris_depth_cpu_deps",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/image:image_file_properties_calculator",
-        "//mediapipe/calculators/image:opencv_encoded_image_to_image_frame_calculator",
-        "//mediapipe/calculators/image:opencv_image_encoder_calculator",
-        "//mediapipe/graphs/iris_tracking/calculators:update_face_landmarks_calculator",
-        "//mediapipe/graphs/iris_tracking/subgraphs:iris_and_depth_renderer_cpu",
-        "//mediapipe/modules/face_landmark:face_landmark_front_cpu",
-        "//mediapipe/modules/iris_landmark:iris_landmark_left_and_right_cpu",
-    ],
-)
-
-cc_library(
-    name = "iris_tracking_cpu_deps",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/graphs/iris_tracking/calculators:update_face_landmarks_calculator",
-        "//mediapipe/graphs/iris_tracking/subgraphs:iris_renderer_cpu",
-        "//mediapipe/modules/face_landmark:face_landmark_front_cpu",
-        "//mediapipe/modules/iris_landmark:iris_landmark_left_and_right_cpu",
-    ],
-)
-
-cc_library(
-    name = "iris_tracking_cpu_video_input_deps",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
-        "//mediapipe/graphs/iris_tracking/calculators:update_face_landmarks_calculator",
-        "//mediapipe/graphs/iris_tracking/subgraphs:iris_renderer_cpu",
-        "//mediapipe/modules/face_landmark:face_landmark_front_cpu",
-        "//mediapipe/modules/iris_landmark:iris_landmark_left_and_right_cpu",
-    ],
-)
-
-cc_library(
-    name = "iris_tracking_gpu_deps",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/graphs/iris_tracking/calculators:update_face_landmarks_calculator",
-        "//mediapipe/graphs/iris_tracking/subgraphs:iris_and_depth_renderer_gpu",
-        "//mediapipe/modules/face_landmark:face_landmark_front_gpu",
-        "//mediapipe/modules/iris_landmark:iris_landmark_left_and_right_gpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "iris_tracking_gpu_binary_graph",
-    graph = "iris_tracking_gpu.pbtxt",
-    output_name = "iris_tracking_gpu.binarypb",
-    deps = [":iris_tracking_gpu_deps"],
-)
diff --git a/mediapipe/graphs/iris_tracking/calculators/BUILD b/mediapipe/graphs/iris_tracking/calculators/BUILD
deleted file mode 100644
index 3a3d57a..0000000
--- a/mediapipe/graphs/iris_tracking/calculators/BUILD
+++ /dev/null
@@ -1,107 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//mediapipe/framework/port:build_config.bzl", "mediapipe_cc_proto_library")
-
-licenses(["notice"])
-
-proto_library(
-    name = "iris_to_render_data_calculator_proto",
-    srcs = ["iris_to_render_data_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_proto",
-        "//mediapipe/util:color_proto",
-        "//mediapipe/util:render_data_proto",
-    ],
-)
-
-mediapipe_cc_proto_library(
-    name = "iris_to_render_data_calculator_cc_proto",
-    srcs = ["iris_to_render_data_calculator.proto"],
-    cc_deps = [
-        "//mediapipe/framework:calculator_cc_proto",
-        "//mediapipe/util:color_cc_proto",
-        "//mediapipe/util:render_data_cc_proto",
-    ],
-    visibility = ["//visibility:public"],
-    deps = [":iris_to_render_data_calculator_proto"],
-)
-
-cc_library(
-    name = "iris_to_render_data_calculator",
-    srcs = ["iris_to_render_data_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":iris_to_render_data_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/formats:landmark_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/util:color_cc_proto",
-        "//mediapipe/util:render_data_cc_proto",
-        "@com_google_absl//absl/strings",
-    ],
-    alwayslink = 1,
-)
-
-proto_library(
-    name = "iris_to_depth_calculator_proto",
-    srcs = ["iris_to_depth_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-mediapipe_cc_proto_library(
-    name = "iris_to_depth_calculator_cc_proto",
-    srcs = ["iris_to_depth_calculator.proto"],
-    cc_deps = [
-        "//mediapipe/framework:calculator_cc_proto",
-    ],
-    visibility = ["//visibility:public"],
-    deps = [":iris_to_depth_calculator_proto"],
-)
-
-cc_library(
-    name = "iris_to_depth_calculator",
-    srcs = ["iris_to_depth_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":iris_to_depth_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/formats:image_file_properties_cc_proto",
-        "//mediapipe/framework/formats:landmark_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "@com_google_absl//absl/strings",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "update_face_landmarks_calculator",
-    srcs = ["update_face_landmarks_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/formats:image_file_properties_cc_proto",
-        "//mediapipe/framework/formats:landmark_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "@com_google_absl//absl/strings",
-    ],
-    alwayslink = 1,
-)
diff --git a/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.cc b/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.cc
deleted file mode 100644
index 3522274..0000000
--- a/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.cc
+++ /dev/null
@@ -1,245 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <cmath>
-#include <memory>
-
-#include "absl/strings/str_cat.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/formats/image_file_properties.pb.h"
-#include "mediapipe/framework/formats/landmark.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.pb.h"
-
-namespace mediapipe {
-
-namespace {
-
-constexpr char kIrisTag[] = "IRIS";
-constexpr char kImageSizeTag[] = "IMAGE_SIZE";
-constexpr char kFocalLengthPixelTag[] = "FOCAL_LENGTH";
-constexpr char kImageFilePropertiesTag[] = "IMAGE_FILE_PROPERTIES";
-constexpr char kLeftIrisDepthTag[] = "LEFT_IRIS_DEPTH_MM";
-constexpr char kRightIrisDepthTag[] = "RIGHT_IRIS_DEPTH_MM";
-constexpr int kNumIrisLandmarksPerEye = 5;
-constexpr float kDepthWeightUpdate = 0.1;
-// Avergae fixed iris size across human beings.
-constexpr float kIrisSizeInMM = 11.8;
-
-inline float GetDepth(float x0, float y0, float x1, float y1) {
-  return std::sqrt((x0 - x1) * (x0 - x1) + (y0 - y1) * (y0 - y1));
-}
-
-inline float GetLandmarkDepth(const NormalizedLandmark& ld0,
-                              const NormalizedLandmark& ld1,
-                              const std::pair<int, int>& image_size) {
-  return GetDepth(ld0.x() * image_size.first, ld0.y() * image_size.second,
-                  ld1.x() * image_size.first, ld1.y() * image_size.second);
-}
-
-float CalculateIrisDiameter(const NormalizedLandmarkList& landmarks,
-                            const std::pair<int, int>& image_size) {
-  const float dist_vert = GetLandmarkDepth(landmarks.landmark(1),
-                                           landmarks.landmark(2), image_size);
-  const float dist_hori = GetLandmarkDepth(landmarks.landmark(3),
-                                           landmarks.landmark(4), image_size);
-  return (dist_hori + dist_vert) / 2.0f;
-}
-
-float CalculateDepth(const NormalizedLandmark& center, float focal_length,
-                     float iris_size, float img_w, float img_h) {
-  std::pair<float, float> origin{img_w / 2.f, img_h / 2.f};
-  const auto y = GetDepth(origin.first, origin.second, center.x() * img_w,
-                          center.y() * img_h);
-  const auto x = std::sqrt(focal_length * focal_length + y * y);
-  const auto depth = kIrisSizeInMM * x / iris_size;
-  return depth;
-}
-
-}  // namespace
-
-// Estimates depth from iris to camera given focal length and image size.
-//
-// Usage example:
-// node {
-//   calculator: "IrisToDepthCalculator"
-//   # A NormalizedLandmarkList contains landmarks for both iris.
-//   input_stream: "IRIS:iris_landmarks"
-//   input_stream: "IMAGE_SIZE:image_size"
-//   # Note: Only one of FOCAL_LENGTH or IMAGE_FILE_PROPERTIES is necessary
-//   # to get focal length in pixels. Sending focal length in pixels to
-//   # this calculator is optional.
-//   input_side_packet: "FOCAL_LENGTH:focal_length_pixel"
-//   # OR
-//   input_side_packet: "IMAGE_FILE_PROPERTIES:image_file_properties"
-//   output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
-//   output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
-// }
-class IrisToDepthCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc) {
-    cc->Inputs().Tag(kIrisTag).Set<NormalizedLandmarkList>();
-    cc->Inputs().Tag(kImageSizeTag).Set<std::pair<int, int>>();
-
-    // Only one of kFocalLengthPixelTag or kImageFilePropertiesTag must exist
-    // if they are present.
-    RET_CHECK(!(cc->InputSidePackets().HasTag(kFocalLengthPixelTag) &&
-                cc->InputSidePackets().HasTag(kImageFilePropertiesTag)));
-    if (cc->InputSidePackets().HasTag(kFocalLengthPixelTag)) {
-      cc->InputSidePackets().Tag(kFocalLengthPixelTag).SetAny();
-    }
-    if (cc->InputSidePackets().HasTag(kImageFilePropertiesTag)) {
-      cc->InputSidePackets()
-          .Tag(kImageFilePropertiesTag)
-          .Set<ImageFileProperties>();
-    }
-    if (cc->Outputs().HasTag(kLeftIrisDepthTag)) {
-      cc->Outputs().Tag(kLeftIrisDepthTag).Set<float>();
-    }
-    if (cc->Outputs().HasTag(kRightIrisDepthTag)) {
-      cc->Outputs().Tag(kRightIrisDepthTag).Set<float>();
-    }
-    return absl::OkStatus();
-  }
-
-  absl::Status Open(CalculatorContext* cc) override;
-
-  absl::Status Process(CalculatorContext* cc) override;
-
- private:
-  float focal_length_pixels_ = -1.f;
-  // TODO: Consolidate the logic when switching to input stream for
-  // focal length.
-  bool compute_depth_from_iris_ = false;
-  float smoothed_left_depth_mm_ = -1.f;
-  float smoothed_right_depth_mm_ = -1.f;
-
-  void GetLeftIris(const NormalizedLandmarkList& lds,
-                   NormalizedLandmarkList* iris);
-  void GetRightIris(const NormalizedLandmarkList& lds,
-                    NormalizedLandmarkList* iris);
-  ::mediapipe::IrisToDepthCalculatorOptions options_;
-};
-REGISTER_CALCULATOR(IrisToDepthCalculator);
-
-absl::Status IrisToDepthCalculator::Open(CalculatorContext* cc) {
-  cc->SetOffset(TimestampDiff(0));
-  if (cc->InputSidePackets().HasTag(kFocalLengthPixelTag)) {
-#if defined(__APPLE__)
-    focal_length_pixels_ = *cc->InputSidePackets()
-                                .Tag(kFocalLengthPixelTag)
-                                .Get<std::unique_ptr<float>>();
-#else
-    focal_length_pixels_ =
-        cc->InputSidePackets().Tag(kFocalLengthPixelTag).Get<float>();
-#endif
-    compute_depth_from_iris_ = true;
-  } else if (cc->InputSidePackets().HasTag(kImageFilePropertiesTag)) {
-    const auto& properties = cc->InputSidePackets()
-                                 .Tag(kImageFilePropertiesTag)
-                                 .Get<ImageFileProperties>();
-    focal_length_pixels_ = properties.focal_length_pixels();
-    compute_depth_from_iris_ = true;
-  }
-
-  options_ = cc->Options<::mediapipe::IrisToDepthCalculatorOptions>();
-  return absl::OkStatus();
-}
-
-absl::Status IrisToDepthCalculator::Process(CalculatorContext* cc) {
-  // Only process if there's input landmarks.
-  if (cc->Inputs().Tag(kIrisTag).IsEmpty()) {
-    return absl::OkStatus();
-  }
-
-  const auto& iris_landmarks =
-      cc->Inputs().Tag(kIrisTag).Get<NormalizedLandmarkList>();
-  RET_CHECK_EQ(iris_landmarks.landmark_size(), kNumIrisLandmarksPerEye * 2)
-      << "Wrong number of iris landmarks";
-
-  std::pair<int, int> image_size;
-  RET_CHECK(!cc->Inputs().Tag(kImageSizeTag).IsEmpty());
-  image_size = cc->Inputs().Tag(kImageSizeTag).Get<std::pair<int, int>>();
-
-  auto left_iris = absl::make_unique<NormalizedLandmarkList>();
-  auto right_iris = absl::make_unique<NormalizedLandmarkList>();
-  GetLeftIris(iris_landmarks, left_iris.get());
-  GetRightIris(iris_landmarks, right_iris.get());
-
-  const auto left_iris_size = CalculateIrisDiameter(*left_iris, image_size);
-  const auto right_iris_size = CalculateIrisDiameter(*right_iris, image_size);
-
-#if defined(__APPLE__)
-  if (cc->InputSidePackets().HasTag(kFocalLengthPixelTag)) {
-    focal_length_pixels_ = *cc->InputSidePackets()
-                                .Tag(kFocalLengthPixelTag)
-                                .Get<std::unique_ptr<float>>();
-  }
-#endif
-
-  if (compute_depth_from_iris_ && focal_length_pixels_ > 0) {
-    const auto left_depth =
-        CalculateDepth(left_iris->landmark(0), focal_length_pixels_,
-                       left_iris_size, image_size.first, image_size.second);
-    const auto right_depth =
-        CalculateDepth(right_iris->landmark(0), focal_length_pixels_,
-                       right_iris_size, image_size.first, image_size.second);
-    smoothed_left_depth_mm_ =
-        smoothed_left_depth_mm_ < 0 || std::isinf(smoothed_left_depth_mm_)
-            ? left_depth
-            : smoothed_left_depth_mm_ * (1 - kDepthWeightUpdate) +
-                  left_depth * kDepthWeightUpdate;
-    smoothed_right_depth_mm_ =
-        smoothed_right_depth_mm_ < 0 || std::isinf(smoothed_right_depth_mm_)
-            ? right_depth
-            : smoothed_right_depth_mm_ * (1 - kDepthWeightUpdate) +
-                  right_depth * kDepthWeightUpdate;
-
-    if (cc->Outputs().HasTag(kLeftIrisDepthTag)) {
-      cc->Outputs()
-          .Tag(kLeftIrisDepthTag)
-          .AddPacket(MakePacket<float>(smoothed_left_depth_mm_)
-                         .At(cc->InputTimestamp()));
-    }
-    if (cc->Outputs().HasTag(kRightIrisDepthTag)) {
-      cc->Outputs()
-          .Tag(kRightIrisDepthTag)
-          .AddPacket(MakePacket<float>(smoothed_right_depth_mm_)
-                         .At(cc->InputTimestamp()));
-    }
-  }
-  return absl::OkStatus();
-}
-
-void IrisToDepthCalculator::GetLeftIris(const NormalizedLandmarkList& lds,
-                                        NormalizedLandmarkList* iris) {
-  // Center, top, bottom, left, right
-  *iris->add_landmark() = lds.landmark(options_.left_iris_center_index());
-  *iris->add_landmark() = lds.landmark(options_.left_iris_top_index());
-  *iris->add_landmark() = lds.landmark(options_.left_iris_bottom_index());
-  *iris->add_landmark() = lds.landmark(options_.left_iris_left_index());
-  *iris->add_landmark() = lds.landmark(options_.left_iris_right_index());
-}
-
-void IrisToDepthCalculator::GetRightIris(const NormalizedLandmarkList& lds,
-                                         NormalizedLandmarkList* iris) {
-  // Center, top, bottom, left, right
-  *iris->add_landmark() = lds.landmark(options_.right_iris_center_index());
-  *iris->add_landmark() = lds.landmark(options_.right_iris_top_index());
-  *iris->add_landmark() = lds.landmark(options_.right_iris_bottom_index());
-  *iris->add_landmark() = lds.landmark(options_.right_iris_left_index());
-  *iris->add_landmark() = lds.landmark(options_.right_iris_right_index());
-}
-}  // namespace mediapipe
diff --git a/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.proto b/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.proto
deleted file mode 100644
index 786cd30..0000000
--- a/mediapipe/graphs/iris_tracking/calculators/iris_to_depth_calculator.proto
+++ /dev/null
@@ -1,39 +0,0 @@
-// Copyright 2019 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-
-message IrisToDepthCalculatorOptions {
-  extend CalculatorOptions {
-    optional IrisToDepthCalculatorOptions ext = 303429002;
-  }
-
-  // Indices of correspondent left iris landmarks in input stream.
-  optional int32 left_iris_center_index = 1 [default = 0];
-  optional int32 left_iris_top_index = 2 [default = 2];
-  optional int32 left_iris_bottom_index = 3 [default = 4];
-  optional int32 left_iris_left_index = 4 [default = 3];
-  optional int32 left_iris_right_index = 5 [default = 1];
-
-  // Indices of correspondent right iris landmarks in input stream.
-  optional int32 right_iris_center_index = 6 [default = 5];
-  optional int32 right_iris_top_index = 7 [default = 7];
-  optional int32 right_iris_bottom_index = 8 [default = 9];
-  optional int32 right_iris_left_index = 9 [default = 6];
-  optional int32 right_iris_right_index = 10 [default = 8];
-}
diff --git a/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.cc b/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.cc
deleted file mode 100644
index c19db2a..0000000
--- a/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.cc
+++ /dev/null
@@ -1,318 +0,0 @@
-// Copyright 2019 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <cmath>
-#include <memory>
-
-#include "absl/strings/str_cat.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/formats/landmark.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.pb.h"
-#include "mediapipe/util/color.pb.h"
-#include "mediapipe/util/render_data.pb.h"
-
-namespace mediapipe {
-
-namespace {
-
-constexpr char kIrisTag[] = "IRIS";
-constexpr char kRenderDataTag[] = "RENDER_DATA";
-constexpr char kImageSizeTag[] = "IMAGE_SIZE";
-constexpr char kLeftIrisDepthTag[] = "LEFT_IRIS_DEPTH_MM";
-constexpr char kRightIrisDepthTag[] = "RIGHT_IRIS_DEPTH_MM";
-constexpr char kOvalLabel[] = "OVAL";
-constexpr float kFontHeightScale = 1.5f;
-constexpr int kNumIrisLandmarksPerEye = 5;
-// TODO: Source.
-constexpr float kIrisSizeInMM = 11.8;
-
-inline void SetColor(RenderAnnotation* annotation, const Color& color) {
-  annotation->mutable_color()->set_r(color.r());
-  annotation->mutable_color()->set_g(color.g());
-  annotation->mutable_color()->set_b(color.b());
-}
-
-inline float GetDepth(float x0, float y0, float x1, float y1) {
-  return std::sqrt((x0 - x1) * (x0 - x1) + (y0 - y1) * (y0 - y1));
-}
-
-inline float GetLandmarkDepth(const NormalizedLandmark& ld0,
-                              const NormalizedLandmark& ld1,
-                              const std::pair<int, int>& image_size) {
-  return GetDepth(ld0.x() * image_size.first, ld0.y() * image_size.second,
-                  ld1.x() * image_size.first, ld1.y() * image_size.second);
-}
-
-float CalculateIrisDiameter(const NormalizedLandmarkList& landmarks,
-                            const std::pair<int, int>& image_size) {
-  const float dist_vert = GetLandmarkDepth(landmarks.landmark(1),
-                                           landmarks.landmark(2), image_size);
-  const float dist_hori = GetLandmarkDepth(landmarks.landmark(3),
-                                           landmarks.landmark(4), image_size);
-  return (dist_hori + dist_vert) / 2.0f;
-}
-
-float CalculateDepth(const NormalizedLandmark& center, float focal_length,
-                     float iris_size, float img_w, float img_h) {
-  std::pair<float, float> origin{img_w / 2.f, img_h / 2.f};
-  const auto y = GetDepth(origin.first, origin.second, center.x() * img_w,
-                          center.y() * img_h);
-  const auto x = std::sqrt(focal_length * focal_length + y * y);
-  const auto depth = kIrisSizeInMM * x / iris_size;
-  return depth;
-}
-
-}  // namespace
-
-// Converts iris landmarks to render data and estimates depth from the camera if
-// focal length and image size. The depth will be rendered as part of the render
-// data on the frame.
-//
-// Usage example:
-// node {
-//   calculator: "IrisToRenderDataCalculator"
-//   input_stream: "IRIS:iris_landmarks"
-//   input_stream: "IMAGE_SIZE:image_size"
-//   # Note: Only one of FOCAL_LENGTH or IMAGE_FILE_PROPERTIES is necessary
-//   # to get focal length in pixels. Sending focal length in pixels to
-//   # this calculator is optional.
-//   input_side_packet: "FOCAL_LENGTH:focal_length_pixel"
-//   # OR
-//   input_side_packet: "IMAGE_FILE_PROPERTIES:image_file_properties"
-//   output_stream: "RENDER_DATA:iris_render_data"
-//   output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
-//   output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
-//   node_options: {
-//     [type.googleapis.com/mediapipe.IrisToRenderDataCalculatorOptions] {
-//       color { r: 255 g: 255 b: 255 }
-//       thickness: 2.0
-//       font_height_px: 50
-//       horizontal_offset_px: 200
-//       vertical_offset_px: 200
-//       location: TOP_LEFT
-//     }
-//   }
-// }
-class IrisToRenderDataCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc) {
-    cc->Inputs().Tag(kIrisTag).Set<NormalizedLandmarkList>();
-    cc->Outputs().Tag(kRenderDataTag).Set<RenderData>();
-    cc->Inputs().Tag(kImageSizeTag).Set<std::pair<int, int>>();
-
-    if (cc->Inputs().HasTag(kLeftIrisDepthTag)) {
-      cc->Inputs().Tag(kLeftIrisDepthTag).Set<float>();
-    }
-    if (cc->Inputs().HasTag(kRightIrisDepthTag)) {
-      cc->Inputs().Tag(kRightIrisDepthTag).Set<float>();
-    }
-    return absl::OkStatus();
-  }
-
-  absl::Status Open(CalculatorContext* cc) override;
-
-  absl::Status Process(CalculatorContext* cc) override;
-
- private:
-  void RenderIris(const NormalizedLandmarkList& iris_landmarks,
-                  const IrisToRenderDataCalculatorOptions& options,
-                  const std::pair<int, int>& image_size, float iris_size,
-                  RenderData* render_data);
-  void GetLeftIris(const NormalizedLandmarkList& lds,
-                   NormalizedLandmarkList* iris);
-  void GetRightIris(const NormalizedLandmarkList& lds,
-                    NormalizedLandmarkList* iris);
-
-  void AddTextRenderData(const IrisToRenderDataCalculatorOptions& options,
-                         const std::pair<int, int>& image_size,
-                         const std::vector<std::string>& lines,
-                         RenderData* render_data);
-
-  static RenderAnnotation* AddOvalRenderData(
-      const IrisToRenderDataCalculatorOptions& options,
-      RenderData* render_data);
-  static RenderAnnotation* AddPointRenderData(
-      const IrisToRenderDataCalculatorOptions& options,
-      RenderData* render_data);
-};
-REGISTER_CALCULATOR(IrisToRenderDataCalculator);
-
-absl::Status IrisToRenderDataCalculator::Open(CalculatorContext* cc) {
-  cc->SetOffset(TimestampDiff(0));
-  return absl::OkStatus();
-}
-
-absl::Status IrisToRenderDataCalculator::Process(CalculatorContext* cc) {
-  // Only process if there's input landmarks.
-  if (cc->Inputs().Tag(kIrisTag).IsEmpty()) {
-    return absl::OkStatus();
-  }
-  const auto& options =
-      cc->Options<::mediapipe::IrisToRenderDataCalculatorOptions>();
-
-  const auto& iris_landmarks =
-      cc->Inputs().Tag(kIrisTag).Get<NormalizedLandmarkList>();
-  RET_CHECK_EQ(iris_landmarks.landmark_size(), kNumIrisLandmarksPerEye * 2)
-      << "Wrong number of iris landmarks";
-
-  std::pair<int, int> image_size;
-  RET_CHECK(!cc->Inputs().Tag(kImageSizeTag).IsEmpty());
-  image_size = cc->Inputs().Tag(kImageSizeTag).Get<std::pair<int, int>>();
-
-  auto render_data = absl::make_unique<RenderData>();
-  auto left_iris = absl::make_unique<NormalizedLandmarkList>();
-  auto right_iris = absl::make_unique<NormalizedLandmarkList>();
-  GetLeftIris(iris_landmarks, left_iris.get());
-  GetRightIris(iris_landmarks, right_iris.get());
-
-  const auto left_iris_size = CalculateIrisDiameter(*left_iris, image_size);
-  const auto right_iris_size = CalculateIrisDiameter(*right_iris, image_size);
-  RenderIris(*left_iris, options, image_size, left_iris_size,
-             render_data.get());
-  RenderIris(*right_iris, options, image_size, right_iris_size,
-             render_data.get());
-
-  std::vector<std::string> lines;
-  std::string line;
-  if (cc->Inputs().HasTag(kLeftIrisDepthTag) &&
-      !cc->Inputs().Tag(kLeftIrisDepthTag).IsEmpty()) {
-    const float left_iris_depth =
-        cc->Inputs().Tag(kLeftIrisDepthTag).Get<float>();
-    if (!std::isinf(left_iris_depth)) {
-      line = "Left : ";
-      absl::StrAppend(&line, ":", std::round(left_iris_depth / 10), " cm");
-      lines.emplace_back(line);
-    }
-  }
-  if (cc->Inputs().HasTag(kRightIrisDepthTag) &&
-      !cc->Inputs().Tag(kRightIrisDepthTag).IsEmpty()) {
-    const float right_iris_depth =
-        cc->Inputs().Tag(kRightIrisDepthTag).Get<float>();
-    if (!std::isinf(right_iris_depth)) {
-      line = "Right : ";
-      absl::StrAppend(&line, ":", std::round(right_iris_depth / 10), " cm");
-      lines.emplace_back(line);
-    }
-  }
-  AddTextRenderData(options, image_size, lines, render_data.get());
-
-  cc->Outputs()
-      .Tag(kRenderDataTag)
-      .Add(render_data.release(), cc->InputTimestamp());
-  return absl::OkStatus();
-}
-
-void IrisToRenderDataCalculator::AddTextRenderData(
-    const IrisToRenderDataCalculatorOptions& options,
-    const std::pair<int, int>& image_size,
-    const std::vector<std::string>& lines, RenderData* render_data) {
-  int label_baseline_px = options.vertical_offset_px();
-  float label_height_px =
-      std::ceil(options.font_height_px() * kFontHeightScale);
-  if (options.location() == IrisToRenderDataCalculatorOptions::TOP_LEFT) {
-    label_baseline_px += label_height_px;
-  } else if (options.location() ==
-             IrisToRenderDataCalculatorOptions::BOTTOM_LEFT) {
-    label_baseline_px += image_size.second - label_height_px * lines.size();
-  }
-  const auto label_left_px = options.horizontal_offset_px();
-  for (int i = 0; i < lines.size(); ++i) {
-    auto* label_annotation = render_data->add_render_annotations();
-    label_annotation->set_thickness(5);
-
-    label_annotation->mutable_color()->set_r(255);
-    label_annotation->mutable_color()->set_g(0);
-    label_annotation->mutable_color()->set_b(0);
-    //
-    auto* text = label_annotation->mutable_text();
-    text->set_display_text(lines[i]);
-    text->set_font_height(options.font_height_px());
-    text->set_left(label_left_px);
-    text->set_baseline(label_baseline_px + i * label_height_px);
-    text->set_font_face(options.font_face());
-  }
-}
-
-void IrisToRenderDataCalculator::RenderIris(
-    const NormalizedLandmarkList& iris_landmarks,
-    const IrisToRenderDataCalculatorOptions& options,
-    const std::pair<int, int>& image_size, float iris_size,
-    RenderData* render_data) {
-  auto* oval_data_render = AddOvalRenderData(options, render_data);
-  auto* oval_data = oval_data_render->mutable_oval();
-  const float iris_radius = iris_size / 2.f;
-  const auto& iris_center = iris_landmarks.landmark(0);
-
-  oval_data->mutable_rectangle()->set_top(iris_center.y() -
-                                          iris_radius / image_size.second);
-  oval_data->mutable_rectangle()->set_bottom(iris_center.y() +
-                                             iris_radius / image_size.second);
-  oval_data->mutable_rectangle()->set_left(iris_center.x() -
-                                           iris_radius / image_size.first);
-  oval_data->mutable_rectangle()->set_right(iris_center.x() +
-                                            iris_radius / image_size.first);
-  oval_data->mutable_rectangle()->set_normalized(true);
-
-  for (int i = 0; i < iris_landmarks.landmark_size(); ++i) {
-    const NormalizedLandmark& landmark = iris_landmarks.landmark(i);
-    auto* landmark_data_render = AddPointRenderData(options, render_data);
-    auto* landmark_data = landmark_data_render->mutable_point();
-    landmark_data->set_normalized(true);
-    landmark_data->set_x(landmark.x());
-    landmark_data->set_y(landmark.y());
-  }
-}
-
-void IrisToRenderDataCalculator::GetLeftIris(const NormalizedLandmarkList& lds,
-                                             NormalizedLandmarkList* iris) {
-  // Center, top, bottom, left, right
-  *iris->add_landmark() = lds.landmark(0);
-  *iris->add_landmark() = lds.landmark(2);
-  *iris->add_landmark() = lds.landmark(4);
-  *iris->add_landmark() = lds.landmark(3);
-  *iris->add_landmark() = lds.landmark(1);
-}
-
-void IrisToRenderDataCalculator::GetRightIris(const NormalizedLandmarkList& lds,
-                                              NormalizedLandmarkList* iris) {
-  // Center, top, bottom, left, right
-  *iris->add_landmark() = lds.landmark(5);
-  *iris->add_landmark() = lds.landmark(7);
-  *iris->add_landmark() = lds.landmark(9);
-  *iris->add_landmark() = lds.landmark(6);
-  *iris->add_landmark() = lds.landmark(8);
-}
-
-RenderAnnotation* IrisToRenderDataCalculator::AddOvalRenderData(
-    const IrisToRenderDataCalculatorOptions& options, RenderData* render_data) {
-  auto* oval_data_annotation = render_data->add_render_annotations();
-  oval_data_annotation->set_scene_tag(kOvalLabel);
-
-  SetColor(oval_data_annotation, options.oval_color());
-  oval_data_annotation->set_thickness(options.oval_thickness());
-  return oval_data_annotation;
-}
-
-RenderAnnotation* IrisToRenderDataCalculator::AddPointRenderData(
-    const IrisToRenderDataCalculatorOptions& options, RenderData* render_data) {
-  auto* landmark_data_annotation = render_data->add_render_annotations();
-  SetColor(landmark_data_annotation, options.landmark_color());
-  landmark_data_annotation->set_thickness(options.landmark_thickness());
-
-  return landmark_data_annotation;
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.proto b/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.proto
deleted file mode 100644
index e0fc677..0000000
--- a/mediapipe/graphs/iris_tracking/calculators/iris_to_render_data_calculator.proto
+++ /dev/null
@@ -1,62 +0,0 @@
-// Copyright 2019 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-import "mediapipe/util/color.proto";
-
-message IrisToRenderDataCalculatorOptions {
-  extend CalculatorOptions {
-    optional IrisToRenderDataCalculatorOptions ext = 289530040;
-  }
-
-  // Color of the oval.
-  optional Color oval_color = 1;
-  // Color of the landmarks.
-  optional Color landmark_color = 9;
-
-  // Thickness of the drawing of landmarks and iris oval.
-  optional double oval_thickness = 2 [default = 1.0];
-  optional double landmark_thickness = 10 [default = 1.0];
-
-  // The font height in absolute pixels.
-  optional int32 font_height_px = 3 [default = 50];
-
-  // The offset of the starting text in horizontal direction in absolute pixels.
-  optional int32 horizontal_offset_px = 7 [default = 0];
-  // The offset of the starting text in vertical direction in absolute pixels.
-  optional int32 vertical_offset_px = 8 [default = 0];
-
-  // Specifies the font for the text. Font must be one of the following from
-  // OpenCV:
-  // cv::FONT_HERSHEY_SIMPLEX (0)
-  // cv::FONT_HERSHEY_PLAIN (1)
-  // cv::FONT_HERSHEY_DUPLEX (2)
-  // cv::FONT_HERSHEY_COMPLEX (3)
-  // cv::FONT_HERSHEY_TRIPLEX (4)
-  // cv::FONT_HERSHEY_COMPLEX_SMALL (5)
-  // cv::FONT_HERSHEY_SCRIPT_SIMPLEX (6)
-  // cv::FONT_HERSHEY_SCRIPT_COMPLEX (7)
-  optional int32 font_face = 5 [default = 0];
-
-  // Label location.
-  enum Location {
-    TOP_LEFT = 0;
-    BOTTOM_LEFT = 1;
-  }
-  optional Location location = 6 [default = TOP_LEFT];
-}
diff --git a/mediapipe/graphs/iris_tracking/calculators/update_face_landmarks_calculator.cc b/mediapipe/graphs/iris_tracking/calculators/update_face_landmarks_calculator.cc
deleted file mode 100644
index de9549a..0000000
--- a/mediapipe/graphs/iris_tracking/calculators/update_face_landmarks_calculator.cc
+++ /dev/null
@@ -1,268 +0,0 @@
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <cmath>
-#include <memory>
-
-#include "absl/strings/str_cat.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/formats/landmark.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-
-namespace mediapipe {
-
-namespace {
-
-constexpr char kFaceLandmarksTag[] = "FACE_LANDMARKS";
-constexpr char kNewEyeLandmarksTag[] = "NEW_EYE_LANDMARKS";
-constexpr char kUpdatedFaceLandmarksTag[] = "UPDATED_FACE_LANDMARKS";
-
-constexpr int kNumFaceLandmarks = 468;
-// 71 landamrks for left eye and 71 landmarks for right eye.
-constexpr int kNumEyeLandmarks = 142;
-
-constexpr int kEyeLandmarkIndicesInFaceLandmarks[] = {
-    // Left eye
-    // eye lower contour
-    33,
-    7,
-    163,
-    144,
-    145,
-    153,
-    154,
-    155,
-    133,
-    // eye upper contour (excluding corners)
-    246,
-    161,
-    160,
-    159,
-    158,
-    157,
-    173,
-    // halo x2 lower contour
-    130,
-    25,
-    110,
-    24,
-    23,
-    22,
-    26,
-    112,
-    243,
-    // halo x2 upper contour (excluding corners)
-    247,
-    30,
-    29,
-    27,
-    28,
-    56,
-    190,
-    // halo x3 lower contour
-    226,
-    31,
-    228,
-    229,
-    230,
-    231,
-    232,
-    233,
-    244,
-    // halo x3 upper contour (excluding corners)
-    113,
-    225,
-    224,
-    223,
-    222,
-    221,
-    189,
-    // halo x4 upper contour (no lower because of mesh structure)
-    // or eyebrow inner contour
-    35,
-    124,
-    46,
-    53,
-    52,
-    65,
-    // halo x5 lower contour
-    143,
-    111,
-    117,
-    118,
-    119,
-    120,
-    121,
-    128,
-    245,
-    // halo x5 upper contour (excluding corners)
-    // or eyebrow outer contour
-    156,
-    70,
-    63,
-    105,
-    66,
-    107,
-    55,
-    193,
-
-    // Right eye
-    // eye lower contour
-    263,
-    249,
-    390,
-    373,
-    374,
-    380,
-    381,
-    382,
-    362,
-    // eye upper contour (excluding corners)
-    466,
-    388,
-    387,
-    386,
-    385,
-    384,
-    398,
-    // halo x2 lower contour
-    359,
-    255,
-    339,
-    254,
-    253,
-    252,
-    256,
-    341,
-    463,
-    // halo x2 upper contour (excluding corners)
-    467,
-    260,
-    259,
-    257,
-    258,
-    286,
-    414,
-    // halo x3 lower contour
-    446,
-    261,
-    448,
-    449,
-    450,
-    451,
-    452,
-    453,
-    464,
-    // halo x3 upper contour (excluding corners)
-    342,
-    445,
-    444,
-    443,
-    442,
-    441,
-    413,
-    // halo x4 upper contour (no lower because of mesh structure)
-    // or eyebrow inner contour
-    265,
-    353,
-    276,
-    283,
-    282,
-    295,
-    // halo x5 lower contour
-    372,
-    340,
-    346,
-    347,
-    348,
-    349,
-    350,
-    357,
-    465,
-    // halo x5 upper contour (excluding corners)
-    // or eyebrow outer contour
-    383,
-    300,
-    293,
-    334,
-    296,
-    336,
-    285,
-    417,
-};
-
-}  // namespace
-
-// Update face landmarks with new (e.g., refined) values. Currently only updates
-// landmarks around the eyes.
-//
-// Usage example:
-// node {
-//   calculator: "UpdateFaceLandmarksCalculator"
-//   input_stream: "NEW_EYE_LANDMARKS:new_eye_landmarks"
-//   input_stream: "FACE_LANDMARKS:face_landmarks"
-//   output_stream: "UPDATED_FACE_LANDMARKS:refine_face_landmarks"
-// }
-//
-class UpdateFaceLandmarksCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc) {
-    cc->Inputs().Tag(kFaceLandmarksTag).Set<NormalizedLandmarkList>();
-    cc->Inputs().Tag(kNewEyeLandmarksTag).Set<NormalizedLandmarkList>();
-
-    cc->Outputs().Tag(kUpdatedFaceLandmarksTag).Set<NormalizedLandmarkList>();
-
-    return absl::OkStatus();
-  }
-  absl::Status Open(CalculatorContext* cc) {
-    cc->SetOffset(TimestampDiff(0));
-    return absl::OkStatus();
-  }
-
-  absl::Status Process(CalculatorContext* cc) override;
-};
-REGISTER_CALCULATOR(UpdateFaceLandmarksCalculator);
-
-absl::Status UpdateFaceLandmarksCalculator::Process(CalculatorContext* cc) {
-  if (cc->Inputs().Tag(kFaceLandmarksTag).IsEmpty() ||
-      cc->Inputs().Tag(kNewEyeLandmarksTag).IsEmpty()) {
-    return absl::OkStatus();
-  }
-  const auto& face_landmarks =
-      cc->Inputs().Tag(kFaceLandmarksTag).Get<NormalizedLandmarkList>();
-  const auto& new_eye_landmarks =
-      cc->Inputs().Tag(kNewEyeLandmarksTag).Get<NormalizedLandmarkList>();
-
-  RET_CHECK_EQ(face_landmarks.landmark_size(), kNumFaceLandmarks)
-      << "Wrong number of face landmarks";
-  RET_CHECK_EQ(new_eye_landmarks.landmark_size(), kNumEyeLandmarks)
-      << "Wrong number of face landmarks";
-
-  auto refined_face_landmarks =
-      absl::make_unique<NormalizedLandmarkList>(face_landmarks);
-  for (int i = 0; i < kNumEyeLandmarks; ++i) {
-    const auto& refined_ld = new_eye_landmarks.landmark(i);
-    const int id = kEyeLandmarkIndicesInFaceLandmarks[i];
-    refined_face_landmarks->mutable_landmark(id)->set_x(refined_ld.x());
-    refined_face_landmarks->mutable_landmark(id)->set_y(refined_ld.y());
-    refined_face_landmarks->mutable_landmark(id)->set_z(refined_ld.z());
-    refined_face_landmarks->mutable_landmark(id)->set_visibility(
-        refined_ld.visibility());
-  }
-  cc->Outputs()
-      .Tag(kUpdatedFaceLandmarksTag)
-      .Add(refined_face_landmarks.release(), cc->InputTimestamp());
-
-  return absl::OkStatus();
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/graphs/iris_tracking/iris_depth_cpu.pbtxt b/mediapipe/graphs/iris_tracking/iris_depth_cpu.pbtxt
deleted file mode 100644
index 3597e7f..0000000
--- a/mediapipe/graphs/iris_tracking/iris_depth_cpu.pbtxt
+++ /dev/null
@@ -1,159 +0,0 @@
-# MediaPipe graph that performs iris distance computation on desktop with
-# TensorFlow Lite on CPU.
-# Used in the example in
-# mediapipie/examples/desktop/iris_tracking:iris_depth_from_image_desktop.
-
-# Raw image bytes. (std::string)
-input_stream: "input_image_bytes"
-
-# Image with all the detections rendered. (ImageFrame)
-output_stream: "output_image"
-# Estimated depth in mm from the camera to the left iris of the face (if any) in
-# the image. (float)
-output_stream: "left_iris_depth_mm"
-# Estimated depth in mm from the camera to the right iris of the face (if any)
-# in the image. (float)
-output_stream: "right_iris_depth_mm"
-
-# Computes the focal length in pixels based on EXIF information stored in the
-# image file. The output is an ImageFileProperties object containing relevant
-# image EXIF information along with focal length in pixels.
-node {
-  calculator: "ImageFilePropertiesCalculator"
-  input_stream: "input_image_bytes"
-  output_side_packet: "image_file_properties"
-}
-
-# Converts a raw string with encoded image bytes into an ImageFrame object
-# via OpenCV so that it can be processed by downstream calculators.
-node {
-  calculator: "OpenCvEncodedImageToImageFrameCalculator"
-  input_stream: "input_image_bytes"
-  output_stream: "input_image"
-}
-
-# Defines how many faces to detect. Iris tracking currently only handles one
-# face (left and right eye), and therefore this should always be set to 1.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:0:num_faces"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 1 }
-    }
-  }
-}
-
-# Detects faces and corresponding landmarks.
-node {
-  calculator: "FaceLandmarkFrontCpu"
-  input_stream: "IMAGE:input_image"
-  input_side_packet: "NUM_FACES:num_faces"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-  output_stream: "DETECTIONS:face_detections"
-  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-}
-
-# Gets the very first and only face from "multi_face_landmarks" vector.
-node {
-  calculator: "SplitNormalizedLandmarkListVectorCalculator"
-  input_stream: "multi_face_landmarks"
-  output_stream: "face_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Gets the very first and only face rect from "face_rects_from_landmarks"
-# vector.
-node {
-  calculator: "SplitNormalizedRectVectorCalculator"
-  input_stream: "face_rects_from_landmarks"
-  output_stream: "face_rect"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Gets two landmarks which define left eye boundary.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "face_landmarks"
-  output_stream: "left_eye_boundary_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 33 end: 34 }
-      ranges: { begin: 133 end: 134 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Gets two landmarks which define right eye boundary.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "face_landmarks"
-  output_stream: "right_eye_boundary_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 362 end: 363 }
-      ranges: { begin: 263 end: 264 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Detects iris landmarks, eye contour landmarks, and corresponding rect (ROI).
-node {
-  calculator: "IrisLandmarkLeftAndRightCpu"
-  input_stream: "IMAGE:input_image"
-  input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
-  input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
-  output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
-  output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
-  output_stream: "LEFT_EYE_ROI:left_eye_rect_from_landmarks"
-  output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
-  output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
-  output_stream: "RIGHT_EYE_ROI:right_eye_rect_from_landmarks"
-}
-
-node {
-  calculator: "ConcatenateNormalizedLandmarkListCalculator"
-  input_stream: "left_eye_contour_landmarks"
-  input_stream: "right_eye_contour_landmarks"
-  output_stream: "refined_eye_landmarks"
-}
-
-node {
-  calculator: "UpdateFaceLandmarksCalculator"
-  input_stream: "NEW_EYE_LANDMARKS:refined_eye_landmarks"
-  input_stream: "FACE_LANDMARKS:face_landmarks"
-  output_stream: "UPDATED_FACE_LANDMARKS:updated_face_landmarks"
-}
-
-# Renders annotations and overlays them on top of the input images.
-node {
-  calculator: "IrisAndDepthRendererCpu"
-  input_stream: "IMAGE:input_image"
-  input_stream: "FACE_LANDMARKS:updated_face_landmarks"
-  input_stream: "EYE_LANDMARKS_LEFT:left_eye_contour_landmarks"
-  input_stream: "EYE_LANDMARKS_RIGHT:right_eye_contour_landmarks"
-  input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
-  input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
-  input_stream: "NORM_RECT:face_rect"
-  input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
-  input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
-  input_stream: "DETECTIONS:face_detections"
-  input_side_packet: "IMAGE_FILE_PROPERTIES:image_file_properties"
-  output_stream: "IRIS_LANDMARKS:iris_landmarks"
-  output_stream: "IMAGE:output_image"
-  output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
-  output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
-}
diff --git a/mediapipe/graphs/iris_tracking/iris_tracking_cpu.pbtxt b/mediapipe/graphs/iris_tracking/iris_tracking_cpu.pbtxt
deleted file mode 100644
index c0a3857..0000000
--- a/mediapipe/graphs/iris_tracking/iris_tracking_cpu.pbtxt
+++ /dev/null
@@ -1,142 +0,0 @@
-# MediaPipe graph that performs iris tracking on desktop with TensorFlow Lite
-# on CPU.
-# Used in the example in
-# mediapipie/examples/desktop/iris_tracking:iris_tracking_cpu.
-
-# CPU image. (ImageFrame)
-input_stream: "input_video"
-
-# CPU image. (ImageFrame)
-output_stream: "output_video"
-# Face landmarks with iris. (NormalizedLandmarkList)
-output_stream: "face_landmarks_with_iris"
-
-# Defines how many faces to detect. Iris tracking currently only handles one
-# face (left and right eye), and therefore this should always be set to 1.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:0:num_faces"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 1 }
-    }
-  }
-}
-
-# Detects faces and corresponding landmarks.
-node {
-  calculator: "FaceLandmarkFrontCpu"
-  input_stream: "IMAGE:input_video"
-  input_side_packet: "NUM_FACES:num_faces"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-  output_stream: "DETECTIONS:face_detections"
-  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-}
-
-# Gets the very first and only face from "multi_face_landmarks" vector.
-node {
-  calculator: "SplitNormalizedLandmarkListVectorCalculator"
-  input_stream: "multi_face_landmarks"
-  output_stream: "face_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Gets the very first and only face rect from "face_rects_from_landmarks"
-# vector.
-node {
-  calculator: "SplitNormalizedRectVectorCalculator"
-  input_stream: "face_rects_from_landmarks"
-  output_stream: "face_rect"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Gets two landmarks which define left eye boundary.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "face_landmarks"
-  output_stream: "left_eye_boundary_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 33 end: 34 }
-      ranges: { begin: 133 end: 134 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Gets two landmarks which define right eye boundary.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "face_landmarks"
-  output_stream: "right_eye_boundary_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 362 end: 363 }
-      ranges: { begin: 263 end: 264 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Detects iris landmarks, eye contour landmarks, and corresponding rect (ROI).
-node {
-  calculator: "IrisLandmarkLeftAndRightCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
-  input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
-  output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
-  output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
-  output_stream: "LEFT_EYE_ROI:left_eye_rect_from_landmarks"
-  output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
-  output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
-  output_stream: "RIGHT_EYE_ROI:right_eye_rect_from_landmarks"
-}
-
-node {
-  calculator: "ConcatenateNormalizedLandmarkListCalculator"
-  input_stream: "left_eye_contour_landmarks"
-  input_stream: "right_eye_contour_landmarks"
-  output_stream: "refined_eye_landmarks"
-}
-
-node {
-  calculator: "UpdateFaceLandmarksCalculator"
-  input_stream: "NEW_EYE_LANDMARKS:refined_eye_landmarks"
-  input_stream: "FACE_LANDMARKS:face_landmarks"
-  output_stream: "UPDATED_FACE_LANDMARKS:updated_face_landmarks"
-}
-
-# Renders annotations and overlays them on top of the input images.
-node {
-  calculator: "IrisRendererCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "FACE_LANDMARKS:updated_face_landmarks"
-  input_stream: "EYE_LANDMARKS_LEFT:left_eye_contour_landmarks"
-  input_stream: "EYE_LANDMARKS_RIGHT:right_eye_contour_landmarks"
-  input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
-  input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
-  input_stream: "NORM_RECT:face_rect"
-  input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
-  input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
-  input_stream: "DETECTIONS:face_detections"
-  output_stream: "IRIS_LANDMARKS:iris_landmarks"
-  output_stream: "IMAGE:output_video"
-}
-
-node {
-  calculator: "ConcatenateNormalizedLandmarkListCalculator"
-  input_stream: "updated_face_landmarks"
-  input_stream: "iris_landmarks"
-  output_stream: "face_landmarks_with_iris"
-}
diff --git a/mediapipe/graphs/iris_tracking/iris_tracking_cpu_video_input.pbtxt b/mediapipe/graphs/iris_tracking/iris_tracking_cpu_video_input.pbtxt
deleted file mode 100644
index 82229bd..0000000
--- a/mediapipe/graphs/iris_tracking/iris_tracking_cpu_video_input.pbtxt
+++ /dev/null
@@ -1,153 +0,0 @@
-# MediaPipe graph that performs iris tracking on desktop with TensorFlow Lite
-# on CPU.
-
-# max_queue_size limits the number of packets enqueued on any input stream
-# by throttling inputs to the graph. This makes the graph only process one
-# frame per time.
-max_queue_size: 1
-
-# Decodes an input video file into images and a video header.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:input_video"
-  output_stream: "VIDEO_PRESTREAM:input_video_header"
-}
-
-# Defines how many faces to detect. Iris tracking currently only handles one
-# face (left and right eye), and therefore this should always be set to 1.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:0:num_faces"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 1 }
-    }
-  }
-}
-
-# Detects faces and corresponding landmarks.
-node {
-  calculator: "FaceLandmarkFrontCpu"
-  input_stream: "IMAGE:input_video"
-  input_side_packet: "NUM_FACES:num_faces"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-  output_stream: "DETECTIONS:face_detections"
-  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-}
-
-# Gets the very first and only face from "multi_face_landmarks" vector.
-node {
-  calculator: "SplitNormalizedLandmarkListVectorCalculator"
-  input_stream: "multi_face_landmarks"
-  output_stream: "face_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Gets the very first and only face rect from "face_rects_from_landmarks"
-# vector.
-node {
-  calculator: "SplitNormalizedRectVectorCalculator"
-  input_stream: "face_rects_from_landmarks"
-  output_stream: "face_rect"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Gets two landmarks which define left eye boundary.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "face_landmarks"
-  output_stream: "left_eye_boundary_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 33 end: 34 }
-      ranges: { begin: 133 end: 134 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Gets two landmarks which define right eye boundary.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "face_landmarks"
-  output_stream: "right_eye_boundary_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 362 end: 363 }
-      ranges: { begin: 263 end: 264 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Detects iris landmarks, eye contour landmarks, and corresponding rect (ROI).
-node {
-  calculator: "IrisLandmarkLeftAndRightCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
-  input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
-  output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
-  output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
-  output_stream: "LEFT_EYE_ROI:left_eye_rect_from_landmarks"
-  output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
-  output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
-  output_stream: "RIGHT_EYE_ROI:right_eye_rect_from_landmarks"
-}
-
-node {
-  calculator: "ConcatenateNormalizedLandmarkListCalculator"
-  input_stream: "left_eye_contour_landmarks"
-  input_stream: "right_eye_contour_landmarks"
-  output_stream: "refined_eye_landmarks"
-}
-
-node {
-  calculator: "UpdateFaceLandmarksCalculator"
-  input_stream: "NEW_EYE_LANDMARKS:refined_eye_landmarks"
-  input_stream: "FACE_LANDMARKS:face_landmarks"
-  output_stream: "UPDATED_FACE_LANDMARKS:updated_face_landmarks"
-}
-
-# Renders annotations and overlays them on top of the input images.
-node {
-  calculator: "IrisRendererCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "FACE_LANDMARKS:updated_face_landmarks"
-  input_stream: "EYE_LANDMARKS_LEFT:left_eye_contour_landmarks"
-  input_stream: "EYE_LANDMARKS_RIGHT:right_eye_contour_landmarks"
-  input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
-  input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
-  input_stream: "NORM_RECT:face_rect"
-  input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
-  input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
-  input_stream: "DETECTIONS:face_detections"
-  output_stream: "IRIS_LANDMARKS:iris_landmarks"
-  output_stream: "IMAGE:output_video"
-}
-
-# Encodes the annotated images into a video file, adopting properties specified
-# in the input video header, e.g., video framerate.
-node {
-  calculator: "OpenCvVideoEncoderCalculator"
-  input_stream: "VIDEO:output_video"
-  input_stream: "VIDEO_PRESTREAM:input_video_header"
-  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
-      codec: "avc1"
-      video_format: "mp4"
-    }
-  }
-}
diff --git a/mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt b/mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt
deleted file mode 100644
index 505a951..0000000
--- a/mediapipe/graphs/iris_tracking/iris_tracking_gpu.pbtxt
+++ /dev/null
@@ -1,163 +0,0 @@
-# MediaPipe graph that performs iris tracking with TensorFlow Lite on GPU.
-# Used in the examples in
-# mediapipie/examples/android/src/java/com/mediapipe/apps/iristrackinggpu and
-
-# GPU buffer. (GpuBuffer)
-input_stream: "input_video"
-
-# GPU buffer. (GpuBuffer)
-output_stream: "output_video"
-# Face landmarks with iris. (NormalizedLandmarkList)
-output_stream: "face_landmarks_with_iris"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Defines how many faces to detect. Iris tracking currently only handles one
-# face (left and right eye), and therefore this should always be set to 1.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:num_faces"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 1 }
-    }
-  }
-}
-
-# Detects faces and corresponding landmarks.
-node {
-  calculator: "FaceLandmarkFrontGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_side_packet: "NUM_FACES:num_faces"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-  output_stream: "DETECTIONS:face_detections"
-  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-}
-
-# Gets the very first and only face from "multi_face_landmarks" vector.
-node {
-  calculator: "SplitNormalizedLandmarkListVectorCalculator"
-  input_stream: "multi_face_landmarks"
-  output_stream: "face_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Gets the very first and only face rect from "face_rects_from_landmarks"
-# vector.
-node {
-  calculator: "SplitNormalizedRectVectorCalculator"
-  input_stream: "face_rects_from_landmarks"
-  output_stream: "face_rect"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Gets two landmarks which define left eye boundary.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "face_landmarks"
-  output_stream: "left_eye_boundary_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 33 end: 34 }
-      ranges: { begin: 133 end: 134 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Gets two landmarks which define right eye boundary.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "face_landmarks"
-  output_stream: "right_eye_boundary_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 362 end: 363 }
-      ranges: { begin: 263 end: 264 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Detects iris landmarks, eye contour landmarks, and corresponding rect (ROI).
-node {
-  calculator: "IrisLandmarkLeftAndRightGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
-  input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
-  output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
-  output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
-  output_stream: "LEFT_EYE_ROI:left_eye_rect_from_landmarks"
-  output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
-  output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
-  output_stream: "RIGHT_EYE_ROI:right_eye_rect_from_landmarks"
-}
-
-node {
-  calculator: "ConcatenateNormalizedLandmarkListCalculator"
-  input_stream: "left_eye_contour_landmarks"
-  input_stream: "right_eye_contour_landmarks"
-  output_stream: "refined_eye_landmarks"
-}
-
-node {
-  calculator: "UpdateFaceLandmarksCalculator"
-  input_stream: "NEW_EYE_LANDMARKS:refined_eye_landmarks"
-  input_stream: "FACE_LANDMARKS:face_landmarks"
-  output_stream: "UPDATED_FACE_LANDMARKS:updated_face_landmarks"
-}
-
-# Renders annotations and overlays them on top of the input images.
-node {
-  calculator: "IrisAndDepthRendererGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "FACE_LANDMARKS:updated_face_landmarks"
-  input_stream: "EYE_LANDMARKS_LEFT:left_eye_contour_landmarks"
-  input_stream: "EYE_LANDMARKS_RIGHT:right_eye_contour_landmarks"
-  input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
-  input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
-  input_stream: "NORM_RECT:face_rect"
-  input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
-  input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
-  input_stream: "DETECTIONS:face_detections"
-  input_side_packet: "FOCAL_LENGTH:focal_length_pixel"
-  output_stream: "IRIS_LANDMARKS:iris_landmarks"
-  output_stream: "IMAGE:output_video"
-}
-
-node {
-  calculator: "ConcatenateNormalizedLandmarkListCalculator"
-  input_stream: "updated_face_landmarks"
-  input_stream: "iris_landmarks"
-  output_stream: "face_landmarks_with_iris"
-}
diff --git a/mediapipe/graphs/iris_tracking/subgraphs/BUILD b/mediapipe/graphs/iris_tracking/subgraphs/BUILD
deleted file mode 100644
index d37c550..0000000
--- a/mediapipe/graphs/iris_tracking/subgraphs/BUILD
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "renderer_calculators",
-    deps = [
-        "//mediapipe/calculators/core:concatenate_normalized_landmark_list_calculator",
-        "//mediapipe/calculators/core:concatenate_vector_calculator",
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_data_calculator",
-        "//mediapipe/graphs/face_mesh/calculators:face_landmarks_to_render_data_calculator",
-        "//mediapipe/graphs/iris_tracking/calculators:iris_to_render_data_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "iris_and_depth_renderer_gpu",
-    graph = "iris_and_depth_renderer_gpu.pbtxt",
-    register_as = "IrisAndDepthRendererGpu",
-    deps = [
-        ":renderer_calculators",
-        "//mediapipe/graphs/iris_tracking/calculators:iris_to_depth_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "iris_renderer_cpu",
-    graph = "iris_renderer_cpu.pbtxt",
-    register_as = "IrisRendererCpu",
-    deps = [
-        ":renderer_calculators",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "iris_and_depth_renderer_cpu",
-    graph = "iris_and_depth_renderer_cpu.pbtxt",
-    register_as = "IrisAndDepthRendererCpu",
-    deps = [
-        ":renderer_calculators",
-        "//mediapipe/graphs/iris_tracking/calculators:iris_to_depth_calculator",
-    ],
-)
diff --git a/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_cpu.pbtxt b/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_cpu.pbtxt
deleted file mode 100644
index fad6d4a..0000000
--- a/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_cpu.pbtxt
+++ /dev/null
@@ -1,267 +0,0 @@
-# MediaPipe iris tracking rendering subgraph.
-
-type: "IrisAndDepthRendererCpu"
-
-input_stream: "IMAGE:input_image"
-input_stream: "DETECTIONS:detections"
-input_stream: "FACE_LANDMARKS:face_landmarks"
-input_stream: "EYE_LANDMARKS_LEFT:all_left_eye_contour_landmarks"
-input_stream: "EYE_LANDMARKS_RIGHT:all_right_eye_contour_landmarks"
-input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
-input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
-input_stream: "NORM_RECT:rect"
-input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
-input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
-input_side_packet: "IMAGE_FILE_PROPERTIES:image_file_properties"
-output_stream: "IRIS_LANDMARKS:iris_landmarks"
-output_stream: "IMAGE:output_image"
-output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
-output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
-
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "all_left_eye_contour_landmarks"
-  output_stream: "left_eye_contour_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 15 }
-    }
-  }
-}
-
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "all_right_eye_contour_landmarks"
-  output_stream: "right_eye_contour_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 15 }
-    }
-  }
-}
-
-# Concatenate iris landmarks from both eyes.
-node {
-  calculator: "ConcatenateNormalizedLandmarkListCalculator"
-  input_stream: "left_iris_landmarks"
-  input_stream: "right_iris_landmarks"
-  output_stream: "iris_landmarks"
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "FaceLandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:face_landmarks"
-  output_stream: "RENDER_DATA:face_landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 150 g: 0 b: 0 }
-      connection_color { r: 0 g: 150 b: 0 }
-      thickness: 2
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:input_image"
-  output_stream: "SIZE:image_size"
-}
-
-# Maps detection label IDs to the corresponding label text ("Face").
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "detections"
-  output_stream: "labeled_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
-      label: "Face"
-    }
-  }
-}
-
-# Converts detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:labeled_detections"
-  output_stream: "RENDER_DATA:detection_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:left_eye_contour_landmarks"
-  output_stream: "RENDER_DATA:left_eye_contour_landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 4
-      landmark_connections: 4
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 8
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 12
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 14
-      landmark_connections: 0
-      landmark_connections: 9
-      landmark_connections: 8
-      landmark_connections: 14
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 255 g: 0 b: 0 }
-      visualize_landmark_depth: false
-      thickness: 1.0
-    }
-  }
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:right_eye_contour_landmarks"
-  output_stream: "RENDER_DATA:right_eye_contour_landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 4
-      landmark_connections: 4
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 8
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 12
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 14
-      landmark_connections: 0
-      landmark_connections: 9
-      landmark_connections: 8
-      landmark_connections: 14
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 255 g: 0 b: 0 }
-      visualize_landmark_depth: false
-      thickness: 1.0
-    }
-  }
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:rect"
-  output_stream: "RENDER_DATA:rect_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:right_eye_rect_from_landmarks"
-  output_stream: "RENDER_DATA:right_eye_rect_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:left_eye_rect_from_landmarks"
-  output_stream: "RENDER_DATA:left_eye_rect_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-node {
-  calculator: "IrisToDepthCalculator"
-  input_stream: "IRIS:iris_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_side_packet: "IMAGE_FILE_PROPERTIES:image_file_properties"
-  output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
-  output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
-}
-
-node {
-  calculator: "IrisToRenderDataCalculator"
-  input_stream: "IRIS:iris_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
-  input_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
-  output_stream: "RENDER_DATA:iris_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.IrisToRenderDataCalculatorOptions] {
-      oval_color { r: 0 g: 0 b: 255 }
-      landmark_color { r: 0 g: 255 b: 0 }
-      oval_thickness: 2.0
-      landmark_thickness: 1.0
-      font_height_px: 50
-      horizontal_offset_px: 200
-      vertical_offset_px: 200
-      location: TOP_LEFT
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_image"
-  input_stream: "detection_render_data"
-  input_stream: "face_landmarks_render_data"
-  input_stream: "right_eye_contour_landmarks_render_data"
-  input_stream: "left_eye_contour_landmarks_render_data"
-  input_stream: "iris_render_data"
-  output_stream: "IMAGE:output_image"
-}
diff --git a/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_gpu.pbtxt b/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_gpu.pbtxt
deleted file mode 100644
index ba043d3..0000000
--- a/mediapipe/graphs/iris_tracking/subgraphs/iris_and_depth_renderer_gpu.pbtxt
+++ /dev/null
@@ -1,270 +0,0 @@
-# MediaPipe iris tracking rendering subgraph.
-
-type: "IrisAndDepthRendererGpu"
-
-input_stream: "IMAGE:input_image"
-input_stream: "DETECTIONS:detections"
-input_stream: "FACE_LANDMARKS:face_landmarks"
-input_stream: "EYE_LANDMARKS_LEFT:all_left_eye_contour_landmarks"
-input_stream: "EYE_LANDMARKS_RIGHT:all_right_eye_contour_landmarks"
-input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
-input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
-input_stream: "NORM_RECT:rect"
-input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
-input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
-input_side_packet: "FOCAL_LENGTH:focal_length_pixel"
-output_stream: "IRIS_LANDMARKS:iris_landmarks"
-output_stream: "IMAGE:output_image"
-
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "all_left_eye_contour_landmarks"
-  output_stream: "left_eye_contour_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 15 }
-    }
-  }
-}
-
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "all_right_eye_contour_landmarks"
-  output_stream: "right_eye_contour_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 15 }
-    }
-  }
-}
-
-# Concatenate iris landmarks from both eyes.
-node {
-  calculator: "ConcatenateNormalizedLandmarkListCalculator"
-  input_stream: "left_iris_landmarks"
-  input_stream: "right_iris_landmarks"
-  output_stream: "iris_landmarks"
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "FaceLandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:face_landmarks"
-  output_stream: "RENDER_DATA:face_landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 150 g: 0 b: 0 }
-      connection_color { r: 0 g: 150 b: 0 }
-      thickness: 2
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:input_image"
-  output_stream: "SIZE:image_size"
-}
-
-# Maps detection label IDs to the corresponding label text ("Face").
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "detections"
-  output_stream: "labeled_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
-      label: "Face"
-    }
-  }
-}
-
-# Converts detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:labeled_detections"
-  output_stream: "RENDER_DATA:detection_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:left_eye_contour_landmarks"
-  output_stream: "RENDER_DATA:left_eye_contour_landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 4
-      landmark_connections: 4
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 8
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 12
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 14
-      landmark_connections: 0
-      landmark_connections: 9
-      landmark_connections: 8
-      landmark_connections: 14
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 255 g: 0 b: 0 }
-      visualize_landmark_depth: false
-      thickness: 2.0
-    }
-  }
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:right_eye_contour_landmarks"
-  output_stream: "RENDER_DATA:right_eye_contour_landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 4
-      landmark_connections: 4
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 8
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 12
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 14
-      landmark_connections: 0
-      landmark_connections: 9
-      landmark_connections: 8
-      landmark_connections: 14
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 255 g: 0 b: 0 }
-      visualize_landmark_depth: false
-      thickness: 2.0
-    }
-  }
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:rect"
-  output_stream: "RENDER_DATA:rect_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:right_eye_rect_from_landmarks"
-  output_stream: "RENDER_DATA:right_eye_rect_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:left_eye_rect_from_landmarks"
-  output_stream: "RENDER_DATA:left_eye_rect_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-node {
-  calculator: "IrisToDepthCalculator"
-  input_stream: "IRIS:iris_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_side_packet: "FOCAL_LENGTH:focal_length_pixel"
-  output_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
-  output_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
-}
-
-node {
-  calculator: "IrisToRenderDataCalculator"
-  input_stream: "IRIS:iris_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "LEFT_IRIS_DEPTH_MM:left_iris_depth_mm"
-  input_stream: "RIGHT_IRIS_DEPTH_MM:right_iris_depth_mm"
-  output_stream: "RENDER_DATA:iris_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.IrisToRenderDataCalculatorOptions] {
-      oval_color { r: 0 g: 0 b: 255 }
-      landmark_color { r: 0 g: 255 b: 0 }
-      oval_thickness: 4.0
-      landmark_thickness: 2.0
-      font_height_px: 50
-      horizontal_offset_px: 200
-      vertical_offset_px: 200
-      location: TOP_LEFT
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:input_image"
-  input_stream: "detection_render_data"
-  input_stream: "face_landmarks_render_data"
-  input_stream: "right_eye_contour_landmarks_render_data"
-  input_stream: "left_eye_contour_landmarks_render_data"
-  input_stream: "iris_render_data"
-  output_stream: "IMAGE_GPU:output_image"
-  node_options: {
-    [type.googleapis.com/mediapipe.AnnotationOverlayCalculatorOptions] {
-      gpu_scale_factor: 0.5
-    }
-  }
-}
diff --git a/mediapipe/graphs/iris_tracking/subgraphs/iris_renderer_cpu.pbtxt b/mediapipe/graphs/iris_tracking/subgraphs/iris_renderer_cpu.pbtxt
deleted file mode 100644
index 81a3c90..0000000
--- a/mediapipe/graphs/iris_tracking/subgraphs/iris_renderer_cpu.pbtxt
+++ /dev/null
@@ -1,254 +0,0 @@
-# MediaPipe iris tracking rendering subgraph.
-
-type: "IrisRendererCpu"
-
-input_stream: "IMAGE:input_image"
-input_stream: "DETECTIONS:detections"
-input_stream: "FACE_LANDMARKS:face_landmarks"
-input_stream: "EYE_LANDMARKS_LEFT:all_left_eye_contour_landmarks"
-input_stream: "EYE_LANDMARKS_RIGHT:all_right_eye_contour_landmarks"
-input_stream: "IRIS_LANDMARKS_LEFT:left_iris_landmarks"
-input_stream: "IRIS_LANDMARKS_RIGHT:right_iris_landmarks"
-input_stream: "NORM_RECT:rect"
-input_stream: "LEFT_EYE_RECT:left_eye_rect_from_landmarks"
-input_stream: "RIGHT_EYE_RECT:right_eye_rect_from_landmarks"
-output_stream: "IRIS_LANDMARKS:iris_landmarks"
-output_stream: "IMAGE:output_image"
-
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "all_left_eye_contour_landmarks"
-  output_stream: "left_eye_contour_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 15 }
-    }
-  }
-}
-
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "all_right_eye_contour_landmarks"
-  output_stream: "right_eye_contour_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 15 }
-    }
-  }
-}
-
-# Concatenate iris landmarks from both eyes.
-node {
-  calculator: "ConcatenateNormalizedLandmarkListCalculator"
-  input_stream: "left_iris_landmarks"
-  input_stream: "right_iris_landmarks"
-  output_stream: "iris_landmarks"
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "FaceLandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:face_landmarks"
-  output_stream: "RENDER_DATA:face_landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 150 g: 0 b: 0 }
-      connection_color { r: 0 g: 150 b: 0 }
-      thickness: 2
-      visualize_landmark_depth: false
-    }
-  }
-}
-
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:input_image"
-  output_stream: "SIZE:image_size"
-}
-
-# Maps detection label IDs to the corresponding label text ("Face").
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "detections"
-  output_stream: "labeled_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
-      label: "Face"
-    }
-  }
-}
-
-# Converts detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:labeled_detections"
-  output_stream: "RENDER_DATA:detection_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:left_eye_contour_landmarks"
-  output_stream: "RENDER_DATA:left_eye_contour_landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 4
-      landmark_connections: 4
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 8
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 12
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 14
-      landmark_connections: 0
-      landmark_connections: 9
-      landmark_connections: 8
-      landmark_connections: 14
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 255 g: 0 b: 0 }
-      visualize_landmark_depth: false
-      thickness: 1.0
-    }
-  }
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:right_eye_contour_landmarks"
-  output_stream: "RENDER_DATA:right_eye_contour_landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 4
-      landmark_connections: 4
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 7
-      landmark_connections: 7
-      landmark_connections: 8
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 12
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 14
-      landmark_connections: 0
-      landmark_connections: 9
-      landmark_connections: 8
-      landmark_connections: 14
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 255 g: 0 b: 0 }
-      visualize_landmark_depth: false
-      thickness: 1.0
-    }
-  }
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:rect"
-  output_stream: "RENDER_DATA:rect_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:right_eye_rect_from_landmarks"
-  output_stream: "RENDER_DATA:right_eye_rect_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:left_eye_rect_from_landmarks"
-  output_stream: "RENDER_DATA:left_eye_rect_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-node {
-  calculator: "IrisToRenderDataCalculator"
-  input_stream: "IRIS:iris_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "RENDER_DATA:iris_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.IrisToRenderDataCalculatorOptions] {
-      oval_color { r: 0 g: 0 b: 255 }
-      landmark_color { r: 0 g: 255 b: 0 }
-      oval_thickness: 4.0
-      landmark_thickness: 2.0
-      font_height_px: 50
-      horizontal_offset_px: 200
-      vertical_offset_px: 200
-      location: TOP_LEFT
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_image"
-  input_stream: "detection_render_data"
-  input_stream: "face_landmarks_render_data"
-  input_stream: "right_eye_contour_landmarks_render_data"
-  input_stream: "left_eye_contour_landmarks_render_data"
-  input_stream: "iris_render_data"
-  output_stream: "IMAGE:output_image"
-}
diff --git a/mediapipe/graphs/media_sequence/BUILD b/mediapipe/graphs/media_sequence/BUILD
deleted file mode 100644
index e989147..0000000
--- a/mediapipe/graphs/media_sequence/BUILD
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "clipped_images_from_file_at_24fps_calculators",
-    deps = [
-        "//mediapipe/calculators/core:packet_resampler_calculator",
-        "//mediapipe/calculators/image:opencv_image_encoder_calculator",
-        "//mediapipe/calculators/image:scale_image_calculator",
-        "//mediapipe/calculators/tensorflow:pack_media_sequence_calculator",
-        "//mediapipe/calculators/tensorflow:string_to_sequence_example_calculator",
-        "//mediapipe/calculators/tensorflow:unpack_media_sequence_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-    ],
-)
-
-cc_library(
-    name = "tvl1_flow_and_rgb_from_file_calculators",
-    deps = [
-        "//mediapipe/calculators/core:packet_inner_join_calculator",
-        "//mediapipe/calculators/core:packet_resampler_calculator",
-        "//mediapipe/calculators/core:sequence_shift_calculator",
-        "//mediapipe/calculators/image:opencv_image_encoder_calculator",
-        "//mediapipe/calculators/image:scale_image_calculator",
-        "//mediapipe/calculators/tensorflow:pack_media_sequence_calculator",
-        "//mediapipe/calculators/tensorflow:string_to_sequence_example_calculator",
-        "//mediapipe/calculators/tensorflow:unpack_media_sequence_calculator",
-        "//mediapipe/calculators/video:flow_to_image_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-        "//mediapipe/calculators/video:tvl1_optical_flow_calculator",
-    ],
-)
diff --git a/mediapipe/graphs/media_sequence/clipped_images_from_file_at_24fps.pbtxt b/mediapipe/graphs/media_sequence/clipped_images_from_file_at_24fps.pbtxt
deleted file mode 100644
index e3c6a51..0000000
--- a/mediapipe/graphs/media_sequence/clipped_images_from_file_at_24fps.pbtxt
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Convert the string input into a decoded SequenceExample.
-node {
-  calculator: "StringToSequenceExampleCalculator"
-  input_side_packet: "STRING:input_sequence_example"
-  output_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
-}
-
-# Unpack the data path and clip timing from the SequenceExample.
-node {
-  calculator: "UnpackMediaSequenceCalculator"
-  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
-  output_side_packet: "DATA_PATH:input_video_path"
-  output_side_packet: "RESAMPLER_OPTIONS:packet_resampler_options"
-  node_options: {
-    [type.googleapis.com/mediapipe.UnpackMediaSequenceCalculatorOptions]: {
-      base_packet_resampler_options: {
-        frame_rate: 24.0
-        base_timestamp: 0
-      }
-    }
-  }
-}
-
-# Decode the entire video.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:decoded_frames"
-}
-
-# Extract the subset of frames we want to keep.
-node {
-  calculator: "PacketResamplerCalculator"
-  input_stream: "decoded_frames"
-  output_stream: "sampled_frames"
-  input_side_packet: "OPTIONS:packet_resampler_options"
-}
-
-# Encode the images to store in the SequenceExample.
-node {
-  calculator: "OpenCvImageEncoderCalculator"
-  input_stream: "sampled_frames"
-  output_stream: "encoded_frames"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvImageEncoderCalculatorOptions]: {
-      quality: 80
-    }
-  }
-}
-
-# Store the images in the SequenceExample.
-node {
-  calculator: "PackMediaSequenceCalculator"
-  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
-  output_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
-  input_stream: "IMAGE:encoded_frames"
-}
-
-# Serialize the SequenceExample to a string for storage.
-node {
-  calculator: "StringToSequenceExampleCalculator"
-  input_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
-  output_side_packet: "STRING:output_sequence_example"
-}
diff --git a/mediapipe/graphs/media_sequence/tvl1_flow_and_rgb_from_file.pbtxt b/mediapipe/graphs/media_sequence/tvl1_flow_and_rgb_from_file.pbtxt
deleted file mode 100644
index 032fc36..0000000
--- a/mediapipe/graphs/media_sequence/tvl1_flow_and_rgb_from_file.pbtxt
+++ /dev/null
@@ -1,153 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Convert the string input into a decoded SequenceExample.
-node {
-  calculator: "StringToSequenceExampleCalculator"
-  input_side_packet: "STRING:input_sequence_example"
-  output_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
-}
-
-# Unpack the data path and clip timing from the SequenceExample.
-node {
-  calculator: "UnpackMediaSequenceCalculator"
-  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
-  output_side_packet: "DATA_PATH:input_video_path"
-  output_side_packet: "RESAMPLER_OPTIONS:packet_resampler_options"
-  node_options: {
-    [type.googleapis.com/mediapipe.UnpackMediaSequenceCalculatorOptions]: {
-      base_packet_resampler_options: {
-        frame_rate: 25.0
-        base_timestamp: 0
-      }
-    }
-  }
-}
-
-# Decode the entire video.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:decoded_frames"
-}
-
-# Extract the subset of frames we want to keep.
-node {
-  calculator: "PacketResamplerCalculator"
-  input_stream: "decoded_frames"
-  output_stream: "sampled_frames"
-  input_side_packet: "OPTIONS:packet_resampler_options"
-}
-
-# Fit the images into the target size.
-node: {
-  calculator: "ScaleImageCalculator"
-  input_stream: "sampled_frames"
-  output_stream: "scaled_frames"
-  node_options: {
-    [type.googleapis.com/mediapipe.ScaleImageCalculatorOptions]: {
-      target_height: 256
-      preserve_aspect_ratio: true
-    }
-  }
-}
-
-# Shift the the timestamps of packets along a stream.
-# With a packet_offset of -1, the first packet will be dropped, the second will
-# be output with the timestamp of the first, the third with the timestamp of
-# the second, and so on.
-node: {
-  calculator: "SequenceShiftCalculator"
-  input_stream: "scaled_frames"
-  output_stream: "shifted_scaled_frames"
-  node_options: {
-    [type.googleapis.com/mediapipe.SequenceShiftCalculatorOptions]: {
-      packet_offset: -1
-    }
-  }
-}
-
-# Join the original input stream and the one that is shifted by one packet.
-node: {
-  calculator: "PacketInnerJoinCalculator"
-  input_stream: "scaled_frames"
-  input_stream: "shifted_scaled_frames"
-  output_stream: "first_frames"
-  output_stream: "second_frames"
-}
-
-# Compute the forward optical flow.
-node {
-  calculator: "Tvl1OpticalFlowCalculator"
-  input_stream: "FIRST_FRAME:first_frames"
-  input_stream: "SECOND_FRAME:second_frames"
-  output_stream: "FORWARD_FLOW:forward_flow"
-  max_in_flight: 32
-}
-
-# Convert an optical flow to be an image frame with 2 channels (v_x and v_y),
-# each channel is quantized to 0-255.
-node: {
-  calculator: "FlowToImageCalculator"
-  input_stream: "forward_flow"
-  output_stream: "flow_frames"
-  node_options: {
-    [type.googleapis.com/mediapipe.FlowToImageCalculatorOptions]: {
-      min_value: -20.0
-      max_value: 20.0
-    }
-  }
-}
-
-# Encode the optical flow images to store in the SequenceExample.
-node {
-  calculator: "OpenCvImageEncoderCalculator"
-  input_stream: "flow_frames"
-  output_stream: "encoded_flow_frames"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvImageEncoderCalculatorOptions]: {
-      quality: 100
-    }
-  }
-}
-
-# Encode the rgb images to store in the SequenceExample.
-node {
-  calculator: "OpenCvImageEncoderCalculator"
-  input_stream: "scaled_frames"
-  output_stream: "encoded_frames"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvImageEncoderCalculatorOptions]: {
-      quality: 100
-    }
-  }
-}
-
-# Store the images in the SequenceExample.
-node {
-  calculator: "PackMediaSequenceCalculator"
-  input_stream: "IMAGE:encoded_frames"
-  input_stream: "FORWARD_FLOW_ENCODED:encoded_flow_frames"
-  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
-  output_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
-}
-
-# Serialize the SequenceExample to a string for storage.
-node {
-  calculator: "StringToSequenceExampleCalculator"
-  input_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
-  output_side_packet: "STRING:output_sequence_example"
-}
-
-num_threads: 32
diff --git a/mediapipe/graphs/object_detection/BUILD b/mediapipe/graphs/object_detection/BUILD
deleted file mode 100644
index ef53fd2..0000000
--- a/mediapipe/graphs/object_detection/BUILD
+++ /dev/null
@@ -1,94 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "mobile_calculators",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/tflite:tflite_converter_calculator",
-        "//mediapipe/calculators/tflite:tflite_inference_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_detections_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
-        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
-        "//mediapipe/gpu:image_frame_to_gpu_buffer_calculator",
-    ],
-)
-
-cc_library(
-    name = "desktop_tensorflow_calculators",
-    deps = [
-        "//mediapipe/calculators/tensorflow:image_frame_to_tensor_calculator",
-        "//mediapipe/calculators/tensorflow:lapped_tensor_buffer_calculator",
-        "//mediapipe/calculators/tensorflow:object_detection_tensors_to_detections_calculator",
-        "//mediapipe/calculators/tensorflow:tensor_squeeze_dimensions_calculator",
-        "//mediapipe/calculators/tensorflow:tensorflow_inference_calculator",
-        "//mediapipe/calculators/tensorflow:tensorflow_session_from_saved_model_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
-    ],
-)
-
-cc_library(
-    name = "desktop_tflite_calculators",
-    deps = [
-        "//mediapipe/calculators/core:concatenate_vector_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/tflite:tflite_converter_calculator",
-        "//mediapipe/calculators/tflite:tflite_inference_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_detections_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
-    ],
-)
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-mediapipe_binary_graph(
-    name = "mobile_cpu_binary_graph",
-    graph = "object_detection_mobile_cpu.pbtxt",
-    output_name = "mobile_cpu.binarypb",
-    deps = [":mobile_calculators"],
-)
-
-mediapipe_binary_graph(
-    name = "mobile_gpu_binary_graph",
-    graph = "object_detection_mobile_gpu.pbtxt",
-    output_name = "mobile_gpu.binarypb",
-    deps = [":mobile_calculators"],
-)
diff --git a/mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt b/mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt
deleted file mode 100644
index 98b9fab..0000000
--- a/mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt
+++ /dev/null
@@ -1,174 +0,0 @@
-# MediaPipe graph that performs object detection with TensorFlow Lite on CPU.
-# Used in the examples in
-# mediapipe/examples/desktop/object_detection:object_detection_cpu.
-
-# Images on CPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for
-# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
-# generating the corresponding detections before it passes through another
-# image. All images that come in while waiting are dropped, limiting the number
-# of in-flight images between this calculator and
-# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
-# from queuing up incoming images and data excessively, which leads to increased
-# latency and memory usage, unwanted in real-time mobile applications. It also
-# eliminates unnecessarily computation, e.g., a transformed image produced by
-# ImageTransformationCalculator may get dropped downstream if the subsequent
-# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
-# processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:detections"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Transforms the input image on CPU to a 320x320 image. To scale the image, by
-# default it uses the STRETCH scale mode that maps the entire input image to the
-# entire transformed image. As a result, image aspect ratio may be changed and
-# objects in the image may be deformed (stretched or squeezed), but the object
-# detection model used in this graph is agnostic to that deformation.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "IMAGE:transformed_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 320
-      output_height: 320
-    }
-  }
-}
-
-# Converts the transformed input image on CPU into an image tensor stored as a
-# TfLiteTensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE:transformed_input_video"
-  output_stream: "TENSORS:image_tensor"
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  output_stream: "TENSORS:detection_tensors"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  node_options: {
-    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
-      num_layers: 6
-      min_scale: 0.2
-      max_scale: 0.95
-      input_size_height: 320
-      input_size_width: 320
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 16
-      strides: 32
-      strides: 64
-      strides: 128
-      strides: 256
-      strides: 512
-      aspect_ratios: 1.0
-      aspect_ratios: 2.0
-      aspect_ratios: 0.5
-      aspect_ratios: 3.0
-      aspect_ratios: 0.3333
-      reduce_boxes_in_lowest_layer: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TfLiteTensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
-      num_classes: 91
-      num_boxes: 2034
-      num_coords: 4
-      ignore_classes: 0
-      sigmoid_score: true
-      apply_exponential_on_box_size: true
-      x_scale: 10.0
-      y_scale: 10.0
-      h_scale: 5.0
-      w_scale: 5.0
-      min_score_thresh: 0.6
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "detections"
-  output_stream: "filtered_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
-      min_suppression_threshold: 0.4
-      max_num_detections: 3
-      overlap_type: INTERSECTION_OVER_UNION
-      return_empty_detections: true
-    }
-  }
-}
-
-# Maps detection label IDs to the corresponding label text. The label map is
-# provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "output_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
-      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
-    }
-  }
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/object_detection/object_detection_desktop_tensorflow_graph.pbtxt b/mediapipe/graphs/object_detection/object_detection_desktop_tensorflow_graph.pbtxt
deleted file mode 100644
index f12eeb6..0000000
--- a/mediapipe/graphs/object_detection/object_detection_desktop_tensorflow_graph.pbtxt
+++ /dev/null
@@ -1,130 +0,0 @@
-# MediaPipe graph that performs object detection on desktop with TensorFlow
-# on CPU.
-# Used in the example in
-# mediapipie/examples/desktop/object_detection:object_detection_tensorflow.
-
-# Decodes an input video file into images and a video header.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:input_video"
-  output_stream: "VIDEO_PRESTREAM:input_video_header"
-}
-
-# Converts the input image into an image tensor as a tensorflow::Tensor.
-node {
-  calculator: "ImageFrameToTensorCalculator"
-  input_stream: "input_video"
-  output_stream: "image_tensor"
-}
-
-# Generates a single side packet containing a TensorFlow session from a saved
-# model. The directory path that contains the saved model is specified in the
-# saved_model_path option, and the name of the saved model file has to be
-# "saved_model.pb".
-node {
-  calculator: "TensorFlowSessionFromSavedModelCalculator"
-  output_side_packet: "SESSION:object_detection_session"
-  node_options: {
-    [type.googleapis.com/mediapipe.TensorFlowSessionFromSavedModelCalculatorOptions]: {
-      saved_model_path: "mediapipe/models/object_detection_saved_model"
-    }
-  }
-}
-
-# Runs a TensorFlow session (specified as an input side packet) that takes an
-# image tensor and outputs multiple tensors that describe the objects detected
-# in the image. The batch_size option is set to 1 to disable batching entirely.
-# Note that the particular TensorFlow model used in this session handles image
-# scaling internally before the object-detection inference, and therefore no
-# additional calculator for image transformation is needed in this MediaPipe
-# graph.
-node: {
-  calculator: "TensorFlowInferenceCalculator"
-  input_side_packet: "SESSION:object_detection_session"
-  input_stream: "INPUTS:image_tensor"
-  output_stream: "DETECTION_BOXES:detection_boxes_tensor"
-  output_stream: "DETECTION_CLASSES:detection_classes_tensor"
-  output_stream: "DETECTION_SCORES:detection_scores_tensor"
-  output_stream: "NUM_DETECTIONS:num_detections_tensor"
-  node_options: {
-    [type.googleapis.com/mediapipe.TensorFlowInferenceCalculatorOptions]: {
-      batch_size: 1
-    }
-  }
-}
-
-# Decodes the detection tensors from the TensorFlow model into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "ObjectDetectionTensorsToDetectionsCalculator"
-  input_stream: "BOXES:detection_boxes_tensor"
-  input_stream: "SCORES:detection_scores_tensor"
-  input_stream: "CLASSES:detection_classes_tensor"
-  input_stream: "NUM_DETECTIONS:num_detections_tensor"
-  output_stream: "DETECTIONS:detections"
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "detections"
-  output_stream: "filtered_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
-      min_suppression_threshold: 0.4
-      min_score_threshold: 0.6
-      max_num_detections: 10
-      overlap_type: INTERSECTION_OVER_UNION
-    }
-  }
-}
-
-# Maps detection label IDs to the corresponding label text. The label map is
-# provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "output_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
-      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
-    }
-  }
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE:output_video"
-}
-
-# Encodes the annotated images into a video file, adopting properties specified
-# in the input video header, e.g., video framerate.
-node {
-  calculator: "OpenCvVideoEncoderCalculator"
-  input_stream: "VIDEO:output_video"
-  input_stream: "VIDEO_PRESTREAM:input_video_header"
-  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
-      codec: "avc1"
-      video_format: "mp4"
-    }
-  }
-}
diff --git a/mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt b/mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt
deleted file mode 100644
index 15aa2cd..0000000
--- a/mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt
+++ /dev/null
@@ -1,180 +0,0 @@
-# MediaPipe graph that performs object detection on desktop with TensorFlow Lite
-# on CPU.
-# Used in the example in
-# mediapipe/examples/desktop/object_detection:object_detection_tflite.
-
-# max_queue_size limits the number of packets enqueued on any input stream
-# by throttling inputs to the graph. This makes the graph only process one
-# frame per time.
-max_queue_size: 1
-
-# Decodes an input video file into images and a video header.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:input_video"
-  output_stream: "VIDEO_PRESTREAM:input_video_header"
-}
-
-# Transforms the input image on CPU to a 320x320 image. To scale the image, by
-# default it uses the STRETCH scale mode that maps the entire input image to the
-# entire transformed image. As a result, image aspect ratio may be changed and
-# objects in the image may be deformed (stretched or squeezed), but the object
-# detection model used in this graph is agnostic to that deformation.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:input_video"
-  output_stream: "IMAGE:transformed_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 320
-      output_height: 320
-    }
-  }
-}
-
-# Converts the transformed input image on CPU into an image tensor as a
-# TfLiteTensor. The zero_center option is set to true to normalize the
-# pixel values to [-1.f, 1.f] as opposed to [0.f, 1.f].
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE:transformed_input_video"
-  output_stream: "TENSORS:image_tensor"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteConverterCalculatorOptions] {
-      zero_center: true
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  output_stream: "TENSORS:detection_tensors"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  node_options: {
-    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
-      num_layers: 6
-      min_scale: 0.2
-      max_scale: 0.95
-      input_size_height: 320
-      input_size_width: 320
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 16
-      strides: 32
-      strides: 64
-      strides: 128
-      strides: 256
-      strides: 512
-      aspect_ratios: 1.0
-      aspect_ratios: 2.0
-      aspect_ratios: 0.5
-      aspect_ratios: 3.0
-      aspect_ratios: 0.3333
-      reduce_boxes_in_lowest_layer: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TfLiteTensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
-      num_classes: 91
-      num_boxes: 2034
-      num_coords: 4
-      ignore_classes: 0
-      apply_exponential_on_box_size: true
-
-      x_scale: 10.0
-      y_scale: 10.0
-      h_scale: 5.0
-      w_scale: 5.0
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "detections"
-  output_stream: "filtered_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
-      min_suppression_threshold: 0.4
-      min_score_threshold: 0.6
-      max_num_detections: 5
-      overlap_type: INTERSECTION_OVER_UNION
-    }
-  }
-}
-
-# Maps detection label IDs to the corresponding label text. The label map is
-# provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "output_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
-      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
-    }
-  }
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE:output_video"
-}
-
-# Encodes the annotated images into a video file, adopting properties specified
-# in the input video header, e.g., video framerate.
-node {
-  calculator: "OpenCvVideoEncoderCalculator"
-  input_stream: "VIDEO:output_video"
-  input_stream: "VIDEO_PRESTREAM:input_video_header"
-  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
-      codec: "avc1"
-      video_format: "mp4"
-    }
-  }
-}
diff --git a/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt b/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt
deleted file mode 100644
index 8256179..0000000
--- a/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt
+++ /dev/null
@@ -1,193 +0,0 @@
-# MediaPipe graph that performs object detection with TensorFlow Lite on CPU.
-# Used in the examples in
-# mediapipe/examples/android/src/java/com/mediapipe/apps/objectdetectioncpu and
-# mediapipe/examples/ios/objectdetectioncpu.
-
-# Images on GPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Transfers the input image from GPU to CPU memory for the purpose of
-# demonstrating a CPU-based pipeline. Note that the input image on GPU has the
-# origin defined at the bottom-left corner (OpenGL convention). As a result,
-# the transferred image on CPU also shares the same representation.
-node: {
-  calculator: "GpuBufferToImageFrameCalculator"
-  input_stream: "input_video"
-  output_stream: "input_video_cpu"
-}
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for
-# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
-# generating the corresponding detections before it passes through another
-# image. All images that come in while waiting are dropped, limiting the number
-# of in-flight images between this calculator and
-# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
-# from queuing up incoming images and data excessively, which leads to increased
-# latency and memory usage, unwanted in real-time mobile applications. It also
-# eliminates unnecessarily computation, e.g., a transformed image produced by
-# ImageTransformationCalculator may get dropped downstream if the subsequent
-# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
-# processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video_cpu"
-  input_stream: "FINISHED:detections"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video_cpu"
-}
-
-# Transforms the input image on CPU to a 320x320 image. To scale the image, by
-# default it uses the STRETCH scale mode that maps the entire input image to the
-# entire transformed image. As a result, image aspect ratio may be changed and
-# objects in the image may be deformed (stretched or squeezed), but the object
-# detection model used in this graph is agnostic to that deformation.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:throttled_input_video_cpu"
-  output_stream: "IMAGE:transformed_input_video_cpu"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 320
-      output_height: 320
-    }
-  }
-}
-
-# Converts the transformed input image on CPU into an image tensor stored as a
-# TfLiteTensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE:transformed_input_video_cpu"
-  output_stream: "TENSORS:image_tensor"
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  output_stream: "TENSORS:detection_tensors"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  node_options: {
-    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
-      num_layers: 6
-      min_scale: 0.2
-      max_scale: 0.95
-      input_size_height: 320
-      input_size_width: 320
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 16
-      strides: 32
-      strides: 64
-      strides: 128
-      strides: 256
-      strides: 512
-      aspect_ratios: 1.0
-      aspect_ratios: 2.0
-      aspect_ratios: 0.5
-      aspect_ratios: 3.0
-      aspect_ratios: 0.3333
-      reduce_boxes_in_lowest_layer: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TfLiteTensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
-      num_classes: 91
-      num_boxes: 2034
-      num_coords: 4
-      ignore_classes: 0
-      sigmoid_score: true
-      apply_exponential_on_box_size: true
-      x_scale: 10.0
-      y_scale: 10.0
-      h_scale: 5.0
-      w_scale: 5.0
-      min_score_thresh: 0.6
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "detections"
-  output_stream: "filtered_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
-      min_suppression_threshold: 0.4
-      max_num_detections: 3
-      overlap_type: INTERSECTION_OVER_UNION
-      return_empty_detections: true
-    }
-  }
-}
-
-# Maps detection label IDs to the corresponding label text. The label map is
-# provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "output_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
-      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
-    }
-  }
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:throttled_input_video_cpu"
-  input_stream: "render_data"
-  output_stream: "IMAGE:output_video_cpu"
-}
-
-# Transfers the annotated image from CPU back to GPU memory, to be sent out of
-# the graph.
-node: {
-  calculator: "ImageFrameToGpuBufferCalculator"
-  input_stream: "output_video_cpu"
-  output_stream: "output_video"
-}
diff --git a/mediapipe/graphs/object_detection/object_detection_mobile_gpu.pbtxt b/mediapipe/graphs/object_detection/object_detection_mobile_gpu.pbtxt
deleted file mode 100644
index 1ed66e8..0000000
--- a/mediapipe/graphs/object_detection/object_detection_mobile_gpu.pbtxt
+++ /dev/null
@@ -1,175 +0,0 @@
-# MediaPipe graph that performs object detection with TensorFlow Lite on GPU.
-# Used in the examples in
-# mediapipe/examples/android/src/java/com/mediapipe/apps/objectdetectiongpu and
-# mediapipe/examples/ios/objectdetectiongpu.
-
-# Images on GPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for
-# TfLiteTensorsToDetectionsCalculator downstream in the graph to finish
-# generating the corresponding detections before it passes through another
-# image. All images that come in while waiting are dropped, limiting the number
-# of in-flight images between this calculator and
-# TfLiteTensorsToDetectionsCalculator to 1. This prevents the nodes in between
-# from queuing up incoming images and data excessively, which leads to increased
-# latency and memory usage, unwanted in real-time mobile applications. It also
-# eliminates unnecessarily computation, e.g., a transformed image produced by
-# ImageTransformationCalculator may get dropped downstream if the subsequent
-# TfLiteConverterCalculator or TfLiteInferenceCalculator is still busy
-# processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:detections"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Transforms the input image on GPU to a 320x320 image. To scale the image, by
-# default it uses the STRETCH scale mode that maps the entire input image to the
-# entire transformed image. As a result, image aspect ratio may be changed and
-# objects in the image may be deformed (stretched or squeezed), but the object
-# detection model used in this graph is agnostic to that deformation.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  output_stream: "IMAGE_GPU:transformed_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 320
-      output_height: 320
-    }
-  }
-}
-
-# Converts the transformed input image on GPU into an image tensor stored as a
-# TfLiteTensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE_GPU:transformed_input_video"
-  output_stream: "TENSORS_GPU:image_tensor"
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS_GPU:image_tensor"
-  output_stream: "TENSORS_GPU:detection_tensors"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  node_options: {
-    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
-      num_layers: 6
-      min_scale: 0.2
-      max_scale: 0.95
-      input_size_height: 320
-      input_size_width: 320
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 16
-      strides: 32
-      strides: 64
-      strides: 128
-      strides: 256
-      strides: 512
-      aspect_ratios: 1.0
-      aspect_ratios: 2.0
-      aspect_ratios: 0.5
-      aspect_ratios: 3.0
-      aspect_ratios: 0.3333
-      reduce_boxes_in_lowest_layer: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TfLiteTensorsToDetectionsCalculator"
-  input_stream: "TENSORS_GPU:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
-      num_classes: 91
-      num_boxes: 2034
-      num_coords: 4
-      ignore_classes: 0
-      sigmoid_score: true
-      apply_exponential_on_box_size: true
-      x_scale: 10.0
-      y_scale: 10.0
-      h_scale: 5.0
-      w_scale: 5.0
-      min_score_thresh: 0.6
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "detections"
-  output_stream: "filtered_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
-      min_suppression_threshold: 0.4
-      max_num_detections: 3
-      overlap_type: INTERSECTION_OVER_UNION
-      return_empty_detections: true
-    }
-  }
-}
-
-# Maps detection label IDs to the corresponding label text. The label map is
-# provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "output_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
-      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
-    }
-  }
-}
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  input_stream: "render_data"
-  output_stream: "IMAGE_GPU:output_video"
-}
diff --git a/mediapipe/graphs/object_detection_3d/BUILD b/mediapipe/graphs/object_detection_3d/BUILD
deleted file mode 100644
index 7ba00c0..0000000
--- a/mediapipe/graphs/object_detection_3d/BUILD
+++ /dev/null
@@ -1,80 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-exports_files(glob([
-    "*.pbtxt",
-]))
-
-cc_library(
-    name = "mobile_calculators",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_cropping_calculator",
-        "//mediapipe/graphs/object_detection_3d/calculators:annotations_to_model_matrices_calculator",
-        "//mediapipe/graphs/object_detection_3d/calculators:gl_animation_overlay_calculator",
-        "//mediapipe/modules/objectron:objectron_gpu",
-    ],
-)
-
-cc_library(
-    name = "mobile_calculators_1stage",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/calculators/core:packet_resampler_calculator",
-        "//mediapipe/calculators/image:image_cropping_calculator",
-        "//mediapipe/gpu:gl_scaler_calculator",
-        "//mediapipe/graphs/object_detection_3d/calculators:annotations_to_model_matrices_calculator",
-        "//mediapipe/graphs/object_detection_3d/calculators:gl_animation_overlay_calculator",
-        "//mediapipe/modules/objectron:objectron_detection_1stage_gpu",
-        "//mediapipe/modules/objectron:objectron_tracking_1stage_gpu",
-    ],
-)
-
-cc_library(
-    name = "desktop_cpu_calculators",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
-        "//mediapipe/graphs/object_detection_3d/subgraphs:renderer_cpu",
-        "//mediapipe/modules/objectron:objectron_cpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "mobile_gpu_binary_graph",
-    graph = "object_occlusion_tracking.pbtxt",
-    output_name = "mobile_gpu_binary_graph.binarypb",
-    visibility = ["//visibility:public"],
-    deps = [":mobile_calculators"],
-)
-
-mediapipe_binary_graph(
-    name = "mobile_gpu_1stage_binary_graph",
-    graph = "object_occlusion_tracking_1stage.pbtxt",
-    output_name = "mobile_gpu_1stage_binary_graph.binarypb",
-    visibility = ["//visibility:public"],
-    deps = [":mobile_calculators_1stage"],
-)
diff --git a/mediapipe/graphs/object_detection_3d/calculators/BUILD b/mediapipe/graphs/object_detection_3d/calculators/BUILD
deleted file mode 100644
index 8f80312..0000000
--- a/mediapipe/graphs/object_detection_3d/calculators/BUILD
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_proto_library(
-    name = "gl_animation_overlay_calculator_proto",
-    srcs = ["gl_animation_overlay_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "annotations_to_model_matrices_calculator_proto",
-    srcs = ["annotations_to_model_matrices_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "model_matrix_proto",
-    srcs = ["model_matrix.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "annotations_to_render_data_calculator_proto",
-    srcs = ["annotations_to_render_data_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_proto",
-        "//mediapipe/util:color_proto",
-    ],
-)
-
-cc_library(
-    name = "gl_animation_overlay_calculator",
-    srcs = ["gl_animation_overlay_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":gl_animation_overlay_calculator_cc_proto",
-        ":model_matrix_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/gpu:gl_calculator_helper",
-        "//mediapipe/gpu:shader_util",
-        "//mediapipe/modules/objectron/calculators:camera_parameters_cc_proto",
-        "//mediapipe/util/android:asset_manager_util",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "annotations_to_model_matrices_calculator",
-    srcs = ["annotations_to_model_matrices_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":annotations_to_model_matrices_calculator_cc_proto",
-        ":model_matrix_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework:calculator_options_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/modules/objectron/calculators:annotation_cc_proto",
-        "//mediapipe/modules/objectron/calculators:box",
-        "//mediapipe/util:color_cc_proto",
-        "@com_google_absl//absl/memory",
-        "@com_google_absl//absl/strings",
-        "@eigen_archive//:eigen3",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "annotations_to_render_data_calculator",
-    srcs = ["annotations_to_render_data_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":annotations_to_render_data_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework:calculator_options_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/modules/objectron/calculators:annotation_cc_proto",
-        "//mediapipe/util:color_cc_proto",
-        "//mediapipe/util:render_data_cc_proto",
-        "@com_google_absl//absl/memory",
-        "@com_google_absl//absl/strings",
-    ],
-    alwayslink = 1,
-)
diff --git a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.cc b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.cc
deleted file mode 100644
index 183f6fc..0000000
--- a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.cc
+++ /dev/null
@@ -1,215 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <memory>
-
-#include "Eigen/Core"
-#include "Eigen/Dense"
-#include "Eigen/Geometry"
-#include "absl/memory/memory.h"
-#include "absl/strings/str_cat.h"
-#include "absl/strings/str_join.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/calculator_options.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.pb.h"
-#include "mediapipe/graphs/object_detection_3d/calculators/model_matrix.pb.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/box.h"
-#include "mediapipe/util/color.pb.h"
-
-namespace mediapipe {
-
-namespace {
-
-constexpr char kAnnotationTag[] = "ANNOTATIONS";
-constexpr char kModelMatricesTag[] = "MODEL_MATRICES";
-
-using Matrix3fRM = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
-using Matrix4fRM = Eigen::Matrix<float, 4, 4, Eigen::RowMajor>;
-
-}  // namespace
-
-// Converts the box prediction from Objectron Model to the Model matrices
-// to be rendered.
-//
-// Input:
-//  ANNOTATIONS - Frame annotations with lifted 3D points, the points are in
-//     Objectron coordinate system.
-// Output:
-//  MODEL_MATRICES - Result ModelMatrices, in OpenGL coordinate system.
-//
-// Usage example:
-// node {
-//  calculator: "AnnotationsToModelMatricesCalculator"
-//  input_stream: "ANNOTATIONS:objects"
-//  output_stream: "MODEL_MATRICES:model_matrices"
-//}
-
-class AnnotationsToModelMatricesCalculator : public CalculatorBase {
- public:
-  AnnotationsToModelMatricesCalculator() {}
-  ~AnnotationsToModelMatricesCalculator() override {}
-  AnnotationsToModelMatricesCalculator(
-      const AnnotationsToModelMatricesCalculator&) = delete;
-  AnnotationsToModelMatricesCalculator& operator=(
-      const AnnotationsToModelMatricesCalculator&) = delete;
-
-  static absl::Status GetContract(CalculatorContract* cc);
-
-  absl::Status Open(CalculatorContext* cc) override;
-
-  absl::Status Process(CalculatorContext* cc) override;
-
- private:
-  absl::Status GetModelMatricesForAnnotations(
-      const FrameAnnotation& annotations,
-      TimedModelMatrixProtoList* model_matrix_list);
-
-  AnnotationsToModelMatricesCalculatorOptions options_;
-  Eigen::Vector3f model_scale_;
-  Matrix4fRM model_transformation_;
-};
-REGISTER_CALCULATOR(AnnotationsToModelMatricesCalculator);
-
-absl::Status AnnotationsToModelMatricesCalculator::GetContract(
-    CalculatorContract* cc) {
-  RET_CHECK(cc->Inputs().HasTag(kAnnotationTag)) << "No input stream found.";
-  if (cc->Inputs().HasTag(kAnnotationTag)) {
-    cc->Inputs().Tag(kAnnotationTag).Set<FrameAnnotation>();
-  }
-
-  if (cc->Outputs().HasTag(kModelMatricesTag)) {
-    cc->Outputs().Tag(kModelMatricesTag).Set<TimedModelMatrixProtoList>();
-  }
-
-  if (cc->InputSidePackets().HasTag("MODEL_SCALE")) {
-    cc->InputSidePackets().Tag("MODEL_SCALE").Set<float[]>();
-  }
-
-  if (cc->InputSidePackets().HasTag("MODEL_TRANSFORMATION")) {
-    cc->InputSidePackets().Tag("MODEL_TRANSFORMATION").Set<float[]>();
-  }
-  return absl::OkStatus();
-}
-
-absl::Status AnnotationsToModelMatricesCalculator::Open(CalculatorContext* cc) {
-  RET_CHECK(cc->Inputs().HasTag(kAnnotationTag));
-
-  cc->SetOffset(TimestampDiff(0));
-  options_ = cc->Options<AnnotationsToModelMatricesCalculatorOptions>();
-
-  if (cc->InputSidePackets().HasTag("MODEL_SCALE")) {
-    model_scale_ = Eigen::Map<const Eigen::Vector3f>(
-        cc->InputSidePackets().Tag("MODEL_SCALE").Get<float[]>());
-  } else if (options_.model_scale_size() == 3) {
-    model_scale_ =
-        Eigen::Map<const Eigen::Vector3f>(options_.model_scale().data());
-  } else {
-    model_scale_.setOnes();
-  }
-
-  if (cc->InputSidePackets().HasTag("MODEL_TRANSFORMATION")) {
-    model_transformation_ = Eigen::Map<const Matrix4fRM>(
-        cc->InputSidePackets().Tag("MODEL_TRANSFORMATION").Get<float[]>());
-  } else if (options_.model_transformation_size() == 16) {
-    model_transformation_ =
-        Eigen::Map<const Matrix4fRM>(options_.model_transformation().data());
-  } else {
-    model_transformation_.setIdentity();
-  }
-
-  return absl::OkStatus();
-}
-
-absl::Status AnnotationsToModelMatricesCalculator::Process(
-    CalculatorContext* cc) {
-  auto model_matrices = std::make_unique<TimedModelMatrixProtoList>();
-
-  const FrameAnnotation& annotations =
-      cc->Inputs().Tag(kAnnotationTag).Get<FrameAnnotation>();
-
-  if (!GetModelMatricesForAnnotations(annotations, model_matrices.get()).ok()) {
-    return absl::InvalidArgumentError("Error in GetModelMatricesForBoxes");
-  }
-  cc->Outputs()
-      .Tag(kModelMatricesTag)
-      .Add(model_matrices.release(), cc->InputTimestamp());
-
-  return absl::OkStatus();
-}
-
-absl::Status
-AnnotationsToModelMatricesCalculator::GetModelMatricesForAnnotations(
-    const FrameAnnotation& annotations,
-    TimedModelMatrixProtoList* model_matrix_list) {
-  if (model_matrix_list == nullptr) {
-    return absl::InvalidArgumentError("model_matrix_list is nullptr");
-  }
-  model_matrix_list->clear_model_matrix();
-
-  for (const auto& object : annotations.annotations()) {
-    TimedModelMatrixProto* model_matrix = model_matrix_list->add_model_matrix();
-    model_matrix->set_id(object.object_id());
-
-    // Get object rotation, translation and scale.
-    const auto object_rotation =
-        Eigen::Map<const Matrix3fRM>(object.rotation().data());
-    const auto object_translation =
-        Eigen::Map<const Eigen::Vector3f>(object.translation().data());
-    const auto object_scale =
-        Eigen::Map<const Eigen::Vector3f>(object.scale().data());
-
-    // Compose object transformation matrix.
-    Matrix4fRM object_transformation;
-    object_transformation.setIdentity();
-    object_transformation.topLeftCorner<3, 3>() = object_rotation;
-    object_transformation.topRightCorner<3, 1>() = object_translation;
-
-    Matrix4fRM model_view;
-    Matrix4fRM objectron_model;
-    // The reference view is
-    //
-    // ref <<  0.,  0.,  1.,  0.,
-    //        -1.,  0., 0.,  0.,
-    //         0.,  -1.,  0.,  0.,
-    //         0.,  0.,  0.,  1.;
-    // We have objectron_model * model = model_view, to get objectron_model:
-    // objectron_model = model_view * model^-1
-    // clang-format off
-    objectron_model << 1.0, 0.0, 0.0, 0.0,
-                       0.0, -1., 0.0, 0.0,
-                       0.0, 0.0, 1.0, 0.0,
-                       0.0, 0.0, 0.0, 1.0;
-    // clang-format on
-
-    // Re-scale the CAD model to the scale of the estimated bounding box.
-    const Eigen::Vector3f scale = model_scale_.cwiseProduct(object_scale);
-    const Matrix4fRM model =
-        model_transformation_.array().colwise() * scale.homogeneous().array();
-
-    // Finally compute the model_view matrix.
-    model_view = objectron_model * object_transformation * model;
-
-    for (int i = 0; i < model_view.rows(); ++i) {
-      for (int j = 0; j < model_view.cols(); ++j) {
-        model_matrix->add_matrix_entries(model_view(i, j));
-      }
-    }
-  }
-  return absl::OkStatus();
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.proto b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.proto
deleted file mode 100644
index c0159d4..0000000
--- a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_model_matrices_calculator.proto
+++ /dev/null
@@ -1,33 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-
-message AnnotationsToModelMatricesCalculatorOptions {
-  extend CalculatorOptions {
-    optional AnnotationsToModelMatricesCalculatorOptions ext = 290166283;
-  }
-
-  // Vector of size 3 indicating the scale vector [x, y, z]. We will re-scale
-  // the model size with this vector. (Defaults to [1., 1., 1.])
-  repeated float model_scale = 1;
-
-  // 4x4 Row major matrix denoting the transformation from the model to the
-  // Deep Pursuit 3D coordinate system (where front is +z, and up is +y).
-  repeated float model_transformation = 2;
-}
diff --git a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.cc b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.cc
deleted file mode 100644
index 65bff77..0000000
--- a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.cc
+++ /dev/null
@@ -1,271 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "absl/memory/memory.h"
-#include "absl/strings/str_cat.h"
-#include "absl/strings/str_join.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/calculator_options.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.pb.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/util/color.pb.h"
-#include "mediapipe/util/render_data.pb.h"
-
-namespace mediapipe {
-
-namespace {
-
-constexpr char kAnnotationTag[] = "ANNOTATIONS";
-constexpr char kRenderDataTag[] = "RENDER_DATA";
-constexpr char kKeypointLabel[] = "KEYPOINT";
-constexpr int kMaxLandmarkThickness = 18;
-
-inline void SetColor(RenderAnnotation* annotation, const Color& color) {
-  annotation->mutable_color()->set_r(color.r());
-  annotation->mutable_color()->set_g(color.g());
-  annotation->mutable_color()->set_b(color.b());
-}
-
-// Remap x from range [lo hi] to range [0 1] then multiply by scale.
-inline float Remap(float x, float lo, float hi, float scale) {
-  return (x - lo) / (hi - lo + 1e-6) * scale;
-}
-
-inline void GetMinMaxZ(const FrameAnnotation& annotations, float* z_min,
-                       float* z_max) {
-  *z_min = std::numeric_limits<float>::max();
-  *z_max = std::numeric_limits<float>::min();
-  // Use a global depth scale for all the objects in the scene
-  for (const auto& object : annotations.annotations()) {
-    for (const auto& keypoint : object.keypoints()) {
-      *z_min = std::min(keypoint.point_2d().depth(), *z_min);
-      *z_max = std::max(keypoint.point_2d().depth(), *z_max);
-    }
-  }
-}
-
-void SetColorSizeValueFromZ(float z, float z_min, float z_max,
-                            RenderAnnotation* render_annotation) {
-  const int color_value = 255 - static_cast<int>(Remap(z, z_min, z_max, 255));
-  ::mediapipe::Color color;
-  color.set_r(color_value);
-  color.set_g(color_value);
-  color.set_b(color_value);
-  SetColor(render_annotation, color);
-  const int thickness = static_cast<int>((1.f - Remap(z, z_min, z_max, 1)) *
-                                         kMaxLandmarkThickness);
-  render_annotation->set_thickness(thickness);
-}
-
-}  // namespace
-
-// A calculator that converts FrameAnnotation proto to RenderData proto for
-// visualization. The input should be the FrameAnnotation proto buffer. It is
-// also possible to specify the connections between landmarks.
-//
-// Example config:
-// node {
-//   calculator: "AnnotationsToRenderDataCalculator"
-//   input_stream: "ANNOTATIONS:annotations"
-//   output_stream: "RENDER_DATA:render_data"
-//   options {
-//     [AnnotationsToRenderDataCalculator.ext] {
-//       landmark_connections: [0, 1, 1, 2]
-//       landmark_color { r: 0 g: 255 b: 0 }
-//       connection_color { r: 0 g: 255 b: 0 }
-//       thickness: 4.0
-//     }
-//   }
-// }
-class AnnotationsToRenderDataCalculator : public CalculatorBase {
- public:
-  AnnotationsToRenderDataCalculator() {}
-  ~AnnotationsToRenderDataCalculator() override {}
-  AnnotationsToRenderDataCalculator(const AnnotationsToRenderDataCalculator&) =
-      delete;
-  AnnotationsToRenderDataCalculator& operator=(
-      const AnnotationsToRenderDataCalculator&) = delete;
-
-  static absl::Status GetContract(CalculatorContract* cc);
-
-  absl::Status Open(CalculatorContext* cc) override;
-
-  absl::Status Process(CalculatorContext* cc) override;
-
- private:
-  static void SetRenderAnnotationColorThickness(
-      const AnnotationsToRenderDataCalculatorOptions& options,
-      RenderAnnotation* render_annotation);
-  static RenderAnnotation* AddPointRenderData(
-      const AnnotationsToRenderDataCalculatorOptions& options,
-      RenderData* render_data);
-
-  // Add a command to draw a line in the rendering queue. The line is drawn from
-  // (start_x, start_y) to (end_x, end_y). The input x,y can either be in pixel
-  // or normalized coordinate [0, 1] as indicated by the normalized flag.
-  static void AddConnectionToRenderData(
-      float start_x, float start_y, float end_x, float end_y,
-      const AnnotationsToRenderDataCalculatorOptions& options, bool normalized,
-      RenderData* render_data);
-
-  // Same as above function. Instead of using color data to render the line, it
-  // re-colors the line according to the two depth value. gray_val1 is the color
-  // of the starting point and gray_val2 is the color of the ending point. The
-  // line is colored using gradient color from gray_val1 to gray_val2. The
-  // gray_val ranges from [0 to 255] for black to white.
-  static void AddConnectionToRenderData(
-      float start_x, float start_y, float end_x, float end_y,
-      const AnnotationsToRenderDataCalculatorOptions& options, bool normalized,
-      int gray_val1, int gray_val2, RenderData* render_data);
-
-  AnnotationsToRenderDataCalculatorOptions options_;
-};
-REGISTER_CALCULATOR(AnnotationsToRenderDataCalculator);
-
-absl::Status AnnotationsToRenderDataCalculator::GetContract(
-    CalculatorContract* cc) {
-  RET_CHECK(cc->Inputs().HasTag(kAnnotationTag)) << "No input stream found.";
-  if (cc->Inputs().HasTag(kAnnotationTag)) {
-    cc->Inputs().Tag(kAnnotationTag).Set<FrameAnnotation>();
-  }
-  cc->Outputs().Tag(kRenderDataTag).Set<RenderData>();
-
-  return absl::OkStatus();
-}
-
-absl::Status AnnotationsToRenderDataCalculator::Open(CalculatorContext* cc) {
-  cc->SetOffset(TimestampDiff(0));
-  options_ = cc->Options<AnnotationsToRenderDataCalculatorOptions>();
-
-  return absl::OkStatus();
-}
-
-absl::Status AnnotationsToRenderDataCalculator::Process(CalculatorContext* cc) {
-  auto render_data = absl::make_unique<RenderData>();
-  bool visualize_depth = options_.visualize_landmark_depth();
-  float z_min = 0.f;
-  float z_max = 0.f;
-
-  if (cc->Inputs().HasTag(kAnnotationTag)) {
-    const auto& annotations =
-        cc->Inputs().Tag(kAnnotationTag).Get<FrameAnnotation>();
-    RET_CHECK_EQ(options_.landmark_connections_size() % 2, 0)
-        << "Number of entries in landmark connections must be a multiple of 2";
-
-    if (visualize_depth) {
-      GetMinMaxZ(annotations, &z_min, &z_max);
-      // Only change rendering if there are actually z values other than 0.
-      visualize_depth &= ((z_max - z_min) > 1e-3);
-    }
-
-    for (const auto& object : annotations.annotations()) {
-      for (const auto& keypoint : object.keypoints()) {
-        auto* keypoint_data_render =
-            AddPointRenderData(options_, render_data.get());
-        auto* point = keypoint_data_render->mutable_point();
-        if (visualize_depth) {
-          SetColorSizeValueFromZ(keypoint.point_2d().depth(), z_min, z_max,
-                                 keypoint_data_render);
-        }
-
-        point->set_normalized(true);
-        point->set_x(keypoint.point_2d().x());
-        point->set_y(keypoint.point_2d().y());
-      }
-
-      // Add edges
-      for (int i = 0; i < options_.landmark_connections_size(); i += 2) {
-        const auto& ld0 =
-            object.keypoints(options_.landmark_connections(i)).point_2d();
-        const auto& ld1 =
-            object.keypoints(options_.landmark_connections(i + 1)).point_2d();
-        const bool normalized = true;
-
-        if (visualize_depth) {
-          const int gray_val1 =
-              255 - static_cast<int>(Remap(ld0.depth(), z_min, z_max, 255));
-          const int gray_val2 =
-              255 - static_cast<int>(Remap(ld1.depth(), z_min, z_max, 255));
-          AddConnectionToRenderData(ld0.x(), ld0.y(), ld1.x(), ld1.y(),
-                                    options_, normalized, gray_val1, gray_val2,
-                                    render_data.get());
-        } else {
-          AddConnectionToRenderData(ld0.x(), ld0.y(), ld1.x(), ld1.y(),
-                                    options_, normalized, render_data.get());
-        }
-      }
-    }
-  }
-
-  cc->Outputs()
-      .Tag(kRenderDataTag)
-      .Add(render_data.release(), cc->InputTimestamp());
-
-  return absl::OkStatus();
-}
-
-void AnnotationsToRenderDataCalculator::AddConnectionToRenderData(
-    float start_x, float start_y, float end_x, float end_y,
-    const AnnotationsToRenderDataCalculatorOptions& options, bool normalized,
-    int gray_val1, int gray_val2, RenderData* render_data) {
-  auto* connection_annotation = render_data->add_render_annotations();
-  RenderAnnotation::GradientLine* line =
-      connection_annotation->mutable_gradient_line();
-  line->set_x_start(start_x);
-  line->set_y_start(start_y);
-  line->set_x_end(end_x);
-  line->set_y_end(end_y);
-  line->set_normalized(normalized);
-  line->mutable_color1()->set_r(gray_val1);
-  line->mutable_color1()->set_g(gray_val1);
-  line->mutable_color1()->set_b(gray_val1);
-  line->mutable_color2()->set_r(gray_val2);
-  line->mutable_color2()->set_g(gray_val2);
-  line->mutable_color2()->set_b(gray_val2);
-  connection_annotation->set_thickness(options.thickness());
-}
-
-void AnnotationsToRenderDataCalculator::AddConnectionToRenderData(
-    float start_x, float start_y, float end_x, float end_y,
-    const AnnotationsToRenderDataCalculatorOptions& options, bool normalized,
-    RenderData* render_data) {
-  auto* connection_annotation = render_data->add_render_annotations();
-  RenderAnnotation::Line* line = connection_annotation->mutable_line();
-  line->set_x_start(start_x);
-  line->set_y_start(start_y);
-  line->set_x_end(end_x);
-  line->set_y_end(end_y);
-  line->set_normalized(normalized);
-  SetColor(connection_annotation, options.connection_color());
-  connection_annotation->set_thickness(options.thickness());
-}
-
-RenderAnnotation* AnnotationsToRenderDataCalculator::AddPointRenderData(
-    const AnnotationsToRenderDataCalculatorOptions& options,
-    RenderData* render_data) {
-  auto* landmark_data_annotation = render_data->add_render_annotations();
-  landmark_data_annotation->set_scene_tag(kKeypointLabel);
-  SetRenderAnnotationColorThickness(options, landmark_data_annotation);
-  return landmark_data_annotation;
-}
-
-void AnnotationsToRenderDataCalculator::SetRenderAnnotationColorThickness(
-    const AnnotationsToRenderDataCalculatorOptions& options,
-    RenderAnnotation* render_annotation) {
-  SetColor(render_annotation, options.landmark_color());
-  render_annotation->set_thickness(options.thickness());
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.proto b/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.proto
deleted file mode 100644
index 1e04d95..0000000
--- a/mediapipe/graphs/object_detection_3d/calculators/annotations_to_render_data_calculator.proto
+++ /dev/null
@@ -1,43 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-import "mediapipe/util/color.proto";
-
-message AnnotationsToRenderDataCalculatorOptions {
-  extend CalculatorOptions {
-    optional AnnotationsToRenderDataCalculatorOptions ext = 267644238;
-  }
-
-  // Specifies the landmarks to be connected in the drawing. For example, the
-  // landmark_connections value of [0, 1, 1, 2] specifies two connections: one
-  // that connects landmarks with index 0 and 1, and another that connects
-  // landmarks with index 1 and 2.
-  repeated int32 landmark_connections = 1;
-
-  // Color of the landmarks.
-  optional Color landmark_color = 2;
-  // Color of the connections.
-  optional Color connection_color = 3;
-
-  // Thickness of the drawing of landmarks and connections.
-  optional double thickness = 4 [default = 1.0];
-
-  // Change color and size of rendered landmarks based on its z value.
-  optional bool visualize_landmark_depth = 5 [default = true];
-}
diff --git a/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc b/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc
deleted file mode 100644
index 9bc43ba..0000000
--- a/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.cc
+++ /dev/null
@@ -1,947 +0,0 @@
-// Copyright 2020 Google LLC
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#if defined(__ANDROID__)
-#include "mediapipe/util/android/asset_manager_util.h"
-#else
-#include <fstream>
-#include <iostream>
-#endif
-
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/gpu/gl_calculator_helper.h"
-#include "mediapipe/gpu/shader_util.h"
-#include "mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.pb.h"
-#include "mediapipe/graphs/object_detection_3d/calculators/model_matrix.pb.h"
-#include "mediapipe/modules/objectron/calculators/camera_parameters.pb.h"
-
-namespace mediapipe {
-
-namespace {
-
-#if defined(GL_DEBUG)
-#define GLCHECK(command) \
-  command;               \
-  if (int err = glGetError()) LOG(ERROR) << "GL error detected: " << err;
-#else
-#define GLCHECK(command) command
-#endif
-
-// For ease of use, we prefer ImageFrame on Android and GpuBuffer otherwise.
-#if defined(__ANDROID__)
-typedef ImageFrame AssetTextureFormat;
-#else
-typedef GpuBuffer AssetTextureFormat;
-#endif
-
-enum { ATTRIB_VERTEX, ATTRIB_TEXTURE_POSITION, ATTRIB_NORMAL, NUM_ATTRIBUTES };
-static const int kNumMatrixEntries = 16;
-
-// Hard-coded MVP Matrix for testing.
-static const float kModelMatrix[] = {0.83704215,  -0.36174262, 0.41049102, 0.0,
-                                     0.06146407,  0.8076706,   0.5864218,  0.0,
-                                     -0.54367524, -0.4656292,  0.69828844, 0.0,
-                                     0.0,         0.0,         -98.64117,  1.0};
-
-// Loads a texture from an input side packet, and streams in an animation file
-// from a filename given in another input side packet, and renders the animation
-// over the screen according to the input timestamp and desired animation FPS.
-//
-// Inputs:
-//   VIDEO (GpuBuffer, optional):
-//     If provided, the input buffer will be assumed to be unique, and will be
-//     consumed by this calculator and rendered to directly.  The output video
-//     buffer will then be the released reference to the input video buffer.
-//   MODEL_MATRICES (TimedModelMatrixProtoList, optional):
-//     If provided, will set the model matrices for the objects to be rendered
-//     during future rendering calls.
-//   TEXTURE (ImageFrame on Android / GpuBuffer on iOS, semi-optional):
-//     Texture to use with animation file. Texture is REQUIRED to be passed into
-//     the calculator, but can be passed in as a Side Packet OR Input Stream.
-//
-// Input side packets:
-//   TEXTURE (ImageFrame on Android / GpuBuffer on iOS, semi-optional):
-//     Texture to use with animation file. Texture is REQUIRED to be passed into
-//     the calculator, but can be passed in as a Side Packet OR Input Stream.
-//   ANIMATION_ASSET (String, required):
-//     Path of animation file to load and render. The file format expects an
-//     arbitrary number of animation frames, concatenated directly together,
-//     with each animation frame looking like:
-//     HEADER
-//     VERTICES
-//     TEXTURE_COORDS
-//     INDICES
-//     The header consists of 3 int32 lengths, the sizes of the vertex data,
-//     the texcoord data, and the index data, respectively. Let us call those
-//     N1, N2, and N3. Then we expect N1 float32's for vertex information
-//     (x1,y1,z1,x2,y2,z2,etc.), followed by N2 float32's for texcoord
-//     information (u1,v1,u2,v2,u3,v3,etc.), followed by N3 shorts/int16's
-//     for triangle indices (a1,b1,c1,a2,b2,c2,etc.).
-//   CAMERA_PARAMETERS_PROTO_STRING (String, optional):
-//     Serialized proto std::string of CameraParametersProto. We need this to
-//     get the right aspect ratio and field of view.
-// Options:
-//   aspect_ratio: the ratio between the rendered image width and height.
-//     It will be ignored if CAMERA_PARAMETERS_PROTO_STRING input side packet
-//     is provided.
-//   vertical_fov_degrees: vertical field of view in degrees.
-//     It will be ignored if CAMERA_PARAMETERS_PROTO_STRING input side packet
-//     is provided.
-//   z_clipping_plane_near: near plane value for z-clipping.
-//   z_clipping_plane_far: far plane value for z-clipping.
-//   animation_speed_fps: speed at which to cycle through animation frames (in
-//     frames per second).
-//
-// Outputs:
-//   OUTPUT, or index 0 (GpuBuffer):
-//     Frames filled with the given texture.
-
-// Simple helper-struct for containing the parsed geometry data from a 3D
-// animation frame for rendering.
-struct TriangleMesh {
-  int index_count = 0;  // Needed for glDrawElements rendering call
-  std::unique_ptr<float[]> normals = nullptr;
-  std::unique_ptr<float[]> vertices = nullptr;
-  std::unique_ptr<float[]> texture_coords = nullptr;
-  std::unique_ptr<int16[]> triangle_indices = nullptr;
-};
-
-typedef std::unique_ptr<float[]> ModelMatrix;
-
-}  // namespace
-
-class GlAnimationOverlayCalculator : public CalculatorBase {
- public:
-  GlAnimationOverlayCalculator() {}
-  ~GlAnimationOverlayCalculator();
-
-  static absl::Status GetContract(CalculatorContract *cc);
-
-  absl::Status Open(CalculatorContext *cc) override;
-  absl::Status Process(CalculatorContext *cc) override;
-
- private:
-  bool has_video_stream_ = false;
-  bool has_model_matrix_stream_ = false;
-  bool has_mask_model_matrix_stream_ = false;
-  bool has_occlusion_mask_ = false;
-
-  GlCalculatorHelper helper_;
-  bool initialized_ = false;
-  GlTexture texture_;
-  GlTexture mask_texture_;
-
-  GLuint renderbuffer_ = 0;
-  bool depth_buffer_created_ = false;
-
-  GLuint program_ = 0;
-  GLint texture_uniform_ = -1;
-  GLint perspective_matrix_uniform_ = -1;
-  GLint model_matrix_uniform_ = -1;
-
-  std::vector<TriangleMesh> triangle_meshes_;
-  std::vector<TriangleMesh> mask_meshes_;
-  Timestamp animation_start_time_;
-  int frame_count_ = 0;
-  float animation_speed_fps_;
-
-  std::vector<ModelMatrix> current_model_matrices_;
-  std::vector<ModelMatrix> current_mask_model_matrices_;
-
-  // Perspective matrix for rendering, to be applied to all model matrices
-  // prior to passing through to the shader as a MVP matrix.  Initialized during
-  // first image packet read.
-  float perspective_matrix_[kNumMatrixEntries];
-
-  void ComputeAspectRatioAndFovFromCameraParameters(
-      const CameraParametersProto &camera_parameters, float *aspect_ratio,
-      float *vertical_fov_degrees);
-
-  int GetAnimationFrameIndex(Timestamp timestamp);
-  absl::Status GlSetup();
-  absl::Status GlBind(const TriangleMesh &triangle_mesh,
-                      const GlTexture &texture);
-  absl::Status GlRender(const TriangleMesh &triangle_mesh,
-                        const float *model_matrix);
-  void InitializePerspectiveMatrix(float aspect_ratio,
-                                   float vertical_fov_degrees, float z_near,
-                                   float z_far);
-  void LoadModelMatrices(const TimedModelMatrixProtoList &model_matrices,
-                         std::vector<ModelMatrix> *current_model_matrices);
-  void CalculateTriangleMeshNormals(int normals_len,
-                                    TriangleMesh *triangle_mesh);
-  void Normalize3f(float input[3]);
-
-#if !defined(__ANDROID__)
-  // Asset loading routine for all non-Android platforms.
-  bool LoadAnimation(const std::string &filename);
-#else
-  // Asset loading for all Android platforms.
-  bool LoadAnimationAndroid(const std::string &filename,
-                            std::vector<TriangleMesh> *mesh);
-  bool ReadBytesFromAsset(AAsset *asset, void *buffer, int num_bytes_to_read);
-#endif
-};
-REGISTER_CALCULATOR(GlAnimationOverlayCalculator);
-
-// static
-absl::Status GlAnimationOverlayCalculator::GetContract(CalculatorContract *cc) {
-  MP_RETURN_IF_ERROR(
-      GlCalculatorHelper::SetupInputSidePackets(&(cc->InputSidePackets())));
-  if (cc->Inputs().HasTag("VIDEO")) {
-    // Currently used only for size and timestamp.
-    cc->Inputs().Tag("VIDEO").Set<GpuBuffer>();
-  }
-  TagOrIndex(&(cc->Outputs()), "OUTPUT", 0).Set<GpuBuffer>();
-
-  if (cc->Inputs().HasTag("MODEL_MATRICES")) {
-    cc->Inputs().Tag("MODEL_MATRICES").Set<TimedModelMatrixProtoList>();
-  }
-  if (cc->Inputs().HasTag("MASK_MODEL_MATRICES")) {
-    cc->Inputs().Tag("MASK_MODEL_MATRICES").Set<TimedModelMatrixProtoList>();
-  }
-
-  // Must have texture as Input Stream or Side Packet
-  if (cc->InputSidePackets().HasTag("TEXTURE")) {
-    cc->InputSidePackets().Tag("TEXTURE").Set<AssetTextureFormat>();
-  } else {
-    cc->Inputs().Tag("TEXTURE").Set<AssetTextureFormat>();
-  }
-
-  cc->InputSidePackets().Tag("ANIMATION_ASSET").Set<std::string>();
-  if (cc->InputSidePackets().HasTag("CAMERA_PARAMETERS_PROTO_STRING")) {
-    cc->InputSidePackets()
-        .Tag("CAMERA_PARAMETERS_PROTO_STRING")
-        .Set<std::string>();
-  }
-
-  if (cc->InputSidePackets().HasTag("MASK_TEXTURE")) {
-    cc->InputSidePackets().Tag("MASK_TEXTURE").Set<AssetTextureFormat>();
-  }
-  if (cc->InputSidePackets().HasTag("MASK_ASSET")) {
-    cc->InputSidePackets().Tag("MASK_ASSET").Set<std::string>();
-  }
-
-  return absl::OkStatus();
-}
-
-void GlAnimationOverlayCalculator::CalculateTriangleMeshNormals(
-    int normals_len, TriangleMesh *triangle_mesh) {
-  // Set triangle_mesh normals for shader usage
-  triangle_mesh->normals.reset(new float[normals_len]);
-  // Used for storing the vertex normals prior to averaging
-  std::vector<float> vertex_normals_sum(normals_len, 0.0f);
-  // Compute every triangle surface normal and store them for averaging
-  for (int idx = 0; idx < triangle_mesh->index_count; idx += 3) {
-    int v_idx[3];
-    v_idx[0] = triangle_mesh->triangle_indices.get()[idx];
-    v_idx[1] = triangle_mesh->triangle_indices.get()[idx + 1];
-    v_idx[2] = triangle_mesh->triangle_indices.get()[idx + 2];
-    // (V1) vertex X,Y,Z indices in triangle_mesh.vertices
-    const float v1x = triangle_mesh->vertices[v_idx[0] * 3];
-    const float v1y = triangle_mesh->vertices[v_idx[0] * 3 + 1];
-    const float v1z = triangle_mesh->vertices[v_idx[0] * 3 + 2];
-    // (V2) vertex X,Y,Z indices in triangle_mesh.vertices
-    const float v2x = triangle_mesh->vertices[v_idx[1] * 3];
-    const float v2y = triangle_mesh->vertices[v_idx[1] * 3 + 1];
-    const float v2z = triangle_mesh->vertices[v_idx[1] * 3 + 2];
-    // (V3) vertex X,Y,Z indices in triangle_mesh.vertices
-    const float v3x = triangle_mesh->vertices[v_idx[2] * 3];
-    const float v3y = triangle_mesh->vertices[v_idx[2] * 3 + 1];
-    const float v3z = triangle_mesh->vertices[v_idx[2] * 3 + 2];
-    // Calculate normals from vertices
-    // V2 - V1
-    const float ax = v2x - v1x;
-    const float ay = v2y - v1y;
-    const float az = v2z - v1z;
-    // V3 - V1
-    const float bx = v3x - v1x;
-    const float by = v3y - v1y;
-    const float bz = v3z - v1z;
-    // Calculate cross product
-    const float normal_x = ay * bz - az * by;
-    const float normal_y = az * bx - ax * bz;
-    const float normal_z = ax * by - ay * bx;
-    // The normals calculated above must be normalized if we wish to prevent
-    // triangles with a larger surface area from dominating the normal
-    // calculations, however, none of our current models require this
-    // normalization.
-
-    // Add connected normal to each associated vertex
-    // It is also necessary to increment each vertex denominator for averaging
-    for (int i = 0; i < 3; i++) {
-      vertex_normals_sum[v_idx[i] * 3] += normal_x;
-      vertex_normals_sum[v_idx[i] * 3 + 1] += normal_y;
-      vertex_normals_sum[v_idx[i] * 3 + 2] += normal_z;
-    }
-  }
-
-  // Combine all triangle normals connected to each vertex by adding the X,Y,Z
-  // value of each adjacent triangle surface normal to every vertex and then
-  // averaging the combined value.
-  for (int idx = 0; idx < normals_len; idx += 3) {
-    float normal[3];
-    normal[0] = vertex_normals_sum[idx];
-    normal[1] = vertex_normals_sum[idx + 1];
-    normal[2] = vertex_normals_sum[idx + 2];
-    Normalize3f(normal);
-    triangle_mesh->normals.get()[idx] = normal[0];
-    triangle_mesh->normals.get()[idx + 1] = normal[1];
-    triangle_mesh->normals.get()[idx + 2] = normal[2];
-  }
-}
-
-void GlAnimationOverlayCalculator::Normalize3f(float input[3]) {
-  float product = 0.0;
-  product += input[0] * input[0];
-  product += input[1] * input[1];
-  product += input[2] * input[2];
-  float magnitude = sqrt(product);
-  input[0] /= magnitude;
-  input[1] /= magnitude;
-  input[2] /= magnitude;
-}
-
-// Helper function for initializing our perspective matrix.
-void GlAnimationOverlayCalculator::InitializePerspectiveMatrix(
-    float aspect_ratio, float fov_degrees, float z_near, float z_far) {
-  // Standard perspective projection matrix calculations.
-  const float f = 1.0f / std::tan(fov_degrees * M_PI / 360.0f);
-  for (int i = 0; i < kNumMatrixEntries; i++) {
-    perspective_matrix_[i] = 0;
-  }
-  const float denom = 1.0f / (z_near - z_far);
-  perspective_matrix_[0] = f / aspect_ratio;
-  perspective_matrix_[5] = f;
-  perspective_matrix_[10] = (z_near + z_far) * denom;
-  perspective_matrix_[11] = -1.0f;
-  perspective_matrix_[14] = 2.0f * z_far * z_near * denom;
-}
-
-#if defined(__ANDROID__)
-// Helper function for reading in a specified number of bytes from an Android
-// asset.  Returns true if successfully reads in all bytes into buffer.
-bool GlAnimationOverlayCalculator::ReadBytesFromAsset(AAsset *asset,
-                                                      void *buffer,
-                                                      int num_bytes_to_read) {
-  // Most file systems use block sizes of 4KB or 8KB; ideally we'd choose a
-  // small multiple of the block size for best input streaming performance, so
-  // we go for a reasobably safe buffer size of 8KB = 8*1024 bytes.
-  static const int kMaxChunkSize = 8192;
-
-  int bytes_left = num_bytes_to_read;
-  int bytes_read = 1;  // any value > 0 here just to start looping.
-
-  // Treat as uint8_t array so we can deal in single byte arithmetic easily.
-  uint8_t *currBufferIndex = reinterpret_cast<uint8_t *>(buffer);
-  while (bytes_read > 0 && bytes_left > 0) {
-    bytes_read = AAsset_read(asset, (void *)currBufferIndex,
-                             std::min(bytes_left, kMaxChunkSize));
-    bytes_left -= bytes_read;
-    currBufferIndex += bytes_read;
-  }
-  // At least log any I/O errors encountered.
-  if (bytes_read < 0) {
-    LOG(ERROR) << "Error reading from AAsset: " << bytes_read;
-    return false;
-  }
-  if (bytes_left > 0) {
-    // Reached EOF before reading in specified number of bytes.
-    LOG(WARNING) << "Reached EOF before reading in specified number of bytes.";
-    return false;
-  }
-  return true;
-}
-
-// The below asset streaming code is Android-only, making use of the platform
-// JNI helper classes AAssetManager and AAsset.
-bool GlAnimationOverlayCalculator::LoadAnimationAndroid(
-    const std::string &filename, std::vector<TriangleMesh> *meshes) {
-  mediapipe::AssetManager *mediapipe_asset_manager =
-      Singleton<mediapipe::AssetManager>::get();
-  AAssetManager *asset_manager = mediapipe_asset_manager->GetAssetManager();
-  if (!asset_manager) {
-    LOG(ERROR) << "Failed to access Android asset manager.";
-    return false;
-  }
-
-  // New read-bytes stuff here!  First we open file for streaming.
-  AAsset *asset = AAssetManager_open(asset_manager, filename.c_str(),
-                                     AASSET_MODE_STREAMING);
-  if (!asset) {
-    LOG(ERROR) << "Failed to open animation asset: " << filename;
-    return false;
-  }
-
-  // And now, while we are able to stream in more frames, we do so.
-  frame_count_ = 0;
-  int32 lengths[3];
-  while (ReadBytesFromAsset(asset, (void *)lengths, sizeof(lengths[0]) * 3)) {
-    // About to start reading the next animation frame.  Stream it in here.
-    // Each frame stores first the object counts of its three arrays
-    // (vertices, texture coordinates, triangle indices; respectively), and
-    // then stores each of those arrays as a byte dump, in order.
-    meshes->emplace_back();
-    TriangleMesh &triangle_mesh = meshes->back();
-    // Try to read in vertices (4-byte floats)
-    triangle_mesh.vertices.reset(new float[lengths[0]]);
-    if (!ReadBytesFromAsset(asset, (void *)triangle_mesh.vertices.get(),
-                            sizeof(float) * lengths[0])) {
-      LOG(ERROR) << "Failed to read vertices for frame " << frame_count_;
-      return false;
-    }
-    // Try to read in texture coordinates (4-byte floats)
-    triangle_mesh.texture_coords.reset(new float[lengths[1]]);
-    if (!ReadBytesFromAsset(asset, (void *)triangle_mesh.texture_coords.get(),
-                            sizeof(float) * lengths[1])) {
-      LOG(ERROR) << "Failed to read tex-coords for frame " << frame_count_;
-      return false;
-    }
-    // Try to read in indices (2-byte shorts)
-    triangle_mesh.index_count = lengths[2];
-    triangle_mesh.triangle_indices.reset(new int16[lengths[2]]);
-    if (!ReadBytesFromAsset(asset, (void *)triangle_mesh.triangle_indices.get(),
-                            sizeof(int16) * lengths[2])) {
-      LOG(ERROR) << "Failed to read indices for frame " << frame_count_;
-      return false;
-    }
-
-    // Set the normals for this triangle_mesh
-    CalculateTriangleMeshNormals(lengths[0], &triangle_mesh);
-
-    frame_count_++;
-  }
-  AAsset_close(asset);
-
-  LOG(INFO) << "Finished parsing " << frame_count_ << " animation frames.";
-  if (meshes->empty()) {
-    LOG(ERROR) << "No animation frames were parsed!  Erroring out calculator.";
-    return false;
-  }
-  return true;
-}
-
-#else  // defined(__ANDROID__)
-
-bool GlAnimationOverlayCalculator::LoadAnimation(const std::string &filename) {
-  std::ifstream infile(filename.c_str(), std::ifstream::binary);
-  if (!infile) {
-    LOG(ERROR) << "Error opening asset with filename: " << filename;
-    return false;
-  }
-
-  frame_count_ = 0;
-  int32 lengths[3];
-  while (true) {
-    // See if we have more initial size counts to read in.
-    infile.read((char *)(lengths), sizeof(lengths[0]) * 3);
-    if (!infile) {
-      // No more frames to read.  Close out.
-      infile.close();
-      break;
-    }
-
-    triangle_meshes_.emplace_back();
-    TriangleMesh &triangle_mesh = triangle_meshes_.back();
-
-    // Try to read in vertices (4-byte floats).
-    triangle_mesh.vertices.reset(new float[lengths[0]]);
-    infile.read((char *)(triangle_mesh.vertices.get()),
-                sizeof(float) * lengths[0]);
-    if (!infile) {
-      LOG(ERROR) << "Failed to read vertices for frame " << frame_count_;
-      return false;
-    }
-
-    // Try to read in texture coordinates (4-byte floats)
-    triangle_mesh.texture_coords.reset(new float[lengths[1]]);
-    infile.read((char *)(triangle_mesh.texture_coords.get()),
-                sizeof(float) * lengths[1]);
-    if (!infile) {
-      LOG(ERROR) << "Failed to read texture coordinates for frame "
-                 << frame_count_;
-      return false;
-    }
-
-    // Try to read in the triangle indices (2-byte shorts)
-    triangle_mesh.index_count = lengths[2];
-    triangle_mesh.triangle_indices.reset(new int16[lengths[2]]);
-    infile.read((char *)(triangle_mesh.triangle_indices.get()),
-                sizeof(int16) * lengths[2]);
-    if (!infile) {
-      LOG(ERROR) << "Failed to read triangle indices for frame "
-                 << frame_count_;
-      return false;
-    }
-
-    // Set the normals for this triangle_mesh
-    CalculateTriangleMeshNormals(lengths[0], &triangle_mesh);
-
-    frame_count_++;
-  }
-
-  LOG(INFO) << "Finished parsing " << frame_count_ << " animation frames.";
-  if (triangle_meshes_.empty()) {
-    LOG(ERROR) << "No animation frames were parsed!  Erroring out calculator.";
-    return false;
-  }
-  return true;
-}
-
-#endif
-
-void GlAnimationOverlayCalculator::ComputeAspectRatioAndFovFromCameraParameters(
-    const CameraParametersProto &camera_parameters, float *aspect_ratio,
-    float *vertical_fov_degrees) {
-  CHECK(aspect_ratio != nullptr);
-  CHECK(vertical_fov_degrees != nullptr);
-  *aspect_ratio =
-      camera_parameters.portrait_width() / camera_parameters.portrait_height();
-  *vertical_fov_degrees =
-      std::atan(camera_parameters.portrait_height() * 0.5f) * 2 * 180 / M_PI;
-}
-
-absl::Status GlAnimationOverlayCalculator::Open(CalculatorContext *cc) {
-  cc->SetOffset(TimestampDiff(0));
-  MP_RETURN_IF_ERROR(helper_.Open(cc));
-
-  const auto &options = cc->Options<GlAnimationOverlayCalculatorOptions>();
-
-  animation_speed_fps_ = options.animation_speed_fps();
-
-  // Construct projection matrix using input side packets or option
-  float aspect_ratio;
-  float vertical_fov_degrees;
-  if (cc->InputSidePackets().HasTag("CAMERA_PARAMETERS_PROTO_STRING")) {
-    const std::string &camera_parameters_proto_string =
-        cc->InputSidePackets()
-            .Tag("CAMERA_PARAMETERS_PROTO_STRING")
-            .Get<std::string>();
-    CameraParametersProto camera_parameters_proto;
-    camera_parameters_proto.ParseFromString(camera_parameters_proto_string);
-    ComputeAspectRatioAndFovFromCameraParameters(
-        camera_parameters_proto, &aspect_ratio, &vertical_fov_degrees);
-  } else {
-    aspect_ratio = options.aspect_ratio();
-    vertical_fov_degrees = options.vertical_fov_degrees();
-  }
-
-  // when constructing projection matrix.
-  InitializePerspectiveMatrix(aspect_ratio, vertical_fov_degrees,
-                              options.z_clipping_plane_near(),
-                              options.z_clipping_plane_far());
-
-  // See what streams we have.
-  has_video_stream_ = cc->Inputs().HasTag("VIDEO");
-  has_model_matrix_stream_ = cc->Inputs().HasTag("MODEL_MATRICES");
-  has_mask_model_matrix_stream_ = cc->Inputs().HasTag("MASK_MODEL_MATRICES");
-
-  // Try to load in the animation asset in a platform-specific manner.
-  const std::string &asset_name =
-      cc->InputSidePackets().Tag("ANIMATION_ASSET").Get<std::string>();
-  bool loaded_animation = false;
-#if defined(__ANDROID__)
-  if (cc->InputSidePackets().HasTag("MASK_ASSET")) {
-    has_occlusion_mask_ = true;
-    const std::string &mask_asset_name =
-        cc->InputSidePackets().Tag("MASK_ASSET").Get<std::string>();
-    loaded_animation = LoadAnimationAndroid(mask_asset_name, &mask_meshes_);
-    if (!loaded_animation) {
-      LOG(ERROR) << "Failed to load mask asset.";
-      return absl::UnknownError("Failed to load mask asset.");
-    }
-  }
-  loaded_animation = LoadAnimationAndroid(asset_name, &triangle_meshes_);
-#else
-  loaded_animation = LoadAnimation(asset_name);
-#endif
-  if (!loaded_animation) {
-    LOG(ERROR) << "Failed to load animation asset.";
-    return absl::UnknownError("Failed to load animation asset.");
-  }
-
-  return helper_.RunInGlContext([this, &cc]() -> absl::Status {
-    if (cc->InputSidePackets().HasTag("MASK_TEXTURE")) {
-      const auto &mask_texture =
-          cc->InputSidePackets().Tag("MASK_TEXTURE").Get<AssetTextureFormat>();
-      mask_texture_ = helper_.CreateSourceTexture(mask_texture);
-    }
-
-    // Load in all static texture data if it exists
-    if (cc->InputSidePackets().HasTag("TEXTURE")) {
-      const auto &input_texture =
-          cc->InputSidePackets().Tag("TEXTURE").Get<AssetTextureFormat>();
-      texture_ = helper_.CreateSourceTexture(input_texture);
-    }
-
-    VLOG(2) << "Input texture size: " << texture_.width() << ", "
-            << texture_.height() << std::endl;
-
-    return absl::OkStatus();
-  });
-}
-
-int GlAnimationOverlayCalculator::GetAnimationFrameIndex(Timestamp timestamp) {
-  double seconds_delta = timestamp.Seconds() - animation_start_time_.Seconds();
-  int64_t frame_index =
-      static_cast<int64_t>(seconds_delta * animation_speed_fps_);
-  frame_index %= frame_count_;
-  return static_cast<int>(frame_index);
-}
-
-void GlAnimationOverlayCalculator::LoadModelMatrices(
-    const TimedModelMatrixProtoList &model_matrices,
-    std::vector<ModelMatrix> *current_model_matrices) {
-  current_model_matrices->clear();
-  for (int i = 0; i < model_matrices.model_matrix_size(); ++i) {
-    const auto &model_matrix = model_matrices.model_matrix(i);
-    CHECK(model_matrix.matrix_entries_size() == kNumMatrixEntries)
-        << "Invalid Model Matrix";
-    current_model_matrices->emplace_back();
-    ModelMatrix &new_matrix = current_model_matrices->back();
-    new_matrix.reset(new float[kNumMatrixEntries]);
-    for (int j = 0; j < kNumMatrixEntries; j++) {
-      // Model matrices streamed in using ROW-MAJOR format, but we want
-      // COLUMN-MAJOR for rendering, so we transpose here.
-      int col = j % 4;
-      int row = j / 4;
-      new_matrix[row + col * 4] = model_matrix.matrix_entries(j);
-    }
-  }
-}
-
-absl::Status GlAnimationOverlayCalculator::Process(CalculatorContext *cc) {
-  return helper_.RunInGlContext([this, &cc]() -> absl::Status {
-    if (!initialized_) {
-      MP_RETURN_IF_ERROR(GlSetup());
-      initialized_ = true;
-      animation_start_time_ = cc->InputTimestamp();
-    }
-
-    // Process model matrices, if any are being streamed in, and update our
-    // list.
-    current_model_matrices_.clear();
-    if (has_model_matrix_stream_ &&
-        !cc->Inputs().Tag("MODEL_MATRICES").IsEmpty()) {
-      const TimedModelMatrixProtoList &model_matrices =
-          cc->Inputs().Tag("MODEL_MATRICES").Get<TimedModelMatrixProtoList>();
-      LoadModelMatrices(model_matrices, &current_model_matrices_);
-    }
-
-    current_mask_model_matrices_.clear();
-    if (has_mask_model_matrix_stream_ &&
-        !cc->Inputs().Tag("MASK_MODEL_MATRICES").IsEmpty()) {
-      const TimedModelMatrixProtoList &model_matrices =
-          cc->Inputs()
-              .Tag("MASK_MODEL_MATRICES")
-              .Get<TimedModelMatrixProtoList>();
-      LoadModelMatrices(model_matrices, &current_mask_model_matrices_);
-    }
-
-    // Arbitrary default width and height for output destination texture, in the
-    // event that we don't have a valid and unique input buffer to overlay.
-    int width = 640;
-    int height = 480;
-
-    GlTexture dst;
-    std::unique_ptr<GpuBuffer> input_frame(nullptr);
-    if (has_video_stream_ && !(cc->Inputs().Tag("VIDEO").IsEmpty())) {
-      auto result = cc->Inputs().Tag("VIDEO").Value().Consume<GpuBuffer>();
-      if (result.ok()) {
-        input_frame = std::move(result).value();
-#if !MEDIAPIPE_GPU_BUFFER_USE_CV_PIXEL_BUFFER
-        input_frame->GetGlTextureBufferSharedPtr()->Reuse();
-#endif
-        width = input_frame->width();
-        height = input_frame->height();
-        dst = helper_.CreateSourceTexture(*input_frame);
-      } else {
-        LOG(ERROR) << "Unable to consume input video frame for overlay!";
-        LOG(ERROR) << "Status returned was: " << result.status();
-        dst = helper_.CreateDestinationTexture(width, height);
-      }
-    } else if (!has_video_stream_) {
-      dst = helper_.CreateDestinationTexture(width, height);
-    } else {
-      // We have an input video stream, but not for this frame. Don't render!
-      return absl::OkStatus();
-    }
-    helper_.BindFramebuffer(dst);
-
-    if (!depth_buffer_created_) {
-      // Create our private depth buffer.
-      GLCHECK(glGenRenderbuffers(1, &renderbuffer_));
-      GLCHECK(glBindRenderbuffer(GL_RENDERBUFFER, renderbuffer_));
-      GLCHECK(glRenderbufferStorage(GL_RENDERBUFFER, GL_DEPTH_COMPONENT16,
-                                    width, height));
-      GLCHECK(glFramebufferRenderbuffer(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
-                                        GL_RENDERBUFFER, renderbuffer_));
-      GLCHECK(glBindRenderbuffer(GL_RENDERBUFFER, 0));
-      depth_buffer_created_ = true;
-    }
-
-    // Re-bind our depth renderbuffer to our FBO depth attachment here.
-    GLCHECK(glBindRenderbuffer(GL_RENDERBUFFER, renderbuffer_));
-    GLCHECK(glFramebufferRenderbuffer(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
-                                      GL_RENDERBUFFER, renderbuffer_));
-    GLenum status = GLCHECK(glCheckFramebufferStatus(GL_FRAMEBUFFER));
-    if (status != GL_FRAMEBUFFER_COMPLETE) {
-      LOG(ERROR) << "Incomplete framebuffer with status: " << status;
-    }
-    GLCHECK(glClear(GL_DEPTH_BUFFER_BIT));
-
-    if (has_occlusion_mask_) {
-      glColorMask(GL_FALSE, GL_FALSE, GL_FALSE, GL_FALSE);
-      const TriangleMesh &mask_frame = mask_meshes_.front();
-      MP_RETURN_IF_ERROR(GlBind(mask_frame, mask_texture_));
-      // Draw objects using our latest model matrix stream packet.
-      for (const ModelMatrix &model_matrix : current_mask_model_matrices_) {
-        MP_RETURN_IF_ERROR(GlRender(mask_frame, model_matrix.get()));
-      }
-    }
-
-    glColorMask(GL_TRUE, GL_TRUE, GL_TRUE, GL_TRUE);
-    int frame_index = GetAnimationFrameIndex(cc->InputTimestamp());
-    const TriangleMesh &current_frame = triangle_meshes_[frame_index];
-
-    // Load dynamic texture if it exists
-    if (cc->Inputs().HasTag("TEXTURE")) {
-      const auto &input_texture =
-          cc->Inputs().Tag("TEXTURE").Get<AssetTextureFormat>();
-      texture_ = helper_.CreateSourceTexture(input_texture);
-    }
-
-    MP_RETURN_IF_ERROR(GlBind(current_frame, texture_));
-    if (has_model_matrix_stream_) {
-      // Draw objects using our latest model matrix stream packet.
-      for (const ModelMatrix &model_matrix : current_model_matrices_) {
-        MP_RETURN_IF_ERROR(GlRender(current_frame, model_matrix.get()));
-      }
-    } else {
-      // Just draw one object to a static model matrix.
-      MP_RETURN_IF_ERROR(GlRender(current_frame, kModelMatrix));
-    }
-
-    // Disable vertex attributes
-    GLCHECK(glDisableVertexAttribArray(ATTRIB_VERTEX));
-    GLCHECK(glDisableVertexAttribArray(ATTRIB_TEXTURE_POSITION));
-    GLCHECK(glDisableVertexAttribArray(ATTRIB_NORMAL));
-
-    // Disable depth test
-    GLCHECK(glDisable(GL_DEPTH_TEST));
-
-    // Unbind texture
-    GLCHECK(glActiveTexture(GL_TEXTURE1));
-    GLCHECK(glBindTexture(texture_.target(), 0));
-
-    // Unbind depth buffer
-    GLCHECK(glBindRenderbuffer(GL_RENDERBUFFER, 0));
-
-    GLCHECK(glFlush());
-
-    auto output = dst.GetFrame<GpuBuffer>();
-    dst.Release();
-    TagOrIndex(&(cc->Outputs()), "OUTPUT", 0)
-        .Add(output.release(), cc->InputTimestamp());
-    GLCHECK(glFrontFace(GL_CCW));
-    return absl::OkStatus();
-  });
-}
-
-absl::Status GlAnimationOverlayCalculator::GlSetup() {
-  // Load vertex and fragment shaders
-  const GLint attr_location[NUM_ATTRIBUTES] = {
-      ATTRIB_VERTEX,
-      ATTRIB_TEXTURE_POSITION,
-      ATTRIB_NORMAL,
-  };
-  const GLchar *attr_name[NUM_ATTRIBUTES] = {
-      "position",
-      "texture_coordinate",
-      "normal",
-  };
-
-  const GLchar *vert_src = R"(
-    // Perspective projection matrix for rendering / clipping
-    uniform mat4 perspectiveMatrix;
-
-    // Matrix defining the currently rendered object model
-    uniform mat4 modelMatrix;
-
-    // vertex position in threespace
-    attribute vec4 position;
-    attribute vec3 normal;
-
-    // texture coordinate for each vertex in normalized texture space (0..1)
-    attribute mediump vec4 texture_coordinate;
-
-    // texture coordinate for fragment shader (will be interpolated)
-    varying mediump vec2 sampleCoordinate;
-    varying mediump vec3 vNormal;
-
-    void main() {
-      sampleCoordinate = texture_coordinate.xy;
-      mat4 mvpMatrix = perspectiveMatrix * modelMatrix;
-      gl_Position = mvpMatrix * position;
-
-      // TODO: Pass in rotation submatrix with no scaling or transforms to prevent
-      // breaking vNormal in case of model matrix having non-uniform scaling
-      vec4 tmpNormal = mvpMatrix * vec4(normal, 1.0);
-      vec4 transformedZero = mvpMatrix * vec4(0.0, 0.0, 0.0, 1.0);
-      tmpNormal = tmpNormal - transformedZero;
-      vNormal = normalize(tmpNormal.xyz);
-    }
-  )";
-
-  const GLchar *frag_src = R"(
-    precision mediump float;
-
-    varying vec2 sampleCoordinate;  // texture coordinate (0..1)
-    varying vec3 vNormal;
-    uniform sampler2D texture;  // texture to shade with
-    const float kPi = 3.14159265359;
-
-    // Define ambient lighting factor that is applied to our texture in order to
-    // generate ambient lighting of the scene on the object. Range is [0.0-1.0],
-    // with the factor being proportional to the brightness of the lighting in the
-    // scene being applied to the object
-    const float kAmbientLighting = 0.75;
-
-    // Define RGB values for light source
-    const vec3 kLightColor = vec3(0.25);
-    // Exponent for directional lighting that governs diffusion of surface light
-    const float kExponent = 1.0;
-    // Define direction of lighting effect source
-    const vec3 lightDir = vec3(0.0, -1.0, -0.6);
-    // Hard-coded view direction
-    const vec3 viewDir = vec3(0.0, 0.0, -1.0);
-
-    // DirectionalLighting procedure imported from Lullaby @ https://github.com/google/lullaby
-    // Calculate and return the color (diffuse and specular together) reflected by
-    // a directional light.
-    vec3 GetDirectionalLight(vec3 pos, vec3 normal, vec3 viewDir, vec3 lightDir, vec3 lightColor, float exponent) {
-      // Intensity of the diffuse light. Saturate to keep within the 0-1 range.
-      float normal_dot_light_dir = dot(-normal, -lightDir);
-      float intensity = clamp(normal_dot_light_dir, 0.0, 1.0);
-      // Calculate the diffuse light
-      vec3 diffuse = intensity * lightColor;
-      // http://www.rorydriscoll.com/2009/01/25/energy-conservation-in-games/
-      float kEnergyConservation = (2.0 + exponent) / (2.0 * kPi);
-      vec3 reflect_dir = reflect(lightDir, -normal);
-      // Intensity of the specular light
-      float view_dot_reflect = dot(-viewDir, reflect_dir);
-      // Use an epsilon for pow because pow(x,y) is undefined if x < 0 or x == 0
-      // and y <= 0 (GLSL Spec 8.2)
-      const float kEpsilon = 1e-5;
-      intensity = kEnergyConservation * pow(clamp(view_dot_reflect, kEpsilon, 1.0),
-       exponent);
-      // Specular color:
-      vec3 specular = intensity * lightColor;
-      return diffuse + specular;
-    }
-
-    void main() {
-      // Sample the texture, retrieving an rgba pixel value
-      vec4 pixel = texture2D(texture, sampleCoordinate);
-      // If the alpha (background) value is near transparent, then discard the
-      // pixel, this allows the rendering of transparent background GIFs
-      // TODO: Adding a toggle to perform pixel alpha discarding for transparent
-      // GIFs (prevent interference with Objectron system).
-      if (pixel.a < 0.2) discard;
-
-      // Generate directional lighting effect
-      vec3 lighting = GetDirectionalLight(gl_FragCoord.xyz, vNormal, viewDir, lightDir, kLightColor, kExponent);
-      // Apply both ambient and directional lighting to our texture
-      gl_FragColor = vec4((vec3(kAmbientLighting) + lighting) * pixel.rgb, 1.0);
-    }
-  )";
-
-  // Shader program
-  GLCHECK(GlhCreateProgram(vert_src, frag_src, NUM_ATTRIBUTES,
-                           (const GLchar **)&attr_name[0], attr_location,
-                           &program_));
-  RET_CHECK(program_) << "Problem initializing the program.";
-  texture_uniform_ = GLCHECK(glGetUniformLocation(program_, "texture"));
-  perspective_matrix_uniform_ =
-      GLCHECK(glGetUniformLocation(program_, "perspectiveMatrix"));
-  model_matrix_uniform_ =
-      GLCHECK(glGetUniformLocation(program_, "modelMatrix"));
-  return absl::OkStatus();
-}
-
-absl::Status GlAnimationOverlayCalculator::GlBind(
-    const TriangleMesh &triangle_mesh, const GlTexture &texture) {
-  GLCHECK(glUseProgram(program_));
-
-  // Disable backface culling to allow occlusion effects.
-  // Some options for solid arbitrary 3D geometry rendering
-  GLCHECK(glEnable(GL_BLEND));
-  GLCHECK(glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA));
-  GLCHECK(glEnable(GL_DEPTH_TEST));
-  GLCHECK(glFrontFace(GL_CW));
-  GLCHECK(glDepthMask(GL_TRUE));
-  GLCHECK(glDepthFunc(GL_LESS));
-
-  // Clear our depth buffer before starting draw calls
-  GLCHECK(glVertexAttribPointer(ATTRIB_VERTEX, 3, GL_FLOAT, 0, 0,
-                                triangle_mesh.vertices.get()));
-  GLCHECK(glEnableVertexAttribArray(ATTRIB_VERTEX));
-  GLCHECK(glVertexAttribPointer(ATTRIB_TEXTURE_POSITION, 2, GL_FLOAT, 0, 0,
-                                triangle_mesh.texture_coords.get()));
-  GLCHECK(glEnableVertexAttribArray(ATTRIB_TEXTURE_POSITION));
-  GLCHECK(glVertexAttribPointer(ATTRIB_NORMAL, 3, GL_FLOAT, 0, 0,
-                                triangle_mesh.normals.get()));
-  GLCHECK(glEnableVertexAttribArray(ATTRIB_NORMAL));
-  GLCHECK(glActiveTexture(GL_TEXTURE1));
-  GLCHECK(glBindTexture(texture.target(), texture.name()));
-
-  // We previously bound it to GL_TEXTURE1
-  GLCHECK(glUniform1i(texture_uniform_, 1));
-
-  GLCHECK(glUniformMatrix4fv(perspective_matrix_uniform_, 1, GL_FALSE,
-                             perspective_matrix_));
-  return absl::OkStatus();
-}
-
-absl::Status GlAnimationOverlayCalculator::GlRender(
-    const TriangleMesh &triangle_mesh, const float *model_matrix) {
-  GLCHECK(glUniformMatrix4fv(model_matrix_uniform_, 1, GL_FALSE, model_matrix));
-  GLCHECK(glDrawElements(GL_TRIANGLES, triangle_mesh.index_count,
-                         GL_UNSIGNED_SHORT,
-                         triangle_mesh.triangle_indices.get()));
-  return absl::OkStatus();
-}
-
-GlAnimationOverlayCalculator::~GlAnimationOverlayCalculator() {
-  helper_.RunInGlContext([this] {
-    if (program_) {
-      GLCHECK(glDeleteProgram(program_));
-      program_ = 0;
-    }
-    if (depth_buffer_created_) {
-      GLCHECK(glDeleteRenderbuffers(1, &renderbuffer_));
-      renderbuffer_ = 0;
-    }
-    if (texture_.width() > 0) {
-      texture_.Release();
-    }
-    if (mask_texture_.width() > 0) {
-      mask_texture_.Release();
-    }
-  });
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.proto b/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.proto
deleted file mode 100644
index 4966f0a..0000000
--- a/mediapipe/graphs/object_detection_3d/calculators/gl_animation_overlay_calculator.proto
+++ /dev/null
@@ -1,41 +0,0 @@
-// Copyright 2019 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-
-message GlAnimationOverlayCalculatorOptions {
-  extend CalculatorOptions {
-    optional GlAnimationOverlayCalculatorOptions ext = 174760573;
-  }
-
-  // Default aspect ratio of rendering target width over height.
-  // This specific value is for 3:4 view. Do not change this default value.
-  optional float aspect_ratio = 1 [default = 0.75];
-  // Default vertical field of view in degrees. This specific default value
-  // is arbitrary. Do not change this default value. If you want to use
-  // a different vertical_fov_degrees, set it in the options.
-  optional float vertical_fov_degrees = 2 [default = 70.0];
-
-  // Perspective projection matrix z-clipping near plane value.
-  optional float z_clipping_plane_near = 3 [default = 0.1];
-  // Perspective projection matrix z-clipping far plane value.
-  optional float z_clipping_plane_far = 4 [default = 1000.0];
-
-  // Speed at which to play the animation (in frames per second).
-  optional float animation_speed_fps = 5 [default = 25.0];
-}
diff --git a/mediapipe/graphs/object_detection_3d/calculators/model_matrix.proto b/mediapipe/graphs/object_detection_3d/calculators/model_matrix.proto
deleted file mode 100644
index 406cc9f..0000000
--- a/mediapipe/graphs/object_detection_3d/calculators/model_matrix.proto
+++ /dev/null
@@ -1,48 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-message TimedModelMatrixProto {
-  // 4x4 model matrix stored in ROW major order.
-  repeated float matrix_entries = 1 [packed = true];
-  // Timestamp of this model matrix in milliseconds.
-  optional int64 time_msec = 2 [default = 0];
-  // Unique per object id
-  optional int32 id = 3 [default = -1];
-}
-
-message TimedModelMatrixProtoList {
-  repeated TimedModelMatrixProto model_matrix = 1;
-}
-
-// For convenience, when the desired information or transformation can be
-// encoded into vectors (e.g. when the matrix represents a scale or Euler-angle-
-// based rotation operation.)
-message TimedVectorProto {
-  // The vector values themselves.
-  repeated float vector_entries = 1 [packed = true];
-
-  // Timestamp of this vector in milliseconds.
-  optional int64 time_msec = 2 [default = 0];
-
-  // Unique per object id
-  optional int32 id = 3 [default = -1];
-}
-
-message TimedVectorProtoList {
-  repeated TimedVectorProto vector_list = 1;
-}
diff --git a/mediapipe/graphs/object_detection_3d/obj_parser/BUILD b/mediapipe/graphs/object_detection_3d/obj_parser/BUILD
deleted file mode 100644
index 3b84cc8..0000000
--- a/mediapipe/graphs/object_detection_3d/obj_parser/BUILD
+++ /dev/null
@@ -1,33 +0,0 @@
-# Copyright 2021 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-java_library(
-    name = "obj_parser_lib",
-    srcs = [
-        "ObjParserMain.java",
-        "SimpleObjParser.java",
-    ],
-    javacopts = ["-Xep:DefaultPackage:OFF"],
-)
-
-java_binary(
-    name = "ObjParser",
-    javacopts = ["-Xep:DefaultPackage:OFF"],
-    main_class = "ObjParserMain",
-    runtime_deps = [
-        ":obj_parser_lib",
-    ],
-)
diff --git a/mediapipe/graphs/object_detection_3d/obj_parser/ObjParserMain.java b/mediapipe/graphs/object_detection_3d/obj_parser/ObjParserMain.java
deleted file mode 100644
index 80e639d..0000000
--- a/mediapipe/graphs/object_detection_3d/obj_parser/ObjParserMain.java
+++ /dev/null
@@ -1,205 +0,0 @@
-// Copyright 2021 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-import static java.nio.charset.StandardCharsets.UTF_8;
-
-import java.io.BufferedWriter;
-import java.io.File;
-import java.io.FileFilter;
-import java.io.FileOutputStream;
-import java.io.OutputStream;
-import java.io.OutputStreamWriter;
-import java.io.PrintWriter;
-import java.nio.ByteBuffer;
-import java.nio.ByteOrder;
-import java.util.ArrayList;
-import java.util.Arrays;
-
-/**
- * Class for running desktop-side parsing/packing routines on .obj AR assets. Usage: ObjParser
- * --input_dir=[INPUT_DIRECTORY] --output_dir=[OUTPUT_DIRECTORY] where INPUT_DIRECTORY is the folder
- * with asset obj files to process, and OUTPUT_DIRECTORY is the folder where processed asset uuu
- * file should be placed.
- *
- * <p>NOTE: Directories are assumed to be absolute paths.
- */
-public final class ObjParserMain {
-  // Simple FileFilter implementation to let us walk over only our .obj files in a particular
-  // directory.
-  private static final class ObjFileFilter implements FileFilter {
-    ObjFileFilter() {
-      // Nothing to do here.
-    }
-
-    @Override
-    public boolean accept(File file) {
-      return file.getName().endsWith(".obj");
-    }
-  }
-
-  // File extension for binary output files; tagged onto end of initial file extension.
-  private static final String BINARY_FILE_EXT = ".uuu";
-  private static final String INPUT_DIR_FLAG = "--input_dir=";
-  private static final String OUTPUT_DIR_FLAG = "--output_dir=";
-  private static final float DEFAULT_VERTEX_SCALE_FACTOR = 30.0f;
-  private static final double NS_TO_SECONDS = 1e9;
-
-  public final PrintWriter writer;
-
-  public ObjParserMain() {
-    super();
-    this.writer = new PrintWriter(new BufferedWriter(new OutputStreamWriter(System.out, UTF_8)));
-  }
-
-  // Simple overridable logging function.
-  protected void logString(String infoLog) {
-    writer.println(infoLog);
-  }
-
-  /*
-   * Main program logic: parse command-line arguments and perform actions.
-   */
-  public void run(String inDirectory, String outDirectory) {
-    if (inDirectory.isEmpty()) {
-      logString("Error: Must provide input directory with " + INPUT_DIR_FLAG);
-      return;
-    }
-    if (outDirectory.isEmpty()) {
-      logString("Error: Must provide output directory with " + OUTPUT_DIR_FLAG);
-      return;
-    }
-
-    File dirAsFile = new File(inDirectory);
-    ObjFileFilter objFileFilter = new ObjFileFilter();
-    File[] objFiles = dirAsFile.listFiles(objFileFilter);
-
-    FileOutputStream outputStream = null;
-    logString("Parsing directory: " + inDirectory);
-    // We need frames processed in correct order.
-    Arrays.sort(objFiles);
-
-    for (File objFile : objFiles) {
-      String fileName = objFile.getAbsolutePath();
-
-      // Just take the file name of the first processed frame.
-      if (outputStream == null) {
-        String outputFileName = outDirectory + objFile.getName() + BINARY_FILE_EXT;
-        try {
-          // Create new file here, if we can.
-          outputStream = new FileOutputStream(outputFileName);
-          logString("Created outfile: " + outputFileName);
-        } catch (Exception e) {
-          logString("Error creating outfile: " + e.toString());
-          e.printStackTrace(writer);
-          return;
-        }
-      }
-
-      // Process each file into the stream.
-      logString("Processing file: " + fileName);
-      processFile(fileName, outputStream);
-    }
-
-    // Finally close the stream out.
-    try {
-      if (outputStream != null) {
-        outputStream.close();
-      }
-    } catch (Exception e) {
-      logString("Error trying to close output stream: " + e.toString());
-      e.printStackTrace(writer);
-    }
-  }
-
-  /*
-   * Entrypoint for command-line executable.
-   */
-  public static void main(String[] args) {
-    // Parse flags
-    String inDirectory = "";
-    String outDirectory = "";
-    for (int i = 0; i < args.length; i++) {
-      if (args[i].startsWith(INPUT_DIR_FLAG)) {
-        inDirectory = args[i].substring(INPUT_DIR_FLAG.length());
-        // Make sure this will be treated as a directory
-        if (!inDirectory.endsWith("/")) {
-          inDirectory += "/";
-        }
-      }
-      if (args[i].startsWith(OUTPUT_DIR_FLAG)) {
-        outDirectory = args[i].substring(OUTPUT_DIR_FLAG.length());
-        // Make sure this will be treated as a directory
-        if (!outDirectory.endsWith("/")) {
-          outDirectory += "/";
-        }
-      }
-    }
-    ObjParserMain parser = new ObjParserMain();
-    parser.run(inDirectory, outDirectory);
-    parser.writer.flush();
-  }
-
-  /*
-   * Internal helper function to parse a .obj from an infile name and stream the resulting data
-   * directly out in binary-dump format to outputStream.
-   */
-  private void processFile(String infileName, OutputStream outputStream) {
-    long start = System.nanoTime();
-
-    // First we parse the obj.
-    SimpleObjParser objParser = new SimpleObjParser(infileName, DEFAULT_VERTEX_SCALE_FACTOR);
-    if (!objParser.parse()) {
-      logString("Error parsing .obj file before processing");
-      return;
-    }
-
-    final float[] vertices = objParser.getVertices();
-    final float[] textureCoords = objParser.getTextureCoords();
-    final ArrayList<Short> triangleList = objParser.getTriangles();
-
-    // Overall byte count to stream: 12 for the 3 list-length ints, and then 4 for each vertex and
-    // texCoord int, and finally 2 for each triangle index short.
-    final int bbSize =
-        12 + 4 * vertices.length + 4 * textureCoords.length + 2 * triangleList.size();
-
-    // Ensure ByteBuffer is native order, just like we want to read it in, but is NOT direct, so
-    // we can call .array() on it.
-    ByteBuffer bb = ByteBuffer.allocate(bbSize);
-    bb.order(ByteOrder.nativeOrder());
-
-    bb.putInt(vertices.length);
-    bb.putInt(textureCoords.length);
-    bb.putInt(triangleList.size());
-    logString(String.format("Writing... Vertices: %d, TextureCoords: %d, Indices: %d.%n",
-        vertices.length, textureCoords.length, triangleList.size()));
-    for (float vertex : vertices) {
-      bb.putFloat(vertex);
-    }
-    for (float textureCoord : textureCoords) {
-      bb.putFloat(textureCoord);
-    }
-    for (Short vertexIndex : triangleList) {
-      bb.putShort(vertexIndex.shortValue());
-    }
-    bb.position(0);
-    try {
-      outputStream.write(bb.array(), 0, bbSize);
-      logString(String.format("Processing successful!  Took %.4f seconds.%n",
-          (System.nanoTime() - start) / NS_TO_SECONDS));
-    } catch (Exception e) {
-      logString("Error writing during processing: " + e.toString());
-      e.printStackTrace(writer);
-    }
-  }
-}
diff --git a/mediapipe/graphs/object_detection_3d/obj_parser/SimpleObjParser.java b/mediapipe/graphs/object_detection_3d/obj_parser/SimpleObjParser.java
deleted file mode 100644
index 937fdff..0000000
--- a/mediapipe/graphs/object_detection_3d/obj_parser/SimpleObjParser.java
+++ /dev/null
@@ -1,386 +0,0 @@
-// Copyright 2021 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-import static java.nio.charset.StandardCharsets.UTF_8;
-
-import java.io.BufferedReader;
-import java.io.IOException;
-import java.nio.file.Files;
-import java.nio.file.Paths;
-import java.util.ArrayList;
-import java.util.HashMap;
-import java.util.Map;
-
-/**
- * Class for parsing a single .obj file into openGL-usable pieces.
- *
- * <p>Usage:
- *
- * <p>SimpleObjParser objParser = new SimpleObjParser("animations/cow/cow320.obj", .015f);
- *
- * <p>if (objParser.parse()) { ... }
- */
-public class SimpleObjParser {
-  private static class ShortPair {
-    private final Short first;
-    private final Short second;
-
-    public ShortPair(Short newFirst, Short newSecond) {
-      first = newFirst;
-      second = newSecond;
-    }
-
-    public Short getFirst() {
-      return first;
-    }
-
-    public Short getSecond() {
-      return second;
-    }
-  }
-
-  private static final String TAG = SimpleObjParser.class.getSimpleName();
-  private static final boolean DEBUG = false;
-  private static final int INVALID_INDEX = -1;
-  private static final int POSITIONS_COORDS_PER_VERTEX = 3;
-  private static final int TEXTURE_COORDS_PER_VERTEX = 2;
-  private final String fileName;
-
-  // Since .obj doesn't tie together texture coordinates and vertex
-  // coordinates, but OpenGL does, we need to keep a map of all such pairings that occur in
-  // our face list.
-  private final HashMap<ShortPair, Short> vertexTexCoordMap;
-
-  // Internal (de-coupled) unique vertices and texture coordinates
-  private ArrayList<Float> vertices;
-  private ArrayList<Float> textureCoords;
-
-  // Data we expose to openGL for rendering
-  private float[] finalizedVertices;
-  private float[] finalizedTextureCoords;
-  private ArrayList<Short> finalizedTriangles;
-
-  // So we only display warnings about dropped w-coordinates once
-  private boolean vertexCoordIgnoredWarning;
-  private boolean textureCoordIgnoredWarning;
-  private boolean startedProcessingFaces;
-
-  private int numPrimitiveVertices;
-  private int numPrimitiveTextureCoords;
-  private int numPrimitiveFaces;
-
-  // For scratchwork, so we don't have to keep reallocating
-  private float[] tempCoords;
-
-  // We scale all our position coordinates uniformly by this factor
-  private float objectUniformScaleFactor;
-
-  public SimpleObjParser(String objFile, float scaleFactor) {
-    objectUniformScaleFactor = scaleFactor;
-
-    fileName = objFile;
-    vertices = new ArrayList<Float>();
-    textureCoords = new ArrayList<Float>();
-
-    vertexTexCoordMap = new HashMap<ShortPair, Short>();
-    finalizedTriangles = new ArrayList<Short>();
-
-    tempCoords = new float[Math.max(POSITIONS_COORDS_PER_VERTEX, TEXTURE_COORDS_PER_VERTEX)];
-    numPrimitiveFaces = 0;
-
-    vertexCoordIgnoredWarning = false;
-    textureCoordIgnoredWarning = false;
-    startedProcessingFaces = false;
-  }
-
-  // Simple helper wrapper function
-  private void debugLogString(String message) {
-    if (DEBUG) {
-      System.out.println(message);
-    }
-  }
-
-  private void parseVertex(String[] linePieces) {
-    // Note: Traditionally xyzw is acceptable as a format, with w defaulting to 1.0, but for now
-    // we only parse xyz.
-    if (linePieces.length < POSITIONS_COORDS_PER_VERTEX + 1
-        || linePieces.length > POSITIONS_COORDS_PER_VERTEX + 2) {
-      System.out.println("Malformed vertex coordinate specification, assuming xyz format only.");
-      return;
-    } else if (linePieces.length == POSITIONS_COORDS_PER_VERTEX + 2 && !vertexCoordIgnoredWarning) {
-      System.out.println(
-          "Only x, y, and z parsed for vertex coordinates; w coordinates will be ignored.");
-      vertexCoordIgnoredWarning = true;
-    }
-
-    boolean success = true;
-    try {
-      for (int i = 1; i < POSITIONS_COORDS_PER_VERTEX + 1; i++) {
-        tempCoords[i - 1] = Float.parseFloat(linePieces[i]);
-      }
-    } catch (NumberFormatException e) {
-      success = false;
-      System.out.println("Malformed vertex coordinate error: " + e.toString());
-    }
-
-    if (success) {
-      for (int i = 0; i < POSITIONS_COORDS_PER_VERTEX; i++) {
-        vertices.add(Float.valueOf(tempCoords[i] * objectUniformScaleFactor));
-      }
-    }
-  }
-
-  private void parseTextureCoordinate(String[] linePieces) {
-    // Similar to vertices, uvw is acceptable as a format, with w defaulting to 0.0, but for now we
-    // only parse uv.
-    if (linePieces.length < TEXTURE_COORDS_PER_VERTEX + 1
-        || linePieces.length > TEXTURE_COORDS_PER_VERTEX + 2) {
-      System.out.println("Malformed texture coordinate specification, assuming uv format only.");
-      return;
-    } else if (linePieces.length == (TEXTURE_COORDS_PER_VERTEX + 2)
-        && !textureCoordIgnoredWarning) {
-      debugLogString("Only u and v parsed for texture coordinates; w coordinates will be ignored.");
-      textureCoordIgnoredWarning = true;
-    }
-
-    boolean success = true;
-    try {
-      for (int i = 1; i < TEXTURE_COORDS_PER_VERTEX + 1; i++) {
-        tempCoords[i - 1] = Float.parseFloat(linePieces[i]);
-      }
-    } catch (NumberFormatException e) {
-      success = false;
-      System.out.println("Malformed texture coordinate error: " + e.toString());
-    }
-
-    if (success) {
-      // .obj files treat (0,0) as top-left, compared to bottom-left for openGL.  So invert "v"
-      // texture coordinate only here.
-      textureCoords.add(Float.valueOf(tempCoords[0]));
-      textureCoords.add(Float.valueOf(1.0f - tempCoords[1]));
-    }
-  }
-
-  // Will return INVALID_INDEX if error occurs, and otherwise will return finalized (combined)
-  // index, adding and hashing new combinations as it sees them.
-  private short parseAndProcessCombinedVertexCoord(String coordString) {
-    String[] coords = coordString.split("/");
-    try {
-      // Parse vertex and texture indices; 1-indexed from front if positive and from end of list if
-      // negative.
-      short vertexIndex = Short.parseShort(coords[0]);
-      short textureIndex = Short.parseShort(coords[1]);
-      if (vertexIndex > 0) {
-        vertexIndex--;
-      } else {
-        vertexIndex = (short) (vertexIndex + numPrimitiveVertices);
-      }
-      if (textureIndex > 0) {
-        textureIndex--;
-      } else {
-        textureIndex = (short) (textureIndex + numPrimitiveTextureCoords);
-      }
-
-      // Combine indices and look up in pair map.
-      ShortPair indexPair = new ShortPair(Short.valueOf(vertexIndex), Short.valueOf(textureIndex));
-      Short combinedIndex = vertexTexCoordMap.get(indexPair);
-      if (combinedIndex == null) {
-        short numIndexPairs = (short) vertexTexCoordMap.size();
-        vertexTexCoordMap.put(indexPair, numIndexPairs);
-        return numIndexPairs;
-      } else {
-        return combinedIndex.shortValue();
-      }
-    } catch (NumberFormatException e) {
-      // Failure to parse coordinates as shorts
-      return INVALID_INDEX;
-    }
-  }
-
-  // Note: it is assumed that face list occurs AFTER vertex and texture coordinate lists finish in
-  //  the obj file format.
-  private void parseFace(String[] linePieces) {
-    if (linePieces.length < 4) {
-      System.out.println("Malformed face index list: there must be at least 3 indices per face");
-      return;
-    }
-
-    short[] faceIndices = new short[linePieces.length - 1];
-    boolean success = true;
-    for (int i = 1; i < linePieces.length; i++) {
-      short faceIndex = parseAndProcessCombinedVertexCoord(linePieces[i]);
-
-      if (faceIndex < 0) {
-        System.out.println(faceIndex);
-        System.out.println("Malformed face index: " + linePieces[i]);
-        success = false;
-        break;
-      }
-      faceIndices[i - 1] = faceIndex;
-    }
-
-    if (success) {
-      numPrimitiveFaces++;
-      // Manually triangulate the face under the assumption that the points are coplanar, the poly
-      // is convex, and the points are listed in either clockwise or anti-clockwise orientation.
-      for (int i = 1; i < faceIndices.length - 1; i++) {
-        // We use a triangle fan here, so first point is part of all triangles
-        finalizedTriangles.add(faceIndices[0]);
-        finalizedTriangles.add(faceIndices[i]);
-        finalizedTriangles.add(faceIndices[i + 1]);
-      }
-    }
-  }
-
-  // Iterate over map and reconstruct proper vertex/texture coordinate pairings.
-  private boolean constructFinalCoordinatesFromMap() {
-    final int numIndexPairs = vertexTexCoordMap.size();
-    // XYZ vertices and UV texture coordinates
-    finalizedVertices = new float[POSITIONS_COORDS_PER_VERTEX * numIndexPairs];
-    finalizedTextureCoords = new float[TEXTURE_COORDS_PER_VERTEX * numIndexPairs];
-    try {
-      for (Map.Entry<ShortPair, Short> entry : vertexTexCoordMap.entrySet()) {
-        ShortPair indexPair = entry.getKey();
-        short rawVertexIndex = indexPair.getFirst().shortValue();
-        short rawTexCoordIndex = indexPair.getSecond().shortValue();
-        short finalIndex = entry.getValue().shortValue();
-        for (int i = 0; i < POSITIONS_COORDS_PER_VERTEX; i++) {
-          finalizedVertices[POSITIONS_COORDS_PER_VERTEX * finalIndex + i]
-              = vertices.get(rawVertexIndex * POSITIONS_COORDS_PER_VERTEX + i);
-        }
-        for (int i = 0; i < TEXTURE_COORDS_PER_VERTEX; i++) {
-          finalizedTextureCoords[TEXTURE_COORDS_PER_VERTEX * finalIndex + i]
-              = textureCoords.get(rawTexCoordIndex * TEXTURE_COORDS_PER_VERTEX + i);
-        }
-      }
-    } catch (NumberFormatException e) {
-      System.out.println("Malformed index in vertex/texture coordinate mapping.");
-      return false;
-    }
-    return true;
-  }
-
-  /**
-   * Returns the vertex position coordinate list (x1, y1, z1, x2, y2, z2, ...) after a successful
-   * call to parse().
-   */
-  public float[] getVertices() {
-    return finalizedVertices;
-  }
-
-  /**
-   * Returns the vertex texture coordinate list (u1, v1, u2, v2, ...) after a successful call to
-   * parse().
-   */
-  public float[] getTextureCoords() {
-    return finalizedTextureCoords;
-  }
-
-  /**
-   * Returns the list of indices (a1, b1, c1, a2, b2, c2, ...) after a successful call to parse().
-   * Each (a, b, c) triplet specifies a triangle to be rendered, with a, b, and c Short objects used
-   * to index into the coordinates returned by getVertices() and getTextureCoords().<p></p>
-   * For example, a Short index representing 5 should be used to index into vertices[15],
-   * vertices[16], and vertices[17], as well as textureCoords[10] and textureCoords[11].
-   */
-  public ArrayList<Short> getTriangles() {
-    return finalizedTriangles;
-  }
-
-  /**
-   * Attempts to locate and read the specified .obj file, and parse it accordingly.  None of the
-   * getter functions in this class will return valid results until a value of true is returned
-   * from this function.
-   * @return true on success.
-   */
-  public boolean parse() {
-    boolean success = true;
-    BufferedReader reader = null;
-    try {
-      reader = Files.newBufferedReader(Paths.get(fileName), UTF_8);
-      String line;
-      while ((line = reader.readLine()) != null) {
-        // Skip over lines with no characters
-        if (line.length() < 1) {
-          continue;
-        }
-
-        // Ignore comment lines entirely
-        if (line.charAt(0) == '#') {
-          continue;
-        }
-
-        // Split into pieces based on whitespace, and process according to first command piece
-        String[] linePieces = line.split(" +");
-        switch (linePieces[0]) {
-          case "v":
-            // Add vertex
-            if (startedProcessingFaces) {
-              throw new IOException("Vertices must all be declared before faces in obj files.");
-            }
-            parseVertex(linePieces);
-            break;
-          case "vt":
-            // Add texture coordinate
-            if (startedProcessingFaces) {
-              throw new IOException(
-                  "Texture coordinates must all be declared before faces in obj files.");
-            }
-            parseTextureCoordinate(linePieces);
-            break;
-          case "f":
-            // Vertex and texture coordinate lists should be locked into place by now
-            if (!startedProcessingFaces) {
-              startedProcessingFaces = true;
-              numPrimitiveVertices = vertices.size() / POSITIONS_COORDS_PER_VERTEX;
-              numPrimitiveTextureCoords = textureCoords.size() / TEXTURE_COORDS_PER_VERTEX;
-            }
-            // Add face
-            parseFace(linePieces);
-            break;
-          default:
-            // Unknown or unused directive: ignoring
-            // Note: We do not yet process vertex normals or curves, so we ignore {vp, vn, s}
-            // Note: We assume only a single object, so we ignore {g, o}
-            // Note: We also assume a single texture, which we process independently, so we ignore
-            // {mtllib, usemtl}
-            break;
-        }
-      }
-
-      // If we made it all the way through, then we have a vertex-to-tex-coord pair mapping, so
-      // construct our final vertex and texture coordinate lists now.
-      success = constructFinalCoordinatesFromMap();
-
-    } catch (IOException e) {
-      success = false;
-      System.out.println("Failure to parse obj file: " + e.toString());
-    } finally {
-      try {
-        if (reader != null) {
-          reader.close();
-        }
-      } catch (IOException e) {
-        System.out.println("Couldn't close reader");
-      }
-    }
-    if (success) {
-      debugLogString("Successfully parsed " + numPrimitiveVertices + " vertices and "
-          + numPrimitiveTextureCoords + " texture coordinates into " + vertexTexCoordMap.size()
-          + " combined vertices and " + numPrimitiveFaces + " faces, represented as a mesh of "
-          + finalizedTriangles.size() / 3 + " triangles.");
-    }
-    return success;
-  }
-}
diff --git a/mediapipe/graphs/object_detection_3d/obj_parser/obj_cleanup.sh b/mediapipe/graphs/object_detection_3d/obj_parser/obj_cleanup.sh
deleted file mode 100755
index 1573387..0000000
--- a/mediapipe/graphs/object_detection_3d/obj_parser/obj_cleanup.sh
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/bin/bash
-
-# Copyright 2021 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# The SimpleObjParser expects the obj commands to follow v/vt/f order. This
-# little script will read all the obj files in a directory and sort the
-# existing obj commands inside them to also follow this order (so all v lines
-# will appear before all vt lines, which will appear before all f lines).
-
-# Usage: ./obj_cleanup.sh input_folder output_folder
-# input_folder and output_folder paths can be absolute or relative.
-
-input_folder=$1
-output_folder=$2
-if [[ "${input_folder}" == "" ]]; then
-  echo "input_folder must be defined.  Usage: ./obj_cleanup.sh input_folder output_folder"
-  exit 1
-fi
-if [[ "${output_folder}" == "" ]]; then
-  echo "output_folder must be defined.  Usage: ./obj_cleanup.sh input_folder output_folder"
-  exit 1
-fi
-
-# Find all the obj files and remove the directory name
-# Interestingly, piping | sed 's!.obj!!  also removed the extension obj too.
-find "${input_folder}" -name "*.obj" | sed 's!.*/!!' | sort |
-while IFS= read -r filename; do
-  echo "Clean up ${filename}"
-  cat "${input_folder}/${filename}" | grep 'v ' > "${output_folder}/${filename}"
-  cat "${input_folder}/${filename}" | grep 'vt ' >> "${output_folder}/${filename}"
-  cat "${input_folder}/${filename}" | grep 'f ' >> "${output_folder}/${filename}"
-done
diff --git a/mediapipe/graphs/object_detection_3d/object_occlusion_tracking.pbtxt b/mediapipe/graphs/object_detection_3d/object_occlusion_tracking.pbtxt
deleted file mode 100644
index 10b11de..0000000
--- a/mediapipe/graphs/object_detection_3d/object_occlusion_tracking.pbtxt
+++ /dev/null
@@ -1,122 +0,0 @@
-# MediaPipe graph that performs box tracking with TensorFlow Lite on GPU.
-
-# Images coming into and out of the graph.
-input_stream: "input_video"
-input_stream: "WIDTH:input_width"
-input_stream: "HEIGHT:input_height"
-input_side_packet: "LABELS_CSV:allowed_labels"
-input_side_packet: "MODEL_SCALE:model_scale"
-input_side_packet: "MODEL_TRANSFORMATION:model_transformation"
-input_side_packet: "TEXTURE:box_texture"
-input_side_packet: "MAX_NUM_OBJECTS:max_num_objects"
-input_side_packet: "ANIMATION_ASSET:box_asset_name"
-input_side_packet: "MASK_TEXTURE:obj_texture"
-input_side_packet: "MASK_ASSET:obj_asset_name"
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Crops the image from the center to the size WIDTHxHEIGHT.
-node: {
-  calculator: "ImageCroppingCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  output_stream: "IMAGE_GPU:throttled_input_video_3x4"
-  input_stream: "WIDTH:input_width"
-  input_stream: "HEIGHT:input_height"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageCroppingCalculatorOptions] {
-      border_mode: BORDER_REPLICATE
-    }
-  }
-}
-
-node {
-  calculator: "ObjectronGpuSubgraph"
-  input_stream: "IMAGE_GPU:throttled_input_video_3x4"
-  input_side_packet: "LABELS_CSV:allowed_labels"
-  input_side_packet: "MAX_NUM_OBJECTS:max_num_objects"
-  output_stream: "FRAME_ANNOTATION:lifted_objects"
-}
-
-# The rendering nodes:
-# We are rendering two meshes: 1) a 3D bounding box, which we overlay directly
-# on the texture, and 2) a virtual object, which we use as an occlusion mask.
-# These models are designed using different tools, so we supply a transformation
-# to bring both of them to the Objectron's coordinate system.
-
-# Creates a model matrices for the tracked object given the lifted 3D points.
-# This calculator does two things: 1) Estimates object's pose (orientation,
-# translation, and scale) from the 3D vertices, and
-# 2) bring the object from the objectron's coordinate system to the renderer
-# (OpenGL) coordinate system. Since the final goal is to render a mesh file on
-# top of the object, we also supply a transformation to bring the mesh to the
-# objectron's coordinate system, and rescale mesh to the unit size.
-node {
-  calculator: "AnnotationsToModelMatricesCalculator"
-  input_stream: "ANNOTATIONS:lifted_objects"
-  output_stream: "MODEL_MATRICES:model_matrices"
-  node_options: {
-    [type.googleapis.com/mediapipe.AnnotationsToModelMatricesCalculatorOptions] {
-      # Re-scale the CAD model to the size of a unit box
-      model_scale: [0.04, 0.04, 0.04]
-      # Bring the box CAD model to objectron's coordinate system. This
-      # is equivalent of -pi/2 rotation along the y-axis (right-hand rule):
-      # Eigen::AngleAxisf(-M_PI / 2., Eigen::Vector3f::UnitY())
-      model_transformation: [0.0,  0.0, -1.0,  0.0]
-      model_transformation: [0.0,  1.0,  0.0,  0.0]
-      model_transformation: [1.0,  0.0,  0.0,  0.0]
-      model_transformation: [0.0,  0.0,  0.0,  1.0]
-    }
-  }
-}
-
-# Compute the model matrices for the CAD model of the virtual object, to be used
-# as an occlusion mask. The model will be rendered at the exact same location as
-# the bounding box.
-node {
-  calculator: "AnnotationsToModelMatricesCalculator"
-  input_stream: "ANNOTATIONS:lifted_objects"
-  input_side_packet: "MODEL_SCALE:model_scale"
-  input_side_packet: "MODEL_TRANSFORMATION:model_transformation"
-  output_stream: "MODEL_MATRICES:mask_model_matrices"
-}
-
-# Render everything together. First we render the 3D bounding box animation,
-# then we render the occlusion mask.
-node: {
-  calculator: "GlAnimationOverlayCalculator"
-  input_stream: "VIDEO:throttled_input_video_3x4"
-  input_stream: "MODEL_MATRICES:model_matrices"
-  input_stream: "MASK_MODEL_MATRICES:mask_model_matrices"
-  output_stream: "output_video"
-  input_side_packet: "TEXTURE:box_texture"
-  input_side_packet: "ANIMATION_ASSET:box_asset_name"
-  input_side_packet: "MASK_TEXTURE:obj_texture"
-  input_side_packet: "MASK_ASSET:obj_asset_name"
-  node_options: {
-    [type.googleapis.com/mediapipe.GlAnimationOverlayCalculatorOptions] {
-      aspect_ratio: 0.75
-      vertical_fov_degrees: 70.
-      animation_speed_fps: 25
-    }
-  }
-}
diff --git a/mediapipe/graphs/object_detection_3d/object_occlusion_tracking_1stage.pbtxt b/mediapipe/graphs/object_detection_3d/object_occlusion_tracking_1stage.pbtxt
deleted file mode 100644
index bda02b2..0000000
--- a/mediapipe/graphs/object_detection_3d/object_occlusion_tracking_1stage.pbtxt
+++ /dev/null
@@ -1,133 +0,0 @@
-# MediaPipe object detection 3D with tracking graph.
-
-# Images on GPU coming into and out of the graph.
-input_stream: "input_video"
-input_stream: "input_width"
-input_stream: "input_height"
-output_stream: "output_video"
-
-# Crops the image from the center to the size WIDTHxHEIGHT.
-node: {
-  calculator: "ImageCroppingCalculator"
-  input_stream: "IMAGE_GPU:input_video"
-  output_stream: "IMAGE_GPU:input_video_4x3"
-  input_stream: "WIDTH:input_width"
-  input_stream: "HEIGHT:input_height"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageCroppingCalculatorOptions] {
-      border_mode: BORDER_REPLICATE
-    }
-  }
-}
-
-# Creates a copy of the input_video stream. At the end of the graph, the
-# GlAnimationOverlayCalculator will consume the input_video texture and draws
-# on top of it.
-node: {
-  calculator: "GlScalerCalculator"
-  input_stream: "VIDEO:input_video_4x3"
-  output_stream: "VIDEO:input_video_copy"
-}
-
-# Resamples the images by specific frame rate. This calculator is used to
-# control the frequecy of subsequent calculators/subgraphs, e.g. less power
-# consumption for expensive process.
-node {
-  calculator: "PacketResamplerCalculator"
-  input_stream: "DATA:input_video_copy"
-  output_stream: "DATA:sampled_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.PacketResamplerCalculatorOptions] {
-      frame_rate: 5
-    }
-  }
-}
-
-node {
-  calculator: "ObjectronDetection1StageSubgraphGpu"
-  input_stream: "IMAGE_GPU:sampled_input_video"
-  output_stream: "ANNOTATIONS:objects"
-}
-
-node {
-  calculator: "ObjectronTracking1StageSubgraphGpu"
-  input_stream: "FRAME_ANNOTATION:objects"
-  input_stream: "IMAGE_GPU:input_video_copy"
-  output_stream: "LIFTED_FRAME_ANNOTATION:lifted_tracked_objects"
-}
-
-# The rendering nodes:
-# We are rendering two meshes: 1) a 3D bounding box, which we overlay directly
-# on the texture, and 2) a shoe CAD model, which we use as an occlusion mask.
-# These models are designed using different tools, so we supply a transformation
-# to bring both of them to the Objectron's coordinate system.
-
-# Creates a model matrices for the tracked object given the lifted 3D points.
-# This calculator does two things: 1) Estimates object's pose (orientation,
-# translation, and scale) from the 3D vertices, and
-# 2) bring the object from the objectron's coordinate system to the renderer
-# (OpenGL) coordinate system. Since the final goal is to render a mesh file on
-# top of the object, we also supply a transformation to bring the mesh to the
-# objectron's coordinate system, and rescale mesh to the unit size.
-node {
-  calculator: "AnnotationsToModelMatricesCalculator"
-  input_stream: "ANNOTATIONS:lifted_tracked_objects"
-  output_stream: "MODEL_MATRICES:model_matrices"
-  node_options: {
-    [type.googleapis.com/mediapipe.AnnotationsToModelMatricesCalculatorOptions] {
-      # Re-scale the CAD model to the size of a unit box
-      model_scale: [0.05, 0.05, 0.05]
-      # Bring the box CAD model to objectron's coordinate system. This
-      # is equivalent of -pi/2 rotation along the y-axis (right-hand rule):
-      # Eigen::AngleAxisf(-M_PI / 2., Eigen::Vector3f::UnitY())
-      model_transformation: [0.0,  0.0, -1.0,  0.0]
-      model_transformation: [0.0,  1.0,  0.0,  0.0]
-      model_transformation: [1.0,  0.0,  0.0,  0.0]
-      model_transformation: [0.0,  0.0,  0.0,  1.0]
-    }
-  }
-}
-
-# Compute the model matrices for the CAD model of the chair, to be used as an
-# occlusion mask. The model will be rendered at the exact same location as the
-# bounding box.
-node {
-  calculator: "AnnotationsToModelMatricesCalculator"
-  input_stream: "ANNOTATIONS:lifted_tracked_objects"
-  output_stream: "MODEL_MATRICES:mask_model_matrices"
-  node_options: {
-    [type.googleapis.com/mediapipe.AnnotationsToModelMatricesCalculatorOptions] {
-      # Re-scale the CAD model to the size of a unit box
-      model_scale: [0.15, 0.1, 0.15]
-      # Bring the CAD model to Deep Pursuit 3D's coordinate system. This
-      # is equivalent of -pi/2 rotation along the x-axis:
-      # Eigen::AngleAxisf(-M_PI / 2., Eigen::Vector3f::UnitX())
-      model_transformation: [1.0,  0.0,  0.0,  0.0]
-      model_transformation: [0.0,  1.0,  0.0,  -10.0]
-      model_transformation: [0.0,  0.0,  -1.0,  0.0]
-      model_transformation: [0.0,  0.0,  0.0,  1.0]
-    }
-  }
-}
-
-# Render everything together. First we render the 3D bounding box animation,
-# then we render the occlusion mask.
-node:{
-  calculator:"GlAnimationOverlayCalculator"
-  input_stream:"VIDEO:input_video_4x3"
-  input_stream:"MODEL_MATRICES:model_matrices"
-  input_stream:"MASK_MODEL_MATRICES:mask_model_matrices"
-  output_stream:"output_video"
-  input_side_packet:"TEXTURE:box_texture"
-  input_side_packet:"ANIMATION_ASSET:box_asset_name"
-  input_side_packet:"MASK_TEXTURE:obj_texture"
-  input_side_packet:"MASK_ASSET:obj_asset_name"
-  node_options: {
-    [type.googleapis.com/mediapipe.GlAnimationOverlayCalculatorOptions] {
-    # Output resolution is 480x640 with the aspect ratio of 0.75
-      aspect_ratio: 0.75
-      vertical_fov_degrees: 70.
-      animation_speed_fps: 25
-    }
-  }
-}
diff --git a/mediapipe/graphs/object_detection_3d/objectron_desktop_cpu.pbtxt b/mediapipe/graphs/object_detection_3d/objectron_desktop_cpu.pbtxt
deleted file mode 100644
index 0a962d7..0000000
--- a/mediapipe/graphs/object_detection_3d/objectron_desktop_cpu.pbtxt
+++ /dev/null
@@ -1,60 +0,0 @@
-# MediaPipe Objectron 3D object detection on Desktop CPU.
-input_side_packet: "INPUT_FILE_PATH:input_video_path"
-input_side_packet: "FILE_PATH:0:box_landmark_model_path"
-input_side_packet: "LABELS_CSV:allowed_labels"
-input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
-
-# Generates side packet with max number of objects to detect/track.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:max_num_objects"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { int_value: 5 }
-    }
-  }
-}
-
-# Decodes an input video file into images and a video header.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:input_video"
-  output_stream: "VIDEO_PRESTREAM:input_video_header"
-}
-
-# Run Objectron subgraph.
-node {
-  calculator: "ObjectronCpuSubgraph"
-  input_stream: "IMAGE:input_video"
-  input_side_packet: "MODEL_PATH:box_landmark_model_path"
-  input_side_packet: "LABELS_CSV:allowed_labels"
-  input_side_packet: "MAX_NUM_OBJECTS:max_num_objects"
-  output_stream: "MULTI_LANDMARKS:box_landmarks"
-  output_stream: "NORM_RECTS:box_rect"
-}
-
-# Subgraph that renders annotations and overlays them on top of the input
-# images (see renderer_cpu.pbtxt).
-node {
-  calculator: "RendererSubgraph"
-  input_stream: "IMAGE:input_video"
-  input_stream: "MULTI_LANDMARKS:box_landmarks"
-  input_stream: "NORM_RECTS:box_rect"
-  output_stream: "IMAGE:output_video"
-}
-
-# Encodes the annotated images into a video file, adopting properties specified
-# in the input video header, e.g., video framerate.
-node {
-  calculator: "OpenCvVideoEncoderCalculator"
-  input_stream: "VIDEO:output_video"
-  input_stream: "VIDEO_PRESTREAM:input_video_header"
-  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
-      codec: "avc1"
-      video_format: "mp4"
-    }
-  }
-}
diff --git a/mediapipe/graphs/object_detection_3d/subgraphs/BUILD b/mediapipe/graphs/object_detection_3d/subgraphs/BUILD
deleted file mode 100644
index 524ef9f..0000000
--- a/mediapipe/graphs/object_detection_3d/subgraphs/BUILD
+++ /dev/null
@@ -1,37 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "renderer_cpu",
-    graph = "renderer_cpu.pbtxt",
-    register_as = "RendererSubgraph",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/calculators/core:begin_loop_calculator",
-        "//mediapipe/calculators/core:end_loop_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_data_calculator",
-    ],
-)
diff --git a/mediapipe/graphs/object_detection_3d/subgraphs/renderer_cpu.pbtxt b/mediapipe/graphs/object_detection_3d/subgraphs/renderer_cpu.pbtxt
deleted file mode 100644
index 0f275a7..0000000
--- a/mediapipe/graphs/object_detection_3d/subgraphs/renderer_cpu.pbtxt
+++ /dev/null
@@ -1,75 +0,0 @@
-# MediaPipe Objectron vertices/landmarks rendering CPU subgraph.
-
-type: "RendererSubgraph"
-
-input_stream: "IMAGE:input_image"
-input_stream: "MULTI_LANDMARKS:multi_landmarks"
-input_stream: "NORM_RECTS:multi_rect"
-output_stream: "IMAGE:output_image"
-
-# Outputs each element of multi_landmarks at a fake timestamp for the rest
-# of the graph to process. At the end of the loop, outputs the BATCH_END
-# timestamp for downstream calculators to inform them that all elements in the
-# vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITERABLE:multi_landmarks"
-  output_stream: "ITEM:single_landmarks"
-  output_stream: "BATCH_END:landmark_timestamp"
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:single_landmarks"
-  output_stream: "RENDER_DATA:single_landmark_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: [1, 2] # edge 1-2
-      landmark_connections: [1, 3] # edge 1-3
-      landmark_connections: [1, 5] # edge 1-5
-      landmark_connections: [2, 4] # edge 2-4
-      landmark_connections: [2, 6] # edge 2-6
-      landmark_connections: [3, 4] # edge 3-4
-      landmark_connections: [3, 7] # edge 3-7
-      landmark_connections: [4, 8] # edge 4-8
-      landmark_connections: [5, 6] # edge 5-6
-      landmark_connections: [5, 7] # edge 5-7
-      landmark_connections: [6, 8] # edge 6-8
-      landmark_connections: [7, 8] # edge 7-8
-      landmark_color { r: 255 g: 0 b: 0 }
-      connection_color { r: 0 g: 255 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-node {
-  calculator: "EndLoopRenderDataCalculator"
-  input_stream: "ITEM:single_landmark_render_data"
-  input_stream: "BATCH_END:landmark_timestamp"
-  output_stream: "ITERABLE:multi_landmarks_render_data"
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECTS:multi_rect"
-  output_stream: "RENDER_DATA:multi_rect_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_image"
-  input_stream: "VECTOR:multi_landmarks_render_data"
-  input_stream: "multi_rect_render_data"
-  output_stream: "IMAGE:output_image"
-}
diff --git a/mediapipe/graphs/pose_tracking/BUILD b/mediapipe/graphs/pose_tracking/BUILD
deleted file mode 100644
index 26f607c..0000000
--- a/mediapipe/graphs/pose_tracking/BUILD
+++ /dev/null
@@ -1,56 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "pose_tracking_gpu_deps",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/graphs/pose_tracking/subgraphs:pose_renderer_gpu",
-        "//mediapipe/modules/pose_landmark:pose_landmark_gpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "pose_tracking_gpu_binary_graph",
-    graph = "pose_tracking_gpu.pbtxt",
-    output_name = "pose_tracking_gpu.binarypb",
-    deps = [":pose_tracking_gpu_deps"],
-)
-
-cc_library(
-    name = "pose_tracking_cpu_deps",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/graphs/pose_tracking/subgraphs:pose_renderer_cpu",
-        "//mediapipe/modules/pose_landmark:pose_landmark_cpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "pose_tracking_cpu_binary_graph",
-    graph = "pose_tracking_cpu.pbtxt",
-    output_name = "pose_tracking_cpu.binarypb",
-    deps = [":pose_tracking_cpu_deps"],
-)
diff --git a/mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt b/mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt
deleted file mode 100644
index 31d847e..0000000
--- a/mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt
+++ /dev/null
@@ -1,63 +0,0 @@
-# MediaPipe graph that performs pose tracking with TensorFlow Lite on CPU.
-
-# CPU buffer. (ImageFrame)
-input_stream: "input_video"
-
-# Output image with rendered results. (ImageFrame)
-output_stream: "output_video"
-# Pose landmarks. (NormalizedLandmarkList)
-output_stream: "pose_landmarks"
-
-# Generates side packet to enable segmentation.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:enable_segmentation"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { bool_value: true }
-    }
-  }
-}
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Subgraph that detects poses and corresponding landmarks.
-node {
-  calculator: "PoseLandmarkCpu"
-  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "LANDMARKS:pose_landmarks"
-  output_stream: "SEGMENTATION_MASK:segmentation_mask"
-  output_stream: "DETECTION:pose_detection"
-  output_stream: "ROI_FROM_LANDMARKS:roi_from_landmarks"
-}
-
-# Subgraph that renders pose-landmark annotation onto the input image.
-node {
-  calculator: "PoseRendererCpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "LANDMARKS:pose_landmarks"
-  input_stream: "SEGMENTATION_MASK:segmentation_mask"
-  input_stream: "DETECTION:pose_detection"
-  input_stream: "ROI:roi_from_landmarks"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt b/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt
deleted file mode 100644
index 35be3f0..0000000
--- a/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt
+++ /dev/null
@@ -1,63 +0,0 @@
-# MediaPipe graph that performs pose tracking with TensorFlow Lite on GPU.
-
-# GPU buffer. (GpuBuffer)
-input_stream: "input_video"
-
-# Output image with rendered results. (GpuBuffer)
-output_stream: "output_video"
-# Pose landmarks. (NormalizedLandmarkList)
-output_stream: "pose_landmarks"
-
-# Generates side packet to enable segmentation.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:enable_segmentation"
-  node_options: {
-    [type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
-      packet { bool_value: true }
-    }
-  }
-}
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Subgraph that detects poses and corresponding landmarks.
-node {
-  calculator: "PoseLandmarkGpu"
-  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "LANDMARKS:pose_landmarks"
-  output_stream: "SEGMENTATION_MASK:segmentation_mask"
-  output_stream: "DETECTION:pose_detection"
-  output_stream: "ROI_FROM_LANDMARKS:roi_from_landmarks"
-}
-
-# Subgraph that renders pose-landmark annotation onto the input image.
-node {
-  calculator: "PoseRendererGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "LANDMARKS:pose_landmarks"
-  input_stream: "SEGMENTATION_MASK:segmentation_mask"
-  input_stream: "DETECTION:pose_detection"
-  input_stream: "ROI:roi_from_landmarks"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/pose_tracking/subgraphs/BUILD b/mediapipe/graphs/pose_tracking/subgraphs/BUILD
deleted file mode 100644
index fa34640..0000000
--- a/mediapipe/graphs/pose_tracking/subgraphs/BUILD
+++ /dev/null
@@ -1,52 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "pose_renderer_gpu",
-    graph = "pose_renderer_gpu.pbtxt",
-    register_as = "PoseRendererGpu",
-    deps = [
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/image:recolor_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_scale_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_renderer_cpu",
-    graph = "pose_renderer_cpu.pbtxt",
-    register_as = "PoseRendererCpu",
-    deps = [
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/image:recolor_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_scale_calculator",
-    ],
-)
diff --git a/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_cpu.pbtxt b/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_cpu.pbtxt
deleted file mode 100644
index e176765..0000000
--- a/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_cpu.pbtxt
+++ /dev/null
@@ -1,292 +0,0 @@
-# MediaPipe pose landmarks rendering subgraph.
-
-type: "PoseRendererCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:input_image"
-# Pose landmarks. (NormalizedLandmarkList)
-input_stream: "LANDMARKS:pose_landmarks"
-# Segmentation mask. (ImageFrame in ImageFormat::VEC32F1)
-input_stream: "SEGMENTATION_MASK:segmentation_mask"
-# Region of interest calculated based on landmarks. (NormalizedRect)
-input_stream: "ROI:roi"
-# Detected pose. (Detection)
-input_stream: "DETECTION:detection"
-
-# CPU image with rendered data. (ImageFrame)
-output_stream: "IMAGE:output_image"
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:input_image"
-  output_stream: "SIZE:image_size"
-}
-
-# Calculates rendering scale based on the pose roi.
-node {
-  calculator: "RectToRenderScaleCalculator"
-  input_stream: "NORM_RECT:roi"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "RENDER_SCALE:render_scale"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderScaleCalculatorOptions] {
-      multiplier: 0.0012
-    }
-  }
-}
-
-# Converts detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTION:detection"
-  output_stream: "RENDER_DATA:detection_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "visible_pose_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 25 }
-    }
-  }
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:pose_landmarks"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 7
-      landmark_connections: 0
-      landmark_connections: 4
-      landmark_connections: 4
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 8
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 11
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 15
-      landmark_connections: 15
-      landmark_connections: 17
-      landmark_connections: 15
-      landmark_connections: 19
-      landmark_connections: 15
-      landmark_connections: 21
-      landmark_connections: 17
-      landmark_connections: 19
-      landmark_connections: 12
-      landmark_connections: 14
-      landmark_connections: 14
-      landmark_connections: 16
-      landmark_connections: 16
-      landmark_connections: 18
-      landmark_connections: 16
-      landmark_connections: 20
-      landmark_connections: 16
-      landmark_connections: 22
-      landmark_connections: 18
-      landmark_connections: 20
-      landmark_connections: 11
-      landmark_connections: 23
-      landmark_connections: 12
-      landmark_connections: 24
-      landmark_connections: 23
-      landmark_connections: 24
-      landmark_connections: 23
-      landmark_connections: 25
-      landmark_connections: 24
-      landmark_connections: 26
-      landmark_connections: 25
-      landmark_connections: 27
-      landmark_connections: 26
-      landmark_connections: 28
-      landmark_connections: 27
-      landmark_connections: 29
-      landmark_connections: 28
-      landmark_connections: 30
-      landmark_connections: 29
-      landmark_connections: 31
-      landmark_connections: 30
-      landmark_connections: 32
-      landmark_connections: 27
-      landmark_connections: 31
-      landmark_connections: 28
-      landmark_connections: 32
-
-      landmark_color { r: 255 g: 255 b: 255 }
-      connection_color { r: 255 g: 255 b: 255 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# Take left pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "landmarks_left_side"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 1 end: 4 }
-      ranges: { begin: 7 end: 8 }
-      ranges: { begin: 9 end: 10 }
-      ranges: { begin: 11 end: 12 }
-      ranges: { begin: 13 end: 14 }
-      ranges: { begin: 15 end: 16 }
-      ranges: { begin: 17 end: 18 }
-      ranges: { begin: 19 end: 20 }
-      ranges: { begin: 21 end: 22 }
-      ranges: { begin: 23 end: 24 }
-
-      combine_outputs: true
-    }
-  }
-}
-
-# Take right pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "landmarks_right_side"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 4 end: 7 }
-      ranges: { begin: 8 end: 9 }
-      ranges: { begin: 10 end: 11 }
-      ranges: { begin: 12 end: 13 }
-      ranges: { begin: 14 end: 15 }
-      ranges: { begin: 16 end: 17 }
-      ranges: { begin: 18 end: 19 }
-      ranges: { begin: 20 end: 21 }
-      ranges: { begin: 22 end: 23 }
-      ranges: { begin: 24 end: 25 }
-
-      combine_outputs: true
-    }
-  }
-}
-
-# Render pose joints as big white circles.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:visible_pose_landmarks"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_background_joints_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 255 g: 255 b: 255 }
-      connection_color { r: 255 g: 255 b: 255 }
-      thickness: 5.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# Render pose left side joints as orange circles (inside white ones).
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks_left_side"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_left_joints_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 255 g: 138 b: 0 }
-      connection_color { r: 255 g: 138 b: 0 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# Render pose right side joints as cyan circles (inside white ones).
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks_right_side"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_right_joints_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 0 g: 217 b: 231 }
-      connection_color { r: 0 g: 217 b: 231 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "RENDER_DATA:roi_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Colors the segmentation mask with the color specified in the option.
-node {
-  calculator: "RecolorCalculator"
-  input_stream: "IMAGE:input_image"
-  input_stream: "MASK:segmentation_mask"
-  output_stream: "IMAGE:segmented_image"
-  node_options: {
-    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
-      color { r: 0 g: 0 b: 255 }
-      mask_channel: RED
-      invert_mask: true
-      adjust_with_luminance: false
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:segmented_image"
-  input_stream: "detection_render_data"
-  input_stream: "landmarks_render_data"
-  input_stream: "landmarks_background_joints_render_data"
-  input_stream: "landmarks_left_joints_render_data"
-  input_stream: "landmarks_right_joints_render_data"
-  input_stream: "roi_render_data"
-  output_stream: "IMAGE:output_image"
-}
diff --git a/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_gpu.pbtxt b/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_gpu.pbtxt
deleted file mode 100644
index 4d680c6..0000000
--- a/mediapipe/graphs/pose_tracking/subgraphs/pose_renderer_gpu.pbtxt
+++ /dev/null
@@ -1,292 +0,0 @@
-# MediaPipe pose landmarks rendering subgraph.
-
-type: "PoseRendererGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:input_image"
-# Pose landmarks. (NormalizedLandmarkList)
-input_stream: "LANDMARKS:pose_landmarks"
-# Segmentation mask. (GpuBuffer in RGBA, with the same mask values in R and A)
-input_stream: "SEGMENTATION_MASK:segmentation_mask"
-# Region of interest calculated based on landmarks. (NormalizedRect)
-input_stream: "ROI:roi"
-# Detected pose. (Detection)
-input_stream: "DETECTION:detection"
-
-# GPU image with rendered data. (GpuBuffer)
-output_stream: "IMAGE:output_image"
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:input_image"
-  output_stream: "SIZE:image_size"
-}
-
-# Calculates rendering scale based on the pose roi.
-node {
-  calculator: "RectToRenderScaleCalculator"
-  input_stream: "NORM_RECT:roi"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "RENDER_SCALE:render_scale"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderScaleCalculatorOptions] {
-      multiplier: 0.0012
-    }
-  }
-}
-
-# Converts detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTION:detection"
-  output_stream: "RENDER_DATA:detection_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 0 g: 255 b: 0 }
-    }
-  }
-}
-
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "visible_pose_landmarks"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 0 end: 25 }
-    }
-  }
-}
-
-# Converts landmarks to drawing primitives for annotation overlay.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:pose_landmarks"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_connections: 0
-      landmark_connections: 1
-      landmark_connections: 1
-      landmark_connections: 2
-      landmark_connections: 2
-      landmark_connections: 3
-      landmark_connections: 3
-      landmark_connections: 7
-      landmark_connections: 0
-      landmark_connections: 4
-      landmark_connections: 4
-      landmark_connections: 5
-      landmark_connections: 5
-      landmark_connections: 6
-      landmark_connections: 6
-      landmark_connections: 8
-      landmark_connections: 9
-      landmark_connections: 10
-      landmark_connections: 11
-      landmark_connections: 12
-      landmark_connections: 11
-      landmark_connections: 13
-      landmark_connections: 13
-      landmark_connections: 15
-      landmark_connections: 15
-      landmark_connections: 17
-      landmark_connections: 15
-      landmark_connections: 19
-      landmark_connections: 15
-      landmark_connections: 21
-      landmark_connections: 17
-      landmark_connections: 19
-      landmark_connections: 12
-      landmark_connections: 14
-      landmark_connections: 14
-      landmark_connections: 16
-      landmark_connections: 16
-      landmark_connections: 18
-      landmark_connections: 16
-      landmark_connections: 20
-      landmark_connections: 16
-      landmark_connections: 22
-      landmark_connections: 18
-      landmark_connections: 20
-      landmark_connections: 11
-      landmark_connections: 23
-      landmark_connections: 12
-      landmark_connections: 24
-      landmark_connections: 23
-      landmark_connections: 24
-      landmark_connections: 23
-      landmark_connections: 25
-      landmark_connections: 24
-      landmark_connections: 26
-      landmark_connections: 25
-      landmark_connections: 27
-      landmark_connections: 26
-      landmark_connections: 28
-      landmark_connections: 27
-      landmark_connections: 29
-      landmark_connections: 28
-      landmark_connections: 30
-      landmark_connections: 29
-      landmark_connections: 31
-      landmark_connections: 30
-      landmark_connections: 32
-      landmark_connections: 27
-      landmark_connections: 31
-      landmark_connections: 28
-      landmark_connections: 32
-
-      landmark_color { r: 255 g: 255 b: 255 }
-      connection_color { r: 255 g: 255 b: 255 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# Take left pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "landmarks_left_side"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 1 end: 4 }
-      ranges: { begin: 7 end: 8 }
-      ranges: { begin: 9 end: 10 }
-      ranges: { begin: 11 end: 12 }
-      ranges: { begin: 13 end: 14 }
-      ranges: { begin: 15 end: 16 }
-      ranges: { begin: 17 end: 18 }
-      ranges: { begin: 19 end: 20 }
-      ranges: { begin: 21 end: 22 }
-      ranges: { begin: 23 end: 24 }
-
-      combine_outputs: true
-    }
-  }
-}
-
-# Take right pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "landmarks_right_side"
-  node_options: {
-    [type.googleapis.com/mediapipe.SplitVectorCalculatorOptions] {
-      ranges: { begin: 4 end: 7 }
-      ranges: { begin: 8 end: 9 }
-      ranges: { begin: 10 end: 11 }
-      ranges: { begin: 12 end: 13 }
-      ranges: { begin: 14 end: 15 }
-      ranges: { begin: 16 end: 17 }
-      ranges: { begin: 18 end: 19 }
-      ranges: { begin: 20 end: 21 }
-      ranges: { begin: 22 end: 23 }
-      ranges: { begin: 24 end: 25 }
-
-      combine_outputs: true
-    }
-  }
-}
-
-# Render pose joints as big white circles.
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:visible_pose_landmarks"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_background_joints_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 255 g: 255 b: 255 }
-      connection_color { r: 255 g: 255 b: 255 }
-      thickness: 5.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# Render pose left side joints as orange circles (inside white ones).
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks_left_side"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_left_joints_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 255 g: 138 b: 0 }
-      connection_color { r: 255 g: 138 b: 0 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# Render pose right side joints as cyan circles (inside white ones).
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks_right_side"
-  input_stream: "RENDER_SCALE:render_scale"
-  output_stream: "RENDER_DATA:landmarks_right_joints_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 0 g: 217 b: 231 }
-      connection_color { r: 0 g: 217 b: 231 }
-      thickness: 3.0
-      visualize_landmark_depth: false
-      utilize_visibility: true
-      visibility_threshold: 0.5
-    }
-  }
-}
-
-# Converts normalized rects to drawing primitives for annotation overlay.
-node {
-  calculator: "RectToRenderDataCalculator"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "RENDER_DATA:roi_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.RectToRenderDataCalculatorOptions] {
-      filled: false
-      color { r: 255 g: 0 b: 0 }
-      thickness: 4.0
-    }
-  }
-}
-
-# Colors the segmentation mask with the color specified in the option.
-node {
-  calculator: "RecolorCalculator"
-  input_stream: "IMAGE_GPU:input_image"
-  input_stream: "MASK_GPU:segmentation_mask"
-  output_stream: "IMAGE_GPU:segmented_image"
-  node_options: {
-    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
-      color { r: 0 g: 0 b: 255 }
-      mask_channel: RED
-      invert_mask: true
-      adjust_with_luminance: false
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:segmented_image"
-  input_stream: "detection_render_data"
-  input_stream: "landmarks_render_data"
-  input_stream: "landmarks_background_joints_render_data"
-  input_stream: "landmarks_left_joints_render_data"
-  input_stream: "landmarks_right_joints_render_data"
-  input_stream: "roi_render_data"
-  output_stream: "IMAGE_GPU:output_image"
-}
diff --git a/mediapipe/graphs/selfie_segmentation/BUILD b/mediapipe/graphs/selfie_segmentation/BUILD
deleted file mode 100644
index ddca178..0000000
--- a/mediapipe/graphs/selfie_segmentation/BUILD
+++ /dev/null
@@ -1,54 +0,0 @@
-# Copyright 2021 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "selfie_segmentation_gpu_deps",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:recolor_calculator",
-        "//mediapipe/modules/selfie_segmentation:selfie_segmentation_gpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "selfie_segmentation_gpu_binary_graph",
-    graph = "selfie_segmentation_gpu.pbtxt",
-    output_name = "selfie_segmentation_gpu.binarypb",
-    deps = [":selfie_segmentation_gpu_deps"],
-)
-
-cc_library(
-    name = "selfie_segmentation_cpu_deps",
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:recolor_calculator",
-        "//mediapipe/modules/selfie_segmentation:selfie_segmentation_cpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "selfie_segmentation_cpu_binary_graph",
-    graph = "selfie_segmentation_cpu.pbtxt",
-    output_name = "selfie_segmentation_cpu.binarypb",
-    deps = [":selfie_segmentation_cpu_deps"],
-)
diff --git a/mediapipe/graphs/selfie_segmentation/selfie_segmentation_cpu.pbtxt b/mediapipe/graphs/selfie_segmentation/selfie_segmentation_cpu.pbtxt
deleted file mode 100644
index db1b479..0000000
--- a/mediapipe/graphs/selfie_segmentation/selfie_segmentation_cpu.pbtxt
+++ /dev/null
@@ -1,52 +0,0 @@
-# MediaPipe graph that performs selfie segmentation with TensorFlow Lite on CPU.
-
-# CPU buffer. (ImageFrame)
-input_stream: "input_video"
-
-# Output image with rendered results. (ImageFrame)
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Subgraph that performs selfie segmentation.
-node {
-  calculator: "SelfieSegmentationCpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "SEGMENTATION_MASK:segmentation_mask"
-}
-
-
-# Colors the selfie segmentation with the color specified in the option.
-node {
-  calculator: "RecolorCalculator"
-  input_stream: "IMAGE:throttled_input_video"
-  input_stream: "MASK:segmentation_mask"
-  output_stream: "IMAGE:output_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
-      color { r: 0 g: 0 b: 255 }
-      mask_channel: RED
-      invert_mask: true
-      adjust_with_luminance: false
-    }
-  }
-}
diff --git a/mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt b/mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt
deleted file mode 100644
index 08d4c36..0000000
--- a/mediapipe/graphs/selfie_segmentation/selfie_segmentation_gpu.pbtxt
+++ /dev/null
@@ -1,52 +0,0 @@
-# MediaPipe graph that performs selfie segmentation with TensorFlow Lite on GPU.
-
-# GPU buffer. (GpuBuffer)
-input_stream: "input_video"
-
-# Output image with rendered results. (GpuBuffer)
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control. It passes through
-# the very first incoming image unaltered, and waits for downstream nodes
-# (calculators and subgraphs) in the graph to finish their tasks before it
-# passes through another image. All images that come in while waiting are
-# dropped, limiting the number of in-flight images in most part of the graph to
-# 1. This prevents the downstream nodes from queuing up incoming images and data
-# excessively, which leads to increased latency and memory usage, unwanted in
-# real-time mobile applications. It also eliminates unnecessarily computation,
-# e.g., the output produced by a node may get dropped downstream if the
-# subsequent nodes are still busy processing previous inputs.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:output_video"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Subgraph that performs selfie segmentation.
-node {
-  calculator: "SelfieSegmentationGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "SEGMENTATION_MASK:segmentation_mask"
-}
-
-
-# Colors the selfie segmentation with the color specified in the option.
-node {
-  calculator: "RecolorCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  input_stream: "MASK_GPU:segmentation_mask"
-  output_stream: "IMAGE_GPU:output_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
-      color { r: 0 g: 0 b: 255 }
-      mask_channel: RED
-      invert_mask: true
-      adjust_with_luminance: false
-    }
-  }
-}
diff --git a/mediapipe/graphs/template_matching/BUILD b/mediapipe/graphs/template_matching/BUILD
deleted file mode 100644
index bc254d2..0000000
--- a/mediapipe/graphs/template_matching/BUILD
+++ /dev/null
@@ -1,67 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "template_matching_deps",
-    deps = [
-        "//mediapipe/calculators/image:feature_detector_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/tflite:tflite_converter_calculator",
-        "//mediapipe/calculators/tflite:tflite_inference_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_floats_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:landmarks_to_render_data_calculator",
-        "//mediapipe/calculators/util:timed_box_list_id_to_label_calculator",
-        "//mediapipe/calculators/util:timed_box_list_to_render_data_calculator",
-        "//mediapipe/calculators/video:box_detector_calculator",
-    ],
-)
-
-cc_library(
-    name = "desktop_calculators",
-    deps = [
-        ":template_matching_deps",
-        "//mediapipe/calculators/image:opencv_encoded_image_to_image_frame_calculator",
-        "//mediapipe/calculators/util:local_file_pattern_contents_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
-    ],
-)
-
-cc_library(
-    name = "mobile_calculators",
-    deps = [
-        ":template_matching_deps",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "mobile_cpu_binary_graph",
-    graph = "template_matching_mobile_cpu.pbtxt",
-    output_name = "mobile_cpu.binarypb",
-    deps = [":mobile_calculators"],
-)
diff --git a/mediapipe/graphs/template_matching/index_building.pbtxt b/mediapipe/graphs/template_matching/index_building.pbtxt
deleted file mode 100644
index 8228139..0000000
--- a/mediapipe/graphs/template_matching/index_building.pbtxt
+++ /dev/null
@@ -1,92 +0,0 @@
-# MediaPipe graph that build feature descriptors index for specific target.
-
-# max_queue_size limits the number of packets enqueued on any input stream
-# by throttling inputs to the graph. This makes the graph only process one
-# frame per time.
-max_queue_size: 1
-
-# Decodes an input video file into images and a video header.
-node {
-  calculator: "LocalFilePatternContentsCalculator"
-  input_side_packet: "FILE_DIRECTORY:file_directory"
-  input_side_packet: "FILE_SUFFIX:file_suffix"
-  output_stream: "CONTENTS:encoded_image"
-}
-
-node {
-  calculator: "OpenCvEncodedImageToImageFrameCalculator"
-  input_stream: "encoded_image"
-  output_stream: "image_frame"
-}
-
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:image_frame"
-  output_stream: "IMAGE:scaled_image_frame"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 320
-      output_height: 320
-      scale_mode: FILL_AND_CROP
-    }
-  }
-}
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:scaled_image_frame"
-  output_stream: "SIZE:input_video_size"
-}
-
-node {
-  calculator: "FeatureDetectorCalculator"
-  input_stream: "IMAGE:scaled_image_frame"
-  output_stream: "FEATURES:features"
-  output_stream: "LANDMARKS:landmarks"
-  output_stream: "PATCHES:patches"
-  node_options: {
-    [type.googleapis.com/mediapipe.FeatureDetectorCalculatorOptions] {
-      max_features: 400
-    }
-  }
-}
-
-# input tensors: 200*32*32*1 float
-# output tensors: 200*40 float, only first keypoint.size()*40 is knift features,
-# rest is padded by zero.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS:patches"
-  output_stream: "TENSORS:knift_feature_tensors"
-  input_stream_handler {
-    input_stream_handler: "DefaultInputStreamHandler"
-  }
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/knift_float_400.tflite"
-    }
-  }
-}
-
-node {
-  calculator: "TfLiteTensorsToFloatsCalculator"
-  input_stream: "TENSORS:knift_feature_tensors"
-  output_stream: "FLOATS:knift_feature_floats"
-}
-
-node {
-  calculator: "BoxDetectorCalculator"
-  input_side_packet: "OUTPUT_INDEX_FILENAME:output_index_filename"
-  input_stream: "FEATURES:features"
-  input_stream: "IMAGE_SIZE:input_video_size"
-  input_stream: "DESCRIPTORS:knift_feature_floats"
-
-  node_options: {
-    [type.googleapis.com/mediapipe.BoxDetectorCalculatorOptions] {
-      detector_options {
-        index_type: OPENCV_BF
-        detect_every_n_frame: 1
-      }
-    }
-  }
-}
diff --git a/mediapipe/graphs/template_matching/template_matching_desktop.pbtxt b/mediapipe/graphs/template_matching/template_matching_desktop.pbtxt
deleted file mode 100644
index d44a7e5..0000000
--- a/mediapipe/graphs/template_matching/template_matching_desktop.pbtxt
+++ /dev/null
@@ -1,141 +0,0 @@
-# MediaPipe graph that performs object detection on desktop with TensorFlow Lite
-# on CPU.
-# Used in the example in
-# mediapipe/examples/desktop/template_matching:template_matching_tflite
-
-# max_queue_size limits the number of packets enqueued on any input stream
-# by throttling inputs to the graph. This makes the graph only process one
-# frame per time.
-max_queue_size: 1
-
-# Decodes an input video file into images and a video header.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:input_video"
-  output_stream: "VIDEO_PRESTREAM:input_video_header"
-}
-
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:input_video"
-  output_stream: "IMAGE:scaled_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 640
-      output_height: 640
-      scale_mode: FILL_AND_CROP
-    }
-  }
-}
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:scaled_input_video"
-  output_stream: "SIZE:input_video_size"
-}
-
-node {
-  calculator: "FeatureDetectorCalculator"
-  input_stream: "IMAGE:scaled_input_video"
-  output_stream: "FEATURES:features"
-  output_stream: "LANDMARKS:landmarks"
-  output_stream: "PATCHES:patches"
-}
-
-# input tensors: 200*32*32*1 float
-# output tensors: 200*40 float, only first keypoint.size()*40 is knift features,
-# rest is padded by zero.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS:patches"
-  output_stream: "TENSORS:knift_feature_tensors"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/knift_float.tflite"
-    }
-  }
-}
-
-node {
-  calculator: "TfLiteTensorsToFloatsCalculator"
-  input_stream: "TENSORS:knift_feature_tensors"
-  output_stream: "FLOATS:knift_feature_floats"
-}
-
-node {
-  calculator: "BoxDetectorCalculator"
-  input_stream: "FEATURES:features"
-  input_stream: "IMAGE_SIZE:input_video_size"
-  input_stream: "DESCRIPTORS:knift_feature_floats"
-  output_stream: "BOXES:detections"
-
-  node_options: {
-    [type.googleapis.com/mediapipe.BoxDetectorCalculatorOptions] {
-      detector_options {
-        index_type: OPENCV_BF
-        detect_every_n_frame: 1
-      }
-      index_proto_filename: "mediapipe/models/knift_index.pb"
-    }
-  }
-}
-
-node {
-  calculator: "TimedBoxListIdToLabelCalculator"
-  input_stream: "detections"
-  output_stream: "labeled_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.TimedBoxListIdToLabelCalculatorOptions] {
-      label_map_path: "mediapipe/models/knift_labelmap.txt"
-    }
-  }
-}
-
-node {
-  calculator: "TimedBoxListToRenderDataCalculator"
-  input_stream: "BOX_LIST:labeled_detections"
-  output_stream: "RENDER_DATA:box_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.TimedBoxListToRenderDataCalculatorOptions] {
-      box_color { r: 255 g: 0 b: 0 }
-      thickness: 5.0
-    }
-  }
-}
-
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks"
-  output_stream: "RENDER_DATA:landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 0 g: 255 b: 0 }
-      thickness: 2.0
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_video"
-  input_stream: "box_render_data"
-  input_stream: "landmarks_render_data"
-  output_stream: "IMAGE:output_video"
-}
-
-# Encodes the annotated images into a video file, adopting properties specified
-# in the input video header, e.g., video framerate.
-node {
-  calculator: "OpenCvVideoEncoderCalculator"
-  input_stream: "VIDEO:output_video"
-  input_stream: "VIDEO_PRESTREAM:input_video_header"
-  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
-      codec: "avc1"
-      video_format: "mp4"
-    }
-  }
-}
diff --git a/mediapipe/graphs/template_matching/template_matching_mobile_cpu.pbtxt b/mediapipe/graphs/template_matching/template_matching_mobile_cpu.pbtxt
deleted file mode 100644
index e02e12d..0000000
--- a/mediapipe/graphs/template_matching/template_matching_mobile_cpu.pbtxt
+++ /dev/null
@@ -1,137 +0,0 @@
-# MediaPipe graph that performs template matching with TensorFlow Lite on CPU.
-# Used in the examples in
-# mediapipe/examples/android/src/java/com/mediapipe/apps/templatematchingcpu
-
-# Images on GPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Throttles the images flowing downstream for flow control.
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "input_video"
-  input_stream: "FINISHED:detections"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_input_video"
-}
-
-# Transfers the input image from GPU to CPU memory.
-node: {
-  calculator: "GpuBufferToImageFrameCalculator"
-  input_stream: "throttled_input_video"
-  output_stream: "input_video_cpu"
-}
-
-# Scale the image's longer side to 640, keeping aspect ratio.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:input_video_cpu"
-  output_stream: "IMAGE:transformed_input_video_cpu"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 640
-      output_height: 640
-      scale_mode: FILL_AND_CROP
-    }
-  }
-}
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:transformed_input_video_cpu"
-  output_stream: "SIZE:input_video_size"
-}
-
-node {
-  calculator: "FeatureDetectorCalculator"
-  input_stream: "IMAGE:transformed_input_video_cpu"
-  output_stream: "FEATURES:features"
-  output_stream: "LANDMARKS:landmarks"
-  output_stream: "PATCHES:patches"
-}
-
-# input tensors: 200*32*32*1 float
-# output tensors: 200*40 float, only first keypoint.size()*40 is knift features,
-# rest is padded by zero.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS:patches"
-  output_stream: "TENSORS:knift_feature_tensors"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/knift_float.tflite"
-      delegate { xnnpack {} }
-    }
-  }
-}
-
-node {
-  calculator: "TfLiteTensorsToFloatsCalculator"
-  input_stream: "TENSORS:knift_feature_tensors"
-  output_stream: "FLOATS:knift_feature_floats"
-}
-
-node {
-  calculator: "BoxDetectorCalculator"
-  input_stream: "FEATURES:features"
-  input_stream: "IMAGE_SIZE:input_video_size"
-  input_stream: "DESCRIPTORS:knift_feature_floats"
-  output_stream: "BOXES:detections"
-
-  node_options: {
-    [type.googleapis.com/mediapipe.BoxDetectorCalculatorOptions] {
-      detector_options {
-        index_type: OPENCV_BF
-        detect_every_n_frame: 1
-      }
-      index_proto_filename: "mediapipe/models/knift_index.pb"
-    }
-  }
-}
-
-node {
-  calculator: "TimedBoxListIdToLabelCalculator"
-  input_stream: "detections"
-  output_stream: "labeled_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.TimedBoxListIdToLabelCalculatorOptions] {
-      label_map_path: "mediapipe/models/knift_labelmap.txt"
-    }
-  }
-}
-
-node {
-  calculator: "TimedBoxListToRenderDataCalculator"
-  input_stream: "BOX_LIST:labeled_detections"
-  output_stream: "RENDER_DATA:box_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.TimedBoxListToRenderDataCalculatorOptions] {
-      box_color { r: 255 g: 0 b: 0 }
-      thickness: 5.0
-    }
-  }
-}
-
-node {
-  calculator: "LandmarksToRenderDataCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks"
-  output_stream: "RENDER_DATA:landmarks_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LandmarksToRenderDataCalculatorOptions] {
-      landmark_color { r: 0 g: 255 b: 0 }
-      thickness: 2.0
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:throttled_input_video"
-  input_stream: "box_render_data"
-  input_stream: "landmarks_render_data"
-  output_stream: "IMAGE_GPU:output_video"
-}
diff --git a/mediapipe/graphs/tracking/BUILD b/mediapipe/graphs/tracking/BUILD
deleted file mode 100644
index 9e6e75f..0000000
--- a/mediapipe/graphs/tracking/BUILD
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_binary_graph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "mobile_calculators",
-    deps = [
-        "//mediapipe/calculators/core:packet_resampler_calculator",
-        "//mediapipe/graphs/tracking/subgraphs:object_detection_gpu",
-        "//mediapipe/graphs/tracking/subgraphs:object_tracking_gpu",
-        "//mediapipe/graphs/tracking/subgraphs:renderer_gpu",
-    ],
-)
-
-cc_library(
-    name = "desktop_calculators",
-    deps = [
-        "//mediapipe/calculators/core:packet_resampler_calculator",
-        "//mediapipe/graphs/tracking/subgraphs:object_detection_cpu",
-        "//mediapipe/graphs/tracking/subgraphs:object_tracking_cpu",
-        "//mediapipe/graphs/tracking/subgraphs:renderer_cpu",
-    ],
-)
-
-mediapipe_binary_graph(
-    name = "mobile_gpu_binary_graph",
-    graph = "object_detection_tracking_mobile_gpu.pbtxt",
-    output_name = "mobile_gpu.binarypb",
-    deps = [":mobile_calculators"],
-)
diff --git a/mediapipe/graphs/tracking/object_detection_tracking_desktop_live.pbtxt b/mediapipe/graphs/tracking/object_detection_tracking_desktop_live.pbtxt
deleted file mode 100644
index 4b21ee5..0000000
--- a/mediapipe/graphs/tracking/object_detection_tracking_desktop_live.pbtxt
+++ /dev/null
@@ -1,45 +0,0 @@
-# MediaPipe graph that performs object detection and tracking with TensorFlow
-# Lite on CPU.
-# Used in the examples in
-# mediapipie/examples/desktop/object_tracking/
-
-# Images on CPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Resamples the images by specific frame rate. This calculator is used to
-# control the frequecy of subsequent calculators/subgraphs, e.g. less power
-# consumption for expensive process.
-node {
-  calculator: "PacketResamplerCalculator"
-  input_stream: "DATA:input_video"
-  output_stream: "DATA:throttled_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.PacketResamplerCalculatorOptions] {
-      frame_rate: 3
-    }
-  }
-}
-
-# Subgraph that detections objects (see object_detection_cpu.pbtxt).
-node {
-  calculator: "ObjectDetectionSubgraphCpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "DETECTIONS:output_detections"
-}
-
-# Subgraph that tracks objects (see object_tracking_cpu.pbtxt).
-node {
-  calculator: "ObjectTrackingSubgraphCpu"
-  input_stream: "VIDEO:input_video"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "DETECTIONS:tracked_detections"
-}
-
-# Subgraph that renders annotations and overlays them on top of input images (see renderer_cpu.pbtxt).
-node {
-  calculator: "RendererSubgraphCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "DETECTIONS:tracked_detections"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/tracking/object_detection_tracking_mobile_gpu.pbtxt b/mediapipe/graphs/tracking/object_detection_tracking_mobile_gpu.pbtxt
deleted file mode 100644
index 0ef9830..0000000
--- a/mediapipe/graphs/tracking/object_detection_tracking_mobile_gpu.pbtxt
+++ /dev/null
@@ -1,46 +0,0 @@
-# MediaPipe graph that performs object detection and tracking with TensorFlow
-# Lite on GPU.
-# Used in the examples in
-# mediapipie/examples/android/src/java/com/mediapipe/apps/objecttrackinggpu
-
-# Images on GPU coming into and out of the graph.
-input_stream: "input_video"
-output_stream: "output_video"
-
-# Resamples the images by specific frame rate. This calculator is used to
-# control the frequecy of subsequent calculators/subgraphs, e.g. less power
-# consumption for expensive process.
-node {
-  calculator: "PacketResamplerCalculator"
-  input_stream: "DATA:input_video"
-  output_stream: "DATA:throttled_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.PacketResamplerCalculatorOptions] {
-      frame_rate: 0.5
-    }
-  }
-}
-
-# Subgraph that detections objects (see object_detection_gpu.pbtxt).
-node {
-  calculator: "ObjectDetectionSubgraphGpu"
-  input_stream: "IMAGE:throttled_input_video"
-  output_stream: "DETECTIONS:output_detections"
-}
-
-# Subgraph that tracks objects (see object_tracking_gpu.pbtxt).
-node {
-  calculator: "ObjectTrackingSubgraphGpu"
-  input_stream: "VIDEO:input_video"
-  input_stream: "DETECTIONS:output_detections"
-  output_stream: "DETECTIONS:tracked_detections"
-}
-
-# Subgraph that renders annotations and overlays them on top of the input
-# images (see renderer_gpu.pbtxt).
-node {
-  calculator: "RendererSubgraphGpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "DETECTIONS:tracked_detections"
-  output_stream: "IMAGE:output_video"
-}
diff --git a/mediapipe/graphs/tracking/subgraphs/BUILD b/mediapipe/graphs/tracking/subgraphs/BUILD
deleted file mode 100644
index 16f87f3..0000000
--- a/mediapipe/graphs/tracking/subgraphs/BUILD
+++ /dev/null
@@ -1,129 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "box_tracking_gpu",
-    graph = "box_tracking_gpu.pbtxt",
-    register_as = "BoxTrackingSubgraphGpu",
-    deps = [
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/video:box_tracker_calculator",
-        "//mediapipe/calculators/video:flow_packager_calculator",
-        "//mediapipe/calculators/video:motion_analysis_calculator",
-        "//mediapipe/framework/stream_handler:immediate_input_stream_handler",
-        "//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
-        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "box_tracking_cpu",
-    graph = "box_tracking_cpu.pbtxt",
-    register_as = "BoxTrackingSubgraphCpu",
-    deps = [
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/video:box_tracker_calculator",
-        "//mediapipe/calculators/video:flow_packager_calculator",
-        "//mediapipe/calculators/video:motion_analysis_calculator",
-        "//mediapipe/framework/stream_handler:immediate_input_stream_handler",
-        "//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "object_tracking_gpu",
-    graph = "object_tracking_gpu.pbtxt",
-    register_as = "ObjectTrackingSubgraphGpu",
-    deps = [
-        "//mediapipe/calculators/util:detection_unique_id_calculator",
-        "//mediapipe/calculators/util:detections_to_timed_box_list_calculator",
-        "//mediapipe/calculators/video:tracked_detection_manager_calculator",
-        "//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
-        "//mediapipe/graphs/tracking/subgraphs:box_tracking_gpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "object_tracking_cpu",
-    graph = "object_tracking_cpu.pbtxt",
-    register_as = "ObjectTrackingSubgraphCpu",
-    deps = [
-        "//mediapipe/calculators/util:detection_unique_id_calculator",
-        "//mediapipe/calculators/util:detections_to_timed_box_list_calculator",
-        "//mediapipe/calculators/video:tracked_detection_manager_calculator",
-        "//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
-        "//mediapipe/graphs/tracking/subgraphs:box_tracking_cpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "object_detection_gpu",
-    graph = "object_detection_gpu.pbtxt",
-    register_as = "ObjectDetectionSubgraphGpu",
-    deps = [
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/tflite:tflite_converter_calculator",
-        "//mediapipe/calculators/tflite:tflite_inference_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_detections_calculator",
-        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "object_detection_cpu",
-    graph = "object_detection_cpu.pbtxt",
-    register_as = "ObjectDetectionSubgraphCpu",
-    deps = [
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/tflite:tflite_converter_calculator",
-        "//mediapipe/calculators/tflite:tflite_inference_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_detections_calculator",
-        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "renderer_gpu",
-    graph = "renderer_gpu.pbtxt",
-    register_as = "RendererSubgraphGpu",
-    deps = [
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_data_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "renderer_cpu",
-    graph = "renderer_cpu.pbtxt",
-    register_as = "RendererSubgraphCpu",
-    deps = [
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:detections_to_render_data_calculator",
-        "//mediapipe/calculators/util:rect_to_render_data_calculator",
-    ],
-)
diff --git a/mediapipe/graphs/tracking/subgraphs/box_tracking_cpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/box_tracking_cpu.pbtxt
deleted file mode 100644
index b8c4e2f..0000000
--- a/mediapipe/graphs/tracking/subgraphs/box_tracking_cpu.pbtxt
+++ /dev/null
@@ -1,119 +0,0 @@
-# MediaPipe box tracking subgraph.
-
-type: "BoxTrackingSubgraphCpu"
-
-input_stream: "VIDEO:input_video"
-input_stream: "BOXES:start_pos"
-input_stream: "CANCEL_ID:cancel_object_id"
-output_stream: "BOXES:boxes"
-
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:input_video"
-  output_stream: "IMAGE:downscaled_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 320
-      output_height: 240
-    }
-  }
-}
-
-# Performs motion analysis on an incoming video stream.
-node: {
-  calculator: "MotionAnalysisCalculator"
-  input_stream: "VIDEO:downscaled_input_video"
-  output_stream: "CAMERA:camera_motion"
-  output_stream: "FLOW:region_flow"
-
-  node_options: {
-    [type.googleapis.com/mediapipe.MotionAnalysisCalculatorOptions]: {
-      analysis_options {
-        analysis_policy: ANALYSIS_POLICY_CAMERA_MOBILE
-        flow_options {
-          fast_estimation_min_block_size: 100
-          top_inlier_sets: 1
-          frac_inlier_error_threshold: 3e-3
-          downsample_mode: DOWNSAMPLE_TO_INPUT_SIZE
-          verification_distance: 5.0
-          verify_long_feature_acceleration: true
-          verify_long_feature_trigger_ratio: 0.1
-          tracking_options {
-            max_features: 500
-            adaptive_extraction_levels: 2
-            min_eig_val_settings {
-              adaptive_lowest_quality_level: 2e-4
-            }
-            klt_tracker_implementation: KLT_OPENCV
-          }
-        }
-      }
-    }
-  }
-}
-
-# Reads optical flow fields defined in
-# mediapipe/framework/formats/motion/optical_flow_field.h,
-# returns a VideoFrame with 2 channels (v_x and v_y), each channel is quantized
-# to 0-255.
-node: {
-  calculator: "FlowPackagerCalculator"
-  input_stream: "FLOW:region_flow"
-  input_stream: "CAMERA:camera_motion"
-  output_stream: "TRACKING:tracking_data"
-
-  node_options: {
-    [type.googleapis.com/mediapipe.FlowPackagerCalculatorOptions]: {
-      flow_packager_options: {
-        binary_tracking_data_support: false
-      }
-    }
-  }
-}
-
-# Tracks box positions over time.
-node: {
-  calculator: "BoxTrackerCalculator"
-  input_stream: "TRACKING:tracking_data"
-  input_stream: "TRACK_TIME:input_video"
-  input_stream: "START_POS:start_pos"
-  input_stream: "CANCEL_OBJECT_ID:cancel_object_id"
-  input_stream_info: {
-    tag_index: "CANCEL_OBJECT_ID"
-    back_edge: true
-  }
-  output_stream: "BOXES:boxes"
-
-  input_stream_handler {
-    input_stream_handler: "SyncSetInputStreamHandler"
-    options {
-      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
-        sync_set {
-          tag_index: "TRACKING"
-          tag_index: "TRACK_TIME"
-        }
-        sync_set {
-          tag_index: "START_POS"
-        }
-        sync_set {
-          tag_index: "CANCEL_OBJECT_ID"
-        }
-      }
-    }
-  }
-
-  node_options: {
-    [type.googleapis.com/mediapipe.BoxTrackerCalculatorOptions]: {
-      tracker_options: {
-        track_step_options {
-          track_object_and_camera: true
-          tracking_degrees: TRACKING_DEGREE_OBJECT_SCALE
-          inlier_spring_force: 0.0
-          static_motion_temporal_ratio: 3e-2
-        }
-      }
-      visualize_tracking_data: false
-      streaming_track_data_cache_size: 100
-    }
-  }
-}
diff --git a/mediapipe/graphs/tracking/subgraphs/box_tracking_gpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/box_tracking_gpu.pbtxt
deleted file mode 100644
index cab2b77..0000000
--- a/mediapipe/graphs/tracking/subgraphs/box_tracking_gpu.pbtxt
+++ /dev/null
@@ -1,126 +0,0 @@
-# MediaPipe box tracking subgraph.
-
-type: "BoxTrackingSubgraphGpu"
-
-input_stream: "VIDEO:input_video"
-input_stream: "BOXES:start_pos"
-input_stream: "CANCEL_ID:cancel_object_id"
-output_stream: "BOXES:boxes"
-
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:input_video"
-  output_stream: "IMAGE_GPU:downscaled_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 240
-      output_height: 320
-    }
-  }
-}
-
-# Converts GPU buffer to ImageFrame for processing tracking.
-node: {
-  calculator: "GpuBufferToImageFrameCalculator"
-  input_stream: "downscaled_input_video"
-  output_stream: "downscaled_input_video_cpu"
-}
-
-# Performs motion analysis on an incoming video stream.
-node: {
-  calculator: "MotionAnalysisCalculator"
-  input_stream: "VIDEO:downscaled_input_video_cpu"
-  output_stream: "CAMERA:camera_motion"
-  output_stream: "FLOW:region_flow"
-
-  node_options: {
-    [type.googleapis.com/mediapipe.MotionAnalysisCalculatorOptions]: {
-      analysis_options {
-        analysis_policy: ANALYSIS_POLICY_CAMERA_MOBILE
-        flow_options {
-          fast_estimation_min_block_size: 100
-          top_inlier_sets: 1
-          frac_inlier_error_threshold: 3e-3
-          downsample_mode: DOWNSAMPLE_TO_INPUT_SIZE
-          verification_distance: 5.0
-          verify_long_feature_acceleration: true
-          verify_long_feature_trigger_ratio: 0.1
-          tracking_options {
-            max_features: 500
-            adaptive_extraction_levels: 2
-            min_eig_val_settings {
-              adaptive_lowest_quality_level: 2e-4
-            }
-            klt_tracker_implementation: KLT_OPENCV
-          }
-        }
-      }
-    }
-  }
-}
-
-# Reads optical flow fields defined in
-# mediapipe/framework/formats/motion/optical_flow_field.h,
-# returns a VideoFrame with 2 channels (v_x and v_y), each channel is quantized
-# to 0-255.
-node: {
-  calculator: "FlowPackagerCalculator"
-  input_stream: "FLOW:region_flow"
-  input_stream: "CAMERA:camera_motion"
-  output_stream: "TRACKING:tracking_data"
-
-  node_options: {
-    [type.googleapis.com/mediapipe.FlowPackagerCalculatorOptions]: {
-      flow_packager_options: {
-        binary_tracking_data_support: false
-      }
-    }
-  }
-}
-
-# Tracks box positions over time.
-node: {
-  calculator: "BoxTrackerCalculator"
-  input_stream: "TRACKING:tracking_data"
-  input_stream: "TRACK_TIME:input_video"
-  input_stream: "START_POS:start_pos"
-  input_stream: "CANCEL_OBJECT_ID:cancel_object_id"
-  input_stream_info: {
-    tag_index: "CANCEL_OBJECT_ID"
-    back_edge: true
-  }
-  output_stream: "BOXES:boxes"
-
-  input_stream_handler {
-    input_stream_handler: "SyncSetInputStreamHandler"
-    options {
-      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
-        sync_set {
-          tag_index: "TRACKING"
-          tag_index: "TRACK_TIME"
-        }
-        sync_set {
-          tag_index: "START_POS"
-        }
-        sync_set {
-          tag_index: "CANCEL_OBJECT_ID"
-        }
-      }
-    }
-  }
-
-  node_options: {
-    [type.googleapis.com/mediapipe.BoxTrackerCalculatorOptions]: {
-      tracker_options: {
-        track_step_options {
-          track_object_and_camera: true
-          tracking_degrees: TRACKING_DEGREE_OBJECT_SCALE
-          inlier_spring_force: 0.0
-          static_motion_temporal_ratio: 3e-2
-        }
-      }
-      visualize_tracking_data: false
-      streaming_track_data_cache_size: 100
-    }
-  }
-}
diff --git a/mediapipe/graphs/tracking/subgraphs/object_detection_cpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/object_detection_cpu.pbtxt
deleted file mode 100644
index 54d6af3..0000000
--- a/mediapipe/graphs/tracking/subgraphs/object_detection_cpu.pbtxt
+++ /dev/null
@@ -1,128 +0,0 @@
-# MediaPipe object detection subgraph.
-
-type: "ObjectDetectionSubgraphCpu"
-
-input_stream: "IMAGE:input_video"
-output_stream: "DETECTIONS:output_detections"
-
-# Transforms the input image on CPU to a 320x320 image. To scale the image, by
-# default it uses the STRETCH scale mode that maps the entire input image to the
-# entire transformed image. As a result, image aspect ratio may be changed and
-# objects in the image may be deformed (stretched or squeezed), but the object
-# detection model used in this graph is agnostic to that deformation.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:input_video"
-  output_stream: "IMAGE:transformed_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 320
-      output_height: 320
-    }
-  }
-}
-
-# Converts the transformed input image on CPU into an image tensor stored as a
-# TfLiteTensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE:transformed_input_video"
-  output_stream: "TENSORS:image_tensor"
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  output_stream: "TENSORS:detection_tensors"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  node_options: {
-    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
-      num_layers: 6
-      min_scale: 0.2
-      max_scale: 0.95
-      input_size_height: 320
-      input_size_width: 320
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 16
-      strides: 32
-      strides: 64
-      strides: 128
-      strides: 256
-      strides: 512
-      aspect_ratios: 1.0
-      aspect_ratios: 2.0
-      aspect_ratios: 0.5
-      aspect_ratios: 3.0
-      aspect_ratios: 0.3333
-      reduce_boxes_in_lowest_layer: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TfLiteTensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
-      num_classes: 91
-      num_boxes: 2034
-      num_coords: 4
-      ignore_classes: 0
-      sigmoid_score: true
-      apply_exponential_on_box_size: true
-      x_scale: 10.0
-      y_scale: 10.0
-      h_scale: 5.0
-      w_scale: 5.0
-      min_score_thresh: 0.6
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "detections"
-  output_stream: "filtered_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
-      min_suppression_threshold: 0.4
-      max_num_detections: 3
-      overlap_type: INTERSECTION_OVER_UNION
-      return_empty_detections: true
-    }
-  }
-}
-
-# Maps detection label IDs to the corresponding label text. The label map is
-# provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "output_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
-      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
-    }
-  }
-}
diff --git a/mediapipe/graphs/tracking/subgraphs/object_detection_gpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/object_detection_gpu.pbtxt
deleted file mode 100644
index f3cc2c8..0000000
--- a/mediapipe/graphs/tracking/subgraphs/object_detection_gpu.pbtxt
+++ /dev/null
@@ -1,128 +0,0 @@
-# MediaPipe object detection subgraph.
-
-type: "ObjectDetectionSubgraphGpu"
-
-input_stream: "IMAGE:input_video"
-output_stream: "DETECTIONS:output_detections"
-
-# Transforms the input image on GPU to a 320x320 image. To scale the image, by
-# default it uses the STRETCH scale mode that maps the entire input image to the
-# entire transformed image. As a result, image aspect ratio may be changed and
-# objects in the image may be deformed (stretched or squeezed), but the object
-# detection model used in this graph is agnostic to that deformation.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:input_video"
-  output_stream: "IMAGE_GPU:transformed_input_video"
-  node_options: {
-    [type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
-      output_width: 320
-      output_height: 320
-    }
-  }
-}
-
-# Converts the transformed input image on GPU into an image tensor stored as a
-# TfLiteTensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE_GPU:transformed_input_video"
-  output_stream: "TENSORS_GPU:image_tensor"
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS_GPU:image_tensor"
-  output_stream: "TENSORS_GPU:detection_tensors"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
-      model_path: "mediapipe/models/ssdlite_object_detection.tflite"
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  node_options: {
-    [type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
-      num_layers: 6
-      min_scale: 0.2
-      max_scale: 0.95
-      input_size_height: 320
-      input_size_width: 320
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 16
-      strides: 32
-      strides: 64
-      strides: 128
-      strides: 256
-      strides: 512
-      aspect_ratios: 1.0
-      aspect_ratios: 2.0
-      aspect_ratios: 0.5
-      aspect_ratios: 3.0
-      aspect_ratios: 0.3333
-      reduce_boxes_in_lowest_layer: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TfLiteTensorsToDetectionsCalculator"
-  input_stream: "TENSORS_GPU:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
-      num_classes: 91
-      num_boxes: 2034
-      num_coords: 4
-      ignore_classes: 0
-      sigmoid_score: true
-      apply_exponential_on_box_size: true
-      x_scale: 10.0
-      y_scale: 10.0
-      h_scale: 5.0
-      w_scale: 5.0
-      min_score_thresh: 0.6
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "detections"
-  output_stream: "filtered_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
-      min_suppression_threshold: 0.4
-      max_num_detections: 3
-      overlap_type: INTERSECTION_OVER_UNION
-      return_empty_detections: true
-    }
-  }
-}
-
-# Maps detection label IDs to the corresponding label text. The label map is
-# provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "output_detections"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
-      label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
-    }
-  }
-}
diff --git a/mediapipe/graphs/tracking/subgraphs/object_tracking_cpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/object_tracking_cpu.pbtxt
deleted file mode 100644
index 9ac7978..0000000
--- a/mediapipe/graphs/tracking/subgraphs/object_tracking_cpu.pbtxt
+++ /dev/null
@@ -1,56 +0,0 @@
-# MediaPipe object tracking subgraph.
-
-type: "ObjectTrackingSubgraphCpu"
-
-input_stream: "VIDEO:input_video"
-input_stream: "DETECTIONS:new_detections"
-output_stream: "DETECTIONS:tracked_detections"
-
-# Assigns an unique id for each new detection.
-node {
-  calculator: "DetectionUniqueIdCalculator"
-  input_stream: "DETECTIONS:new_detections"
-  output_stream: "DETECTIONS:detections_with_id"
-}
-
-# Converts detections to TimedBox protos which are used as initial location
-# for tracking.
-node {
-  calculator: "DetectionsToTimedBoxListCalculator"
-  input_stream: "DETECTIONS:detections_with_id"
-  output_stream: "BOXES:start_pos"
-}
-
-# Subgraph that tracks boxes (see box_tracking_cpu.pbtxt).
-node {
-  calculator: "BoxTrackingSubgraphCpu"
-  input_stream: "VIDEO:input_video"
-  input_stream: "BOXES:start_pos"
-  input_stream: "CANCEL_ID:cancel_object_id"
-  output_stream: "BOXES:boxes"
-}
-
-# Managers new detected objects and objects that are being tracked.
-# It associates the duplicated detections and updates the locations of
-# detections from tracking.
-node: {
-  calculator: "TrackedDetectionManagerCalculator"
-  input_stream: "DETECTIONS:detections_with_id"
-  input_stream: "TRACKING_BOXES:boxes"
-  output_stream: "DETECTIONS:tracked_detections"
-  output_stream: "CANCEL_OBJECT_ID:cancel_object_id"
-
-  input_stream_handler {
-    input_stream_handler: "SyncSetInputStreamHandler"
-    options {
-      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
-        sync_set {
-          tag_index: "TRACKING_BOXES"
-        }
-        sync_set {
-          tag_index: "DETECTIONS"
-        }
-      }
-    }
-  }
-}
diff --git a/mediapipe/graphs/tracking/subgraphs/object_tracking_gpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/object_tracking_gpu.pbtxt
deleted file mode 100644
index ab27dbd..0000000
--- a/mediapipe/graphs/tracking/subgraphs/object_tracking_gpu.pbtxt
+++ /dev/null
@@ -1,56 +0,0 @@
-# MediaPipe object tracking subgraph.
-
-type: "ObjectTrackingSubgraphGpu"
-
-input_stream: "VIDEO:input_video"
-input_stream: "DETECTIONS:new_detections"
-output_stream: "DETECTIONS:tracked_detections"
-
-# Assigns an unique id for each new detection.
-node {
-  calculator: "DetectionUniqueIdCalculator"
-  input_stream: "DETECTIONS:new_detections"
-  output_stream: "DETECTIONS:detections_with_id"
-}
-
-# Converts detections to TimedBox protos which are used as initial location
-# for tracking.
-node {
-  calculator: "DetectionsToTimedBoxListCalculator"
-  input_stream: "DETECTIONS:detections_with_id"
-  output_stream: "BOXES:start_pos"
-}
-
-# Subgraph that tracks boxes (see box_tracking_gpu.pbtxt).
-node {
-  calculator: "BoxTrackingSubgraphGpu"
-  input_stream: "VIDEO:input_video"
-  input_stream: "BOXES:start_pos"
-  input_stream: "CANCEL_ID:cancel_object_id"
-  output_stream: "BOXES:boxes"
-}
-
-# Managers new detected objects and objects that are being tracked.
-# It associates the duplicated detections and updates the locations of
-# detections from tracking.
-node: {
-  calculator: "TrackedDetectionManagerCalculator"
-  input_stream: "DETECTIONS:detections_with_id"
-  input_stream: "TRACKING_BOXES:boxes"
-  output_stream: "DETECTIONS:tracked_detections"
-  output_stream: "CANCEL_OBJECT_ID:cancel_object_id"
-
-  input_stream_handler {
-    input_stream_handler: "SyncSetInputStreamHandler"
-    options {
-      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
-        sync_set {
-          tag_index: "TRACKING_BOXES"
-        }
-        sync_set {
-          tag_index: "DETECTIONS"
-        }
-      }
-    }
-  }
-}
diff --git a/mediapipe/graphs/tracking/subgraphs/renderer_cpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/renderer_cpu.pbtxt
deleted file mode 100644
index 665126a..0000000
--- a/mediapipe/graphs/tracking/subgraphs/renderer_cpu.pbtxt
+++ /dev/null
@@ -1,29 +0,0 @@
-# MediaPipe object tracking rendering subgraph.
-
-type: "RendererSubgraphCpu"
-
-input_stream: "IMAGE:input_image"
-input_stream: "DETECTIONS:detections"
-output_stream: "IMAGE:output_image"
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:detections"
-  output_stream: "RENDER_DATA:detections_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-      render_detection_id: true
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_image"
-  input_stream: "detections_render_data"
-  output_stream: "IMAGE:output_image"
-}
diff --git a/mediapipe/graphs/tracking/subgraphs/renderer_gpu.pbtxt b/mediapipe/graphs/tracking/subgraphs/renderer_gpu.pbtxt
deleted file mode 100644
index e94fb6d..0000000
--- a/mediapipe/graphs/tracking/subgraphs/renderer_gpu.pbtxt
+++ /dev/null
@@ -1,29 +0,0 @@
-# MediaPipe object tracking rendering subgraph.
-
-type: "RendererSubgraphGpu"
-
-input_stream: "IMAGE:input_image"
-input_stream: "DETECTIONS:detections"
-output_stream: "IMAGE:output_image"
-
-# Converts the detections to drawing primitives for annotation overlay.
-node {
-  calculator: "DetectionsToRenderDataCalculator"
-  input_stream: "DETECTIONS:detections"
-  output_stream: "RENDER_DATA:detections_render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
-      thickness: 4.0
-      color { r: 255 g: 0 b: 0 }
-      render_detection_id: true
-    }
-  }
-}
-
-# Draws annotations and overlays them on top of the input images.
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE_GPU:input_image"
-  input_stream: "detections_render_data"
-  output_stream: "IMAGE_GPU:output_image"
-}
diff --git a/mediapipe/graphs/youtube8m/BUILD b/mediapipe/graphs/youtube8m/BUILD
deleted file mode 100644
index 7318a8c..0000000
--- a/mediapipe/graphs/youtube8m/BUILD
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "yt8m_feature_extraction_calculators",
-    deps = [
-        "//mediapipe/calculators/audio:audio_decoder_calculator",
-        "//mediapipe/calculators/audio:basic_time_series_calculators",
-        "//mediapipe/calculators/audio:mfcc_mel_calculators",
-        "//mediapipe/calculators/audio:rational_factor_resample_calculator",
-        "//mediapipe/calculators/audio:spectrogram_calculator",
-        "//mediapipe/calculators/audio:stabilized_log_calculator",
-        "//mediapipe/calculators/audio:time_series_framer_calculator",
-        "//mediapipe/calculators/core:add_header_calculator",
-        "//mediapipe/calculators/core:matrix_multiply_calculator",
-        "//mediapipe/calculators/core:matrix_subtract_calculator",
-        "//mediapipe/calculators/core:matrix_to_vector_calculator",
-        "//mediapipe/calculators/core:packet_cloner_calculator",
-        "//mediapipe/calculators/core:packet_resampler_calculator",
-        "//mediapipe/calculators/tensorflow:image_frame_to_tensor_calculator",
-        "//mediapipe/calculators/tensorflow:matrix_to_tensor_calculator",
-        "//mediapipe/calculators/tensorflow:pack_media_sequence_calculator",
-        "//mediapipe/calculators/tensorflow:string_to_sequence_example_calculator",
-        "//mediapipe/calculators/tensorflow:tensor_squeeze_dimensions_calculator",
-        "//mediapipe/calculators/tensorflow:tensor_to_matrix_calculator",
-        "//mediapipe/calculators/tensorflow:tensorflow_inference_calculator",
-        "//mediapipe/calculators/tensorflow:tensorflow_session_from_frozen_graph_calculator",
-        "//mediapipe/calculators/tensorflow:unpack_media_sequence_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-    ],
-)
-
-cc_library(
-    name = "yt8m_inference_calculators_deps",
-    deps = [
-        "//mediapipe/calculators/core:concatenate_vector_calculator",
-        "//mediapipe/calculators/core:dequantize_byte_array_calculator",
-        "//mediapipe/calculators/core:packet_cloner_calculator",
-        "//mediapipe/calculators/core:side_packet_to_stream_calculator",
-        "//mediapipe/calculators/core:string_to_int_calculator",
-        "//mediapipe/calculators/tensorflow:lapped_tensor_buffer_calculator",
-        "//mediapipe/calculators/tensorflow:string_to_sequence_example_calculator",
-        "//mediapipe/calculators/tensorflow:tensor_to_vector_float_calculator",
-        "//mediapipe/calculators/tensorflow:tensorflow_inference_calculator",
-        "//mediapipe/calculators/tensorflow:tensorflow_session_from_saved_model_calculator",
-        "//mediapipe/calculators/tensorflow:tfrecord_reader_calculator",
-        "//mediapipe/calculators/tensorflow:unpack_media_sequence_calculator",
-        "//mediapipe/calculators/tensorflow:unpack_yt8m_sequence_example_calculator",
-        "//mediapipe/calculators/tensorflow:vector_float_to_tensor_calculator",
-        "//mediapipe/calculators/tensorflow:vector_int_to_tensor_calculator",
-        "//mediapipe/calculators/util:annotation_overlay_calculator",
-        "//mediapipe/calculators/util:labels_to_render_data_calculator",
-        "//mediapipe/calculators/util:local_file_contents_calculator",
-        "//mediapipe/calculators/util:top_k_scores_calculator",
-        "//mediapipe/calculators/video:opencv_video_decoder_calculator",
-        "//mediapipe/calculators/video:opencv_video_encoder_calculator",
-    ],
-)
diff --git a/mediapipe/graphs/youtube8m/feature_extraction.pbtxt b/mediapipe/graphs/youtube8m/feature_extraction.pbtxt
deleted file mode 100644
index 89d1053..0000000
--- a/mediapipe/graphs/youtube8m/feature_extraction.pbtxt
+++ /dev/null
@@ -1,295 +0,0 @@
-input_side_packet: "input_sequence_example"
-input_side_packet: "inception3_pca_mean_matrix"
-input_side_packet: "inception3_pca_projection_matrix"
-input_side_packet: "vggish_pca_mean_matrix"
-input_side_packet: "vggish_pca_projection_matrix"
-output_side_packet: "sequence_example_to_serialize"
-
-node {
-  calculator: "StringToSequenceExampleCalculator"
-  input_side_packet: "STRING:input_sequence_example"
-  output_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
-}
-
-node {
-  calculator: "UnpackMediaSequenceCalculator"
-  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
-  output_side_packet: "DATA_PATH:input_file"
-  output_side_packet: "RESAMPLER_OPTIONS:packet_resampler_options"
-  output_side_packet: "AUDIO_DECODER_OPTIONS:audio_decoder_options"
-  node_options: {
-    [type.googleapis.com/mediapipe.UnpackMediaSequenceCalculatorOptions]: {
-      base_packet_resampler_options {
-        frame_rate: 1.0
-        base_timestamp: 0
-      }
-      base_audio_decoder_options {
-        audio_stream { stream_index: 0 }
-      }
-    }
-  }
-}
-
-# Decode the entire video.
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_file"
-  output_stream: "VIDEO:decoded_frames"
-}
-
-# Extract the subset of frames we want to keep.
-node {
-  calculator: "PacketResamplerCalculator"
-  input_stream: "decoded_frames"
-  output_stream: "sampled_decoded_frames"
-  input_side_packet: "OPTIONS:packet_resampler_options"
-}
-
-node {
-  calculator: "ImageFrameToTensorCalculator"
-  input_stream: "sampled_decoded_frames"
-  output_stream: "tensor_frame"
-}
-
-node {
-  calculator: "TensorFlowSessionFromFrozenGraphCalculator"
-  output_side_packet: "SESSION:session"
-  node_options: {
-    [type.googleapis.com/mediapipe.TensorFlowSessionFromFrozenGraphCalculatorOptions]: {
-      graph_proto_path: "/tmp/mediapipe/classify_image_graph_def.pb"
-      tag_to_tensor_names {
-        key: "IMG_UINT8"
-        value: "DecodeJpeg:0"
-      }
-      tag_to_tensor_names {
-        key: "INCEPTION_POOL3"
-        value: "pool_3/_reshape:0"
-      }
-    }
-  }
-}
-
-node {
-  calculator: "TensorFlowInferenceCalculator"
-  input_side_packet: "SESSION:session"
-  input_stream: "IMG_UINT8:tensor_frame"
-  output_stream: "INCEPTION_POOL3:inception3_hidden_activation_single_element_batch"
-  node_options: {
-    [type.googleapis.com/mediapipe.TensorFlowInferenceCalculatorOptions]: {
-      signature_name: ""
-      batch_size: 1
-      add_batch_dim_to_tensors: false
-    }
-  }
-}
-
-# Remove the batch dimension.
-node: {
-  calculator: "TensorSqueezeDimensionsCalculator"
-  input_stream: "inception3_hidden_activation_single_element_batch"
-  output_stream: "inception3_hidden_activation"
-  node_options: {
-    [type.googleapis.com/mediapipe.TensorSqueezeDimensionsCalculatorOptions]: {
-      dim: 0
-    }
-  }
-}
-
-node {
-  calculator: "TensorToMatrixCalculator"
-  input_stream: "TENSOR:inception3_hidden_activation"
-  output_stream: "MATRIX:inception3_hidden_activation_matrix"
-}
-
-node {
-  calculator: "MatrixSubtractCalculator"
-  input_stream: "MINUEND:inception3_hidden_activation_matrix"
-  input_side_packet: "SUBTRAHEND:inception3_pca_mean_matrix"
-  output_stream: "mean_subtracted_inception3_matrix"
-}
-node {
-  calculator: "MatrixMultiplyCalculator"
-  input_stream: "mean_subtracted_inception3_matrix"
-  input_side_packet: "inception3_pca_projection_matrix"
-  output_stream: "pca_inception3_matrix"
-}
-node {
-  calculator: "MatrixToVectorCalculator"
-  input_stream: "pca_inception3_matrix"
-  output_stream: "pca_inception3_vf"
-}
-
-######################## END OF VISUAL ###########################
-
-######################## BEGIN OF AUDIO ##########################
-node {
-  calculator: "AudioDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_file"
-  input_side_packet: "OPTIONS:audio_decoder_options"
-  output_stream: "AUDIO:audio"
-  output_stream: "AUDIO_HEADER:audio_header"
-}
-
-node {
-  calculator: "AddHeaderCalculator"
-  input_stream: "DATA:audio"
-  input_stream: "HEADER:audio_header"
-  output_stream: "media_audio"
-}
-
-# Always convert the audio to mono.
-node {
-  calculator: "AverageTimeSeriesAcrossChannelsCalculator"
-  input_stream: "media_audio"
-  output_stream: "mono_waveform"
-}
-
-node {
-  calculator: "RationalFactorResampleCalculator"
-  input_stream: "mono_waveform"
-  output_stream: "resampled_waveform"
-  node_options: {
-    [type.googleapis.com/mediapipe.RationalFactorResampleCalculatorOptions] {
-      target_sample_rate: 16000.0
-    }
-  }
-}
-node {
-  calculator: "SpectrogramCalculator"
-  input_stream: "resampled_waveform"
-  output_stream: "spectrogram_squared_magnitude"
-  node_options: {
-    [type.googleapis.com/mediapipe.SpectrogramCalculatorOptions] {
-      frame_duration_seconds: 0.025
-      frame_overlap_seconds: 0.015
-      output_type: SQUARED_MAGNITUDE
-    }
-  }
-}
-node {
-  calculator: "MelSpectrumCalculator"
-  # MelSpectrumCalculator expects SQUARED_MAGNITUDE input, but its output is in
-  # linear magnitude units.
-  input_stream: "spectrogram_squared_magnitude"
-  output_stream: "mel_spectrum_magnitude"
-  node_options: {
-    [type.googleapis.com/mediapipe.MelSpectrumCalculatorOptions] {
-      # Follow the 'wideband' or '16kHz' speech convention.
-      channel_count: 64
-      min_frequency_hertz: 125.0
-      max_frequency_hertz: 7500.0
-    }
-  }
-}
-node {
-  calculator: "StabilizedLogCalculator"
-  input_stream: "mel_spectrum_magnitude"
-  output_stream: "log_mel_spectrum_magnitude"
-  node_options: {
-    [type.googleapis.com/mediapipe.StabilizedLogCalculatorOptions] {
-      stabilizer: 0.01
-    }
-  }
-}
-node {
-  calculator: "TimeSeriesFramerCalculator"
-  input_stream: "log_mel_spectrum_magnitude"
-  output_stream: "log_mel_spectrum_magnitude_with_context"
-  node_options: {
-    [type.googleapis.com/mediapipe.TimeSeriesFramerCalculatorOptions] {
-      frame_duration_seconds: 0.96
-      frame_overlap_seconds: -0.04
-    }
-  }
-}
-node {
-  calculator: "MatrixToTensorCalculator"
-  input_stream: "log_mel_spectrum_magnitude_with_context"
-  output_stream: "log_mel_spectrum_magnitude_tensor"
-  node_options: {
-    [type.googleapis.com/mediapipe.MatrixToTensorCalculatorOptions] {
-      transpose: true
-    }
-  }
-}
-
-node {
-  calculator: "TensorFlowSessionFromFrozenGraphCalculator"
-  output_side_packet: "SESSION:vggish_session"
-  node_options: {
-    [type.googleapis.com/mediapipe.TensorFlowSessionFromFrozenGraphCalculatorOptions]: {
-      graph_proto_path: "/tmp/mediapipe/vggish_new.pb"
-      tag_to_tensor_names {
-        key: "INPUT"
-        value: "vggish/input_features:0"
-      }
-      tag_to_tensor_names {
-        key: "VGGISH"
-        value: "vggish/fc2/BiasAdd:0"
-      }
-    }
-  }
-}
-
-node {
-  calculator: "TensorFlowInferenceCalculator"
-  input_side_packet: "SESSION:vggish_session"
-  input_stream: "INPUT:log_mel_spectrum_magnitude_tensor"
-  output_stream: "VGGISH:vggish_tensor"
-  node_options: {
-    [type.googleapis.com/mediapipe.TensorFlowInferenceCalculatorOptions]: {
-      signature_name: ""
-      batch_size: 128
-    }
-  }
-}
-
-node {
-  calculator: "TensorToMatrixCalculator"
-  input_stream: "REFERENCE:log_mel_spectrum_magnitude_with_context"
-  input_stream: "TENSOR:vggish_tensor"
-  output_stream: "MATRIX:vggish_matrix"
-  node_options: {
-    [type.googleapis.com/mediapipe.TensorToMatrixCalculatorOptions] {
-      time_series_header_overrides {
-        num_channels: 128
-        num_samples: 1
-      }
-    }
-  }
-}
-
-node {
-  calculator: "MatrixSubtractCalculator"
-  input_stream: "MINUEND:vggish_matrix"
-  input_side_packet: "SUBTRAHEND:vggish_pca_mean_matrix"
-  output_stream: "mean_subtracted_vggish_matrix"
-}
-node {
-  calculator: "MatrixMultiplyCalculator"
-  input_stream: "mean_subtracted_vggish_matrix"
-  input_side_packet: "vggish_pca_projection_matrix"
-  output_stream: "pca_vggish_matrix"
-}
-node {
-  calculator: "MatrixToVectorCalculator"
-  input_stream: "pca_vggish_matrix"
-  output_stream: "pca_vggish_vf"
-}
-
-# Store the features in the SequenceExample.
-node {
-  calculator: "PackMediaSequenceCalculator"
-  input_side_packet: "SEQUENCE_EXAMPLE:parsed_sequence_example"
-  output_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
-  input_stream: "FLOAT_FEATURE_RGB:pca_inception3_vf"
-  input_stream: "FLOAT_FEATURE_AUDIO:pca_vggish_vf"
-}
-
-# Serialize the SequenceExample to a string for storage.
-node {
-  calculator: "StringToSequenceExampleCalculator"
-  input_side_packet: "SEQUENCE_EXAMPLE:sequence_example_to_serialize"
-  output_side_packet: "STRING:output_sequence_example"
-}
-
diff --git a/mediapipe/graphs/youtube8m/label_map.txt b/mediapipe/graphs/youtube8m/label_map.txt
deleted file mode 100644
index a3ed470..0000000
--- a/mediapipe/graphs/youtube8m/label_map.txt
+++ /dev/null
@@ -1,3862 +0,0 @@
-Game
-Video game
-Vehicle
-Concert
-Musician
-Cartoon
-Performance art
-Car
-Dance
-Guitar
-String instrument
-Food
-Association football
-Musical ensemble
-Music video
-Animal
-Animation
-Motorsport
-Pet
-Racing
-Recipe
-Mobile phone
-Cooking
-Smartphone
-Gadget
-Trailer (promotion)
-Toy
-Minecraft
-Drum kit
-Cuisine
-Motorcycle
-Piano
-Dish (food)
-Drum
-Acoustic guitar
-Action-adventure game
-Call of Duty
-Electric guitar
-Drummer
-Cosmetics
-Keyboard instrument
-Choir
-Strategy video game
-Fishing
-Aircraft
-Train
-Airplane
-Pianist
-Sports car
-Art
-Hair
-Rail transport
-Basketball
-Cycling
-Orchestra
-Motorcycling
-Transport
-Musical keyboard
-Bicycle
-Fish
-Outdoor recreation
-Disc jockey
-Machine
-Sports game
-Radio-controlled model
-Hairstyle
-Fashion
-Dog
-Skateboarding
-Fighting game
-Basketball moves
-Wedding
-Skateboard
-IPhone
-Personal computer
-Truck
-Boat
-Railroad car
-Snare drum
-American football
-Drawing
-Pokémon
-Winter sport
-Tractor
-Naruto
-Grand Theft Auto V
-Cymbal
-Horse
-House
-Festival
-Engine
-Highlight film
-Boxing
-World of Warcraft
-Call of Duty: Black Ops II
-Four-wheel drive
-Bird
-Violin
-Skateboarding trick
-Christmas
-Weight training
-Recreational fishing
-Warcraft
-Ice skating
-Driving
-Video game console
-Microsoft Windows
-Airline
-Pokémon (video game series)
-Landing
-Combat
-League of Legends
-Vegetable
-Model aircraft
-Airliner
-Samsung Galaxy
-Sport utility vehicle
-Electronic keyboard
-Hockey
-Radio-controlled aircraft
-???
-Eye shadow
-Cooking show
-Dessert
-Battlefield (series)
-Slam dunk
-Plant
-Painting
-Drifting (motorsport)
-Rallying
-Lego
-Tablet computer
-Call of Duty: Modern Warfare 2
-Comedy (drama)
-Grand Theft Auto: San Andreas
-Off-road vehicle
-The Walt Disney Company
-Locomotive
-Takeoff
-RuneScape
-Puppy
-Amusement park
-Call of Duty: Modern Warfare 3
-Motocross
-Dragon Ball
-Airport
-Photography
-Call of Duty: Black Ops
-Shoe
-Radio-controlled car
-Sonic the Hedgehog
-Skatepark
-Bride
-First-person shooter
-Accordion
-Jet aircraft
-Mascara
-Halo (series)
-Camera
-Final Fantasy
-Skiing
-Gym
-Aviation
-Mountain bike
-Marching band
-???
-Extreme sport
-FIFA 15
-Brass instrument
-Sasuke Uchiha
-Cat
-Sedan (automobile)
-Pickup truck
-Meat
-BMW
-Parade
-Cake
-Supercar
-Aquarium
-Weather
-Weapon
-Nail (anatomy)
-Surfing
-PlayStation 3
-Room
-Call of Duty 4: Modern Warfare
-Helicopter
-Laptop
-Saxophone
-Star Wars
-Goku
-Hotel
-Xbox 360
-Arcade game
-Doll
-News presenter
-Exhaust system
-Volkswagen
-Hatchback
-Action figure
-Computer
-Carnival
-Lipstick
-Wii
-Sonic the Hedgehog (character)
-School
-Ballet
-Eye liner
-Heavy equipment
-IPad
-Running
-Baking
-Rapid transit
-Coupé
-Road bicycle
-Card game
-Nail polish
-Playing card
-Bus
-Counter-Strike (video game)
-Gardening
-Outline of meals
-Nail art
-Tank
-???
-Bollywood
-Tennis
-Ship
-BMX bike
-Drink
-Grand Theft Auto IV
-Snowboarding
-Mountain biking
-Rouge (cosmetics)
-Super Smash Bros.
-???
-Street Fighter
-Stadium
-Underwater
-Hunting
-Kickflip
-Metin2
-The Sims
-Viola
-Pony
-PlayStation 4
-Television
-???
-Beach
-Manicure
-Chocolate
-Wood
-Snow
-Sneakers
-???
-Roller coaster
-Afro-textured hair
-Timbales
-Need for Speed
-Robot
-Paper
-Gymnastics
-Farm
-Diatonic button accordion
-Fighter aircraft
-Sketch (drawing)
-Mercedes-Benz
-Chevrolet
-Batman
-Loudspeaker
-Tool
-Nike, Inc.
-Race track
-Ski
-Underwater diving
-Computer hardware
-Garden
-Paint
-Cello
-Digital camera
-Scooter (motorcycle)
-Motorboat
-Harry Potter
-???
-GoPro
-Assassin's Creed
-Fishing rod
-Battlefield 3
-IPod
-Nature
-Dota 2
-Tree
-My Little Pony
-Dress
-Xbox One
-Train station
-Firefighter
-Jeep
-Rail transport modelling
-Resort
-Flute
-Touhou Project
-Fruit
-Chicken as food
-Knife
-Dashcam
-Clash of Clans
-Kitchen
-Slide show
-The Legend of Zelda
-Fireworks
-Swimming pool
-Rugby football
-Building
-Kitten
-Television advertisement
-???
-Battlefield 4
-Horse racing
-MapleStory
-Subwoofer
-Flour
-IPod Touch
-World of Tanks
-Music festival
-Comedian
-Figurine
-Kingdom Hearts
-Manga
-Wrestling
-Trumpet
-Xbox
-Model (person)
-Jumping
-Dough
-FIFA 13
-Pro Evolution Soccer
-Resident Evil
-Eye
-Guitar Hero
-Enduro
-Home appliance
-News program
-Watch
-Audi
-Off-road racing
-Ice dancing
-Construction
-Organ (music)
-PlayStation Portable
-Figure skating
-Fiddle
-WWE 2K
-Climbing
-Spider-Man
-Braid
-Muscle
-The Elder Scrolls V: Skyrim
-Nintendo 3DS
-Fire
-Human swimming
-BMW Motorrad
-One Piece
-Wildlife
-Apartment
-Dressage
-Scuba diving
-Call of Duty: Ghosts
-Eating
-Kickboxing
-Egg as food
-Origami
-The Elder Scrolls
-Ford Mustang
-Fishing lure
-Light
-Running back
-Air force
-M.U.G.E.N
-Transformers
-Living room
-Soldier
-Bag
-Ballroom dance
-Gohan
-Kayak
-Sheet music
-Destiny (video game)
-Wall
-Church (building)
-Sewing
-Chipmunk
-Surfboard
-Concealer
-Drag racing
-Mega Man
-Walt Disney World
-Chicken
-Parachuting
-Classic car
-Furniture
-Jewellery
-Recreational vehicle
-Call of Duty: Advanced Warfare
-Street Fighter IV
-Sakura Haruno
-Restaurant
-Halo 3
-Wheelie
-Mario Kart
-Headphones
-Factory
-Yu-Gi-Oh! Trading Card Game
-Speedometer
-Circus
-Muscle car
-Bedroom
-Tekken
-Graffiti
-River
-Lighting
-Guitar amplifier
-Knitting
-Call of Duty: Zombies
-PlayStation
-Radio-controlled helicopter
-Cookware and bakeware
-Trail
-Camping
-University
-Indian cuisine
-Multiplayer online battle arena
-Ball
-Nightclub
-Book
-Lego minifigure
-PlayStation 2
-Dodge
-Garry's Mod
-Camera lens
-Hockey puck
-Barbie
-Thomas the Tank Engine
-Go-kart
-Vegetarian cuisine
-Monster High
-Yacht
-Collectible card game
-Auto Race (Japanese sport)
-Role-playing game
-Madden NFL
-Unidentified flying object
-Longboard (skateboard)
-Toddler
-Digital single-lens reflex camera
-Xbox (console)
-Rail freight transport
-Honda Civic
-Convertible
-The Sims 2
-Lamborghini
-Printer (computing)
-Cream
-Parrot
-Tire
-Quadcopter
-Littlest Pet Shop
-Wii U
-Planet
-???
-The Sims 3
-Sony Xperia
-Salad
-Sailboat
-Cruise ship
-Unmanned aerial vehicle
-Naruto: Ultimate Ninja
-Barbecue
-Mortal Kombat
-Slot machine
-Longboarding
-Halo: Reach
-Paragliding
-Bread
-Monster Hunter
-Stitch (textile arts)
-Dofus
-StarCraft II: Wings of Liberty
-Game controller
-Gears of War
-Mud bogging
-Snowboard
-Synthesia
-Wig
-Road bicycle racing
-Wheel
-Macintosh
-Home improvement
-Printing
-Insect
-Road
-Parachute
-Cattle
-Hair coloring
-IPhone 4S
-Advertising
-Potato
-Runway
-Van
-Zoo
-Handheld game console
-Water
-Rock Band
-Volkswagen Golf
-Bathroom
-Stunt performer
-Bleach (manga)
-Metal Gear
-Santa Claus
-Hiking
-Samsung Electronics
-Runway (fashion)
-Elevator
-Cricket
-Gran Turismo (series)
-Fire engine
-Kinder Surprise
-Play-Doh
-Grilling
-Eyelash
-Table tennis
-Fiat Automobiles
-Dragon
-Lion
-Nintendo Entertainment System
-PlayStation (console)
-Stallion
-Ice skate
-Baseball park
-Flamenco
-Steam engine
-Plough
-Farming Simulator
-Soup
-Snowmobile
-Mare
-Counter-Strike: Source
-Sail
-Squat (exercise)
-Bass (fish)
-Banjo
-Harmonica
-Quartet
-Drum stick
-IPhone 5
-Reptile
-Prayer
-T-shirt
-Talent show
-Rice
-Roasting
-Diablo III
-CrossFire (video game)
-Renault
-Pizza
-Trombone
-Chevrolet Camaro
-Barbell
-Ryu (Street Fighter)
-Clay
-Beyblade
-Lake
-Sauce
-???
-Cube
-Forza (series)
-Cookie
-Taiko no Tatsujin
-Mixtape
-Medicine
-Door
-Monster
-Call of Duty: World at War
-Mud
-Computer keyboard
-Clarinet
-Defense of the Ancients
-Sora (Kingdom Hearts)
-Computer monitor
-Super Street Fighter IV
-PlayStation Vita
-Guild Wars
-Album
-Model car
-Tenor saxophone
-The Twilight Saga (film series)
-Rubik's Cube
-Sailor Moon
-Teacher
-Mixing console
-Card manipulation
-Combine harvester
-Boeing 737
-Bull
-Fish as food
-Cheese
-Concrete
-Board game
-Moped
-Puzzle
-Lego Star Wars
-Poker
-Portrait
-Luigi
-Dining room
-Pokémon X and Y
-Floor
-Asus
-Inuyasha
-Livestock
-Lawn mower
-Tibia (video game)
-Tabletop game
-Iron Man
-Tomato
-Juice
-Final Fantasy VII
-Lip gloss
-Super Smash Bros. Melee
-Central processing unit
-Sitcom
-Cockpit
-Emergency vehicle
-FIFA 12
-Bodyboarding
-Earth
-The Lego Group
-Ice cream
-Microphone
-Rallycross
-Website
-Table (furniture)
-Ice
-Magic: The Gathering
-Ninja
-Darth Vader
-Saw
-Mickey Mouse
-Handbag
-The King of Fighters
-Ballet dancer
-Samsung Galaxy Note series
-Washing machine
-Zee TV
-Point Blank (2008 video game)
-Gibson Les Paul
-Dune buggy
-DayZ (video game)
-Television set
-Dirt track racing
-Edward Cullen
-Beauty salon
-Hetalia: Axis Powers
-Vampire
-Gliding
-Batman: Arkham
-Mountain
-Rain
-Shark
-Waterfall
-DarkOrbit
-Bagpipes
-Comics
-Rock climbing
-Skin
-Arena
-IPhone 4
-ARMA (series)
-Super Smash Bros. for Nintendo 3DS and Wii U
-Curry
-Pasta
-Halo 4
-Superman
-Icing (food)
-Google Nexus
-Marathon
-Deer
-Guitar Hero III: Legends of Rock
-Balloon
-Goalkeeper (association football)
-Red Bull
-Nissan GT-R
-Noodle
-Fishing bait
-Pencil
-Plants vs. Zombies
-Athlete
-Computer case
-Stretching
-Terrier
-Outer space
-Textile
-Mercedes-AMG
-Hard disk drive
-Biceps
-Handball
-Land Rover
-Kamen Rider Series
-Parakeet
-Bear
-Rim (wheel)
-Chevrolet Corvette
-Battery (electricity)
-Milk
-Roblox
-BMW M3
-Christmas decoration
-Moon
-Microsoft Lumia
-Combat Arms (video game)
-Maize
-Cargo
-Headset (audio)
-Bee
-Helmet
-Street art
-Clown
-Tattoo
-Cupcake
-Traxxas
-Money
-Hatsune Miku: Project DIVA
-Bead
-Angry Birds
-Movieclips
-Optimus Prime
-MacBook
-Mass Effect
-Bowser (character)
-Sega Genesis
-Pachinko
-Jedi
-Jeep Wrangler
-Dragon Ball Z: Budokai Tenkaichi
-Tales (series)
-Loader (equipment)
-Water park
-Beef
-Sewing machine
-Beer
-Glass
-Silage
-Seafood
-Gran Turismo 5
-Harp
-Joker (comics)
-Volkswagen Beetle
-???
-BlackBerry
-AdventureQuest Worlds
-Bowling
-Guild Wars 2
-Dragon Quest
-Washing
-Mermaid
-Cue stick
-Boot
-Stir frying
-Grand Theft Auto: Vice City
-Penguin
-Acrylic paint
-Cocktail
-Kingdom Hearts II
-Coral
-Borderlands 2
-Telephone
-Gears of War (video game)
-Far Cry
-Tractor pulling
-Rock Band (video game)
-Crane (machine)
-Updo
-Stuffed toy
-Lawn
-Tekken (video game)
-Airbus A320 family
-IPhone 5S
-Watercolor painting
-Ten-pin bowling
-Duck
-Pokémon Trading Card Game
-Oven
-Subaru Impreza
-Porsche 911
-Backpack
-Carl Johnson (Grand Theft Auto)
-German Shepherd
-Turtle
-Metal
-Left 4 Dead
-Ultralight aviation
-Comic book
-Batting (cricket)
-Tram
-Mower
-Reef aquarium
-???
-Swing (dance)
-Lego City
-Game Boy Advance
-Diesel engine
-Pitcher
-Dance studio
-Hamburger
-Cake decorating
-Left 4 Dead 2
-Bible
-Candy
-Vacuum cleaner
-Pokémon Omega Ruby and Alpha Sapphire
-Sowing
-Roof
-Donkey Kong
-Trout
-Coin
-Tent
-Digimon
-Costume
-Warface
-Sandwich
-BMW 3 Series
-Star Wars: The Old Republic
-Trampoline
-Pipe organ
-Latin dance
-Aerobics
-Aion: Upheaval
-Supermoto
-Netbook
-Gift
-Strum
-Mitsubishi Lancer Evolution
-Drum and bugle corps (modern)
-Gramophone record
-Gundam (mobile suit)
-Euro Truck Simulator 2
-Tai chi
-Teenage Mutant Ninja Turtles
-Aerobatics
-Wedding dress
-Hair conditioner
-Achievement (video gaming)
-Boeing 777
-Shadow the Hedgehog
-Boeing 747
-Simba
-Silkroad Online
-Kindergarten
-Smartwatch
-Computer mouse
-Bell
-Museum
-Rabbit
-Total War (series)
-DVD
-Devil May Cry
-Face
-Lathe
-Five Nights at Freddy's
-Logging
-String quartet
-Bridge
-Super Mario Bros.
-Fishing reel
-Badminton
-Clock
-Stove
-Wine
-Subaru
-Leather
-IPad 2
-Terraria
-Attack on Titan
-Bottle
-Kick
-Police officer
-Raw foodism
-Video card
-Alpine skiing
-String (music)
-StarCraft (video game)
-Roadster (automobile)
-Steak
-Hearthstone (video game)
-Solo dance
-Foreign exchange market
-God of War (series)
-Hulk (comics)
-Easter egg
-Ceiling
-Yo-kai Watch
-Wakeboarding
-Monster truck
-McDonald's
-Assassin's Creed III
-Chopper (motorcycle)
-Largemouth bass
-Roller skating
-Glider (aircraft)
-Jacket
-Marimba
-Christmas tree
-Sand
-Afro
-MacBook Pro
-Booster pack
-Dark Souls II
-Bartender
-Quarterback
-Illustration
-ARMA 2
-Star Trek
-Itachi Uchiha
-Hot rod
-Saints Row
-Freeza
-Need for Speed: Most Wanted (2012 video game)
-Hair twists
-Super Mario World
-Crash Bandicoot
-Pork
-Shampoo
-Mask
-Hair iron
-Marvel vs. Capcom
-Castlevania
-Halo 2
-Battery charger
-Tower defense
-BBC
-Kawasaki motorcycles
-Link (The Legend of Zelda)
-Muffler
-Nintendo 64
-Marriage proposal
-Fingerboard (skateboard)
-Beehive
-Pokémon HeartGold and SoulSilver
-Bowling ball
-Tower of Saviors
-Artificial nails
-Final Fantasy XIII
-Chair
-Hijab
-Juggling
-Nissan Skyline
-Anpanman
-Car wash
-Kite
-Diablo (video game)
-Resident Evil 4
-Candy Crush Saga
-Rocket
-Video game arcade cabinet
-Whale
-Glider (sailplane)
-Flooring
-Kingdom Hearts (video game)
-???
-Fast food
-Mandolin
-Metal detector
-Cinema 4D
-Ash Ketchum
-Router (computing)
-Yamaha YZF-R1
-Uncharted
-DC Comics
-Egg
-Lexus
-Ollie (skateboarding)
-Hamster
-Chainsaw
-Galaxy
-Embroidery
-Suite (hotel)
-Brush
-Electronic drum
-Gran Turismo 6
-NBA 2K15
-Dolphin
-Salmon
-Window
-Drill
-Pen
-Backpacking (wilderness)
-Torte
-Web page
-Dreadlocks
-Hot Wheels
-Brake
-Tuba
-Volcano
-Ibiza
-Dragon Age
-Mini
-Perfect World (video game)
-Knot
-Tails (character)
-Thunderstorm
-Video camera
-Smoothie
-Crossover (automobile)
-Condominium
-Desert
-Pump
-Strawberry
-Coffeemaker
-The Legend of Zelda: Ocarina of Time
-Tarot
-Architecture
-Portal (video game)
-Dynasty Warriors
-Lightning McQueen
-Pirates of the Caribbean (film series)
-Tile
-Battlefield: Bad Company 2
-Sketch comedy
-Aikido
-V8 engine
-Sailor Moon (character)
-Lamborghini Aventador
-Carp fishing
-Kirby (series)
-Banana
-Police car
-Laser lighting display
-Necklace
-???
-WWE '13
-Mini (marque)
-Tanki Online
-Oil
-Radio-controlled boat
-Dinosaur
-Pie
-President of the United States
-NBA 2K14
-Labrador Retriever
-Blender
-Plarail
-Captain America
-Electric locomotive
-Street racing
-Need for Speed: Most Wanted (2005 video game)
-Canoe
-Golf club
-Sheep
-Bar
-CDJ
-Lace
-Gold
-Glove
-Halo: Combat Evolved
-Alphabet
-Fender Telecaster
-IPhone 3GS
-Beadwork
-Personal water craft
-Dietary supplement
-James Bond
-Ragnarok Online
-French braid
-Road racing
-Star
-Dean Winchester
-Snake
-Seed
-Christmas lights
-Plaster
-Trunks (Dragon Ball)
-Forage harvester
-Cartoon Network
-Honda CBR series
-Battlefield Hardline
-Tekken 6
-Glitter
-Ford Focus
-Roland V-Drums
-Ski-Doo
-Tyrannosaurus
-New Super Mario Bros.
-Cue sports
-Rainbow Loom
-Samsung Galaxy S III
-Glasses
-Italian cuisine
-RollerCoaster Tycoon 3
-Pig
-Lock (security device)
-The Lord of the Rings (film series)
-Military parade
-Elephant
-Pull-up (exercise)
-Eyelash extensions
-Ring (jewellery)
-Minivan
-Coca-Cola
-Mural
-Love song
-Portal 2
-Mortal Kombat (2011 video game)
-Yarn
-Pokémon Ruby and Sapphire
-Dragon Nest
-Japanese cuisine
-Resident Evil 5
-Jeans
-Map
-Pikachu
-Sun
-Pond
-Bulldog
-Greenhouse
-Škoda Auto
-Baby transport
-Apple
-The Doctor (Doctor Who)
-Turbine
-Naruto: Ultimate Ninja Storm
-Watch Dogs
-VHS
-Ariel (Disney)
-Sculpture
-Bulldozer
-Transformice
-Sushi
-Home run
-Fountain
-Slopestyle
-Fullmetal Alchemist
-Ultimate Marvel vs. Capcom 3
-Automotive lighting
-Lightsaber
-Chevrolet Silverado
-Honey
-Wangan Midnight
-Sword
-Toilet
-Super Mario Galaxy
-Akuma (Street Fighter)
-Shiva
-Bed
-Toy train
-Manufacturing
-Ram Trucks
-Stuffing
-Biscuit
-Kia Motors
-Spa
-Samsung Galaxy S II
-Demolition
-Airbus A330
-Breakfast
-Airbus A380
-Pancake
-Kawasaki Ninja
-Mitsubishi Lancer
-Mushroom
-Grand Theft Auto: The Lost and Damned
-Microsoft Flight Simulator
-Spacecraft
-Logo
-Stock car racing
-Goat
-Pool (cue sports)
-Assassin's Creed (video game)
-Majin Boo
-Vespa
-???
-Samsung Galaxy S4
-Assassin's Creed IV: Black Flag
-Batman: Arkham City
-Monkey
-Death Note
-WWE 2K15
-Pumpkin
-Shopping mall
-Rose
-Cola
-Minnie Mouse
-Caporales
-Jet Ski
-World of Warcraft: Wrath of the Lich King
-Winter
-Prom
-Karaoke box
-Minibike
-RFactor
-Art exhibition
-Plush
-Chocolate cake
-Ford F-Series
-Soap
-Knuckles the Echidna
-Dump truck
-Giant panda
-Dance Dance Revolution
-Princess
-Street food
-Flashlight
-Animal Crossing
-Pilates
-Pipe band
-Toyota Land Cruiser
-Lara Croft
-Jumbotron
-Ferrari F430
-Cell (Dragon Ball)
-BMW 3 Series (E36)
-Injustice: Gods Among Us
-Dumbbell
-Samsung Galaxy Tab series
-Bodyweight exercise
-Penalty kick (association football)
-Lizard
-City
-Bionicle
-Kirby (character)
-WWE 2K14
-Pokémon Battle Revolution
-Sonic the Hedgehog (1991 video game)
-Alliance of Valiant Arms
-Racket (sports equipment)
-K-1
-Acer Inc.
-Recorder (musical instrument)
-Earring
-National park
-The Elder Scrolls IV: Oblivion
-Audi R8
-Clothes dryer
-Military band
-Silver
-Warcraft III: Reign of Chaos
-Classroom
-Samsung Galaxy S5
-Black cat
-Scarf
-Kratos (God of War)
-Skylanders
-Super Robot Wars
-Electric car
-Video lesson
-Smoking (cooking)
-Antenna (radio)
-Sonic Generations
-Butter
-Chess
-Hello Kitty
-Goldfish
-Carrot
-Blu-ray
-Squirrel
-Balloon (aeronautics)
-Microwave oven
-Range Rover
-Wool
-TalesRunner
-IPad Mini
-Pokémon Emerald
-Inflatable boat
-Bull riding
-Football boot
-Gears of War 2
-Bugatti Veyron
-Airbrush
-Brick
-Avengers (comics)
-Plants vs. Zombies 2: It's About Time
-United States Navy
-Ball (association football)
-Volkswagen Gol
-Yo-yo
-Forza Motorsport 4
-Logitech
-Shirt
-Golden Retriever
-Alarm device
-Water slide
-Paramotor
-Fondant icing
-Acrobatic gymnastics
-Coach (sport)
-The Witcher 3: Wild Hunt
-Tabla
-Kinect
-Zee Bangla
-???
-Cabinetry
-Quilt
-Claw crane
-Spyro (series)
-Yoshi
-Tekken Tag Tournament 2
-Diamond
-Samsung Galaxy S series
-BMW 3 Series (E46)
-Tiger
-Number
-Traffic
-Metalworking
-Haruhi Suzumiya
-Gown
-Luxury yacht
-Yuna (Final Fantasy)
-Station wagon
-Softball
-The Legend of Zelda: Twilight Princess HD
-Dungeon Fighter Online
-Plasticine
-LG Optimus series
-Source (game engine)
-Battlefield 2
-BMW 3 Series (E30)
-Ink
-Half-Life 2
-Hitman (series)
-Inline skates
-Remote control
-Mercedes-Benz C-Class
-The Sims 4
-Harlem Shake (meme)
-Magic Kingdom
-Dune
-Prince of Persia
-Final Fantasy XIV
-Marvel Universe
-Draco Malfoy
-Ram Pickup
-DC Universe Online
-Assassin's Creed II
-Mars
-Xylophone
-Dragon Age: Inquisition
-Game Boy
-Carpet
-Roxas (Kingdom Hearts)
-Balance beam
-Mass Effect 2
-Dragon Ball Xenoverse
-Call of Duty: Black Ops – Zombies
-Cadillac
-Guinea pig
-The Hobbit (film series)
-Need for Speed: World
-Pastry
-Chapel
-Rayman
-Armour
-Mouse
-Assassin's Creed: Brotherhood
-Lord Voldemort
-Magnet
-The Sims (video game)
-Rubber band
-Grocery store
-Reborn doll
-Ford GT
-WWE '12
-PlanetSide 2
-Jaguar Cars
-Volvo Cars
-Jeep Cherokee (SJ)
-Homer Simpson
-USB flash drive
-Torero
-Persona (series)
-Model railroad layout
-Buttercream
-Serve (tennis)
-Ferrari 458
-Honda Accord
-Chevrolet Impala
-Command & Conquer
-Warframe
-Chrysler (brand)
-Standup paddleboarding
-Pretty Cure
-Campsite
-Final Fantasy VIII
-Audi A4
-Sailing ship
-Rafting
-Custom car
-Belle (Disney)
-Rowing (sport)
-Jeep Grand Cherokee
-Wire
-BMW M5
-Hula hoop
-Pinball
-Spaghetti
-Monster Hunter Freedom Unite
-Far Cry 4
-Pro Evolution Soccer 2015
-Test Drive (series)
-Motorcycle helmet
-Router (woodworking)
-Cave
-Cheesecake
-Birthday cake
-Suzuki Jimny
-New Super Mario Bros. Wii
-Ezio Auditore da Firenze
-Fisherman
-Mime artist
-Roller skates
-Pump It Up (video game series)
-Dissidia Final Fantasy
-Supercharger
-Gemstone
-Titanfall
-Downhill
-Medal
-Garbage truck
-Forehand
-Heroes of Newerth
-Plastic
-???
-Astronaut
-Guitar Hero World Tour
-ArcheAge
-Lowrider
-Police dog
-Toyota Corolla
-Ford Fiesta
-Helmet camera
-Cabal Online
-Assassin's Creed Unity
-Ceramic
-Kidō Senshi Gundam: Senjō no Kizuna
-Hot air balloon
-Shower
-Donald Duck
-Multi Theft Auto
-Rock Band 3
-Porsche 911 GT3
-Stick figure
-Sled
-Lemon
-Frog
-Mexican Creole hairless pig
-Forklift
-Dog agility
-Kettlebell
-Shelby Mustang
-Candle
-Bowling (cricket)
-Kick (football)
-Electric vehicle
-Oboe
-Desktop computer
-Wing Chun
-Statue
-DayZ (mod)
-Eagle
-Fire station
-Nike Air Max
-Rage (video game)
-Woodturning
-Fireplace
-Volkswagen Jetta
-Madison Square Garden
-Fly tying
-Spore (2008 video game)
-Hammond organ
-Sam Winchester
-The Pink Panther
-Saints Row: The Third
-Cherry blossom
-Doraemon
-WWE action figures
-Marvel vs. Capcom 3: Fate of Two Worlds
-Bugatti Automobiles
-Fire Emblem
-Border Collie
-Aircraft carrier
-Snow blower
-Culinary art
-Ken Masters
-Seafight
-Sport bike
-Dentist
-Easter egg (media)
-Joystick
-Tuna
-Crysis 2
-Audi Quattro
-Academy Awards
-Ponytail
-Ramen
-Hummer
-Fishing tackle
-Final Fantasy X-2
-Coupon
-Porsche Carrera
-Wood carving
-Rocksmith
-Wallet
-Refrigerator
-Koi
-Battlefield Heroes
-Phonograph
-Onion
-Biceps curl
-Trainz
-Hat
-Jubeat
-Nissan Skyline GT-R
-Mattel
-GameCube
-LittleBigPlanet 2
-Epiphone
-Inazuma Eleven
-Soft tennis
-Killer whale
-Hair straightening
-Merienda
-The Witcher (video game)
-Skate (video game)
-Live for Speed
-Rooster
-Chihuahua (dog)
-Triangle
-Land Rover Defender
-Marvel Legends
-Trousers
-SD Gundam Capsule Fighter
-Ratchet & Clank
-Doughnut
-Hatsune Miku: Project DIVA F
-Bouzouki
-Domestic canary
-Half-Life (video game)
-Raven (comics)
-Black Butler
-Mario Kart 8
-Chili pepper
-BMW 5 Series
-Hail
-Ouran High School Host Club
-Brain
-Chinese cuisine
-Playmobil
-Model building
-Ribbon
-Pit bike
-Sonic Unleashed
-Solar panel
-Orange (fruit)
-Otis Elevator Company
-Mu Online
-Hang gliding
-Path of Exile
-Animal Crossing: New Leaf
-Steel guitar
-Sword Art Online
-Lego Ninjago
-Paddle
-Second Life
-Aikatsu!
-IPhone 5C
-Gothic (series)
-Batman: Arkham Asylum
-Carburetor
-Crab
-Espresso machine
-The Phantom of the Opera (1986 musical)
-Hellsing
-Spider
-Super Mario Galaxy 2
-Duel Masters Trading Card Game
-Drywall
-Laundry
-United States Air Force
-Assassin's Creed: Revelations
-Corel
-Omelette
-Composer
-Ford Escort (Europe)
-Grape
-Honda CB600F
-Tea
-Elmo
-Temple
-Need for Speed: Carbon
-Catamaran
-Perfect World (company)
-Skate 3
-Missile
-Infomercial
-Chevrolet Chevelle
-Airport terminal
-Crysis (video game)
-StepMania
-Red Dead Redemption
-Atari
-Couch
-The Idolmaster
-Beatmania IIDX
-Big wave surfing
-Tokyo Mew Mew
-Wheat
-Warhammer Fantasy Battle
-Rock (geology)
-Snowplow
-Submarine
-Doctor Eggman
-Wood flooring
-Bangs (hair)
-Yamaha YZF-R6
-Pontiac Firebird
-Red Dead
-Field hockey
-Vineyard
-Waterfowl hunting
-Domestic pigeon
-Toyota Hilux
-CNET
-Preacher
-Sonic Adventure
-Lamborghini Murciélago
-Marinera
-Screen printing
-Crazyracing Kartrider
-The Legend of Zelda: Majora's Mask
-Sunglasses
-Log cabin
-Fungus
-Wedding photography
-Flag
-Devil May Cry 4
-Cappuccino
-Flamenco guitar
-Projector
-Rock dove
-The Elder Scrolls Online
-LittleBigPlanet (2008 video game)
-Digital video recorder
-Djembe
-Vending machine
-Mehndi
-Telescope
-Flyff
-Pattern (sewing)
-Stairs
-Nissan 350Z
-Cell (biology)
-Need for Speed: Underground 2
-Incandescent light bulb
-Gallon
-Greeting card
-Balloon modelling
-Sensor
-Realm of the Mad God
-Nest
-Writing
-Logic Pro
-Opel Astra
-Campervan
-Cooked rice
-Muffin
-Wind power
-Hedgehog
-Soft drink
-Calculator
-Harness racing
-Buick
-Beast (Disney)
-Destroyer
-Point guard
-Forza Horizon
-Mercedes-Benz SLS AMG
-Supermarket
-Catfish
-Final Fantasy XI
-The Last of Us
-Battleship
-Dodge Challenger
-Peter Pan
-Metal Gear Solid 4: Guns of the Patriots
-Toyota 86
-Bakery
-Compact disc
-Backhoe
-Saddle
-Total Drama Island
-Erhu
-Bumblebee (Transformers)
-Cajón
-Beatmania
-Ice rink
-Child safety seat
-Honda S2000
-Samsung Galaxy Note II
-Higurashi When They Cry
-Union Pacific Railroad
-BMW 3 Series (E90)
-V6 engine
-BlazBlue
-Rottweiler
-Necktie
-Image scanner
-White-tailed deer
-TV4 (Sweden)
-Bishop
-Need for Speed: Hot Pursuit (2010 video game)
-Princess Peach
-Rust (video game)
-Doom (1993 video game)
-Fender Custom Shop
-Smite (video game)
-Nissan Silvia
-???
-Pudding
-Sephiroth (Final Fantasy)
-Irish dance
-MacBook Air
-Commodore 64
-IMac
-Space Shuttle
-Automobile repair shop
-Collie
-Dragon Age: Origins
-Sangokushi Taisen
-Calligraphy
-Black belt (martial arts)
-???
-Valve
-Crisis Core: Final Fantasy VII
-Two-stroke engine
-Killzone (series)
-Full moon
-Hunter × Hunter
-New York City Subway
-Latte
-Mercedes-Benz S-Class
-Tetris
-Samurai
-Predator (alien)
-Arabian horse
-Mercedes-Benz E-Class
-Spinach
-Dōjinshi
-Polar bear
-Body piercing
-Amazon Kindle
-Biology
-Key (lock)
-Mobile Suit Gundam: Extreme Vs.
-Rappelz
-Bobber (motorcycle)
-Toy balloon
-Mexican cuisine
-Rope
-Taco
-Taxicab
-Infestation: Survivor Stories
-Clutch
-PlayStation Network
-Garage (residential)
-Milkshake
-Cloud Strife
-Honda Integra
-Eintopf
-Primary school
-Kingdom Hearts Birth by Sleep
-Resident Evil (1996 video game)
-Foal
-GameSpot
-Castle
-Human hair color
-Scorpion (Mortal Kombat)
-Poultry
-Poodle
-Vans
-Forza Horizon 2
-Zero (Mega Man)
-Toyota Camry
-Chemical reaction
-Test Drive Unlimited 2
-Bacon
-Mario Party
-18 Wheels of Steel
-Goose
-Sausage
-Compost
-Cucumber
-French horn
-Analog synthesizer
-Siamese fighting fish
-???
-Las Vegas Strip
-Crysis 3
-School bus
-Oculus Rift
-Carnival Cruise Line
-Honda CBR600RR
-Pokémon Red and Blue
-Autobot
-Christ (title)
-Cockatiel
-Ace Combat
-Mazda MX-5
-Countertop
-Safari
-Final Fantasy XIV: A Realm Reborn
-Track (rail transport)
-Ganon
-Two-wheel tractor
-???
-Watermelon
-Paper plane
-Rainbow trout
-???
-Tony Hawk's (series)
-Korean cuisine
-Lip balm
-Angry Birds (video game)
-Lead guitar
-Pug
-Monster Hunter Tri
-Playground
-God of War III
-Herd
-Niko Bellic
-Bungee jumping
-Soil
-Subway Surfers
-Hindu temple
-Audi A6
-Hogwarts
-Eggplant
-Mabinogi (video game)
-Sugar
-Makeup brush
-Rocksmith 2014
-Ocean
-Asphalt (series)
-Dental braces
-Bob cut
-Nissan 240SX
-Cement
-Sharpening
-Leopard
-United States Army
-Tom and Jerry
-Xbox 360 controller
-Dragon Ball: Raging Blast 2
-Winnie the Pooh (franchise)
-Trophy
-Inazuma Eleven (manga)
-Owl
-Street Fighter II: The World Warrior
-Golf ball
-Floyd Mayweather Jr. vs. Manny Pacquiao
-Belt (clothing)
-Slender: The Eight Pages
-Test Drive Unlimited
-Super Mario Bros. 3
-Power supply
-Retail
-Venom (comics)
-IPad (3rd generation)
-Teddy bear
-Denim
-Baseball bat
-Halo 3: ODST
-Train Simulator (Dovetail Games)
-Bowhunting
-Lotus Cars
-Pineapple
-Boeing 737 Next Generation
-Audi A3
-Dreamcast
-City-building game
-Diablo II
-Suzuki Hayabusa
-Gamepad
-Electrical wiring
-Kitchen stove
-Yamaha Aerox
-Monster Hunter Portable 3rd
-BMX racing
-Katara (Avatar: The Last Airbender)
-HP Pavilion (computer)
-Emirates (airline)
-Amiga
-Touchscreen
-Winter storm
-Driver (video game series)
-Pac-Man
-Fantage
-Land Rover Discovery
-Flash (photography)
-Human back
-Intermodal container
-Infiniti
-Guilty Gear
-Animal shelter
-Butterfly
-Piccolo (Dragon Ball)
-Bicycle frame
-Boeing 787 Dreamliner
-Toontown Online
-Renault Mégane
-Age of Empires
-Canyon
-Ski jumping
-Lumber
-Carousel
-Phantasy Star Online 2
-Dodge Viper
-Madden NFL 13
-A-18 Hornet
-String trimmer
-Mattress
-Mixer (cooking)
-Sub-Zero (Mortal Kombat)
-Ford Ranger (North America)
-ESPN
-ABS-CBN News and Current Affairs
-Synchronised swimming
-G-Shock
-???
-Angel
-Champion
-Horse show
-???
-Rurouni Kenshin
-Halo 5: Guardians
-Coconut
-Deep frying
-Dollhouse
-Campus
-Volkswagen Golf Mk6
-Curtain
-Mountain pass
-Dojo
-Boiler
-PRS Guitars
-Diesel locomotive
-Monster Hunter 4
-French Bulldog
-Prince (Prince of Persia)
-Fixed-gear bicycle
-Ninja Gaiden
-Samsung Galaxy Note 3
-Opel Corsa
-Jack Sparrow
-Boeing 767
-Lexus IS
-Tales of Symphonia
-Autumn
-Inline skating
-Filter (aquarium)
-Naruto Shippuden: Ultimate Ninja Storm Generations
-Garmon
-Flower bouquet
-SimCity
-Gravy
-Bully (video game)
-French fries
-Kawasaki Ninja 250R
-Rock fishing
-Batman: Arkham Origins
-Ceiling fan
-Audi TT
-Space Marines (Warhammer 40,000)
-Acer Aspire
-D.Gray-man
-Duct tape
-Electromagnetic coil
-Heroes of the Storm
-Tom Clancy's Ghost Recon
-Sponge cake
-Steelpan
-Modem
-The King of Fighters 2002
-Dying Light
-Need for Speed: Shift
-Riot Games
-Rainbow
-Bean
-Chevrolet Opala
-Reborn!
-Floral design
-Megatron
-Kawasaki Ninja ZX-6R
-Agriculture
-Cottage
-Television presenter
-Metal Gear Solid V: The Phantom Pain
-Juicing
-BioShock
-Plymouth (automobile)
-Crêpe
-Fist of the North Star
-The Legend of Zelda: The Wind Waker
-X-Men
-Piston
-Deck (building)
-Nativity scene
-Sega Saturn
-Stardoll
-Just Dance (video game)
-Chun-Li
-BMW R1200GS
-LG G3
-Fisheye lens
-Dragon Ball: Raging Blast
-Big Boss (Metal Gear)
-Dam
-Gel
-JBL
-Dachshund
-Bane (comics)
-E-reader
-The Lord of the Rings Online
-Ferb Fletcher
-Yeast
-Monastery
-Vampire Knight
-Vodka
-IPhone 3G
-Tricycle
-Metal Slug (series)
-Steel
-LED lamp
-Geometry Dash
-Dominoes
-Gibson Les Paul Custom
-Street Fighter III: 3rd Strike
-Hay
-Honda CR-X
-Spray painting
-Flip Video
-Bald eagle
-God of War II
-Clay animation
-Tomato sauce
-Clone trooper
-Beagle
-Popcorn
-Rubber stamp
-Clannad (visual novel)
-Fried rice
-Moto G (1st generation)
-Toyota Prius
-Mega Man Battle Network
-Doom II: Hell on Earth
-Grand Theft Auto: Vice City Stories
-Deadpool
-Phantasy Star
-Lock picking
-Sugar paste
-Chevrolet Caprice
-???
-Herb
-The Legend of Zelda: Skyward Sword
-Domesticated turkey
-Final Fantasy VI
-BMW S1000RR
-Mitsubishi Pajero
-Mazda3
-IKEA
-Chevrolet S-10
-Paper Mario
-India TV
-Tow truck
-Orochimaru (Naruto)
-Ape
-Line (geometry)
-Kawasaki Ninja ZX-10R
-Aerosol spray
-Power supply unit (computer)
-Zucchini
-Doberman Pinscher
-Wolfenstein (series)
-Contortion
-Fertilizer
-Cooler Master
-Highway
-Chocolate brownie
-Street Fighter III
-Tsubasa: Reservoir Chronicle
-Parking
-Olaf (Disney)
-Frets on Fire
-Multi-function printer
-Suzuki GSX-R1000
-Lush (company)
-Hang (instrument)
-Nexus 7 (2012)
-Skyscraper
-Gorilla
-Ōendan
-Puff pastry
-Crossbow
-Forza Motorsport 5
-Uncharted 2: Among Thieves
-Pokémon Mystery Dungeon
-Closet
-???
-Daytona International Speedway
-VTEC
-Cheerleading
-Slot car
-Garden railway
-Albert Wesker
-Naruto Shippuden: Ultimate Ninja Storm 2
-Sewing needle
-Trials (series)
-Sheriff Woody
-K
-Straw
-Mitsubishi Eclipse
-Frisbee
-TrackMania
-Manure
-Chocolate chip
-Cart
-Borderlands: The Pre-Sequel
-Diving
-Wood-burning stove
-Medal game
-Chrono Trigger
-Sherlock Holmes
-Library
-Volkswagen Golf Mk2
-Guzheng
-Malinois dog
-Goofy
-Pedal steel guitar
-Virtua Fighter 5
-Lego Marvel Super Heroes
-Kantai Collection
-Electric violin
-Firewood
-Devil May Cry 3: Dante's Awakening
-Digital painting
-Flair bartending
-Boxer (dog)
-Melon
-Low-carbohydrate diet
-Škoda Octavia
-The Crew (video game)
-Unicycle
-GAZ
-Gummy bear
-Marker pen
-Need for Speed: The Run
-Dead Space (2008 video game)
-Duke Nukem
-Dirt 3
-Movie theater
-Final Fantasy XIII-2
-Comet
-WWE SmackDown vs. Raw 2010
-Gran Turismo 4
-Star Wars: Battlefront II
-Lamb and mutton
-Ant
-Loki (comics)
-Percy the Small Engine
-Villain
-Plumbing
-Avocado
-BioShock Infinite
-Dormitory
-Mango
-Lucky Star (manga)
-Shadow the Hedgehog (video game)
-Cabbage
-Peanut butter
-Didgeridoo
-Hard Rock Cafe
-Donkey Kong Country
-Amazon.com
-Star Wars Battlefront (2015 video game)
-Harpsichord
-Aston Martin Vantage (2005)
-Suzuki Swift
-Crocodile
-Jet engine
-Sonic the Hedgehog 2
-Delta Air Lines
-Harry Potter and the Deathly Hallows
-Trunk (car)
-Zangief
-Brave Frontier
-Chuck E. Cheese's
-Iori Yagami
-Robotics
-Kebab
-Cheeseburger
-Hatsune Miku: Project DIVA F 2nd
-Humbucker
-Camcorder
-Mega Man X (video game)
-Landscape
-Shih Tzu
-Volkswagen Golf Mk4
-Pollution
-Guppy
-Coffeehouse
-Killer Instinct
-Crusher
-Allods Online
-???
-Boeing 757
-Eclipse
-Meatball
-Saints Row 2
-Roulette
-Grand Theft Auto: Liberty City Stories
-Walleye
-Walmart
-Bearing (mechanical)
-Forest
-Forever 21
-Canvas
-Rat rod
-Soulcalibur V
-Sonic the Hedgehog (2006 video game)
-Multirotor
-???
-LG G2
-Moisturizer
-Halo: The Master Chief Collection
-SEAT León
-Skylanders: Swap Force
-Pan flute
-Chevrolet Tahoe
-Metal Gear Online
-Fiat 126
-Mount & Blade: Warband
-Kennel
-Vibraphone
-Satellite
-Yamaha Raptor 700R
-Sonic & Knuckles
-Honda Fit
-Caridea
-Armored Core
-Bull Terrier
-Firefighting
-Catwoman
-Octopus
-Fencing
-Sitar
-Limousine
-Nintendo DSi
-HTC One (M8)
-McDonnell Douglas F-15 Eagle
-Rat
-GoldenEye 007 (1997 video game)
-Gasoline
-Ken (doll)
-Quadracycle
-Dead or Alive (series)
-Microsoft Surface
-Scooby-Doo
-Landscape painting
-Toyota Land Cruiser Prado
-Hair removal
-Sink
-Mount & Blade
-BMW 5 Series (E39)
-Mewtwo
-Mambo (music)
-The Witcher 2: Assassins of Kings
-North American P-51 Mustang
-Alien (creature in Alien franchise)
-Cloud
-Forge
-Christian Church
-Tom Clancy's Rainbow Six
-Mirror
-Chevrolet Big-Block engine
-Chevrolet Corvette (C6)
-Abarth
-Mazda RX-8
-Pendant
-Metal Gear Solid 3: Snake Eater
-Buffet
-Haunted house
-Cockatoo
-Royal Air Force
-The Embodiment of Scarlet Devil
-LG G series
-Fishing vessel
-DualShock
-Sonic Heroes
-Drawer (furniture)
-BMW 1 Series
-Werewolf
-DatPiff
-Koi pond
-Toyota Celica
-Twelve-string guitar
-Potato chip
-Stargate
-Killer Instinct (2013 video game)
-Caramel
-Sprite (computer graphics)
-NHL 14
-Ham
-Sky
-Sweater
-Chocolate chip cookie
-stay night
-Text (literary theory)
-Skate 2
-Engraving
-Final Fantasy XV
-Cornrows
-Light Yagami
-Floristry
-Sly Cooper
-Volkswagen Golf Mk5
-Snowman
-???
-Vox (musical equipment)
-Happy Farm
-Orc
-Suit (clothing)
-PC game
-Ace Online
-Saints Row IV
-Slingshot
-Dead Island
-Ratchet (Ratchet & Clank)
-Gears of War: Judgment
-Dragon Quest X
-Furby
-Crayon Shin-chan
-Soprano saxophone
-Tifa Lockhart
-European perch
-Patio
-Fried chicken
-Sawmill
-Mirror's Edge
-Canon PowerShot
-Guitar Hero: Warriors of Rock
-Rome: Total War
-Hummer H2
-Radar
-Final Fantasy IV
-Table saw
-Barista
-BMW 7 Series
-Camel
-Windows Media Video
-Felt
-Audi S4
-Cowboy
-Molding (process)
-Contact lens
-Fiat Punto
-The Hobbit
-Indoor cycling
-Sunset
-???
-Persian cat
-Hitman: Absolution
-Battlefield: Bad Company
-Eren Yeager
-Sinterklaas
-Crash Bandicoot (video game)
-Midnight Club: Los Angeles
-Metal Gear Rising: Revengeance
-Hand-to-hand combat
-Avon Products
-Log splitter
-Stormtrooper (Star Wars)
-Epic Rap Battles of History
-Shed
-Walking
-Belt (mechanical)
-Hot dog
-Sock
-Chicken coop
-Humpback whale
-Character (arts)
-Peugeot 106
-Toast
-Princess Jasmine
-Exercise ball
-Fox
-Green Lantern
-Looney Tunes
-Wedding ring
-Tap (valve)
-Charizard
-Mii
-Rolls-Royce Limited
-Copic
-Mega Man Zero (video game)
-Jak and Daxter
-Priston Tale
-Glacier
-IPod Nano
-Banknote
-Mario & Sonic at the Olympic Games
-Hero Factory
-Bamboo
-Fillet (cut)
-Stencil
-Winch
-Dogfight
-Treadmill
-Bassoon
-Staffordshire Bull Terrier
-Cardboard
-Epiphone Les Paul
-Compact Cassette
-Gelatin
-White House
-Suitcase
-MX vs. ATV
-Clank (Ratchet & Clank)
-Beach volleyball
-Loadout
-Batter (cooking)
-Zack Fair
-Cliff
-Baggage
-Cream cheese
-Lantern
-Naruto: Clash of Ninja
-Treasure
-Raccoon
-Mini 4WD
-Robotic vacuum cleaner
-Gate
-Ribs (food)
-Oatmeal
-Water filter
-Super Mario Sunshine
-Animal Crossing: City Folk
-Driver's license
-Asus ZenFone
-American black bear
-Little Red Riding Hood
-???
-Stable
-Gashapon
-Need for Speed: Underground
-Dishwasher
-Frying pan
-Schutzhund
-Mario Kart 7
-Disney Infinity
-Saab Automobile
-F-Zero
-Halloween costume
-Thor (Marvel Comics)
-Foam
-Tokyo Ghoul
-Chevrolet Monte Carlo
-Flush toilet
-Axe
-Worms (series)
-Marble
-Driver's education
-Madden NFL 12
-Pressure washing
-Christmas ornament
-Buffalo wing
-Duct (flow)
-Indiana Jones
-Chart
-Yoshi's Island
-Subaru Forester
-Scar (The Lion King)
-Mousse
-Lalaloopsy
-Micropterus
-Gibson SG
-Express train
-Citroën C4
-Submission wrestling
-Broccoli
-Donkey Kong Country 2: Diddy's Kong Quest
-Barrel organ
-Mega Man 2
-Dragon boat
-New Super Mario Bros. U
-Gecko
-Pillow
-Kemenche
-Porsche Cayenne
-???
-Shift 2: Unleashed
-Bomberman
-Dungeons & Dragons
-BeamNG.drive
-AdventureQuest
-Mario Kart 64
-Disc brake
-Bloons Tower Defense
-Forza Motorsport 3
-Guitar Center
-Super Smash Bros. (video game)
-Fiat Uno
-Printed circuit board
-Porcelain
-E-book
-Macaroni
-Lego Friends
-Max Payne 3
-StarCraft II: Heart of the Swarm
-Medal of Honor: Warfighter
-Kamaz
-Air France
-Porsche Carrera GT
-Black Rock Shooter
-Rosary
-Halo Wars
-Car dealership
-Toys "R" Us
-Total War: Rome II
-Need for Speed: ProStreet
-Mansion
-Cheetah
-Marshmallow
-Shorts
-Unturned
-Charango
-Lithium polymer battery
-Sea turtle
-Vatican City
-Starbucks
-Emergency vehicle lighting
-Volkswagen Golf Mk1
-Lupin the Third
-Pearl
-Wii Sports
-Hero
-Chrysler 300
-GMC (automobile)
-Charm bracelet
-Kamen Rider Battle: Ganbaride
-Ys (series)
-Asus Eee Pad Transformer
-BMW 5 Series (E60)
-Ford Mustang SVT Cobra
-Autocross
-Royal icing
-Laboratory
-Peugeot 206
-Maltese (dog)
-Soulcalibur IV
-Wardrobe
-Garlic
-Tugboat
-Luke Skywalker
-Electronic circuit
-Coat (clothing)
-Passenger
-???
-Cactus
-Ford Crown Victoria
-Elfen Lied
-Circular saw
-Radha
-Welsh Corgi
-Eiffel Tower
-Softail
-Bajo sexto
-Lobster
-Colt (horse)
-Solar eclipse
-Greyhound
-Pepsi
-Black Widow (Natasha Romanova)
-Virtua Fighter
-Filly
-Canning
-Fat
-Goth subculture
-Slow cooker
-Lightning (Final Fantasy)
-Water polo
-Apple pie
-Inkjet printing
-Mercedes-Benz SLK-Class
-Bandsaw
-Cammy
-Fight Night (EA video game series)
-Tortoise
-Multicooker
-Ferret
-Dipping sauce
-Circle
-Rocket launch
-Pembroke Welsh Corgi
-Cold porcelain
-Battlefield Play4Free
-ThinkPad
-BMW X6
-???
-Sony Xperia Z
-Selfie
-Mahjong
-Cherry
-IPod Touch (5th generation)
-Colin McRae: Dirt 2
-Tekken 5
-Shawl
-Ultron
-Guitar pick
-Elk
-Sunrise
-Amusement arcade
-Hammock
-Decoupage
-Mug
-Sander
-Autogyro
-Woodchipper
-Texas Instruments
-Baby Alive
-Tarantula
-Shrub
-Donkey Kong (video game)
-Coating
-Steirische Harmonika
-Racing wheel
-Raphael (Teenage Mutant Ninja Turtles)
-Bank
-Opel Vectra
-Skull
-Sand art and play
-Birth
-Lasagne
-Infinity Ward
-Philippine cuisine
-Custard
-Lettuce
-Megami Tensei
-Flappy Bird
-Sleeping Dogs (video game)
-Fender Jazz Bass
-Devil Kings
-Blouse
-Notebook
-Aloe vera
-Funko
-Lelouch Lamperouge
-Macramé
-Casserole
-Capacitor
-I Wanna Be the Guy
-Hose
-Subaru Legacy
-Star Citizen
-Sabian
-Ventriloquism
-Call of Duty (video game)
-Kindle Fire
-Starfire (Koriand'r)
-Zeus
-Microscope
-Basket
-Coyote
-Bart Simpson
-Volvo FH
-Spinnerbait
-Honda CR-V
-Sony Xperia Z1
-Satan
-Mercedes-Benz Sprinter
-Team roping
-Jeep Cherokee (XJ)
-Friendship bracelet
-Leonardo (Teenage Mutant Ninja Turtles)
-Single track (mountain biking)
-Chickpea
-Vegetable carving
-???
-Spark plug
-Akita (dog)
-Canoeing
-Recumbent bicycle
-Boom Beach
-Puppetry
-Sport stacking
-Kendama
-Punching bag
-Staples Center
-Marvel vs. Capcom 2: New Age of Heroes
-Apple TV
-Davul
-Scratchcard
-Disgaea
-Larva
-Used car
-DmC: Devil May Cry
-Kyo Kusanagi
-Mega Man (video game)
-K'Nex
-Burger King
-Dungeon crawl
-Pro Evolution Soccer 2009
-Blueberry
-Village
-Convenience store
-Golf cart
-BMW M6
-Fiber
-Resistance (series)
-Picture frame
-Trouble in Terrorist Town
-Volkswagen Type 2
-Domestic pig
-Grand Tourer Injection
-Alucard (Hellsing)
-Aerith Gainsborough
-Batmobile
-Gummi candy
-Cauliflower
-Marlin
-Gold medal
-Shin Megami Tensei: Persona 3
-Table football
-Shikamaru Nara
-Truggy
-Ford Explorer
-Chevrolet Cruze
-American Airlines
-Jupiter
-Galaxy Nexus
-KFC
-Spec Ops: The Line
-Rigs of Rods
-EA Sports UFC
-Plastic bottle
-Hubble Space Telescope
-Barn
-Hand
-Star Wars: Battlefront (2004 video game)
-Digimon Masters
-Gibson ES-335
-Waffle
-Paper model
-Ressha Sentai ToQger
-Gas tungsten arc welding
-Pavement (architecture)
-Sonic & Sega All-Stars Racing
-???
-Palace
-Stealth game
-God of War (2005 video game)
-Mazda6
-Dragon Age II
-Warhammer Online: Age of Reckoning
-Switch
-Grizzly bear
-???
-H.A.V.E. Online
-Lowlands (festival)
-Wok
-Window blind
-Nokia N8
-Android Wear
-V10 engine
-Toyota Tundra
-Marble (toy)
-Alligator
-Screencast
-Range Rover Sport
-Moose
-Polo
-Laminate flooring
-BVE Trainsim
-Baby sling
-Garage door
-Compact car
-Dishonored
-Parrot AR.Drone
-Giraffe
-Need for Speed Rivals
-McLaren 12C
-Pork ribs
-Track cycling
-Don't Starve
-Marvel: Avengers Alliance
-Popeye
-Ford Mondeo
-HTC One (M7)
-Pyramid
-Asphalt
-Beetle
-Canon EOS 600D
-Oldsmobile Cutlass
-Suzuki GSX-R750
-Audi A8
-World of Warcraft: The Burning Crusade
-Homing pigeon
-NHL 15
-Touring motorcycle
-Goblin
-Nissan 370Z
-Metro: Last Light
-Skylanders: Giants
-Ran Online
-Gear
-Mercedes-Benz G-Class
-Travian
-Burnout Paradise
-Tag team
-Electric motorcycles and scooters
-Kazuya Mishima
-Serious Sam
-Nexus 7 (2013)
-Super Paper Mario
-Doodle
-Gelatin dessert
-Andalusian horse
-Warrior
-Ferrari 360
-DVD player
-WildStar (video game)
-Hyundai Genesis
-Chutney
-Pizzica
-Dead Rising 2
-Potter's wheel
-Yoda
-Cylinder (engine)
-M. Bison
-Metal Gear Solid: Peace Walker
-Masonry
-Edward Elric
-Split (gymnastics)
-Mario Kart DS
-Ghost Rider
-Grand Theft Auto: Episodes from Liberty City
-F1 2012 (video game)
-Cookie Monster
-Red hair
-Nami (One Piece)
-Canon EF lens mount
-Finger
-Asteroid
-Nissan Navara
-Riddler
-Traffic light
-Nikon Coolpix series
-Dragonica
-Broth
-Metal Gear Solid 2: Sons of Liberty
-Samsung Galaxy Y
-Wedding cake
-Half-pipe
-Gothic II
-Vehicle horn
-Motor oil
-Credit card
-Resident Evil 2
-British Airways
-Great Dane
-Stain
-Super Mario 3D World
-Yamaha YZ125
-Atari 2600
-Rover (space exploration)
-Cayman
-Ragdoll
-Basement
-Betta
-Mobile home
-Heroes of Might and Magic
-Photograph
-Wreath
-Universe of The Legend of Zelda
-Lamborghini Diablo
-Albus Dumbledore
-BlackBerry Bold
-Prototype 2
-Soybean
-Hurdling
-Spock
-Sony Xperia Z2
-Monopoly (game)
-Fruit preserves
-SimCity (2013 video game)
-Cutlet
-Volkswagen Touareg
-Aerosol paint
-Risotto
-Toyota 4Runner
-Driveclub
-Moshing
-Total War: Shogun 2
-Elf
-Hot tub
-President
-NHL 13
-Rudolph the Red-Nosed Reindeer
-Bugs Bunny
-Mario & Luigi: Superstar Saga
-Tulip
-Paper Mario: The Thousand-Year Door
-Hammer
-EarthBound
-Meta Knight
-La Tale
-Shadow of the Colossus
-GLaDOS
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-Orbit
-God of War: Ascension
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-???
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-Tales of Vesperia
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-???
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-Ale
-Meringue
-Canon EOS 7D
-Shaolin Kung Fu
-Hawk
-Donkey Kong Country Returns
-The Salvation Army
-Brown trout
-Sugarcane
-Cake pop
-Suzuki Bandit series
-Green tea
-Warehouse
-Appalachian dulcimer
-Kermit the Frog
-Unicorn
-Fountain pen
-Acer Iconia
-Master System
-Robocraft
-Merlin
-Sweet potato
-Alice's Adventures in Wonderland
-Solar flare
-DigiTech
-Saturn
-Flash (comics)
-Reindeer
-Justice League
-Line Rider
-Runes of Magic
-Chevrolet Suburban
-Michael Myers (Halloween)
-Need for Speed: Undercover
-Wand
-Chevrolet Malibu
-Coal
-Antena 3 (Spain)
-Driver: San Francisco
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-Stingray
-Thermostat
-Toph Beifong
-Vert ramp
-Ridge Racer
-Goat Simulator
-Lineage (video game)
-CNBC
-Juri (Street Fighter)
-TARDIS
-Pigeon racing
-Lap steel guitar
-Shovel
-Mosaic
-Monster Retsuden Oreca Battle
-Pair skating
-Wallpaper
-The Simpsons: Tapped Out
-The Elder Scrolls III: Morrowind
-Padel (sport)
-Fender (vehicle)
-Furnace
-Nissan Altima
-Cornet
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-Lockheed Martin F-35 Lightning II
-Electribe
-Alesis
-Motorola Razr
-Halo: Combat Evolved Anniversary
-Darksiders
-Neo Geo (system)
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-Milking
-Pluto (Disney)
-Peanut
-Verona Arena
-Chubby Bunny
-Jerry Mouse
-Corvette Stingray (concept car)
-Cigarette
-Cube World
-???
-Cybertron
-Dacia Duster
-Pastel
-Transformer
-Split screen (computer graphics)
-Sukhoi Su-27
-Gabrielle (Xena: Warrior Princess)
-Opel Kadett
-Nokia Lumia 920
-Twin-turbo
-Jiraiya (Naruto)
-The Legend of Zelda: A Link to the Past
-Crappie
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-???
-Super Mario 3D Land
-???
-DragonFable
-Aragorn
-Crash Bandicoot 2: Cortex Strikes Back
-Southwest Airlines
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-Passport
-Porsche Panamera
-Airship
-Tuxedo Mask
-Tom Clancy's Ghost Recon: Future Soldier
-Melty Blood
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-Audi Q7
-Bell pepper
-Chewing gum
-Drinking water
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-Kenshiro
-Patrick Drake and Robin Scorpio
-Miniature wargaming
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-Captain Falcon
-J-Stars Victory VS
-Imperishable Night
-Citrus
-Drift trike
-Optical illusion
-Command & Conquer: Red Alert 3
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-Mayonnaise
-Quake III Arena
-Keychain
-God Mode
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-Crocodilia
-Black and white
-Llanero
-Monorail
-Nova
-G.I. Joe
-S.T.A.L.K.E.R.: Call of Pripyat
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-Wine tasting
-Olive
-Ultra Series
-Beat 'em up
-Jellyfish
-Lego Legends of Chima
-Sauna
-Tom Clancy's Splinter Cell: Blacklist
-Starscream
-Aang
-Misty (Pokémon)
-IPad Air
-Ice pop
-Lute
-Jigsaw puzzle
-Baritone saxophone
-BMW Z4
-Mana (series)
-Motorized bicycle
-Dalmatian (dog)
-Bose Corporation
-Burton Snowboards
-Kingdom Hearts: Chain of Memories
-Mass Rapid Transit (Singapore)
-Boombox
-Napkin
-Chimpanzee
-Guitar Hero: Metallica
-Radar detector
-Honda NSX
-Empire: Total War
-Darts
-Light fixture
-Super Mario Bros. 2
-Temple Run
-Kristoff (Disney)
-Adrenalyn XL
-Tatra (company)
-Mini-Z
-Tin can
-Market garden
-Mercedes-Benz Actros
-Hug
-Whipped cream
-Wasp
-Oni
-Princess Daisy
-Constellation
-HTC One X
-Fender Precision Bass
-Prawn
-Christmas card
-Handbell
-Coconut milk
-Toshiba Satellite
-Riven
-Referee
-Dragon's Dogma
-Dalek
-Folding bicycle
-2 Days
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-Resident Evil: Revelations
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-Clothes iron
-Order & Chaos Online
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-Crème caramel
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-Tom Clancy's Splinter Cell: Conviction
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-Stitch (Lilo & Stitch)
-Calf
-Cars 2 (video game)
-Crayfish
-Engagement ring
-Infamous Second Son
-Jukebox
-Biryani
-DJ Hero
-Super GT
-Chameleon
-Oyster
-Warcraft III: The Frozen Throne
-Dynasty Warriors 7
-Postage stamp
-Derek Shepherd
-Plotter
-Amnesia: The Dark Descent
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-Rayman Legends
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-Paiste
-Mercedes-Benz CLS-Class
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-GameTrailers
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-SaGa (series)
-Nollie
-Company of Heroes
-Green Arrow
-Naruto Uzumaki
-DeWalt
-Putter
-Family
-Transistor
-SOCOM (series)
-Pea
-Social media
-Aliens vs. Predator (2010 video game)
-HTC HD2
-Ducati Monster
-Aggressive inline skating
-Maserati GranTurismo
-PortAventura World
-Lego Batman: The Videogame
-Energy drink
-Turban
-Pokémon Yellow
-Alaskan Malamute
-Monica's Gang
-Suzuki Vitara
-Black Desert Online
-Zara (retailer)
-Just Dance 2015
-Maid Sama!
-Disguise
-Kidney
-Water well
-Farmer
-Toyota RAV4
-Night
-DJMax
-Richter-tuned harmonica
-Real Racing 3
-Solid Snake
-United States dollar
-F1 2010 (video game)
-Samsung Galaxy Ace
-Trials Evolution
-Cadillac CTS
-Daihatsu
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-Xperia Play
-Rookie
-Timing belt (camshaft)
-Monster Energy
-Ork (Warhammer 40,000)
-Toyota JZ engine
-Drive-through
-Spektrum RC
-Hyundai Sonata
-Chinchilla
-Wii Sports Resort
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-Whitewater slalom
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-Bayonetta
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-PlayStation All-Stars Battle Royale
-Lego Minecraft
-???
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-SEAT Ibiza
-Converse (shoe company)
-Atlantic bluefin tuna
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-???
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-Kawasaki Z1000
-Shrimp and prawn as food
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-Carpet cleaning
-Lost Planet
-Sonic the Hedgehog CD
-Final Fantasy V
-F1 2013 (video game)
-Modelling clay
-Audi Sportback concept
-WWE All Stars
-Mitsubishi Outlander
-Punch-Out!!
-Disney Infinity: Marvel Super Heroes
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-Willy Wonka
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-Eurofighter Typhoon
-H1Z1: Just Survive
-Fakie
-Super Mario RPG
-Dance Central 3
-Puppet
-Cursor (user interface)
-Prince of Persia: Warrior Within
-Ultimate Mortal Kombat 3
-Macross
-Upholstery
-The Binding of Isaac (video game)
-Deathstroke
-The King of Fighters '98
-Dragon Ball Z: Battle of Z
-Theatre organ
-Valve Corporation
-Age of Conan
-GameStop
-Unreal Tournament
-Metroid Prime
-Annie (musical)
-Cinderella (Disney character)
-Eric Cartman
-The Prince of Tennis
-Kia Sportage
-Vase
-Nightwing
-Wing
-Gouken
-Loft
-Ferris wheel
-Newspaper
-Cash
-A Certain Magical Index
-Pretty Rhythm
-Marionette
-Swing (seat)
-He-Man
-Cook (profession)
-Bentley Continental GT
-Shaman King
-Hakuōki
-Essential oil
-Balalaika
-Baja 1000
-Hummingbird
-PSA HDi engine
-Nissan Sentra
-???
-Infamous (video game)
-Game Boy Color
-343 Industries
-Six Flags Magic Mountain
-Woozworld
-It's a Small World
-Star Fox 64
-Xenoblade Chronicles
-TurboGrafx-16
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-HTC Evo 4G
-Super Metroid
-Label
-Gothic (video game)
-Samsung Galaxy Gear
-???
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-Yamaha MT-09
-Mortal Kombat: Armageddon
-Angry Birds Star Wars
-Aerography (arts)
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-Hyundai Elantra
-MG Cars
-Tesla Model S
-Castlevania: Symphony of the Night
-Body armor
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-Tekken 5: Dark Resurrection
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-Wedding invitation
-Porsche 930
-Whey protein
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-Hatter (Alice's Adventures in Wonderland)
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-The Lion King (musical)
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-Kingdom Hearts III
-Zipper
-Leaf
-Samsung Galaxy Note 10.1
-Bansuri
-BMW 5 Series (F10)
-Australian Shepherd
-Crash Bandicoot: Warped
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-Tilapia
-Peugeot 205
-AC Cobra
-Tin whistle
-Tooth brushing
-Battlefield 1942
-Virginia Tech
-Quarry
-Amphibious ATV
-Dome
-Portable stove
-Sound system (Jamaican)
-Suikoden
-Lunar eclipse
-Tiramisu
-Inazuma Eleven GO (video game)
-Nissan 300ZX
-Neverwinter (video game)
-Axle
-Altaïr Ibn-La'Ahad
-Radiator
-Resident Evil (2002 video game)
-Prince of Persia: The Sands of Time
-Crop circle
-Rhinoceros
-???
-Bookcase
-Common quail
-The Hunger Games
-Mercedes-Benz A-Class
-Sarah Walker (Chuck)
-Cinnamon
-Hiru TV
-Bread roll
-Magician (fantasy)
-Lotion
-Killzone 3
-Cadillac Escalade
-Silhouette
-Swan
-Lemonade
-Trabant
-Mojito
-Fossil
-Macy's
-Silk
-Puma SE
-Nissan Maxima
-Battlefield 2142
-Twisted Metal
-Olive oil
-Wii Remote
-Universal Studios Hollywood
-Berserk (manga)
-Wellington boot
-Tomb Raider: Anniversary
-Almond
-Audi RS 6
-Ladder
-Fire Emblem Awakening
-Stained glass
-Tape recorder
-Emerald
-Ford Fusion (Americas)
-Iguana
-Might and Magic
-Pluto
-Mazda Raceway Laguna Seca
-Air Force 1 (shoe)
-Pub
-Oshun
-Honda K engine
-Nerd
-Renault 5
-F1 2011 (video game)
-Windscreen wiper
-Lex Luthor
-Track racing
-Escalator
-Charlie Brown
-Chauffeur
-Soba
-Window film
-Bowl
-Alarm clock
-Pokémon Mystery Dungeon: Explorers of Time and Explorers of Darkness
-Roomba
-Honda Shadow
-Lightning Returns: Final Fantasy XIII
-LATAM Brasil
-Top
-American Bulldog
-Legoland
-Caterpillar
-Windows Phone 8
-Automated teller machine
-Samsung Galaxy S III Mini
-Portrait photography
-Office
-Para Para
-Hockey stick
-Singapore Airlines
-Volvo S60
-Udon
-Chevrolet K5 Blazer
-Bath & Body Works
-Segway PT
-Castlevania: Lords of Shadow
-Mario Kart: Double Dash
-Mew (Pokémon)
-Walkman
-Mentos
-Jilbāb
-Canter and gallop
-Cinderella
-Skylanders: Trap Team
-Lego Duplo
-Morgan le Fay
-Decal
-Handycam
-Women's Tennis Association
-Yeti
-Multi-valve
-Pokémon Stadium
-Matryoshka doll
-Lexus LFA
-Keirin
-???
-Honda Prelude
-Burrito
-Midna
-Shuriken
-New Super Mario Bros. 2
-Nebula
-BlackBerry PlayBook
-Typography
-Hare
-Mohawk hairstyle
-Onsen
-Jet pack
-Wagon
-Just Dance 3
-Nissan S30
-Noah's Ark
-Ronald McDonald
-Bombardier Dash 8
-Raspberry
-Hair dryer
-The Simpsons: Hit & Run
-Still life
-Ice climbing
-Lada Riva
-Port
-Compound bow
-Resident Evil 3: Nemesis
-R2-D2
-Sand animation
-ABS-CBN (television network)
-Leica Camera
-Final Fantasy (video game)
-Arkham Asylum
-Dynasty Warriors 8
-Text messaging
-Nursery (room)
-Donkey Kong 64
-Star Wars Jedi Knight: Jedi Academy
-Typing
-Mapex Drums
-Granado Espada
-Calendar
-UFC Undisputed 3
-Airbag
-DMC World DJ Championships
-Gingerbread
-Rayman Origins
-Lamborghini Reventón
-Trials Fusion
-Mafia (video game)
-Paso Fino
-???
-Sport kite
-Taco Bell
-Envelope
-Mazdaspeed3
-Transformers: Generation 1
-Empanada
-Mega Man 3
-Transformers: Fall of Cybertron
-Rosalina (character)
-Mosquito
-Volkswagen Tiguan
-Metal Gear Solid V: Ground Zeroes
-Marmalade
-Pandeiro
-Miss Saigon
-Yosemite National Park
-Dutch Warmblood
-Pre-flight safety demonstration
-Citroën Saxo
-Mack Trucks
-Medley swimming
-???
-Spindle (tool)
-Greek cuisine
-Hyundai Santa Fe
-Chili con carne
-Poster
-Kawasaki Ninja 300
-Baby food
-Grand Theft Auto (Game Boy Advance)
-Sim racing
-Chromebook
-Peter Griffin
-Stainless steel
-Beverage can
-Pixie cut
-Chevrolet SS (concept car)
-Chokehold
-Bullion
-Super Mario Kart
-The Sims FreePlay
-Giant Bicycles
-Sgt. Frog
-Age of Empires II
-Abadá
-Kingdom Hearts HD 1.5 Remix
-Blackjack
-Canon EOS 60D
-Filling station
-Plywood
-Pheasant
-Wilson Sporting Goods
-Comb
-Lighthouse
-Rock and Roll Hall of Fame
-Tōshirō Hitsugaya
-Tales of the Abyss
-Maze
-Resident Evil: Operation Raccoon City
-Cimbalom
-???
-Monkey Island (series)
-Civilization V
-Venus
-Peugeot 207
-The Amazing Spider-Man (2012 video game)
-Chrono Cross
-New Balance
-Dassault Rafale
-Daredevil (Marvel Comics character)
-Silent Hill 2
-Beanie (seamed cap)
-Nut (fruit)
-Jill Valentine
-Scion tC
-Percy Jackson
-Lord of the Dance (musical)
-Far Cry (video game)
-Star Wars: The Force Unleashed II
-Memory card
-Motorola Droid
-Skylanders: Spyro's Adventure
-Yamaha DT125
-Audi Q5
-Jaguar
-Jaguar XJ
-Animal Crossing: Wild World
-Cockroach
-Wetsuit
-Funny Car
-FarmVille
-The Sims 3: Pets
-Peel (fruit)
-Melting
-Aurora (Disney character)
-Dry ice
-Star Ocean
-Duke Nukem Forever
-Toribash
-Yamaha YZ250
-Tekken 3
-Orihime Inoue
-Spyro: Year of the Dragon
-Eight-string guitar
-Sonic Riders
-Penny (The Big Bang Theory)
-Honda XR series
-Neodymium magnet toys
-Leatherman
-Maximum Destruction
-Super Mario 64 DS
-Unreal Tournament 3
-Health club
-Chrysler Hemi engine
-The North Face
-CBS News
-Pentium
-Cannon
-London Fashion Week
-Military tactics
-Smallmouth bass
-Leopard gecko
-Top (clothing)
-Fable III
-Panasonic Lumix DMC-GH4
-Sikorsky UH-60 Black Hawk
-Blue Dragon
-Loudspeaker enclosure
-Ōkami
-Tribal Wars
-Hot chocolate
-Beetroot
-???
-Nokia N97
-Blue Exorcist
-???
-Sonic and the Black Knight
-Headscarf
-Plasma display
-Woody Woodpecker
-???
-Beyblade: Shogun Steel
-29er (bicycle)
-QR code
-Dyson (company)
-Yanmar
-Gladiator
-Nissan Pathfinder
-Nissan X-Trail
-Autofocus
-King Dedede
-Zoo Tycoon 2
-Wheat tortilla
-Team Rocket
-Classical ballet
-New York City Police Department
-Heihachi Mishima
-Crochet hook
-Pencil case
-Gods Eater Burst
-???
-DS 3
-Periodic table
-General Electric
-Nissan Juke
-Lollipop
-Jaguar F-Type
-MechWarrior Online
-Dodge Neon SRT-4
-Fried egg
-Revell
-Indoor soccer
-Gratin
-Punisher
-Washburn Guitars
-Caster board
-Eldar (Warhammer 40,000)
-Final Fantasy Type-0
-NBA 2K10
-The Lord of the Rings: The Battle for Middle-earth II
-Texas Longhorns
-3D television
-Scorpion
-Warhammer 40,000: Dawn of War II
-Burpee (exercise)
-The Order: 1886
-Poptropica
-Tomb Raider: Legend
-Pelmeni
-Bánh
-PriPara
-Legacy of Kain
-Bowser Jr.
-Yonex
-Humanoid robot
-Sony Ericsson Xperia X10
-Rain gutter
-FIFA Street (2012 video game)
-Castle Crashers
-Meteoroid
-Macaroni and cheese
-Sega CD
-Mac Mini
-Tales of Xillia
-Sonic Lost World
-Orphanage
-Siku Toys
-Lego Batman 3: Beyond Gotham
-Daenerys Targaryen
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-Town
-Command & Conquer: Generals
-Samurai Shodown
-ZX Spectrum
-Quake Live
-Weighing scale
-Dead Frontier
-Wolfenstein: The New Order
-Colin McRae: Dirt
-Square dance
-Assassin's Creed Rogue
-Airboat
-Uncharted: Drake's Fortune
-Diddy Kong
-Yamaha Motif
-Theremin
-Rilakkuma
-Tie-dye
-Flip-flops
-Cylinder
-Gothic 3
-Unreal (1998 video game)
-Beyond: Two Souls
-Umbrella
-Dream Club
-Gradius
-Nexus One
-Nokia N900
-Tamagotchi
-Husband
-Sleeping bag
-Look-alike
-Papaya
-Mother 3
-The Beatles: Rock Band
-Prince of Persia: The Two Thrones
-???
-Darth Maul
-Knife sharpening
-Meteor shower
-Flugelhorn
-One Piece: Pirate Warriors
-Asterix
-Talk box
-With Your Destiny
-Alan Wake
-Barcode
-Recurve bow
-Diaper bag
-Ferrari F12berlinetta
-Taskbar
-Mortar (masonry)
-Toner (skin care)
-Freddy Krueger
-Marriott International
-Mass Effect (video game)
-Hawkeye (comics)
-Killing Floor (video game)
-Chibiusa
-Screenshot
-Pear
-Injury
-Kia Sorento
-Shredder (Teenage Mutant Ninja Turtles)
-Lifeguard
-Kei car
-Fight Night Champion
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-Gamblerz
diff --git a/mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt b/mediapipe/graphs/youtube8m/local_video_model_inference.pbtxt
deleted file mode 100644
index 12ed2cb..0000000
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-  input_stream: "SCORES:prediction_vector"
-  output_stream: "TOP_K_INDEXES:top_k_indexes"
-  output_stream: "TOP_K_SCORES:top_k_scores"
-  output_stream: "TOP_K_LABELS:top_k_labels"
-  node_options: {
-    [type.googleapis.com/mediapipe.TopKScoresCalculatorOptions]: {
-      top_k: 3
-      label_map_path: "mediapipe/graphs/youtube8m/label_map.txt"
-    }
-  }
-}
-
-node {
-  calculator: "OpenCvVideoDecoderCalculator"
-  input_side_packet: "INPUT_FILE_PATH:input_video_path"
-  output_stream: "VIDEO:input_video"
-  output_stream: "VIDEO_PRESTREAM:input_video_header"
-}
-
-node {
-  calculator: "LabelsToRenderDataCalculator"
-  input_stream: "LABELS:top_k_labels"
-  input_stream: "SCORES:top_k_scores"
-  input_stream: "VIDEO_PRESTREAM:input_video_header"
-  output_stream: "RENDER_DATA:render_data"
-  node_options: {
-    [type.googleapis.com/mediapipe.LabelsToRenderDataCalculatorOptions]: {
-      color { r: 255 g: 0 b: 0 }
-      color { r: 0 g: 255 b: 0 }
-      color { r: 0 g: 0 b: 255 }
-      thickness: 2.0
-      font_height_px: 20
-      max_num_labels: 3
-      location: TOP_LEFT
-    }
-  }
-}
-
-node {
-  calculator: "PacketClonerCalculator"
-  input_stream: "render_data"
-  input_stream: "input_video"
-  output_stream: "synchronized_render_data"
-}
-
-node {
-  calculator: "AnnotationOverlayCalculator"
-  input_stream: "IMAGE:input_video"
-  input_stream: "synchronized_render_data"
-  output_stream: "IMAGE:output_video"
-}
-
-node {
-  calculator: "OpenCvVideoEncoderCalculator"
-  input_stream: "VIDEO:output_video"
-  input_stream: "VIDEO_PRESTREAM:input_video_header"
-  input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
-  node_options: {
-    [type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
-      codec: "avc1"
-      video_format: "mp4"
-    }
-  }
-}
-
diff --git a/mediapipe/graphs/youtube8m/yt8m_dataset_model_inference.pbtxt b/mediapipe/graphs/youtube8m/yt8m_dataset_model_inference.pbtxt
deleted file mode 100644
index 38a0257..0000000
--- a/mediapipe/graphs/youtube8m/yt8m_dataset_model_inference.pbtxt
+++ /dev/null
@@ -1,139 +0,0 @@
-input_side_packet: "desired_segment_size"
-input_side_packet: "record_index"
-input_side_packet: "tfrecord_path"
-output_side_packet: "yt8m_id"
-output_stream: "annotation_summary"
-
-node {
-  calculator: "StringToInt32Calculator"
-  input_side_packet: "record_index"
-  output_side_packet: "record_index_int"
-}
-
-node {
-  calculator: "StringToInt32Calculator"
-  input_side_packet: "desired_segment_size"
-  output_side_packet: "desired_segment_size_int"
-}
-
-node {
-  calculator: "TFRecordReaderCalculator"
-  input_side_packet: "TFRECORD_PATH:tfrecord_path"
-  input_side_packet: "RECORD_INDEX:record_index_int"
-  output_side_packet: "SEQUENCE_EXAMPLE:yt8m_sequence_example"
-}
-
-node {
-  calculator: "UnpackYt8mSequenceExampleCalculator"
-  input_side_packet: "YT8M_SEQUENCE_EXAMPLE:yt8m_sequence_example"
-  input_side_packet: "DESIRED_SEGMENT_SIZE:desired_segment_size_int"
-  output_side_packet: "YT8M_ID:yt8m_id"
-  output_side_packet: "SEGMENT_SIZE:segment_size"
-  output_side_packet: "LAPPED_TENSOR_BUFFER_CALCULATOR_OPTIONS:lapped_tensor_buffer_calculator_options"
-  output_stream: "QUANTIZED_RGB_FEATURE:quantized_rgb_feature"
-  output_stream: "QUANTIZED_AUDIO_FEATURE:quantized_audio_feature"
-}
-
-node {
-  calculator: "DequantizeByteArrayCalculator"
-  input_stream: "ENCODED:quantized_rgb_feature"
-  output_stream: "FLOAT_VECTOR:rgb_feature_vector"
-  node_options: {
-    [type.googleapis.com/mediapipe.DequantizeByteArrayCalculatorOptions]: {
-      max_quantized_value: 2
-      min_quantized_value: -2
-    }
-  }
-}
-
-node {
-  calculator: "DequantizeByteArrayCalculator"
-  input_stream: "ENCODED:quantized_audio_feature"
-  output_stream: "FLOAT_VECTOR:audio_feature_vector"
-  node_options: {
-    [type.googleapis.com/mediapipe.DequantizeByteArrayCalculatorOptions]: {
-      max_quantized_value: 2
-      min_quantized_value: -2
-    }
-  }
-}
-
-node {
-  calculator: "ConcatenateFloatVectorCalculator"
-  input_stream: "rgb_feature_vector"
-  input_stream: "audio_feature_vector"
-  output_stream: "feature_vector"
-}
-
-node {
-  calculator: "VectorFloatToTensorCalculator"
-  input_stream: "feature_vector"
-  output_stream: "feature_tensor"
-}
-
-node {
-  calculator: "LappedTensorBufferCalculator"
-  input_stream: "feature_tensor"
-  input_side_packet: "CALCULATOR_OPTIONS:lapped_tensor_buffer_calculator_options"
-  output_stream: "lapped_feature_tensor"
-}
-
-node {
-  calculator: "SidePacketToStreamCalculator"
-  input_side_packet: "segment_size"
-  output_stream: "AT_ZERO:segment_size_int_stream"
-}
-
-node {
-  calculator: "VectorIntToTensorCalculator"
-  input_stream: "SINGLE_INT:segment_size_int_stream"
-  output_stream: "TENSOR_OUT:segment_size_tensor"
-}
-
-node {
-  calculator: "PacketClonerCalculator"
-  input_stream: "segment_size_tensor"
-  input_stream: "lapped_feature_tensor"
-  output_stream: "synced_segment_size_tensor"
-}
-
-node {
-  calculator: "TensorFlowSessionFromSavedModelCalculator"
-  output_side_packet: "SESSION:session"
-  node_options: {
-    [type.googleapis.com/mediapipe.TensorFlowSessionFromSavedModelCalculatorOptions]: {
-      saved_model_path: "/tmp/mediapipe/saved_model"
-    }
-  }
-}
-
-node: {
-  calculator: "TensorFlowInferenceCalculator"
-  input_side_packet: "SESSION:session"
-  input_stream: "NUM_FRAMES:synced_segment_size_tensor"
-  input_stream: "RGB_AND_AUDIO:lapped_feature_tensor"
-  output_stream: "PREDICTIONS:prediction_tensor"
-  node_options: {
-    [type.googleapis.com/mediapipe.TensorFlowInferenceCalculatorOptions]: {
-      batch_size: 32
-    }
-  }
-}
-
-node {
-  calculator: "TensorToVectorFloatCalculator"
-  input_stream: "prediction_tensor"
-  output_stream: "prediction_vector"
-}
-
-node {
-  calculator: "TopKScoresCalculator"
-  input_stream: "SCORES:prediction_vector"
-  output_stream: "SUMMARY:annotation_summary"
-  node_options: {
-    [type.googleapis.com/mediapipe.TopKScoresCalculatorOptions]: {
-      top_k: 9
-      label_map_path: "mediapipe/graphs/youtube8m/label_map.txt"
-    }
-  }
-}
diff --git a/mediapipe/modules/README.md b/mediapipe/modules/README.md
deleted file mode 100644
index 12ec103..0000000
--- a/mediapipe/modules/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
-# Modules
-
-Each module (represented as a subfolder) provides subgraphs and corresponding resources (e.g. tflite models) to perform domain-specific tasks (e.g. detect faces, detect face landmarks).
-
-*Modules listed below are already used in some of `mediapipe/graphs` and more graphs are being migrated to use existing and upcoming modules.*
-
-| Module | Description |
-| :--- | :--- |
-| [`face_detection`](face_detection/README.md) | Subgraphs to detect faces. |
-| [`face_geometry`](face_geometry/README.md) | Subgraphs to extract face geometry. |
-| [`face_landmark`](face_landmark/README.md) | Subgraphs to detect and track face landmarks. |
-| [`hand_landmark`](hand_landmark/README.md) | Subgraphs to detect and track hand landmarks. |
-| [`holistic_landmark`](holistic_landmark/README.md) | Subgraphs to detect and track holistic pose which consists of pose, face and hand landmarks. |
-| [`iris_landmark`](iris_landmark/README.md) | Subgraphs to detect iris landmarks. |
-| [`palm_detection`](palm_detection/README.md) | Subgraphs to detect palms/hands. |
-| [`pose_detection`](pose_detection/README.md) | Subgraphs to detect poses. |
-| [`pose_landmark`](pose_landmark/README.md) | Subgraphs to detect and track pose landmarks. |
-| [`objectron`](objectron/README.md) | Subgraphs to detect and track 3D objects. |
diff --git a/mediapipe/modules/face_detection/BUILD b/mediapipe/modules/face_detection/BUILD
deleted file mode 100644
index b1cddeb..0000000
--- a/mediapipe/modules/face_detection/BUILD
+++ /dev/null
@@ -1,150 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "face_detection_short_range_by_roi_cpu",
-    graph = "face_detection_short_range_by_roi_cpu.pbtxt",
-    register_as = "FaceDetectionShortRangeByRoiCpu",
-    deps = [
-        ":face_detection_short_range_common",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/util:to_image_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_short_range_by_roi_gpu",
-    graph = "face_detection_short_range_by_roi_gpu.pbtxt",
-    register_as = "FaceDetectionShortRangeByRoiGpu",
-    deps = [
-        ":face_detection_short_range_common",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/util:to_image_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_short_range_cpu",
-    graph = "face_detection_short_range_cpu.pbtxt",
-    register_as = "FaceDetectionShortRangeCpu",
-    deps = [
-        ":face_detection_short_range_common",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/util:to_image_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_short_range_gpu",
-    graph = "face_detection_short_range_gpu.pbtxt",
-    register_as = "FaceDetectionShortRangeGpu",
-    deps = [
-        ":face_detection_short_range_common",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/util:to_image_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_short_range_common",
-    graph = "face_detection_short_range_common.pbtxt",
-    register_as = "FaceDetectionShortRangeCommon",
-    deps = [
-        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/util:detection_projection_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_full_range_cpu",
-    graph = "face_detection_full_range_cpu.pbtxt",
-    register_as = "FaceDetectionFullRangeCpu",
-    deps = [
-        ":face_detection_full_range_common",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/util:to_image_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_full_range_gpu",
-    graph = "face_detection_full_range_gpu.pbtxt",
-    register_as = "FaceDetectionFullRangeGpu",
-    deps = [
-        ":face_detection_full_range_common",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/util:to_image_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_full_range_common",
-    graph = "face_detection_full_range_common.pbtxt",
-    register_as = "FaceDetectionFullRangeCommon",
-    deps = [
-        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/util:detection_projection_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_short_range_image",
-    graph = "face_detection_short_range_image.pbtxt",
-    register_as = "FaceDetectionShortRangeImage",
-    deps = [
-        ":face_detection_short_range_common",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_full_range_image",
-    graph = "face_detection_full_range_image.pbtxt",
-    register_as = "FaceDetectionFullRangeImage",
-    deps = [
-        ":face_detection_full_range_common",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-    ],
-)
-
-exports_files(
-    srcs = [
-        "face_detection_full_range.tflite",
-        "face_detection_full_range_sparse.tflite",
-        "face_detection_short_range.tflite",
-    ],
-)
diff --git a/mediapipe/modules/face_detection/README.md b/mediapipe/modules/face_detection/README.md
deleted file mode 100644
index 17cf27b..0000000
--- a/mediapipe/modules/face_detection/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# face_detection
-
-Subgraphs|Details
-:--- | :---
-[`FaceDetectionFullRangeCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_full_range_cpu.pbtxt)| Detects faces. Works best for faces within 5 meters from the camera. (CPU input, and inference is executed on CPU.)
-[`FaceDetectionFullRangeGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_full_range_gpu.pbtxt)| Detects faces. Works best for faces within 5 meters from the camera. (GPU input, and inference is executed on GPU.)
-[`FaceDetectionShortRangeCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range_cpu.pbtxt)| Detects faces. Works best for faces within 2 meters from the camera. (CPU input, and inference is executed on CPU.)
-[`FaceDetectionShortRangeGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range_gpu.pbtxt)| Detects faces. Works best for faces within 2 meters from the camera. (GPU input, and inference is executed on GPU.)
diff --git a/mediapipe/modules/face_detection/face_detection_full_range.tflite b/mediapipe/modules/face_detection/face_detection_full_range.tflite
deleted file mode 100755
index 98c5c16..0000000
Binary files a/mediapipe/modules/face_detection/face_detection_full_range.tflite and /dev/null differ
diff --git a/mediapipe/modules/face_detection/face_detection_full_range_common.pbtxt b/mediapipe/modules/face_detection/face_detection_full_range_common.pbtxt
deleted file mode 100644
index 937e8be..0000000
--- a/mediapipe/modules/face_detection/face_detection_full_range_common.pbtxt
+++ /dev/null
@@ -1,102 +0,0 @@
-# MediaPipe graph performing common processing to detect faces using
-# face_detection_full_range_sparse.tflite model, currently consisting of tensor
-# post processing.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceDetectionFullRangeCommon"
-#     input_stream: "TENSORS:detection_tensors"
-#     input_stream: "MATRIX:transform_matrix"
-#     output_stream: "DETECTIONS:detections"
-#   }
-
-type: "FaceDetectionShortRangeCommon"
-
-# Detection tensors. (std::vector<Tensor>)
-input_stream: "TENSORS:detection_tensors"
-
-# A 4x4 row-major-order matrix that maps a point represented in the detection
-# tensors to a desired coordinate system, e.g., in the original input image
-# before scaling/cropping. (std::array<float, 16>)
-input_stream: "MATRIX:transform_matrix"
-
-# Detected faces. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  options: {
-    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
-      num_layers: 1
-      min_scale: 0.1484375
-      max_scale: 0.75
-      input_size_height: 192
-      input_size_width: 192
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 4
-      aspect_ratios: 1.0
-      fixed_anchor_size: true
-      interpolated_scale_aspect_ratio: 0.0
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:unfiltered_detections"
-  options: {
-    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
-      num_classes: 1
-      num_boxes: 2304
-      num_coords: 16
-      box_coord_offset: 0
-      keypoint_coord_offset: 4
-      num_keypoints: 6
-      num_values_per_keypoint: 2
-      sigmoid_score: true
-      score_clipping_thresh: 100.0
-      reverse_output_order: true
-      x_scale: 192.0
-      y_scale: 192.0
-      h_scale: 192.0
-      w_scale: 192.0
-      min_score_thresh: 0.6
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "unfiltered_detections"
-  output_stream: "filtered_detections"
-  options: {
-    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
-      min_suppression_threshold: 0.3
-      overlap_type: INTERSECTION_OVER_UNION
-      algorithm: WEIGHTED
-    }
-  }
-}
-
-# Projects the detections from input tensor to the corresponding locations on
-# the original image (input to the graph).
-node {
-  calculator: "DetectionProjectionCalculator"
-  input_stream: "DETECTIONS:filtered_detections"
-  input_stream: "PROJECTION_MATRIX:transform_matrix"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/face_detection/face_detection_full_range_cpu.pbtxt b/mediapipe/modules/face_detection/face_detection_full_range_cpu.pbtxt
deleted file mode 100644
index 2350401..0000000
--- a/mediapipe/modules/face_detection/face_detection_full_range_cpu.pbtxt
+++ /dev/null
@@ -1,80 +0,0 @@
-# MediaPipe graph to detect faces. (CPU input, and inference is executed on
-# CPU.)
-#
-# It is required that "face_detection_full_range_sparse.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceDetectionFullRangeCpu"
-#     input_stream: "IMAGE:image"
-#     output_stream: "DETECTIONS:face_detections"
-#   }
-
-type: "FaceDetectionFullRangeCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# Detected faces. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Converts the input CPU image (ImageFrame) to the multi-backend image type
-# (Image).
-node: {
-  calculator: "ToImageCalculator"
-  input_stream: "IMAGE_CPU:image"
-  output_stream: "IMAGE:multi_backend_image"
-}
-
-# Transforms the input image into a 192x192 tensor while keeping the aspect
-# ratio (what is expected by the corresponding face detection model), resulting
-# in potential letterboxing in the transformed image.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:multi_backend_image"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "MATRIX:transform_matrix"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 192
-      output_tensor_height: 192
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
-      delegate {
-        xnnpack {}
-      }
-    }
-  }
-}
-
-# Performs tensor post processing to generate face detections.
-node {
-  calculator: "FaceDetectionFullRangeCommon"
-  input_stream: "TENSORS:detection_tensors"
-  input_stream: "MATRIX:transform_matrix"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/face_detection/face_detection_full_range_gpu.pbtxt b/mediapipe/modules/face_detection/face_detection_full_range_gpu.pbtxt
deleted file mode 100644
index 703b717..0000000
--- a/mediapipe/modules/face_detection/face_detection_full_range_gpu.pbtxt
+++ /dev/null
@@ -1,80 +0,0 @@
-# MediaPipe graph to detect faces. (GPU input, and inference is executed on
-# GPU.)
-#
-# It is required that "face_detection_full_range_sparse.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceDetectionFullRangeGpu"
-#     input_stream: "IMAGE:image"
-#     output_stream: "DETECTIONS:face_detections"
-#   }
-
-type: "FaceDetectionFullRangeGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# Detected faces. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Converts the input GPU image (GpuBuffer) to the multi-backend image type
-# (Image).
-node: {
-  calculator: "ToImageCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "IMAGE:multi_backend_image"
-}
-
-# Transforms the input image into a 128x128 tensor while keeping the aspect
-# ratio (what is expected by the corresponding face detection model), resulting
-# in potential letterboxing in the transformed image.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:multi_backend_image"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "MATRIX:transform_matrix"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 192
-      output_tensor_height: 192
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
-      #
-      delegate: { gpu { use_advanced_gpu_api: true } }
-    }
-  }
-}
-
-# Performs tensor post processing to generate face detections.
-node {
-  calculator: "FaceDetectionFullRangeCommon"
-  input_stream: "TENSORS:detection_tensors"
-  input_stream: "MATRIX:transform_matrix"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/face_detection/face_detection_full_range_image.pbtxt b/mediapipe/modules/face_detection/face_detection_full_range_image.pbtxt
deleted file mode 100644
index 4e0bc0b..0000000
--- a/mediapipe/modules/face_detection/face_detection_full_range_image.pbtxt
+++ /dev/null
@@ -1,86 +0,0 @@
-# MediaPipe graph to detect faces. (GPU/CPU input, and inference is executed on
-# GPU.)
-#
-# It is required that "face_detection_full_range_sparse.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
-# path during execution.
-
-type: "FaceDetectionFullRangeImage"
-
-# Image. (Image)
-input_stream: "IMAGE:image"
-
-# The throttled input image. (Image)
-output_stream: "IMAGE:throttled_image"
-# Detected faces. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "image"
-  input_stream: "FINISHED:detections"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_image"
-  options: {
-    [mediapipe.FlowLimiterCalculatorOptions.ext] {
-      max_in_flight: 1
-      max_in_queue: 1
-    }
-  }
-}
-
-# Transforms the input image into a 128x128 tensor while keeping the aspect
-# ratio (what is expected by the corresponding face detection model), resulting
-# in potential letterboxing in the transformed image.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:throttled_image"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "MATRIX:transform_matrix"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 192
-      output_tensor_height: 192
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-      gpu_origin: CONVENTIONAL
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-# TODO: Use GraphOptions to modify the delegate field to be
-# `delegate { xnnpack {} }` for the CPU only use cases.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite"
-      #
-      delegate: { gpu { use_advanced_gpu_api: true } }
-    }
-  }
-}
-
-# Performs tensor post processing to generate face detections.
-node {
-  calculator: "FaceDetectionFullRangeCommon"
-  input_stream: "TENSORS:detection_tensors"
-  input_stream: "MATRIX:transform_matrix"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite b/mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite
deleted file mode 100755
index 9575d8c..0000000
Binary files a/mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite and /dev/null differ
diff --git a/mediapipe/modules/face_detection/face_detection_short_range.tflite b/mediapipe/modules/face_detection/face_detection_short_range.tflite
deleted file mode 100755
index 659bce8..0000000
Binary files a/mediapipe/modules/face_detection/face_detection_short_range.tflite and /dev/null differ
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_by_roi_cpu.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_by_roi_cpu.pbtxt
deleted file mode 100644
index b3adfeb..0000000
--- a/mediapipe/modules/face_detection/face_detection_short_range_by_roi_cpu.pbtxt
+++ /dev/null
@@ -1,83 +0,0 @@
-# MediaPipe graph to detect faces. (CPU input, and inference is executed on
-# CPU.)
-#
-# It is required that "face_detection_short_range.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceDetectionShortRangeByRoiCpu"
-#     input_stream: "IMAGE:image"
-#     input_stream: "ROI:roi"
-#     output_stream: "DETECTIONS:face_detections"
-#   }
-
-type: "FaceDetectionShortRangeByRoiCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# ROI (region of interest) within the given image where faces should be
-# detected. (NormalizedRect)
-input_stream: "ROI:roi"
-
-# Detected faces. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Converts the input CPU image (ImageFrame) to the multi-backend image type
-# (Image).
-node: {
-  calculator: "ToImageCalculator"
-  input_stream: "IMAGE_CPU:image"
-  output_stream: "IMAGE:multi_backend_image"
-}
-
-# Transforms specified region of image into 128x128 tensor keeping aspect ratio
-# (padding tensor if needed).
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:multi_backend_image"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "MATRIX:transform_matrix"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 128
-      output_tensor_height: 128
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-      delegate { xnnpack {} }
-    }
-  }
-}
-
-# Performs tensor post processing to generate face detections.
-node {
-  calculator: "FaceDetectionShortRangeCommon"
-  input_stream: "TENSORS:detection_tensors"
-  input_stream: "MATRIX:transform_matrix"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_by_roi_gpu.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_by_roi_gpu.pbtxt
deleted file mode 100644
index 1bd08e9..0000000
--- a/mediapipe/modules/face_detection/face_detection_short_range_by_roi_gpu.pbtxt
+++ /dev/null
@@ -1,83 +0,0 @@
-# MediaPipe graph to detect faces. (CPU input, and inference is executed on
-# CPU.)
-#
-# It is required that "face_detection_short_range.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceDetectionShortRangeByRoiGpu"
-#     input_stream: "IMAGE:image"
-#     input_stream: "ROI:roi"
-#     output_stream: "DETECTIONS:face_detections"
-#   }
-
-type: "FaceDetectionShortRangeByRoiGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# ROI (region of interest) within the given image where faces should be
-# detected. (NormalizedRect)
-input_stream: "ROI:roi"
-
-# Detected faces. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Converts the input GPU image (GpuBuffer) to the multi-backend image type
-# (Image).
-node: {
-  calculator: "ToImageCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "IMAGE:multi_backend_image"
-}
-
-# Transforms specified region of image into 128x128 tensor keeping aspect ratio
-# (padding tensor if needed).
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:multi_backend_image"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "MATRIX:transform_matrix"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 128
-      output_tensor_height: 128
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-    }
-  }
-}
-
-# Performs tensor post processing to generate face detections.
-node {
-  calculator: "FaceDetectionShortRangeCommon"
-  input_stream: "TENSORS:detection_tensors"
-  input_stream: "MATRIX:transform_matrix"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_common.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_common.pbtxt
deleted file mode 100644
index 4a6a54f..0000000
--- a/mediapipe/modules/face_detection/face_detection_short_range_common.pbtxt
+++ /dev/null
@@ -1,103 +0,0 @@
-# MediaPipe graph performing common processing to detect faces, currently
-# consisting of tensor post processing.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceDetectionShortRangeCommon"
-#     input_stream: "TENSORS:detection_tensors"
-#     input_stream: "MATRIX:transform_matrix"
-#     output_stream: "DETECTIONS:detections"
-#   }
-
-type: "FaceDetectionShortRangeCommon"
-
-# Detection tensors. (std::vector<Tensor>)
-input_stream: "TENSORS:detection_tensors"
-
-# A 4x4 row-major-order matrix that maps a point represented in the detection
-# tensors to a desired coordinate system, e.g., in the original input image
-# before scaling/cropping. (std::array<float, 16>)
-input_stream: "MATRIX:transform_matrix"
-
-# Detected faces. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  options: {
-    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
-      num_layers: 4
-      min_scale: 0.1484375
-      max_scale: 0.75
-      input_size_height: 128
-      input_size_width: 128
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 8
-      strides: 16
-      strides: 16
-      strides: 16
-      aspect_ratios: 1.0
-      fixed_anchor_size: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:unfiltered_detections"
-  options: {
-    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
-      num_classes: 1
-      num_boxes: 896
-      num_coords: 16
-      box_coord_offset: 0
-      keypoint_coord_offset: 4
-      num_keypoints: 6
-      num_values_per_keypoint: 2
-      sigmoid_score: true
-      score_clipping_thresh: 100.0
-      reverse_output_order: true
-      x_scale: 128.0
-      y_scale: 128.0
-      h_scale: 128.0
-      w_scale: 128.0
-      min_score_thresh: 0.5
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "unfiltered_detections"
-  output_stream: "filtered_detections"
-  options: {
-    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
-      min_suppression_threshold: 0.3
-      overlap_type: INTERSECTION_OVER_UNION
-      algorithm: WEIGHTED
-    }
-  }
-}
-
-# Projects the detections from input tensor to the corresponding locations on
-# the original image (input to the graph).
-node {
-  calculator: "DetectionProjectionCalculator"
-  input_stream: "DETECTIONS:filtered_detections"
-  input_stream: "PROJECTION_MATRIX:transform_matrix"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_cpu.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_cpu.pbtxt
deleted file mode 100644
index 0db2420..0000000
--- a/mediapipe/modules/face_detection/face_detection_short_range_cpu.pbtxt
+++ /dev/null
@@ -1,78 +0,0 @@
-# MediaPipe graph to detect faces. (CPU input, and inference is executed on
-# CPU.)
-#
-# It is required that "face_detection_short_range.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceDetectionShortRangeCpu"
-#     input_stream: "IMAGE:image"
-#     output_stream: "DETECTIONS:face_detections"
-#   }
-
-type: "FaceDetectionShortRangeCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# Detected faces. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Converts the input CPU image (ImageFrame) to the multi-backend image type
-# (Image).
-node: {
-  calculator: "ToImageCalculator"
-  input_stream: "IMAGE_CPU:image"
-  output_stream: "IMAGE:multi_backend_image"
-}
-
-# Transforms the input image into a 128x128 tensor while keeping the aspect
-# ratio (what is expected by the corresponding face detection model), resulting
-# in potential letterboxing in the transformed image.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:multi_backend_image"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "MATRIX:transform_matrix"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 128
-      output_tensor_height: 128
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-      delegate { xnnpack {} }
-    }
-  }
-}
-
-# Performs tensor post processing to generate face detections.
-node {
-  calculator: "FaceDetectionShortRangeCommon"
-  input_stream: "TENSORS:detection_tensors"
-  input_stream: "MATRIX:transform_matrix"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_gpu.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_gpu.pbtxt
deleted file mode 100644
index d30644b..0000000
--- a/mediapipe/modules/face_detection/face_detection_short_range_gpu.pbtxt
+++ /dev/null
@@ -1,78 +0,0 @@
-# MediaPipe graph to detect faces. (CPU input, and inference is executed on
-# CPU.)
-#
-# It is required that "face_detection_short_range.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceDetectionShortRangeGpu"
-#     input_stream: "IMAGE:image"
-#     output_stream: "DETECTIONS:face_detections"
-#   }
-
-type: "FaceDetectionShortRangeGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# Detected faces. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Converts the input GPU image (GpuBuffer) to the multi-backend image type
-# (Image).
-node: {
-  calculator: "ToImageCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "IMAGE:multi_backend_image"
-}
-
-# Transforms the input image into a 128x128 tensor while keeping the aspect
-# ratio (what is expected by the corresponding face detection model), resulting
-# in potential letterboxing in the transformed image.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:multi_backend_image"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "MATRIX:transform_matrix"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 128
-      output_tensor_height: 128
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-    }
-  }
-}
-
-# Performs tensor post processing to generate face detections.
-node {
-  calculator: "FaceDetectionShortRangeCommon"
-  input_stream: "TENSORS:detection_tensors"
-  input_stream: "MATRIX:transform_matrix"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/face_detection/face_detection_short_range_image.pbtxt b/mediapipe/modules/face_detection/face_detection_short_range_image.pbtxt
deleted file mode 100644
index a259041..0000000
--- a/mediapipe/modules/face_detection/face_detection_short_range_image.pbtxt
+++ /dev/null
@@ -1,94 +0,0 @@
-# MediaPipe graph to detect faces. (GPU/CPU input, and inference is executed on
-# GPU.)
-#
-# It is required that "face_detection_short_range.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceDetectionShortRangeCpu"
-#     input_stream: "IMAGE:image"
-#     output_stream: "DETECTIONS:face_detections"
-#   }
-
-type: "FaceDetectionShortRangeCpu"
-
-# Image. (Image)
-input_stream: "IMAGE:image"
-
-# The throttled input image. (Image)
-output_stream: "IMAGE:throttled_image"
-# Detected faces. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "image"
-  input_stream: "FINISHED:detections"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_image"
-  options: {
-    [mediapipe.FlowLimiterCalculatorOptions.ext] {
-      max_in_flight: 1
-      max_in_queue: 1
-    }
-  }
-}
-
-# Transforms the input image into a 128x128 tensor while keeping the aspect
-# ratio (what is expected by the corresponding face detection model), resulting
-# in potential letterboxing in the transformed image.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:throttled_image"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "MATRIX:transform_matrix"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 128
-      output_tensor_height: 128
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-      gpu_origin: CONVENTIONAL
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-# TODO: Use GraphOptions to modify the delegate field to be
-# `delegate { xnnpack {} }` for the CPU only use cases.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-
-      #
-      delegate: { gpu { use_advanced_gpu_api: true } }
-    }
-  }
-}
-
-# Performs tensor post processing to generate face detections.
-node {
-  calculator: "FaceDetectionShortRangeCommon"
-  input_stream: "TENSORS:detection_tensors"
-  input_stream: "MATRIX:transform_matrix"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/face_geometry/BUILD b/mediapipe/modules/face_geometry/BUILD
deleted file mode 100644
index c1f9967..0000000
--- a/mediapipe/modules/face_geometry/BUILD
+++ /dev/null
@@ -1,137 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
-load("//mediapipe/framework/tool:mediapipe_graph.bzl", "mediapipe_simple_subgraph")
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "face_geometry",
-    graph = "face_geometry.pbtxt",
-    register_as = "FaceGeometry",
-    deps = [
-        ":geometry_pipeline_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_geometry_from_detection",
-    graph = "face_geometry_from_detection.pbtxt",
-    register_as = "FaceGeometryFromDetection",
-    deps = [
-        ":geometry_pipeline_calculator",
-        "//mediapipe/calculators/core:begin_loop_calculator",
-        "//mediapipe/calculators/core:end_loop_calculator",
-        "//mediapipe/calculators/util:detection_to_landmarks_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_geometry_from_landmarks",
-    graph = "face_geometry_from_landmarks.pbtxt",
-    register_as = "FaceGeometryFromLandmarks",
-    deps = [
-        ":geometry_pipeline_calculator",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "effect_renderer_calculator_proto",
-    srcs = ["effect_renderer_calculator.proto"],
-    deps = [
-        "//mediapipe/framework:calculator_options_proto",
-    ],
-)
-
-cc_library(
-    name = "effect_renderer_calculator",
-    srcs = ["effect_renderer_calculator.cc"],
-    deps = [
-        ":effect_renderer_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/formats:image_frame",
-        "//mediapipe/framework/formats:image_frame_opencv",
-        "//mediapipe/framework/port:opencv_core",
-        "//mediapipe/framework/port:opencv_imgcodecs",
-        "//mediapipe/framework/port:opencv_imgproc",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/framework/port:statusor",
-        "//mediapipe/gpu:gl_calculator_helper",
-        "//mediapipe/gpu:gpu_buffer",
-        "//mediapipe/modules/face_geometry/libs:effect_renderer",
-        "//mediapipe/modules/face_geometry/libs:validation_utils",
-        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:face_geometry_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:mesh_3d_cc_proto",
-        "//mediapipe/util:resource_util",
-        "@com_google_absl//absl/types:optional",
-    ],
-    alwayslink = 1,
-)
-
-mediapipe_proto_library(
-    name = "env_generator_calculator_proto",
-    srcs = ["env_generator_calculator.proto"],
-    deps = [
-        "//mediapipe/framework:calculator_options_proto",
-        "//mediapipe/modules/face_geometry/protos:environment_proto",
-    ],
-)
-
-cc_library(
-    name = "env_generator_calculator",
-    srcs = ["env_generator_calculator.cc"],
-    deps = [
-        ":env_generator_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/modules/face_geometry/libs:validation_utils",
-        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
-    ],
-    alwayslink = 1,
-)
-
-mediapipe_proto_library(
-    name = "geometry_pipeline_calculator_proto",
-    srcs = ["geometry_pipeline_calculator.proto"],
-    deps = [
-        "//mediapipe/framework:calculator_options_proto",
-    ],
-)
-
-cc_library(
-    name = "geometry_pipeline_calculator",
-    srcs = ["geometry_pipeline_calculator.cc"],
-    deps = [
-        ":geometry_pipeline_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/formats:landmark_cc_proto",
-        "//mediapipe/framework/port:logging",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/framework/port:statusor",
-        "//mediapipe/modules/face_geometry/libs:geometry_pipeline",
-        "//mediapipe/modules/face_geometry/libs:validation_utils",
-        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:face_geometry_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_cc_proto",
-        "//mediapipe/util:resource_util",
-        "@com_google_absl//absl/memory",
-    ],
-    alwayslink = 1,
-)
diff --git a/mediapipe/modules/face_geometry/README.md b/mediapipe/modules/face_geometry/README.md
deleted file mode 100644
index 8427ea6..0000000
--- a/mediapipe/modules/face_geometry/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# face_geometry
-
-Protos|Details
-:--- | :---
-[`face_geometry.Environment`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/environment.proto)| Describes an environment; includes the camera frame origin point location as well as virtual camera parameters.
-[`face_geometry.GeometryPipelineMetadata`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.proto)| Describes metadata needed to estimate face geometry based on the face landmark module result.
-[`face_geometry.FaceGeometry`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/face_geometry.proto)| Describes geometry data for a single face; includes a face mesh surface and a face pose in a given environment.
-[`face_geometry.Mesh3d`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/protos/mesh_3d.proto)| Describes a 3D mesh surface.
-
-Calculators|Details
-:--- | :---
-[`FaceGeometryEnvGeneratorCalculator`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/env_generator_calculator.cc)| Generates an environment that describes a virtual scene.
-[`FaceGeometryPipelineCalculator`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc)| Extracts face geometry for multiple faces from a vector of landmark lists.
-[`FaceGeometryEffectRendererCalculator`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/effect_renderer_calculator.cc)| Renders a face effect.
-
-Subgraphs|Details
-:--- | :---
-[`FaceGeometryFromDetection`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry_from_detection.pbtxt)| Extracts geometry from face detection for multiple faces.
-[`FaceGeometryFromLandmarks`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt)| Extracts geometry from face landmarks for multiple faces.
-[`FaceGeometry`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_geometry/face_geometry.pbtxt)| Extracts geometry from face landmarks for multiple faces. Deprecated, please use `FaceGeometryFromLandmarks` in the new code.
diff --git a/mediapipe/modules/face_geometry/data/BUILD b/mediapipe/modules/face_geometry/data/BUILD
deleted file mode 100644
index 1661a22..0000000
--- a/mediapipe/modules/face_geometry/data/BUILD
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//mediapipe/framework:encode_binary_proto.bzl", "encode_binary_proto")
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-encode_binary_proto(
-    name = "geometry_pipeline_metadata_detection",
-    input = "geometry_pipeline_metadata_detection.pbtxt",
-    message_type = "mediapipe.face_geometry.GeometryPipelineMetadata",
-    output = "geometry_pipeline_metadata_detection.binarypb",
-    deps = [
-        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_proto",
-    ],
-)
-
-encode_binary_proto(
-    name = "geometry_pipeline_metadata_landmarks",
-    input = "geometry_pipeline_metadata_landmarks.pbtxt",
-    message_type = "mediapipe.face_geometry.GeometryPipelineMetadata",
-    output = "geometry_pipeline_metadata_landmarks.binarypb",
-    deps = [
-        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_proto",
-    ],
-)
-
-# For backward-compatibility reasons, generate `geometry_pipeline_metadata.binarypb` from
-# the `geometry_pipeline_metadata_landmarks.pbtxt` definition.
-encode_binary_proto(
-    name = "geometry_pipeline_metadata",
-    input = "geometry_pipeline_metadata_landmarks.pbtxt",
-    message_type = "mediapipe.face_geometry.GeometryPipelineMetadata",
-    output = "geometry_pipeline_metadata.binarypb",
-    deps = [
-        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_proto",
-    ],
-)
-
-# These canonical face model files are not meant to be used in runtime, but rather for asset
-# creation and/or reference.
-exports_files([
-    "canonical_face_model.fbx",
-    "canonical_face_model.obj",
-    "canonical_face_model_uv_visualization.png",
-])
diff --git a/mediapipe/modules/face_geometry/data/canonical_face_model.fbx b/mediapipe/modules/face_geometry/data/canonical_face_model.fbx
deleted file mode 100644
index 8e9d24a..0000000
Binary files a/mediapipe/modules/face_geometry/data/canonical_face_model.fbx and /dev/null differ
diff --git a/mediapipe/modules/face_geometry/data/canonical_face_model.obj b/mediapipe/modules/face_geometry/data/canonical_face_model.obj
deleted file mode 100644
index 0e666d1..0000000
--- a/mediapipe/modules/face_geometry/data/canonical_face_model.obj
+++ /dev/null
@@ -1,1834 +0,0 @@
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diff --git a/mediapipe/modules/face_geometry/data/canonical_face_model_uv_visualization.png b/mediapipe/modules/face_geometry/data/canonical_face_model_uv_visualization.png
deleted file mode 100644
index 2acd991..0000000
Binary files a/mediapipe/modules/face_geometry/data/canonical_face_model_uv_visualization.png and /dev/null differ
diff --git a/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_detection.pbtxt b/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_detection.pbtxt
deleted file mode 100644
index c4389a6..0000000
--- a/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_detection.pbtxt
+++ /dev/null
@@ -1,78 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-input_source: FACE_DETECTION_PIPELINE
-procrustes_landmark_basis { landmark_id: 0 weight: 1.0 }
-procrustes_landmark_basis { landmark_id: 1 weight: 1.0 }
-procrustes_landmark_basis { landmark_id: 2 weight: 1.0 }
-procrustes_landmark_basis { landmark_id: 3 weight: 1.0 }
-procrustes_landmark_basis { landmark_id: 4 weight: 1.0 }
-procrustes_landmark_basis { landmark_id: 5 weight: 1.0 }
-# NOTE: the triangular topology of the face meshes is only useful when derived
-#       from the 468 face landmarks, not from the 6 face detection landmarks
-#       (keypoints). The former don't cover the entire face and this mesh is
-#       defined here only to comply with the API. It should be considered as
-#       a placeholder and/or for debugging purposes.
-#
-#       Use the face geometry derived from the face detection landmarks
-#       (keypoints) for the face pose transformation matrix, not the mesh.
-canonical_mesh: {
-  vertex_type: VERTEX_PT
-  primitive_type: TRIANGLE
-  vertex_buffer: -3.1511454582214355
-  vertex_buffer: 2.6246179342269897
-  vertex_buffer: 3.4656630754470825
-  vertex_buffer: 0.349575996398926
-  vertex_buffer: 0.38137748837470997
-  vertex_buffer: 3.1511454582214355
-  vertex_buffer: 2.6246179342269897
-  vertex_buffer: 3.4656630754470825
-  vertex_buffer: 0.650443494319916
-  vertex_buffer: 0.38137999176979054
-  vertex_buffer: 0.0
-  vertex_buffer: -1.126865029335022
-  vertex_buffer: 7.475604057312012
-  vertex_buffer: 0.500025987625122
-  vertex_buffer: 0.547487020492554
-  vertex_buffer: 0.0
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diff --git a/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_landmarks.pbtxt b/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_landmarks.pbtxt
deleted file mode 100644
index 8dfb463..0000000
--- a/mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_landmarks.pbtxt
+++ /dev/null
@@ -1,5086 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
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-}
diff --git a/mediapipe/modules/face_geometry/effect_renderer_calculator.cc b/mediapipe/modules/face_geometry/effect_renderer_calculator.cc
deleted file mode 100644
index f353b8f..0000000
--- a/mediapipe/modules/face_geometry/effect_renderer_calculator.cc
+++ /dev/null
@@ -1,284 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "absl/types/optional.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/formats/image_frame.h"
-#include "mediapipe/framework/formats/image_frame_opencv.h"
-#include "mediapipe/framework/port/opencv_core_inc.h"       // NOTYPO
-#include "mediapipe/framework/port/opencv_imgcodecs_inc.h"  // NOTYPO
-#include "mediapipe/framework/port/opencv_imgproc_inc.h"    // NOTYPO
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/framework/port/status_macros.h"
-#include "mediapipe/framework/port/statusor.h"
-#include "mediapipe/gpu/gl_calculator_helper.h"
-#include "mediapipe/gpu/gpu_buffer.h"
-#include "mediapipe/modules/face_geometry/effect_renderer_calculator.pb.h"
-#include "mediapipe/modules/face_geometry/libs/effect_renderer.h"
-#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
-#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
-#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
-#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
-#include "mediapipe/util/resource_util.h"
-
-namespace mediapipe {
-namespace {
-
-static constexpr char kEnvironmentTag[] = "ENVIRONMENT";
-static constexpr char kImageGpuTag[] = "IMAGE_GPU";
-static constexpr char kMultiFaceGeometryTag[] = "MULTI_FACE_GEOMETRY";
-
-// A calculator that renders a visual effect for multiple faces.
-//
-// Inputs:
-//   IMAGE_GPU (`GpuBuffer`, required):
-//     A buffer containing input image.
-//
-//   MULTI_FACE_GEOMETRY (`std::vector<face_geometry::FaceGeometry>`, optional):
-//     A vector of face geometry data.
-//
-//     If absent, the input GPU buffer is copied over into the output GPU buffer
-//     without any effect being rendered.
-//
-// Input side packets:
-//   ENVIRONMENT (`face_geometry::Environment`, required)
-//     Describes an environment; includes the camera frame origin point location
-//     as well as virtual camera parameters.
-//
-// Output:
-//   IMAGE_GPU (`GpuBuffer`, required):
-//     A buffer with a visual effect being rendered for multiple faces.
-//
-// Options:
-//   effect_texture_path (`string`, required):
-//     Defines a path for the visual effect texture file. The effect texture is
-//     later rendered on top of the effect mesh.
-//
-//     The texture file format must be supported by the OpenCV image decoder. It
-//     must also define either an RGB or an RGBA texture.
-//
-//   effect_mesh_3d_path (`string`, optional):
-//     Defines a path for the visual effect mesh 3D file. The effect mesh is
-//     later "attached" to the face and is driven by the face pose
-//     transformation matrix.
-//
-//     The mesh 3D file format must be the binary `face_geometry.Mesh3d` proto.
-//
-//     If is not present, the runtime face mesh will be used as the effect mesh
-//     - this mode is handy for facepaint effects.
-//
-class EffectRendererCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc) {
-    MP_RETURN_IF_ERROR(mediapipe::GlCalculatorHelper::UpdateContract(cc))
-        << "Failed to update contract for the GPU helper!";
-
-    cc->InputSidePackets()
-        .Tag(kEnvironmentTag)
-        .Set<face_geometry::Environment>();
-    cc->Inputs().Tag(kImageGpuTag).Set<GpuBuffer>();
-    cc->Inputs()
-        .Tag(kMultiFaceGeometryTag)
-        .Set<std::vector<face_geometry::FaceGeometry>>();
-    cc->Outputs().Tag(kImageGpuTag).Set<GpuBuffer>();
-
-    return mediapipe::GlCalculatorHelper::UpdateContract(cc);
-  }
-
-  absl::Status Open(CalculatorContext* cc) override {
-    cc->SetOffset(mediapipe::TimestampDiff(0));
-
-    MP_RETURN_IF_ERROR(gpu_helper_.Open(cc))
-        << "Failed to open the GPU helper!";
-    return gpu_helper_.RunInGlContext([&]() -> absl::Status {
-      const auto& options =
-          cc->Options<FaceGeometryEffectRendererCalculatorOptions>();
-
-      const auto& environment = cc->InputSidePackets()
-                                    .Tag(kEnvironmentTag)
-                                    .Get<face_geometry::Environment>();
-
-      MP_RETURN_IF_ERROR(face_geometry::ValidateEnvironment(environment))
-          << "Invalid environment!";
-
-      absl::optional<face_geometry::Mesh3d> effect_mesh_3d;
-      if (options.has_effect_mesh_3d_path()) {
-        ASSIGN_OR_RETURN(effect_mesh_3d,
-                         ReadMesh3dFromFile(options.effect_mesh_3d_path()),
-                         _ << "Failed to read the effect 3D mesh from file!");
-
-        MP_RETURN_IF_ERROR(face_geometry::ValidateMesh3d(*effect_mesh_3d))
-            << "Invalid effect 3D mesh!";
-      }
-
-      ASSIGN_OR_RETURN(ImageFrame effect_texture,
-                       ReadTextureFromFile(options.effect_texture_path()),
-                       _ << "Failed to read the effect texture from file!");
-
-      ASSIGN_OR_RETURN(effect_renderer_,
-                       CreateEffectRenderer(environment, effect_mesh_3d,
-                                            std::move(effect_texture)),
-                       _ << "Failed to create the effect renderer!");
-
-      return absl::OkStatus();
-    });
-  }
-
-  absl::Status Process(CalculatorContext* cc) override {
-    // The `IMAGE_GPU` stream is required to have a non-empty packet. In case
-    // this requirement is not met, there's nothing to be processed at the
-    // current timestamp.
-    if (cc->Inputs().Tag(kImageGpuTag).IsEmpty()) {
-      return absl::OkStatus();
-    }
-
-    return gpu_helper_.RunInGlContext([this, cc]() -> absl::Status {
-      const auto& input_gpu_buffer =
-          cc->Inputs().Tag(kImageGpuTag).Get<GpuBuffer>();
-
-      GlTexture input_gl_texture =
-          gpu_helper_.CreateSourceTexture(input_gpu_buffer);
-
-      GlTexture output_gl_texture = gpu_helper_.CreateDestinationTexture(
-          input_gl_texture.width(), input_gl_texture.height());
-
-      std::vector<face_geometry::FaceGeometry> empty_multi_face_geometry;
-      const auto& multi_face_geometry =
-          cc->Inputs().Tag(kMultiFaceGeometryTag).IsEmpty()
-              ? empty_multi_face_geometry
-              : cc->Inputs()
-                    .Tag(kMultiFaceGeometryTag)
-                    .Get<std::vector<face_geometry::FaceGeometry>>();
-
-      // Validate input multi face geometry data.
-      for (const face_geometry::FaceGeometry& face_geometry :
-           multi_face_geometry) {
-        MP_RETURN_IF_ERROR(face_geometry::ValidateFaceGeometry(face_geometry))
-            << "Invalid face geometry!";
-      }
-
-      MP_RETURN_IF_ERROR(effect_renderer_->RenderEffect(
-          multi_face_geometry, input_gl_texture.width(),
-          input_gl_texture.height(), input_gl_texture.target(),
-          input_gl_texture.name(), output_gl_texture.target(),
-          output_gl_texture.name()))
-          << "Failed to render the effect!";
-
-      std::unique_ptr<GpuBuffer> output_gpu_buffer =
-          output_gl_texture.GetFrame<GpuBuffer>();
-
-      cc->Outputs()
-          .Tag(kImageGpuTag)
-          .AddPacket(mediapipe::Adopt<GpuBuffer>(output_gpu_buffer.release())
-                         .At(cc->InputTimestamp()));
-
-      output_gl_texture.Release();
-      input_gl_texture.Release();
-
-      return absl::OkStatus();
-    });
-  }
-
-  ~EffectRendererCalculator() {
-    gpu_helper_.RunInGlContext([this]() { effect_renderer_.reset(); });
-  }
-
- private:
-  static absl::StatusOr<ImageFrame> ReadTextureFromFile(
-      const std::string& texture_path) {
-    ASSIGN_OR_RETURN(std::string texture_blob,
-                     ReadContentBlobFromFile(texture_path),
-                     _ << "Failed to read texture blob from file!");
-
-    // Use OpenCV image decoding functionality to finish reading the texture.
-    std::vector<char> texture_blob_vector(texture_blob.begin(),
-                                          texture_blob.end());
-    cv::Mat decoded_mat =
-        cv::imdecode(texture_blob_vector, cv::IMREAD_UNCHANGED);
-
-    RET_CHECK(decoded_mat.type() == CV_8UC3 || decoded_mat.type() == CV_8UC4)
-        << "Texture must have `char` as the underlying type and "
-           "must have either 3 or 4 channels!";
-
-    ImageFormat::Format image_format = ImageFormat::UNKNOWN;
-    cv::Mat output_mat;
-    switch (decoded_mat.channels()) {
-      case 3:
-        image_format = ImageFormat::SRGB;
-        cv::cvtColor(decoded_mat, output_mat, cv::COLOR_BGR2RGB);
-        break;
-
-      case 4:
-        image_format = ImageFormat::SRGBA;
-        cv::cvtColor(decoded_mat, output_mat, cv::COLOR_BGRA2RGBA);
-        break;
-
-      default:
-        RET_CHECK_FAIL()
-            << "Unexpected number of channels; expected 3 or 4, got "
-            << decoded_mat.channels() << "!";
-    }
-
-    ImageFrame output_image_frame(image_format, output_mat.size().width,
-                                  output_mat.size().height,
-                                  ImageFrame::kGlDefaultAlignmentBoundary);
-
-    output_mat.copyTo(formats::MatView(&output_image_frame));
-
-    return output_image_frame;
-  }
-
-  static absl::StatusOr<face_geometry::Mesh3d> ReadMesh3dFromFile(
-      const std::string& mesh_3d_path) {
-    ASSIGN_OR_RETURN(std::string mesh_3d_blob,
-                     ReadContentBlobFromFile(mesh_3d_path),
-                     _ << "Failed to read mesh 3D blob from file!");
-
-    face_geometry::Mesh3d mesh_3d;
-    RET_CHECK(mesh_3d.ParseFromString(mesh_3d_blob))
-        << "Failed to parse a mesh 3D proto from a binary blob!";
-
-    return mesh_3d;
-  }
-
-  static absl::StatusOr<std::string> ReadContentBlobFromFile(
-      const std::string& unresolved_path) {
-    ASSIGN_OR_RETURN(std::string resolved_path,
-                     mediapipe::PathToResourceAsFile(unresolved_path),
-                     _ << "Failed to resolve path! Path = " << unresolved_path);
-
-    std::string content_blob;
-    MP_RETURN_IF_ERROR(
-        mediapipe::GetResourceContents(resolved_path, &content_blob))
-        << "Failed to read content blob! Resolved path = " << resolved_path;
-
-    return content_blob;
-  }
-
-  mediapipe::GlCalculatorHelper gpu_helper_;
-  std::unique_ptr<face_geometry::EffectRenderer> effect_renderer_;
-};
-
-}  // namespace
-
-using FaceGeometryEffectRendererCalculator = EffectRendererCalculator;
-
-REGISTER_CALCULATOR(FaceGeometryEffectRendererCalculator);
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/face_geometry/effect_renderer_calculator.proto b/mediapipe/modules/face_geometry/effect_renderer_calculator.proto
deleted file mode 100644
index 6c23903..0000000
--- a/mediapipe/modules/face_geometry/effect_renderer_calculator.proto
+++ /dev/null
@@ -1,46 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator_options.proto";
-
-message FaceGeometryEffectRendererCalculatorOptions {
-  extend CalculatorOptions {
-    optional FaceGeometryEffectRendererCalculatorOptions ext = 323693808;
-  }
-
-  // Defines a path for the visual effect texture file. The effect texture is
-  // later rendered on top of the effect mesh.
-  //
-  // Please be aware about the difference between the CPU texture memory layout
-  // and the GPU texture sampler coordinate space. This renderer follows
-  // conventions discussed here: https://open.gl/textures
-  //
-  // The texture file format must be supported by the OpenCV image decoder. It
-  // must also define either an RGB or an RGBA texture.
-  optional string effect_texture_path = 1;
-
-  // Defines a path for the visual effect mesh 3D file. The effect mesh is later
-  // "attached" to the face and is driven by the face pose transformation
-  // matrix.
-  //
-  // The mesh 3D file format must be the binary `face_system.Mesh3d` proto.
-  //
-  // If is not present, the runtime face mesh will be used as the effect mesh
-  // - this mode is handy for facepaint effects.
-  optional string effect_mesh_3d_path = 2;
-}
diff --git a/mediapipe/modules/face_geometry/env_generator_calculator.cc b/mediapipe/modules/face_geometry/env_generator_calculator.cc
deleted file mode 100644
index 2e95a66..0000000
--- a/mediapipe/modules/face_geometry/env_generator_calculator.cc
+++ /dev/null
@@ -1,81 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/framework/port/status_macros.h"
-#include "mediapipe/modules/face_geometry/env_generator_calculator.pb.h"
-#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
-#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
-
-namespace mediapipe {
-namespace {
-
-static constexpr char kEnvironmentTag[] = "ENVIRONMENT";
-
-// A calculator that generates an environment, which describes a virtual scene.
-//
-// Output side packets:
-//   ENVIRONMENT (`face_geometry::Environment`, required)
-//     Describes an environment; includes the camera frame origin point location
-//     as well as virtual camera parameters.
-//
-// Options:
-//   environment (`face_geometry.Environment`, required):
-//     Defines an environment to be packed as the output side packet.
-//
-//     Must be valid (for details, please refer to the proto message definition
-//     comments and/or `modules/face_geometry/libs/validation_utils.h/cc`)
-//
-class EnvGeneratorCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc) {
-    cc->OutputSidePackets()
-        .Tag(kEnvironmentTag)
-        .Set<face_geometry::Environment>();
-    return absl::OkStatus();
-  }
-
-  absl::Status Open(CalculatorContext* cc) override {
-    cc->SetOffset(mediapipe::TimestampDiff(0));
-
-    const face_geometry::Environment& environment =
-        cc->Options<FaceGeometryEnvGeneratorCalculatorOptions>().environment();
-
-    MP_RETURN_IF_ERROR(face_geometry::ValidateEnvironment(environment))
-        << "Invalid environment!";
-
-    cc->OutputSidePackets()
-        .Tag(kEnvironmentTag)
-        .Set(mediapipe::MakePacket<face_geometry::Environment>(environment));
-
-    return absl::OkStatus();
-  }
-
-  absl::Status Process(CalculatorContext* cc) override {
-    return absl::OkStatus();
-  }
-
-  absl::Status Close(CalculatorContext* cc) override {
-    return absl::OkStatus();
-  }
-};
-
-}  // namespace
-
-using FaceGeometryEnvGeneratorCalculator = EnvGeneratorCalculator;
-
-REGISTER_CALCULATOR(FaceGeometryEnvGeneratorCalculator);
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/face_geometry/env_generator_calculator.proto b/mediapipe/modules/face_geometry/env_generator_calculator.proto
deleted file mode 100644
index dea2ae0..0000000
--- a/mediapipe/modules/face_geometry/env_generator_calculator.proto
+++ /dev/null
@@ -1,32 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator_options.proto";
-import "mediapipe/modules/face_geometry/protos/environment.proto";
-
-message FaceGeometryEnvGeneratorCalculatorOptions {
-  extend CalculatorOptions {
-    optional FaceGeometryEnvGeneratorCalculatorOptions ext = 323693810;
-  }
-
-  // Defines an environment to be packed as the output side packet.
-  //
-  // Must be valid (for details, please refer to the proto message definition
-  // comments and/or `modules/face_geometry/libs/validation_utils.h/cc`)
-  optional face_geometry.Environment environment = 1;
-}
diff --git a/mediapipe/modules/face_geometry/face_geometry.pbtxt b/mediapipe/modules/face_geometry/face_geometry.pbtxt
deleted file mode 100644
index 76228d4..0000000
--- a/mediapipe/modules/face_geometry/face_geometry.pbtxt
+++ /dev/null
@@ -1,48 +0,0 @@
-# MediaPipe graph to extract geometry from face landmarks for multiple faces.
-#
-# It is required that "geometry_pipeline_metadata.binarypb" is available at
-# "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata.binarypb"
-# path during execution.
-#
-# This is a deprecated subgraph kept for backward-compatibility reasons. Please,
-# be explicit and use the `FaceGeometryFromLandmarks` subgraph in the new code
-# to enable the same runtime behaviour.
-
-type: "FaceGeometry"
-
-# The size of the input frame. The first element of the pair is the frame width;
-# the other one is the frame height.
-#
-# The face landmarks should have been detected on a frame with the same
-# ratio. If used as-is, the resulting face geometry visualization should be
-# happening on a frame with the same ratio as well.
-#
-# (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# Collection of detected/predicted faces, each represented as a list of face
-# landmarks. (std::vector<NormalizedLandmarkList>)
-input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
-
-# Environment that describes the current virtual scene.
-# (face_geometry::Environment)
-input_side_packet: "ENVIRONMENT:environment"
-
-# A list of geometry data for each detected face.
-# (std::vector<face_geometry::FaceGeometry>)
-output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-
-# Extracts face geometry for multiple faces from a vector of face landmark
-# lists.
-node {
-  calculator: "FaceGeometryPipelineCalculator"
-  input_side_packet: "ENVIRONMENT:environment"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
-  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-  options: {
-    [mediapipe.FaceGeometryPipelineCalculatorOptions.ext] {
-      metadata_path: "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata.binarypb"
-    }
-  }
-}
diff --git a/mediapipe/modules/face_geometry/face_geometry_from_detection.pbtxt b/mediapipe/modules/face_geometry/face_geometry_from_detection.pbtxt
deleted file mode 100644
index f570286..0000000
--- a/mediapipe/modules/face_geometry/face_geometry_from_detection.pbtxt
+++ /dev/null
@@ -1,87 +0,0 @@
-# MediaPipe graph to extract geometry from face detection for multiple faces.
-#
-# It is required that "geometry_pipeline_metadata_detection.binarypb" is
-# available at
-# "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_detection.binarypb"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceGeometryFromDetection"
-#     input_stream: "IMAGE_SIZE:image_size"
-#     input_stream: "MULTI_FACE_DETECTION:multi_face_detection"
-#     input_side_packet: "ENVIRONMENT:environment"
-#     output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-#   }
-
-type: "FaceGeometryFromDetection"
-
-# The size of the input frame. The first element of the pair is the frame width;
-# the other one is the frame height.
-#
-# The face landmarks should have been detected on a frame with the same
-# ratio. If used as-is, the resulting face geometry visualization should be
-# happening on a frame with the same ratio as well.
-#
-# (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# Collection of detected/predicted faces, each represented as a detection.
-# (std::vector<DETECTION>)
-input_stream: "MULTI_FACE_DETECTION:multi_face_detection"
-
-# Environment that describes the current virtual scene.
-# (face_geometry::Environment)
-input_side_packet: "ENVIRONMENT:environment"
-
-# A list of geometry data for each detected face.
-# (std::vector<face_geometry::FaceGeometry>)
-#
-# NOTE: the triangular topology of the face meshes is only useful when derived
-#       from the 468 face landmarks, not from the 6 face detection landmarks
-#       (keypoints). The former don't cover the entire face and this mesh is
-#       defined here only to comply with the API. It should be considered as
-#       a placeholder and/or for debugging purposes.
-#
-#       Use the face geometry derived from the face detection landmarks
-#       (keypoints) for the face pose transformation matrix, not the mesh.
-output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-
-# Begin iterating over a vector of the face detections.
-node {
-  calculator: "BeginLoopDetectionCalculator"
-  input_stream: "ITERABLE:multi_face_detection"
-  output_stream: "ITEM:face_detection"
-  output_stream: "BATCH_END:detection_timestamp"
-}
-
-# Extracts face detection keypoints as a normalized landmarks.
-node {
-  calculator: "DetectionToLandmarksCalculator"
-  input_stream: "DETECTION:face_detection"
-  output_stream: "LANDMARKS:face_landmarks"
-}
-
-# End iterating over a vector of the face detections and receive a vector of
-# face landmark lists as a result.
-node {
-  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITEM:face_landmarks"
-  input_stream: "BATCH_END:detection_timestamp"
-  output_stream: "ITERABLE:multi_face_landmarks"
-}
-
-# Extracts face geometry for multiple faces from a vector of face detection
-# landmark lists.
-node {
-  calculator: "FaceGeometryPipelineCalculator"
-  input_side_packet: "ENVIRONMENT:environment"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
-  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-  options: {
-    [mediapipe.FaceGeometryPipelineCalculatorOptions.ext] {
-      metadata_path: "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_detection.binarypb"
-    }
-  }
-}
diff --git a/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt b/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt
deleted file mode 100644
index 3291476..0000000
--- a/mediapipe/modules/face_geometry/face_geometry_from_landmarks.pbtxt
+++ /dev/null
@@ -1,54 +0,0 @@
-# MediaPipe graph to extract geometry from face landmarks for multiple faces.
-#
-# It is required that "geometry_pipeline_metadata_from_landmark.binarypb" is
-# available at
-# "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_from_landmarks.binarypb"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceGeometryFromLandmarks"
-#     input_stream: "IMAGE_SIZE:image_size"
-#     input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
-#     input_side_packet: "ENVIRONMENT:environment"
-#     output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-#   }
-
-type: "FaceGeometryFromLandmarks"
-
-# The size of the input frame. The first element of the pair is the frame width;
-# the other one is the frame height.
-#
-# The face landmarks should have been detected on a frame with the same
-# ratio. If used as-is, the resulting face geometry visualization should be
-# happening on a frame with the same ratio as well.
-#
-# (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# Collection of detected/predicted faces, each represented as a list of face
-# landmarks. (std::vector<NormalizedLandmarkList>)
-input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
-
-# Environment that describes the current virtual scene.
-# (face_geometry::Environment)
-input_side_packet: "ENVIRONMENT:environment"
-
-# A list of geometry data for each detected face.
-# (std::vector<face_geometry::FaceGeometry>)
-output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-
-# Extracts face geometry for multiple faces from a vector of face landmark
-# lists.
-node {
-  calculator: "FaceGeometryPipelineCalculator"
-  input_side_packet: "ENVIRONMENT:environment"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "MULTI_FACE_LANDMARKS:multi_face_landmarks"
-  output_stream: "MULTI_FACE_GEOMETRY:multi_face_geometry"
-  options: {
-    [mediapipe.FaceGeometryPipelineCalculatorOptions.ext] {
-      metadata_path: "mediapipe/modules/face_geometry/data/geometry_pipeline_metadata_landmarks.binarypb"
-    }
-  }
-}
diff --git a/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc b/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc
deleted file mode 100644
index 87e710e..0000000
--- a/mediapipe/modules/face_geometry/geometry_pipeline_calculator.cc
+++ /dev/null
@@ -1,197 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <memory>
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "absl/memory/memory.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/formats/landmark.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/framework/port/status_macros.h"
-#include "mediapipe/framework/port/statusor.h"
-#include "mediapipe/modules/face_geometry/geometry_pipeline_calculator.pb.h"
-#include "mediapipe/modules/face_geometry/libs/geometry_pipeline.h"
-#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
-#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
-#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
-#include "mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.pb.h"
-#include "mediapipe/util/resource_util.h"
-
-namespace mediapipe {
-namespace {
-
-static constexpr char kEnvironmentTag[] = "ENVIRONMENT";
-static constexpr char kImageSizeTag[] = "IMAGE_SIZE";
-static constexpr char kMultiFaceGeometryTag[] = "MULTI_FACE_GEOMETRY";
-static constexpr char kMultiFaceLandmarksTag[] = "MULTI_FACE_LANDMARKS";
-
-// A calculator that renders a visual effect for multiple faces.
-//
-// Inputs:
-//   IMAGE_SIZE (`std::pair<int, int>`, required):
-//     The size of the current frame. The first element of the pair is the frame
-//     width; the other one is the frame height.
-//
-//     The face landmarks should have been detected on a frame with the same
-//     ratio. If used as-is, the resulting face geometry visualization should be
-//     happening on a frame with the same ratio as well.
-//
-//   MULTI_FACE_LANDMARKS (`std::vector<NormalizedLandmarkList>`, required):
-//     A vector of face landmark lists.
-//
-// Input side packets:
-//   ENVIRONMENT (`face_geometry::Environment`, required)
-//     Describes an environment; includes the camera frame origin point location
-//     as well as virtual camera parameters.
-//
-// Output:
-//   MULTI_FACE_GEOMETRY (`std::vector<face_geometry::FaceGeometry>`, required):
-//     A vector of face geometry data.
-//
-// Options:
-//   metadata_path (`string`, optional):
-//     Defines a path for the geometry pipeline metadata file.
-//
-//     The geometry pipeline metadata file format must be the binary
-//     `face_geometry.GeometryPipelineMetadata` proto.
-//
-class GeometryPipelineCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc) {
-    cc->InputSidePackets()
-        .Tag(kEnvironmentTag)
-        .Set<face_geometry::Environment>();
-    cc->Inputs().Tag(kImageSizeTag).Set<std::pair<int, int>>();
-    cc->Inputs()
-        .Tag(kMultiFaceLandmarksTag)
-        .Set<std::vector<NormalizedLandmarkList>>();
-    cc->Outputs()
-        .Tag(kMultiFaceGeometryTag)
-        .Set<std::vector<face_geometry::FaceGeometry>>();
-
-    return absl::OkStatus();
-  }
-
-  absl::Status Open(CalculatorContext* cc) override {
-    cc->SetOffset(mediapipe::TimestampDiff(0));
-
-    const auto& options = cc->Options<FaceGeometryPipelineCalculatorOptions>();
-
-    ASSIGN_OR_RETURN(
-        face_geometry::GeometryPipelineMetadata metadata,
-        ReadMetadataFromFile(options.metadata_path()),
-        _ << "Failed to read the geometry pipeline metadata from file!");
-
-    MP_RETURN_IF_ERROR(
-        face_geometry::ValidateGeometryPipelineMetadata(metadata))
-        << "Invalid geometry pipeline metadata!";
-
-    const face_geometry::Environment& environment =
-        cc->InputSidePackets()
-            .Tag(kEnvironmentTag)
-            .Get<face_geometry::Environment>();
-
-    MP_RETURN_IF_ERROR(face_geometry::ValidateEnvironment(environment))
-        << "Invalid environment!";
-
-    ASSIGN_OR_RETURN(
-        geometry_pipeline_,
-        face_geometry::CreateGeometryPipeline(environment, metadata),
-        _ << "Failed to create a geometry pipeline!");
-
-    return absl::OkStatus();
-  }
-
-  absl::Status Process(CalculatorContext* cc) override {
-    // Both the `IMAGE_SIZE` and the `MULTI_FACE_LANDMARKS` streams are required
-    // to have a non-empty packet. In case this requirement is not met, there's
-    // nothing to be processed at the current timestamp.
-    if (cc->Inputs().Tag(kImageSizeTag).IsEmpty() ||
-        cc->Inputs().Tag(kMultiFaceLandmarksTag).IsEmpty()) {
-      return absl::OkStatus();
-    }
-
-    const auto& image_size =
-        cc->Inputs().Tag(kImageSizeTag).Get<std::pair<int, int>>();
-    const auto& multi_face_landmarks =
-        cc->Inputs()
-            .Tag(kMultiFaceLandmarksTag)
-            .Get<std::vector<NormalizedLandmarkList>>();
-
-    auto multi_face_geometry =
-        absl::make_unique<std::vector<face_geometry::FaceGeometry>>();
-
-    ASSIGN_OR_RETURN(
-        *multi_face_geometry,
-        geometry_pipeline_->EstimateFaceGeometry(
-            multi_face_landmarks,  //
-            /*frame_width*/ image_size.first,
-            /*frame_height*/ image_size.second),
-        _ << "Failed to estimate face geometry for multiple faces!");
-
-    cc->Outputs()
-        .Tag(kMultiFaceGeometryTag)
-        .AddPacket(mediapipe::Adopt<std::vector<face_geometry::FaceGeometry>>(
-                       multi_face_geometry.release())
-                       .At(cc->InputTimestamp()));
-
-    return absl::OkStatus();
-  }
-
-  absl::Status Close(CalculatorContext* cc) override {
-    return absl::OkStatus();
-  }
-
- private:
-  static absl::StatusOr<face_geometry::GeometryPipelineMetadata>
-  ReadMetadataFromFile(const std::string& metadata_path) {
-    ASSIGN_OR_RETURN(std::string metadata_blob,
-                     ReadContentBlobFromFile(metadata_path),
-                     _ << "Failed to read a metadata blob from file!");
-
-    face_geometry::GeometryPipelineMetadata metadata;
-    RET_CHECK(metadata.ParseFromString(metadata_blob))
-        << "Failed to parse a metadata proto from a binary blob!";
-
-    return metadata;
-  }
-
-  static absl::StatusOr<std::string> ReadContentBlobFromFile(
-      const std::string& unresolved_path) {
-    ASSIGN_OR_RETURN(std::string resolved_path,
-                     mediapipe::PathToResourceAsFile(unresolved_path),
-                     _ << "Failed to resolve path! Path = " << unresolved_path);
-
-    std::string content_blob;
-    MP_RETURN_IF_ERROR(
-        mediapipe::GetResourceContents(resolved_path, &content_blob))
-        << "Failed to read content blob! Resolved path = " << resolved_path;
-
-    return content_blob;
-  }
-
-  std::unique_ptr<face_geometry::GeometryPipeline> geometry_pipeline_;
-};
-
-}  // namespace
-
-using FaceGeometryPipelineCalculator = GeometryPipelineCalculator;
-
-REGISTER_CALCULATOR(FaceGeometryPipelineCalculator);
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/face_geometry/geometry_pipeline_calculator.proto b/mediapipe/modules/face_geometry/geometry_pipeline_calculator.proto
deleted file mode 100644
index 638bb45..0000000
--- a/mediapipe/modules/face_geometry/geometry_pipeline_calculator.proto
+++ /dev/null
@@ -1,27 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator_options.proto";
-
-message FaceGeometryPipelineCalculatorOptions {
-  extend CalculatorOptions {
-    optional FaceGeometryPipelineCalculatorOptions ext = 323693812;
-  }
-
-  optional string metadata_path = 1;
-}
diff --git a/mediapipe/modules/face_geometry/libs/BUILD b/mediapipe/modules/face_geometry/libs/BUILD
deleted file mode 100644
index 35dc451..0000000
--- a/mediapipe/modules/face_geometry/libs/BUILD
+++ /dev/null
@@ -1,103 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "effect_renderer",
-    srcs = ["effect_renderer.cc"],
-    hdrs = ["effect_renderer.h"],
-    deps = [
-        ":mesh_3d_utils",
-        ":validation_utils",
-        "//mediapipe/framework/formats:image_format_cc_proto",
-        "//mediapipe/framework/formats:image_frame",
-        "//mediapipe/framework/formats:matrix_data_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/framework/port:statusor",
-        "//mediapipe/gpu:gl_base",
-        "//mediapipe/gpu:shader_util",
-        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:face_geometry_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:mesh_3d_cc_proto",
-        "@com_google_absl//absl/memory",
-        "@com_google_absl//absl/types:optional",
-    ],
-)
-
-cc_library(
-    name = "geometry_pipeline",
-    srcs = ["geometry_pipeline.cc"],
-    hdrs = ["geometry_pipeline.h"],
-    deps = [
-        ":mesh_3d_utils",
-        ":procrustes_solver",
-        ":validation_utils",
-        "//mediapipe/framework/formats:landmark_cc_proto",
-        "//mediapipe/framework/formats:matrix",
-        "//mediapipe/framework/formats:matrix_data_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/framework/port:statusor",
-        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:face_geometry_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:mesh_3d_cc_proto",
-        "@com_google_absl//absl/memory",
-        "@eigen_archive//:eigen3",
-    ],
-)
-
-cc_library(
-    name = "mesh_3d_utils",
-    srcs = ["mesh_3d_utils.cc"],
-    hdrs = ["mesh_3d_utils.h"],
-    deps = [
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:statusor",
-        "//mediapipe/modules/face_geometry/protos:mesh_3d_cc_proto",
-    ],
-)
-
-cc_library(
-    name = "procrustes_solver",
-    srcs = ["procrustes_solver.cc"],
-    hdrs = ["procrustes_solver.h"],
-    deps = [
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/framework/port:statusor",
-        "@com_google_absl//absl/memory",
-        "@eigen_archive//:eigen3",
-    ],
-)
-
-cc_library(
-    name = "validation_utils",
-    srcs = ["validation_utils.cc"],
-    hdrs = ["validation_utils.h"],
-    deps = [
-        ":mesh_3d_utils",
-        "//mediapipe/framework/formats:matrix_data_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/modules/face_geometry/protos:environment_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:face_geometry_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:geometry_pipeline_metadata_cc_proto",
-        "//mediapipe/modules/face_geometry/protos:mesh_3d_cc_proto",
-    ],
-)
diff --git a/mediapipe/modules/face_geometry/libs/effect_renderer.cc b/mediapipe/modules/face_geometry/libs/effect_renderer.cc
deleted file mode 100644
index 27a54e0..0000000
--- a/mediapipe/modules/face_geometry/libs/effect_renderer.cc
+++ /dev/null
@@ -1,733 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/face_geometry/libs/effect_renderer.h"
-
-#include <cmath>
-#include <cstdint>
-#include <limits>
-#include <memory>
-#include <utility>
-#include <vector>
-
-#include "absl/memory/memory.h"
-#include "absl/types/optional.h"
-#include "mediapipe/framework/formats/image_format.pb.h"
-#include "mediapipe/framework/formats/image_frame.h"
-#include "mediapipe/framework/formats/matrix_data.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/framework/port/status_macros.h"
-#include "mediapipe/framework/port/statusor.h"
-#include "mediapipe/gpu/gl_base.h"
-#include "mediapipe/gpu/shader_util.h"
-#include "mediapipe/modules/face_geometry/libs/mesh_3d_utils.h"
-#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
-#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
-#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
-#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
-
-namespace mediapipe::face_geometry {
-namespace {
-
-struct RenderableMesh3d {
-  static absl::StatusOr<RenderableMesh3d> CreateFromProtoMesh3d(
-      const Mesh3d& proto_mesh_3d) {
-    Mesh3d::VertexType vertex_type = proto_mesh_3d.vertex_type();
-
-    RenderableMesh3d renderable_mesh_3d;
-    renderable_mesh_3d.vertex_size = GetVertexSize(vertex_type);
-    ASSIGN_OR_RETURN(
-        renderable_mesh_3d.vertex_position_size,
-        GetVertexComponentSize(vertex_type, VertexComponent::POSITION),
-        _ << "Failed to get the position vertex size!");
-    ASSIGN_OR_RETURN(
-        renderable_mesh_3d.tex_coord_position_size,
-        GetVertexComponentSize(vertex_type, VertexComponent::TEX_COORD),
-        _ << "Failed to get the tex coord vertex size!");
-    ASSIGN_OR_RETURN(
-        renderable_mesh_3d.vertex_position_offset,
-        GetVertexComponentOffset(vertex_type, VertexComponent::POSITION),
-        _ << "Failed to get the position vertex offset!");
-    ASSIGN_OR_RETURN(
-        renderable_mesh_3d.tex_coord_position_offset,
-        GetVertexComponentOffset(vertex_type, VertexComponent::TEX_COORD),
-        _ << "Failed to get the tex coord vertex offset!");
-
-    switch (proto_mesh_3d.primitive_type()) {
-      case Mesh3d::TRIANGLE:
-        renderable_mesh_3d.primitive_type = GL_TRIANGLES;
-        break;
-
-      default:
-        RET_CHECK_FAIL() << "Only triangle primitive types are supported!";
-    }
-
-    renderable_mesh_3d.vertex_buffer.reserve(
-        proto_mesh_3d.vertex_buffer_size());
-    for (float vertex_element : proto_mesh_3d.vertex_buffer()) {
-      renderable_mesh_3d.vertex_buffer.push_back(vertex_element);
-    }
-
-    renderable_mesh_3d.index_buffer.reserve(proto_mesh_3d.index_buffer_size());
-    for (uint32_t index_element : proto_mesh_3d.index_buffer()) {
-      RET_CHECK_LE(index_element, std::numeric_limits<uint16_t>::max())
-          << "Index buffer elements must fit into the `uint16` type in order "
-             "to be renderable!";
-
-      renderable_mesh_3d.index_buffer.push_back(
-          static_cast<uint16_t>(index_element));
-    }
-
-    return renderable_mesh_3d;
-  }
-
-  uint32_t vertex_size;
-  uint32_t vertex_position_size;
-  uint32_t tex_coord_position_size;
-  uint32_t vertex_position_offset;
-  uint32_t tex_coord_position_offset;
-  uint32_t primitive_type;
-
-  std::vector<float> vertex_buffer;
-  std::vector<uint16_t> index_buffer;
-};
-
-class Texture {
- public:
-  static absl::StatusOr<std::unique_ptr<Texture>> WrapExternalTexture(
-      GLuint handle, GLenum target, int width, int height) {
-    RET_CHECK(handle) << "External texture must have a non-null handle!";
-    return absl::WrapUnique(new Texture(handle, target, width, height,
-                                        /*is_owned*/ false));
-  }
-
-  static absl::StatusOr<std::unique_ptr<Texture>> CreateFromImageFrame(
-      const ImageFrame& image_frame) {
-    RET_CHECK(image_frame.IsAligned(ImageFrame::kGlDefaultAlignmentBoundary))
-        << "Image frame memory must be aligned for GL usage!";
-
-    RET_CHECK(image_frame.Width() > 0 && image_frame.Height() > 0)
-        << "Image frame must have positive dimensions!";
-
-    RET_CHECK(image_frame.Format() == ImageFormat::SRGB ||
-              image_frame.Format() == ImageFormat::SRGBA)
-        << "Image frame format must be either SRGB or SRGBA!";
-
-    GLint image_format;
-    switch (image_frame.NumberOfChannels()) {
-      case 3:
-        image_format = GL_RGB;
-        break;
-      case 4:
-        image_format = GL_RGBA;
-        break;
-      default:
-        RET_CHECK_FAIL()
-            << "Unexpected number of channels; expected 3 or 4, got "
-            << image_frame.NumberOfChannels() << "!";
-    }
-
-    GLuint handle;
-    glGenTextures(1, &handle);
-    RET_CHECK(handle) << "Failed to initialize an OpenGL texture!";
-
-    glBindTexture(GL_TEXTURE_2D, handle);
-    glTexParameteri(GL_TEXTURE_2D, GL_NEAREST_MIPMAP_LINEAR, GL_LINEAR);
-    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR);
-    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE);
-    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE);
-    glTexImage2D(GL_TEXTURE_2D, 0, image_format, image_frame.Width(),
-                 image_frame.Height(), 0, image_format, GL_UNSIGNED_BYTE,
-                 image_frame.PixelData());
-    glGenerateMipmap(GL_TEXTURE_2D);
-    glBindTexture(GL_TEXTURE_2D, 0);
-
-    return absl::WrapUnique(new Texture(
-        handle, GL_TEXTURE_2D, image_frame.Width(), image_frame.Height(),
-        /*is_owned*/ true));
-  }
-
-  ~Texture() {
-    if (is_owned_) {
-      glDeleteProgram(handle_);
-    }
-  }
-
-  GLuint handle() const { return handle_; }
-  GLenum target() const { return target_; }
-  int width() const { return width_; }
-  int height() const { return height_; }
-
- private:
-  Texture(GLuint handle, GLenum target, int width, int height, bool is_owned)
-      : handle_(handle),
-        target_(target),
-        width_(width),
-        height_(height),
-        is_owned_(is_owned) {}
-
-  GLuint handle_;
-  GLenum target_;
-  int width_;
-  int height_;
-  bool is_owned_;
-};
-
-class RenderTarget {
- public:
-  static absl::StatusOr<std::unique_ptr<RenderTarget>> Create() {
-    GLuint framebuffer_handle;
-    glGenFramebuffers(1, &framebuffer_handle);
-    RET_CHECK(framebuffer_handle)
-        << "Failed to initialize an OpenGL framebuffer!";
-
-    return absl::WrapUnique(new RenderTarget(framebuffer_handle));
-  }
-
-  ~RenderTarget() {
-    glDeleteFramebuffers(1, &framebuffer_handle_);
-    // Renderbuffer handle might have never been created if this render target
-    // is destroyed before `SetColorbuffer()` is called for the first time.
-    if (renderbuffer_handle_) {
-      glDeleteFramebuffers(1, &renderbuffer_handle_);
-    }
-  }
-
-  absl::Status SetColorbuffer(const Texture& colorbuffer_texture) {
-    glBindFramebuffer(GL_FRAMEBUFFER, framebuffer_handle_);
-    glViewport(0, 0, colorbuffer_texture.width(), colorbuffer_texture.height());
-
-    glActiveTexture(GL_TEXTURE0);
-    glBindTexture(colorbuffer_texture.target(), colorbuffer_texture.handle());
-    glFramebufferTexture2D(GL_FRAMEBUFFER, GL_COLOR_ATTACHMENT0,
-                           colorbuffer_texture.target(),
-                           colorbuffer_texture.handle(),
-                           /*level*/ 0);
-    glBindTexture(colorbuffer_texture.target(), 0);
-
-    // If the existing depth buffer has different dimensions, delete it.
-    if (renderbuffer_handle_ &&
-        (viewport_width_ != colorbuffer_texture.width() ||
-         viewport_height_ != colorbuffer_texture.height())) {
-      glDeleteRenderbuffers(1, &renderbuffer_handle_);
-      renderbuffer_handle_ = 0;
-    }
-
-    // If there is no depth buffer, create one.
-    if (!renderbuffer_handle_) {
-      glGenRenderbuffers(1, &renderbuffer_handle_);
-      RET_CHECK(renderbuffer_handle_)
-          << "Failed to initialize an OpenGL renderbuffer!";
-      glBindRenderbuffer(GL_RENDERBUFFER, renderbuffer_handle_);
-      glRenderbufferStorage(GL_RENDERBUFFER, GL_DEPTH_COMPONENT16,
-                            colorbuffer_texture.width(),
-                            colorbuffer_texture.height());
-      glFramebufferRenderbuffer(GL_FRAMEBUFFER, GL_DEPTH_ATTACHMENT,
-                                GL_RENDERBUFFER, renderbuffer_handle_);
-      glBindRenderbuffer(GL_RENDERBUFFER, 0);
-    }
-
-    viewport_width_ = colorbuffer_texture.width();
-    viewport_height_ = colorbuffer_texture.height();
-
-    glBindFramebuffer(GL_FRAMEBUFFER, 0);
-    glFlush();
-
-    return absl::OkStatus();
-  }
-
-  void Bind() const {
-    glBindFramebuffer(GL_FRAMEBUFFER, framebuffer_handle_);
-    glViewport(0, 0, viewport_width_, viewport_height_);
-  }
-
-  void Unbind() const { glBindFramebuffer(GL_FRAMEBUFFER, 0); }
-
-  void Clear() const {
-    Bind();
-    glEnable(GL_DEPTH_TEST);
-    glDepthMask(GL_TRUE);
-
-    glClearColor(0.f, 0.f, 0.f, 0.f);
-    glClearDepthf(1.f);
-    glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);
-
-    glDepthMask(GL_FALSE);
-    glDisable(GL_DEPTH_TEST);
-
-    Unbind();
-    glFlush();
-  }
-
- private:
-  explicit RenderTarget(GLuint framebuffer_handle)
-      : framebuffer_handle_(framebuffer_handle),
-        renderbuffer_handle_(0),
-        viewport_width_(-1),
-        viewport_height_(-1) {}
-
-  GLuint framebuffer_handle_;
-  GLuint renderbuffer_handle_;
-  int viewport_width_;
-  int viewport_height_;
-};
-
-class Renderer {
- public:
-  enum class RenderMode { OPAQUE, OVERDRAW, OCCLUSION };
-
-  static absl::StatusOr<std::unique_ptr<Renderer>> Create() {
-    static const GLint kAttrLocation[NUM_ATTRIBUTES] = {
-        ATTRIB_VERTEX,
-        ATTRIB_TEXTURE_POSITION,
-    };
-    static const GLchar* kAttrName[NUM_ATTRIBUTES] = {
-        "position",
-        "tex_coord",
-    };
-
-    static const GLchar* kVertSrc = R"(
-      uniform mat4 projection_mat;
-      uniform mat4 model_mat;
-
-      attribute vec4 position;
-      attribute vec4 tex_coord;
-
-      varying vec2 v_tex_coord;
-
-      void main() {
-        v_tex_coord = tex_coord.xy;
-        gl_Position = projection_mat * model_mat * position;
-      }
-    )";
-
-    static const GLchar* kFragSrc = R"(
-      precision mediump float;
-
-      varying vec2 v_tex_coord;
-      uniform sampler2D texture;
-
-      void main() {
-        gl_FragColor = texture2D(texture, v_tex_coord);
-      }
-    )";
-
-    GLuint program_handle = 0;
-    GlhCreateProgram(kVertSrc, kFragSrc, NUM_ATTRIBUTES,
-                     (const GLchar**)&kAttrName[0], kAttrLocation,
-                     &program_handle);
-    RET_CHECK(program_handle) << "Problem initializing the texture program!";
-    GLint projection_mat_uniform =
-        glGetUniformLocation(program_handle, "projection_mat");
-    GLint model_mat_uniform = glGetUniformLocation(program_handle, "model_mat");
-    GLint texture_uniform = glGetUniformLocation(program_handle, "texture");
-
-    RET_CHECK_NE(projection_mat_uniform, -1)
-        << "Failed to find `projection_mat` uniform!";
-    RET_CHECK_NE(model_mat_uniform, -1)
-        << "Failed to find `model_mat` uniform!";
-    RET_CHECK_NE(texture_uniform, -1) << "Failed to find `texture` uniform!";
-
-    return absl::WrapUnique(new Renderer(program_handle, projection_mat_uniform,
-                                         model_mat_uniform, texture_uniform));
-  }
-
-  ~Renderer() { glDeleteProgram(program_handle_); }
-
-  absl::Status Render(const RenderTarget& render_target, const Texture& texture,
-                      const RenderableMesh3d& mesh_3d,
-                      const std::array<float, 16>& projection_mat,
-                      const std::array<float, 16>& model_mat,
-                      RenderMode render_mode) const {
-    glUseProgram(program_handle_);
-    // Set up the GL state.
-    glEnable(GL_BLEND);
-    glFrontFace(GL_CCW);
-    switch (render_mode) {
-      case RenderMode::OPAQUE:
-        glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);
-        glEnable(GL_DEPTH_TEST);
-        glDepthMask(GL_TRUE);
-        break;
-
-      case RenderMode::OVERDRAW:
-        glBlendFunc(GL_ONE, GL_ZERO);
-        glDisable(GL_DEPTH_TEST);
-        glDepthMask(GL_FALSE);
-        break;
-
-      case RenderMode::OCCLUSION:
-        glBlendFunc(GL_ZERO, GL_ONE);
-        glEnable(GL_DEPTH_TEST);
-        glDepthMask(GL_TRUE);
-        break;
-    }
-
-    render_target.Bind();
-    // Set up vertex attributes.
-    glVertexAttribPointer(
-        ATTRIB_VERTEX, mesh_3d.vertex_position_size, GL_FLOAT, 0,
-        mesh_3d.vertex_size * sizeof(float),
-        mesh_3d.vertex_buffer.data() + mesh_3d.vertex_position_offset);
-    glEnableVertexAttribArray(ATTRIB_VERTEX);
-    glVertexAttribPointer(
-        ATTRIB_TEXTURE_POSITION, mesh_3d.tex_coord_position_size, GL_FLOAT, 0,
-        mesh_3d.vertex_size * sizeof(float),
-        mesh_3d.vertex_buffer.data() + mesh_3d.tex_coord_position_offset);
-    glEnableVertexAttribArray(ATTRIB_TEXTURE_POSITION);
-    // Set up textures and uniforms.
-    glActiveTexture(GL_TEXTURE1);
-    glBindTexture(texture.target(), texture.handle());
-    glUniform1i(texture_uniform_, 1);
-    glUniformMatrix4fv(projection_mat_uniform_, 1, GL_FALSE,
-                       projection_mat.data());
-    glUniformMatrix4fv(model_mat_uniform_, 1, GL_FALSE, model_mat.data());
-    // Draw the mesh.
-    glDrawElements(mesh_3d.primitive_type, mesh_3d.index_buffer.size(),
-                   GL_UNSIGNED_SHORT, mesh_3d.index_buffer.data());
-    // Unbind textures and uniforms.
-    glActiveTexture(GL_TEXTURE1);
-    glBindTexture(texture.target(), 0);
-    render_target.Unbind();
-    // Unbind vertex attributes.
-    glDisableVertexAttribArray(ATTRIB_TEXTURE_POSITION);
-    glDisableVertexAttribArray(ATTRIB_VERTEX);
-    // Restore the GL state.
-    glDepthMask(GL_FALSE);
-    glDisable(GL_DEPTH_TEST);
-    glDisable(GL_BLEND);
-
-    glUseProgram(0);
-    glFlush();
-
-    return absl::OkStatus();
-  }
-
- private:
-  enum { ATTRIB_VERTEX, ATTRIB_TEXTURE_POSITION, NUM_ATTRIBUTES };
-
-  Renderer(GLuint program_handle, GLint projection_mat_uniform,
-           GLint model_mat_uniform, GLint texture_uniform)
-      : program_handle_(program_handle),
-        projection_mat_uniform_(projection_mat_uniform),
-        model_mat_uniform_(model_mat_uniform),
-        texture_uniform_(texture_uniform) {}
-
-  GLuint program_handle_;
-  GLint projection_mat_uniform_;
-  GLint model_mat_uniform_;
-  GLint texture_uniform_;
-};
-
-class EffectRendererImpl : public EffectRenderer {
- public:
-  EffectRendererImpl(
-      const Environment& environment,
-      std::unique_ptr<RenderTarget> render_target,
-      std::unique_ptr<Renderer> renderer,
-      RenderableMesh3d&& renderable_quad_mesh_3d,
-      absl::optional<RenderableMesh3d>&& renderable_effect_mesh_3d,
-      std::unique_ptr<Texture> empty_color_texture,
-      std::unique_ptr<Texture> effect_texture)
-      : environment_(environment),
-        render_target_(std::move(render_target)),
-        renderer_(std::move(renderer)),
-        renderable_quad_mesh_3d_(std::move(renderable_quad_mesh_3d)),
-        renderable_effect_mesh_3d_(std::move(renderable_effect_mesh_3d)),
-        empty_color_texture_(std::move(empty_color_texture)),
-        effect_texture_(std::move(effect_texture)),
-        identity_matrix_(Create4x4IdentityMatrix()) {}
-
-  absl::Status RenderEffect(
-      const std::vector<FaceGeometry>& multi_face_geometry,
-      int frame_width,            //
-      int frame_height,           //
-      GLenum src_texture_target,  //
-      GLuint src_texture_name,    //
-      GLenum dst_texture_target,  //
-      GLuint dst_texture_name) {
-    // Validate input arguments.
-    MP_RETURN_IF_ERROR(ValidateFrameDimensions(frame_width, frame_height))
-        << "Invalid frame dimensions!";
-    RET_CHECK(src_texture_name > 0 && dst_texture_name > 0)
-        << "Both source and destination texture names must be non-null!";
-    RET_CHECK_NE(src_texture_name, dst_texture_name)
-        << "Source and destination texture names must be different!";
-
-    // Validate all input face geometries.
-    for (const FaceGeometry& face_geometry : multi_face_geometry) {
-      MP_RETURN_IF_ERROR(ValidateFaceGeometry(face_geometry))
-          << "Invalid face geometry!";
-    }
-
-    // Wrap both source and destination textures.
-    ASSIGN_OR_RETURN(
-        std::unique_ptr<Texture> src_texture,
-        Texture::WrapExternalTexture(src_texture_name, src_texture_target,
-                                     frame_width, frame_height),
-        _ << "Failed to wrap the external source texture");
-    ASSIGN_OR_RETURN(
-        std::unique_ptr<Texture> dst_texture,
-        Texture::WrapExternalTexture(dst_texture_name, dst_texture_target,
-                                     frame_width, frame_height),
-        _ << "Failed to wrap the external destination texture");
-
-    // Set the destination texture as the color buffer. Then, clear both the
-    // color and the depth buffers for the render target.
-    MP_RETURN_IF_ERROR(render_target_->SetColorbuffer(*dst_texture))
-        << "Failed to set the destination texture as the colorbuffer!";
-    render_target_->Clear();
-
-    // Render the source texture on top of the quad mesh (i.e. make a copy)
-    // into the render target.
-    MP_RETURN_IF_ERROR(renderer_->Render(
-        *render_target_, *src_texture, renderable_quad_mesh_3d_,
-        identity_matrix_, identity_matrix_, Renderer::RenderMode::OVERDRAW))
-        << "Failed to render the source texture on top of the quad mesh!";
-
-    // Extract pose transform matrices and meshes from the face geometry data;
-    const int num_faces = multi_face_geometry.size();
-
-    std::vector<std::array<float, 16>> face_pose_transform_matrices(num_faces);
-    std::vector<RenderableMesh3d> renderable_face_meshes(num_faces);
-    for (int i = 0; i < num_faces; ++i) {
-      const FaceGeometry& face_geometry = multi_face_geometry[i];
-
-      // Extract the face pose transformation matrix.
-      ASSIGN_OR_RETURN(
-          face_pose_transform_matrices[i],
-          Convert4x4MatrixDataToArrayFormat(
-              face_geometry.pose_transform_matrix()),
-          _ << "Failed to extract the face pose transformation matrix!");
-
-      // Extract the face mesh as a renderable.
-      ASSIGN_OR_RETURN(
-          renderable_face_meshes[i],
-          RenderableMesh3d::CreateFromProtoMesh3d(face_geometry.mesh()),
-          _ << "Failed to extract a renderable face mesh!");
-    }
-
-    // Create a perspective matrix using the frame aspect ratio.
-    std::array<float, 16> perspective_matrix = CreatePerspectiveMatrix(
-        /*aspect_ratio*/ static_cast<float>(frame_width) / frame_height);
-
-    // Render a face mesh occluder for each face.
-    for (int i = 0; i < num_faces; ++i) {
-      const std::array<float, 16>& face_pose_transform_matrix =
-          face_pose_transform_matrices[i];
-      const RenderableMesh3d& renderable_face_mesh = renderable_face_meshes[i];
-
-      // Render the face mesh using the empty color texture, i.e. the face
-      // mesh occluder.
-      //
-      // For occlusion, the pose transformation is moved ~1mm away from camera
-      // in order to allow the face mesh texture to be rendered without
-      // failing the depth test.
-      std::array<float, 16> occlusion_face_pose_transform_matrix =
-          face_pose_transform_matrix;
-      occlusion_face_pose_transform_matrix[14] -= 0.1f;  // ~ 1mm
-      MP_RETURN_IF_ERROR(renderer_->Render(
-          *render_target_, *empty_color_texture_, renderable_face_mesh,
-          perspective_matrix, occlusion_face_pose_transform_matrix,
-          Renderer::RenderMode::OCCLUSION))
-          << "Failed to render the face mesh occluder!";
-    }
-
-    // Render the main face mesh effect component for each face.
-    for (int i = 0; i < num_faces; ++i) {
-      const std::array<float, 16>& face_pose_transform_matrix =
-          face_pose_transform_matrices[i];
-
-      // If there is no effect 3D mesh provided, then the face mesh itself is
-      // used as a topology for rendering (for example, this can be used for
-      // facepaint effects or AR makeup).
-      const RenderableMesh3d& main_effect_mesh_3d =
-          renderable_effect_mesh_3d_ ? *renderable_effect_mesh_3d_
-                                     : renderable_face_meshes[i];
-
-      MP_RETURN_IF_ERROR(renderer_->Render(
-          *render_target_, *effect_texture_, main_effect_mesh_3d,
-          perspective_matrix, face_pose_transform_matrix,
-          Renderer::RenderMode::OPAQUE))
-          << "Failed to render the main effect pass!";
-    }
-
-    // At this point in the code, the destination texture must contain the
-    // correctly renderer effect, so we should just return.
-    return absl::OkStatus();
-  }
-
- private:
-  std::array<float, 16> CreatePerspectiveMatrix(float aspect_ratio) const {
-    static constexpr float kDegreesToRadians = M_PI / 180.f;
-
-    std::array<float, 16> perspective_matrix;
-    perspective_matrix.fill(0.f);
-
-    const auto& env_camera = environment_.perspective_camera();
-    // Standard perspective projection matrix calculations.
-    const float f = 1.0f / std::tan(kDegreesToRadians *
-                                    env_camera.vertical_fov_degrees() / 2.f);
-
-    const float denom = 1.0f / (env_camera.near() - env_camera.far());
-    perspective_matrix[0] = f / aspect_ratio;
-    perspective_matrix[5] = f;
-    perspective_matrix[10] = (env_camera.near() + env_camera.far()) * denom;
-    perspective_matrix[11] = -1.f;
-    perspective_matrix[14] = 2.f * env_camera.far() * env_camera.near() * denom;
-
-    // If the environment's origin point location is in the top left corner,
-    // then skip additional flip along Y-axis is required to render correctly.
-    if (environment_.origin_point_location() ==
-        OriginPointLocation::TOP_LEFT_CORNER) {
-      perspective_matrix[5] *= -1.f;
-    }
-
-    return perspective_matrix;
-  }
-
-  static std::array<float, 16> Create4x4IdentityMatrix() {
-    return {1.f, 0.f, 0.f, 0.f,  //
-            0.f, 1.f, 0.f, 0.f,  //
-            0.f, 0.f, 1.f, 0.f,  //
-            0.f, 0.f, 0.f, 1.f};
-  }
-
-  static absl::StatusOr<std::array<float, 16>>
-  Convert4x4MatrixDataToArrayFormat(const MatrixData& matrix_data) {
-    RET_CHECK(matrix_data.rows() == 4 &&  //
-              matrix_data.cols() == 4 &&  //
-              matrix_data.packed_data_size() == 16)
-        << "The matrix data must define a 4x4 matrix!";
-
-    std::array<float, 16> matrix_array;
-    for (int i = 0; i < 16; i++) {
-      matrix_array[i] = matrix_data.packed_data(i);
-    }
-
-    // Matrix array must be in the OpenGL-friendly column-major order. If
-    // `matrix_data` is in the row-major order, then transpose.
-    if (matrix_data.layout() == MatrixData::ROW_MAJOR) {
-      std::swap(matrix_array[1], matrix_array[4]);
-      std::swap(matrix_array[2], matrix_array[8]);
-      std::swap(matrix_array[3], matrix_array[12]);
-      std::swap(matrix_array[6], matrix_array[9]);
-      std::swap(matrix_array[7], matrix_array[13]);
-      std::swap(matrix_array[11], matrix_array[14]);
-    }
-
-    return matrix_array;
-  }
-
-  Environment environment_;
-
-  std::unique_ptr<RenderTarget> render_target_;
-  std::unique_ptr<Renderer> renderer_;
-
-  RenderableMesh3d renderable_quad_mesh_3d_;
-  absl::optional<RenderableMesh3d> renderable_effect_mesh_3d_;
-
-  std::unique_ptr<Texture> empty_color_texture_;
-  std::unique_ptr<Texture> effect_texture_;
-
-  std::array<float, 16> identity_matrix_;
-};
-
-Mesh3d CreateQuadMesh3d() {
-  static constexpr float kQuadMesh3dVertexBuffer[] = {
-      -1.f, -1.f, 0.f, 0.f, 0.f,  //
-      1.f,  -1.f, 0.f, 1.f, 0.f,  //
-      -1.f, 1.f,  0.f, 0.f, 1.f,  //
-      1.f,  1.f,  0.f, 1.f, 1.f,  //
-  };
-  static constexpr uint16_t kQuadMesh3dIndexBuffer[] = {0, 1, 2, 1, 3, 2};
-
-  static constexpr int kQuadMesh3dVertexBufferSize =
-      sizeof(kQuadMesh3dVertexBuffer) / sizeof(float);
-  static constexpr int kQuadMesh3dIndexBufferSize =
-      sizeof(kQuadMesh3dIndexBuffer) / sizeof(uint16_t);
-
-  Mesh3d quad_mesh_3d;
-  quad_mesh_3d.set_vertex_type(Mesh3d::VERTEX_PT);
-  quad_mesh_3d.set_primitive_type(Mesh3d::TRIANGLE);
-  for (int i = 0; i < kQuadMesh3dVertexBufferSize; ++i) {
-    quad_mesh_3d.add_vertex_buffer(kQuadMesh3dVertexBuffer[i]);
-  }
-  for (int i = 0; i < kQuadMesh3dIndexBufferSize; ++i) {
-    quad_mesh_3d.add_index_buffer(kQuadMesh3dIndexBuffer[i]);
-  }
-
-  return quad_mesh_3d;
-}
-
-ImageFrame CreateEmptyColorTexture() {
-  static constexpr ImageFormat::Format kEmptyColorTextureFormat =
-      ImageFormat::SRGBA;
-  static constexpr int kEmptyColorTextureWidth = 1;
-  static constexpr int kEmptyColorTextureHeight = 1;
-
-  ImageFrame empty_color_texture(
-      kEmptyColorTextureFormat, kEmptyColorTextureWidth,
-      kEmptyColorTextureHeight, ImageFrame::kGlDefaultAlignmentBoundary);
-  empty_color_texture.SetToZero();
-
-  return empty_color_texture;
-}
-
-}  // namespace
-
-absl::StatusOr<std::unique_ptr<EffectRenderer>> CreateEffectRenderer(
-    const Environment& environment,                //
-    const absl::optional<Mesh3d>& effect_mesh_3d,  //
-    ImageFrame&& effect_texture) {
-  MP_RETURN_IF_ERROR(ValidateEnvironment(environment))
-      << "Invalid environment!";
-  if (effect_mesh_3d) {
-    MP_RETURN_IF_ERROR(ValidateMesh3d(*effect_mesh_3d))
-        << "Invalid effect 3D mesh!";
-  }
-
-  ASSIGN_OR_RETURN(std::unique_ptr<RenderTarget> render_target,
-                   RenderTarget::Create(),
-                   _ << "Failed to create a render target!");
-  ASSIGN_OR_RETURN(std::unique_ptr<Renderer> renderer, Renderer::Create(),
-                   _ << "Failed to create a renderer!");
-  ASSIGN_OR_RETURN(RenderableMesh3d renderable_quad_mesh_3d,
-                   RenderableMesh3d::CreateFromProtoMesh3d(CreateQuadMesh3d()),
-                   _ << "Failed to create a renderable quad mesh!");
-  absl::optional<RenderableMesh3d> renderable_effect_mesh_3d;
-  if (effect_mesh_3d) {
-    ASSIGN_OR_RETURN(renderable_effect_mesh_3d,
-                     RenderableMesh3d::CreateFromProtoMesh3d(*effect_mesh_3d),
-                     _ << "Failed to create a renderable effect mesh!");
-  }
-  ASSIGN_OR_RETURN(std::unique_ptr<Texture> empty_color_gl_texture,
-                   Texture::CreateFromImageFrame(CreateEmptyColorTexture()),
-                   _ << "Failed to create an empty color texture!");
-  ASSIGN_OR_RETURN(std::unique_ptr<Texture> effect_gl_texture,
-                   Texture::CreateFromImageFrame(effect_texture),
-                   _ << "Failed to create an effect texture!");
-
-  std::unique_ptr<EffectRenderer> result =
-      absl::make_unique<EffectRendererImpl>(
-          environment, std::move(render_target), std::move(renderer),
-          std::move(renderable_quad_mesh_3d),
-          std::move(renderable_effect_mesh_3d),
-          std::move(empty_color_gl_texture), std::move(effect_gl_texture));
-
-  return result;
-}
-
-}  // namespace mediapipe::face_geometry
diff --git a/mediapipe/modules/face_geometry/libs/effect_renderer.h b/mediapipe/modules/face_geometry/libs/effect_renderer.h
deleted file mode 100644
index 71330e7..0000000
--- a/mediapipe/modules/face_geometry/libs/effect_renderer.h
+++ /dev/null
@@ -1,92 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_MODULES_FACE_GEOMETRY_LIBS_EFFECT_RENDERER_H_
-#define MEDIAPIPE_MODULES_FACE_GEOMETRY_LIBS_EFFECT_RENDERER_H_
-
-#include <memory>
-#include <vector>
-
-#include "absl/types/optional.h"
-#include "mediapipe/framework/formats/image_frame.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/framework/port/statusor.h"
-#include "mediapipe/gpu/gl_base.h"
-#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
-#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
-#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
-
-namespace mediapipe::face_geometry {
-
-// Encapsulates a stateful face effect renderer.
-class EffectRenderer {
- public:
-  virtual ~EffectRenderer() = default;
-
-  // Renders a face effect based on the multiple facial geometries.
-  //
-  // Must be called in the same GL context as was used upon initialization.
-  //
-  // Each of the `multi_face_geometry` must be valid (for details, please refer
-  // to the proto message definition comments and/or `validation_utils.h/cc`).
-  // Additionally, all face mesh index buffer elements must fit into the
-  // `uint16` type in order to be renderable.
-  //
-  // Both `frame_width` and `frame_height` must be positive.
-  //
-  // Both `src_texture_name` and `dst_texture_name` must be positive and
-  // reference existing OpenGL textures in the current context. They should also
-  // reference different textures as the in-place effect rendering is not yet
-  // supported.
-  virtual absl::Status RenderEffect(
-      const std::vector<FaceGeometry>& multi_face_geometry,
-      int frame_width,            //
-      int frame_height,           //
-      GLenum src_texture_target,  //
-      GLuint src_texture_name,    //
-      GLenum dst_texture_target,  //
-      GLuint dst_texture_name) = 0;
-};
-
-// Creates an instance of `EffectRenderer`.
-//
-// `effect_mesh_3d` defines a rigid 3d mesh which is "attached" to the face and
-// is driven by the face pose transformation matrix. If is not present, the
-// runtime face mesh will be used as the effect mesh - this mode is handy for
-// facepaint effects. In both rendering modes, the face mesh is first rendered
-// as an occluder straight into the depth buffer. This step helps to create a
-// more believable effect via hiding invisible elements behind the face surface.
-//
-// `effect_texture` defines the color texture to be rendered on top of the
-// effect mesh. Please be aware about the difference between the CPU texture
-// memory layout and the GPU texture sampler coordinate space. This renderer
-// follows conventions discussed here: https://open.gl/textures
-//
-// Must be called in the same GL context as will be used for rendering.
-//
-// Both `environment` and `effect_mesh_3d` (is present) must be valid (for
-// details, please refer to the proto message definition comments and/or
-// `validation_utils.h/cc`). Additionally, `effect_mesh_3d`s index buffer
-// elements must fit into the `uint16` type in order to be renderable.
-//
-// `effect_texture` must have positive dimensions. Its format must be either
-// `SRGB` or `SRGBA`. Its memory must be aligned for GL usage.
-absl::StatusOr<std::unique_ptr<EffectRenderer>> CreateEffectRenderer(
-    const Environment& environment,                //
-    const absl::optional<Mesh3d>& effect_mesh_3d,  //
-    ImageFrame&& effect_texture);
-
-}  // namespace mediapipe::face_geometry
-
-#endif  // MEDIAPIPE_MODULES_FACE_GEOMETRY_LIBS_EFFECT_RENDERER_H_
diff --git a/mediapipe/modules/face_geometry/libs/geometry_pipeline.cc b/mediapipe/modules/face_geometry/libs/geometry_pipeline.cc
deleted file mode 100644
index bcfce7c..0000000
--- a/mediapipe/modules/face_geometry/libs/geometry_pipeline.cc
+++ /dev/null
@@ -1,466 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/face_geometry/libs/geometry_pipeline.h"
-
-#include <cmath>
-#include <cstdint>
-#include <memory>
-#include <utility>
-#include <vector>
-
-#include "Eigen/Core"
-#include "absl/memory/memory.h"
-#include "mediapipe/framework/formats/landmark.pb.h"
-#include "mediapipe/framework/formats/matrix.h"
-#include "mediapipe/framework/formats/matrix_data.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/framework/port/status_macros.h"
-#include "mediapipe/framework/port/statusor.h"
-#include "mediapipe/modules/face_geometry/libs/mesh_3d_utils.h"
-#include "mediapipe/modules/face_geometry/libs/procrustes_solver.h"
-#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
-#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
-#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
-#include "mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.pb.h"
-#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
-
-namespace mediapipe::face_geometry {
-namespace {
-
-struct PerspectiveCameraFrustum {
-  // NOTE: all arguments must be validated prior to calling this constructor.
-  PerspectiveCameraFrustum(const PerspectiveCamera& perspective_camera,
-                           int frame_width, int frame_height) {
-    static constexpr float kDegreesToRadians = 3.14159265358979323846f / 180.f;
-
-    const float height_at_near =
-        2.f * perspective_camera.near() *
-        std::tan(0.5f * kDegreesToRadians *
-                 perspective_camera.vertical_fov_degrees());
-
-    const float width_at_near = frame_width * height_at_near / frame_height;
-
-    left = -0.5f * width_at_near;
-    right = 0.5f * width_at_near;
-    bottom = -0.5f * height_at_near;
-    top = 0.5f * height_at_near;
-    near = perspective_camera.near();
-    far = perspective_camera.far();
-  }
-
-  float left;
-  float right;
-  float bottom;
-  float top;
-  float near;
-  float far;
-};
-
-class ScreenToMetricSpaceConverter {
- public:
-  ScreenToMetricSpaceConverter(
-      OriginPointLocation origin_point_location,      //
-      InputSource input_source,                       //
-      Eigen::Matrix3Xf&& canonical_metric_landmarks,  //
-      Eigen::VectorXf&& landmark_weights,             //
-      std::unique_ptr<ProcrustesSolver> procrustes_solver)
-      : origin_point_location_(origin_point_location),
-        input_source_(input_source),
-        canonical_metric_landmarks_(std::move(canonical_metric_landmarks)),
-        landmark_weights_(std::move(landmark_weights)),
-        procrustes_solver_(std::move(procrustes_solver)) {}
-
-  // Converts `screen_landmark_list` into `metric_landmark_list` and estimates
-  // the `pose_transform_mat`.
-  //
-  // Here's the algorithm summary:
-  //
-  // (1) Project X- and Y- screen landmark coordinates at the Z near plane.
-  //
-  // (2) Estimate a canonical-to-runtime landmark set scale by running the
-  //     Procrustes solver using the screen runtime landmarks.
-  //
-  //     On this iteration, screen landmarks are used instead of unprojected
-  //     metric landmarks as it is not safe to unproject due to the relative
-  //     nature of the input screen landmark Z coordinate.
-  //
-  // (3) Use the canonical-to-runtime scale from (2) to unproject the screen
-  //     landmarks. The result is referenced as "intermediate landmarks" because
-  //     they are the first estimation of the resuling metric landmarks, but are
-  //     not quite there yet.
-  //
-  // (4) Estimate a canonical-to-runtime landmark set scale by running the
-  //     Procrustes solver using the intermediate runtime landmarks.
-  //
-  // (5) Use the product of the scale factors from (2) and (4) to unproject
-  //     the screen landmarks the second time. This is the second and the final
-  //     estimation of the metric landmarks.
-  //
-  // (6) Multiply each of the metric landmarks by the inverse pose
-  //     transformation matrix to align the runtime metric face landmarks with
-  //     the canonical metric face landmarks.
-  //
-  // Note: the input screen landmarks are in the left-handed coordinate system,
-  //       however any metric landmarks - including the canonical metric
-  //       landmarks, the final runtime metric landmarks and any intermediate
-  //       runtime metric landmarks - are in the right-handed coordinate system.
-  //
-  //       To keep the logic correct, the landmark set handedness is changed any
-  //       time the screen-to-metric semantic barrier is passed.
-  absl::Status Convert(const NormalizedLandmarkList& screen_landmark_list,  //
-                       const PerspectiveCameraFrustum& pcf,                 //
-                       LandmarkList& metric_landmark_list,                  //
-                       Eigen::Matrix4f& pose_transform_mat) const {
-    RET_CHECK_EQ(screen_landmark_list.landmark_size(),
-                 canonical_metric_landmarks_.cols())
-        << "The number of landmarks doesn't match the number passed upon "
-           "initialization!";
-
-    Eigen::Matrix3Xf screen_landmarks;
-    ConvertLandmarkListToEigenMatrix(screen_landmark_list, screen_landmarks);
-
-    ProjectXY(pcf, screen_landmarks);
-    const float depth_offset = screen_landmarks.row(2).mean();
-
-    // 1st iteration: don't unproject XY because it's unsafe to do so due to
-    //                the relative nature of the Z coordinate. Instead, run the
-    //                first estimation on the projected XY and use that scale to
-    //                unproject for the 2nd iteration.
-    Eigen::Matrix3Xf intermediate_landmarks(screen_landmarks);
-    ChangeHandedness(intermediate_landmarks);
-
-    ASSIGN_OR_RETURN(const float first_iteration_scale,
-                     EstimateScale(intermediate_landmarks),
-                     _ << "Failed to estimate first iteration scale!");
-
-    // 2nd iteration: unproject XY using the scale from the 1st iteration.
-    intermediate_landmarks = screen_landmarks;
-    MoveAndRescaleZ(pcf, depth_offset, first_iteration_scale,
-                    intermediate_landmarks);
-    UnprojectXY(pcf, intermediate_landmarks);
-    ChangeHandedness(intermediate_landmarks);
-
-    // For face detection input landmarks, re-write Z-coord from the canonical
-    // landmarks.
-    if (input_source_ == InputSource::FACE_DETECTION_PIPELINE) {
-      Eigen::Matrix4f intermediate_pose_transform_mat;
-      MP_RETURN_IF_ERROR(procrustes_solver_->SolveWeightedOrthogonalProblem(
-          canonical_metric_landmarks_, intermediate_landmarks,
-          landmark_weights_, intermediate_pose_transform_mat))
-          << "Failed to estimate pose transform matrix!";
-
-      intermediate_landmarks.row(2) =
-          (intermediate_pose_transform_mat *
-           canonical_metric_landmarks_.colwise().homogeneous())
-              .row(2);
-    }
-    ASSIGN_OR_RETURN(const float second_iteration_scale,
-                     EstimateScale(intermediate_landmarks),
-                     _ << "Failed to estimate second iteration scale!");
-
-    // Use the total scale to unproject the screen landmarks.
-    const float total_scale = first_iteration_scale * second_iteration_scale;
-    MoveAndRescaleZ(pcf, depth_offset, total_scale, screen_landmarks);
-    UnprojectXY(pcf, screen_landmarks);
-    ChangeHandedness(screen_landmarks);
-
-    // At this point, screen landmarks are converted into metric landmarks.
-    Eigen::Matrix3Xf& metric_landmarks = screen_landmarks;
-
-    MP_RETURN_IF_ERROR(procrustes_solver_->SolveWeightedOrthogonalProblem(
-        canonical_metric_landmarks_, metric_landmarks, landmark_weights_,
-        pose_transform_mat))
-        << "Failed to estimate pose transform matrix!";
-
-    // For face detection input landmarks, re-write Z-coord from the canonical
-    // landmarks and run the pose transform estimation again.
-    if (input_source_ == InputSource::FACE_DETECTION_PIPELINE) {
-      metric_landmarks.row(2) =
-          (pose_transform_mat *
-           canonical_metric_landmarks_.colwise().homogeneous())
-              .row(2);
-
-      MP_RETURN_IF_ERROR(procrustes_solver_->SolveWeightedOrthogonalProblem(
-          canonical_metric_landmarks_, metric_landmarks, landmark_weights_,
-          pose_transform_mat))
-          << "Failed to estimate pose transform matrix!";
-    }
-
-    // Multiply each of the metric landmarks by the inverse pose
-    // transformation matrix to align the runtime metric face landmarks with
-    // the canonical metric face landmarks.
-    metric_landmarks = (pose_transform_mat.inverse() *
-                        metric_landmarks.colwise().homogeneous())
-                           .topRows(3);
-
-    ConvertEigenMatrixToLandmarkList(metric_landmarks, metric_landmark_list);
-
-    return absl::OkStatus();
-  }
-
- private:
-  void ProjectXY(const PerspectiveCameraFrustum& pcf,
-                 Eigen::Matrix3Xf& landmarks) const {
-    float x_scale = pcf.right - pcf.left;
-    float y_scale = pcf.top - pcf.bottom;
-    float x_translation = pcf.left;
-    float y_translation = pcf.bottom;
-
-    if (origin_point_location_ == OriginPointLocation::TOP_LEFT_CORNER) {
-      landmarks.row(1) = 1.f - landmarks.row(1).array();
-    }
-
-    landmarks =
-        landmarks.array().colwise() * Eigen::Array3f(x_scale, y_scale, x_scale);
-    landmarks.colwise() += Eigen::Vector3f(x_translation, y_translation, 0.f);
-  }
-
-  absl::StatusOr<float> EstimateScale(Eigen::Matrix3Xf& landmarks) const {
-    Eigen::Matrix4f transform_mat;
-    MP_RETURN_IF_ERROR(procrustes_solver_->SolveWeightedOrthogonalProblem(
-        canonical_metric_landmarks_, landmarks, landmark_weights_,
-        transform_mat))
-        << "Failed to estimate canonical-to-runtime landmark set transform!";
-
-    return transform_mat.col(0).norm();
-  }
-
-  static void MoveAndRescaleZ(const PerspectiveCameraFrustum& pcf,
-                              float depth_offset, float scale,
-                              Eigen::Matrix3Xf& landmarks) {
-    landmarks.row(2) =
-        (landmarks.array().row(2) - depth_offset + pcf.near) / scale;
-  }
-
-  static void UnprojectXY(const PerspectiveCameraFrustum& pcf,
-                          Eigen::Matrix3Xf& landmarks) {
-    landmarks.row(0) =
-        landmarks.row(0).cwiseProduct(landmarks.row(2)) / pcf.near;
-    landmarks.row(1) =
-        landmarks.row(1).cwiseProduct(landmarks.row(2)) / pcf.near;
-  }
-
-  static void ChangeHandedness(Eigen::Matrix3Xf& landmarks) {
-    landmarks.row(2) *= -1.f;
-  }
-
-  static void ConvertLandmarkListToEigenMatrix(
-      const NormalizedLandmarkList& landmark_list,
-      Eigen::Matrix3Xf& eigen_matrix) {
-    eigen_matrix = Eigen::Matrix3Xf(3, landmark_list.landmark_size());
-    for (int i = 0; i < landmark_list.landmark_size(); ++i) {
-      const auto& landmark = landmark_list.landmark(i);
-      eigen_matrix(0, i) = landmark.x();
-      eigen_matrix(1, i) = landmark.y();
-      eigen_matrix(2, i) = landmark.z();
-    }
-  }
-
-  static void ConvertEigenMatrixToLandmarkList(
-      const Eigen::Matrix3Xf& eigen_matrix, LandmarkList& landmark_list) {
-    landmark_list.Clear();
-
-    for (int i = 0; i < eigen_matrix.cols(); ++i) {
-      auto& landmark = *landmark_list.add_landmark();
-      landmark.set_x(eigen_matrix(0, i));
-      landmark.set_y(eigen_matrix(1, i));
-      landmark.set_z(eigen_matrix(2, i));
-    }
-  }
-
-  const OriginPointLocation origin_point_location_;
-  const InputSource input_source_;
-  Eigen::Matrix3Xf canonical_metric_landmarks_;
-  Eigen::VectorXf landmark_weights_;
-
-  std::unique_ptr<ProcrustesSolver> procrustes_solver_;
-};
-
-class GeometryPipelineImpl : public GeometryPipeline {
- public:
-  GeometryPipelineImpl(
-      const PerspectiveCamera& perspective_camera,  //
-      const Mesh3d& canonical_mesh,                 //
-      uint32_t canonical_mesh_vertex_size,          //
-      uint32_t canonical_mesh_num_vertices,
-      uint32_t canonical_mesh_vertex_position_offset,
-      std::unique_ptr<ScreenToMetricSpaceConverter> space_converter)
-      : perspective_camera_(perspective_camera),
-        canonical_mesh_(canonical_mesh),
-        canonical_mesh_vertex_size_(canonical_mesh_vertex_size),
-        canonical_mesh_num_vertices_(canonical_mesh_num_vertices),
-        canonical_mesh_vertex_position_offset_(
-            canonical_mesh_vertex_position_offset),
-        space_converter_(std::move(space_converter)) {}
-
-  absl::StatusOr<std::vector<FaceGeometry>> EstimateFaceGeometry(
-      const std::vector<NormalizedLandmarkList>& multi_face_landmarks,
-      int frame_width, int frame_height) const override {
-    MP_RETURN_IF_ERROR(ValidateFrameDimensions(frame_width, frame_height))
-        << "Invalid frame dimensions!";
-
-    // Create a perspective camera frustum to be shared for geometry estimation
-    // per each face.
-    PerspectiveCameraFrustum pcf(perspective_camera_, frame_width,
-                                 frame_height);
-
-    std::vector<FaceGeometry> multi_face_geometry;
-
-    // From this point, the meaning of "face landmarks" is clarified further as
-    // "screen face landmarks". This is done do distinguish from "metric face
-    // landmarks" that are derived during the face geometry estimation process.
-    for (const NormalizedLandmarkList& screen_face_landmarks :
-         multi_face_landmarks) {
-      // Having a too compact screen landmark list will result in numerical
-      // instabilities, therefore such faces are filtered.
-      if (IsScreenLandmarkListTooCompact(screen_face_landmarks)) {
-        continue;
-      }
-
-      // Convert the screen landmarks into the metric landmarks and get the pose
-      // transformation matrix.
-      LandmarkList metric_face_landmarks;
-      Eigen::Matrix4f pose_transform_mat;
-      MP_RETURN_IF_ERROR(space_converter_->Convert(screen_face_landmarks, pcf,
-                                                   metric_face_landmarks,
-                                                   pose_transform_mat))
-          << "Failed to convert landmarks from the screen to the metric space!";
-
-      // Pack geometry data for this face.
-      FaceGeometry face_geometry;
-      Mesh3d* mutable_mesh = face_geometry.mutable_mesh();
-      // Copy the canonical face mesh as the face geometry mesh.
-      mutable_mesh->CopyFrom(canonical_mesh_);
-      // Replace XYZ vertex mesh coodinates with the metric landmark positions.
-      for (int i = 0; i < canonical_mesh_num_vertices_; ++i) {
-        uint32_t vertex_buffer_offset = canonical_mesh_vertex_size_ * i +
-                                        canonical_mesh_vertex_position_offset_;
-
-        mutable_mesh->set_vertex_buffer(vertex_buffer_offset,
-                                        metric_face_landmarks.landmark(i).x());
-        mutable_mesh->set_vertex_buffer(vertex_buffer_offset + 1,
-                                        metric_face_landmarks.landmark(i).y());
-        mutable_mesh->set_vertex_buffer(vertex_buffer_offset + 2,
-                                        metric_face_landmarks.landmark(i).z());
-      }
-      // Populate the face pose transformation matrix.
-      mediapipe::MatrixDataProtoFromMatrix(
-          pose_transform_mat, face_geometry.mutable_pose_transform_matrix());
-
-      multi_face_geometry.push_back(face_geometry);
-    }
-
-    return multi_face_geometry;
-  }
-
- private:
-  static bool IsScreenLandmarkListTooCompact(
-      const NormalizedLandmarkList& screen_landmarks) {
-    float mean_x = 0.f;
-    float mean_y = 0.f;
-    for (int i = 0; i < screen_landmarks.landmark_size(); ++i) {
-      const auto& landmark = screen_landmarks.landmark(i);
-      mean_x += (landmark.x() - mean_x) / static_cast<float>(i + 1);
-      mean_y += (landmark.y() - mean_y) / static_cast<float>(i + 1);
-    }
-
-    float max_sq_dist = 0.f;
-    for (const auto& landmark : screen_landmarks.landmark()) {
-      const float d_x = landmark.x() - mean_x;
-      const float d_y = landmark.y() - mean_y;
-      max_sq_dist = std::max(max_sq_dist, d_x * d_x + d_y * d_y);
-    }
-
-    static constexpr float kIsScreenLandmarkListTooCompactThreshold = 1e-3f;
-    return std::sqrt(max_sq_dist) <= kIsScreenLandmarkListTooCompactThreshold;
-  }
-
-  const PerspectiveCamera perspective_camera_;
-  const Mesh3d canonical_mesh_;
-  const uint32_t canonical_mesh_vertex_size_;
-  const uint32_t canonical_mesh_num_vertices_;
-  const uint32_t canonical_mesh_vertex_position_offset_;
-
-  std::unique_ptr<ScreenToMetricSpaceConverter> space_converter_;
-};
-
-}  // namespace
-
-absl::StatusOr<std::unique_ptr<GeometryPipeline>> CreateGeometryPipeline(
-    const Environment& environment, const GeometryPipelineMetadata& metadata) {
-  MP_RETURN_IF_ERROR(ValidateEnvironment(environment))
-      << "Invalid environment!";
-  MP_RETURN_IF_ERROR(ValidateGeometryPipelineMetadata(metadata))
-      << "Invalid geometry pipeline metadata!";
-
-  const auto& canonical_mesh = metadata.canonical_mesh();
-  RET_CHECK(HasVertexComponent(canonical_mesh.vertex_type(),
-                               VertexComponent::POSITION))
-      << "Canonical face mesh must have the `POSITION` vertex component!";
-  RET_CHECK(HasVertexComponent(canonical_mesh.vertex_type(),
-                               VertexComponent::TEX_COORD))
-      << "Canonical face mesh must have the `TEX_COORD` vertex component!";
-
-  uint32_t canonical_mesh_vertex_size =
-      GetVertexSize(canonical_mesh.vertex_type());
-  uint32_t canonical_mesh_num_vertices =
-      canonical_mesh.vertex_buffer_size() / canonical_mesh_vertex_size;
-  uint32_t canonical_mesh_vertex_position_offset =
-      GetVertexComponentOffset(canonical_mesh.vertex_type(),
-                               VertexComponent::POSITION)
-          .value();
-
-  // Put the Procrustes landmark basis into Eigen matrices for an easier access.
-  Eigen::Matrix3Xf canonical_metric_landmarks =
-      Eigen::Matrix3Xf::Zero(3, canonical_mesh_num_vertices);
-  Eigen::VectorXf landmark_weights =
-      Eigen::VectorXf::Zero(canonical_mesh_num_vertices);
-
-  for (int i = 0; i < canonical_mesh_num_vertices; ++i) {
-    uint32_t vertex_buffer_offset =
-        canonical_mesh_vertex_size * i + canonical_mesh_vertex_position_offset;
-
-    canonical_metric_landmarks(0, i) =
-        canonical_mesh.vertex_buffer(vertex_buffer_offset);
-    canonical_metric_landmarks(1, i) =
-        canonical_mesh.vertex_buffer(vertex_buffer_offset + 1);
-    canonical_metric_landmarks(2, i) =
-        canonical_mesh.vertex_buffer(vertex_buffer_offset + 2);
-  }
-
-  for (const WeightedLandmarkRef& wlr : metadata.procrustes_landmark_basis()) {
-    uint32_t landmark_id = wlr.landmark_id();
-    landmark_weights(landmark_id) = wlr.weight();
-  }
-
-  std::unique_ptr<GeometryPipeline> result =
-      absl::make_unique<GeometryPipelineImpl>(
-          environment.perspective_camera(), canonical_mesh,
-          canonical_mesh_vertex_size, canonical_mesh_num_vertices,
-          canonical_mesh_vertex_position_offset,
-          absl::make_unique<ScreenToMetricSpaceConverter>(
-              environment.origin_point_location(),
-              metadata.input_source() == InputSource::DEFAULT
-                  ? InputSource::FACE_LANDMARK_PIPELINE
-                  : metadata.input_source(),
-              std::move(canonical_metric_landmarks),
-              std::move(landmark_weights),
-              CreateFloatPrecisionProcrustesSolver()));
-
-  return result;
-}
-
-}  // namespace mediapipe::face_geometry
diff --git a/mediapipe/modules/face_geometry/libs/geometry_pipeline.h b/mediapipe/modules/face_geometry/libs/geometry_pipeline.h
deleted file mode 100644
index ffa779c..0000000
--- a/mediapipe/modules/face_geometry/libs/geometry_pipeline.h
+++ /dev/null
@@ -1,67 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_FACE_GEOMETRY_LIBS_GEOMETRY_PIPELINE_H_
-#define MEDIAPIPE_FACE_GEOMETRY_LIBS_GEOMETRY_PIPELINE_H_
-
-#include <memory>
-#include <vector>
-
-#include "mediapipe/framework/formats/landmark.pb.h"
-#include "mediapipe/framework/port/statusor.h"
-#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
-#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
-#include "mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.pb.h"
-
-namespace mediapipe::face_geometry {
-
-// Encapsulates a stateless estimator of facial geometry in a Metric space based
-// on the normalized face landmarks in the Screen space.
-class GeometryPipeline {
- public:
-  virtual ~GeometryPipeline() = default;
-
-  // Estimates geometry data for multiple faces.
-  //
-  // Returns an error status if any of the passed arguments is invalid.
-  //
-  // The result includes face geometry data for a subset of the input faces,
-  // however geometry data for some faces might be missing. This may happen if
-  // it'd be unstable to estimate the facial geometry based on a corresponding
-  // face landmark list for any reason (for example, if the landmark list is too
-  // compact).
-  //
-  // Each face landmark list must have the same number of landmarks as was
-  // passed upon initialization via the canonical face mesh (as a part of the
-  // geometry pipeline metadata).
-  //
-  // Both `frame_width` and `frame_height` must be positive.
-  virtual absl::StatusOr<std::vector<FaceGeometry>> EstimateFaceGeometry(
-      const std::vector<NormalizedLandmarkList>& multi_face_landmarks,
-      int frame_width, int frame_height) const = 0;
-};
-
-// Creates an instance of `GeometryPipeline`.
-//
-// Both `environment` and `metadata` must be valid (for details, please refer to
-// the proto message definition comments and/or `validation_utils.h/cc`).
-//
-// Canonical face mesh (defined as a part of `metadata`) must have the
-// `POSITION` and the `TEX_COORD` vertex components.
-absl::StatusOr<std::unique_ptr<GeometryPipeline>> CreateGeometryPipeline(
-    const Environment& environment, const GeometryPipelineMetadata& metadata);
-
-}  // namespace mediapipe::face_geometry
-
-#endif  // MEDIAPIPE_FACE_GEOMETRY_LIBS_GEOMETRY_PIPELINE_H_
diff --git a/mediapipe/modules/face_geometry/libs/mesh_3d_utils.cc b/mediapipe/modules/face_geometry/libs/mesh_3d_utils.cc
deleted file mode 100644
index 2078ec6..0000000
--- a/mediapipe/modules/face_geometry/libs/mesh_3d_utils.cc
+++ /dev/null
@@ -1,103 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/face_geometry/libs/mesh_3d_utils.h"
-
-#include <cstdint>
-#include <cstdlib>
-
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/statusor.h"
-#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
-
-namespace mediapipe::face_geometry {
-namespace {
-
-bool HasVertexComponentVertexPT(VertexComponent vertex_component) {
-  switch (vertex_component) {
-    case VertexComponent::POSITION:
-    case VertexComponent::TEX_COORD:
-      return true;
-
-    default:
-      return false;
-  }
-}
-
-uint32_t GetVertexComponentSizeVertexPT(VertexComponent vertex_component) {
-  switch (vertex_component) {
-    case VertexComponent::POSITION:
-      return 3;
-    case VertexComponent::TEX_COORD:
-      return 2;
-  }
-}
-
-uint32_t GetVertexComponentOffsetVertexPT(VertexComponent vertex_component) {
-  switch (vertex_component) {
-    case VertexComponent::POSITION:
-      return 0;
-    case VertexComponent::TEX_COORD:
-      return GetVertexComponentSizeVertexPT(VertexComponent::POSITION);
-  }
-}
-
-}  // namespace
-
-std::size_t GetVertexSize(Mesh3d::VertexType vertex_type) {
-  switch (vertex_type) {
-    case Mesh3d::VERTEX_PT:
-      return GetVertexComponentSizeVertexPT(VertexComponent::POSITION) +
-             GetVertexComponentSizeVertexPT(VertexComponent::TEX_COORD);
-  }
-}
-
-std::size_t GetPrimitiveSize(Mesh3d::PrimitiveType primitive_type) {
-  switch (primitive_type) {
-    case Mesh3d::TRIANGLE:
-      return 3;
-  }
-}
-
-bool HasVertexComponent(Mesh3d::VertexType vertex_type,
-                        VertexComponent vertex_component) {
-  switch (vertex_type) {
-    case Mesh3d::VERTEX_PT:
-      return HasVertexComponentVertexPT(vertex_component);
-  }
-}
-
-absl::StatusOr<uint32_t> GetVertexComponentOffset(
-    Mesh3d::VertexType vertex_type, VertexComponent vertex_component) {
-  RET_CHECK(HasVertexComponentVertexPT(vertex_component))
-      << "A given vertex type doesn't have the requested component!";
-
-  switch (vertex_type) {
-    case Mesh3d::VERTEX_PT:
-      return GetVertexComponentOffsetVertexPT(vertex_component);
-  }
-}
-
-absl::StatusOr<uint32_t> GetVertexComponentSize(
-    Mesh3d::VertexType vertex_type, VertexComponent vertex_component) {
-  RET_CHECK(HasVertexComponentVertexPT(vertex_component))
-      << "A given vertex type doesn't have the requested component!";
-
-  switch (vertex_type) {
-    case Mesh3d::VERTEX_PT:
-      return GetVertexComponentSizeVertexPT(vertex_component);
-  }
-}
-
-}  // namespace mediapipe::face_geometry
diff --git a/mediapipe/modules/face_geometry/libs/mesh_3d_utils.h b/mediapipe/modules/face_geometry/libs/mesh_3d_utils.h
deleted file mode 100644
index a320aae..0000000
--- a/mediapipe/modules/face_geometry/libs/mesh_3d_utils.h
+++ /dev/null
@@ -1,51 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_FACE_GEOMETRY_LIBS_MESH_3D_UTILS_H_
-#define MEDIAPIPE_FACE_GEOMETRY_LIBS_MESH_3D_UTILS_H_
-
-#include <cstdint>
-#include <cstdlib>
-
-#include "mediapipe/framework/port/statusor.h"
-#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
-
-namespace mediapipe::face_geometry {
-
-enum class VertexComponent { POSITION, TEX_COORD };
-
-std::size_t GetVertexSize(Mesh3d::VertexType vertex_type);
-
-std::size_t GetPrimitiveSize(Mesh3d::PrimitiveType primitive_type);
-
-bool HasVertexComponent(Mesh3d::VertexType vertex_type,
-                        VertexComponent vertex_component);
-
-// Computes the vertex component offset.
-//
-// Returns an error status if a given vertex type doesn't have the requested
-// component.
-absl::StatusOr<uint32_t> GetVertexComponentOffset(
-    Mesh3d::VertexType vertex_type, VertexComponent vertex_component);
-
-// Computes the vertex component size.
-//
-// Returns an error status if a given vertex type doesn't have the requested
-// component.
-absl::StatusOr<uint32_t> GetVertexComponentSize(
-    Mesh3d::VertexType vertex_type, VertexComponent vertex_component);
-
-}  // namespace mediapipe::face_geometry
-
-#endif  // MEDIAPIPE_FACE_GEOMETRY_LIBS_MESH_3D_UTILS_H_
diff --git a/mediapipe/modules/face_geometry/libs/procrustes_solver.cc b/mediapipe/modules/face_geometry/libs/procrustes_solver.cc
deleted file mode 100644
index 2ffae0e..0000000
--- a/mediapipe/modules/face_geometry/libs/procrustes_solver.cc
+++ /dev/null
@@ -1,266 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/face_geometry/libs/procrustes_solver.h"
-
-#include <cmath>
-#include <memory>
-
-#include "Eigen/Dense"
-#include "absl/memory/memory.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/framework/port/status_macros.h"
-#include "mediapipe/framework/port/statusor.h"
-
-namespace mediapipe {
-namespace face_geometry {
-namespace {
-
-class FloatPrecisionProcrustesSolver : public ProcrustesSolver {
- public:
-  FloatPrecisionProcrustesSolver() = default;
-
-  absl::Status SolveWeightedOrthogonalProblem(
-      const Eigen::Matrix3Xf& source_points,  //
-      const Eigen::Matrix3Xf& target_points,  //
-      const Eigen::VectorXf& point_weights,
-      Eigen::Matrix4f& transform_mat) const override {
-    // Validate inputs.
-    MP_RETURN_IF_ERROR(ValidateInputPoints(source_points, target_points))
-        << "Failed to validate weighted orthogonal problem input points!";
-    MP_RETURN_IF_ERROR(
-        ValidatePointWeights(source_points.cols(), point_weights))
-        << "Failed to validate weighted orthogonal problem point weights!";
-
-    // Extract square root from the point weights.
-    Eigen::VectorXf sqrt_weights = ExtractSquareRoot(point_weights);
-
-    // Try to solve the WEOP problem.
-    MP_RETURN_IF_ERROR(InternalSolveWeightedOrthogonalProblem(
-        source_points, target_points, sqrt_weights, transform_mat))
-        << "Failed to solve the WEOP problem!";
-
-    return absl::OkStatus();
-  }
-
- private:
-  static constexpr float kAbsoluteErrorEps = 1e-9f;
-
-  static absl::Status ValidateInputPoints(
-      const Eigen::Matrix3Xf& source_points,
-      const Eigen::Matrix3Xf& target_points) {
-    RET_CHECK_GT(source_points.cols(), 0)
-        << "The number of source points must be positive!";
-
-    RET_CHECK_EQ(source_points.cols(), target_points.cols())
-        << "The number of source and target points must be equal!";
-
-    return absl::OkStatus();
-  }
-
-  static absl::Status ValidatePointWeights(
-      int num_points, const Eigen::VectorXf& point_weights) {
-    RET_CHECK_GT(point_weights.size(), 0)
-        << "The number of point weights must be positive!";
-
-    RET_CHECK_EQ(point_weights.size(), num_points)
-        << "The number of points and point weights must be equal!";
-
-    float total_weight = 0.f;
-    for (int i = 0; i < num_points; ++i) {
-      RET_CHECK_GE(point_weights(i), 0.f)
-          << "Each point weight must be non-negative!";
-
-      total_weight += point_weights(i);
-    }
-
-    RET_CHECK_GT(total_weight, kAbsoluteErrorEps)
-        << "The total point weight is too small!";
-
-    return absl::OkStatus();
-  }
-
-  static Eigen::VectorXf ExtractSquareRoot(
-      const Eigen::VectorXf& point_weights) {
-    Eigen::VectorXf sqrt_weights(point_weights);
-    for (int i = 0; i < sqrt_weights.size(); ++i) {
-      sqrt_weights(i) = std::sqrt(sqrt_weights(i));
-    }
-
-    return sqrt_weights;
-  }
-
-  // Combines a 3x3 rotation-and-scale matrix and a 3x1 translation vector into
-  // a single 4x4 transformation matrix.
-  static Eigen::Matrix4f CombineTransformMatrix(const Eigen::Matrix3f& r_and_s,
-                                                const Eigen::Vector3f& t) {
-    Eigen::Matrix4f result = Eigen::Matrix4f::Identity();
-    result.leftCols(3).topRows(3) = r_and_s;
-    result.col(3).topRows(3) = t;
-
-    return result;
-  }
-
-  // The weighted problem is thoroughly addressed in Section 2.4 of:
-  // D. Akca, Generalized Procrustes analysis and its applications
-  // in photogrammetry, 2003, https://doi.org/10.3929/ethz-a-004656648
-  //
-  // Notable differences in the code presented here are:
-  //
-  //   * In the paper, the weights matrix W_p is Cholesky-decomposed as Q^T Q.
-  //     Our W_p is diagonal (equal to diag(sqrt_weights^2)),
-  //     so we can just set Q = diag(sqrt_weights) instead.
-  //
-  //   * In the paper, the problem is presented as
-  //     (for W_k = I and W_p = tranposed(Q) Q):
-  //     || Q (c A T + j tranposed(t) - B) || -> min.
-  //
-  //     We reformulate it as an equivalent minimization of the transpose's
-  //     norm:
-  //     || (c tranposed(T) tranposed(A) - tranposed(B)) tranposed(Q) || -> min,
-  //     where tranposed(A) and tranposed(B) are the source and the target point
-  //     clouds, respectively, c tranposed(T) is the rotation+scaling R sought
-  //     for, and Q is diag(sqrt_weights).
-  //
-  //     Most of the derivations are therefore transposed.
-  //
-  // Note: the output `transform_mat` argument is used instead of `StatusOr<>`
-  // return type in order to avoid Eigen memory alignment issues. Details:
-  // https://eigen.tuxfamily.org/dox/group__TopicStructHavingEigenMembers.html
-  static absl::Status InternalSolveWeightedOrthogonalProblem(
-      const Eigen::Matrix3Xf& sources, const Eigen::Matrix3Xf& targets,
-      const Eigen::VectorXf& sqrt_weights, Eigen::Matrix4f& transform_mat) {
-    // tranposed(A_w).
-    Eigen::Matrix3Xf weighted_sources =
-        sources.array().rowwise() * sqrt_weights.array().transpose();
-    // tranposed(B_w).
-    Eigen::Matrix3Xf weighted_targets =
-        targets.array().rowwise() * sqrt_weights.array().transpose();
-
-    // w = tranposed(j_w) j_w.
-    float total_weight = sqrt_weights.cwiseProduct(sqrt_weights).sum();
-
-    // Let C = (j_w tranposed(j_w)) / (tranposed(j_w) j_w).
-    // Note that C = tranposed(C), hence (I - C) = tranposed(I - C).
-    //
-    // tranposed(A_w) C = tranposed(A_w) j_w tranposed(j_w) / w =
-    // (tranposed(A_w) j_w) tranposed(j_w) / w = c_w tranposed(j_w),
-    //
-    // where c_w = tranposed(A_w) j_w / w is a k x 1 vector calculated here:
-    Eigen::Matrix3Xf twice_weighted_sources =
-        weighted_sources.array().rowwise() * sqrt_weights.array().transpose();
-    Eigen::Vector3f source_center_of_mass =
-        twice_weighted_sources.rowwise().sum() / total_weight;
-    // tranposed((I - C) A_w) = tranposed(A_w) (I - C) =
-    // tranposed(A_w) - tranposed(A_w) C = tranposed(A_w) - c_w tranposed(j_w).
-    Eigen::Matrix3Xf centered_weighted_sources =
-        weighted_sources - source_center_of_mass * sqrt_weights.transpose();
-
-    Eigen::Matrix3f rotation;
-    MP_RETURN_IF_ERROR(ComputeOptimalRotation(
-        weighted_targets * centered_weighted_sources.transpose(), rotation))
-        << "Failed to compute the optimal rotation!";
-    ASSIGN_OR_RETURN(
-        float scale,
-        ComputeOptimalScale(centered_weighted_sources, weighted_sources,
-                            weighted_targets, rotation),
-        _ << "Failed to compute the optimal scale!");
-
-    // R = c tranposed(T).
-    Eigen::Matrix3f rotation_and_scale = scale * rotation;
-
-    // Compute optimal translation for the weighted problem.
-
-    // tranposed(B_w - c A_w T) = tranposed(B_w) - R tranposed(A_w) in (54).
-    const auto pointwise_diffs =
-        weighted_targets - rotation_and_scale * weighted_sources;
-    // Multiplication by j_w is a respectively weighted column sum.
-    // (54) from the paper.
-    const auto weighted_pointwise_diffs =
-        pointwise_diffs.array().rowwise() * sqrt_weights.array().transpose();
-    Eigen::Vector3f translation =
-        weighted_pointwise_diffs.rowwise().sum() / total_weight;
-
-    transform_mat = CombineTransformMatrix(rotation_and_scale, translation);
-
-    return absl::OkStatus();
-  }
-
-  // `design_matrix` is a transposed LHS of (51) in the paper.
-  //
-  // Note: the output `rotation` argument is used instead of `StatusOr<>`
-  // return type in order to avoid Eigen memory alignment issues. Details:
-  // https://eigen.tuxfamily.org/dox/group__TopicStructHavingEigenMembers.html
-  static absl::Status ComputeOptimalRotation(
-      const Eigen::Matrix3f& design_matrix, Eigen::Matrix3f& rotation) {
-    RET_CHECK_GT(design_matrix.norm(), kAbsoluteErrorEps)
-        << "Design matrix norm is too small!";
-
-    Eigen::JacobiSVD<Eigen::Matrix3f> svd(
-        design_matrix, Eigen::ComputeFullU | Eigen::ComputeFullV);
-
-    Eigen::Matrix3f postrotation = svd.matrixU();
-    Eigen::Matrix3f prerotation = svd.matrixV().transpose();
-
-    // Disallow reflection by ensuring that det(`rotation`) = +1 (and not -1),
-    // see "4.6 Constrained orthogonal Procrustes problems"
-    // in the Gower & Dijksterhuis's book "Procrustes Analysis".
-    // We flip the sign of the least singular value along with a column in W.
-    //
-    // Note that now the sum of singular values doesn't work for scale
-    // estimation due to this sign flip.
-    if (postrotation.determinant() * prerotation.determinant() <
-        static_cast<float>(0)) {
-      postrotation.col(2) *= static_cast<float>(-1);
-    }
-
-    // Transposed (52) from the paper.
-    rotation = postrotation * prerotation;
-    return absl::OkStatus();
-  }
-
-  static absl::StatusOr<float> ComputeOptimalScale(
-      const Eigen::Matrix3Xf& centered_weighted_sources,
-      const Eigen::Matrix3Xf& weighted_sources,
-      const Eigen::Matrix3Xf& weighted_targets,
-      const Eigen::Matrix3f& rotation) {
-    // tranposed(T) tranposed(A_w) (I - C).
-    const auto rotated_centered_weighted_sources =
-        rotation * centered_weighted_sources;
-    // Use the identity trace(A B) = sum(A * B^T)
-    // to avoid building large intermediate matrices (* is Hadamard product).
-    // (53) from the paper.
-    float numerator =
-        rotated_centered_weighted_sources.cwiseProduct(weighted_targets).sum();
-    float denominator =
-        centered_weighted_sources.cwiseProduct(weighted_sources).sum();
-
-    RET_CHECK_GT(denominator, kAbsoluteErrorEps)
-        << "Scale expression denominator is too small!";
-    RET_CHECK_GT(numerator / denominator, kAbsoluteErrorEps)
-        << "Scale is too small!";
-
-    return numerator / denominator;
-  }
-};
-
-}  // namespace
-
-std::unique_ptr<ProcrustesSolver> CreateFloatPrecisionProcrustesSolver() {
-  return absl::make_unique<FloatPrecisionProcrustesSolver>();
-}
-
-}  // namespace face_geometry
-}  // namespace mediapipe
diff --git a/mediapipe/modules/face_geometry/libs/procrustes_solver.h b/mediapipe/modules/face_geometry/libs/procrustes_solver.h
deleted file mode 100644
index c34b8f6..0000000
--- a/mediapipe/modules/face_geometry/libs/procrustes_solver.h
+++ /dev/null
@@ -1,70 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_FACE_GEOMETRY_LIBS_PROCRUSTES_SOLVER_H_
-#define MEDIAPIPE_FACE_GEOMETRY_LIBS_PROCRUSTES_SOLVER_H_
-
-#include <memory>
-
-#include "Eigen/Dense"
-#include "mediapipe/framework/port/status.h"
-
-namespace mediapipe::face_geometry {
-
-// Encapsulates a stateless solver for the Weighted Extended Orthogonal
-// Procrustes (WEOP) Problem, as defined in Section 2.4 of
-// https://doi.org/10.3929/ethz-a-004656648.
-//
-// Given the source and the target point clouds, the algorithm estimates
-// a 4x4 transformation matrix featuring the following semantic components:
-//
-//   * Uniform scale
-//   * Rotation
-//   * Translation
-//
-// The matrix maps the source point cloud into the target point cloud minimizing
-// the Mean Squared Error.
-class ProcrustesSolver {
- public:
-  virtual ~ProcrustesSolver() = default;
-
-  // Solves the Weighted Extended Orthogonal Procrustes (WEOP) Problem.
-  //
-  // All `source_points`, `target_points` and `point_weights` must define the
-  // same number of points. Elements of `point_weights` must be non-negative.
-  //
-  // A too small diameter of either of the point clouds will likely lead to
-  // numerical instabilities and failure to estimate the transformation.
-  //
-  // A too small point cloud total weight will likely lead to numerical
-  // instabilities and failure to estimate the transformation too.
-  //
-  // Small point coordinate deviation for either of the point cloud will likely
-  // result in a failure as it will make the solution very unstable if possible.
-  //
-  // Note: the output `transform_mat` argument is used instead of `StatusOr<>`
-  // return type in order to avoid Eigen memory alignment issues. Details:
-  // https://eigen.tuxfamily.org/dox/group__TopicStructHavingEigenMembers.html
-  virtual absl::Status SolveWeightedOrthogonalProblem(
-      const Eigen::Matrix3Xf& source_points,  //
-      const Eigen::Matrix3Xf& target_points,  //
-      const Eigen::VectorXf& point_weights,   //
-      Eigen::Matrix4f& transform_mat) const = 0;
-};
-
-std::unique_ptr<ProcrustesSolver> CreateFloatPrecisionProcrustesSolver();
-
-}  // namespace mediapipe::face_geometry
-
-#endif  // MEDIAPIPE_FACE_GEOMETRY_LIBS_PROCRUSTES_SOLVER_H_
diff --git a/mediapipe/modules/face_geometry/libs/validation_utils.cc b/mediapipe/modules/face_geometry/libs/validation_utils.cc
deleted file mode 100644
index eb4fd08..0000000
--- a/mediapipe/modules/face_geometry/libs/validation_utils.cc
+++ /dev/null
@@ -1,126 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/face_geometry/libs/validation_utils.h"
-
-#include <cstdint>
-#include <cstdlib>
-
-#include "mediapipe/framework/formats/matrix_data.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/framework/port/status_macros.h"
-#include "mediapipe/modules/face_geometry/libs/mesh_3d_utils.h"
-#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
-#include "mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.pb.h"
-#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
-
-namespace mediapipe::face_geometry {
-
-absl::Status ValidatePerspectiveCamera(
-    const PerspectiveCamera& perspective_camera) {
-  static constexpr float kAbsoluteErrorEps = 1e-9f;
-
-  RET_CHECK_GT(perspective_camera.near(), kAbsoluteErrorEps)
-      << "Near Z must be greater than 0 with a margin of 10^{-9}!";
-
-  RET_CHECK_GT(perspective_camera.far(),
-               perspective_camera.near() + kAbsoluteErrorEps)
-      << "Far Z must be greater than Near Z with a margin of 10^{-9}!";
-
-  RET_CHECK_GT(perspective_camera.vertical_fov_degrees(), kAbsoluteErrorEps)
-      << "Vertical FOV must be positive with a margin of 10^{-9}!";
-
-  RET_CHECK_LT(perspective_camera.vertical_fov_degrees() + kAbsoluteErrorEps,
-               180.f)
-      << "Vertical FOV must be less than 180 degrees with a margin of 10^{-9}";
-
-  return absl::OkStatus();
-}
-
-absl::Status ValidateEnvironment(const Environment& environment) {
-  MP_RETURN_IF_ERROR(
-      ValidatePerspectiveCamera(environment.perspective_camera()))
-      << "Invalid perspective camera!";
-
-  return absl::OkStatus();
-}
-
-absl::Status ValidateMesh3d(const Mesh3d& mesh_3d) {
-  const std::size_t vertex_size = GetVertexSize(mesh_3d.vertex_type());
-  const std::size_t primitive_type = GetPrimitiveSize(mesh_3d.primitive_type());
-
-  RET_CHECK_EQ(mesh_3d.vertex_buffer_size() % vertex_size, 0)
-      << "Vertex buffer size must a multiple of the vertex size!";
-
-  RET_CHECK_EQ(mesh_3d.index_buffer_size() % primitive_type, 0)
-      << "Index buffer size must a multiple of the primitive size!";
-
-  const int num_vertices = mesh_3d.vertex_buffer_size() / vertex_size;
-  for (uint32_t idx : mesh_3d.index_buffer()) {
-    RET_CHECK_LT(idx, num_vertices)
-        << "All mesh indices must refer to an existing vertex!";
-  }
-
-  return absl::OkStatus();
-}
-
-absl::Status ValidateFaceGeometry(const FaceGeometry& face_geometry) {
-  MP_RETURN_IF_ERROR(ValidateMesh3d(face_geometry.mesh())) << "Invalid mesh!";
-
-  static constexpr char kInvalid4x4MatrixMessage[] =
-      "Pose transformation matrix must be a 4x4 matrix!";
-
-  const MatrixData& pose_transform_matrix =
-      face_geometry.pose_transform_matrix();
-  RET_CHECK_EQ(pose_transform_matrix.rows(), 4) << kInvalid4x4MatrixMessage;
-  RET_CHECK_EQ(pose_transform_matrix.rows(), 4) << kInvalid4x4MatrixMessage;
-  RET_CHECK_EQ(pose_transform_matrix.packed_data_size(), 16)
-      << kInvalid4x4MatrixMessage;
-
-  return absl::OkStatus();
-}
-
-absl::Status ValidateGeometryPipelineMetadata(
-    const GeometryPipelineMetadata& metadata) {
-  MP_RETURN_IF_ERROR(ValidateMesh3d(metadata.canonical_mesh()))
-      << "Invalid canonical mesh!";
-
-  RET_CHECK_GT(metadata.procrustes_landmark_basis_size(), 0)
-
-      << "Procrustes landmark basis must be non-empty!";
-
-  const int num_vertices =
-      metadata.canonical_mesh().vertex_buffer_size() /
-      GetVertexSize(metadata.canonical_mesh().vertex_type());
-  for (const WeightedLandmarkRef& wlr : metadata.procrustes_landmark_basis()) {
-    RET_CHECK_LT(wlr.landmark_id(), num_vertices)
-        << "All Procrustes basis indices must refer to an existing canonical "
-           "mesh vertex!";
-
-    RET_CHECK_GE(wlr.weight(), 0.f)
-        << "All Procrustes basis landmarks must have a non-negative weight!";
-  }
-
-  return absl::OkStatus();
-}
-
-absl::Status ValidateFrameDimensions(int frame_width, int frame_height) {
-  RET_CHECK_GT(frame_width, 0) << "Frame width must be positive!";
-  RET_CHECK_GT(frame_height, 0) << "Frame height must be positive!";
-
-  return absl::OkStatus();
-}
-
-}  // namespace mediapipe::face_geometry
diff --git a/mediapipe/modules/face_geometry/libs/validation_utils.h b/mediapipe/modules/face_geometry/libs/validation_utils.h
deleted file mode 100644
index c0a7e08..0000000
--- a/mediapipe/modules/face_geometry/libs/validation_utils.h
+++ /dev/null
@@ -1,70 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_FACE_GEOMETRY_LIBS_VALIDATION_UTILS_H_
-#define MEDIAPIPE_FACE_GEOMETRY_LIBS_VALIDATION_UTILS_H_
-
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/modules/face_geometry/protos/environment.pb.h"
-#include "mediapipe/modules/face_geometry/protos/face_geometry.pb.h"
-#include "mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.pb.h"
-#include "mediapipe/modules/face_geometry/protos/mesh_3d.pb.h"
-
-namespace mediapipe::face_geometry {
-
-// Validates `perspective_camera`.
-//
-// Near Z must be greater than 0 with a margin of `1e-9`.
-// Far Z must be greater than Near Z with a margin of `1e-9`.
-// Vertical FOV must be in range (0, 180) with a margin of `1e-9` on the range
-// edges.
-absl::Status ValidatePerspectiveCamera(
-    const PerspectiveCamera& perspective_camera);
-
-// Validates `environment`.
-//
-// Environment's perspective camera must be valid.
-absl::Status ValidateEnvironment(const Environment& environment);
-
-// Validates `mesh_3d`.
-//
-// Mesh vertex buffer size must a multiple of the vertex size.
-// Mesh index buffer size must a multiple of the primitive size.
-// All mesh indices must reference an existing mesh vertex.
-absl::Status ValidateMesh3d(const Mesh3d& mesh_3d);
-
-// Validates `face_geometry`.
-//
-// Face mesh must be valid.
-// Face pose transformation matrix must be a 4x4 matrix.
-absl::Status ValidateFaceGeometry(const FaceGeometry& face_geometry);
-
-// Validates `metadata`.
-//
-// Canonical face mesh must be valid.
-// Procrustes landmark basis must be non-empty.
-// All Procrustes basis indices must reference an existing canonical mesh
-// vertex.
-// All Procrustes basis landmarks must have a non-negative weight.
-absl::Status ValidateGeometryPipelineMetadata(
-    const GeometryPipelineMetadata& metadata);
-
-// Validates frame dimensions.
-//
-// Both frame width and frame height must be positive.
-absl::Status ValidateFrameDimensions(int frame_width, int frame_height);
-
-}  // namespace mediapipe::face_geometry
-
-#endif  // MEDIAPIPE_FACE_GEOMETRY_LIBS_VALIDATION_UTILS_H_
diff --git a/mediapipe/modules/face_geometry/protos/BUILD b/mediapipe/modules/face_geometry/protos/BUILD
deleted file mode 100644
index 48b7b66..0000000
--- a/mediapipe/modules/face_geometry/protos/BUILD
+++ /dev/null
@@ -1,46 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_proto_library(
-    name = "environment_proto",
-    srcs = ["environment.proto"],
-)
-
-mediapipe_proto_library(
-    name = "face_geometry_proto",
-    srcs = ["face_geometry.proto"],
-    deps = [
-        ":mesh_3d_proto",
-        "//mediapipe/framework/formats:matrix_data_proto",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "geometry_pipeline_metadata_proto",
-    srcs = ["geometry_pipeline_metadata.proto"],
-    deps = [
-        ":mesh_3d_proto",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "mesh_3d_proto",
-    srcs = ["mesh_3d.proto"],
-)
diff --git a/mediapipe/modules/face_geometry/protos/environment.proto b/mediapipe/modules/face_geometry/protos/environment.proto
deleted file mode 100644
index cca3f29..0000000
--- a/mediapipe/modules/face_geometry/protos/environment.proto
+++ /dev/null
@@ -1,84 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe.face_geometry;
-
-option java_package = "com.google.mediapipe.modules.facegeometry";
-option java_outer_classname = "EnvironmentProto";
-
-// Defines the (0, 0) origin point location of the environment.
-//
-// The variation in the origin point location can be traced back to the memory
-// layout of the camera video frame buffers.
-//
-// Usually, the memory layout for most CPU (and also some GPU) camera video
-// frame buffers results in having the (0, 0) origin point located in the
-// Top Left corner.
-//
-// On the contrary, the memory layout for most GPU camera video frame buffers
-// results in having the (0, 0) origin point located in the Bottom Left corner.
-//
-// Let's consider the following example:
-//
-// (A) ---------------+
-//               ___  |
-//  |     (1)    | |  |
-//  |     / \    | |  |
-//  |    |---|===|-|  |
-//  |    |---|   | |  |
-//  |   /     \  | |  |
-//  |  |       | | |  |
-//  |  |  (2)  |=| |  |
-//  |  |       | | |  |
-//  |  |_______| |_|  |
-//  |   |@| |@|  | |  |
-//  | ___________|_|_ |
-//                    |
-// (B) ---------------+
-//
-// On this example, (1) and (2) have the same X coordinate regardless of the
-// origin point location. However, having the origin point located at (A)
-// (Top Left corner) results in (1) having a smaller Y coordinate if compared to
-// (2). Similarly, having the origin point located at (B) (Bottom Left corner)
-// results in (1) having a greater Y coordinate if compared to (2).
-//
-// Providing the correct origin point location for your environment and making
-// sure all the input landmarks are in-sync with this location is crucial
-// for receiving the correct output face geometry and visual renders.
-enum OriginPointLocation {
-  BOTTOM_LEFT_CORNER = 1;
-  TOP_LEFT_CORNER = 2;
-}
-
-// The perspective camera is defined through its vertical FOV angle and the
-// Z-clipping planes. The aspect ratio is a runtime variable for the face
-// geometry module and should be provided alongside the face landmarks in order
-// to estimate the face geometry on a given frame.
-//
-// More info on Perspective Cameras:
-// http://www.songho.ca/opengl/gl_projectionmatrix.html#perspective
-message PerspectiveCamera {
-  // `0 < vertical_fov_degrees < 180`.
-  optional float vertical_fov_degrees = 1;
-  // `0 < near < far`.
-  optional float near = 2;
-  optional float far = 3;
-}
-
-message Environment {
-  optional OriginPointLocation origin_point_location = 1;
-  optional PerspectiveCamera perspective_camera = 2;
-}
diff --git a/mediapipe/modules/face_geometry/protos/face_geometry.proto b/mediapipe/modules/face_geometry/protos/face_geometry.proto
deleted file mode 100644
index b91a7d7..0000000
--- a/mediapipe/modules/face_geometry/protos/face_geometry.proto
+++ /dev/null
@@ -1,60 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe.face_geometry;
-
-import "mediapipe/framework/formats/matrix_data.proto";
-import "mediapipe/modules/face_geometry/protos/mesh_3d.proto";
-
-option java_package = "com.google.mediapipe.modules.facegeometry";
-option java_outer_classname = "FaceGeometryProto";
-
-// Defines the face geometry pipeline estimation result format.
-message FaceGeometry {
-  // Defines a mesh surface for a face. The face mesh vertex IDs are the same as
-  // the face landmark IDs.
-  //
-  // XYZ coordinates exist in the right-handed Metric 3D space configured by an
-  // environment. UV coodinates are taken from the canonical face mesh model.
-  //
-  // XY coordinates are guaranteed to match the screen positions of
-  // the input face landmarks after (1) being multiplied by the face pose
-  // transformation matrix and then (2) being projected with a perspective
-  // camera matrix of the same environment.
-  //
-  // NOTE: the triangular topology of the face mesh is only useful when derived
-  //       from the 468 face landmarks, not from the 6 face detection landmarks
-  //       (keypoints). The former don't cover the entire face and this mesh is
-  //       defined here only to comply with the API. It should be considered as
-  //       a placeholder and/or for debugging purposes.
-  //
-  //       Use the face geometry derived from the face detection landmarks
-  //       (keypoints) for the face pose transformation matrix, not the mesh.
-  optional Mesh3d mesh = 1;
-
-  // Defines a face pose transformation matrix, which provides mapping from
-  // the static canonical face model to the runtime face. Tries to distinguish
-  // a head pose change from a facial expression change and to only reflect the
-  // former.
-  //
-  // Is a 4x4 matrix and contains only the following components:
-  //   * Uniform scale
-  //   * Rotation
-  //   * Translation
-  //
-  // The last row is guaranteed to be `[0 0 0 1]`.
-  optional MatrixData pose_transform_matrix = 2;
-}
diff --git a/mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.proto b/mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.proto
deleted file mode 100644
index dac0e25..0000000
--- a/mediapipe/modules/face_geometry/protos/geometry_pipeline_metadata.proto
+++ /dev/null
@@ -1,63 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe.face_geometry;
-
-import "mediapipe/modules/face_geometry/protos/mesh_3d.proto";
-
-option java_package = "com.google.mediapipe.modules.facegeometry";
-option java_outer_classname = "GeometryPipelineMetadataProto";
-
-enum InputSource {
-  DEFAULT = 0;  // FACE_LANDMARK_PIPELINE
-  FACE_LANDMARK_PIPELINE = 1;
-  FACE_DETECTION_PIPELINE = 2;
-}
-
-message WeightedLandmarkRef {
-  // Defines the landmark ID. References an existing face landmark ID.
-  optional uint32 landmark_id = 1;
-  // Defines the landmark weight. The larger the weight the more influence this
-  // landmark has in the basis.
-  //
-  // Is positive.
-  optional float weight = 2;
-}
-
-// Next field ID: 4
-message GeometryPipelineMetadata {
-  // Defines the source of the input landmarks to let the underlying geometry
-  // pipeline to adjust in order to produce the best results.
-  //
-  // Face landmark pipeline is expected to produce 3D landmarks with relative Z
-  // coordinate, which is scaled as the X coordinate assuming the weak
-  // perspective projection camera model.
-  //
-  // Face landmark pipeline is expected to produce 2D landmarks with Z
-  // coordinate being equal to 0.
-  optional InputSource input_source = 3;
-  // Defines a mesh surface for a canonical face. The canonical face mesh vertex
-  // IDs are the same as the face landmark IDs.
-  //
-  // XYZ coordinates are defined in centimeter units.
-  optional Mesh3d canonical_mesh = 1;
-  // Defines a weighted landmark basis for running the Procrustes solver
-  // algorithm inside the geometry pipeline.
-  //
-  // A good basis sets face landmark weights in way to distinguish a head pose
-  // change from a facial expression change and to only respond to the former.
-  repeated WeightedLandmarkRef procrustes_landmark_basis = 2;
-}
diff --git a/mediapipe/modules/face_geometry/protos/mesh_3d.proto b/mediapipe/modules/face_geometry/protos/mesh_3d.proto
deleted file mode 100644
index 4db45c1..0000000
--- a/mediapipe/modules/face_geometry/protos/mesh_3d.proto
+++ /dev/null
@@ -1,41 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe.face_geometry;
-
-option java_package = "com.google.mediapipe.modules.facegeometry";
-option java_outer_classname = "Mesh3dProto";
-
-message Mesh3d {
-  enum VertexType {
-    // Is defined by 5 coordinates: Position (XYZ) + Texture coordinate (UV).
-    VERTEX_PT = 0;
-  }
-
-  enum PrimitiveType {
-    // Is defined by 3 indices: triangle vertex IDs.
-    TRIANGLE = 0;
-  }
-
-  optional VertexType vertex_type = 1;
-  optional PrimitiveType primitive_type = 2;
-  // Vertex buffer size is a multiple of the vertex size (e.g., 5 for
-  // VERTEX_PT).
-  repeated float vertex_buffer = 3;
-  // Index buffer size is a multiple of the primitive size (e.g., 3 for
-  // TRIANGLE).
-  repeated uint32 index_buffer = 4;
-}
diff --git a/mediapipe/modules/face_landmark/BUILD b/mediapipe/modules/face_landmark/BUILD
deleted file mode 100644
index f155e46..0000000
--- a/mediapipe/modules/face_landmark/BUILD
+++ /dev/null
@@ -1,190 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "face_landmark_cpu",
-    graph = "face_landmark_cpu.pbtxt",
-    register_as = "FaceLandmarkCpu",
-    deps = [
-        ":face_landmarks_model_loader",
-        ":tensors_to_face_landmarks",
-        ":tensors_to_face_landmarks_with_attention",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-        "//mediapipe/calculators/util:thresholding_calculator",
-        "//mediapipe/framework/tool:switch_container",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_landmark_gpu",
-    graph = "face_landmark_gpu.pbtxt",
-    register_as = "FaceLandmarkGpu",
-    deps = [
-        ":face_landmarks_model_loader",
-        ":tensors_to_face_landmarks",
-        ":tensors_to_face_landmarks_with_attention",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-        "//mediapipe/calculators/util:thresholding_calculator",
-        "//mediapipe/framework/tool:switch_container",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_landmark_front_cpu",
-    graph = "face_landmark_front_cpu.pbtxt",
-    register_as = "FaceLandmarkFrontCpu",
-    deps = [
-        ":face_detection_front_detection_to_roi",
-        ":face_landmark_cpu",
-        ":face_landmark_landmarks_to_roi",
-        "//mediapipe/calculators/core:begin_loop_calculator",
-        "//mediapipe/calculators/core:clip_vector_size_calculator",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:end_loop_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/util:association_norm_rect_calculator",
-        "//mediapipe/calculators/util:collection_has_min_size_calculator",
-        "//mediapipe/modules/face_detection:face_detection_short_range_cpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_landmark_front_gpu",
-    graph = "face_landmark_front_gpu.pbtxt",
-    register_as = "FaceLandmarkFrontGpu",
-    deps = [
-        ":face_detection_front_detection_to_roi",
-        ":face_landmark_gpu",
-        ":face_landmark_landmarks_to_roi",
-        "//mediapipe/calculators/core:begin_loop_calculator",
-        "//mediapipe/calculators/core:clip_vector_size_calculator",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:end_loop_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/util:association_norm_rect_calculator",
-        "//mediapipe/calculators/util:collection_has_min_size_calculator",
-        "//mediapipe/modules/face_detection:face_detection_short_range_gpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_landmark_front_cpu_image",
-    graph = "face_landmark_front_cpu_image.pbtxt",
-    register_as = "FaceLandmarkFrontCpuImage",
-    deps = [
-        ":face_landmark_front_cpu",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_landmark_front_gpu_image",
-    graph = "face_landmark_front_gpu_image.pbtxt",
-    register_as = "FaceLandmarkFrontGpuImage",
-    deps = [
-        ":face_landmark_front_gpu",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-    ],
-)
-
-exports_files(
-    srcs = [
-        "face_landmark.tflite",
-        "face_landmark_with_attention.tflite",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_front_detection_to_roi",
-    graph = "face_detection_front_detection_to_roi.pbtxt",
-    register_as = "FaceDetectionFrontDetectionToRoi",
-    deps = [
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_landmark_landmarks_to_roi",
-    graph = "face_landmark_landmarks_to_roi.pbtxt",
-    register_as = "FaceLandmarkLandmarksToRoi",
-    deps = [
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_landmarks_model_loader",
-    graph = "face_landmarks_model_loader.pbtxt",
-    register_as = "FaceLandmarksModelLoader",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/tflite:tflite_model_calculator",
-        "//mediapipe/calculators/util:local_file_contents_calculator",
-        "//mediapipe/framework/tool:switch_container",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "tensors_to_face_landmarks",
-    graph = "tensors_to_face_landmarks.pbtxt",
-    register_as = "TensorsToFaceLandmarks",
-    deps = [
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "tensors_to_face_landmarks_with_attention",
-    graph = "tensors_to_face_landmarks_with_attention.pbtxt",
-    register_as = "TensorsToFaceLandmarksWithAttention",
-    deps = [
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/util:landmarks_refinement_calculator",
-    ],
-)
diff --git a/mediapipe/modules/face_landmark/README.md b/mediapipe/modules/face_landmark/README.md
deleted file mode 100644
index eed21a2..0000000
--- a/mediapipe/modules/face_landmark/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# face_landmark
-
-Subgraphs|Details
-:--- | :---
-[`FaceLandmarkCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_cpu.pbtxt)| Detects landmarks on a single face. (CPU input, and inference is executed on CPU.)
-[`FaceLandmarkGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_gpu.pbtxt)| Detects landmarks on a single face. (GPU input, and inference is executed on GPU)
-[`FaceLandmarkFrontCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_front_cpu.pbtxt)| Detects and tracks landmarks on multiple faces. (CPU input, and inference is executed on CPU)
-[`FaceLandmarkFrontGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt)| Detects and tracks landmarks on multiple faces. (GPU input, and inference is executed on GPU.)
-
diff --git a/mediapipe/modules/face_landmark/face_detection_front_detection_to_roi.pbtxt b/mediapipe/modules/face_landmark/face_detection_front_detection_to_roi.pbtxt
deleted file mode 100644
index acc9476..0000000
--- a/mediapipe/modules/face_landmark/face_detection_front_detection_to_roi.pbtxt
+++ /dev/null
@@ -1,47 +0,0 @@
-# MediaPipe graph to calculate face region of interest (ROI) from the very
-# first face detection in the vector of detections provided by
-# "FaceDetectionShortRangeCpu" or "FaceDetectionShortRangeGpu"
-#
-# NOTE: this graph is subject to change and should not be used directly.
-
-type: "FaceDetectionFrontDetectionToRoi"
-
-# Face detection. (Detection)
-input_stream: "DETECTION:detection"
-# Frame size (width and height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-# ROI according to the first detection of input detections. (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Converts results of face detection into a rectangle (normalized by image size)
-# that encloses the face and is rotated such that the line connecting left eye
-# and right eye is aligned with the X-axis of the rectangle.
-node {
-  calculator: "DetectionsToRectsCalculator"
-  input_stream: "DETECTION:detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:initial_roi"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 0  # Left eye.
-      rotation_vector_end_keypoint_index: 1  # Right eye.
-      rotation_vector_target_angle_degrees: 0
-    }
-  }
-}
-
-# Expands and shifts the rectangle that contains the face so that it's likely
-# to cover the entire face.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:initial_roi"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 1.5
-      scale_y: 1.5
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/face_landmark/face_landmark.tflite b/mediapipe/modules/face_landmark/face_landmark.tflite
deleted file mode 100755
index 573285d..0000000
Binary files a/mediapipe/modules/face_landmark/face_landmark.tflite and /dev/null differ
diff --git a/mediapipe/modules/face_landmark/face_landmark_cpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_cpu.pbtxt
deleted file mode 100644
index 4604fc7..0000000
--- a/mediapipe/modules/face_landmark/face_landmark_cpu.pbtxt
+++ /dev/null
@@ -1,184 +0,0 @@
-# MediaPipe graph to detect/predict face landmarks. (CPU input, and inference is
-# executed on CPU.)
-#
-# It is required that "face_landmark.tflite" is available at
-# "mediapipe/modules/face_landmark/face_landmark.tflite"
-# path during execution if `with_attention` is not set or set to `false`.
-#
-# It is required that "face_landmark_with_attention.tflite" is available at
-# "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
-# path during execution if `with_attention` is set to `true`.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceLandmarkCpu"
-#     input_stream: "IMAGE:image"
-#     input_stream: "ROI:face_roi"
-#     input_side_packet: "WITH_ATTENTION:with_attention"
-#     output_stream: "LANDMARKS:face_landmarks"
-#   }
-
-type: "FaceLandmarkCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-# ROI (region of interest) within the given image where a face is located.
-# (NormalizedRect)
-input_stream: "ROI:roi"
-# Whether to run face mesh model with attention on lips and eyes. (bool)
-# Attention provides more accuracy on lips and eye regions as well as iris
-# landmarks.
-input_side_packet: "WITH_ATTENTION:with_attention"
-
-# 468 or 478 facial landmarks within the given ROI. (NormalizedLandmarkList)
-#
-# Number of landmarks depends on the WITH_ATTENTION flag. If it's `true` - then
-# there will be 478 landmarks with refined lips, eyes and irises (10 extra
-# landmarks are for irises), otherwise 468 non-refined landmarks are returned.
-#
-# NOTE: if a face is not present within the given ROI, for this particular
-# timestamp there will not be an output packet in the LANDMARKS stream. However,
-# the MediaPipe framework will internally inform the downstream calculators of
-# the absence of this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:face_landmarks"
-
-# Transforms the input image into a 192x192 tensor.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:image"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "TENSORS:input_tensors"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 192
-      output_tensor_height: 192
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-    }
-  }
-}
-
-# Loads the face landmarks TF Lite model.
-node {
-  calculator: "FaceLandmarksModelLoader"
-  input_side_packet: "WITH_ATTENTION:with_attention"
-  output_side_packet: "MODEL:model"
-}
-
-# Generates a single side packet containing a TensorFlow Lite op resolver that
-# supports custom ops needed by the model used in this graph.
-node {
-  calculator: "TfLiteCustomOpResolverCalculator"
-  output_side_packet: "op_resolver"
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  input_side_packet: "MODEL:model"
-  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
-  output_stream: "TENSORS:output_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      delegate { xnnpack {} }
-    }
-  }
-}
-
-# Splits a vector of tensors into landmark tensors and face flag tensor.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "ENABLE:with_attention"
-  input_stream: "output_tensors"
-  output_stream: "landmark_tensors"
-  output_stream: "face_flag_tensor"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      contained_node: {
-        calculator: "SplitTensorVectorCalculator"
-        options: {
-          [mediapipe.SplitVectorCalculatorOptions.ext] {
-            ranges: { begin: 0 end: 1 }
-            ranges: { begin: 1 end: 2 }
-          }
-        }
-      }
-      contained_node: {
-        calculator: "SplitTensorVectorCalculator"
-        options: {
-          [mediapipe.SplitVectorCalculatorOptions.ext] {
-            ranges: { begin: 0 end: 6 }
-            ranges: { begin: 6 end: 7 }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Converts the face-flag tensor into a float that represents the confidence
-# score of face presence.
-node {
-  calculator: "TensorsToFloatsCalculator"
-  input_stream: "TENSORS:face_flag_tensor"
-  output_stream: "FLOAT:face_presence_score"
-  options {
-    [mediapipe.TensorsToFloatsCalculatorOptions.ext] {
-      activation: SIGMOID
-    }
-  }
-}
-
-# Applies a threshold to the confidence score to determine whether a face is
-# present.
-node {
-  calculator: "ThresholdingCalculator"
-  input_stream: "FLOAT:face_presence_score"
-  output_stream: "FLAG:face_presence"
-  options: {
-    [mediapipe.ThresholdingCalculatorOptions.ext] {
-      threshold: 0.5
-    }
-  }
-}
-
-# Drop landmarks tensors if face is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "landmark_tensors"
-  input_stream: "ALLOW:face_presence"
-  output_stream: "ensured_landmark_tensors"
-}
-
-# Decodes the landmark tensors into a vector of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "ENABLE:with_attention"
-  input_stream: "TENSORS:ensured_landmark_tensors"
-  output_stream: "LANDMARKS:landmarks"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      contained_node: {
-        calculator: "TensorsToFaceLandmarks"
-      }
-      contained_node: {
-        calculator: "TensorsToFaceLandmarksWithAttention"
-      }
-    }
-  }
-}
-
-# Projects the landmarks from the cropped face image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "NORM_LANDMARKS:face_landmarks"
-}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_cpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_cpu.pbtxt
deleted file mode 100644
index 70a57b0..0000000
--- a/mediapipe/modules/face_landmark/face_landmark_front_cpu.pbtxt
+++ /dev/null
@@ -1,247 +0,0 @@
-# MediaPipe graph to detect/predict face landmarks. (CPU input, and inference is
-# executed on CPU.) This graph tries to skip face detection as much as possible
-# by using previously detected/predicted landmarks for new images.
-#
-# It is required that "face_detection_short_range.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-# path during execution.
-#
-# It is required that "face_landmark.tflite" is available at
-# "mediapipe/modules/face_landmark/face_landmark.tflite"
-# path during execution if `with_attention` is not set or set to `false`.
-#
-# It is required that "face_landmark_with_attention.tflite" is available at
-# "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
-# path during execution if `with_attention` is set to `true`.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceLandmarkFrontCpu"
-#     input_stream: "IMAGE:image"
-#     input_side_packet: "NUM_FACES:num_faces"
-#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-#     input_side_packet: "WITH_ATTENTION:with_attention"
-#     output_stream: "LANDMARKS:multi_face_landmarks"
-#   }
-
-type: "FaceLandmarkFrontCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# Max number of faces to detect/track. (int)
-input_side_packet: "NUM_FACES:num_faces"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Whether to run face mesh model with attention on lips and eyes. (bool)
-# Attention provides more accuracy on lips and eye regions as well as iris
-# landmarks.
-input_side_packet: "WITH_ATTENTION:with_attention"
-
-# Collection of detected/predicted faces, each represented as a list of 468 face
-# landmarks. (std::vector<NormalizedLandmarkList>)
-# NOTE: there will not be an output packet in the LANDMARKS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:multi_face_landmarks"
-
-# Extra outputs (for debugging, for instance).
-# Detected faces. (std::vector<Detection>)
-output_stream: "DETECTIONS:face_detections"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-# Regions of interest calculated based on face detections.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-
-# When the optional input side packet "use_prev_landmarks" is either absent or
-# set to true, uses the landmarks on the previous image to help localize
-# landmarks on the current image.
-node {
-  calculator: "GateCalculator"
-  input_side_packet: "ALLOW:use_prev_landmarks"
-  input_stream: "prev_face_rects_from_landmarks"
-  output_stream: "gated_prev_face_rects_from_landmarks"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      allow: true
-    }
-  }
-}
-
-# Determines if an input vector of NormalizedRect has a size greater than or
-# equal to the provided num_faces.
-node {
-  calculator: "NormalizedRectVectorHasMinSizeCalculator"
-  input_stream: "ITERABLE:gated_prev_face_rects_from_landmarks"
-  input_side_packet: "num_faces"
-  output_stream: "prev_has_enough_faces"
-}
-
-# Drops the incoming image if enough faces have already been identified from the
-# previous image. Otherwise, passes the incoming image through to trigger a new
-# round of face detection.
-node {
-  calculator: "GateCalculator"
-  input_stream: "image"
-  input_stream: "DISALLOW:prev_has_enough_faces"
-  output_stream: "gated_image"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      empty_packets_as_allow: true
-    }
-  }
-}
-
-# Detects faces.
-node {
-  calculator: "FaceDetectionShortRangeCpu"
-  input_stream: "IMAGE:gated_image"
-  output_stream: "DETECTIONS:all_face_detections"
-}
-
-# Makes sure there are no more detections than the provided num_faces.
-node {
-  calculator: "ClipDetectionVectorSizeCalculator"
-  input_stream: "all_face_detections"
-  output_stream: "face_detections"
-  input_side_packet: "num_faces"
-}
-
-# Calculate size of the image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:gated_image"
-  output_stream: "SIZE:gated_image_size"
-}
-
-# Outputs each element of face_detections at a fake timestamp for the rest of
-# the graph to process. Clones the image size packet for each face_detection at
-# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
-# for downstream calculators to inform them that all elements in the vector have
-# been processed.
-node {
-  calculator: "BeginLoopDetectionCalculator"
-  input_stream: "ITERABLE:face_detections"
-  input_stream: "CLONE:gated_image_size"
-  output_stream: "ITEM:face_detection"
-  output_stream: "CLONE:detections_loop_image_size"
-  output_stream: "BATCH_END:detections_loop_end_timestamp"
-}
-
-# Calculates region of interest based on face detections, so that can be used
-# to detect landmarks.
-node {
-  calculator: "FaceDetectionFrontDetectionToRoi"
-  input_stream: "DETECTION:face_detection"
-  input_stream: "IMAGE_SIZE:detections_loop_image_size"
-  output_stream: "ROI:face_rect_from_detection"
-}
-
-# Collects a NormalizedRect for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:face_rect_from_detection"
-  input_stream: "BATCH_END:detections_loop_end_timestamp"
-  output_stream: "ITERABLE:face_rects_from_detections"
-}
-
-# Performs association between NormalizedRect vector elements from previous
-# image and rects based on face detections from the current image. This
-# calculator ensures that the output face_rects vector doesn't contain
-# overlapping regions based on the specified min_similarity_threshold.
-node {
-  calculator: "AssociationNormRectCalculator"
-  input_stream: "face_rects_from_detections"
-  input_stream: "gated_prev_face_rects_from_landmarks"
-  output_stream: "face_rects"
-  options: {
-    [mediapipe.AssociationCalculatorOptions.ext] {
-      min_similarity_threshold: 0.5
-    }
-  }
-}
-
-# Calculate size of the image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Outputs each element of face_rects at a fake timestamp for the rest of the
-# graph to process. Clones image and image size packets for each
-# single_face_rect at the fake timestamp. At the end of the loop, outputs the
-# BATCH_END timestamp for downstream calculators to inform them that all
-# elements in the vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedRectCalculator"
-  input_stream: "ITERABLE:face_rects"
-  input_stream: "CLONE:0:image"
-  input_stream: "CLONE:1:image_size"
-  output_stream: "ITEM:face_rect"
-  output_stream: "CLONE:0:landmarks_loop_image"
-  output_stream: "CLONE:1:landmarks_loop_image_size"
-  output_stream: "BATCH_END:landmarks_loop_end_timestamp"
-}
-
-# Detects face landmarks within specified region of interest of the image.
-node {
-  calculator: "FaceLandmarkCpu"
-  input_stream: "IMAGE:landmarks_loop_image"
-  input_stream: "ROI:face_rect"
-  input_side_packet: "WITH_ATTENTION:with_attention"
-  output_stream: "LANDMARKS:face_landmarks"
-}
-
-# Calculates region of interest based on face landmarks, so that can be reused
-# for subsequent image.
-node {
-  calculator: "FaceLandmarkLandmarksToRoi"
-  input_stream: "LANDMARKS:face_landmarks"
-  input_stream: "IMAGE_SIZE:landmarks_loop_image_size"
-  output_stream: "ROI:face_rect_from_landmarks"
-}
-
-# Collects a set of landmarks for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITEM:face_landmarks"
-  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
-  output_stream: "ITERABLE:multi_face_landmarks"
-}
-
-# Collects a NormalizedRect for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:face_rect_from_landmarks"
-  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
-  output_stream: "ITERABLE:face_rects_from_landmarks"
-}
-
-# Caches face rects calculated from landmarks, and upon the arrival of the next
-# input image, sends out the cached rects with timestamps replaced by that of
-# the input image, essentially generating a packet that carries the previous
-# face rects. Note that upon the arrival of the very first input image, a
-# timestamp bound update occurs to jump start the feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image"
-  input_stream: "LOOP:face_rects_from_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_face_rects_from_landmarks"
-}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_cpu_image.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_cpu_image.pbtxt
deleted file mode 100644
index 7d0c46a..0000000
--- a/mediapipe/modules/face_landmark/face_landmark_front_cpu_image.pbtxt
+++ /dev/null
@@ -1,87 +0,0 @@
-# MediaPipe graph to detect/predict face landmarks on CPU.
-
-type: "FaceLandmarkFrontCpuImage"
-
-# Input image. (Image)
-input_stream: "IMAGE:image"
-
-# Max number of faces to detect/track. (int)
-input_side_packet: "NUM_FACES:num_faces"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Whether to run face mesh model with attention on lips and eyes. (bool)
-# Attention provides more accuracy on lips and eye regions as well as iris
-# landmarks.
-input_side_packet: "WITH_ATTENTION:with_attention"
-
-# The throttled input image. (Image)
-output_stream: "IMAGE:throttled_image"
-# Collection of detected/predicted faces, each represented as a list of 468 face
-# landmarks. (std::vector<NormalizedLandmarkList>)
-# NOTE: there will not be an output packet in the LANDMARKS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:multi_face_landmarks"
-
-# Extra outputs (for debugging, for instance).
-# Detected faces. (std::vector<Detection>)
-output_stream: "DETECTIONS:face_detections"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-# Regions of interest calculated based on face detections.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "image"
-  input_stream: "FINISHED:multi_face_landmarks"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_image"
-  options: {
-    [mediapipe.FlowLimiterCalculatorOptions.ext] {
-      max_in_flight: 1
-      max_in_queue: 1
-    }
-  }
-}
-
-# Converts Image to ImageFrame for FaceLandmarkFrontCpu to consume.
-node {
-  calculator: "FromImageCalculator"
-  input_stream: "IMAGE:throttled_image"
-  output_stream: "IMAGE_CPU:raw_image_frame"
-  output_stream: "SOURCE_ON_GPU:is_gpu_image"
-}
-
-# TODO: Remove the extra flipping once adopting MlImage.
-# If the source images are on gpu, flip the data vertically before sending them
-# into FaceLandmarkFrontCpu. This maybe needed because OpenGL represents images
-# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
-# general assumes the image origin is at the top-left corner.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:raw_image_frame"
-  input_stream: "FLIP_VERTICALLY:is_gpu_image"
-  output_stream: "IMAGE:image_frame"
-}
-
-node {
-  calculator: "FaceLandmarkFrontCpu"
-  input_stream: "IMAGE:image_frame"
-  input_side_packet: "NUM_FACES:num_faces"
-  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-  input_side_packet: "WITH_ATTENTION:with_attention"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-  output_stream: "DETECTIONS:face_detections"
-  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt
deleted file mode 100644
index fd89565..0000000
--- a/mediapipe/modules/face_landmark/face_landmark_front_gpu.pbtxt
+++ /dev/null
@@ -1,247 +0,0 @@
-# MediaPipe graph to detect/predict face landmarks. (GPU input, and inference is
-# executed on GPU.) This graph tries to skip face detection as much as possible
-# by using previously detected/predicted landmarks for new images.
-#
-# It is required that "face_detection_short_range.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-# path during execution.
-#
-# It is required that "face_landmark.tflite" is available at
-# "mediapipe/modules/face_landmark/face_landmark.tflite"
-# path during execution if `with_attention` is not set or set to `false`.
-#
-# It is required that "face_landmark_with_attention.tflite" is available at
-# "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
-# path during execution if `with_attention` is set to `true`.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceLandmarkFrontGpu"
-#     input_stream: "IMAGE:image"
-#     input_side_packet: "NUM_FACES:num_faces"
-#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-#     input_side_packet: "WITH_ATTENTION:with_attention"
-#     output_stream: "LANDMARKS:multi_face_landmarks"
-#   }
-
-type: "FaceLandmarkFrontGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# Max number of faces to detect/track. (int)
-input_side_packet: "NUM_FACES:num_faces"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Whether to run face mesh model with attention on lips and eyes. (bool)
-# Attention provides more accuracy on lips and eye regions as well as iris
-# landmarks.
-input_side_packet: "WITH_ATTENTION:with_attention"
-
-# Collection of detected/predicted faces, each represented as a list of 468 face
-# landmarks. (std::vector<NormalizedLandmarkList>)
-# NOTE: there will not be an output packet in the LANDMARKS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:multi_face_landmarks"
-
-# Extra outputs (for debugging, for instance).
-# Detected faces. (std::vector<Detection>)
-output_stream: "DETECTIONS:face_detections"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-# Regions of interest calculated based on face detections.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-
-# When the optional input side packet "use_prev_landmarks" is either absent or
-# set to true, uses the landmarks on the previous image to help localize
-# landmarks on the current image.
-node {
-  calculator: "GateCalculator"
-  input_side_packet: "ALLOW:use_prev_landmarks"
-  input_stream: "prev_face_rects_from_landmarks"
-  output_stream: "gated_prev_face_rects_from_landmarks"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      allow: true
-    }
-  }
-}
-
-# Determines if an input vector of NormalizedRect has a size greater than or
-# equal to the provided num_faces.
-node {
-  calculator: "NormalizedRectVectorHasMinSizeCalculator"
-  input_stream: "ITERABLE:gated_prev_face_rects_from_landmarks"
-  input_side_packet: "num_faces"
-  output_stream: "prev_has_enough_faces"
-}
-
-# Drops the incoming image if enough faces have already been identified from the
-# previous image. Otherwise, passes the incoming image through to trigger a new
-# round of face detection.
-node {
-  calculator: "GateCalculator"
-  input_stream: "image"
-  input_stream: "DISALLOW:prev_has_enough_faces"
-  output_stream: "gated_image"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      empty_packets_as_allow: true
-    }
-  }
-}
-
-# Detects faces.
-node {
-  calculator: "FaceDetectionShortRangeGpu"
-  input_stream: "IMAGE:gated_image"
-  output_stream: "DETECTIONS:all_face_detections"
-}
-
-# Makes sure there are no more detections than the provided num_faces.
-node {
-  calculator: "ClipDetectionVectorSizeCalculator"
-  input_stream: "all_face_detections"
-  output_stream: "face_detections"
-  input_side_packet: "num_faces"
-}
-
-# Calculate size of the image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:gated_image"
-  output_stream: "SIZE:gated_image_size"
-}
-
-# Outputs each element of face_detections at a fake timestamp for the rest of
-# the graph to process. Clones the image size packet for each face_detection at
-# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
-# for downstream calculators to inform them that all elements in the vector have
-# been processed.
-node {
-  calculator: "BeginLoopDetectionCalculator"
-  input_stream: "ITERABLE:face_detections"
-  input_stream: "CLONE:gated_image_size"
-  output_stream: "ITEM:face_detection"
-  output_stream: "CLONE:detections_loop_image_size"
-  output_stream: "BATCH_END:detections_loop_end_timestamp"
-}
-
-# Calculates region of interest based on face detections, so that can be used
-# to detect landmarks.
-node {
-  calculator: "FaceDetectionFrontDetectionToRoi"
-  input_stream: "DETECTION:face_detection"
-  input_stream: "IMAGE_SIZE:detections_loop_image_size"
-  output_stream: "ROI:face_rect_from_detection"
-}
-
-# Collects a NormalizedRect for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:face_rect_from_detection"
-  input_stream: "BATCH_END:detections_loop_end_timestamp"
-  output_stream: "ITERABLE:face_rects_from_detections"
-}
-
-# Performs association between NormalizedRect vector elements from previous
-# image and rects based on face detections from the current image. This
-# calculator ensures that the output face_rects vector doesn't contain
-# overlapping regions based on the specified min_similarity_threshold.
-node {
-  calculator: "AssociationNormRectCalculator"
-  input_stream: "face_rects_from_detections"
-  input_stream: "gated_prev_face_rects_from_landmarks"
-  output_stream: "face_rects"
-  options: {
-    [mediapipe.AssociationCalculatorOptions.ext] {
-      min_similarity_threshold: 0.5
-    }
-  }
-}
-
-# Calculate size of the image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Outputs each element of face_rects at a fake timestamp for the rest of the
-# graph to process. Clones image and image size packets for each
-# single_face_rect at the fake timestamp. At the end of the loop, outputs the
-# BATCH_END timestamp for downstream calculators to inform them that all
-# elements in the vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedRectCalculator"
-  input_stream: "ITERABLE:face_rects"
-  input_stream: "CLONE:0:image"
-  input_stream: "CLONE:1:image_size"
-  output_stream: "ITEM:face_rect"
-  output_stream: "CLONE:0:landmarks_loop_image"
-  output_stream: "CLONE:1:landmarks_loop_image_size"
-  output_stream: "BATCH_END:landmarks_loop_end_timestamp"
-}
-
-# Detects face landmarks within specified region of interest of the image.
-node {
-  calculator: "FaceLandmarkGpu"
-  input_stream: "IMAGE:landmarks_loop_image"
-  input_stream: "ROI:face_rect"
-  input_side_packet: "WITH_ATTENTION:with_attention"
-  output_stream: "LANDMARKS:face_landmarks"
-}
-
-# Calculates region of interest based on face landmarks, so that can be reused
-# for subsequent image.
-node {
-  calculator: "FaceLandmarkLandmarksToRoi"
-  input_stream: "LANDMARKS:face_landmarks"
-  input_stream: "IMAGE_SIZE:landmarks_loop_image_size"
-  output_stream: "ROI:face_rect_from_landmarks"
-}
-
-# Collects a set of landmarks for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITEM:face_landmarks"
-  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
-  output_stream: "ITERABLE:multi_face_landmarks"
-}
-
-# Collects a NormalizedRect for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:face_rect_from_landmarks"
-  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
-  output_stream: "ITERABLE:face_rects_from_landmarks"
-}
-
-# Caches face rects calculated from landmarks, and upon the arrival of the next
-# input image, sends out the cached rects with timestamps replaced by that of
-# the input image, essentially generating a packet that carries the previous
-# face rects. Note that upon the arrival of the very first input image, a
-# timestamp bound update occurs to jump start the feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image"
-  input_stream: "LOOP:face_rects_from_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_face_rects_from_landmarks"
-}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_gpu_image.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_gpu_image.pbtxt
deleted file mode 100644
index 31da4b8..0000000
--- a/mediapipe/modules/face_landmark/face_landmark_front_gpu_image.pbtxt
+++ /dev/null
@@ -1,87 +0,0 @@
-# MediaPipe graph to detect/predict face landmarks on GPU.
-
-type: "FaceLandmarkFrontGpuImage"
-
-# Input image. (Image)
-input_stream: "IMAGE:image"
-
-# Max number of faces to detect/track. (int)
-input_side_packet: "NUM_FACES:num_faces"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Whether to run face mesh model with attention on lips and eyes. (bool)
-# Attention provides more accuracy on lips and eye regions as well as iris
-# landmarks.
-input_side_packet: "WITH_ATTENTION:with_attention"
-
-# The throttled input image. (Image)
-output_stream: "IMAGE:throttled_image"
-# Collection of detected/predicted faces, each represented as a list of 468 face
-# landmarks. (std::vector<NormalizedLandmarkList>)
-# NOTE: there will not be an output packet in the LANDMARKS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:multi_face_landmarks"
-
-# Extra outputs (for debugging, for instance).
-# Detected faces. (std::vector<Detection>)
-output_stream: "DETECTIONS:face_detections"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-# Regions of interest calculated based on face detections.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "image"
-  input_stream: "FINISHED:multi_face_landmarks"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_image"
-  options: {
-    [mediapipe.FlowLimiterCalculatorOptions.ext] {
-      max_in_flight: 1
-      max_in_queue: 1
-    }
-  }
-}
-
-# Converts Image to GpuBuffer for FaceLandmarkFrontGpu to consume.
-node {
-  calculator: "FromImageCalculator"
-  input_stream: "IMAGE:throttled_image"
-  output_stream: "IMAGE_GPU:raw_gpu_buffer"
-  output_stream: "SOURCE_ON_GPU:is_gpu_image"
-}
-
-# TODO: Remove the extra flipping once adopting MlImage.
-# If the source images are on gpu, flip the data vertically before sending them
-# into FaceLandmarkFrontGpu. This maybe needed because OpenGL represents images
-# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
-# general assumes the image origin is at the top-left corner.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:raw_gpu_buffer"
-  input_stream: "FLIP_VERTICALLY:is_gpu_image"
-  output_stream: "IMAGE_GPU:gpu_buffer"
-}
-
-node {
-  calculator: "FaceLandmarkFrontGpu"
-  input_stream: "IMAGE:gpu_buffer"
-  input_side_packet: "NUM_FACES:num_faces"
-  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-  input_side_packet: "WITH_ATTENTION:with_attention"
-  output_stream: "LANDMARKS:multi_face_landmarks"
-  output_stream: "DETECTIONS:face_detections"
-  output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-  output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_side_model_cpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_side_model_cpu.pbtxt
deleted file mode 100644
index d3d26c0..0000000
--- a/mediapipe/modules/face_landmark/face_landmark_front_side_model_cpu.pbtxt
+++ /dev/null
@@ -1,224 +0,0 @@
-# MediaPipe graph to detect/predict face landmarks. (CPU input, and inference is
-# executed on CPU.) This graph tries to skip face detection as much as possible
-# by using previously detected/predicted landmarks for new images.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceLandmarkFrontSideModelCpu"
-#     input_stream: "IMAGE:image"
-#     input_side_packet: "NUM_FACES:num_faces"
-#     input_side_packet: "MODEL:0:face_detection_model"
-#     input_side_packet: "MODEL:1:face_landmark_model"
-#     output_stream: "LANDMARKS:multi_face_landmarks"
-#   }
-
-type: "FaceLandmarkFrontSideModelCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# Max number of faces to detect/track. (int)
-input_side_packet: "NUM_FACES:num_faces"
-# TfLite model to detect faces.
-# (std::unique_ptr<tflite::FlatBufferModel,
-#      std::function<void(tflite::FlatBufferModel*)>>)
-# NOTE: mediapipe/modules/face_detection/face_detection_short_range.tflite
-# model only, can be passed here, otherwise - results are undefined.
-input_side_packet: "MODEL:0:face_detection_model"
-# TfLite model to detect face landmarks.
-# (std::unique_ptr<tflite::FlatBufferModel,
-#      std::function<void(tflite::FlatBufferModel*)>>)
-# NOTE: mediapipe/modules/face_landmark/face_landmark.tflite model
-# only, can be passed here, otherwise - results are undefined.
-input_side_packet: "MODEL:1:face_landmark_model"
-
-# Collection of detected/predicted faces, each represented as a list of 468 face
-# landmarks. (std::vector<NormalizedLandmarkList>)
-# NOTE: there will not be an output packet in the LANDMARKS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:multi_face_landmarks"
-
-# Extra outputs (for debugging, for instance).
-# Detected faces. (std::vector<Detection>)
-output_stream: "DETECTIONS:face_detections"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-# Regions of interest calculated based on face detections.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-
-# Determines if an input vector of NormalizedRect has a size greater than or
-# equal to the provided num_faces.
-node {
-  calculator: "NormalizedRectVectorHasMinSizeCalculator"
-  input_stream: "ITERABLE:prev_face_rects_from_landmarks"
-  input_side_packet: "num_faces"
-  output_stream: "prev_has_enough_faces"
-}
-
-# Drops the incoming image if FaceLandmarkCpu was able to identify face presence
-# in the previous image. Otherwise, passes the incoming image through to trigger
-# a new round of face detection in FaceDetectionShortRangeCpu.
-node {
-  calculator: "GateCalculator"
-  input_stream: "image"
-  input_stream: "DISALLOW:prev_has_enough_faces"
-  output_stream: "gated_image"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      empty_packets_as_allow: true
-    }
-  }
-}
-
-# Detects faces.
-node {
-  calculator: "FaceDetectionShortRangeSideModelCpu"
-  input_stream: "IMAGE:gated_image"
-  input_side_packet: "MODEL:face_detection_model"
-  output_stream: "DETECTIONS:all_face_detections"
-}
-
-# Makes sure there are no more detections than the provided num_faces.
-node {
-  calculator: "ClipDetectionVectorSizeCalculator"
-  input_stream: "all_face_detections"
-  output_stream: "face_detections"
-  input_side_packet: "num_faces"
-}
-
-# Calculate size of the image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:gated_image"
-  output_stream: "SIZE:gated_image_size"
-}
-
-# Outputs each element of face_detections at a fake timestamp for the rest of
-# the graph to process. Clones the image size packet for each face_detection at
-# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
-# for downstream calculators to inform them that all elements in the vector have
-# been processed.
-node {
-  calculator: "BeginLoopDetectionCalculator"
-  input_stream: "ITERABLE:face_detections"
-  input_stream: "CLONE:gated_image_size"
-  output_stream: "ITEM:face_detection"
-  output_stream: "CLONE:detections_loop_image_size"
-  output_stream: "BATCH_END:detections_loop_end_timestamp"
-}
-
-# Calculates region of interest based on face detections, so that can be used
-# to detect landmarks.
-node {
-  calculator: "FaceDetectionFrontDetectionToRoi"
-  input_stream: "DETECTION:face_detection"
-  input_stream: "IMAGE_SIZE:detections_loop_image_size"
-  output_stream: "ROI:face_rect_from_detection"
-}
-
-# Collects a NormalizedRect for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:face_rect_from_detection"
-  input_stream: "BATCH_END:detections_loop_end_timestamp"
-  output_stream: "ITERABLE:face_rects_from_detections"
-}
-
-# Performs association between NormalizedRect vector elements from previous
-# image and rects based on face detections from the current image. This
-# calculator ensures that the output face_rects vector doesn't contain
-# overlapping regions based on the specified min_similarity_threshold.
-node {
-  calculator: "AssociationNormRectCalculator"
-  input_stream: "face_rects_from_detections"
-  input_stream: "prev_face_rects_from_landmarks"
-  output_stream: "face_rects"
-  options: {
-    [mediapipe.AssociationCalculatorOptions.ext] {
-      min_similarity_threshold: 0.5
-    }
-  }
-}
-
-# Calculate size of the image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Outputs each element of face_rects at a fake timestamp for the rest of the
-# graph to process. Clones image and image size packets for each
-# single_face_rect at the fake timestamp. At the end of the loop, outputs the
-# BATCH_END timestamp for downstream calculators to inform them that all
-# elements in the vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedRectCalculator"
-  input_stream: "ITERABLE:face_rects"
-  input_stream: "CLONE:0:image"
-  input_stream: "CLONE:1:image_size"
-  output_stream: "ITEM:face_rect"
-  output_stream: "CLONE:0:landmarks_loop_image"
-  output_stream: "CLONE:1:landmarks_loop_image_size"
-  output_stream: "BATCH_END:landmarks_loop_end_timestamp"
-}
-
-# Detects face landmarks within specified region of interest of the image.
-node {
-  calculator: "FaceLandmarkSideModelCpu"
-  input_stream: "IMAGE:landmarks_loop_image"
-  input_stream: "ROI:face_rect"
-  input_side_packet: "MODEL:face_landmark_model"
-  output_stream: "LANDMARKS:face_landmarks"
-}
-
-# Calculates region of interest based on face landmarks, so that can be reused
-# for subsequent image.
-node {
-  calculator: "FaceLandmarkLandmarksToRoi"
-  input_stream: "LANDMARKS:face_landmarks"
-  input_stream: "IMAGE_SIZE:landmarks_loop_image_size"
-  output_stream: "ROI:face_rect_from_landmarks"
-}
-
-# Collects a set of landmarks for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITEM:face_landmarks"
-  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
-  output_stream: "ITERABLE:multi_face_landmarks"
-}
-
-# Collects a NormalizedRect for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:face_rect_from_landmarks"
-  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
-  output_stream: "ITERABLE:face_rects_from_landmarks"
-}
-
-# Caches face rects calculated from landmarks, and upon the arrival of the next
-# input image, sends out the cached rects with timestamps replaced by that of
-# the input image, essentially generating a packet that carries the previous
-# face rects. Note that upon the arrival of the very first input image, a
-# timestamp bound update occurs to jump start the feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image"
-  input_stream: "LOOP:face_rects_from_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_face_rects_from_landmarks"
-}
diff --git a/mediapipe/modules/face_landmark/face_landmark_front_side_model_gpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_front_side_model_gpu.pbtxt
deleted file mode 100644
index 9832c2f..0000000
--- a/mediapipe/modules/face_landmark/face_landmark_front_side_model_gpu.pbtxt
+++ /dev/null
@@ -1,224 +0,0 @@
-# MediaPipe graph to detect/predict face landmarks. (GPU input, and inference is
-# executed on GPU.) This graph tries to skip face detection as much as possible
-# by using previously detected/predicted landmarks for new images.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceLandmarkFrontSideModelGpu"
-#     input_stream: "IMAGE:image"
-#     input_side_packet: "NUM_FACES:num_faces"
-#     input_side_packet: "MODEL:0:face_detection_model"
-#     input_side_packet: "MODEL:1:face_landmark_model"
-#     output_stream: "LANDMARKS:multi_face_landmarks"
-#   }
-
-type: "FaceLandmarkFrontSideModelGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# Max number of faces to detect/track. (int)
-input_side_packet: "NUM_FACES:num_faces"
-# TfLite model to detect faces.
-# (std::unique_ptr<tflite::FlatBufferModel,
-#      std::function<void(tflite::FlatBufferModel*)>>)
-# NOTE: mediapipe/modules/face_detection/face_detection_short_range.tflite
-# model only, can be passed here, otherwise - results are undefined.
-input_side_packet: "MODEL:0:face_detection_model"
-# TfLite model to detect face landmarks.
-# (std::unique_ptr<tflite::FlatBufferModel,
-#      std::function<void(tflite::FlatBufferModel*)>>)
-# NOTE: mediapipe/modules/face_landmark/face_landmark.tflite model
-# only, can be passed here, otherwise - results are undefined.
-input_side_packet: "MODEL:1:face_landmark_model"
-
-# Collection of detected/predicted faces, each represented as a list of 468 face
-# landmarks. (std::vector<NormalizedLandmarkList>)
-# NOTE: there will not be an output packet in the LANDMARKS stream for this
-# particular timestamp if none of faces detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:multi_face_landmarks"
-
-# Extra outputs (for debugging, for instance).
-# Detected faces. (std::vector<Detection>)
-output_stream: "DETECTIONS:face_detections"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
-# Regions of interest calculated based on face detections.
-# (std::vector<NormalizedRect>)
-output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
-
-# Determines if an input vector of NormalizedRect has a size greater than or
-# equal to the provided num_faces.
-node {
-  calculator: "NormalizedRectVectorHasMinSizeCalculator"
-  input_stream: "ITERABLE:prev_face_rects_from_landmarks"
-  input_side_packet: "num_faces"
-  output_stream: "prev_has_enough_faces"
-}
-
-# Drops the incoming image if FaceLandmarkGpu was able to identify face presence
-# in the previous image. Otherwise, passes the incoming image through to trigger
-# a new round of face detection in FaceDetectionShortRangeGpu.
-node {
-  calculator: "GateCalculator"
-  input_stream: "image"
-  input_stream: "DISALLOW:prev_has_enough_faces"
-  output_stream: "gated_image"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      empty_packets_as_allow: true
-    }
-  }
-}
-
-# Detects faces.
-node {
-  calculator: "FaceDetectionShortRangeSideModelGpu"
-  input_stream: "IMAGE:gated_image"
-  input_side_packet: "MODEL:face_detection_model"
-  output_stream: "DETECTIONS:all_face_detections"
-}
-
-# Makes sure there are no more detections than the provided num_faces.
-node {
-  calculator: "ClipDetectionVectorSizeCalculator"
-  input_stream: "all_face_detections"
-  output_stream: "face_detections"
-  input_side_packet: "num_faces"
-}
-
-# Calculate size of the image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:gated_image"
-  output_stream: "SIZE:gated_image_size"
-}
-
-# Outputs each element of face_detections at a fake timestamp for the rest of
-# the graph to process. Clones the image size packet for each face_detection at
-# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
-# for downstream calculators to inform them that all elements in the vector have
-# been processed.
-node {
-  calculator: "BeginLoopDetectionCalculator"
-  input_stream: "ITERABLE:face_detections"
-  input_stream: "CLONE:gated_image_size"
-  output_stream: "ITEM:face_detection"
-  output_stream: "CLONE:detections_loop_image_size"
-  output_stream: "BATCH_END:detections_loop_end_timestamp"
-}
-
-# Calculates region of interest based on face detections, so that can be used
-# to detect landmarks.
-node {
-  calculator: "FaceDetectionFrontDetectionToRoi"
-  input_stream: "DETECTION:face_detection"
-  input_stream: "IMAGE_SIZE:detections_loop_image_size"
-  output_stream: "ROI:face_rect_from_detection"
-}
-
-# Collects a NormalizedRect for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:face_rect_from_detection"
-  input_stream: "BATCH_END:detections_loop_end_timestamp"
-  output_stream: "ITERABLE:face_rects_from_detections"
-}
-
-# Performs association between NormalizedRect vector elements from previous
-# image and rects based on face detections from the current image. This
-# calculator ensures that the output face_rects vector doesn't contain
-# overlapping regions based on the specified min_similarity_threshold.
-node {
-  calculator: "AssociationNormRectCalculator"
-  input_stream: "face_rects_from_detections"
-  input_stream: "prev_face_rects_from_landmarks"
-  output_stream: "face_rects"
-  options: {
-    [mediapipe.AssociationCalculatorOptions.ext] {
-      min_similarity_threshold: 0.5
-    }
-  }
-}
-
-# Calculate size of the image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Outputs each element of face_rects at a fake timestamp for the rest of the
-# graph to process. Clones image and image size packets for each
-# single_face_rect at the fake timestamp. At the end of the loop, outputs the
-# BATCH_END timestamp for downstream calculators to inform them that all
-# elements in the vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedRectCalculator"
-  input_stream: "ITERABLE:face_rects"
-  input_stream: "CLONE:0:image"
-  input_stream: "CLONE:1:image_size"
-  output_stream: "ITEM:face_rect"
-  output_stream: "CLONE:0:landmarks_loop_image"
-  output_stream: "CLONE:1:landmarks_loop_image_size"
-  output_stream: "BATCH_END:landmarks_loop_end_timestamp"
-}
-
-# Detects face landmarks within specified region of interest of the image.
-node {
-  calculator: "FaceLandmarkSideModelGpu"
-  input_stream: "IMAGE:landmarks_loop_image"
-  input_stream: "ROI:face_rect"
-  input_side_packet: "MODEL:face_landmark_model"
-  output_stream: "LANDMARKS:face_landmarks"
-}
-
-# Calculates region of interest based on face landmarks, so that can be reused
-# for subsequent image.
-node {
-  calculator: "FaceLandmarkLandmarksToRoi"
-  input_stream: "LANDMARKS:face_landmarks"
-  input_stream: "IMAGE_SIZE:landmarks_loop_image_size"
-  output_stream: "ROI:face_rect_from_landmarks"
-}
-
-# Collects a set of landmarks for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITEM:face_landmarks"
-  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
-  output_stream: "ITERABLE:multi_face_landmarks"
-}
-
-# Collects a NormalizedRect for each face into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:face_rect_from_landmarks"
-  input_stream: "BATCH_END:landmarks_loop_end_timestamp"
-  output_stream: "ITERABLE:face_rects_from_landmarks"
-}
-
-# Caches face rects calculated from landmarks, and upon the arrival of the next
-# input image, sends out the cached rects with timestamps replaced by that of
-# the input image, essentially generating a packet that carries the previous
-# face rects. Note that upon the arrival of the very first input image, a
-# timestamp bound update occurs to jump start the feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image"
-  input_stream: "LOOP:face_rects_from_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_face_rects_from_landmarks"
-}
diff --git a/mediapipe/modules/face_landmark/face_landmark_gpu.pbtxt b/mediapipe/modules/face_landmark/face_landmark_gpu.pbtxt
deleted file mode 100644
index 854ceaf..0000000
--- a/mediapipe/modules/face_landmark/face_landmark_gpu.pbtxt
+++ /dev/null
@@ -1,185 +0,0 @@
-# MediaPipe graph to detect/predict face landmarks. (CPU input, and inference is
-# executed on CPU.)
-#
-# It is required that "face_landmark.tflite" is available at
-# "mediapipe/modules/face_landmark/face_landmark.tflite"
-# path during execution if `with_attention` is not set or set to `false`.
-#
-# It is required that "face_landmark_with_attention.tflite" is available at
-# "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
-# path during execution if `with_attention` is set to `true`.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "FaceLandmarkGpu"
-#     input_stream: "IMAGE:image"
-#     input_stream: "ROI:face_roi"
-#     input_side_packet: "WITH_ATTENTION:with_attention"
-#     output_stream: "LANDMARKS:face_landmarks"
-#   }
-
-type: "FaceLandmarkGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-# ROI (region of interest) within the given image where a face is located.
-# (NormalizedRect)
-input_stream: "ROI:roi"
-# Whether to run face mesh model with attention on lips and eyes. (bool)
-# Attention provides more accuracy on lips and eye regions as well as iris
-# landmarks.
-input_side_packet: "WITH_ATTENTION:with_attention"
-
-# 468 or 478 facial landmarks within the given ROI. (NormalizedLandmarkList)
-#
-# Number of landmarks depends on the WITH_ATTENTION flag. If it's `true` - then
-# there will be 478 landmarks with refined lips, eyes and irises (10 extra
-# landmarks are for irises), otherwise 468 non-refined landmarks are returned.
-#
-# NOTE: if a face is not present within the given ROI, for this particular
-# timestamp there will not be an output packet in the LANDMARKS stream. However,
-# the MediaPipe framework will internally inform the downstream calculators of
-# the absence of this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:face_landmarks"
-
-# Transforms the input image into a 192x192 tensor.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE_GPU:image"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "TENSORS:input_tensors"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 192
-      output_tensor_height: 192
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-
-# Loads the face landmarks TF Lite model.
-node {
-  calculator: "FaceLandmarksModelLoader"
-  input_side_packet: "WITH_ATTENTION:with_attention"
-  output_side_packet: "MODEL:model"
-}
-
-# Generates a single side packet containing a TensorFlow Lite op resolver that
-# supports custom ops needed by the model used in this graph.
-node {
-  calculator: "TfLiteCustomOpResolverCalculator"
-  output_side_packet: "op_resolver"
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of GPU tensors representing, for instance, detection boxes/keypoints
-# and scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  input_side_packet: "MODEL:model"
-  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
-  output_stream: "TENSORS:output_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      # Do not remove. Used for generation of XNNPACK/NNAPI graphs.
-    }
-  }
-}
-
-# Splits a vector of tensors into landmark tensors and face flag tensor.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "ENABLE:with_attention"
-  input_stream: "output_tensors"
-  output_stream: "landmark_tensors"
-  output_stream: "face_flag_tensor"
-  options {
-    [mediapipe.SwitchContainerOptions.ext] {
-      contained_node: {
-        calculator: "SplitTensorVectorCalculator"
-        options: {
-          [mediapipe.SplitVectorCalculatorOptions.ext] {
-            ranges: { begin: 0 end: 1 }
-            ranges: { begin: 1 end: 2 }
-          }
-        }
-      }
-      contained_node: {
-        calculator: "SplitTensorVectorCalculator"
-        options: {
-          [mediapipe.SplitVectorCalculatorOptions.ext] {
-            ranges: { begin: 0 end: 6 }
-            ranges: { begin: 6 end: 7 }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Converts the face-flag tensor into a float that represents the confidence
-# score of face presence.
-node {
-  calculator: "TensorsToFloatsCalculator"
-  input_stream: "TENSORS:face_flag_tensor"
-  output_stream: "FLOAT:face_presence_score"
-  options: {
-    [mediapipe.TensorsToFloatsCalculatorOptions.ext] {
-      activation: SIGMOID
-    }
-  }
-}
-
-# Applies a threshold to the confidence score to determine whether a face is
-# present.
-node {
-  calculator: "ThresholdingCalculator"
-  input_stream: "FLOAT:face_presence_score"
-  output_stream: "FLAG:face_presence"
-  options: {
-    [mediapipe.ThresholdingCalculatorOptions.ext] {
-      threshold: 0.5
-    }
-  }
-}
-
-# Drop landmarks tensors if face is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "landmark_tensors"
-  input_stream: "ALLOW:face_presence"
-  output_stream: "ensured_landmark_tensors"
-}
-
-# Decodes the landmark tensors into a vector of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "ENABLE:with_attention"
-  input_stream: "TENSORS:ensured_landmark_tensors"
-  output_stream: "LANDMARKS:landmarks"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      contained_node: {
-        calculator: "TensorsToFaceLandmarks"
-      }
-      contained_node: {
-        calculator: "TensorsToFaceLandmarksWithAttention"
-      }
-    }
-  }
-}
-
-# Projects the landmarks from the cropped face image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "NORM_LANDMARKS:face_landmarks"
-}
diff --git a/mediapipe/modules/face_landmark/face_landmark_landmarks_to_roi.pbtxt b/mediapipe/modules/face_landmark/face_landmark_landmarks_to_roi.pbtxt
deleted file mode 100644
index 9f634b0..0000000
--- a/mediapipe/modules/face_landmark/face_landmark_landmarks_to_roi.pbtxt
+++ /dev/null
@@ -1,54 +0,0 @@
-# MediaPipe graph to calculate face region of interest (ROI) from landmarks
-# detected by "FaceLandmarkCpu" or "FaceLandmarkGpu".
-#
-# NOTE: this graph is subject to change and should not be used directly.
-
-type: "FaceLandmarkLandmarksToRoi"
-
-# Normalized landmarks. (NormalizedLandmarkList)
-input_stream: "LANDMARKS:landmarks"
-# Frame size (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-# ROI according to landmarks. (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Converts face landmarks to a detection that tightly encloses all landmarks.
-node {
-  calculator: "LandmarksToDetectionCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks"
-  output_stream: "DETECTION:face_detection"
-}
-
-# Converts the face detection into a rectangle (normalized by image size)
-# that encloses the face and is rotated such that the line connecting left side
-# of the left eye and right side of the right eye is aligned with the X-axis of
-# the rectangle.
-node {
-  calculator: "DetectionsToRectsCalculator"
-  input_stream: "DETECTION:face_detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:face_rect_from_landmarks"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 33  # Left side of left eye.
-      rotation_vector_end_keypoint_index: 263  # Right side of right eye.
-      rotation_vector_target_angle_degrees: 0
-    }
-  }
-}
-
-# Expands the face rectangle so that in the next video image it's likely to
-# still contain the face even with some motion.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:face_rect_from_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 1.5
-      scale_y: 1.5
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/face_landmark/face_landmark_with_attention.tflite b/mediapipe/modules/face_landmark/face_landmark_with_attention.tflite
deleted file mode 100755
index fe0a93a..0000000
Binary files a/mediapipe/modules/face_landmark/face_landmark_with_attention.tflite and /dev/null differ
diff --git a/mediapipe/modules/face_landmark/face_landmarks_model_loader.pbtxt b/mediapipe/modules/face_landmark/face_landmarks_model_loader.pbtxt
deleted file mode 100644
index ecac1a6..0000000
--- a/mediapipe/modules/face_landmark/face_landmarks_model_loader.pbtxt
+++ /dev/null
@@ -1,58 +0,0 @@
-# MediaPipe graph to load a selected face landmarks TF Lite model.
-
-type: "FaceLandmarksModelLoader"
-
-# Whether to run face mesh model with attention on lips and eyes. (bool)
-# Attention provides more accuracy on lips and eye regions as well as iris
-# landmarks.
-input_side_packet: "WITH_ATTENTION:with_attention"
-
-# TF Lite model represented as a FlatBuffer.
-# (std::unique_ptr<tflite::FlatBufferModel, std::function<void(tflite::FlatBufferModel*)>>)
-output_side_packet: "MODEL:model"
-
-# Determines path to the desired face landmark model file based on specification
-# in the input side packet.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "ENABLE:with_attention"
-  output_side_packet: "PACKET:model_path"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/face_landmark/face_landmark.tflite"
-            }
-          }
-        }
-      }
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/face_landmark/face_landmark_with_attention.tflite"
-            }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Loads the file in the specified path into a blob.
-node {
-  calculator: "LocalFileContentsCalculator"
-  input_side_packet: "FILE_PATH:model_path"
-  output_side_packet: "CONTENTS:model_blob"
-}
-
-# Converts the input blob into a TF Lite model.
-node {
-  calculator: "TfLiteModelCalculator"
-  input_side_packet: "MODEL_BLOB:model_blob"
-  output_side_packet: "MODEL:model"
-}
diff --git a/mediapipe/modules/face_landmark/tensors_to_face_landmarks.pbtxt b/mediapipe/modules/face_landmark/tensors_to_face_landmarks.pbtxt
deleted file mode 100644
index 0adbdf3..0000000
--- a/mediapipe/modules/face_landmark/tensors_to_face_landmarks.pbtxt
+++ /dev/null
@@ -1,24 +0,0 @@
-# MediaPipe graph to transform single tensor into 468 facial landmarks.
-
-type: "TensorsToFaceLandmarks"
-
-# Vector with a single tensor that contains 468 landmarks. (std::vector<Tensor>)
-input_stream: "TENSORS:tensors"
-
-# 468 facial landmarks (NormalizedLandmarkList)
-output_stream: "LANDMARKS:landmarks"
-
-# Decodes the landmark tensors into a vector of lanmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:tensors"
-  output_stream: "NORM_LANDMARKS:landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 468
-      input_image_width: 192
-      input_image_height: 192
-    }
-  }
-}
diff --git a/mediapipe/modules/face_landmark/tensors_to_face_landmarks_with_attention.pbtxt b/mediapipe/modules/face_landmark/tensors_to_face_landmarks_with_attention.pbtxt
deleted file mode 100644
index 4f9b994..0000000
--- a/mediapipe/modules/face_landmark/tensors_to_face_landmarks_with_attention.pbtxt
+++ /dev/null
@@ -1,299 +0,0 @@
-# MediaPipe graph to transform model output tensors into 478 facial landmarks
-# with refined lips, eyes and irises.
-
-type: "TensorsToFaceLandmarksWithAttention"
-
-# Vector with a six tensors to parse landmarks from. (std::vector<Tensor>)
-# Landmark tensors order:
-#   - mesh_tensor
-#   - lips_tensor
-#   - left_eye_tensor
-#   - right_eye_tensor
-#   - left_iris_tensor
-#   - right_iris_tensor
-input_stream: "TENSORS:tensors"
-
-# 478 facial landmarks (NormalizedLandmarkList)
-output_stream: "LANDMARKS:landmarks"
-
-# Splits a vector of tensors into multiple vectors.
-node {
-  calculator: "SplitTensorVectorCalculator"
-  input_stream: "tensors"
-  output_stream: "mesh_tensor"
-  output_stream: "lips_tensor"
-  output_stream: "left_eye_tensor"
-  output_stream: "right_eye_tensor"
-  output_stream: "left_iris_tensor"
-  output_stream: "right_iris_tensor"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-      ranges: { begin: 1 end: 2 }
-      ranges: { begin: 2 end: 3 }
-      ranges: { begin: 3 end: 4 }
-      ranges: { begin: 4 end: 5 }
-      ranges: { begin: 5 end: 6 }
-    }
-  }
-}
-
-# Decodes mesh landmarks tensor into a vector of normalized lanmarks.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:mesh_tensor"
-  output_stream: "NORM_LANDMARKS:mesh_landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 468
-      input_image_width: 192
-      input_image_height: 192
-    }
-  }
-}
-
-# Decodes lips landmarks tensor into a vector of normalized lanmarks.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:lips_tensor"
-  output_stream: "NORM_LANDMARKS:lips_landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 80
-      input_image_width: 192
-      input_image_height: 192
-    }
-  }
-}
-
-# Decodes left eye landmarks tensor into a vector of normalized lanmarks.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:left_eye_tensor"
-  output_stream: "NORM_LANDMARKS:left_eye_landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 71
-      input_image_width: 192
-      input_image_height: 192
-    }
-  }
-}
-
-# Decodes right eye landmarks tensor into a vector of normalized lanmarks.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:right_eye_tensor"
-  output_stream: "NORM_LANDMARKS:right_eye_landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 71
-      input_image_width: 192
-      input_image_height: 192
-    }
-  }
-}
-
-# Decodes left iris landmarks tensor into a vector of normalized lanmarks.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:left_iris_tensor"
-  output_stream: "NORM_LANDMARKS:left_iris_landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 5
-      input_image_width: 192
-      input_image_height: 192
-    }
-  }
-}
-
-# Decodes right iris landmarks tensor into a vector of normalized lanmarks.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:right_iris_tensor"
-  output_stream: "NORM_LANDMARKS:right_iris_landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 5
-      input_image_width: 192
-      input_image_height: 192
-    }
-  }
-}
-
-# Refine mesh landmarks with lips, eyes and irises.
-node {
-  calculator: "LandmarksRefinementCalculator"
-  input_stream: "LANDMARKS:0:mesh_landmarks"
-  input_stream: "LANDMARKS:1:lips_landmarks"
-  input_stream: "LANDMARKS:2:left_eye_landmarks"
-  input_stream: "LANDMARKS:3:right_eye_landmarks"
-  input_stream: "LANDMARKS:4:left_iris_landmarks"
-  input_stream: "LANDMARKS:5:right_iris_landmarks"
-  output_stream: "REFINED_LANDMARKS:landmarks"
-  options: {
-    [mediapipe.LandmarksRefinementCalculatorOptions.ext] {
-      # 0 - mesh
-      refinement: {
-        indexes_mapping: [
-          0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-          20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
-          37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
-          54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70,
-          71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
-          88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,
-          104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117,
-          118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,
-          132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145,
-          146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159,
-          160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173,
-          174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187,
-          188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201,
-          202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215,
-          216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229,
-          230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243,
-          244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,
-          258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271,
-          272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285,
-          286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299,
-          300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313,
-          314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327,
-          328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341,
-          342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355,
-          356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369,
-          370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383,
-          384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397,
-          398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411,
-          412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425,
-          426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439,
-          440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453,
-          454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467
-        ]
-        z_refinement: { copy {} }
-      }
-      # 1 - lips
-      refinement: {
-        indexes_mapping: [
-          # Lower outer.
-          61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291,
-          # Upper outer (excluding corners).
-          185, 40, 39, 37, 0, 267, 269, 270, 409,
-          # Lower inner.
-          78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308,
-          # Upper inner (excluding corners).
-          191, 80, 81, 82, 13, 312, 311, 310, 415,
-          # Lower semi-outer.
-          76, 77, 90, 180, 85, 16, 315, 404, 320, 307, 306,
-          # Upper semi-outer (excluding corners).
-          184, 74, 73, 72, 11, 302, 303, 304, 408,
-          # Lower semi-inner.
-          62, 96, 89, 179, 86, 15, 316, 403, 319, 325, 292,
-          # Upper semi-inner (excluding corners).
-          183, 42, 41, 38, 12, 268, 271, 272, 407
-        ]
-        z_refinement: { none {} }
-      }
-      # 2 - left eye
-      refinement: {
-        indexes_mapping: [
-          # Lower contour.
-          33, 7, 163, 144, 145, 153, 154, 155, 133,
-          # upper contour (excluding corners).
-          246, 161, 160, 159, 158, 157, 173,
-          # Halo x2 lower contour.
-          130, 25, 110, 24, 23, 22, 26, 112, 243,
-          # Halo x2 upper contour (excluding corners).
-          247, 30, 29, 27, 28, 56, 190,
-          # Halo x3 lower contour.
-          226, 31, 228, 229, 230, 231, 232, 233, 244,
-          # Halo x3 upper contour (excluding corners).
-          113, 225, 224, 223, 222, 221, 189,
-          # Halo x4 upper contour (no lower because of mesh structure) or
-          # eyebrow inner contour.
-          35, 124, 46, 53, 52, 65,
-          # Halo x5 lower contour.
-          143, 111, 117, 118, 119, 120, 121, 128, 245,
-          # Halo x5 upper contour (excluding corners) or eyebrow outer contour.
-          156, 70, 63, 105, 66, 107, 55, 193
-        ]
-        z_refinement: { none {} }
-      }
-      # 3 - right eye
-      refinement: {
-        indexes_mapping: [
-          # Lower contour.
-          263, 249, 390, 373, 374, 380, 381, 382, 362,
-          # Upper contour (excluding corners).
-          466, 388, 387, 386, 385, 384, 398,
-          # Halo x2 lower contour.
-          359, 255, 339, 254, 253, 252, 256, 341, 463,
-          # Halo x2 upper contour (excluding corners).
-          467, 260, 259, 257, 258, 286, 414,
-          # Halo x3 lower contour.
-          446, 261, 448, 449, 450, 451, 452, 453, 464,
-          # Halo x3 upper contour (excluding corners).
-          342, 445, 444, 443, 442, 441, 413,
-          # Halo x4 upper contour (no lower because of mesh structure) or
-          # eyebrow inner contour.
-          265, 353, 276, 283, 282, 295,
-          # Halo x5 lower contour.
-          372, 340, 346, 347, 348, 349, 350, 357, 465,
-          # Halo x5 upper contour (excluding corners) or eyebrow outer contour.
-          383, 300, 293, 334, 296, 336, 285, 417
-        ]
-        z_refinement: { none {} }
-      }
-      # 4 - left iris
-      refinement: {
-        indexes_mapping: [
-          # Center.
-          468,
-          # Iris right edge.
-          469,
-          # Iris top edge.
-          470,
-          # Iris left edge.
-          471,
-          # Iris bottom edge.
-          472
-        ]
-        z_refinement: {
-          assign_average: {
-            indexes_for_average: [
-              # Lower contour.
-              33, 7, 163, 144, 145, 153, 154, 155, 133,
-              # Upper contour (excluding corners).
-              246, 161, 160, 159, 158, 157, 173
-            ]
-          }
-        }
-      }
-      # 5 - right iris
-      refinement: {
-        indexes_mapping: [
-          # Center.
-          473,
-          # Iris right edge.
-          474,
-          # Iris top edge.
-          475,
-          # Iris left edge.
-          476,
-          # Iris bottom edge.
-          477
-        ]
-        z_refinement: {
-          assign_average: {
-            indexes_for_average: [
-              # Lower contour.
-              263, 249, 390, 373, 374, 380, 381, 382, 362,
-              # Upper contour (excluding corners).
-              466, 388, 387, 386, 385, 384, 398
-            ]
-          }
-        }
-      }
-    }
-  }
-}
diff --git a/mediapipe/modules/hand_landmark/BUILD b/mediapipe/modules/hand_landmark/BUILD
deleted file mode 100644
index b28dc78..0000000
--- a/mediapipe/modules/hand_landmark/BUILD
+++ /dev/null
@@ -1,171 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-exports_files([
-    "hand_landmark_full.tflite",
-    "hand_landmark_lite.tflite",
-    "handedness.txt",
-])
-
-mediapipe_simple_subgraph(
-    name = "hand_landmark_model_loader",
-    graph = "hand_landmark_model_loader.pbtxt",
-    register_as = "HandLandmarkModelLoader",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/tflite:tflite_model_calculator",
-        "//mediapipe/calculators/util:local_file_contents_calculator",
-        "//mediapipe/framework/tool:switch_container",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmark_cpu",
-    graph = "hand_landmark_cpu.pbtxt",
-    register_as = "HandLandmarkCpu",
-    deps = [
-        ":hand_landmark_model_loader",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_classification_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-        "//mediapipe/calculators/util:thresholding_calculator",
-        "//mediapipe/calculators/util:world_landmark_projection_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmark_gpu",
-    graph = "hand_landmark_gpu.pbtxt",
-    register_as = "HandLandmarkGpu",
-    deps = [
-        ":hand_landmark_model_loader",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_classification_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-        "//mediapipe/calculators/util:thresholding_calculator",
-        "//mediapipe/calculators/util:world_landmark_projection_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmark_tracking_gpu",
-    graph = "hand_landmark_tracking_gpu.pbtxt",
-    register_as = "HandLandmarkTrackingGpu",
-    deps = [
-        ":hand_landmark_gpu",
-        ":hand_landmark_landmarks_to_roi",
-        ":palm_detection_detection_to_roi",
-        "//mediapipe/calculators/core:begin_loop_calculator",
-        "//mediapipe/calculators/core:clip_vector_size_calculator",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:end_loop_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/util:association_norm_rect_calculator",
-        "//mediapipe/calculators/util:collection_has_min_size_calculator",
-        "//mediapipe/calculators/util:filter_collection_calculator",
-        "//mediapipe/modules/palm_detection:palm_detection_gpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmark_tracking_cpu_image",
-    graph = "hand_landmark_tracking_cpu_image.pbtxt",
-    register_as = "HandLandmarkTrackingCpuImage",
-    deps = [
-        ":hand_landmark_tracking_cpu",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmark_tracking_gpu_image",
-    graph = "hand_landmark_tracking_gpu_image.pbtxt",
-    register_as = "HandLandmarkTrackingGpuImage",
-    deps = [
-        ":hand_landmark_tracking_gpu",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmark_tracking_cpu",
-    graph = "hand_landmark_tracking_cpu.pbtxt",
-    register_as = "HandLandmarkTrackingCpu",
-    deps = [
-        ":hand_landmark_cpu",
-        ":hand_landmark_landmarks_to_roi",
-        ":palm_detection_detection_to_roi",
-        "//mediapipe/calculators/core:begin_loop_calculator",
-        "//mediapipe/calculators/core:clip_vector_size_calculator",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:end_loop_calculator",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/util:association_norm_rect_calculator",
-        "//mediapipe/calculators/util:collection_has_min_size_calculator",
-        "//mediapipe/calculators/util:filter_collection_calculator",
-        "//mediapipe/modules/palm_detection:palm_detection_cpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "palm_detection_detection_to_roi",
-    graph = "palm_detection_detection_to_roi.pbtxt",
-    register_as = "PalmDetectionDetectionToRoi",
-    deps = [
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmark_landmarks_to_roi",
-    graph = "hand_landmark_landmarks_to_roi.pbtxt",
-    register_as = "HandLandmarkLandmarksToRoi",
-    deps = [
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-        "//mediapipe/modules/hand_landmark/calculators:hand_landmarks_to_rect_calculator",
-    ],
-)
diff --git a/mediapipe/modules/hand_landmark/README.md b/mediapipe/modules/hand_landmark/README.md
deleted file mode 100644
index 31fe6f7..0000000
--- a/mediapipe/modules/hand_landmark/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# hand_landmark
-
-Subgraphs|Details
-:--- | :---
-[`HandLandmarkCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_cpu.pbtxt)| Detects landmarks of a single hand. (CPU input.)
-[`HandLandmarkGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_gpu.pbtxt)| Detects landmarks of a single hand. (GPU input.)
-[`HandLandmarkTrackingCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu.pbtxt)| Detects and tracks landmarks of multiple hands. (CPU input.)
-[`HandLandmarkTrackingGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu.pbtxt)| Detects and tracks landmarks of multiple hands. (GPU input.)
diff --git a/mediapipe/modules/hand_landmark/calculators/BUILD b/mediapipe/modules/hand_landmark/calculators/BUILD
deleted file mode 100644
index b2a8efe..0000000
--- a/mediapipe/modules/hand_landmark/calculators/BUILD
+++ /dev/null
@@ -1,33 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "hand_landmarks_to_rect_calculator",
-    srcs = ["hand_landmarks_to_rect_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework:calculator_options_cc_proto",
-        "//mediapipe/framework/formats:landmark_cc_proto",
-        "//mediapipe/framework/formats:location_data_cc_proto",
-        "//mediapipe/framework/formats:rect_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-    ],
-    alwayslink = 1,
-)
diff --git a/mediapipe/modules/hand_landmark/calculators/hand_landmarks_to_rect_calculator.cc b/mediapipe/modules/hand_landmark/calculators/hand_landmarks_to_rect_calculator.cc
deleted file mode 100644
index 3e3f5c8..0000000
--- a/mediapipe/modules/hand_landmark/calculators/hand_landmarks_to_rect_calculator.cc
+++ /dev/null
@@ -1,167 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-#include <cmath>
-
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/calculator_options.pb.h"
-#include "mediapipe/framework/formats/landmark.pb.h"
-#include "mediapipe/framework/formats/rect.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-
-namespace mediapipe {
-
-namespace {
-
-constexpr char kNormalizedLandmarksTag[] = "NORM_LANDMARKS";
-constexpr char kNormRectTag[] = "NORM_RECT";
-constexpr char kImageSizeTag[] = "IMAGE_SIZE";
-constexpr int kWristJoint = 0;
-constexpr int kMiddleFingerPIPJoint = 6;
-constexpr int kIndexFingerPIPJoint = 4;
-constexpr int kRingFingerPIPJoint = 8;
-constexpr float kTargetAngle = M_PI * 0.5f;
-
-inline float NormalizeRadians(float angle) {
-  return angle - 2 * M_PI * std::floor((angle - (-M_PI)) / (2 * M_PI));
-}
-
-float ComputeRotation(const NormalizedLandmarkList& landmarks,
-                      const std::pair<int, int>& image_size) {
-  const float x0 = landmarks.landmark(kWristJoint).x() * image_size.first;
-  const float y0 = landmarks.landmark(kWristJoint).y() * image_size.second;
-
-  float x1 = (landmarks.landmark(kIndexFingerPIPJoint).x() +
-              landmarks.landmark(kRingFingerPIPJoint).x()) /
-             2.f;
-  float y1 = (landmarks.landmark(kIndexFingerPIPJoint).y() +
-              landmarks.landmark(kRingFingerPIPJoint).y()) /
-             2.f;
-  x1 = (x1 + landmarks.landmark(kMiddleFingerPIPJoint).x()) / 2.f *
-       image_size.first;
-  y1 = (y1 + landmarks.landmark(kMiddleFingerPIPJoint).y()) / 2.f *
-       image_size.second;
-
-  const float rotation =
-      NormalizeRadians(kTargetAngle - std::atan2(-(y1 - y0), x1 - x0));
-  return rotation;
-}
-
-absl::Status NormalizedLandmarkListToRect(
-    const NormalizedLandmarkList& landmarks,
-    const std::pair<int, int>& image_size, NormalizedRect* rect) {
-  const float rotation = ComputeRotation(landmarks, image_size);
-  const float reverse_angle = NormalizeRadians(-rotation);
-
-  // Find boundaries of landmarks.
-  float max_x = std::numeric_limits<float>::min();
-  float max_y = std::numeric_limits<float>::min();
-  float min_x = std::numeric_limits<float>::max();
-  float min_y = std::numeric_limits<float>::max();
-  for (int i = 0; i < landmarks.landmark_size(); ++i) {
-    max_x = std::max(max_x, landmarks.landmark(i).x());
-    max_y = std::max(max_y, landmarks.landmark(i).y());
-    min_x = std::min(min_x, landmarks.landmark(i).x());
-    min_y = std::min(min_y, landmarks.landmark(i).y());
-  }
-  const float axis_aligned_center_x = (max_x + min_x) / 2.f;
-  const float axis_aligned_center_y = (max_y + min_y) / 2.f;
-
-  // Find boundaries of rotated landmarks.
-  max_x = std::numeric_limits<float>::min();
-  max_y = std::numeric_limits<float>::min();
-  min_x = std::numeric_limits<float>::max();
-  min_y = std::numeric_limits<float>::max();
-  for (int i = 0; i < landmarks.landmark_size(); ++i) {
-    const float original_x =
-        (landmarks.landmark(i).x() - axis_aligned_center_x) * image_size.first;
-    const float original_y =
-        (landmarks.landmark(i).y() - axis_aligned_center_y) * image_size.second;
-
-    const float projected_x = original_x * std::cos(reverse_angle) -
-                              original_y * std::sin(reverse_angle);
-    const float projected_y = original_x * std::sin(reverse_angle) +
-                              original_y * std::cos(reverse_angle);
-
-    max_x = std::max(max_x, projected_x);
-    max_y = std::max(max_y, projected_y);
-    min_x = std::min(min_x, projected_x);
-    min_y = std::min(min_y, projected_y);
-  }
-  const float projected_center_x = (max_x + min_x) / 2.f;
-  const float projected_center_y = (max_y + min_y) / 2.f;
-
-  const float center_x = projected_center_x * std::cos(rotation) -
-                         projected_center_y * std::sin(rotation) +
-                         image_size.first * axis_aligned_center_x;
-  const float center_y = projected_center_x * std::sin(rotation) +
-                         projected_center_y * std::cos(rotation) +
-                         image_size.second * axis_aligned_center_y;
-  const float width = (max_x - min_x) / image_size.first;
-  const float height = (max_y - min_y) / image_size.second;
-
-  rect->set_x_center(center_x / image_size.first);
-  rect->set_y_center(center_y / image_size.second);
-  rect->set_width(width);
-  rect->set_height(height);
-  rect->set_rotation(rotation);
-
-  return absl::OkStatus();
-}
-
-}  // namespace
-
-// A calculator that converts subset of hand landmarks to a bounding box
-// NormalizedRect. The rotation angle of the bounding box is computed based on
-// 1) the wrist joint and 2) the average of PIP joints of index finger, middle
-// finger and ring finger. After rotation, the vector from the wrist to the mean
-// of PIP joints is expected to be vertical with wrist at the bottom and the
-// mean of PIP joints at the top.
-class HandLandmarksToRectCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc) {
-    cc->Inputs().Tag(kNormalizedLandmarksTag).Set<NormalizedLandmarkList>();
-    cc->Inputs().Tag(kImageSizeTag).Set<std::pair<int, int>>();
-    cc->Outputs().Tag(kNormRectTag).Set<NormalizedRect>();
-    return absl::OkStatus();
-  }
-
-  absl::Status Open(CalculatorContext* cc) override {
-    cc->SetOffset(TimestampDiff(0));
-    return absl::OkStatus();
-  }
-
-  absl::Status Process(CalculatorContext* cc) override {
-    if (cc->Inputs().Tag(kNormalizedLandmarksTag).IsEmpty()) {
-      return absl::OkStatus();
-    }
-    RET_CHECK(!cc->Inputs().Tag(kImageSizeTag).IsEmpty());
-
-    std::pair<int, int> image_size =
-        cc->Inputs().Tag(kImageSizeTag).Get<std::pair<int, int>>();
-    const auto& landmarks =
-        cc->Inputs().Tag(kNormalizedLandmarksTag).Get<NormalizedLandmarkList>();
-    auto output_rect = absl::make_unique<NormalizedRect>();
-    MP_RETURN_IF_ERROR(
-        NormalizedLandmarkListToRect(landmarks, image_size, output_rect.get()));
-    cc->Outputs()
-        .Tag(kNormRectTag)
-        .Add(output_rect.release(), cc->InputTimestamp());
-
-    return absl::OkStatus();
-  }
-};
-REGISTER_CALCULATOR(HandLandmarksToRectCalculator);
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_cpu.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_cpu.pbtxt
deleted file mode 100644
index 6ecbfad..0000000
--- a/mediapipe/modules/hand_landmark/hand_landmark_cpu.pbtxt
+++ /dev/null
@@ -1,219 +0,0 @@
-# MediaPipe graph to detect/predict hand landmarks on CPU.
-
-type: "HandLandmarkCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-# ROI (region of interest) within the given image where a palm/hand is located.
-# (NormalizedRect)
-input_stream: "ROI:hand_rect"
-
-# Complexity of the hand landmark model: 0 or 1. Landmark accuracy as well as
-# inference latency generally go up with the model complexity. If unspecified,
-# functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# 21 hand landmarks within the given ROI. (NormalizedLandmarkList)
-# NOTE: if a hand is not present within the given ROI, for this particular
-# timestamp there will not be an output packet in the LANDMARKS stream. However,
-# the MediaPipe framework will internally inform the downstream calculators of
-# the absence of this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:hand_landmarks"
-
-# Hand world landmarks within the given ROI. (LandmarkList)
-# World landmarks are real-world 3D coordinates in meters with the origin in the
-# center of the given ROI.
-#
-# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
-# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
-# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
-# the 3D object itself.
-output_stream: "WORLD_LANDMARKS:hand_world_landmarks"
-
-# Handedness of the detected hand (i.e. is hand left or right).
-# (ClassificationList)
-output_stream: "HANDEDNESS:handedness"
-
-# Transforms a region of image into a 224x224 tensor while keeping the aspect
-# ratio, and therefore may result in potential letterboxing.
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:image"
-  input_stream: "NORM_RECT:hand_rect"
-  output_stream: "TENSORS:input_tensor"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 224
-      output_tensor_height: 224
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-    }
-  }
-}
-
-# Loads the hand landmark TF Lite model.
-node {
-  calculator: "HandLandmarkModelLoader"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  output_side_packet: "MODEL:model"
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_side_packet: "MODEL:model"
-  input_stream: "TENSORS:input_tensor"
-  output_stream: "TENSORS:output_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      delegate {
-        xnnpack {}
-      }
-    }
-  }
-}
-
-# Splits a vector of tensors to multiple vectors according to the ranges
-# specified in option.
-node {
-  calculator: "SplitTensorVectorCalculator"
-  input_stream: "output_tensors"
-  output_stream: "landmark_tensors"
-  output_stream: "hand_flag_tensor"
-  output_stream: "handedness_tensor"
-  output_stream: "world_landmark_tensor"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-      ranges: { begin: 1 end: 2 }
-      ranges: { begin: 2 end: 3 }
-      ranges: { begin: 3 end: 4 }
-    }
-  }
-}
-
-# Converts the hand-flag tensor into a float that represents the confidence
-# score of hand presence.
-node {
-  calculator: "TensorsToFloatsCalculator"
-  input_stream: "TENSORS:hand_flag_tensor"
-  output_stream: "FLOAT:hand_presence_score"
-}
-
-# Applies a threshold to the confidence score to determine whether a hand is
-# present.
-node {
-  calculator: "ThresholdingCalculator"
-  input_stream: "FLOAT:hand_presence_score"
-  output_stream: "FLAG:hand_presence"
-  options: {
-    [mediapipe.ThresholdingCalculatorOptions.ext] {
-      threshold: 0.5
-    }
-  }
-}
-
-# Drops handedness tensor if hand is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "handedness_tensor"
-  input_stream: "ALLOW:hand_presence"
-  output_stream: "ensured_handedness_tensor"
-}
-
-# Converts the handedness tensor into a float that represents the classification
-# score of handedness.
-node {
-  calculator: "TensorsToClassificationCalculator"
-  input_stream: "TENSORS:ensured_handedness_tensor"
-  output_stream: "CLASSIFICATIONS:handedness"
-  options: {
-    [mediapipe.TensorsToClassificationCalculatorOptions.ext] {
-      top_k: 1
-      label_map_path: "mediapipe/modules/hand_landmark/handedness.txt"
-      binary_classification: true
-    }
-  }
-}
-
-# Drops landmarks tensors if hand is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "landmark_tensors"
-  input_stream: "ALLOW:hand_presence"
-  output_stream: "ensured_landmark_tensors"
-}
-
-# Decodes the landmark tensors into a list of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:ensured_landmark_tensors"
-  output_stream: "NORM_LANDMARKS:landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 21
-      input_image_width: 224
-      input_image_height: 224
-      # The additional scaling factor is used to account for the Z coordinate
-      # distribution in the training data.
-      normalize_z: 0.4
-    }
-  }
-}
-
-# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed hand
-# image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (hand
-# image before image transformation).
-node {
-  calculator: "LandmarkLetterboxRemovalCalculator"
-  input_stream: "LANDMARKS:landmarks"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "LANDMARKS:scaled_landmarks"
-}
-
-# Projects the landmarks from the cropped hand image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:scaled_landmarks"
-  input_stream: "NORM_RECT:hand_rect"
-  output_stream: "NORM_LANDMARKS:hand_landmarks"
-}
-
-# Drops world landmarks tensors if hand is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "world_landmark_tensor"
-  input_stream: "ALLOW:hand_presence"
-  output_stream: "ensured_world_landmark_tensor"
-}
-
-# Decodes the landmark tensors into a list of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:ensured_world_landmark_tensor"
-  output_stream: "LANDMARKS:unprojected_world_landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 21
-    }
-  }
-}
-
-# Projects the world landmarks from the cropped hand image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "WorldLandmarkProjectionCalculator"
-  input_stream: "LANDMARKS:unprojected_world_landmarks"
-  input_stream: "NORM_RECT:hand_rect"
-  output_stream: "LANDMARKS:hand_world_landmarks"
-}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_full.tflite b/mediapipe/modules/hand_landmark/hand_landmark_full.tflite
deleted file mode 100755
index a2b0114..0000000
Binary files a/mediapipe/modules/hand_landmark/hand_landmark_full.tflite and /dev/null differ
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_gpu.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_gpu.pbtxt
deleted file mode 100644
index 033ad44..0000000
--- a/mediapipe/modules/hand_landmark/hand_landmark_gpu.pbtxt
+++ /dev/null
@@ -1,213 +0,0 @@
-# MediaPipe graph to detect/predict hand landmarks on CPU.
-
-type: "HandLandmarkGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-# ROI (region of interest) within the given image where a palm/hand is located.
-# (NormalizedRect)
-input_stream: "ROI:hand_rect"
-
-# Complexity of the hand landmark model: 0 or 1. Landmark accuracy as well as
-# inference latency generally go up with the model complexity. If unspecified,
-# functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# 21 hand landmarks within the given ROI. (NormalizedLandmarkList)
-# NOTE: if a hand is not present within the given ROI, for this particular
-# timestamp there will not be an output packet in the LANDMARKS stream. However,
-# the MediaPipe framework will internally inform the downstream calculators of
-# the absence of this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:hand_landmarks"
-
-# Hand world landmarks within the given ROI. (LandmarkList)
-# World landmarks are real-world 3D coordinates in meters with the origin in the
-# center of the given ROI.
-#
-# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
-# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
-# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
-# the 3D object itself.
-output_stream: "WORLD_LANDMARKS:hand_world_landmarks"
-
-# Handedness of the detected hand (i.e. is hand left or right).
-# (ClassificationList)
-output_stream: "HANDEDNESS:handedness"
-
-# Transforms a region of image into a 224x224 tensor while keeping the aspect
-# ratio, and therefore may result in potential letterboxing.
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE_GPU:image"
-  input_stream: "NORM_RECT:hand_rect"
-  output_stream: "TENSORS:input_tensor"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 224
-      output_tensor_height: 224
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-
-# Loads the hand landmark TF Lite model.
-node {
-  calculator: "HandLandmarkModelLoader"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  output_side_packet: "MODEL:model"
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_side_packet: "MODEL:model"
-  input_stream: "TENSORS:input_tensor"
-  output_stream: "TENSORS:output_tensors"
-}
-
-# Splits a vector of tensors to multiple vectors according to the ranges
-# specified in option.
-node {
-  calculator: "SplitTensorVectorCalculator"
-  input_stream: "output_tensors"
-  output_stream: "landmark_tensors"
-  output_stream: "hand_flag_tensor"
-  output_stream: "handedness_tensor"
-  output_stream: "world_landmark_tensor"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-      ranges: { begin: 1 end: 2 }
-      ranges: { begin: 2 end: 3 }
-      ranges: { begin: 3 end: 4 }
-    }
-  }
-}
-
-# Converts the hand-flag tensor into a float that represents the confidence
-# score of hand presence.
-node {
-  calculator: "TensorsToFloatsCalculator"
-  input_stream: "TENSORS:hand_flag_tensor"
-  output_stream: "FLOAT:hand_presence_score"
-}
-
-# Applies a threshold to the confidence score to determine whether a hand is
-# present.
-node {
-  calculator: "ThresholdingCalculator"
-  input_stream: "FLOAT:hand_presence_score"
-  output_stream: "FLAG:hand_presence"
-  options: {
-    [mediapipe.ThresholdingCalculatorOptions.ext] {
-      threshold: 0.5
-    }
-  }
-}
-
-# Drops handedness tensor if hand is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "handedness_tensor"
-  input_stream: "ALLOW:hand_presence"
-  output_stream: "ensured_handedness_tensor"
-}
-
-# Converts the handedness tensor into a float that represents the classification
-# score of handedness.
-node {
-  calculator: "TensorsToClassificationCalculator"
-  input_stream: "TENSORS:ensured_handedness_tensor"
-  output_stream: "CLASSIFICATIONS:handedness"
-  options: {
-    [mediapipe.TensorsToClassificationCalculatorOptions.ext] {
-      top_k: 1
-      label_map_path: "mediapipe/modules/hand_landmark/handedness.txt"
-      binary_classification: true
-    }
-  }
-}
-
-# Drops landmarks tensors if hand is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "landmark_tensors"
-  input_stream: "ALLOW:hand_presence"
-  output_stream: "ensured_landmark_tensors"
-}
-
-# Decodes the landmark tensors into a list of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:ensured_landmark_tensors"
-  output_stream: "NORM_LANDMARKS:landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 21
-      input_image_width: 224
-      input_image_height: 224
-      # The additional scaling factor is used to account for the Z coordinate
-      # distribution in the training data.
-      normalize_z: 0.4
-    }
-  }
-}
-
-# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed hand
-# image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (hand
-# image before image transformation).
-node {
-  calculator: "LandmarkLetterboxRemovalCalculator"
-  input_stream: "LANDMARKS:landmarks"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "LANDMARKS:scaled_landmarks"
-}
-
-# Projects the landmarks from the cropped hand image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:scaled_landmarks"
-  input_stream: "NORM_RECT:hand_rect"
-  output_stream: "NORM_LANDMARKS:hand_landmarks"
-}
-
-# Drops world landmarks tensors if hand is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "world_landmark_tensor"
-  input_stream: "ALLOW:hand_presence"
-  output_stream: "ensured_world_landmark_tensor"
-}
-
-# Decodes the landmark tensors into a list of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:ensured_world_landmark_tensor"
-  output_stream: "LANDMARKS:unprojected_world_landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 21
-    }
-  }
-}
-
-# Projects the world landmarks from the cropped hand image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "WorldLandmarkProjectionCalculator"
-  input_stream: "LANDMARKS:unprojected_world_landmarks"
-  input_stream: "NORM_RECT:hand_rect"
-  output_stream: "LANDMARKS:hand_world_landmarks"
-}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_landmarks_to_roi.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_landmarks_to_roi.pbtxt
deleted file mode 100644
index 1d82d76..0000000
--- a/mediapipe/modules/hand_landmark/hand_landmark_landmarks_to_roi.pbtxt
+++ /dev/null
@@ -1,63 +0,0 @@
-# MediaPipe graph to calculate hand region of interest (ROI) from landmarks
-# detected by "HandLandmarkCpu" or "HandLandmarkGpu".
-
-type: "HandLandmarkLandmarksToRoi"
-
-# Normalized landmarks. (NormalizedLandmarkList)
-input_stream: "LANDMARKS:landmarks"
-# Image size (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# ROI according to landmarks. (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Extracts a subset of the hand landmarks that are relatively more stable across
-# frames (e.g. comparing to finger tips) for computing the bounding box. The box
-# will later be expanded to contain the entire hand. In this approach, it is
-# more robust to drastically changing hand size.
-# The landmarks extracted are: wrist, MCP/PIP of five fingers.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "landmarks"
-  output_stream: "partial_landmarks"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 4 }
-      ranges: { begin: 5 end: 7 }
-      ranges: { begin: 9 end: 11 }
-      ranges: { begin: 13 end: 15 }
-      ranges: { begin: 17 end: 19 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Converts the hand landmarks into a rectangle (normalized by image size)
-# that encloses the hand. The calculator uses a subset of all hand landmarks
-# extracted from SplitNormalizedLandmarkListCalculator above to
-# calculate the bounding box and the rotation of the output rectangle. Please
-# see the comments in the calculator for more detail.
-node {
-  calculator: "HandLandmarksToRectCalculator"
-  input_stream: "NORM_LANDMARKS:partial_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:hand_rect_from_landmarks"
-}
-
-# Expands the hand rectangle so that the box contains the entire hand and it's
-# big enough so that it's likely to still contain the hand even with some motion
-# in the next video frame .
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:hand_rect_from_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 2.0
-      scale_y: 2.0
-      shift_y: -0.1
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_lite.tflite b/mediapipe/modules/hand_landmark/hand_landmark_lite.tflite
deleted file mode 100755
index 0a0a2ba..0000000
Binary files a/mediapipe/modules/hand_landmark/hand_landmark_lite.tflite and /dev/null differ
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_model_loader.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_model_loader.pbtxt
deleted file mode 100644
index c9ecf8a..0000000
--- a/mediapipe/modules/hand_landmark/hand_landmark_model_loader.pbtxt
+++ /dev/null
@@ -1,63 +0,0 @@
-# MediaPipe graph to load a selected hand landmark TF Lite model.
-
-type: "HandLandmarkModelLoader"
-
-# Complexity of the hand landmark model: 0 or 1. Landmark accuracy as well as
-# inference latency generally go up with the model complexity. If unspecified,
-# functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# TF Lite model represented as a FlatBuffer.
-# (std::unique_ptr<tflite::FlatBufferModel, std::function<void(tflite::FlatBufferModel*)>>)
-output_side_packet: "MODEL:model"
-
-# Determines path to the desired pose landmark model file.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "SELECT:model_complexity"
-  output_side_packet: "PACKET:model_path"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      select: 1
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/hand_landmark/hand_landmark_lite.tflite"
-            }
-          }
-        }
-      }
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/hand_landmark/hand_landmark_full.tflite"
-            }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Loads the file in the specified path into a blob.
-node {
-  calculator: "LocalFileContentsCalculator"
-  input_side_packet: "FILE_PATH:model_path"
-  output_side_packet: "CONTENTS:model_blob"
-  options: {
-    [mediapipe.LocalFileContentsCalculatorOptions.ext]: {
-      text_mode: false
-    }
-  }
-}
-
-# Converts the input blob into a TF Lite model.
-node {
-  calculator: "TfLiteModelCalculator"
-  input_side_packet: "MODEL_BLOB:model_blob"
-  output_side_packet: "MODEL:model"
-}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu.pbtxt
deleted file mode 100644
index 2ee8316..0000000
--- a/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu.pbtxt
+++ /dev/null
@@ -1,271 +0,0 @@
-# MediaPipe graph to detect/predict hand landmarks on CPU.
-#
-# The procedure is done in two steps:
-# - locate palms/hands
-# - detect landmarks for each palm/hand.
-# This graph tries to skip palm detection as much as possible by reusing
-# previously detected/predicted landmarks for new images.
-
-type: "HandLandmarkTrackingCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# Max number of hands to detect/track. (int)
-input_side_packet: "NUM_HANDS:num_hands"
-
-# Complexity of hand landmark and palm detection models: 0 or 1. Accuracy as
-# well as inference latency generally go up with the model complexity. If
-# unspecified, functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Collection of detected/predicted hands, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-# NOTE: there will not be an output packet in the LANDMARKS stream for this
-# particular timestamp if none of hands detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:multi_hand_landmarks"
-
-# Collection of detected/predicted hand world landmarks.
-# (std::vector<LandmarkList>)
-#
-# World landmarks are real-world 3D coordinates in meters with the origin in the
-# center of the hand bounding box calculated from the landmarks.
-#
-# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
-# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
-# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
-# the 3D object itself.
-output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
-
-# Collection of handedness of the detected hands (i.e. is hand left or right),
-# each represented as a ClassificationList proto with a single Classification
-# entry. (std::vector<ClassificationList>)
-# Note that handedness is determined assuming the input image is mirrored,
-# i.e., taken with a front-facing/selfie camera with images flipped
-# horizontally.
-output_stream: "HANDEDNESS:multi_handedness"
-
-# Extra outputs (for debugging, for instance).
-# Detected palms. (std::vector<Detection>)
-output_stream: "PALM_DETECTIONS:palm_detections"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
-# Regions of interest calculated based on palm detections.
-# (std::vector<NormalizedRect>)
-output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
-
-# When the optional input side packet "use_prev_landmarks" is either absent or
-# set to true, uses the landmarks on the previous image to help localize
-# landmarks on the current image.
-node {
-  calculator: "GateCalculator"
-  input_side_packet: "ALLOW:use_prev_landmarks"
-  input_stream: "prev_hand_rects_from_landmarks"
-  output_stream: "gated_prev_hand_rects_from_landmarks"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      allow: true
-    }
-  }
-}
-
-# Determines if an input vector of NormalizedRect has a size greater than or
-# equal to the provided num_hands.
-node {
-  calculator: "NormalizedRectVectorHasMinSizeCalculator"
-  input_stream: "ITERABLE:gated_prev_hand_rects_from_landmarks"
-  input_side_packet: "num_hands"
-  output_stream: "prev_has_enough_hands"
-}
-
-# Drops the incoming image if enough hands have already been identified from the
-# previous image. Otherwise, passes the incoming image through to trigger a new
-# round of palm detection.
-node {
-  calculator: "GateCalculator"
-  input_stream: "image"
-  input_stream: "DISALLOW:prev_has_enough_hands"
-  output_stream: "palm_detection_image"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      empty_packets_as_allow: true
-    }
-  }
-}
-
-# Detects palms.
-node {
-  calculator: "PalmDetectionCpu"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_stream: "IMAGE:palm_detection_image"
-  output_stream: "DETECTIONS:all_palm_detections"
-}
-
-# Makes sure there are no more detections than the provided num_hands.
-node {
-  calculator: "ClipDetectionVectorSizeCalculator"
-  input_stream: "all_palm_detections"
-  output_stream: "palm_detections"
-  input_side_packet: "num_hands"
-}
-
-# Extracts image size.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:palm_detection_image"
-  output_stream: "SIZE:palm_detection_image_size"
-}
-
-# Outputs each element of palm_detections at a fake timestamp for the rest of
-# the graph to process. Clones the image size packet for each palm_detection at
-# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
-# for downstream calculators to inform them that all elements in the vector have
-# been processed.
-node {
-  calculator: "BeginLoopDetectionCalculator"
-  input_stream: "ITERABLE:palm_detections"
-  input_stream: "CLONE:palm_detection_image_size"
-  output_stream: "ITEM:palm_detection"
-  output_stream: "CLONE:image_size_for_palms"
-  output_stream: "BATCH_END:palm_detections_timestamp"
-}
-
-# Calculates region of interest (ROI) based on the specified palm.
-node {
-  calculator: "PalmDetectionDetectionToRoi"
-  input_stream: "DETECTION:palm_detection"
-  input_stream: "IMAGE_SIZE:image_size_for_palms"
-  output_stream: "ROI:hand_rect_from_palm_detection"
-}
-
-# Collects a NormalizedRect for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:hand_rect_from_palm_detection"
-  input_stream: "BATCH_END:palm_detections_timestamp"
-  output_stream: "ITERABLE:hand_rects_from_palm_detections"
-}
-
-# Performs association between NormalizedRect vector elements from previous
-# image and rects based on palm detections from the current image. This
-# calculator ensures that the output hand_rects vector doesn't contain
-# overlapping regions based on the specified min_similarity_threshold.
-node {
-  calculator: "AssociationNormRectCalculator"
-  input_stream: "hand_rects_from_palm_detections"
-  input_stream: "gated_prev_hand_rects_from_landmarks"
-  output_stream: "hand_rects"
-  options: {
-    [mediapipe.AssociationCalculatorOptions.ext] {
-      min_similarity_threshold: 0.5
-    }
-  }
-}
-
-# Extracts image size.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_CPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Outputs each element of hand_rects at a fake timestamp for the rest of the
-# graph to process. Clones image and image size packets for each
-# single_hand_rect at the fake timestamp. At the end of the loop, outputs the
-# BATCH_END timestamp for downstream calculators to inform them that all
-# elements in the vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedRectCalculator"
-  input_stream: "ITERABLE:hand_rects"
-  input_stream: "CLONE:0:image"
-  input_stream: "CLONE:1:image_size"
-  output_stream: "ITEM:single_hand_rect"
-  output_stream: "CLONE:0:image_for_landmarks"
-  output_stream: "CLONE:1:image_size_for_landmarks"
-  output_stream: "BATCH_END:hand_rects_timestamp"
-}
-
-# Detect hand landmarks for the specific hand rect.
-node {
-  calculator: "HandLandmarkCpu"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_stream: "IMAGE:image_for_landmarks"
-  input_stream: "ROI:single_hand_rect"
-  output_stream: "LANDMARKS:single_hand_landmarks"
-  output_stream: "WORLD_LANDMARKS:single_hand_world_landmarks"
-  output_stream: "HANDEDNESS:single_handedness"
-}
-
-# Collects the handedness for each single hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs a vector of ClassificationList at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopClassificationListCalculator"
-  input_stream: "ITEM:single_handedness"
-  input_stream: "BATCH_END:hand_rects_timestamp"
-  output_stream: "ITERABLE:multi_handedness"
-}
-
-# Calculate region of interest (ROI) based on detected hand landmarks to reuse
-# on the subsequent runs of the graph.
-node {
-  calculator: "HandLandmarkLandmarksToRoi"
-  input_stream: "IMAGE_SIZE:image_size_for_landmarks"
-  input_stream: "LANDMARKS:single_hand_landmarks"
-  output_stream: "ROI:single_hand_rect_from_landmarks"
-}
-
-# Collects a set of landmarks for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITEM:single_hand_landmarks"
-  input_stream: "BATCH_END:hand_rects_timestamp"
-  output_stream: "ITERABLE:multi_hand_landmarks"
-}
-
-# Collects a set of world landmarks for each hand into a vector. Upon receiving
-# the BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopLandmarkListVectorCalculator"
-  input_stream: "ITEM:single_hand_world_landmarks"
-  input_stream: "BATCH_END:hand_rects_timestamp"
-  output_stream: "ITERABLE:multi_hand_world_landmarks"
-}
-
-# Collects a NormalizedRect for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:single_hand_rect_from_landmarks"
-  input_stream: "BATCH_END:hand_rects_timestamp"
-  output_stream: "ITERABLE:hand_rects_from_landmarks"
-}
-
-# Caches hand rects calculated from landmarks, and upon the arrival of the next
-# input image, sends out the cached rects with timestamps replaced by that of
-# the input image, essentially generating a packet that carries the previous
-# hand rects. Note that upon the arrival of the very first input image, a
-# timestamp bound update occurs to jump start the feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image"
-  input_stream: "LOOP:hand_rects_from_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_hand_rects_from_landmarks"
-}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu_image.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu_image.pbtxt
deleted file mode 100644
index 0bdabb9..0000000
--- a/mediapipe/modules/hand_landmark/hand_landmark_tracking_cpu_image.pbtxt
+++ /dev/null
@@ -1,116 +0,0 @@
-# MediaPipe graph to detect/predict hand landmarks on CPU.
-#
-# The procedure is done in two steps:
-# - locate palms/hands
-# - detect landmarks for each palm/hand.
-# This graph tries to skip palm detection as much as possible by reusing
-# previously detected/predicted landmarks for new images.
-
-type: "HandLandmarkTrackingCpuImage"
-
-# Input image. (Image)
-input_stream: "IMAGE:image"
-
-# Max number of hands to detect/track. (int)
-input_side_packet: "NUM_HANDS:num_hands"
-
-# Complexity of hand landmark and palm detection models: 0 or 1. Accuracy as
-# well as inference latency generally go up with the model complexity. If
-# unspecified, functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# The throttled input image. (Image)
-output_stream: "IMAGE:throttled_image"
-
-# Collection of detected/predicted hands, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-# NOTE: there will not be an output packet in the LANDMARKS stream for this
-# particular timestamp if none of hands detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:multi_hand_landmarks"
-
-# Collection of detected/predicted hand world landmarks.
-# (std::vector<LandmarkList>)
-#
-# World landmarks are real-world 3D coordinates in meters with the origin in the
-# center of the hand bounding box calculated from the landmarks.
-#
-# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
-# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
-# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
-# the 3D object itself.
-output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
-
-# Collection of handedness of the detected hands (i.e. is hand left or right),
-# each represented as a ClassificationList proto with a single Classification
-# entry. (std::vector<ClassificationList>)
-# Note that handedness is determined assuming the input image is mirrored,
-# i.e., taken with a front-facing/selfie camera with images flipped
-# horizontally.
-output_stream: "HANDEDNESS:multi_handedness"
-
-# Extra outputs (for debugging, for instance).
-# Detected palms. (std::vector<Detection>)
-output_stream: "PALM_DETECTIONS:palm_detections"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
-# Regions of interest calculated based on palm detections.
-# (std::vector<NormalizedRect>)
-output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
-
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "image"
-  input_stream: "FINISHED:multi_hand_landmarks"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_image"
-  options: {
-    [mediapipe.FlowLimiterCalculatorOptions.ext] {
-      max_in_flight: 1
-      max_in_queue: 1
-    }
-  }
-}
-
-# Converts Image to ImageFrame for HandLandmarkTrackingCpu to consume.
-node {
-  calculator: "FromImageCalculator"
-  input_stream: "IMAGE:throttled_image"
-  output_stream: "IMAGE_CPU:raw_image_frame"
-  output_stream: "SOURCE_ON_GPU:is_gpu_image"
-}
-
-# TODO: Remove the extra flipping once adopting MlImage.
-# If the source images are on gpu, flip the data vertically before sending them
-# into HandLandmarkTrackingCpu. This maybe needed because OpenGL represents
-# images assuming the image origin is at the bottom-left corner, whereas
-# MediaPipe in general assumes the image origin is at the top-left corner.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:raw_image_frame"
-  input_stream: "FLIP_VERTICALLY:is_gpu_image"
-  output_stream: "IMAGE:image_frame"
-}
-
-node {
-  calculator: "HandLandmarkTrackingCpu"
-  input_stream: "IMAGE:image_frame"
-  input_side_packet: "NUM_HANDS:num_hands"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-  output_stream: "LANDMARKS:multi_hand_landmarks"
-  output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
-  output_stream: "HANDEDNESS:multi_handedness"
-  output_stream: "PALM_DETECTIONS:palm_detections"
-  output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
-  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
-}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu.pbtxt
deleted file mode 100644
index da76f4a..0000000
--- a/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu.pbtxt
+++ /dev/null
@@ -1,272 +0,0 @@
-# MediaPipe graph to detect/predict hand landmarks on GPU.
-#
-# The procedure is done in two steps:
-# - locate palms/hands
-# - detect landmarks for each palm/hand.
-# This graph tries to skip palm detection as much as possible by reusing
-# previously detected/predicted landmarks for new images.
-
-type: "HandLandmarkTrackingGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# Max number of hands to detect/track. (int)
-input_side_packet: "NUM_HANDS:num_hands"
-
-# Complexity of hand landmark and palm detection models: 0 or 1. Accuracy as
-# well as inference latency generally go up with the model complexity. If
-# unspecified, functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Collection of detected/predicted hands, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-# NOTE: there will not be an output packet in the LANDMARKS stream for this
-# particular timestamp if none of hands detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:multi_hand_landmarks"
-
-# Collection of detected/predicted hand world landmarks.
-# (std::vector<LandmarkList>)
-#
-# World landmarks are real-world 3D coordinates in meters with the origin in the
-# center of the hand bounding box calculated from the landmarks.
-#
-# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
-# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
-# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
-# the 3D object itself.
-output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
-
-# Collection of handedness of the detected hands (i.e. is hand left or right),
-# each represented as a ClassificationList proto with a single Classification
-# entry. (std::vector<ClassificationList>)
-# Note that handedness is determined assuming the input image is mirrored,
-# i.e., taken with a front-facing/selfie camera with images flipped
-# horizontally.
-output_stream: "HANDEDNESS:multi_handedness"
-
-# Extra outputs (for debugging, for instance).
-# Detected palms. (std::vector<Detection>)
-output_stream: "PALM_DETECTIONS:palm_detections"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
-# Regions of interest calculated based on palm detections.
-# (std::vector<NormalizedRect>)
-output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
-
-# When the optional input side packet "use_prev_landmarks" is either absent or
-# set to true, uses the landmarks on the previous image to help localize
-# landmarks on the current image.
-node {
-  calculator: "GateCalculator"
-  input_side_packet: "ALLOW:use_prev_landmarks"
-  input_stream: "prev_hand_rects_from_landmarks"
-  output_stream: "gated_prev_hand_rects_from_landmarks"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      allow: true
-    }
-  }
-}
-
-# Determines if an input vector of NormalizedRect has a size greater than or
-# equal to the provided num_hands.
-node {
-  calculator: "NormalizedRectVectorHasMinSizeCalculator"
-  input_stream: "ITERABLE:gated_prev_hand_rects_from_landmarks"
-  input_side_packet: "num_hands"
-  output_stream: "prev_has_enough_hands"
-}
-
-# Drops the incoming image if enough hands have already been identified from the
-# previous image. Otherwise, passes the incoming image through to trigger a new
-# round of palm detection.
-node {
-  calculator: "GateCalculator"
-  input_stream: "image"
-  input_stream: "DISALLOW:prev_has_enough_hands"
-  output_stream: "palm_detection_image"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      empty_packets_as_allow: true
-    }
-  }
-}
-
-# Detects palms.
-node {
-  calculator: "PalmDetectionGpu"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_stream: "IMAGE:palm_detection_image"
-  output_stream: "DETECTIONS:all_palm_detections"
-}
-
-# Makes sure there are no more detections than provided num_hands.
-node {
-  calculator: "ClipDetectionVectorSizeCalculator"
-  input_stream: "all_palm_detections"
-  output_stream: "palm_detections"
-  input_side_packet: "num_hands"
-}
-
-# Extracts image size.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:palm_detection_image"
-  output_stream: "SIZE:palm_detection_image_size"
-}
-
-# Outputs each element of palm_detections at a fake timestamp for the rest of
-# the graph to process. Clones the image_size packet for each palm_detection at
-# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
-# for downstream calculators to inform them that all elements in the vector have
-# been processed.
-node {
-  calculator: "BeginLoopDetectionCalculator"
-  input_stream: "ITERABLE:palm_detections"
-  input_stream: "CLONE:palm_detection_image_size"
-  output_stream: "ITEM:palm_detection"
-  output_stream: "CLONE:image_size_for_palms"
-  output_stream: "BATCH_END:palm_detections_timestamp"
-}
-
-# Calculates region of interest (ROI) base on the specified palm.
-node {
-  calculator: "PalmDetectionDetectionToRoi"
-  input_stream: "DETECTION:palm_detection"
-  input_stream: "IMAGE_SIZE:image_size_for_palms"
-  output_stream: "ROI:hand_rect_from_palm_detection"
-}
-
-# Collects a NormalizedRect for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  name: "EndLoopForPalmDetections"
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:hand_rect_from_palm_detection"
-  input_stream: "BATCH_END:palm_detections_timestamp"
-  output_stream: "ITERABLE:hand_rects_from_palm_detections"
-}
-
-# Performs association between NormalizedRect vector elements from previous
-# image and rects based on palm detections from the current image. This
-# calculator ensures that the output hand_rects vector doesn't contain
-# overlapping regions based on the specified min_similarity_threshold.
-node {
-  calculator: "AssociationNormRectCalculator"
-  input_stream: "hand_rects_from_palm_detections"
-  input_stream: "gated_prev_hand_rects_from_landmarks"
-  output_stream: "hand_rects"
-  options: {
-    [mediapipe.AssociationCalculatorOptions.ext] {
-      min_similarity_threshold: 0.5
-    }
-  }
-}
-
-# Extracts image size.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Outputs each element of hand_rects at a fake timestamp for the rest of the
-# graph to process. Clones image and image size packets for each
-# single_hand_rect at the fake timestamp. At the end of the loop, outputs the
-# BATCH_END timestamp for downstream calculators to inform them that all
-# elements in the vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedRectCalculator"
-  input_stream: "ITERABLE:hand_rects"
-  input_stream: "CLONE:0:image"
-  input_stream: "CLONE:1:image_size"
-  output_stream: "ITEM:single_hand_rect"
-  output_stream: "CLONE:0:image_for_landmarks"
-  output_stream: "CLONE:1:image_size_for_landmarks"
-  output_stream: "BATCH_END:hand_rects_timestamp"
-}
-
-# Detect hand landmarks for the specific hand rect.
-node {
-  calculator: "HandLandmarkGpu"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_stream: "IMAGE:image_for_landmarks"
-  input_stream: "ROI:single_hand_rect"
-  output_stream: "LANDMARKS:single_hand_landmarks"
-  output_stream: "WORLD_LANDMARKS:single_hand_world_landmarks"
-  output_stream: "HANDEDNESS:single_handedness"
-}
-
-# Collects the handedness for each single hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs a vector of ClassificationList at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopClassificationListCalculator"
-  input_stream: "ITEM:single_handedness"
-  input_stream: "BATCH_END:hand_rects_timestamp"
-  output_stream: "ITERABLE:multi_handedness"
-}
-
-# Calculate region of interest (ROI) based on detected hand landmarks to reuse
-# on the subsequent runs of the graph.
-node {
-  calculator: "HandLandmarkLandmarksToRoi"
-  input_stream: "IMAGE_SIZE:image_size_for_landmarks"
-  input_stream: "LANDMARKS:single_hand_landmarks"
-  output_stream: "ROI:single_hand_rect_from_landmarks"
-}
-
-# Collects a set of landmarks for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITEM:single_hand_landmarks"
-  input_stream: "BATCH_END:hand_rects_timestamp"
-  output_stream: "ITERABLE:multi_hand_landmarks"
-}
-
-# Collects a set of world landmarks for each hand into a vector. Upon receiving
-# the BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopLandmarkListVectorCalculator"
-  input_stream: "ITEM:single_hand_world_landmarks"
-  input_stream: "BATCH_END:hand_rects_timestamp"
-  output_stream: "ITERABLE:multi_hand_world_landmarks"
-}
-
-# Collects a NormalizedRect for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedRectCalculator"
-  input_stream: "ITEM:single_hand_rect_from_landmarks"
-  input_stream: "BATCH_END:hand_rects_timestamp"
-  output_stream: "ITERABLE:hand_rects_from_landmarks"
-}
-
-# Caches hand rects calculated from landmarks, and upon the arrival of the next
-# input image, sends out the cached rects with timestamps replaced by that of
-# the input image, essentially generating a packet that carries the previous
-# hand rects. Note that upon the arrival of the very first input image, a
-# timestamp bound update occurs to jump start the feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image"
-  input_stream: "LOOP:hand_rects_from_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_hand_rects_from_landmarks"
-}
diff --git a/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu_image.pbtxt b/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu_image.pbtxt
deleted file mode 100644
index 8b8e466..0000000
--- a/mediapipe/modules/hand_landmark/hand_landmark_tracking_gpu_image.pbtxt
+++ /dev/null
@@ -1,115 +0,0 @@
-# MediaPipe graph to detect/predict hand landmarks on GPU.
-#
-# The procedure is done in two steps:
-# - locate palms/hands
-# - detect landmarks for each palm/hand.
-# This graph tries to skip palm detection as much as possible by reusing
-# previously detected/predicted landmarks for new images.
-
-type: "HandLandmarkTrackingGpuImage"
-
-# Input image. (Image)
-input_stream: "IMAGE:image"
-
-# Max number of hands to detect/track. (int)
-input_side_packet: "NUM_HANDS:num_hands"
-
-# Complexity of hand landmark and palm detection models: 0 or 1. Accuracy as
-# well as inference latency generally go up with the model complexity. If
-# unspecified, functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Collection of detected/predicted hands, each represented as a list of
-# landmarks. (std::vector<NormalizedLandmarkList>)
-# NOTE: there will not be an output packet in the LANDMARKS stream for this
-# particular timestamp if none of hands detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:multi_hand_landmarks"
-
-# Collection of detected/predicted hand world landmarks.
-# (std::vector<LandmarkList>)
-#
-# World landmarks are real-world 3D coordinates in meters with the origin in the
-# center of the hand bounding box calculated from the landmarks.
-#
-# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
-# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
-# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
-# the 3D object itself.
-output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
-
-# Collection of handedness of the detected hands (i.e. is hand left or right),
-# each represented as a ClassificationList proto with a single Classification
-# entry. (std::vector<ClassificationList>)
-# Note that handedness is determined assuming the input image is mirrored,
-# i.e., taken with a front-facing/selfie camera with images flipped
-# horizontally.
-output_stream: "HANDEDNESS:multi_handedness"
-
-# The throttled input image. (Image)
-output_stream: "IMAGE:throttled_image"
-# Extra outputs (for debugging, for instance).
-# Detected palms. (std::vector<Detection>)
-output_stream: "PALM_DETECTIONS:palm_detections"
-# Regions of interest calculated based on landmarks.
-# (std::vector<NormalizedRect>)
-output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
-# Regions of interest calculated based on palm detections.
-# (std::vector<NormalizedRect>)
-output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
-
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "image"
-  input_stream: "FINISHED:multi_hand_landmarks"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_image"
-  options: {
-    [mediapipe.FlowLimiterCalculatorOptions.ext] {
-      max_in_flight: 1
-      max_in_queue: 1
-    }
-  }
-}
-
-# Converts Image to GpuBuffer for HandLandmarkTrackingGpu to consume.
-node {
-  calculator: "FromImageCalculator"
-  input_stream: "IMAGE:throttled_image"
-  output_stream: "IMAGE_GPU:raw_gpu_buffer"
-  output_stream: "SOURCE_ON_GPU:is_gpu_image"
-}
-
-# TODO: Remove the extra flipping once adopting MlImage.
-# If the source images are on gpu, flip the data vertically before sending them
-# into HandLandmarkTrackingGpu. This maybe needed because OpenGL represents
-# images assuming the image origin is at the bottom-left corner, whereas
-# MediaPipe in general assumes the image origin is at the top-left corner.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:raw_gpu_buffer"
-  input_stream: "FLIP_VERTICALLY:is_gpu_image"
-  output_stream: "IMAGE_GPU:gpu_buffer"
-}
-
-node {
-  calculator: "HandLandmarkTrackingGpu"
-  input_stream: "IMAGE:gpu_buffer"
-  input_side_packet: "NUM_HANDS:num_hands"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-  output_stream: "LANDMARKS:multi_hand_landmarks"
-  output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
-  output_stream: "HANDEDNESS:multi_handedness"
-  output_stream: "PALM_DETECTIONS:palm_detections"
-  output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
-  output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
-}
diff --git a/mediapipe/modules/hand_landmark/handedness.txt b/mediapipe/modules/hand_landmark/handedness.txt
deleted file mode 100644
index 9f636db..0000000
--- a/mediapipe/modules/hand_landmark/handedness.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-Left
-Right
diff --git a/mediapipe/modules/hand_landmark/palm_detection_detection_to_roi.pbtxt b/mediapipe/modules/hand_landmark/palm_detection_detection_to_roi.pbtxt
deleted file mode 100644
index 838633b..0000000
--- a/mediapipe/modules/hand_landmark/palm_detection_detection_to_roi.pbtxt
+++ /dev/null
@@ -1,47 +0,0 @@
-# MediaPipe subgraph that calculates hand ROI from palm detection.
-
-type: "PalmDetectionDetectionToRoi"
-
-# Palm detection. (Detection)
-input_stream: "DETECTION:detection"
-# Frame size. (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# ROI (region of interest) according to landmarks, represented as normalized
-# rect. (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Converts results of palm detection into a rectangle (normalized by image size)
-# that encloses the palm and is rotated such that the line connecting center of
-# the wrist and MCP of the middle finger is aligned with the Y-axis of the
-# rectangle.
-node {
-  calculator: "DetectionsToRectsCalculator"
-  input_stream: "DETECTION:detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:raw_roi"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 0  # Center of wrist.
-      rotation_vector_end_keypoint_index: 2  # MCP of middle finger.
-      rotation_vector_target_angle_degrees: 90
-    }
-  }
-}
-
-# Expands and shifts the rectangle that contains the palm so that it's likely
-# to cover the entire hand.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:raw_roi"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 2.6
-      scale_y: 2.6
-      shift_y: -0.5
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/BUILD b/mediapipe/modules/holistic_landmark/BUILD
deleted file mode 100644
index 44854c0..0000000
--- a/mediapipe/modules/holistic_landmark/BUILD
+++ /dev/null
@@ -1,267 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//mediapipe/framework/tool:mediapipe_graph.bzl", "mediapipe_simple_subgraph")
-
-# TODO: revert to private.
-package(default_visibility = ["//visibility:public"])
-
-licenses(["notice"])
-
-exports_files([
-    "hand_recrop.tflite",
-])
-
-mediapipe_simple_subgraph(
-    name = "face_landmarks_from_pose_gpu",
-    graph = "face_landmarks_from_pose_gpu.pbtxt",
-    register_as = "FaceLandmarksFromPoseGpu",
-    deps = [
-        ":face_detection_front_detections_to_roi",
-        ":face_landmarks_from_pose_to_recrop_roi",
-        ":face_tracking",
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/modules/face_detection:face_detection_short_range_by_roi_gpu",
-        "//mediapipe/modules/face_landmark:face_landmark_gpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_landmarks_from_pose_cpu",
-    graph = "face_landmarks_from_pose_cpu.pbtxt",
-    register_as = "FaceLandmarksFromPoseCpu",
-    deps = [
-        ":face_detection_front_detections_to_roi",
-        ":face_landmarks_from_pose_to_recrop_roi",
-        ":face_tracking",
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/modules/face_detection:face_detection_short_range_by_roi_cpu",
-        "//mediapipe/modules/face_landmark:face_landmark_cpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_landmarks_to_roi",
-    graph = "face_landmarks_to_roi.pbtxt",
-    register_as = "FaceLandmarksToRoi",
-    deps = [
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_detection_front_detections_to_roi",
-    graph = "face_detection_front_detections_to_roi.pbtxt",
-    register_as = "FaceDetectionFrontDetectionsToRoi",
-    deps = [
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_tracking",
-    graph = "face_tracking.pbtxt",
-    register_as = "FaceTracking",
-    deps = [
-        ":face_landmarks_to_roi",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/modules/holistic_landmark/calculators:roi_tracking_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "face_landmarks_from_pose_to_recrop_roi",
-    graph = "face_landmarks_from_pose_to_recrop_roi.pbtxt",
-    register_as = "FaceLandmarksFromPoseToRecropRoi",
-    deps = [
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmarks_from_pose_gpu",
-    graph = "hand_landmarks_from_pose_gpu.pbtxt",
-    register_as = "HandLandmarksFromPoseGpu",
-    deps = [
-        ":hand_landmarks_from_pose_to_recrop_roi",
-        ":hand_recrop_by_roi_gpu",
-        ":hand_tracking",
-        ":hand_visibility_from_hand_landmarks_from_pose",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/modules/hand_landmark:hand_landmark_gpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmarks_from_pose_cpu",
-    graph = "hand_landmarks_from_pose_cpu.pbtxt",
-    register_as = "HandLandmarksFromPoseCpu",
-    deps = [
-        ":hand_landmarks_from_pose_to_recrop_roi",
-        ":hand_recrop_by_roi_cpu",
-        ":hand_tracking",
-        ":hand_visibility_from_hand_landmarks_from_pose",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/modules/hand_landmark:hand_landmark_cpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmarks_to_roi",
-    graph = "hand_landmarks_to_roi.pbtxt",
-    register_as = "HandLandmarksToRoi",
-    deps = [
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-        "//mediapipe/modules/hand_landmark/calculators:hand_landmarks_to_rect_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_recrop_by_roi_gpu",
-    graph = "hand_recrop_by_roi_gpu.pbtxt",
-    register_as = "HandRecropByRoiGpu",
-    deps = [
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
-        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_recrop_by_roi_cpu",
-    graph = "hand_recrop_by_roi_cpu.pbtxt",
-    register_as = "HandRecropByRoiCpu",
-    deps = [
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
-        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_tracking",
-    graph = "hand_tracking.pbtxt",
-    register_as = "HandTracking",
-    deps = [
-        ":hand_landmarks_to_roi",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/modules/holistic_landmark/calculators:roi_tracking_calculator",
-    ],
-)
-
-# TODO: parametrize holistic_landmark graph with visibility and make private.
-mediapipe_simple_subgraph(
-    name = "hand_wrist_for_pose",
-    graph = "hand_wrist_for_pose.pbtxt",
-    register_as = "HandWristForPose",
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:side_packet_to_stream_calculator",
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/util:set_landmark_visibility_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmarks_left_and_right_gpu",
-    graph = "hand_landmarks_left_and_right_gpu.pbtxt",
-    register_as = "HandLandmarksLeftAndRightGpu",
-    deps = [
-        ":hand_landmarks_from_pose_gpu",
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmarks_left_and_right_cpu",
-    graph = "hand_landmarks_left_and_right_cpu.pbtxt",
-    register_as = "HandLandmarksLeftAndRightCpu",
-    deps = [
-        ":hand_landmarks_from_pose_cpu",
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_landmarks_from_pose_to_recrop_roi",
-    graph = "hand_landmarks_from_pose_to_recrop_roi.pbtxt",
-    register_as = "HandLandmarksFromPoseToRecropRoi",
-    deps = [
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-        "//mediapipe/modules/holistic_landmark/calculators:hand_detections_from_pose_to_rects_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "hand_visibility_from_hand_landmarks_from_pose",
-    graph = "hand_visibility_from_hand_landmarks_from_pose.pbtxt",
-    register_as = "HandVisibilityFromHandLandmarksFromPose",
-    deps = [
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/util:landmark_visibility_calculator",
-        "//mediapipe/calculators/util:thresholding_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "holistic_landmark_gpu",
-    graph = "holistic_landmark_gpu.pbtxt",
-    register_as = "HolisticLandmarkGpu",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":face_landmarks_from_pose_gpu",
-        ":hand_landmarks_left_and_right_gpu",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/modules/pose_landmark:pose_landmark_gpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "holistic_landmark_cpu",
-    graph = "holistic_landmark_cpu.pbtxt",
-    register_as = "HolisticLandmarkCpu",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":face_landmarks_from_pose_cpu",
-        ":hand_landmarks_left_and_right_cpu",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/modules/pose_landmark:pose_landmark_cpu",
-    ],
-)
diff --git a/mediapipe/modules/holistic_landmark/README.md b/mediapipe/modules/holistic_landmark/README.md
deleted file mode 100644
index d285f15..0000000
--- a/mediapipe/modules/holistic_landmark/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-# holistic_landmark
-
-Subgraphs|Details
-:--- | :---
-[`HolisticLandmarkCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark/holistic_landmark_cpu.pbtxt)| Predicts pose + left/right hand + face landmarks. (CPU input)
-[`HolisticLandmarkGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt)| Predicts pose + left/right hand + face landmarks. (GPU input.)
diff --git a/mediapipe/modules/holistic_landmark/calculators/BUILD b/mediapipe/modules/holistic_landmark/calculators/BUILD
deleted file mode 100644
index c3c0919..0000000
--- a/mediapipe/modules/holistic_landmark/calculators/BUILD
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-cc_library(
-    name = "hand_detections_from_pose_to_rects_calculator",
-    srcs = ["hand_detections_from_pose_to_rects_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:detections_to_rects_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework:calculator_options_cc_proto",
-        "//mediapipe/framework/formats:detection_cc_proto",
-        "//mediapipe/framework/formats:location_data_cc_proto",
-        "//mediapipe/framework/formats:rect_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-    ],
-    alwayslink = 1,
-)
-
-mediapipe_proto_library(
-    name = "roi_tracking_calculator_proto",
-    srcs = ["roi_tracking_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_options_proto",
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-cc_library(
-    name = "roi_tracking_calculator",
-    srcs = ["roi_tracking_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":roi_tracking_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/formats:landmark_cc_proto",
-        "//mediapipe/framework/formats:rect_cc_proto",
-        "//mediapipe/framework/port:logging",
-        "//mediapipe/framework/port:rectangle",
-        "@com_google_absl//absl/strings:str_format",
-    ],
-    alwayslink = 1,
-)
diff --git a/mediapipe/modules/holistic_landmark/calculators/hand_detections_from_pose_to_rects_calculator.cc b/mediapipe/modules/holistic_landmark/calculators/hand_detections_from_pose_to_rects_calculator.cc
deleted file mode 100644
index 5afdb8a..0000000
--- a/mediapipe/modules/holistic_landmark/calculators/hand_detections_from_pose_to_rects_calculator.cc
+++ /dev/null
@@ -1,156 +0,0 @@
-#include <cmath>
-
-#include "mediapipe/calculators/util/detections_to_rects_calculator.h"
-#include "mediapipe/calculators/util/detections_to_rects_calculator.pb.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/calculator_options.pb.h"
-#include "mediapipe/framework/formats/detection.pb.h"
-#include "mediapipe/framework/formats/location_data.pb.h"
-#include "mediapipe/framework/formats/rect.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-
-namespace mediapipe {
-
-namespace {}  // namespace
-
-// Generates a hand ROI based on a hand detection derived from hand-related pose
-// landmarks.
-//
-// Inputs:
-//   DETECTION - Detection.
-//     Detection to convert to ROI. Must contain 3 key points indicating: wrist,
-//     pinky and index fingers.
-//
-//   IMAGE_SIZE - std::pair<int, int>
-//     Image width and height.
-//
-// Outputs:
-//   NORM_RECT - NormalizedRect.
-//     ROI based on passed input.
-//
-// Examples
-// node {
-//   calculator: "HandDetectionsFromPoseToRectsCalculator"
-//   input_stream: "DETECTION:hand_detection_from_pose"
-//   input_stream: "IMAGE_SIZE:image_size"
-//   output_stream: "NORM_RECT:hand_roi_from_pose"
-// }
-class HandDetectionsFromPoseToRectsCalculator
-    : public DetectionsToRectsCalculator {
- public:
-  absl::Status Open(CalculatorContext* cc) override;
-
- private:
-  ::absl::Status DetectionToNormalizedRect(const Detection& detection,
-                                           const DetectionSpec& detection_spec,
-                                           NormalizedRect* rect) override;
-  absl::Status ComputeRotation(const Detection& detection,
-                               const DetectionSpec& detection_spec,
-                               float* rotation) override;
-};
-REGISTER_CALCULATOR(HandDetectionsFromPoseToRectsCalculator);
-
-namespace {
-
-constexpr int kWrist = 0;
-constexpr int kPinky = 1;
-constexpr int kIndex = 2;
-
-constexpr char kImageSizeTag[] = "IMAGE_SIZE";
-
-}  // namespace
-
-::absl::Status HandDetectionsFromPoseToRectsCalculator::Open(
-    CalculatorContext* cc) {
-  RET_CHECK(cc->Inputs().HasTag(kImageSizeTag))
-      << "Image size is required to calculate rotated rect.";
-  cc->SetOffset(TimestampDiff(0));
-  target_angle_ = M_PI * 0.5f;
-  rotate_ = true;
-  options_ = cc->Options<DetectionsToRectsCalculatorOptions>();
-  output_zero_rect_for_empty_detections_ =
-      options_.output_zero_rect_for_empty_detections();
-
-  return ::absl::OkStatus();
-}
-
-::absl::Status
-HandDetectionsFromPoseToRectsCalculator ::DetectionToNormalizedRect(
-    const Detection& detection, const DetectionSpec& detection_spec,
-    NormalizedRect* rect) {
-  const auto& location_data = detection.location_data();
-  const auto& image_size = detection_spec.image_size;
-  RET_CHECK(image_size) << "Image size is required to calculate rotation";
-
-  const float x_wrist =
-      location_data.relative_keypoints(kWrist).x() * image_size->first;
-  const float y_wrist =
-      location_data.relative_keypoints(kWrist).y() * image_size->second;
-
-  const float x_index =
-      location_data.relative_keypoints(kIndex).x() * image_size->first;
-  const float y_index =
-      location_data.relative_keypoints(kIndex).y() * image_size->second;
-
-  const float x_pinky =
-      location_data.relative_keypoints(kPinky).x() * image_size->first;
-  const float y_pinky =
-      location_data.relative_keypoints(kPinky).y() * image_size->second;
-
-  // Estimate middle finger.
-  const float x_middle = (2.f * x_index + x_pinky) / 3.f;
-  const float y_middle = (2.f * y_index + y_pinky) / 3.f;
-
-  // Crop center as middle finger.
-  const float center_x = x_middle;
-  const float center_y = y_middle;
-
-  // Bounding box size as double distance from middle finger to wrist.
-  const float box_size =
-      std::sqrt((x_middle - x_wrist) * (x_middle - x_wrist) +
-                (y_middle - y_wrist) * (y_middle - y_wrist)) *
-      2.0;
-
-  // Set resulting bounding box.
-  rect->set_x_center(center_x / image_size->first);
-  rect->set_y_center(center_y / image_size->second);
-  rect->set_width(box_size / image_size->first);
-  rect->set_height(box_size / image_size->second);
-
-  return ::absl::OkStatus();
-}
-
-absl::Status HandDetectionsFromPoseToRectsCalculator::ComputeRotation(
-    const Detection& detection, const DetectionSpec& detection_spec,
-    float* rotation) {
-  const auto& location_data = detection.location_data();
-  const auto& image_size = detection_spec.image_size;
-  RET_CHECK(image_size) << "Image size is required to calculate rotation";
-
-  const float x_wrist =
-      location_data.relative_keypoints(kWrist).x() * image_size->first;
-  const float y_wrist =
-      location_data.relative_keypoints(kWrist).y() * image_size->second;
-
-  const float x_index =
-      location_data.relative_keypoints(kIndex).x() * image_size->first;
-  const float y_index =
-      location_data.relative_keypoints(kIndex).y() * image_size->second;
-
-  const float x_pinky =
-      location_data.relative_keypoints(kPinky).x() * image_size->first;
-  const float y_pinky =
-      location_data.relative_keypoints(kPinky).y() * image_size->second;
-
-  // Estimate middle finger.
-  const float x_middle = (2.f * x_index + x_pinky) / 3.f;
-  const float y_middle = (2.f * y_index + y_pinky) / 3.f;
-
-  *rotation = NormalizeRadians(
-      target_angle_ - std::atan2(-(y_middle - y_wrist), x_middle - x_wrist));
-
-  return ::absl::OkStatus();
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.cc b/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.cc
deleted file mode 100644
index 0da6cd7..0000000
--- a/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.cc
+++ /dev/null
@@ -1,358 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <math.h>
-
-#include <cstdlib>
-
-#include "absl/strings/str_format.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/formats/landmark.pb.h"
-#include "mediapipe/framework/formats/rect.pb.h"
-#include "mediapipe/framework/port/logging.h"
-#include "mediapipe/framework/port/rectangle.h"
-#include "mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.pb.h"
-
-namespace mediapipe {
-
-namespace {
-
-constexpr char kPrevLandmarksTag[] = "PREV_LANDMARKS";
-constexpr char kPrevLandmarksRectTag[] = "PREV_LANDMARKS_RECT";
-constexpr char kRecropRectTag[] = "RECROP_RECT";
-constexpr char kImageSizeTag[] = "IMAGE_SIZE";
-constexpr char kTrackingRectTag[] = "TRACKING_RECT";
-
-// TODO: Use rect rotation.
-// Verifies that Intersection over Union of previous frame rect and current
-// frame re-crop rect is less than threshold.
-bool IouRequirementsSatisfied(const NormalizedRect& prev_rect,
-                              const NormalizedRect& recrop_rect,
-                              const std::pair<int, int>& image_size,
-                              const float min_iou) {
-  auto r1 = Rectangle_f(prev_rect.x_center() * image_size.first,
-                        prev_rect.y_center() * image_size.second,
-                        prev_rect.width() * image_size.first,
-                        prev_rect.height() * image_size.second);
-  auto r2 = Rectangle_f(recrop_rect.x_center() * image_size.first,
-                        recrop_rect.y_center() * image_size.second,
-                        recrop_rect.width() * image_size.first,
-                        recrop_rect.height() * image_size.second);
-
-  const float intersection_area = r1.Intersect(r2).Area();
-  const float union_area = r1.Area() + r2.Area() - intersection_area;
-
-  const float intersection_threshold = union_area * min_iou;
-  if (intersection_area < intersection_threshold) {
-    VLOG(1) << absl::StrFormat("Lost tracking: IoU intersection %f < %f",
-                               intersection_area, intersection_threshold);
-    return false;
-  }
-
-  return true;
-}
-
-// Verifies that current frame re-crop rect rotation/translation/scale didn't
-// change much comparing to the previous frame rect. Translation and scale are
-// normalized by current frame re-crop rect.
-bool RectRequirementsSatisfied(const NormalizedRect& prev_rect,
-                               const NormalizedRect& recrop_rect,
-                               const std::pair<int, int> image_size,
-                               const float rotation_degrees,
-                               const float translation, const float scale) {
-  // Rotate both rects so that re-crop rect edges are parallel to XY axes. That
-  // will allow to compute x/y translation of the previous frame rect along axes
-  // of the current frame re-crop rect.
-  const float rotation = -recrop_rect.rotation();
-
-  const float cosa = cos(rotation);
-  const float sina = sin(rotation);
-
-  // Rotate previous frame rect and get its parameters.
-  const float prev_rect_x = prev_rect.x_center() * image_size.first * cosa -
-                            prev_rect.y_center() * image_size.second * sina;
-  const float prev_rect_y = prev_rect.x_center() * image_size.first * sina +
-                            prev_rect.y_center() * image_size.second * cosa;
-  const float prev_rect_width = prev_rect.width() * image_size.first;
-  const float prev_rect_height = prev_rect.height() * image_size.second;
-  const float prev_rect_rotation = prev_rect.rotation() / M_PI * 180.f;
-
-  // Rotate current frame re-crop rect and get its parameters.
-  const float recrop_rect_x = recrop_rect.x_center() * image_size.first * cosa -
-                              recrop_rect.y_center() * image_size.second * sina;
-  const float recrop_rect_y = recrop_rect.x_center() * image_size.first * sina +
-                              recrop_rect.y_center() * image_size.second * cosa;
-  const float recrop_rect_width = recrop_rect.width() * image_size.first;
-  const float recrop_rect_height = recrop_rect.height() * image_size.second;
-  const float recrop_rect_rotation = recrop_rect.rotation() / M_PI * 180.f;
-
-  // Rect requirements are satisfied unless one of the checks below fails.
-  bool satisfied = true;
-
-  // Ensure that rotation diff is in [0, 180] range.
-  float rotation_diff = prev_rect_rotation - recrop_rect_rotation;
-  if (rotation_diff > 180.f) {
-    rotation_diff -= 360.f;
-  }
-  if (rotation_diff < -180.f) {
-    rotation_diff += 360.f;
-  }
-  rotation_diff = abs(rotation_diff);
-  if (rotation_diff > rotation_degrees) {
-    satisfied = false;
-    VLOG(1) << absl::StrFormat("Lost tracking: rect rotation %f > %f",
-                               rotation_diff, rotation_degrees);
-  }
-
-  const float x_diff = abs(prev_rect_x - recrop_rect_x);
-  const float x_threshold = recrop_rect_width * translation;
-  if (x_diff > x_threshold) {
-    satisfied = false;
-    VLOG(1) << absl::StrFormat("Lost tracking: rect x translation %f > %f",
-                               x_diff, x_threshold);
-  }
-
-  const float y_diff = abs(prev_rect_y - recrop_rect_y);
-  const float y_threshold = recrop_rect_height * translation;
-  if (y_diff > y_threshold) {
-    satisfied = false;
-    VLOG(1) << absl::StrFormat("Lost tracking: rect y translation %f > %f",
-                               y_diff, y_threshold);
-  }
-
-  const float width_diff = abs(prev_rect_width - recrop_rect_width);
-  const float width_threshold = recrop_rect_width * scale;
-  if (width_diff > width_threshold) {
-    satisfied = false;
-    VLOG(1) << absl::StrFormat("Lost tracking: rect width %f > %f", width_diff,
-                               width_threshold);
-  }
-
-  const float height_diff = abs(prev_rect_height - recrop_rect_height);
-  const float height_threshold = recrop_rect_height * scale;
-  if (height_diff > height_threshold) {
-    satisfied = false;
-    VLOG(1) << absl::StrFormat("Lost tracking: rect height %f > %f",
-                               height_diff, height_threshold);
-  }
-
-  return satisfied;
-}
-
-// Verifies that landmarks from the previous frame are within re-crop rectangle
-// bounds on the current frame.
-bool LandmarksRequirementsSatisfied(const NormalizedLandmarkList& landmarks,
-                                    const NormalizedRect& recrop_rect,
-                                    const std::pair<int, int> image_size,
-                                    const float recrop_rect_margin) {
-  // Rotate both re-crop rectangle and landmarks so that re-crop rectangle edges
-  // are parallel to XY axes. It will allow to easily check if landmarks are
-  // within re-crop rect bounds along re-crop rect axes.
-  //
-  // Rect rotation is specified clockwise. To apply cos/sin functions we
-  // transform it into counterclockwise.
-  const float rotation = -recrop_rect.rotation();
-
-  const float cosa = cos(rotation);
-  const float sina = sin(rotation);
-
-  // Rotate rect.
-  const float rect_x = recrop_rect.x_center() * image_size.first * cosa -
-                       recrop_rect.y_center() * image_size.second * sina;
-  const float rect_y = recrop_rect.x_center() * image_size.first * sina +
-                       recrop_rect.y_center() * image_size.second * cosa;
-  const float rect_width =
-      recrop_rect.width() * image_size.first * (1.f + recrop_rect_margin);
-  const float rect_height =
-      recrop_rect.height() * image_size.second * (1.f + recrop_rect_margin);
-
-  // Get rect bounds.
-  const float rect_left = rect_x - rect_width * 0.5f;
-  const float rect_right = rect_x + rect_width * 0.5f;
-  const float rect_top = rect_y - rect_height * 0.5f;
-  const float rect_bottom = rect_y + rect_height * 0.5f;
-
-  for (int i = 0; i < landmarks.landmark_size(); ++i) {
-    const auto& landmark = landmarks.landmark(i);
-    const float x = landmark.x() * image_size.first * cosa -
-                    landmark.y() * image_size.second * sina;
-    const float y = landmark.x() * image_size.first * sina +
-                    landmark.y() * image_size.second * cosa;
-
-    if (!(rect_left < x && x < rect_right && rect_top < y && y < rect_bottom)) {
-      VLOG(1) << "Lost tracking: landmarks out of re-crop rect";
-      return false;
-    }
-  }
-
-  return true;
-}
-
-}  // namespace
-
-// A calculator to track object rectangle between frames.
-//
-// Calculator checks that all requirements for tracking are satisfied and uses
-// rectangle from the previous frame in this case, otherwise - uses current
-// frame re-crop rectangle.
-//
-// There are several types of tracking requirements that can be configured via
-// options:
-//   IoU: Verifies that IoU of the previous frame rectangle and current frame
-//     re-crop rectangle is less than a given threshold.
-//   Rect parameters: Verifies that rotation/translation/scale of the re-crop
-//     rectangle on the current frame is close to the rectangle from the
-//     previous frame within given thresholds.
-//   Landmarks: Verifies that landmarks from the previous frame are within
-//     the re-crop rectangle on the current frame.
-//
-// Inputs:
-//   PREV_LANDMARKS: Object landmarks from the previous frame.
-//   PREV_LANDMARKS_RECT: Object rectangle based on the landmarks from the
-//     previous frame.
-//   RECROP_RECT: Object re-crop rectangle from the current frame.
-//   IMAGE_SIZE: Image size to transform normalized coordinates to absolute.
-//
-// Outputs:
-//   TRACKING_RECT: Rectangle to use for object prediction on the current frame.
-//     It will be either object rectangle from the previous frame (if all
-//     tracking requirements are satisfied) or re-crop rectangle from the
-//     current frame (if tracking lost the object).
-//
-// Example config:
-//   node {
-//     calculator: "RoiTrackingCalculator"
-//     input_stream: "PREV_LANDMARKS:prev_hand_landmarks"
-//     input_stream: "PREV_LANDMARKS_RECT:prev_hand_landmarks_rect"
-//     input_stream: "RECROP_RECT:hand_recrop_rect"
-//     input_stream: "IMAGE_SIZE:image_size"
-//     output_stream: "TRACKING_RECT:hand_tracking_rect"
-//     options: {
-//       [mediapipe.RoiTrackingCalculatorOptions.ext] {
-//         rect_requirements: {
-//           rotation_degrees: 40.0
-//           translation: 0.2
-//           scale: 0.4
-//         }
-//         landmarks_requirements: {
-//           recrop_rect_margin: -0.1
-//         }
-//       }
-//     }
-//   }
-class RoiTrackingCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc);
-  absl::Status Open(CalculatorContext* cc) override;
-  absl::Status Process(CalculatorContext* cc) override;
-
- private:
-  RoiTrackingCalculatorOptions options_;
-};
-REGISTER_CALCULATOR(RoiTrackingCalculator);
-
-absl::Status RoiTrackingCalculator::GetContract(CalculatorContract* cc) {
-  cc->Inputs().Tag(kPrevLandmarksTag).Set<NormalizedLandmarkList>();
-  cc->Inputs().Tag(kPrevLandmarksRectTag).Set<NormalizedRect>();
-  cc->Inputs().Tag(kRecropRectTag).Set<NormalizedRect>();
-  cc->Inputs().Tag(kImageSizeTag).Set<std::pair<int, int>>();
-  cc->Outputs().Tag(kTrackingRectTag).Set<NormalizedRect>();
-
-  return absl::OkStatus();
-}
-
-absl::Status RoiTrackingCalculator::Open(CalculatorContext* cc) {
-  cc->SetOffset(TimestampDiff(0));
-  options_ = cc->Options<RoiTrackingCalculatorOptions>();
-  return absl::OkStatus();
-}
-
-absl::Status RoiTrackingCalculator::Process(CalculatorContext* cc) {
-  // If there is no current frame re-crop rect (i.e. object is not present on
-  // the current frame) - return empty packet.
-  if (cc->Inputs().Tag(kRecropRectTag).IsEmpty()) {
-    return absl::OkStatus();
-  }
-
-  // If there is no previous rect, but there is current re-crop rect - return
-  // current re-crop rect as is.
-  if (cc->Inputs().Tag(kPrevLandmarksRectTag).IsEmpty()) {
-    cc->Outputs()
-        .Tag(kTrackingRectTag)
-        .AddPacket(cc->Inputs().Tag(kRecropRectTag).Value());
-    return absl::OkStatus();
-  }
-
-  // At this point we have both previous rect (which also means we have previous
-  // landmarks) and currrent re-crop rect.
-  const auto& prev_landmarks =
-      cc->Inputs().Tag(kPrevLandmarksTag).Get<NormalizedLandmarkList>();
-  const auto& prev_rect =
-      cc->Inputs().Tag(kPrevLandmarksRectTag).Get<NormalizedRect>();
-  const auto& recrop_rect =
-      cc->Inputs().Tag(kRecropRectTag).Get<NormalizedRect>();
-  const auto& image_size =
-      cc->Inputs().Tag(kImageSizeTag).Get<std::pair<int, int>>();
-
-  // Keep tracking unless one of the requirements below is not satisfied.
-  bool keep_tracking = true;
-
-  // If IoU of the previous rect and current re-crop rect is lower than allowed
-  // threshold - use current re-crop rect.
-  if (options_.has_iou_requirements() &&
-      !IouRequirementsSatisfied(prev_rect, recrop_rect, image_size,
-                                options_.iou_requirements().min_iou())) {
-    keep_tracking = false;
-  }
-
-  // If previous rect and current re-crop rect differ more than it is allowed by
-  // the augmentations (used during the model training) - use current re-crop
-  // rect.
-  if (options_.has_rect_requirements() &&
-      !RectRequirementsSatisfied(
-          prev_rect, recrop_rect, image_size,
-          options_.rect_requirements().rotation_degrees(),
-          options_.rect_requirements().translation(),
-          options_.rect_requirements().scale())) {
-    keep_tracking = false;
-  }
-
-  // If landmarks from the previous frame are not in the current re-crop rect
-  // (i.e. object moved too fast and using previous frame rect won't cover
-  // landmarks on the current frame) - use current re-crop rect.
-  if (options_.has_landmarks_requirements() &&
-      !LandmarksRequirementsSatisfied(
-          prev_landmarks, recrop_rect, image_size,
-          options_.landmarks_requirements().recrop_rect_margin())) {
-    keep_tracking = false;
-  }
-
-  // If object didn't move a lot comparing to the previous frame - we'll keep
-  // tracking it and will return rect from the previous frame, otherwise -
-  // return re-crop rect from the current frame.
-  if (keep_tracking) {
-    cc->Outputs()
-        .Tag(kTrackingRectTag)
-        .AddPacket(cc->Inputs().Tag(kPrevLandmarksRectTag).Value());
-  } else {
-    cc->Outputs()
-        .Tag(kTrackingRectTag)
-        .AddPacket(cc->Inputs().Tag(kRecropRectTag).Value());
-    VLOG(1) << "Lost tracking: check messages above for details";
-  }
-
-  return absl::OkStatus();
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.proto b/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.proto
deleted file mode 100644
index ec3cf22..0000000
--- a/mediapipe/modules/holistic_landmark/calculators/roi_tracking_calculator.proto
+++ /dev/null
@@ -1,59 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-
-message RoiTrackingCalculatorOptions {
-  extend CalculatorOptions {
-    optional RoiTrackingCalculatorOptions ext = 329994630;
-  }
-
-  // Verifies that Intersection over Union of previous frame rect and current
-  // frame re-crop rect is less than threshold.
-  message IouRequirements {
-    optional float min_iou = 1 [default = 0.5];
-  }
-
-  // Verifies that current frame re-crop rect rotation/translation/scale didn't
-  // change much comparing to the previous frame rect.
-  message RectRequirements {
-    // Allowed rotation change defined in degrees.
-    optional float rotation_degrees = 1 [default = 10.0];
-
-    // Allowed translation change defined as absolute translation normalized by
-    // re-crop rectangle size.
-    optional float translation = 2 [default = 0.1];
-
-    // Allowed scale change defined as absolute translation normalized by
-    // re-crop rectangle size.
-    optional float scale = 3 [default = 0.1];
-  }
-
-  // Verifies that landmarks from the previous frame are within re-crop
-  // rectangle bounds on the current frame.
-  message LandmarksRequirements {
-    // Margin to apply to re-crop rectangle before checking verifing landmarks.
-    optional float recrop_rect_margin = 1 [default = 0.0];
-  }
-
-  optional IouRequirements iou_requirements = 1;
-
-  optional RectRequirements rect_requirements = 2;
-
-  optional LandmarksRequirements landmarks_requirements = 3;
-}
diff --git a/mediapipe/modules/holistic_landmark/face_detection_front_detections_to_roi.pbtxt b/mediapipe/modules/holistic_landmark/face_detection_front_detections_to_roi.pbtxt
deleted file mode 100644
index 7d9fa9e..0000000
--- a/mediapipe/modules/holistic_landmark/face_detection_front_detections_to_roi.pbtxt
+++ /dev/null
@@ -1,48 +0,0 @@
-# Calculates ROI from detections provided by `face_detection_short_range.tflite`
-# model.
-type: "FaceDetectionFrontDetectionsToRoi"
-
-# Detected faces. (std::vector<Detection>)
-input_stream: "DETECTIONS:detections"
-# Image size (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# Refined (more accurate) ROI to use for face landmarks prediction.
-# (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Converts the face detection into a rectangle (normalized by image size)
-# that encloses the face and is rotated such that the line connecting right side
-# of the right eye and left side of the left eye is aligned with the X-axis of
-# the rectangle.
-node {
-  calculator: "DetectionsToRectsCalculator"
-  input_stream: "DETECTIONS:detections"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:raw_roi"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 0  # Right eye.
-      rotation_vector_end_keypoint_index: 1  # Left eye.
-      rotation_vector_target_angle_degrees: 0
-      conversion_mode: USE_KEYPOINTS
-    }
-  }
-}
-
-# Expands and shifts the rectangle that contains the face so that it's likely
-# to cover the entire face.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:raw_roi"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 2.0
-      scale_y: 2.0
-      shift_y: -0.1
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_cpu.pbtxt b/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_cpu.pbtxt
deleted file mode 100644
index 1d99672..0000000
--- a/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_cpu.pbtxt
+++ /dev/null
@@ -1,82 +0,0 @@
-# Predicts face landmarks within an ROI derived from face-related pose
-# landmarks.
-
-type: "FaceLandmarksFromPoseCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:input_video"
-# Face-related pose landmarks. (NormalizedLandmarkList)
-input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
-
-# Whether to run the face landmark model with attention on lips and eyes to
-# provide more accuracy, and additionally output iris landmarks. If unspecified,
-# functions as set to false. (bool)
-input_side_packet: "REFINE_LANDMARKS:refine_landmarks"
-
-# Face landmarks. (NormalizedLandmarkList)
-output_stream: "FACE_LANDMARKS:face_landmarks"
-
-# Debug outputs.
-# Face ROI derived from face-related pose landmarks, which defines the search
-# region for the face detection model. (NormalizedRect)
-output_stream: "FACE_ROI_FROM_POSE:face_roi_from_pose"
-# Refined face crop rectangle predicted by face detection model.
-# (NormalizedRect)
-output_stream: "FACE_ROI_FROM_DETECTION:face_roi_from_detection"
-# Rectangle used to predict face landmarks. (NormalizedRect)
-output_stream: "FACE_TRACKING_ROI:face_tracking_roi"
-
-# TODO: do not predict face when most of the face landmarks from
-# pose are invisible.
-
-# Extracts image size from the input images.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:input_video"
-  output_stream: "SIZE:image_size"
-}
-
-# Gets ROI for re-crop model from face-related pose landmarks.
-node {
-  calculator: "FaceLandmarksFromPoseToRecropRoi"
-  input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:face_roi_from_pose"
-}
-
-# Detects faces within the face ROI calculated from pose landmarks. This is done
-# to refine face ROI for further landmark detection as ROI calculated from
-# pose landmarks may be inaccurate.
-node {
-  calculator: "FaceDetectionShortRangeByRoiCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "ROI:face_roi_from_pose"
-  output_stream: "DETECTIONS:face_detections"
-}
-
-# Calculates refined face ROI.
-node {
-  calculator: "FaceDetectionFrontDetectionsToRoi"
-  input_stream: "DETECTIONS:face_detections"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:face_roi_from_detection"
-}
-
-# Gets face tracking rectangle (either face rectangle from the previous
-# frame or face re-crop rectangle from the current frame) for face prediction.
-node {
-  calculator: "FaceTracking"
-  input_stream: "LANDMARKS:face_landmarks"
-  input_stream: "FACE_RECROP_ROI:face_roi_from_detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "FACE_TRACKING_ROI:face_tracking_roi"
-}
-
-# Predicts face landmarks from the tracking rectangle.
-node {
-  calculator: "FaceLandmarkCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "ROI:face_tracking_roi"
-  input_side_packet: "WITH_ATTENTION:refine_landmarks"
-  output_stream: "LANDMARKS:face_landmarks"
-}
diff --git a/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_gpu.pbtxt b/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_gpu.pbtxt
deleted file mode 100644
index 24a9854..0000000
--- a/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_gpu.pbtxt
+++ /dev/null
@@ -1,82 +0,0 @@
-# Predicts face landmarks within an ROI derived from face-related pose
-# landmarks.
-
-type: "FaceLandmarksFromPoseGpu"
-
-# GPU image. (ImageFrame)
-input_stream: "IMAGE:input_video"
-# Face-related pose landmarks. (NormalizedLandmarkList)
-input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
-
-# Whether to run the face landmark model with attention on lips and eyes to
-# provide more accuracy, and additionally output iris landmarks. If unspecified,
-# functions as set to false. (bool)
-input_side_packet: "REFINE_LANDMARKS:refine_landmarks"
-
-# Face landmarks. (NormalizedLandmarkList)
-output_stream: "FACE_LANDMARKS:face_landmarks"
-
-# Debug outputs.
-# Face ROI derived from face-related pose landmarks, which defines the search
-# region for the face detection model. (NormalizedRect)
-output_stream: "FACE_ROI_FROM_POSE:face_roi_from_pose"
-# Refined face crop rectangle predicted by face detection model.
-# (NormalizedRect)
-output_stream: "FACE_ROI_FROM_DETECTION:face_roi_from_detection"
-# Rectangle used to predict face landmarks. (NormalizedRect)
-output_stream: "FACE_TRACKING_ROI:face_tracking_roi"
-
-# TODO: do not predict face when most of the face landmarks from
-# pose are invisible.
-
-# Extracts image size from the input images.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:input_video"
-  output_stream: "SIZE:image_size"
-}
-
-# Gets ROI for re-crop model from face-related pose landmarks.
-node {
-  calculator: "FaceLandmarksFromPoseToRecropRoi"
-  input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:face_roi_from_pose"
-}
-
-# Detects faces within the face ROI calculated from pose landmarks. This is done
-# to refine face ROI for further landmark detection as ROI calculated from
-# pose landmarks may be inaccurate.
-node {
-  calculator: "FaceDetectionShortRangeByRoiGpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "ROI:face_roi_from_pose"
-  output_stream: "DETECTIONS:face_detections"
-}
-
-# Calculates refined face ROI.
-node {
-  calculator: "FaceDetectionFrontDetectionsToRoi"
-  input_stream: "DETECTIONS:face_detections"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:face_roi_from_detection"
-}
-
-# Gets face tracking rectangle (either face rectangle from the previous
-# frame or face re-crop rectangle from the current frame) for face prediction.
-node {
-  calculator: "FaceTracking"
-  input_stream: "LANDMARKS:face_landmarks"
-  input_stream: "FACE_RECROP_ROI:face_roi_from_detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "FACE_TRACKING_ROI:face_tracking_roi"
-}
-
-# Predicts face landmarks from the tracking rectangle.
-node {
-  calculator: "FaceLandmarkGpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "ROI:face_tracking_roi"
-  input_side_packet: "WITH_ATTENTION:refine_landmarks"
-  output_stream: "LANDMARKS:face_landmarks"
-}
diff --git a/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_to_recrop_roi.pbtxt b/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_to_recrop_roi.pbtxt
deleted file mode 100644
index 65bd340..0000000
--- a/mediapipe/modules/holistic_landmark/face_landmarks_from_pose_to_recrop_roi.pbtxt
+++ /dev/null
@@ -1,51 +0,0 @@
-# Converts face-related pose landmarks to re-crop ROI.
-
-type: "FaceLandmarksFromPoseToRecropRoi"
-
-# Face-related pose landmarks (There should be 11 of them).
-# (NormalizedLandmarkList)
-input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
-# Image size (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# ROI to be used for face detection. (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Converts face-related pose landmarks to a detection that tightly encloses all
-# landmarks.
-node {
-  calculator: "LandmarksToDetectionCalculator"
-  input_stream: "NORM_LANDMARKS:face_landmarks_from_pose"
-  output_stream: "DETECTION:pose_face_detection"
-}
-
-# Converts face detection to a normalized face rectangle.
-node {
-  calculator: "DetectionsToRectsCalculator"
-  input_stream: "DETECTION:pose_face_detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:pose_face_rect"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 5  # Right eye.
-      rotation_vector_end_keypoint_index: 2  # Left eye.
-      rotation_vector_target_angle_degrees: 0
-    }
-  }
-}
-
-# Expands face rectangle so that it becomes big enough for face detector to
-# localize it accurately.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:pose_face_rect"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 3.0
-      scale_y: 3.0
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/face_landmarks_to_roi.pbtxt b/mediapipe/modules/holistic_landmark/face_landmarks_to_roi.pbtxt
deleted file mode 100644
index 8913cc1..0000000
--- a/mediapipe/modules/holistic_landmark/face_landmarks_to_roi.pbtxt
+++ /dev/null
@@ -1,53 +0,0 @@
-# Converts face landmarks to ROI.
-
-type: "FaceLandmarksToRoi"
-
-# Face landmarks. (NormalizedLandmarkList)
-input_stream: "LANDMARKS:face_landmarks"
-# Image size (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# ROI according to landmarks. (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Converts face landmarks to a detection that tightly encloses all landmarks.
-node {
-  calculator: "LandmarksToDetectionCalculator"
-  input_stream: "NORM_LANDMARKS:face_landmarks"
-  output_stream: "DETECTION:face_detection"
-}
-
-# Converts the face detection into a rectangle (normalized by image size)
-# that encloses the face and is rotated such that the line connecting center of
-# the wrist and MCP of the middle finger is aligned with the Y-axis of the
-# rectangle.
-node {
-  calculator: "DetectionsToRectsCalculator"
-  input_stream: "DETECTION:face_detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:face_landmarks_rect_tight"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 33  # Right side of left eye.
-      rotation_vector_end_keypoint_index: 263  # Left side of right eye.
-      rotation_vector_target_angle_degrees: 0
-    }
-  }
-}
-
-# Expands the face rectangle so that it's likely to contain the face even with
-# some motion.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:face_landmarks_rect_tight"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 1.5
-      scale_y: 1.5
-      # TODO: remove `square_long` where appropriat
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/face_tracking.pbtxt b/mediapipe/modules/holistic_landmark/face_tracking.pbtxt
deleted file mode 100644
index 53022d3..0000000
--- a/mediapipe/modules/holistic_landmark/face_tracking.pbtxt
+++ /dev/null
@@ -1,61 +0,0 @@
-# Decides what ROI to use for face landmarks prediction: either previous frame
-# landmarks ROI or the current frame face re-crop ROI.
-
-type: "FaceTracking"
-
-# Face landmarks from the current frame. They will be memorized for tracking on
-# the next frame. (NormalizedLandmarkList)
-input_stream: "LANDMARKS:face_landmarks"
-# Face re-crop ROI from the current frame. (NormalizedRect)
-input_stream: "FACE_RECROP_ROI:face_recrop_roi"
-# Image size (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# Face tracking ROI. Which is either face landmarks ROI from the previous frame
-# if face is still tracked, or face re-crop ROI from the current frame
-# otherwise. (NormalizedRect)
-output_stream: "FACE_TRACKING_ROI:face_tracking_roi"
-
-# Keeps track of face landmarks from the previous frame.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image_size"
-  input_stream: "LOOP:face_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_face_landmarks"
-}
-
-# Gets hand landarmsk rect.
-node {
-  calculator: "FaceLandmarksToRoi"
-  input_stream: "LANDMARKS:prev_face_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:prev_face_landmarks_rect"
-}
-
-# Checks that all requirements for tracking are satisfied and use face rectangle
-# from the previous frame in that case. Otherwise - use face re-crop rectangle
-# from the current frame.
-node {
-  calculator: "RoiTrackingCalculator"
-  input_stream: "PREV_LANDMARKS:prev_face_landmarks"
-  input_stream: "PREV_LANDMARKS_RECT:prev_face_landmarks_rect"
-  input_stream: "RECROP_RECT:face_recrop_roi"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "TRACKING_RECT:face_tracking_roi"
-  options: {
-    [mediapipe.RoiTrackingCalculatorOptions.ext] {
-      rect_requirements: {
-        rotation_degrees: 15.0
-        translation: 0.1
-        scale: 0.3
-      }
-      landmarks_requirements: {
-        recrop_rect_margin: -0.2
-      }
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_cpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_cpu.pbtxt
deleted file mode 100644
index 0a44bcb..0000000
--- a/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_cpu.pbtxt
+++ /dev/null
@@ -1,78 +0,0 @@
-# Predicts hand landmarks within a ROI derived from hand-related pose landmarks.
-
-type: "HandLandmarksFromPoseCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:input_video"
-# Hand-related pose landmarks in [wrist, pinky, index] order.
-# (NormalizedLandmarkList)
-input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
-
-# Hand landmarks. (NormalizedLandmarkList)
-output_stream: "HAND_LANDMARKS:hand_landmarks"
-
-# Debug outputs.
-# Hand ROI derived from hand-related landmarks, which defines the search region
-# for the hand re-crop model. (NormalizedRect)
-output_stream: "HAND_ROI_FROM_POSE:hand_roi_from_pose"
-# Refined hand crop rectangle predicted by hand re-crop model. (NormalizedRect)
-output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
-# Rectangle used to predict hand landmarks. (NormalizedRect)
-output_stream: "HAND_TRACKING_ROI:hand_tracking_roi"
-
-# Gets hand visibility.
-node {
-  calculator: "HandVisibilityFromHandLandmarksFromPose"
-  input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
-  output_stream: "VISIBILITY:hand_visibility"
-}
-
-# Drops hand-related pose landmarks if pose wrist is not visible. It will
-# prevent from predicting hand landmarks on the current frame.
-node {
-  calculator: "GateCalculator"
-  input_stream: "hand_landmarks_from_pose"
-  input_stream: "ALLOW:hand_visibility"
-  output_stream: "ensured_hand_landmarks_from_pose"
-}
-
-# Extracts image size from the input images.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:input_video"
-  output_stream: "SIZE:image_size"
-}
-
-# Gets ROI for re-crop model from hand-related pose landmarks.
-node {
-  calculator: "HandLandmarksFromPoseToRecropRoi"
-  input_stream: "HAND_LANDMARKS_FROM_POSE:ensured_hand_landmarks_from_pose"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:hand_roi_from_pose"
-}
-
-# Predicts hand re-crop rectangle on the current frame.
-node {
-  calculator: "HandRecropByRoiCpu",
-  input_stream: "IMAGE:input_video"
-  input_stream: "ROI:hand_roi_from_pose"
-  output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
-}
-
-# Gets hand tracking rectangle (either hand rectangle from the previous
-# frame or hand re-crop rectangle from the current frame) for hand prediction.
-node {
-  calculator: "HandTracking"
-  input_stream: "LANDMARKS:hand_landmarks"
-  input_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "HAND_TRACKING_ROI:hand_tracking_roi"
-}
-
-# Predicts hand landmarks from the tracking rectangle.
-node {
-  calculator: "HandLandmarkCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "ROI:hand_tracking_roi"
-  output_stream: "LANDMARKS:hand_landmarks"
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_gpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_gpu.pbtxt
deleted file mode 100644
index 0296e7d..0000000
--- a/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_gpu.pbtxt
+++ /dev/null
@@ -1,78 +0,0 @@
-# Predicts hand landmarks within a ROI derived from hand-related pose landmarks.
-
-type: "HandLandmarksFromPoseGpu"
-
-# GPU image. (ImageFrame)
-input_stream: "IMAGE:input_video"
-# Hand-related pose landmarks in [wrist, pinky, index] order.
-# (NormalizedLandmarkList)
-input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
-
-# Hand landmarks. (NormalizedLandmarkList)
-output_stream: "HAND_LANDMARKS:hand_landmarks"
-
-# Debug outputs.
-# Hand ROI derived from hand-related landmarks, which defines the search region
-# for the hand re-crop model. (NormalizedRect)
-output_stream: "HAND_ROI_FROM_POSE:hand_roi_from_pose"
-# Refined hand crop rectangle predicted by hand re-crop model. (NormalizedRect)
-output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
-# Rectangle used to predict hand landmarks. (NormalizedRect)
-output_stream: "HAND_TRACKING_ROI:hand_tracking_roi"
-
-# Gets hand visibility.
-node {
-  calculator: "HandVisibilityFromHandLandmarksFromPose"
-  input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
-  output_stream: "VISIBILITY:hand_visibility"
-}
-
-# Drops hand-related pose landmarks if pose wrist is not visible. It will
-# prevent from predicting hand landmarks on the current frame.
-node {
-  calculator: "GateCalculator"
-  input_stream: "hand_landmarks_from_pose"
-  input_stream: "ALLOW:hand_visibility"
-  output_stream: "ensured_hand_landmarks_from_pose"
-}
-
-# Extracts image size from the input images.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:input_video"
-  output_stream: "SIZE:image_size"
-}
-
-# Gets ROI for re-crop model from hand-related pose landmarks.
-node {
-  calculator: "HandLandmarksFromPoseToRecropRoi"
-  input_stream: "HAND_LANDMARKS_FROM_POSE:ensured_hand_landmarks_from_pose"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:hand_roi_from_pose"
-}
-
-# Predicts hand re-crop rectangle on the current frame.
-node {
-  calculator: "HandRecropByRoiGpu",
-  input_stream: "IMAGE:input_video"
-  input_stream: "ROI:hand_roi_from_pose"
-  output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
-}
-
-# Gets hand tracking rectangle (either hand rectangle from the previous
-# frame or hand re-crop rectangle from the current frame) for hand prediction.
-node {
-  calculator: "HandTracking"
-  input_stream: "LANDMARKS:hand_landmarks"
-  input_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "HAND_TRACKING_ROI:hand_tracking_roi"
-}
-
-# Predicts hand landmarks from the tracking rectangle.
-node {
-  calculator: "HandLandmarkGpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "ROI:hand_tracking_roi"
-  output_stream: "LANDMARKS:hand_landmarks"
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_to_recrop_roi.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_to_recrop_roi.pbtxt
deleted file mode 100644
index 1c2cfe5..0000000
--- a/mediapipe/modules/holistic_landmark/hand_landmarks_from_pose_to_recrop_roi.pbtxt
+++ /dev/null
@@ -1,45 +0,0 @@
-# Converts hand-related pose landmarks to hand re-crop ROI.
-
-type: "HandLandmarksFromPoseToRecropRoi"
-
-# Hand-related pose landmarks in [wrist, pinky, index] order.
-# (NormalizedLandmarkList)
-input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
-# Image size (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# ROI to be used for re-crop prediction. (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Converts hand-related pose landmarks to a detection that tightly encloses all
-# of them.
-node {
-  calculator: "LandmarksToDetectionCalculator"
-  input_stream: "NORM_LANDMARKS:hand_landmarks_from_pose"
-  output_stream: "DETECTION:hand_detection_from_pose"
-}
-
-# Converts hand detection to a normalized hand rectangle.
-node {
-  calculator: "HandDetectionsFromPoseToRectsCalculator"
-  input_stream: "DETECTION:hand_detection_from_pose"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:hand_roi_from_pose"
-}
-
-# Expands the palm rectangle so that it becomes big enough for hand re-crop
-# model to localize it accurately.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:hand_roi_from_pose"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 2.7
-      scale_y: 2.7
-      shift_y: -0.1
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_cpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_cpu.pbtxt
deleted file mode 100644
index 75e0133..0000000
--- a/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_cpu.pbtxt
+++ /dev/null
@@ -1,76 +0,0 @@
-# Predicts left and right hand landmarks within corresponding ROIs derived from
-# hand-related pose landmarks.
-
-type: "HandLandmarksLeftAndRightCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:input_video"
-# Pose landmarks to derive initial hand location from. (NormalizedLandmarkList)
-input_stream: "POSE_LANDMARKS:pose_landmarks"
-
-# Left hand landmarks. (NormalizedLandmarkList)
-output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-# RIght hand landmarks. (NormalizedLandmarkList)
-output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-
-# Debug outputs.
-output_stream: "LEFT_HAND_ROI_FROM_POSE:left_hand_roi_from_pose"
-output_stream: "LEFT_HAND_ROI_FROM_RECROP:left_hand_roi_from_recrop"
-output_stream: "LEFT_HAND_TRACKING_ROI:left_hand_tracking_roi"
-output_stream: "RIGHT_HAND_ROI_FROM_POSE:right_hand_roi_from_pose"
-output_stream: "RIGHT_HAND_ROI_FROM_RECROP:right_hand_roi_from_recrop"
-output_stream: "RIGHT_HAND_TRACKING_ROI:right_hand_tracking_roi"
-
-# Extracts left-hand-related landmarks from the pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "left_hand_landmarks_from_pose"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 15 end: 16 }
-      ranges: { begin: 17 end: 18 }
-      ranges: { begin: 19 end: 20 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Predicts left hand landmarks.
-node {
-  calculator: "HandLandmarksFromPoseCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "HAND_LANDMARKS_FROM_POSE:left_hand_landmarks_from_pose"
-  output_stream: "HAND_LANDMARKS:left_hand_landmarks"
-  # Debug outputs.
-  output_stream: "HAND_ROI_FROM_POSE:left_hand_roi_from_pose"
-  output_stream: "HAND_ROI_FROM_RECROP:left_hand_roi_from_recrop"
-  output_stream: "HAND_TRACKING_ROI:left_hand_tracking_roi"
-}
-
-# Extracts right-hand-related landmarks from the pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "right_hand_landmarks_from_pose"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 16 end: 17 }
-      ranges: { begin: 18 end: 19 }
-      ranges: { begin: 20 end: 21 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Extracts right-hand-related landmarks from the pose landmarks.
-node {
-  calculator: "HandLandmarksFromPoseCpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "HAND_LANDMARKS_FROM_POSE:right_hand_landmarks_from_pose"
-  output_stream: "HAND_LANDMARKS:right_hand_landmarks"
-  # Debug outputs.
-  output_stream: "HAND_ROI_FROM_POSE:right_hand_roi_from_pose"
-  output_stream: "HAND_ROI_FROM_RECROP:right_hand_roi_from_recrop"
-  output_stream: "HAND_TRACKING_ROI:right_hand_tracking_roi"
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_gpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_gpu.pbtxt
deleted file mode 100644
index adeec2b..0000000
--- a/mediapipe/modules/holistic_landmark/hand_landmarks_left_and_right_gpu.pbtxt
+++ /dev/null
@@ -1,76 +0,0 @@
-# Predicts left and right hand landmarks within corresponding ROIs derived from
-# hand-related pose landmarks.
-
-type: "HandLandmarksLeftAndRightGpu"
-
-# GPU image. (ImageFrame)
-input_stream: "IMAGE:input_video"
-# Pose landmarks to derive initial hand location from. (NormalizedLandmarkList)
-input_stream: "POSE_LANDMARKS:pose_landmarks"
-
-# Left hand landmarks. (NormalizedLandmarkList)
-output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-# RIght hand landmarks. (NormalizedLandmarkList)
-output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-
-# Debug outputs.
-output_stream: "LEFT_HAND_ROI_FROM_POSE:left_hand_roi_from_pose"
-output_stream: "LEFT_HAND_ROI_FROM_RECROP:left_hand_roi_from_recrop"
-output_stream: "LEFT_HAND_TRACKING_ROI:left_hand_tracking_roi"
-output_stream: "RIGHT_HAND_ROI_FROM_POSE:right_hand_roi_from_pose"
-output_stream: "RIGHT_HAND_ROI_FROM_RECROP:right_hand_roi_from_recrop"
-output_stream: "RIGHT_HAND_TRACKING_ROI:right_hand_tracking_roi"
-
-# Extracts left-hand-related landmarks from the pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "left_hand_landmarks_from_pose"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 15 end: 16 }
-      ranges: { begin: 17 end: 18 }
-      ranges: { begin: 19 end: 20 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Predicts left hand landmarks.
-node {
-  calculator: "HandLandmarksFromPoseGpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "HAND_LANDMARKS_FROM_POSE:left_hand_landmarks_from_pose"
-  output_stream: "HAND_LANDMARKS:left_hand_landmarks"
-  # Debug outputs.
-  output_stream: "HAND_ROI_FROM_POSE:left_hand_roi_from_pose"
-  output_stream: "HAND_ROI_FROM_RECROP:left_hand_roi_from_recrop"
-  output_stream: "HAND_TRACKING_ROI:left_hand_tracking_roi"
-}
-
-# Extracts right-hand-related landmarks from the pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "right_hand_landmarks_from_pose"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 16 end: 17 }
-      ranges: { begin: 18 end: 19 }
-      ranges: { begin: 20 end: 21 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Extracts right-hand-related landmarks from the pose landmarks.
-node {
-  calculator: "HandLandmarksFromPoseGpu"
-  input_stream: "IMAGE:input_video"
-  input_stream: "HAND_LANDMARKS_FROM_POSE:right_hand_landmarks_from_pose"
-  output_stream: "HAND_LANDMARKS:right_hand_landmarks"
-  # Debug outputs.
-  output_stream: "HAND_ROI_FROM_POSE:right_hand_roi_from_pose"
-  output_stream: "HAND_ROI_FROM_RECROP:right_hand_roi_from_recrop"
-  output_stream: "HAND_TRACKING_ROI:right_hand_tracking_roi"
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_landmarks_to_roi.pbtxt b/mediapipe/modules/holistic_landmark/hand_landmarks_to_roi.pbtxt
deleted file mode 100644
index b874c1d..0000000
--- a/mediapipe/modules/holistic_landmark/hand_landmarks_to_roi.pbtxt
+++ /dev/null
@@ -1,57 +0,0 @@
-# Converts hand landmarks to ROI.
-
-type: "HandLandmarksToRoi"
-
-# Hand landmarks. (NormalizedLandmarkList)
-input_stream: "LANDMARKS:hand_landmarks"
-# Image size (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# ROI according to the hand landmarks. (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Gets hand palm landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "hand_landmarks"
-  output_stream: "palm_landmarks"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 4 }
-      ranges: { begin: 5 end: 7 }
-      ranges: { begin: 9 end: 11 }
-      ranges: { begin: 13 end: 15 }
-      ranges: { begin: 17 end: 19 }
-      combine_outputs: true
-    }
-  }
-}
-
-# Converts the hand landmarks into a rectangle (normalized by image size)
-# that encloses the hand. The calculator uses a subset of all hand landmarks
-# extracted from SplitNormalizedLandmarkListCalculator above to
-# calculate the bounding box and the rotation of the output rectangle. Please
-# see the comments in the calculator for more detail.
-node {
-  calculator: "HandLandmarksToRectCalculator"
-  input_stream: "NORM_LANDMARKS:palm_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:palm_landmarks_rect"
-}
-
-# Expands the hand rectangle so that it's likely to contain the hand even with
-# some motion.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:palm_landmarks_rect"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 2.0
-      scale_y: 2.0
-      shift_y: -0.1
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_recrop.tflite b/mediapipe/modules/holistic_landmark/hand_recrop.tflite
deleted file mode 100755
index dcfd276..0000000
Binary files a/mediapipe/modules/holistic_landmark/hand_recrop.tflite and /dev/null differ
diff --git a/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_cpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_cpu.pbtxt
deleted file mode 100644
index 75141d2..0000000
--- a/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_cpu.pbtxt
+++ /dev/null
@@ -1,137 +0,0 @@
-# Predicts more accurate hand location (re-crop ROI) within a given ROI.
-
-type: "HandRecropByRoiCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:input_video"
-# ROI (region of interest) within the given image where a palm/hand is located.
-# (NormalizedRect)
-input_stream: "ROI:roi"
-
-# Refined (more accurate) ROI to use for hand landmark prediction.
-# (NormalizedRect)
-output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop_refined"
-
-# Transforms hand ROI from the input image to a 256x256 tensor. Preserves aspect
-# ratio, which results in a letterbox padding.
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:input_video"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "TENSORS:initial_crop_tensor"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 256
-      output_tensor_height: 256
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-      # For OpenGL origin should be at the top left corner.
-      gpu_origin: TOP_LEFT,
-    }
-  }
-}
-
-# Predicts hand re-crop rectangle.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:initial_crop_tensor"
-  output_stream: "TENSORS:landmark_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/holistic_landmark/hand_recrop.tflite"
-      delegate { xnnpack {} }
-    }
-  }
-}
-
-# Decodes the landmark tensors into a vector of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model. Two
-# landmarks represent two virtual points: crop and scale of the new crop.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:landmark_tensors"
-  output_stream: "NORM_LANDMARKS:landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 2
-      input_image_width: 256
-      input_image_height: 256
-    }
-  }
-}
-
-# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed hand
-# image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (hand
-# image before image transformation).
-node {
-  calculator: "LandmarkLetterboxRemovalCalculator"
-  input_stream: "LANDMARKS:landmarks"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "LANDMARKS:scaled_landmarks"
-}
-
-# Projects the landmarks from the cropped hand image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:scaled_landmarks"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "NORM_LANDMARKS:alignment_landmarks"
-}
-
-# Converts hand landmarks to a detection that tightly encloses all landmarks.
-node {
-  calculator: "LandmarksToDetectionCalculator"
-  input_stream: "NORM_LANDMARKS:alignment_landmarks"
-  output_stream: "DETECTION:hand_detection"
-}
-
-# Extracts image size from the input images.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:input_video"
-  output_stream: "SIZE:image_size"
-}
-
-# Converts hand detection into a rectangle based on center and scale alignment
-# points.
-node {
-  calculator: "AlignmentPointsRectsCalculator"
-  input_stream: "DETECTION:hand_detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:hand_roi_from_recrop"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 0
-      rotation_vector_end_keypoint_index: 1
-      rotation_vector_target_angle_degrees: -90
-    }
-  }
-}
-
-# TODO: revise hand recrop roi calculation.
-# Slighly moves hand re-crop rectangle from wrist towards fingertips. Due to the
-# new hand cropping logic, crop border is to close to finger tips while a lot of
-# space is below the wrist. And when moving hand up fast (with fingers pointing
-# up) and using hand rect from the previous frame for tracking - fingertips can
-# be cropped. This adjustment partially solves it, but hand cropping logic
-# should be reviewed.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:hand_roi_from_recrop"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "hand_roi_from_recrop_refined"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 1.0
-      scale_y: 1.0
-      shift_y: -0.1
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_gpu.pbtxt b/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_gpu.pbtxt
deleted file mode 100644
index 4fa8f29..0000000
--- a/mediapipe/modules/holistic_landmark/hand_recrop_by_roi_gpu.pbtxt
+++ /dev/null
@@ -1,136 +0,0 @@
-# Predicts more accurate hand location (re-crop ROI) within a given ROI.
-
-type: "HandRecropByRoiGpu"
-
-# GPU image. (ImageFrame)
-input_stream: "IMAGE:input_video"
-# ROI (region of interest) within the given image where a palm/hand is located.
-# (NormalizedRect)
-input_stream: "ROI:roi"
-
-# Refined (more accurate) ROI to use for hand landmark prediction.
-# (NormalizedRect)
-output_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop_refined"
-
-# Transforms hand ROI from the input image to a 256x256 tensor. Preserves aspect
-# ratio, which results in a letterbox padding.
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE_GPU:input_video"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "TENSORS:initial_crop_tensor"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 256
-      output_tensor_height: 256
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-      # For OpenGL origin should be at the top left corner.
-      gpu_origin: TOP_LEFT,
-    }
-  }
-}
-
-# Predicts hand re-crop rectangle.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:initial_crop_tensor"
-  output_stream: "TENSORS:landmark_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/holistic_landmark/hand_recrop.tflite"
-    }
-  }
-}
-
-# Decodes the landmark tensors into a vector of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model. Two
-# landmarks represent two virtual points: crop and scale of the new crop.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:landmark_tensors"
-  output_stream: "NORM_LANDMARKS:landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 2
-      input_image_width: 256
-      input_image_height: 256
-    }
-  }
-}
-
-# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed hand
-# image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (hand
-# image before image transformation).
-node {
-  calculator: "LandmarkLetterboxRemovalCalculator"
-  input_stream: "LANDMARKS:landmarks"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "LANDMARKS:scaled_landmarks"
-}
-
-# Projects the landmarks from the cropped hand image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:scaled_landmarks"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "NORM_LANDMARKS:alignment_landmarks"
-}
-
-# Converts hand landmarks to a detection that tightly encloses all landmarks.
-node {
-  calculator: "LandmarksToDetectionCalculator"
-  input_stream: "NORM_LANDMARKS:alignment_landmarks"
-  output_stream: "DETECTION:hand_detection"
-}
-
-# Extracts image size from the input images.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:input_video"
-  output_stream: "SIZE:image_size"
-}
-
-# Converts hand detection into a rectangle based on center and scale alignment
-# points.
-node {
-  calculator: "AlignmentPointsRectsCalculator"
-  input_stream: "DETECTION:hand_detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:hand_roi_from_recrop"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 0
-      rotation_vector_end_keypoint_index: 1
-      rotation_vector_target_angle_degrees: -90
-    }
-  }
-}
-
-# TODO: revise hand recrop roi calculation.
-# Slighly moves hand re-crop rectangle from wrist towards fingertips. Due to the
-# new hand cropping logic, crop border is to close to finger tips while a lot of
-# space is below the wrist. And when moving hand up fast (with fingers pointing
-# up) and using hand rect from the previous frame for tracking - fingertips can
-# be cropped. This adjustment partially solves it, but hand cropping logic
-# should be reviewed.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:hand_roi_from_recrop"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "hand_roi_from_recrop_refined"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 1.0
-      scale_y: 1.0
-      shift_y: -0.1
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_tracking.pbtxt b/mediapipe/modules/holistic_landmark/hand_tracking.pbtxt
deleted file mode 100644
index 07f734e..0000000
--- a/mediapipe/modules/holistic_landmark/hand_tracking.pbtxt
+++ /dev/null
@@ -1,63 +0,0 @@
-# Decides what ROI to use for hand landmark prediction: either previous frame
-# landmarks ROI or current frame re-crop ROI.
-
-type: "HandTracking"
-
-# Hand landmarks from the current frame. They will be memorized for tracking on
-# the next frame. (NormalizedLandmarkList)
-input_stream: "LANDMARKS:hand_landmarks"
-# Hand re-crop ROI from the current frame. (NormalizedRect)
-input_stream: "HAND_ROI_FROM_RECROP:hand_roi_from_recrop"
-# Image size (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# Hand tracking ROI. Which is either hand landmarks ROI from the previous frame
-# if hand is still tracked, or hand re-crop ROI from the current frame
-# othervise. (NormalizedRect)
-output_stream: "HAND_TRACKING_ROI:hand_tracking_roi"
-
-# Keeps track of hand landmarks from the previous frame.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  # TODO: check that loop works with image size instead of video.
-  input_stream: "MAIN:image_size"
-  input_stream: "LOOP:hand_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_hand_landmarks"
-}
-
-# Gets hand landarmsk rect.
-node {
-  calculator: "HandLandmarksToRoi"
-  input_stream: "LANDMARKS:prev_hand_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:prev_hand_landmarks_roi"
-}
-
-# Checks that all requirements for tracking are satisfied and use hand rectangle
-# from the previous frame in that case. Otherwise - use hand re-crop rectangle
-# from the current frame.
-node {
-  calculator: "RoiTrackingCalculator"
-  input_stream: "PREV_LANDMARKS:prev_hand_landmarks"
-  input_stream: "PREV_LANDMARKS_RECT:prev_hand_landmarks_roi"
-  input_stream: "RECROP_RECT:hand_roi_from_recrop"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "TRACKING_RECT:hand_tracking_roi"
-  options: {
-    [mediapipe.RoiTrackingCalculatorOptions.ext] {
-      rect_requirements: {
-        rotation_degrees: 40.0
-        translation: 0.2
-        # TODO: adjust scale for hand tracking.
-        scale: 0.4
-      }
-      landmarks_requirements: {
-        recrop_rect_margin: -0.1
-      }
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_visibility_from_hand_landmarks_from_pose.pbtxt b/mediapipe/modules/holistic_landmark/hand_visibility_from_hand_landmarks_from_pose.pbtxt
deleted file mode 100644
index 02db672..0000000
--- a/mediapipe/modules/holistic_landmark/hand_visibility_from_hand_landmarks_from_pose.pbtxt
+++ /dev/null
@@ -1,44 +0,0 @@
-# Determines hand visibility from the visibility prediction values in the
-# hand-related pose landmarks.
-
-type: "HandVisibilityFromHandLandmarksFromPose"
-
-# Hand-related pose landmarks in [wrist, pinky, index] order.
-# (NormalizedLandmarkList)
-input_stream: "HAND_LANDMARKS_FROM_POSE:hand_landmarks_from_pose"
-
-# Hand visibility to be used as a trigger for hand landmark prediction. (bool)
-output_stream: "VISIBILITY:wrist_visibility"
-
-# Gets pose wrist landmark.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "hand_landmarks_from_pose"
-  output_stream: "pose_wrist_landmark"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-    }
-  }
-}
-
-# TODO: Use other than pose wrist palm landmarks.
-# Gets pose wrist visiblity.
-node {
-  calculator: "LandmarkVisibilityCalculator"
-  input_stream: "NORM_LANDMARKS:pose_wrist_landmark"
-  output_stream: "VISIBILITY:wrist_visibility_score"
-}
-
-# TODO: ensure the same threshold in rendering.
-# Converts pose wrist visibility score into boolean flag.
-node {
-  calculator: "ThresholdingCalculator"
-  input_stream: "FLOAT:wrist_visibility_score"
-  output_stream: "FLAG:wrist_visibility"
-  options: {
-    [mediapipe.ThresholdingCalculatorOptions.ext] {
-      threshold: 0.1
-    }
-  }
-}
diff --git a/mediapipe/modules/holistic_landmark/hand_wrist_for_pose.pbtxt b/mediapipe/modules/holistic_landmark/hand_wrist_for_pose.pbtxt
deleted file mode 100644
index f6551bb..0000000
--- a/mediapipe/modules/holistic_landmark/hand_wrist_for_pose.pbtxt
+++ /dev/null
@@ -1,52 +0,0 @@
-# Extracts hand wrist landmark to be used instead of pose wrist landmark.
-
-type: "HandWristForPose"
-
-# Hand landmarks to take wrist landmark from. (NormalizedLandmarkList)
-input_stream: "HAND_LANDMARKS:hand_landmarks"
-
-# Hand wrist landmark to replace original pose wrist landmark with updated
-# visibility. (NormalizedLandmarkList)
-output_stream: "WRIST_LANDMARK:hand_wrist_landmark_with_visibility"
-
-# Side packet with constant for visibility score. As score is `x` from
-# `sigmoid(x)` we pick some big value that doesn't affect pose landmarks
-# visibility rendering threshold.
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:0:visible_score_side_packet"
-  options: {
-    [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-      packet { float_value: 100.0 }
-    }
-  }
-}
-
-# Converts side packet with visibility score to a stream.
-node {
-  calculator: "SidePacketToStreamCalculator"
-  input_stream: "TICK:hand_landmarks"
-  input_side_packet: "visible_score_side_packet"
-  output_stream: "AT_TICK:visible_score"
-}
-
-# Extracts wrist landmark from the hand landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "hand_landmarks"
-  output_stream: "hand_wrist_landmark"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-    }
-  }
-}
-
-# Sets wrist landmark visibility score. If HAND_LANDMARKS is non-empty - wrist
-# will always be visible.
-node {
-  calculator: "SetLandmarkVisibilityCalculator"
-  input_stream: "NORM_LANDMARKS:hand_wrist_landmark"
-  input_stream: "VISIBILITY:visible_score"
-  output_stream: "NORM_LANDMARKS:hand_wrist_landmark_with_visibility"
-}
diff --git a/mediapipe/modules/holistic_landmark/holistic_landmark_cpu.pbtxt b/mediapipe/modules/holistic_landmark/holistic_landmark_cpu.pbtxt
deleted file mode 100644
index ce86d1d..0000000
--- a/mediapipe/modules/holistic_landmark/holistic_landmark_cpu.pbtxt
+++ /dev/null
@@ -1,146 +0,0 @@
-# Predicts pose + left/right hand + face landmarks.
-#
-# It is required that:
-# - "face_detection_short_range.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-#
-# - "face_landmark.tflite" is available at
-# "mediapipe/modules/face_landmark/face_landmark.tflite"
-#
-# - "hand_landmark_full.tflite" is available at
-# "mediapipe/modules/hand_landmark/hand_landmark_full.tflite"
-#
-# - "hand_recrop.tflite" is available at
-# "mediapipe/modules/holistic_landmark/hand_recrop.tflite"
-#
-# - "handedness.txt" is available at
-# "mediapipe/modules/hand_landmark/handedness.txt"
-#
-# - "pose_detection.tflite" is available at
-# "mediapipe/modules/pose_detection/pose_detection.tflite"
-#
-# - "pose_landmark_lite.tflite" or "pose_landmark_full.tflite" or
-# "pose_landmark_heavy.tflite" is available at
-# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
-# path respectively during execution, depending on the specification in the
-# MODEL_COMPLEXITY input side packet.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "HolisticLandmarkCpu"
-#     input_stream: "IMAGE:input_video"
-#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-#     input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
-#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-#     input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
-#     input_side_packet: "REFINE_FACE_LANDMARKS:refine_face_landmarks"
-#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-#     output_stream: "POSE_LANDMARKS:pose_landmarks"
-#     output_stream: "FACE_LANDMARKS:face_landmarks"
-#     output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-#     output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-#   }
-#
-# NOTE: if a pose/hand/face output is not present in the image, for this
-# particular timestamp there will not be an output packet in the corresponding
-# output stream below. However, the MediaPipe framework will internally inform
-# the downstream calculators of the absence of this packet so that they don't
-# wait for it unnecessarily.
-
-type: "HolisticLandmarkCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
-# inference latency generally go up with the model complexity. If unspecified,
-# functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Whether to filter landmarks across different input images to reduce jitter.
-# If unspecified, functions as set to true. (bool)
-input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
-
-# Whether to predict the segmentation mask. If unspecified, functions as set to
-# false. (bool)
-input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-
-# Whether to filter segmentation mask across different input images to reduce
-# jitter. If unspecified, functions as set to true. (bool)
-input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
-
-# Whether to run the face landmark model with attention on lips and eyes to
-# provide more accuracy, and additionally output iris landmarks. If unspecified,
-# functions as set to false. (bool)
-input_side_packet: "REFINE_FACE_LANDMARKS:refine_face_landmarks"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Pose landmarks. (NormalizedLandmarkList)
-# 33 pose landmarks.
-output_stream: "POSE_LANDMARKS:pose_landmarks"
-# 33 pose world landmarks. (LandmarkList)
-output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
-# 21 left hand landmarks. (NormalizedLandmarkList)
-output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-# 21 right hand landmarks. (NormalizedLandmarkList)
-output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-# 468 face landmarks. (NormalizedLandmarkList)
-output_stream: "FACE_LANDMARKS:face_landmarks"
-
-# Segmentation mask. (ImageFrame in ImageFormat::VEC32F1)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# Debug outputs
-output_stream: "POSE_ROI:pose_landmarks_roi"
-output_stream: "POSE_DETECTION:pose_detection"
-
-# Predicts pose landmarks.
-node {
-  calculator: "PoseLandmarkCpu"
-  input_stream: "IMAGE:image"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
-  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-  input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
-  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-  output_stream: "LANDMARKS:pose_landmarks"
-  output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
-  output_stream: "SEGMENTATION_MASK:segmentation_mask"
-  output_stream: "ROI_FROM_LANDMARKS:pose_landmarks_roi"
-  output_stream: "DETECTION:pose_detection"
-}
-
-# Predicts left and right hand landmarks based on the initial pose landmarks.
-node {
-  calculator: "HandLandmarksLeftAndRightCpu"
-  input_stream: "IMAGE:image"
-  input_stream: "POSE_LANDMARKS:pose_landmarks"
-  output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-  output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-}
-
-# Extracts face-related pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "face_landmarks_from_pose"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 11 }
-    }
-  }
-}
-
-# Predicts face landmarks based on the initial pose landmarks.
-node {
-  calculator: "FaceLandmarksFromPoseCpu"
-  input_stream: "IMAGE:image"
-  input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
-  input_side_packet: "REFINE_LANDMARKS:refine_face_landmarks"
-  output_stream: "FACE_LANDMARKS:face_landmarks"
-}
diff --git a/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt b/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt
deleted file mode 100644
index 33ed880..0000000
--- a/mediapipe/modules/holistic_landmark/holistic_landmark_gpu.pbtxt
+++ /dev/null
@@ -1,146 +0,0 @@
-# Predicts pose + left/right hand + face landmarks.
-#
-# It is required that:
-# - "face_detection_short_range.tflite" is available at
-# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
-#
-# - "face_landmark.tflite" is available at
-# "mediapipe/modules/face_landmark/face_landmark.tflite"
-#
-# - "hand_landmark_full.tflite" is available at
-# "mediapipe/modules/hand_landmark/hand_landmark_full.tflite"
-#
-# - "hand_recrop.tflite" is available at
-# "mediapipe/modules/holistic_landmark/hand_recrop.tflite"
-#
-# - "handedness.txt" is available at
-# "mediapipe/modules/hand_landmark/handedness.txt"
-#
-# - "pose_detection.tflite" is available at
-# "mediapipe/modules/pose_detection/pose_detection.tflite"
-#
-# - "pose_landmark_lite.tflite" or "pose_landmark_full.tflite" or
-# "pose_landmark_heavy.tflite" is available at
-# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
-# path respectively during execution, depending on the specification in the
-# MODEL_COMPLEXITY input side packet.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "HolisticLandmarkGpu"
-#     input_stream: "IMAGE:input_video"
-#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-#     input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
-#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-#     input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
-#     input_side_packet: "REFINE_FACE_LANDMARKS:refine_face_landmarks"
-#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-#     output_stream: "POSE_LANDMARKS:pose_landmarks"
-#     output_stream: "FACE_LANDMARKS:face_landmarks"
-#     output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-#     output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-#   }
-#
-# NOTE: if a pose/hand/face output is not present in the image, for this
-# particular timestamp there will not be an output packet in the corresponding
-# output stream below. However, the MediaPipe framework will internally inform
-# the downstream calculators of the absence of this packet so that they don't
-# wait for it unnecessarily.
-
-type: "HolisticLandmarkGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
-# inference latency generally go up with the model complexity. If unspecified,
-# functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Whether to filter landmarks across different input images to reduce jitter.
-# If unspecified, functions as set to true. (bool)
-input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
-
-# Whether to predict the segmentation mask. If unspecified, functions as set to
-# false. (bool)
-input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-
-# Whether to filter segmentation mask across different input images to reduce
-# jitter. If unspecified, functions as set to true. (bool)
-input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
-
-# Whether to run the face landmark model with attention on lips and eyes to
-# provide more accuracy, and additionally output iris landmarks. If unspecified,
-# functions as set to false. (bool)
-input_side_packet: "REFINE_FACE_LANDMARKS:refine_face_landmarks"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Pose landmarks. (NormalizedLandmarkList)
-# 33 pose landmarks.
-output_stream: "POSE_LANDMARKS:pose_landmarks"
-# 33 pose world landmarks. (LandmarkList)
-output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
-# 21 left hand landmarks. (NormalizedLandmarkList)
-output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-# 21 right hand landmarks. (NormalizedLandmarkList)
-output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-# 468 face landmarks. (NormalizedLandmarkList)
-output_stream: "FACE_LANDMARKS:face_landmarks"
-
-# Segmentation mask. (GpuBuffer in RGBA, with the same mask values in R and A)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# Debug outputs
-output_stream: "POSE_ROI:pose_landmarks_roi"
-output_stream: "POSE_DETECTION:pose_detection"
-
-# Predicts pose landmarks.
-node {
-  calculator: "PoseLandmarkGpu"
-  input_stream: "IMAGE:image"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
-  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-  input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
-  input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-  output_stream: "LANDMARKS:pose_landmarks"
-  output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
-  output_stream: "SEGMENTATION_MASK:segmentation_mask"
-  output_stream: "ROI_FROM_LANDMARKS:pose_landmarks_roi"
-  output_stream: "DETECTION:pose_detection"
-}
-
-# Predicts left and right hand landmarks based on the initial pose landmarks.
-node {
-  calculator: "HandLandmarksLeftAndRightGpu"
-  input_stream: "IMAGE:image"
-  input_stream: "POSE_LANDMARKS:pose_landmarks"
-  output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
-  output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
-}
-
-# Extracts face-related pose landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "pose_landmarks"
-  output_stream: "face_landmarks_from_pose"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 11 }
-    }
-  }
-}
-
-# Predicts face landmarks based on the initial pose landmarks.
-node {
-  calculator: "FaceLandmarksFromPoseGpu"
-  input_stream: "IMAGE:image"
-  input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
-  input_side_packet: "REFINE_LANDMARKS:refine_face_landmarks"
-  output_stream: "FACE_LANDMARKS:face_landmarks"
-}
diff --git a/mediapipe/modules/iris_landmark/BUILD b/mediapipe/modules/iris_landmark/BUILD
deleted file mode 100644
index e16a79b..0000000
--- a/mediapipe/modules/iris_landmark/BUILD
+++ /dev/null
@@ -1,103 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "iris_landmark_cpu",
-    graph = "iris_landmark_cpu.pbtxt",
-    register_as = "IrisLandmarkCpu",
-    deps = [
-        "//mediapipe/calculators/core:clip_vector_size_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/image:image_cropping_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/tflite:tflite_converter_calculator",
-        "//mediapipe/calculators/tflite:tflite_inference_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_floats_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "iris_landmark_gpu",
-    graph = "iris_landmark_gpu.pbtxt",
-    register_as = "IrisLandmarkGpu",
-    deps = [
-        "//mediapipe/calculators/core:clip_vector_size_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/image:image_cropping_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/tflite:tflite_converter_calculator",
-        "//mediapipe/calculators/tflite:tflite_inference_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_floats_calculator",
-        "//mediapipe/calculators/tflite:tflite_tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "iris_landmark_left_and_right_gpu",
-    graph = "iris_landmark_left_and_right_gpu.pbtxt",
-    register_as = "IrisLandmarkLeftAndRightGpu",
-    deps = [
-        ":iris_landmark_gpu",
-        ":iris_landmark_landmarks_to_roi",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:side_packet_to_stream_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "iris_landmark_left_and_right_cpu",
-    graph = "iris_landmark_left_and_right_cpu.pbtxt",
-    register_as = "IrisLandmarkLeftAndRightCpu",
-    deps = [
-        ":iris_landmark_cpu",
-        ":iris_landmark_landmarks_to_roi",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:side_packet_to_stream_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-    ],
-)
-
-exports_files(
-    srcs = [
-        "iris_landmark.tflite",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "iris_landmark_landmarks_to_roi",
-    graph = "iris_landmark_landmarks_to_roi.pbtxt",
-    register_as = "IrisLandmarkLandmarksToRoi",
-    deps = [
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
diff --git a/mediapipe/modules/iris_landmark/README.md b/mediapipe/modules/iris_landmark/README.md
deleted file mode 100644
index f99fcee..0000000
--- a/mediapipe/modules/iris_landmark/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# iris_landmark
-
-Subgraphs|Details
-:--- | :---
-[`IrisLandmarkCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark/iris_landmark_cpu.pbtxt)| Detects iris landmarks for left or right eye. (CPU input, and inference is executed on CPU.)
-[`IrisLandmarkGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark/iris_landmark_gpu.pbtxt)| Detects iris landmarks for left or right eye. (GPU input, and inference is executed on GPU)
-[`IrisLandmarkLeftAndRightCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_cpu.pbtxt)| Detects iris landmarks for both left and right eyes. (CPU input, and inference is executed on CPU)
-[`IrisLandmarkLeftAndRightGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_gpu.pbtxt)| Detects iris landmarks for both left and right eyes. (GPU input, and inference is executed on GPU.)
diff --git a/mediapipe/modules/iris_landmark/iris_landmark.tflite b/mediapipe/modules/iris_landmark/iris_landmark.tflite
deleted file mode 100755
index 974b910..0000000
Binary files a/mediapipe/modules/iris_landmark/iris_landmark.tflite and /dev/null differ
diff --git a/mediapipe/modules/iris_landmark/iris_landmark_cpu.pbtxt b/mediapipe/modules/iris_landmark/iris_landmark_cpu.pbtxt
deleted file mode 100644
index f2c4b04..0000000
--- a/mediapipe/modules/iris_landmark/iris_landmark_cpu.pbtxt
+++ /dev/null
@@ -1,156 +0,0 @@
-# MediaPipe subgraph to calculate iris landmarks and eye contour landmarks for
-# a single eye. (CPU input, and inference is executed on CPU.)
-#
-# It is required that "iris_landmark.tflite" is available at
-# "mediapipe/modules/iris_landmark/iris_landmark.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "IrisLandmarkCpu"
-#     input_stream: "IMAGE:image"
-#     input_stream: "ROI:eye_roi"
-#     input_stream: "IS_RIGHT_EYE:is_right_eye"
-#     output_stream: "EYE_CONTOUR_LANDMARKS:eye_contour_landmarks"
-#     output_stream: "IRIS_LANDMARKS:iris_landmarks"
-#   }
-
-type: "IrisLandmarkCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-# ROI (region of interest) within the given image where an eye is located.
-# (NormalizedRect)
-input_stream: "ROI:roi"
-# Is right eye. (bool)
-# (Model is trained to detect left eye landmarks only, hence for right eye,
-# flipping is required to immitate left eye.)
-input_stream: "IS_RIGHT_EYE:is_right_eye"
-
-# 71 refined normalized eye contour landmarks. (NormalizedLandmarkList)
-output_stream: "EYE_CONTOUR_LANDMARKS:projected_eye_landmarks"
-# 5 normalized iris landmarks. (NormalizedLandmarkList)
-output_stream: "IRIS_LANDMARKS:projected_iris_landmarks"
-
-node {
-  calculator: "ImageCroppingCalculator"
-  input_stream: "IMAGE:image"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "IMAGE:eye_image"
-  options: {
-    [mediapipe.ImageCroppingCalculatorOptions.ext] {
-      border_mode: BORDER_REPLICATE
-    }
-  }
-}
-
-node {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:eye_image"
-  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
-  output_stream: "IMAGE:transformed_eye_image"
-  output_stream: "LETTERBOX_PADDING:eye_letterbox_padding"
-  options: {
-    [mediapipe.ImageTransformationCalculatorOptions.ext] {
-      output_width: 64
-      output_height: 64
-      scale_mode: FIT
-    }
-  }
-}
-
-# Converts the transformed input image on CPU into an image tensor stored as a
-# TfLiteTensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE:transformed_eye_image"
-  output_stream: "TENSORS:image_tensor"
-  options: {
-    [mediapipe.TfLiteConverterCalculatorOptions.ext] {
-      zero_center: false
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  output_stream: "TENSORS:output_tensors"
-  options: {
-    [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/iris_landmark/iris_landmark.tflite"
-      delegate { xnnpack {} }
-    }
-  }
-}
-
-# Splits a vector of TFLite tensors to multiple vectors according to the ranges
-# specified in option.
-node {
-  calculator: "SplitTfLiteTensorVectorCalculator"
-  input_stream: "output_tensors"
-  output_stream: "eye_landmarks_tensor"
-  output_stream: "iris_landmarks_tensor"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-      ranges: { begin: 1 end: 2 }
-    }
-  }
-}
-
-# Decodes the landmark tensors into a vector of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TfLiteTensorsToLandmarksCalculator"
-  input_stream: "TENSORS:iris_landmarks_tensor"
-  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
-  output_stream: "NORM_LANDMARKS:iris_landmarks"
-  options: {
-    [mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 5
-      input_image_width: 64
-      input_image_height: 64
-    }
-  }
-}
-
-# Decodes the landmark tensors into a vector of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TfLiteTensorsToLandmarksCalculator"
-  input_stream: "TENSORS:eye_landmarks_tensor"
-  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
-  output_stream: "NORM_LANDMARKS:eye_landmarks"
-  options: {
-    [mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 71
-      input_image_width: 64
-      input_image_height: 64
-    }
-  }
-}
-
-node {
-  calculator: "LandmarkLetterboxRemovalCalculator"
-  input_stream: "LANDMARKS:0:iris_landmarks"
-  input_stream: "LANDMARKS:1:eye_landmarks"
-  input_stream: "LETTERBOX_PADDING:eye_letterbox_padding"
-  output_stream: "LANDMARKS:0:padded_iris_landmarks"
-  output_stream: "LANDMARKS:1:padded_eye_landmarks"
-}
-
-# Projects the landmarks from the cropped face image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:0:padded_iris_landmarks"
-  input_stream: "NORM_LANDMARKS:1:padded_eye_landmarks"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "NORM_LANDMARKS:0:projected_iris_landmarks"
-  output_stream: "NORM_LANDMARKS:1:projected_eye_landmarks"
-}
-
diff --git a/mediapipe/modules/iris_landmark/iris_landmark_gpu.pbtxt b/mediapipe/modules/iris_landmark/iris_landmark_gpu.pbtxt
deleted file mode 100644
index 9fb7898..0000000
--- a/mediapipe/modules/iris_landmark/iris_landmark_gpu.pbtxt
+++ /dev/null
@@ -1,162 +0,0 @@
-# MediaPipe subgraph to calculate iris landmarks and eye contour landmarks for
-# a single eye. (GPU input, and inference is executed on GPU.)
-#
-# It is required that "iris_landmark.tflite" is available at
-# "mediapipe/modules/iris_landmark/iris_landmark.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "IrisLandmarkGpu"
-#     input_stream: "IMAGE:image"
-#     input_stream: "ROI:eye_roi"
-#     input_stream: "IS_RIGHT_EYE:is_right_eye"
-#     output_stream: "EYE_CONTOUR_LANDMARKS:eye_contour_landmarks"
-#     output_stream: "IRIS_LANDMARKS:iris_landmarks"
-#   }
-
-type: "IrisLandmarkGpu"
-
-# GPU buffer. (GpuBuffer)
-input_stream: "IMAGE:image"
-# ROI (region of interest) within the given image where an eye is located.
-# (NormalizedRect)
-input_stream: "ROI:roi"
-# Is right eye. (bool)
-# (Model is trained to detect left eye landmarks only, hence for right eye,
-# flipping is required to immitate left eye.)
-input_stream: "IS_RIGHT_EYE:is_right_eye"
-
-# TfLite model to detect iris landmarks.
-# (std::unique_ptr<tflite::FlatBufferModel,
-#      std::function<void(tflite::FlatBufferModel*)>>)
-# NOTE: currently, mediapipe/modules/iris_landmark/iris_landmark.tflite model
-# only, can be passed here, otherwise - results are undefined.
-input_side_packet: "MODEL:model"
-
-# 71 refined normalized eye contour landmarks. (NormalizedLandmarkList)
-output_stream: "EYE_CONTOUR_LANDMARKS:projected_eye_landmarks"
-# 5 normalized iris landmarks. (NormalizedLandmarkList)
-output_stream: "IRIS_LANDMARKS:projected_iris_landmarks"
-
-node {
-  calculator: "ImageCroppingCalculator"
-  input_stream: "IMAGE_GPU:image"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "IMAGE_GPU:eye_image"
-  options: {
-    [mediapipe.ImageCroppingCalculatorOptions.ext] {
-      border_mode: BORDER_REPLICATE
-    }
-  }
-}
-
-node {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:eye_image"
-  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
-  output_stream: "IMAGE_GPU:transformed_eye_image"
-  output_stream: "LETTERBOX_PADDING:eye_letterbox_padding"
-  options: {
-    [mediapipe.ImageTransformationCalculatorOptions.ext] {
-      output_width: 64
-      output_height: 64
-      scale_mode: FIT
-    }
-  }
-}
-
-# Converts the transformed input image on CPU into an image tensor stored as a
-# TfLiteTensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE_GPU:transformed_eye_image"
-  output_stream: "TENSORS_GPU:image_tensor"
-  options: {
-    [mediapipe.TfLiteConverterCalculatorOptions.ext] {
-      zero_center: false
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS_GPU:image_tensor"
-  output_stream: "TENSORS:output_tensors"
-  options: {
-    [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/iris_landmark/iris_landmark.tflite"
-    }
-  }
-}
-
-# Splits a vector of TFLite tensors to multiple vectors according to the ranges
-# specified in option.
-node {
-  calculator: "SplitTfLiteTensorVectorCalculator"
-  input_stream: "output_tensors"
-  output_stream: "eye_landmarks_tensor"
-  output_stream: "iris_landmarks_tensor"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-      ranges: { begin: 1 end: 2 }
-    }
-  }
-}
-
-# Decodes the landmark tensors into a vector of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TfLiteTensorsToLandmarksCalculator"
-  input_stream: "TENSORS:iris_landmarks_tensor"
-  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
-  output_stream: "NORM_LANDMARKS:iris_landmarks"
-  options: {
-    [mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 5
-      input_image_width: 64
-      input_image_height: 64
-    }
-  }
-}
-
-# Decodes the landmark tensors into a vector of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TfLiteTensorsToLandmarksCalculator"
-  input_stream: "TENSORS:eye_landmarks_tensor"
-  input_stream: "FLIP_HORIZONTALLY:is_right_eye"
-  output_stream: "NORM_LANDMARKS:eye_landmarks"
-  options: {
-    [mediapipe.TfLiteTensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 71
-      input_image_width: 64
-      input_image_height: 64
-    }
-  }
-}
-
-node {
-  calculator: "LandmarkLetterboxRemovalCalculator"
-  input_stream: "LANDMARKS:0:iris_landmarks"
-  input_stream: "LANDMARKS:1:eye_landmarks"
-  input_stream: "LETTERBOX_PADDING:eye_letterbox_padding"
-  output_stream: "LANDMARKS:0:padded_iris_landmarks"
-  output_stream: "LANDMARKS:1:padded_eye_landmarks"
-}
-
-# Projects the landmarks from the cropped face image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:0:padded_iris_landmarks"
-  input_stream: "NORM_LANDMARKS:1:padded_eye_landmarks"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "NORM_LANDMARKS:0:projected_iris_landmarks"
-  output_stream: "NORM_LANDMARKS:1:projected_eye_landmarks"
-}
-
diff --git a/mediapipe/modules/iris_landmark/iris_landmark_landmarks_to_roi.pbtxt b/mediapipe/modules/iris_landmark/iris_landmark_landmarks_to_roi.pbtxt
deleted file mode 100644
index fc53a16..0000000
--- a/mediapipe/modules/iris_landmark/iris_landmark_landmarks_to_roi.pbtxt
+++ /dev/null
@@ -1,50 +0,0 @@
-# MediaPipe subgraph to calculate region of interest (ROI) which is then can
-# be used to calculate iris landmarks and eye contour landmarks.
-#
-# NOTE: this graph is subject to change and should not be used directly.
-
-type: "IrisLandmarkLandmarksToRoi"
-
-# List of two normalized landmarks: left and right corners of an eye.
-# (NormalizedLandmarkList)
-input_stream: "LANDMARKS:landmarks"
-# Image size. (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# ROI (region of interest) within the given image where an eye is located.
-# (NormalizedRect)
-output_stream: "ROI:roi"
-
-node {
-  calculator: "LandmarksToDetectionCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks"
-  output_stream: "DETECTION:detection"
-}
-
-node {
-  calculator: "DetectionsToRectsCalculator"
-  input_stream: "DETECTION:detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:raw_roi"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 0
-      rotation_vector_end_keypoint_index: 1
-      rotation_vector_target_angle_degrees: 0
-    }
-  }
-}
-
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:raw_roi"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 2.3
-      scale_y: 2.3
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_cpu.pbtxt b/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_cpu.pbtxt
deleted file mode 100644
index 7fb72de..0000000
--- a/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_cpu.pbtxt
+++ /dev/null
@@ -1,120 +0,0 @@
-# MediaPipe subgraph to calculate iris landmarks and eye contour landmarks for
-# two eyes: left and right. (CPU input, and inference is executed on CPU.)
-#
-# It is required that "iris_landmark.tflite" is available at
-# "mediapipe/modules/iris_landmark/iris_landmark.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "IrisLandmarkLeftAndRightCpu"
-#     input_stream: "IMAGE:image"
-#     input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
-#     input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
-#     output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
-#     output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
-#     output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
-#     output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
-#   }
-
-type: "IrisLandmarkLeftAndRightCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-# List of two landmarks defining LEFT eye boundaries - left and right corners.
-# (NormalizedLandmarkList)
-input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
-# List of two landmarks defining RIGHT eye boundaries - left and right corners.
-# (NormalizedLandmarkList)
-input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
-
-# 71 normalized eye contour landmarks. (NormalizedLandmarkList)
-output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
-# 5 normalized iris landmarks. (NormalizedLandmarkList)
-output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
-# Region of interest used to do calculations for the left eye. (NormalizedRect)
-output_stream: "LEFT_EYE_ROI:left_eye_roi"
-
-# 71 normalized eye contour landmarks. (NormalizedLandmarkList)
-output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
-# 5 normalized iris landmarks. (NormalizedLandmarkList)
-output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
-# Region of interest used to do calculations for the right eye. (NormalizedRect)
-output_stream: "RIGHT_EYE_ROI:right_eye_roi"
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:image"
-  output_stream: "SIZE:image_size"
-}
-
-### Processing left eye ###
-
-node {
-  calculator: "IrisLandmarkLandmarksToRoi"
-  input_stream: "LANDMARKS:left_eye_boundary_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:left_eye_roi"
-}
-
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:left_eye_flag_side_packet"
-  options {
-    [mediapipe.ConstantSidePacketCalculatorOptions.ext] {
-      packet { bool_value: false }
-    }
-  }
-}
-
-node {
-  calculator: "SidePacketToStreamCalculator"
-  input_stream: "TICK:image"
-  input_side_packet: "left_eye_flag_side_packet"
-  output_stream: "AT_TICK:left_eye_flag"
-}
-
-node {
-  calculator: "IrisLandmarkCpu"
-  input_stream: "IMAGE:image"
-  input_stream: "ROI:left_eye_roi"
-  input_stream: "IS_RIGHT_EYE:left_eye_flag"
-  output_stream: "EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
-  output_stream: "IRIS_LANDMARKS:left_iris_landmarks"
-}
-
-### Processing right eye ###
-
-node {
-  calculator: "IrisLandmarkLandmarksToRoi"
-  input_stream: "LANDMARKS:right_eye_boundary_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:right_eye_roi"
-}
-
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:right_eye_flag_side_packet"
-  options {
-    [mediapipe.ConstantSidePacketCalculatorOptions.ext] {
-      packet { bool_value: true }
-    }
-  }
-}
-
-node {
-  calculator: "SidePacketToStreamCalculator"
-  input_stream: "TICK:image"
-  input_side_packet: "right_eye_flag_side_packet"
-  output_stream: "AT_TICK:right_eye_flag"
-}
-
-node {
-  calculator: "IrisLandmarkCpu"
-  input_stream: "IMAGE:image"
-  input_stream: "ROI:right_eye_roi"
-  input_stream: "IS_RIGHT_EYE:right_eye_flag"
-  output_stream: "EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
-  output_stream: "IRIS_LANDMARKS:right_iris_landmarks"
-}
-
diff --git a/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_gpu.pbtxt b/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_gpu.pbtxt
deleted file mode 100644
index eeff026..0000000
--- a/mediapipe/modules/iris_landmark/iris_landmark_left_and_right_gpu.pbtxt
+++ /dev/null
@@ -1,120 +0,0 @@
-# MediaPipe subgraph to calculate iris landmarks and eye contour landmarks for
-# two eyes: left and right. (GPU input, and inference is executed on GPU.)
-#
-# It is required that "iris_landmark.tflite" is available at
-# "mediapipe/modules/iris_landmark/iris_landmark.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "IrisLandmarkLeftAndRightGpu"
-#     input_stream: "IMAGE:image"
-#     input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
-#     input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
-#     output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
-#     output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
-#     output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
-#     output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
-#   }
-
-type: "IrisLandmarkLeftAndRightGpu"
-
-# GPU buffer. (GpuBuffer)
-input_stream: "IMAGE:image"
-# List of two landmarks defining LEFT eye boundaries - left and right corners.
-# (NormalizedLandmarkList)
-input_stream: "LEFT_EYE_BOUNDARY_LANDMARKS:left_eye_boundary_landmarks"
-# List of two landmarks defining RIGHT eye boundaries - left and right corners.
-# (NormalizedLandmarkList)
-input_stream: "RIGHT_EYE_BOUNDARY_LANDMARKS:right_eye_boundary_landmarks"
-
-# 71 normalized eye contour landmarks. (NormalizedLandmarkList)
-output_stream: "LEFT_EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
-# 5 normalized iris landmarks. (NormalizedLandmarkList)
-output_stream: "LEFT_EYE_IRIS_LANDMARKS:left_iris_landmarks"
-# Region of interest used to do calculations for the left eye. (NormalizedRect)
-output_stream: "LEFT_EYE_ROI:left_eye_roi"
-
-# 71 normalized eye contour landmarks. (NormalizedLandmarkList)
-output_stream: "RIGHT_EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
-# 5 normalized iris landmarks. (NormalizedLandmarkList)
-output_stream: "RIGHT_EYE_IRIS_LANDMARKS:right_iris_landmarks"
-# Region of interest used to do calculations for the right eye. (NormalizedRect)
-output_stream: "RIGHT_EYE_ROI:right_eye_roi"
-
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-### Processing left eye ###
-
-node {
-  calculator: "IrisLandmarkLandmarksToRoi"
-  input_stream: "LANDMARKS:left_eye_boundary_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:left_eye_roi"
-}
-
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:left_eye_flag_side_packet"
-  options {
-    [mediapipe.ConstantSidePacketCalculatorOptions.ext] {
-      packet { bool_value: false }
-    }
-  }
-}
-
-node {
-  calculator: "SidePacketToStreamCalculator"
-  input_stream: "TICK:image"
-  input_side_packet: "left_eye_flag_side_packet"
-  output_stream: "AT_TICK:left_eye_flag"
-}
-
-node {
-  calculator: "IrisLandmarkGpu"
-  input_stream: "IMAGE:image"
-  input_stream: "ROI:left_eye_roi"
-  input_stream: "IS_RIGHT_EYE:left_eye_flag"
-  output_stream: "EYE_CONTOUR_LANDMARKS:left_eye_contour_landmarks"
-  output_stream: "IRIS_LANDMARKS:left_iris_landmarks"
-}
-
-### Processing right eye ###
-
-node {
-  calculator: "IrisLandmarkLandmarksToRoi"
-  input_stream: "LANDMARKS:right_eye_boundary_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:right_eye_roi"
-}
-
-node {
-  calculator: "ConstantSidePacketCalculator"
-  output_side_packet: "PACKET:right_eye_flag_side_packet"
-  options {
-    [mediapipe.ConstantSidePacketCalculatorOptions.ext] {
-      packet { bool_value: true }
-    }
-  }
-}
-
-node {
-  calculator: "SidePacketToStreamCalculator"
-  input_stream: "TICK:image"
-  input_side_packet: "right_eye_flag_side_packet"
-  output_stream: "AT_TICK:right_eye_flag"
-}
-
-node {
-  calculator: "IrisLandmarkGpu"
-  input_stream: "IMAGE:image"
-  input_stream: "ROI:right_eye_roi"
-  input_stream: "IS_RIGHT_EYE:right_eye_flag"
-  output_stream: "EYE_CONTOUR_LANDMARKS:right_eye_contour_landmarks"
-  output_stream: "IRIS_LANDMARKS:right_iris_landmarks"
-}
-
diff --git a/mediapipe/modules/objectron/BUILD b/mediapipe/modules/objectron/BUILD
deleted file mode 100644
index cee5768..0000000
--- a/mediapipe/modules/objectron/BUILD
+++ /dev/null
@@ -1,183 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-exports_files([
-    "object_detection_3d_camera.tflite",
-    "object_detection_3d_chair.tflite",
-    "object_detection_3d_chair_1stage.tflite",
-    "object_detection_3d_cup.tflite",
-    "object_detection_3d_sneakers.tflite",
-    "object_detection_3d_sneakers_1stage.tflite",
-    "object_detection_oidv4_labelmap.txt",
-    "object_detection_ssd_mobilenetv2_oidv4_fp16.tflite",
-])
-
-mediapipe_simple_subgraph(
-    name = "objectron_detection_1stage_gpu",
-    graph = "objectron_detection_1stage_gpu.pbtxt",
-    register_as = "ObjectronDetection1StageSubgraphGpu",
-    deps = [
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/tflite:tflite_converter_calculator",
-        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
-        "//mediapipe/calculators/tflite:tflite_inference_calculator",
-        "//mediapipe/modules/objectron/calculators:tflite_tensors_to_objects_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "objectron_tracking_1stage_gpu",
-    graph = "objectron_tracking_1stage_gpu.pbtxt",
-    register_as = "ObjectronTracking1StageSubgraphGpu",
-    deps = [
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/video:box_tracker_calculator",
-        "//mediapipe/calculators/video:flow_packager_calculator",
-        "//mediapipe/calculators/video:motion_analysis_calculator",
-        "//mediapipe/framework/stream_handler:sync_set_input_stream_handler",
-        "//mediapipe/gpu:gpu_buffer_to_image_frame_calculator",
-        "//mediapipe/modules/objectron/calculators:frame_annotation_to_timed_box_list_calculator",
-        "//mediapipe/modules/objectron/calculators:frame_annotation_tracker_calculator",
-        "//mediapipe/modules/objectron/calculators:lift_2d_frame_annotation_to_3d_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "box_landmark_gpu",
-    graph = "box_landmark_gpu.pbtxt",
-    register_as = "BoxLandmarkSubgraph",
-    deps = [
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-        "//mediapipe/calculators/util:thresholding_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "box_landmark_cpu",
-    graph = "box_landmark_cpu.pbtxt",
-    register_as = "BoxLandmarkSubgraph",
-    deps = [
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-        "//mediapipe/calculators/util:thresholding_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "object_detection_oid_v4_gpu",
-    graph = "object_detection_oid_v4_gpu.pbtxt",
-    register_as = "ObjectDetectionOidV4Subgraph",
-    deps = [
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-        "//mediapipe/modules/objectron/calculators:filter_detection_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "object_detection_oid_v4_cpu",
-    graph = "object_detection_oid_v4_cpu.pbtxt",
-    register_as = "ObjectDetectionOidV4Subgraph",
-    deps = [
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/util:detection_label_id_to_text_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-        "//mediapipe/modules/objectron/calculators:filter_detection_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "objectron_cpu",
-    graph = "objectron_cpu.pbtxt",
-    register_as = "ObjectronCpuSubgraph",
-    deps = [
-        ":box_landmark_cpu",
-        ":object_detection_oid_v4_cpu",
-        "//mediapipe/calculators/core:begin_loop_calculator",
-        "//mediapipe/calculators/core:clip_vector_size_calculator",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:end_loop_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/tflite:tflite_model_calculator",
-        "//mediapipe/calculators/util:association_norm_rect_calculator",
-        "//mediapipe/calculators/util:collection_has_min_size_calculator",
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/calculators/util:local_file_contents_calculator",
-        "//mediapipe/modules/objectron/calculators:frame_annotation_to_rect_calculator",
-        "//mediapipe/modules/objectron/calculators:landmarks_to_frame_annotation_calculator",
-        "//mediapipe/modules/objectron/calculators:lift_2d_frame_annotation_to_3d_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "objectron_gpu",
-    graph = "objectron_gpu.pbtxt",
-    register_as = "ObjectronGpuSubgraph",
-    deps = [
-        ":box_landmark_gpu",
-        ":object_detection_oid_v4_gpu",
-        "//mediapipe/calculators/core:begin_loop_calculator",
-        "//mediapipe/calculators/core:clip_vector_size_calculator",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:end_loop_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/util:association_norm_rect_calculator",
-        "//mediapipe/calculators/util:collection_has_min_size_calculator",
-        "//mediapipe/calculators/util:detections_to_rects_calculator",
-        "//mediapipe/modules/objectron/calculators:frame_annotation_to_rect_calculator",
-        "//mediapipe/modules/objectron/calculators:landmarks_to_frame_annotation_calculator",
-        "//mediapipe/modules/objectron/calculators:lift_2d_frame_annotation_to_3d_calculator",
-    ],
-)
diff --git a/mediapipe/modules/objectron/README.md b/mediapipe/modules/objectron/README.md
deleted file mode 100644
index 00883fe..0000000
--- a/mediapipe/modules/objectron/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-# objectron
-
-Subgraphs|Details
-:--- | :---
-[`ObjectronCpuSubgraph`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/objectron_cpu.pbtxt)| Detects and tracks 3D bounding boxes for objects. (CPU input, and inference is executed on CPU.)
-[`ObjectronGpuSubgraph`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/objectron/objectron_gpu.pbtxt)| Detects and tracks 3D bounding boxes for objects. (GPU input, and inference is executed on GPU.)
diff --git a/mediapipe/modules/objectron/box_landmark_cpu.pbtxt b/mediapipe/modules/objectron/box_landmark_cpu.pbtxt
deleted file mode 100644
index bb638d1..0000000
--- a/mediapipe/modules/objectron/box_landmark_cpu.pbtxt
+++ /dev/null
@@ -1,147 +0,0 @@
-# MediaPipe Box landmark localization CPU subgraph.
-
-type: "BoxLandmarkSubgraph"
-
-input_stream: "IMAGE:image"
-input_stream: "NORM_RECT:box_rect"
-input_side_packet: "MODEL:model"
-output_stream: "NORM_LANDMARKS:box_landmarks"
-
-# Extracts image size from the input images.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Expands the rectangle that contain the box so that it's likely to cover the
-# entire box.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:box_rect"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "box_rect_scaled"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 1.5
-      scale_y: 1.5
-      square_long: true
-    }
-  }
-}
-
-# Crops, resizes, and converts the input video into tensor.
-# Preserves aspect ratio of the images.
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:image"
-  input_stream: "NORM_RECT:box_rect_scaled"
-  output_stream: "TENSORS:image_tensor"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 224
-      output_tensor_height: 224
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-      gpu_origin: TOP_LEFT
-      border_mode: BORDER_REPLICATE
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  input_side_packet: "MODEL:model"
-  output_stream: "TENSORS:output_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      delegate { xnnpack {} }
-    }
-  }
-}
-
-# Splits a vector of tensors into multiple vectors.
-node {
-  calculator: "SplitTensorVectorCalculator"
-  input_stream: "output_tensors"
-  output_stream: "landmark_tensors"
-  output_stream: "box_flag_tensor"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-      ranges: { begin: 1 end: 2 }
-    }
-  }
-}
-
-# Converts the box-flag tensor into a float that represents the confidence
-# score of box presence.
-node {
-  calculator: "TensorsToFloatsCalculator"
-  input_stream: "TENSORS:box_flag_tensor"
-  output_stream: "FLOAT:box_presence_score"
-}
-
-# Applies a threshold to the confidence score to determine whether a box is
-# present.
-node {
-  calculator: "ThresholdingCalculator"
-  input_stream: "FLOAT:box_presence_score"
-  output_stream: "FLAG:box_presence"
-  options: {
-    [mediapipe.ThresholdingCalculatorOptions.ext] {
-      threshold: 0.99
-    }
-  }
-}
-
-# Drops landmarks tensors if box is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "landmark_tensors"
-  input_stream: "ALLOW:box_presence"
-  output_stream: "gated_landmark_tensors"
-}
-
-# Decodes the landmark tensors into a list of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:gated_landmark_tensors"
-  output_stream: "NORM_LANDMARKS:landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 9
-      input_image_width: 224
-      input_image_height: 224
-    }
-  }
-}
-
-# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed box
-# image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (box
-# image before image transformation).
-node {
-  calculator: "LandmarkLetterboxRemovalCalculator"
-  input_stream: "LANDMARKS:landmarks"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "LANDMARKS:scaled_landmarks"
-}
-
-# Projects the landmarks from the cropped box image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:scaled_landmarks"
-  input_stream: "NORM_RECT:box_rect_scaled"
-  output_stream: "NORM_LANDMARKS:box_landmarks"
-}
diff --git a/mediapipe/modules/objectron/box_landmark_gpu.pbtxt b/mediapipe/modules/objectron/box_landmark_gpu.pbtxt
deleted file mode 100644
index ac95880..0000000
--- a/mediapipe/modules/objectron/box_landmark_gpu.pbtxt
+++ /dev/null
@@ -1,147 +0,0 @@
-# MediaPipe Box landmark localization GPU subgraph.
-
-type: "BoxLandmarkSubgraph"
-
-input_stream: "IMAGE:image"
-input_stream: "NORM_RECT:box_rect"
-output_stream: "NORM_LANDMARKS:box_landmarks"
-
-# Extracts image size from the input images.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Expands the rectangle that contain the box so that it's likely to cover the
-# entire box.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:box_rect"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "box_rect_scaled"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 1.5
-      scale_y: 1.5
-      square_long: true
-    }
-  }
-}
-
-# Crops, resizes, and converts the input video into tensor.
-# Preserves aspect ratio of the images.
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE_GPU:image"
-  input_stream: "NORM_RECT:box_rect_scaled"
-  output_stream: "TENSORS:image_tensor"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 224
-      output_tensor_height: 224
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-      gpu_origin: TOP_LEFT
-      border_mode: BORDER_REPLICATE
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  output_stream: "TENSORS:output_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "object_detection_3d.tflite"
-      delegate { gpu {} }
-    }
-  }
-}
-
-# Splits a vector of tensors to multiple vectors according to the ranges
-# specified in option.
-node {
-  calculator: "SplitTensorVectorCalculator"
-  input_stream: "output_tensors"
-  output_stream: "landmark_tensors"
-  output_stream: "box_flag_tensor"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-      ranges: { begin: 1 end: 2 }
-    }
-  }
-}
-
-# Converts the box-flag tensor into a float that represents the confidence
-# score of box presence.
-node {
-  calculator: "TensorsToFloatsCalculator"
-  input_stream: "TENSORS:box_flag_tensor"
-  output_stream: "FLOAT:box_presence_score"
-}
-
-# Applies a threshold to the confidence score to determine whether a box is
-# present.
-node {
-  calculator: "ThresholdingCalculator"
-  input_stream: "FLOAT:box_presence_score"
-  output_stream: "FLAG:box_presence"
-  options: {
-    [mediapipe.ThresholdingCalculatorOptions.ext] {
-      threshold: 0.99
-    }
-  }
-}
-
-# Drops landmarks tensors if box is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "landmark_tensors"
-  input_stream: "ALLOW:box_presence"
-  output_stream: "gated_landmark_tensors"
-}
-
-# Decodes the landmark tensors into a list of landmarks, where the landmark
-# coordinates are normalized by the size of the input image to the model.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:gated_landmark_tensors"
-  output_stream: "NORM_LANDMARKS:landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 9
-      input_image_width: 224
-      input_image_height: 224
-    }
-  }
-}
-
-# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed box
-# image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (box
-# image before image transformation).
-node {
-  calculator: "LandmarkLetterboxRemovalCalculator"
-  input_stream: "LANDMARKS:landmarks"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "LANDMARKS:scaled_landmarks"
-}
-
-# Projects the landmarks from the cropped box image to the corresponding
-# locations on the full image before cropping (input to the graph).
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:scaled_landmarks"
-  input_stream: "NORM_RECT:box_rect_scaled"
-  output_stream: "NORM_LANDMARKS:box_landmarks"
-}
diff --git a/mediapipe/modules/objectron/calculators/BUILD b/mediapipe/modules/objectron/calculators/BUILD
deleted file mode 100644
index fb75eb3..0000000
--- a/mediapipe/modules/objectron/calculators/BUILD
+++ /dev/null
@@ -1,407 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load("//mediapipe/framework/port:build_config.bzl", "mediapipe_proto_library")
-load("//mediapipe/framework:mediapipe_register_type.bzl", "mediapipe_register_type")
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_proto_library(
-    name = "object_proto",
-    srcs = ["object.proto"],
-    visibility = ["//visibility:public"],
-)
-
-mediapipe_proto_library(
-    name = "a_r_capture_metadata_proto",
-    srcs = ["a_r_capture_metadata.proto"],
-    visibility = ["//visibility:public"],
-)
-
-mediapipe_proto_library(
-    name = "annotation_proto",
-    srcs = ["annotation_data.proto"],
-    def_options_lib = False,
-    visibility = ["//visibility:public"],
-    deps = [
-        ":a_r_capture_metadata_proto",
-        ":object_proto",
-    ],
-)
-
-mediapipe_register_type(
-    base_name = "annotation",
-    include_headers = ["mediapipe/modules/objectron/calculators/annotation_data.pb.h"],
-    types = [
-        "::mediapipe::FrameAnnotation",
-    ],
-    deps = [":annotation_cc_proto"],
-)
-
-mediapipe_proto_library(
-    name = "camera_parameters_proto",
-    srcs = ["camera_parameters.proto"],
-    visibility = ["//visibility:public"],
-)
-
-mediapipe_proto_library(
-    name = "frame_annotation_tracker_calculator_proto",
-    srcs = ["frame_annotation_tracker_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_options_proto",
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "belief_decoder_config_proto",
-    srcs = ["belief_decoder_config.proto"],
-    visibility = ["//visibility:public"],
-)
-
-mediapipe_proto_library(
-    name = "tflite_tensors_to_objects_calculator_proto",
-    srcs = ["tflite_tensors_to_objects_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":belief_decoder_config_proto",
-        "//mediapipe/framework:calculator_options_proto",
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "tensors_to_objects_calculator_proto",
-    srcs = ["tensors_to_objects_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":belief_decoder_config_proto",
-        "//mediapipe/framework:calculator_options_proto",
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "lift_2d_frame_annotation_to_3d_calculator_proto",
-    srcs = ["lift_2d_frame_annotation_to_3d_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":belief_decoder_config_proto",
-        "//mediapipe/framework:calculator_options_proto",
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "frame_annotation_to_rect_calculator_proto",
-    srcs = ["frame_annotation_to_rect_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_options_proto",
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-mediapipe_proto_library(
-    name = "filter_detection_calculator_proto",
-    srcs = ["filter_detection_calculator.proto"],
-    visibility = ["//visibility:public"],
-    deps = [
-        "//mediapipe/framework:calculator_options_proto",
-        "//mediapipe/framework:calculator_proto",
-    ],
-)
-
-cc_library(
-    name = "box_util",
-    srcs = ["box_util.cc"],
-    hdrs = ["box_util.h"],
-    deps = [
-        "//mediapipe/framework/port:logging",
-        "//mediapipe/framework/port:opencv_core",
-        "//mediapipe/framework/port:opencv_imgproc",
-        "//mediapipe/util/tracking:box_tracker_cc_proto",
-    ],
-)
-
-cc_library(
-    name = "frame_annotation_tracker",
-    srcs = ["frame_annotation_tracker.cc"],
-    hdrs = ["frame_annotation_tracker.h"],
-    deps = [
-        ":annotation_cc_proto",
-        ":box_util",
-        "//mediapipe/framework/port:integral_types",
-        "//mediapipe/framework/port:logging",
-        "//mediapipe/util/tracking:box_tracker_cc_proto",
-        "@com_google_absl//absl/container:btree",
-        "@com_google_absl//absl/container:flat_hash_set",
-    ],
-)
-
-cc_library(
-    name = "epnp",
-    srcs = [
-        "epnp.cc",
-    ],
-    hdrs = [
-        "epnp.h",
-    ],
-    deps = [
-        "//mediapipe/framework/port:logging",
-        "@com_google_absl//absl/status",
-        "@com_google_absl//absl/strings:str_format",
-        "@eigen_archive//:eigen3",
-    ],
-)
-
-cc_library(
-    name = "decoder",
-    srcs = [
-        "decoder.cc",
-    ],
-    hdrs = [
-        "decoder.h",
-    ],
-    deps = [
-        ":annotation_cc_proto",
-        ":belief_decoder_config_cc_proto",
-        ":box",
-        ":epnp",
-        "//mediapipe/framework/port:logging",
-        "//mediapipe/framework/port:opencv_core",
-        "//mediapipe/framework/port:opencv_imgproc",
-        "//mediapipe/framework/port:status",
-        "@com_google_absl//absl/status",
-        "@eigen_archive//:eigen3",
-    ],
-)
-
-cc_library(
-    name = "tensor_util",
-    srcs = [
-        "tensor_util.cc",
-    ],
-    hdrs = [
-        "tensor_util.h",
-    ],
-    deps = [
-        "//mediapipe/framework/formats:tensor",
-        "//mediapipe/framework/port:logging",
-        "//mediapipe/framework/port:opencv_core",
-        "@org_tensorflow//tensorflow/lite:framework",
-    ],
-)
-
-cc_library(
-    name = "box",
-    srcs = [
-        "box.cc",
-        "model.cc",
-    ],
-    hdrs = [
-        "box.h",
-        "model.h",
-        "types.h",
-    ],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":annotation_cc_proto",
-        ":object_cc_proto",
-        "//mediapipe/framework/port:logging",
-        "@eigen_archive//:eigen3",
-    ],
-)
-
-cc_library(
-    name = "frame_annotation_to_timed_box_list_calculator",
-    srcs = ["frame_annotation_to_timed_box_list_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":annotation_cc_proto",
-        ":box_util",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/port:opencv_core",
-        "//mediapipe/framework/port:opencv_imgproc",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/util/tracking:box_tracker_cc_proto",
-        "@com_google_absl//absl/memory",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "frame_annotation_tracker_calculator",
-    srcs = ["frame_annotation_tracker_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":annotation_cc_proto",
-        ":frame_annotation_tracker",
-        ":frame_annotation_tracker_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "//mediapipe/util/tracking:box_tracker_cc_proto",
-        "@com_google_absl//absl/container:flat_hash_set",
-        "@com_google_absl//absl/memory",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "tflite_tensors_to_objects_calculator",
-    srcs = ["tflite_tensors_to_objects_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":annotation_cc_proto",
-        ":belief_decoder_config_cc_proto",
-        ":decoder",
-        ":tensor_util",
-        ":tflite_tensors_to_objects_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/deps:file_path",
-        "//mediapipe/framework/formats:detection_cc_proto",
-        "//mediapipe/framework/port:opencv_core",
-        "//mediapipe/framework/port:ret_check",
-        "@com_google_absl//absl/memory",
-        "@com_google_absl//absl/strings:str_format",
-        "@com_google_absl//absl/types:span",
-        "@eigen_archive//:eigen3",
-        "@org_tensorflow//tensorflow/lite:framework",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "tensors_to_objects_calculator",
-    srcs = ["tensors_to_objects_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":annotation_cc_proto",
-        ":belief_decoder_config_cc_proto",
-        ":decoder",
-        ":tensor_util",
-        ":tensors_to_objects_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/deps:file_path",
-        "//mediapipe/framework/formats:detection_cc_proto",
-        "//mediapipe/framework/port:opencv_core",
-        "//mediapipe/framework/port:ret_check",
-        "@com_google_absl//absl/memory",
-        "@com_google_absl//absl/strings:str_format",
-        "@com_google_absl//absl/types:span",
-        "@eigen_archive//:eigen3",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "lift_2d_frame_annotation_to_3d_calculator",
-    srcs = ["lift_2d_frame_annotation_to_3d_calculator.cc"],
-    visibility = ["//visibility:public"],
-    deps = [
-        ":annotation_cc_proto",
-        ":belief_decoder_config_cc_proto",
-        ":decoder",
-        ":lift_2d_frame_annotation_to_3d_calculator_cc_proto",
-        ":tensor_util",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/deps:file_path",
-        "//mediapipe/framework/formats:detection_cc_proto",
-        "//mediapipe/framework/port:opencv_core",
-        "//mediapipe/framework/port:ret_check",
-        "@com_google_absl//absl/memory",
-        "@com_google_absl//absl/strings:str_format",
-        "@com_google_absl//absl/types:span",
-        "@eigen_archive//:eigen3",
-        "@org_tensorflow//tensorflow/lite:framework",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "frame_annotation_to_rect_calculator",
-    srcs = ["frame_annotation_to_rect_calculator.cc"],
-    deps = [
-        ":annotation_cc_proto",
-        ":frame_annotation_to_rect_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/formats:rect_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "@com_google_absl//absl/memory",
-        "@eigen_archive//:eigen3",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "landmarks_to_frame_annotation_calculator",
-    srcs = ["landmarks_to_frame_annotation_calculator.cc"],
-    deps = [
-        ":annotation_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/formats:landmark_cc_proto",
-        "//mediapipe/framework/port:ret_check",
-        "//mediapipe/framework/port:status",
-        "@com_google_absl//absl/memory",
-    ],
-    alwayslink = 1,
-)
-
-cc_library(
-    name = "filter_detection_calculator",
-    srcs = ["filter_detection_calculator.cc"],
-    deps = [
-        ":filter_detection_calculator_cc_proto",
-        "//mediapipe/framework:calculator_framework",
-        "//mediapipe/framework/formats:detection_cc_proto",
-        "//mediapipe/framework/formats:location_data_cc_proto",
-        "//mediapipe/framework/port:logging",
-        "//mediapipe/framework/port:map_util",
-        "//mediapipe/framework/port:re2",
-        "//mediapipe/framework/port:status",
-        "@com_google_absl//absl/container:node_hash_set",
-        "@com_google_absl//absl/strings",
-    ],
-    alwayslink = 1,
-)
-
-cc_test(
-    name = "box_util_test",
-    srcs = ["box_util_test.cc"],
-    deps = [
-        ":box_util",
-        "//mediapipe/framework/port:gtest_main",
-        "//mediapipe/framework/port:opencv_core",
-        "//mediapipe/util/tracking:box_tracker_cc_proto",
-    ],
-)
-
-cc_test(
-    name = "frame_annotation_tracker_test",
-    srcs = ["frame_annotation_tracker_test.cc"],
-    deps = [
-        ":annotation_cc_proto",
-        ":frame_annotation_tracker",
-        "//mediapipe/framework/port:gtest_main",
-        "//mediapipe/framework/port:logging",
-        "//mediapipe/util/tracking:box_tracker_cc_proto",
-        "@com_google_absl//absl/container:flat_hash_set",
-    ],
-)
diff --git a/mediapipe/modules/objectron/calculators/a_r_capture_metadata.proto b/mediapipe/modules/objectron/calculators/a_r_capture_metadata.proto
deleted file mode 100644
index edc8c4b..0000000
--- a/mediapipe/modules/objectron/calculators/a_r_capture_metadata.proto
+++ /dev/null
@@ -1,551 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-// Info about the camera characteristics used to capture images and depth data.
-// See developer.apple.com/documentation/avfoundation/avcameracalibrationdata
-// for more information.
-message AVCameraCalibrationData {
-  // 3x3 row-major matrix relating a camera's internal properties to an ideal
-  // pinhole-camera model.
-  // See
-  // developer.apple.com/documentation/avfoundation/avcameracalibrationdata/2881135-intrinsicmatrix
-  // for detailed usage information.
-  repeated float intrinsic_matrix = 1 [packed = true];
-
-  // The image dimensions to which the intrinsic_matrix values are relative.
-  optional float intrinsic_matrix_reference_dimension_width = 2;
-  optional float intrinsic_matrix_reference_dimension_height = 3;
-
-  // 3x4 row-major matrix relating a camera's position and orientation to a
-  // world or scene coordinate system. Consists of a unitless 3x3 rotation
-  // matrix (R) on the left and a translation (t) 3x1 vector on the right. The
-  // translation vector's units are millimeters. For example:
-  //
-  //            |r1,1  r2,1  r3,1 | t1|
-  //  [R | t] = |r1,2  r2,2  r3,2 | t2|
-  //            |r1,3  r2,3  r3,3 | t3|
-  //
-  //  is stored as [r11, r21, r31, t1, r12, r22, r32, t2, ...]
-  //
-  // See
-  // developer.apple.com/documentation/avfoundation/avcameracalibrationdata/2881130-extrinsicmatrix?language=objc
-  // for more information.
-  repeated float extrinsic_matrix = 4 [packed = true];
-
-  // The size, in millimeters, of one image pixel.
-  optional float pixel_size = 5;
-
-  // A list of floating-point values describing radial distortions imparted by
-  // the camera lens, for use in rectifying camera images.
-  // See
-  // developer.apple.com/documentation/avfoundation/avcameracalibrationdata/2881129-lensdistortionlookuptable?language=objc
-  // for more information.
-  repeated float lens_distortion_lookup_values = 6 [packed = true];
-
-  // A list of floating-point values describing radial distortions for use in
-  // reapplying camera geometry to a rectified image.
-  // See
-  // developer.apple.com/documentation/avfoundation/avcameracalibrationdata/2881132-inverselensdistortionlookuptable?language=objc
-  // for more information.
-  repeated float inverse_lens_distortion_lookup_values = 7 [packed = true];
-
-  // The offset of the distortion center of the camera lens from the top-left
-  // corner of the image.
-  // See
-  // developer.apple.com/documentation/avfoundation/avcameracalibrationdata/2881131-lensdistortioncenter?language=objc
-  // for more information.
-  optional float lens_distortion_center_x = 8;
-  optional float lens_distortion_center_y = 9;
-}
-
-// Container for depth data information.
-// See developer.apple.com/documentation/avfoundation/avdepthdata for more info.
-message AVDepthData {
-  // PNG representation of the grayscale depth data map. See discussion about
-  // depth_data_map_original_minimum_value, below, for information about how
-  // to interpret the pixel values.
-  optional bytes depth_data_map = 1;
-
-  // Pixel format type of the original captured depth data.
-  // See
-  // developer.apple.com/documentation/corevideo/1563591-pixel_format_identifiers?language=objc
-  // for the complete list of possible pixel format types. This value represents
-  // a string for the associated OSType/FourCharCode.
-  optional string depth_data_type = 2;
-
-  // Indicates the general accuracy of the depth_data_map.
-  // See developer.apple.com/documentation/avfoundation/avdepthdataaccuracy for
-  // more information.
-  enum Accuracy {
-    UNDEFINED_ACCURACY = 0;
-    // Values in the depth map are usable for foreground/background separation
-    // but are not absolutely accurate in the physical world.
-    RELATIVE = 1;
-    // Values in the depth map are absolutely accurate in the physical world.
-    ABSOLUTE = 2;
-  }
-  optional Accuracy depth_data_accuracy = 3 [default = RELATIVE];
-
-  // Indicates whether the depth_data_map contains temporally smoothed data.
-  optional bool depth_data_filtered = 4;
-
-  // Quality of the depth_data_map.
-  enum Quality {
-    UNDEFINED_QUALITY = 0;
-    HIGH = 1;
-    LOW = 2;
-  }
-  optional Quality depth_data_quality = 5;
-
-  // Associated calibration data for the depth_data_map.
-  optional AVCameraCalibrationData camera_calibration_data = 6;
-
-  // The original range of values expressed by the depth_data_map, before
-  // grayscale normalization. For example, if the minimum and maximum values
-  // indicate a range of [0.5, 2.2], and the depth_data_type value indicates
-  // it was a depth map, then white pixels (255, 255, 255) will map to 0.5 and
-  // black pixels (0, 0, 0) will map to 2.2 with the grayscale range linearly
-  // interpolated inbetween. Conversely, if the depth_data_type value indicates
-  // it was a disparity map, then white pixels will map to 2.2 and black pixels
-  // will map to 0.5.
-  optional float depth_data_map_original_minimum_value = 7;
-  optional float depth_data_map_original_maximum_value = 8;
-
-  // The width of the depth buffer map.
-  optional int32 depth_data_map_width = 9;
-
-  // The height of the depth buffer map.
-  optional int32 depth_data_map_height = 10;
-
-  // The row-major flattened array of the depth buffer map pixels. This will be
-  // either a float32 or float16 byte array, depending on 'depth_data_type'.
-  optional bytes depth_data_map_raw_values = 11;
-}
-
-// Estimated scene lighting information associated with a captured video frame.
-// See developer.apple.com/documentation/arkit/arlightestimate for more info.
-message ARLightEstimate {
-  // The estimated intensity, in lumens, of ambient light throughout the scene.
-  optional double ambient_intensity = 1;
-
-  // The estimated color temperature, in degrees Kelvin, of ambient light
-  // throughout the scene.
-  optional double ambient_color_temperature = 2;
-
-  // Data describing the estimated lighting environment in all directions.
-  // Second-level spherical harmonics in separate red, green, and blue data
-  // planes. Thus, this buffer contains 3 sets of 9 coefficients, or a total of
-  // 27 values.
-  // See
-  // https://developer.apple.com/documentation/arkit/ardirectionallightestimate/2928222-sphericalharmonicscoefficients?language=objc
-  // for more information.
-  repeated float spherical_harmonics_coefficients = 3 [packed = true];
-
-  message DirectionVector {
-    optional float x = 1;
-    optional float y = 2;
-    optional float z = 3;
-  }
-  // A vector indicating the orientation of the strongest directional light
-  // source, normalized in the world-coordinate space.
-  // See
-  // https://developer.apple.com/documentation/arkit/ardirectionallightestimate/2928221-primarylightdirection?language=objc
-  // for more information;
-  optional DirectionVector primary_light_direction = 4;
-
-  // The estimated intensity, in lumens, of the strongest directional light
-  // source in the scene.
-  // See
-  // https://developer.apple.com/documentation/arkit/ardirectionallightestimate/2928219-primarylightintensity?language=objc
-  // for more information.
-  optional float primary_light_intensity = 5;
-}
-
-// Information about the camera position and imaging characteristics for a
-// captured video frame.
-// See developer.apple.com/documentation/arkit/arcamera for more information.
-message ARCamera {
-  // The general quality of position tracking available when the camera captured
-  // a frame.
-  enum TrackingState {
-    UNDEFINED_TRACKING_STATE = 0;
-    // Camera position tracking is not available.
-    UNAVAILABLE = 1;
-    // Tracking is available, but the quality of results is questionable.
-    LIMITED = 2;
-    // Camera position tracking is providing optimal results.
-    NORMAL = 3;
-  }
-  optional TrackingState tracking_state = 1 [default = UNAVAILABLE];
-
-  // A possible diagnosis for limited position tracking quality as of when the
-  // frame was captured.
-  enum TrackingStateReason {
-    UNDEFINED_TRACKING_STATE_REASON = 0;
-    // The current tracking state is not limited.
-    NONE = 1;
-    // Not yet enough camera or motion data to provide tracking information.
-    INITIALIZING = 2;
-    // The device is moving too fast for accurate image-based position tracking.
-    EXCESSIVE_MOTION = 3;
-    // Not enough distinguishable features for image-based position tracking.
-    INSUFFICIENT_FEATURES = 4;
-    // Tracking is limited due to a relocalization in progress.
-    RELOCALIZING = 5;
-  }
-  optional TrackingStateReason tracking_state_reason = 2 [default = NONE];
-
-  // 4x4 row-major matrix expressing position and orientation of the camera in
-  // world coordinate space.
-  // See developer.apple.com/documentation/arkit/arcamera/2866108-transform for
-  // more information.
-  repeated float transform = 3 [packed = true];
-
-  // The orientation of the camera, expressed as roll, pitch, and yaw values.
-  message EulerAngles {
-    optional float roll = 1;
-    optional float pitch = 2;
-    optional float yaw = 3;
-  }
-  optional EulerAngles euler_angles = 4;
-
-  // The width and height, in pixels, of the captured camera image.
-  optional int32 image_resolution_width = 5;
-  optional int32 image_resolution_height = 6;
-
-  // 3x3 row-major matrix that converts between the 2D camera plane and 3D world
-  // coordinate space.
-  // See developer.apple.com/documentation/arkit/arcamera/2875730-intrinsics for
-  // usage information.
-  repeated float intrinsics = 7 [packed = true];
-
-  // 4x4 row-major transform matrix appropriate for rendering 3D content to
-  // match the image captured by the camera.
-  // See
-  // developer.apple.com/documentation/arkit/arcamera/2887458-projectionmatrix
-  // for usage information.
-  repeated float projection_matrix = 8 [packed = true];
-
-  // 4x4 row-major transform matrix appropriate for converting from world-space
-  // to camera space. Relativized for the captured_image orientation (i.e.
-  // UILandscapeOrientationRight).
-  // See
-  // https://developer.apple.com/documentation/arkit/arcamera/2921672-viewmatrixfororientation?language=objc
-  // for more information.
-  repeated float view_matrix = 9 [packed = true];
-}
-
-// Container for a 3D mesh describing face topology.
-message ARFaceGeometry {
-  // Each vertex represents a 3D point in the face mesh, in the face coordinate
-  // space.
-  // See developer.apple.com/documentation/arkit/arfacegeometry/2928201-vertices
-  // for more information.
-  message Vertex {
-    optional float x = 1;
-    optional float y = 2;
-    optional float z = 3;
-  }
-  repeated Vertex vertices = 1;
-
-  // The number of elements in the vertices list.
-  optional int32 vertex_count = 2;
-
-  // Each texture coordinate represents UV texture coordinates for the vertex at
-  // the corresponding index in the vertices buffer.
-  // See
-  // developer.apple.com/documentation/arkit/arfacegeometry/2928203-texturecoordinates
-  // for more information.
-  message TextureCoordinate {
-    optional float u = 1;
-    optional float v = 2;
-  }
-  repeated TextureCoordinate texture_coordinates = 3;
-
-  // The number of elements in the texture_coordinates list.
-  optional int32 texture_coordinate_count = 4;
-
-  // Each integer value in this ordered list represents an index into the
-  // vertices and texture_coordinates lists. Each set of three indices
-  // identifies the vertices comprising a single triangle in the mesh. Each set
-  // of three indices forms a triangle, so the number of indices in the
-  // triangle_indices buffer is three times the triangle_count value.
-  // See
-  // developer.apple.com/documentation/arkit/arfacegeometry/2928199-triangleindices
-  // for more information.
-  repeated int32 triangle_indices = 5 [packed = true];
-
-  // The number of triangles described by the triangle_indices buffer.
-  // See
-  // developer.apple.com/documentation/arkit/arfacegeometry/2928207-trianglecount
-  // for more information.
-  optional int32 triangle_count = 6;
-}
-
-// Contains a list of blend shape entries wherein each item maps a specific
-// blend shape location to its associated coefficient.
-message ARBlendShapeMap {
-  message MapEntry {
-    // Identifier for the specific facial feature.
-    // See developer.apple.com/documentation/arkit/arblendshapelocation for a
-    // complete list of identifiers.
-    optional string blend_shape_location = 1;
-
-    // Indicates the current position of the feature relative to its neutral
-    // configuration, ranging from 0.0 (neutral) to 1.0 (maximum movement).
-    optional float blend_shape_coefficient = 2;
-  }
-  repeated MapEntry entries = 1;
-}
-
-// Information about the pose, topology, and expression of a detected face.
-// See developer.apple.com/documentation/arkit/arfaceanchor for more info.
-message ARFaceAnchor {
-  // A coarse triangle mesh representing the topology of the detected face.
-  optional ARFaceGeometry geometry = 1;
-
-  // A map of named coefficients representing the detected facial expression in
-  // terms of the movement of specific facial features.
-  optional ARBlendShapeMap blend_shapes = 2;
-
-  // 4x4 row-major matrix encoding the position, orientation, and scale of the
-  // anchor relative to the world coordinate space.
-  // See
-  // https://developer.apple.com/documentation/arkit/aranchor/2867981-transform?language=objc
-  // for more information.
-  repeated float transform = 3;
-
-  // Indicates whether the anchor's transform is valid. Frames that have a face
-  // anchor with this value set to NO should probably be ignored.
-  optional bool is_tracked = 4;
-}
-
-// Container for a 3D mesh.
-message ARPlaneGeometry {
-  message Vertex {
-    optional float x = 1;
-    optional float y = 2;
-    optional float z = 3;
-  }
-
-  // Each texture coordinate represents UV texture coordinates for the vertex at
-  // the corresponding index in the vertices buffer.
-  // See
-  // https://developer.apple.com/documentation/arkit/arfacegeometry/2928203-texturecoordinates
-  // for more information.
-  message TextureCoordinate {
-    optional float u = 1;
-    optional float v = 2;
-  }
-
-  // A buffer of vertex positions for each point in the plane mesh.
-  repeated Vertex vertices = 1;
-
-  // The number of elements in the vertices buffer.
-  optional int32 vertex_count = 2;
-
-  // A buffer of texture coordinate values for each point in the plane mesh.
-  repeated TextureCoordinate texture_coordinates = 3;
-
-  // The number of elements in the texture_coordinates buffer.
-  optional int32 texture_coordinate_count = 4;
-
-  // Each integer value in this ordered list represents an index into the
-  // vertices and texture_coordinates lists. Each set of three indices
-  // identifies the vertices comprising a single triangle in the mesh. Each set
-  // of three indices forms a triangle, so the number of indices in the
-  // triangle_indices buffer is three times the triangle_count value.
-  // See
-  // https://developer.apple.com/documentation/arkit/arplanegeometry/2941051-triangleindices
-  // for more information.
-  repeated int32 triangle_indices = 5 [packed = true];
-
-  // Each set of three indices forms a triangle, so the number of indices in the
-  // triangle_indices buffer is three times the triangle_count value.
-  // See
-  // https://developer.apple.com/documentation/arkit/arplanegeometry/2941058-trianglecount
-  // for more information.
-  optional int32 triangle_count = 6;
-
-  // Each value in this buffer represents the position of a vertex along the
-  // boundary polygon of the estimated plane. The owning plane anchor's
-  // transform matrix defines the coordinate system for these points.
-  // See
-  // https://developer.apple.com/documentation/arkit/arplanegeometry/2941052-boundaryvertices
-  // for more information.
-  repeated Vertex boundary_vertices = 7;
-
-  // The number of elements in the boundary_vertices buffer.
-  optional int32 boundary_vertex_count = 8;
-}
-
-// Information about the position and orientation of a real-world flat surface.
-// See https://developer.apple.com/documentation/arkit/arplaneanchor for more
-// information.
-message ARPlaneAnchor {
-  enum Alignment {
-    UNDEFINED = 0;
-    // The plane is perpendicular to gravity.
-    HORIZONTAL = 1;
-    // The plane is parallel to gravity.
-    VERTICAL = 2;
-  }
-
-  // Wrapper for a 3D point / vector within the plane. See extent and center
-  // values for more information.
-  message PlaneVector {
-    optional float x = 1;
-    optional float y = 2;
-    optional float z = 3;
-  }
-
-  enum PlaneClassification {
-    NONE = 0;
-    WALL = 1;
-    FLOOR = 2;
-    CEILING = 3;
-    TABLE = 4;
-    SEAT = 5;
-  }
-
-  // The classification status for the plane.
-  enum PlaneClassificationStatus {
-    // The classfication process for the plane anchor has completed but the
-    // result is inconclusive.
-    UNKNOWN = 0;
-    // No classication information can be provided (set on error or if the
-    // device does not support plane classification).
-    UNAVAILABLE = 1;
-    // The classification process has not completed.
-    UNDETERMINED = 2;
-    // The classfication process for the plane anchor has completed.
-    KNOWN = 3;
-  }
-
-  // The ID of the plane.
-  optional string identifier = 1;
-
-  // 4x4 row-major matrix encoding the position, orientation, and scale of the
-  // anchor relative to the world coordinate space.
-  // See
-  // https://developer.apple.com/documentation/arkit/aranchor/2867981-transform
-  // for more information.
-  repeated float transform = 2;
-
-  // The general orientation of the detected plane with respect to gravity.
-  optional Alignment alignment = 3;
-
-  // A coarse triangle mesh representing the general shape of the detected
-  // plane.
-  optional ARPlaneGeometry geometry = 4;
-
-  // The center point of the plane relative to its anchor position.
-  // Although the type of this property is a 3D vector, a plane anchor is always
-  // two-dimensional, and is always positioned in only the x and z directions
-  // relative to its transform position. (That is, the y-component of this
-  // vector is always zero.)
-  // See
-  // https://developer.apple.com/documentation/arkit/arplaneanchor/2882056-center
-  // for more information.
-  optional PlaneVector center = 5;
-
-  // The estimated width and length of the detected plane.
-  // See
-  // https://developer.apple.com/documentation/arkit/arplaneanchor/2882055-extent
-  // for more information.
-  optional PlaneVector extent = 6;
-
-  // A Boolean value that indicates whether plane classification is available on
-  // the current device. On devices without plane classification support, all
-  // plane anchors report a classification value of NONE
-  // and a classification_status value of UNAVAILABLE.
-  optional bool classification_supported = 7;
-
-  // A general characterization of what kind of real-world surface the plane
-  // anchor represents.
-  // See
-  // https://developer.apple.com/documentation/arkit/arplaneanchor/2990936-classification
-  // for more information.
-  optional PlaneClassification classification = 8;
-
-  // The current state of ARKit's process for classifying the plane anchor.
-  // When this property's value is KNOWN, the classification property represents
-  // ARKit's characterization of the real-world surface corresponding to the
-  // plane anchor.
-  // See
-  // https://developer.apple.com/documentation/arkit/arplaneanchor/2990937-classificationstatus
-  // for more information.
-  optional PlaneClassificationStatus classification_status = 9;
-}
-
-// A collection of points in the world coordinate space.
-// See https://developer.apple.com/documentation/arkit/arpointcloud for more
-// information.
-message ARPointCloud {
-  message Point {
-    optional float x = 1;
-    optional float y = 2;
-    optional float z = 3;
-  }
-
-  // The number of points in the cloud.
-  optional int32 count = 1;
-
-  // The list of detected points.
-  repeated Point point = 2;
-
-  // A list of unique identifiers corresponding to detected feature points.
-  // Each identifier in this list corresponds to the point at the same index
-  // in the points array.
-  repeated int64 identifier = 3 [packed = true];
-}
-
-// Video image and face position tracking information.
-// See developer.apple.com/documentation/arkit/arframe for more information.
-message ARFrame {
-  // The timestamp for the frame.
-  optional double timestamp = 1;
-
-  // The depth data associated with the frame. Not all frames have depth data.
-  optional AVDepthData depth_data = 2;
-
-  // The depth data object timestamp associated with the frame. May differ from
-  // the frame timestamp value. Is only set when the frame has depth_data.
-  optional double depth_data_timestamp = 3;
-
-  // Camera information associated with the frame.
-  optional ARCamera camera = 4;
-
-  // Light information associated with the frame.
-  optional ARLightEstimate light_estimate = 5;
-
-  // Face anchor information associated with the frame. Not all frames have an
-  // active face anchor.
-  optional ARFaceAnchor face_anchor = 6;
-
-  // Plane anchors associated with the frame. Not all frames have a plane
-  // anchor. Plane anchors and face anchors are mutually exclusive.
-  repeated ARPlaneAnchor plane_anchor = 7;
-
-  // The current intermediate results of the scene analysis used to perform
-  // world tracking.
-  // See
-  // https://developer.apple.com/documentation/arkit/arframe/2887449-rawfeaturepoints
-  // for more information.
-  optional ARPointCloud raw_feature_points = 8;
-}
diff --git a/mediapipe/modules/objectron/calculators/annotation_data.proto b/mediapipe/modules/objectron/calculators/annotation_data.proto
deleted file mode 100644
index 6c26d29..0000000
--- a/mediapipe/modules/objectron/calculators/annotation_data.proto
+++ /dev/null
@@ -1,108 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto3";
-
-package mediapipe;
-
-import "mediapipe/modules/objectron/calculators/a_r_capture_metadata.proto";
-import "mediapipe/modules/objectron/calculators/object.proto";
-
-// Projection of a 3D point on an image, and its metric depth.
-message NormalizedPoint2D {
-  // x-y position of the 2d keypoint in the image coordinate system.
-  // u,v \in [0, 1], where top left corner is (0, 0) and the bottom-right corner
-  // is (1, 1).
-  float x = 1;
-  float y = 2;
-
-  // The depth of the point in the camera coordinate system (in meters).
-  float depth = 3;
-}
-
-// The 3D point in the camera coordinate system, the scales are in meters.
-message Point3D {
-  float x = 1;
-  float y = 2;
-  float z = 3;
-}
-
-message AnnotatedKeyPoint {
-  int32 id = 1;
-  Point3D point_3d = 2;
-  NormalizedPoint2D point_2d = 3;
-  // Indicates whether this keypoint is hidden or not. The hidden attribute is
-  // determined from the object's skeleton. For box model, none of the keypoints
-  // are hidden.
-  bool hidden = 4;
-}
-
-message ObjectAnnotation {
-  // Reference to the object identifier in ObjectInstance.
-  int32 object_id = 1;
-
-  // For each objects, list all the annotated keypoints here.
-  // E.g. for bounding-boxes, we have 8 keypoints, hands = 21 keypoints, etc.
-  // These normalized points are the projection of the Object's 3D keypoint
-  // on the current frame's camera poses.
-  repeated AnnotatedKeyPoint keypoints = 2;
-
-  // Visibiity of this annotation in a frame.
-  float visibility = 3;
-
-  // 3x3 row-major rotation matrix describing the orientation of the rigid
-  // object's frame of reference in the camera-coordinate system.
-  repeated float rotation = 4;
-
-  // 3x1 vector describing the translation of the rigid object's frame of
-  // reference in the camera-coordinate system in meters.
-  repeated float translation = 5;
-
-  // 3x1 vector describing the scale of the rigid object's frame of reference in
-  // the camera-coordinate system.
-  repeated float scale = 6;
-}
-
-message FrameAnnotation {
-  // Unique frame id, corresponds to images.
-  int32 frame_id = 1;
-
-  // List of the annotated objects in this frame. Depending on how many object
-  // are observable in this frame, we might have non or as much as
-  // sequence.objects_size() annotations.
-  repeated ObjectAnnotation annotations = 2;
-
-  // Information about the camera transformation (in the world coordinate) and
-  // imaging characteristics for a captured video frame.
-  ARCamera camera = 3;
-
-  // The timestamp for the frame.
-  double timestamp = 4;
-
-  // Plane center and normal in camera frame.
-  repeated float plane_center = 5;
-  repeated float plane_normal = 6;
-}
-
-// The sequence protocol contains the annotation data for the entire video clip.
-message Sequence {
-  // List of all the annotated 3D objects in this sequence in the world
-  // Coordinate system. Given the camera poses of each frame (also in the
-  // world-coordinate) these objects bounding boxes can be projected to each
-  // frame to get the per-frame annotation (i.e. image_annotation below).
-  repeated Object objects = 1;
-
-  // List of annotated data per each frame in sequence + frame information.
-  repeated FrameAnnotation frame_annotations = 2;
-}
diff --git a/mediapipe/modules/objectron/calculators/belief_decoder_config.proto b/mediapipe/modules/objectron/calculators/belief_decoder_config.proto
deleted file mode 100644
index f0f10ae..0000000
--- a/mediapipe/modules/objectron/calculators/belief_decoder_config.proto
+++ /dev/null
@@ -1,38 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-message BeliefDecoderConfig {
-  optional float heatmap_threshold = 1 [default = 0.9];
-  // Maximum distance in pixels between two local max heatmap values.
-  optional float local_max_distance = 2 [default = 10.0];
-  // Coefficient of offset_scale.
-  // offset_scale = offset_scale_coef * min(rows, cols).
-  // offset_scale is used to multiply the offset predictions from the network.
-  optional float offset_scale_coef = 3 [default = 0.5, deprecated = true];
-
-  // The radius for vertex voting. Use no voting if the radius is less than or
-  // euqal to 1. Example: 10.
-  optional int32 voting_radius = 4;
-
-  // The number of pixels to determine whether two points are the same.
-  // Example: 5 (voting_radius / 2).
-  optional int32 voting_allowance = 5;
-
-  // The threshold of beliefs, with which the points can vote. Example: 0.2.
-  optional float voting_threshold = 6;
-}
diff --git a/mediapipe/modules/objectron/calculators/box.cc b/mediapipe/modules/objectron/calculators/box.cc
deleted file mode 100644
index bd2ce57..0000000
--- a/mediapipe/modules/objectron/calculators/box.cc
+++ /dev/null
@@ -1,255 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/objectron/calculators/box.h"
-
-#include "Eigen/Core"
-#include "mediapipe/framework/port/logging.h"
-
-namespace mediapipe {
-
-namespace {
-constexpr int kFrontFaceId = 4;
-constexpr int kTopFaceId = 2;
-constexpr int kNumKeypoints = 8 + 1;
-constexpr int kNumberOfAxis = 3;
-constexpr int kEdgesPerAxis = 4;
-
-}  // namespace
-
-Box::Box(const std::string& category)
-    : Model(kBoundingBox, kNumKeypoints, category),
-      bounding_box_(kNumKeypoints) {
-  transformation_.setIdentity();
-
-  scale_ << 0.1, 0.1, 0.1;
-
-  // The vertices are ordered according to the left-hand rule, so the normal
-  // vector of each face will point inward the box.
-  faces_.push_back({5, 6, 8, 7});  // +x on yz plane
-  faces_.push_back({1, 3, 4, 2});  // -x on yz plane
-
-  faces_.push_back({3, 7, 8, 4});  // +y on xz plane = top
-  faces_.push_back({1, 2, 6, 5});  // -y on xz plane
-
-  faces_.push_back({2, 4, 8, 6});  // +z on xy plane = front
-  faces_.push_back({1, 5, 7, 3});  // -z on xy plane
-
-  // Add the edges in the cube, they are sorted according to axis (x-y-z).
-  edges_.push_back({1, 5});
-  edges_.push_back({2, 6});
-  edges_.push_back({3, 7});
-  edges_.push_back({4, 8});
-
-  edges_.push_back({1, 3});
-  edges_.push_back({5, 7});
-  edges_.push_back({2, 4});
-  edges_.push_back({6, 8});
-
-  edges_.push_back({1, 2});
-  edges_.push_back({3, 4});
-  edges_.push_back({5, 6});
-  edges_.push_back({7, 8});
-  Update();
-}
-
-void Box::Update() {
-  // Compute the eight vertices of the bounding box from Box's parameters
-  auto w = scale_[0] / 2.f;
-  auto h = scale_[1] / 2.f;
-  auto d = scale_[2] / 2.f;
-
-  // Define the local coordinate system, w.r.t. the center of the boxs
-  bounding_box_[0] << 0., 0., 0.;
-  bounding_box_[1] << -w, -h, -d;
-  bounding_box_[2] << -w, -h, +d;
-  bounding_box_[3] << -w, +h, -d;
-  bounding_box_[4] << -w, +h, +d;
-  bounding_box_[5] << +w, -h, -d;
-  bounding_box_[6] << +w, -h, +d;
-  bounding_box_[7] << +w, +h, -d;
-  bounding_box_[8] << +w, +h, +d;
-
-  // Convert to world coordinate system
-  for (int i = 0; i < kNumKeypoints; ++i) {
-    bounding_box_[i] =
-        transformation_.topLeftCorner<3, 3>() * bounding_box_[i] +
-        transformation_.col(3).head<3>();
-  }
-}
-
-void Box::Adjust(const std::vector<float>& variables) {
-  Eigen::Vector3f translation;
-  translation << variables[0], variables[1], variables[2];
-  SetTranslation(translation);
-
-  const float roll = variables[3];
-  const float pitch = variables[4];
-  const float yaw = variables[5];
-  SetRotation(roll, pitch, yaw);
-
-  Eigen::Vector3f scale;
-  scale << variables[6], variables[7], variables[8];
-
-  SetScale(scale);
-  Update();
-}
-
-float* Box::GetVertex(size_t vertex_id) {
-  CHECK_LT(vertex_id, kNumKeypoints);
-  return bounding_box_[vertex_id].data();
-}
-
-const float* Box::GetVertex(size_t vertex_id) const {
-  CHECK_LT(vertex_id, kNumKeypoints);
-  return bounding_box_[vertex_id].data();
-}
-
-bool Box::InsideTest(const Eigen::Vector3f& point, int check_axis) const {
-  const float* v0 = GetVertex(1);
-  const float* v1 = GetVertex(2);
-  const float* v2 = GetVertex(3);
-  const float* v4 = GetVertex(5);
-
-  switch (check_axis) {
-    case 1:
-      return (v0[0] <= point[0] && point[0] <= v1[0]);  // X-axis
-    case 2:
-      return (v0[1] <= point[1] && point[1] <= v2[1]);  // Y-axis
-    case 3:
-      return (v0[2] <= point[2] && point[2] <= v4[2]);  // Z-axis
-    default:
-      return false;
-  }
-}
-
-void Box::Deserialize(const Object& obj) {
-  CHECK_EQ(obj.keypoints_size(), kNumKeypoints);
-  Model::Deserialize(obj);
-}
-
-void Box::Serialize(Object* obj) {
-  Model::Serialize(obj);
-  obj->set_type(Object::BOUNDING_BOX);
-  std::vector<Vector3f> local_bounding_box(9);
-  // Define the local coordinate system, w.r.t. the center of the boxs
-  local_bounding_box[0] << 0., 0., 0.;
-  local_bounding_box[1] << -0.5, -0.5, -0.5;
-  local_bounding_box[2] << -0.5, -0.5, +0.5;
-  local_bounding_box[3] << -0.5, +0.5, -0.5;
-  local_bounding_box[4] << -0.5, +0.5, +0.5;
-  local_bounding_box[5] << +0.5, -0.5, -0.5;
-  local_bounding_box[6] << +0.5, -0.5, +0.5;
-  local_bounding_box[7] << +0.5, +0.5, -0.5;
-  local_bounding_box[8] << +0.5, +0.5, +0.5;
-  for (int i = 0; i < kNumKeypoints; ++i) {
-    KeyPoint* keypoint = obj->add_keypoints();
-    keypoint->set_x(local_bounding_box[i][0]);
-    keypoint->set_y(local_bounding_box[i][1]);
-    keypoint->set_z(local_bounding_box[i][2]);
-    keypoint->set_confidence_radius(0.);
-  }
-}
-
-const Face& Box::GetFrontFace() const { return faces_[kFrontFaceId]; }
-
-const Face& Box::GetTopFace() const { return faces_[kTopFaceId]; }
-
-std::pair<Vector3f, Vector3f> Box::GetGroundPlane() const {
-  const Vector3f gravity = Vector3f(0., 1., 0.);
-  int ground_plane_id = 0;
-  float ground_plane_error = 10.0;
-
-  auto get_face_center = [&](const Face& face) {
-    Vector3f center = Vector3f::Zero();
-    for (const int vertex_id : face) {
-      center += Map<const Vector3f>(GetVertex(vertex_id));
-    }
-    center /= face.size();
-    return center;
-  };
-
-  auto get_face_normal = [&](const Face& face, const Vector3f& center) {
-    Vector3f v1 = Map<const Vector3f>(GetVertex(face[0])) - center;
-    Vector3f v2 = Map<const Vector3f>(GetVertex(face[1])) - center;
-    Vector3f normal = v1.cross(v2);
-    return normal;
-  };
-
-  // The ground plane is defined as a plane aligned with gravity.
-  // gravity is the (0, 1, 0) vector in the world coordinate system.
-  const auto& faces = GetFaces();
-  for (int face_id = 0; face_id < faces.size(); face_id += 2) {
-    const auto& face = faces[face_id];
-    Vector3f center = get_face_center(face);
-    Vector3f normal = get_face_normal(face, center);
-    Vector3f w = gravity.cross(normal);
-    const float w_sq_norm = w.squaredNorm();
-    if (w_sq_norm < ground_plane_error) {
-      ground_plane_error = w_sq_norm;
-      ground_plane_id = face_id;
-    }
-  }
-
-  Vector3f center = get_face_center(faces[ground_plane_id]);
-  Vector3f normal = get_face_normal(faces[ground_plane_id], center);
-
-  // For each face, we also have a parallel face that it's normal is also
-  // aligned with gravity vector. We pick the face with lower height (y-value).
-  // The parallel to face 0 is 1, face 2 is 3, and face 4 is 5.
-  int parallel_face_id = ground_plane_id + 1;
-  const auto& parallel_face = faces[parallel_face_id];
-  Vector3f parallel_face_center = get_face_center(parallel_face);
-  Vector3f parallel_face_normal =
-      get_face_normal(parallel_face, parallel_face_center);
-  if (parallel_face_center[1] < center[1]) {
-    center = parallel_face_center;
-    normal = parallel_face_normal;
-  }
-  return {center, normal};
-}
-
-template <typename T>
-void Box::Fit(const std::vector<T>& vertices) {
-  CHECK_EQ(vertices.size(), kNumKeypoints);
-  scale_.setZero();
-  // The scale would remain invariant under rotation and translation.
-  // We can safely estimate the scale from the oriented box.
-  for (int axis = 0; axis < kNumberOfAxis; ++axis) {
-    for (int edge_id = 0; edge_id < kEdgesPerAxis; ++edge_id) {
-      // The edges are stored in quadruples according to each axis
-      const std::array<int, 2>& edge = edges_[axis * kEdgesPerAxis + edge_id];
-      scale_[axis] += (vertices[edge[0]] - vertices[edge[1]]).norm();
-    }
-    scale_[axis] /= kEdgesPerAxis;
-  }
-  // Create a scaled axis-aligned box
-  transformation_.setIdentity();
-  Update();
-
-  using MatrixN3_RM = Eigen::Matrix<float, kNumKeypoints, 3, Eigen::RowMajor>;
-  Eigen::Map<const MatrixN3_RM> v(vertices[0].data());
-  Eigen::Map<const MatrixN3_RM> system(bounding_box_[0].data());
-  auto system_h = system.rowwise().homogeneous().eval();
-  auto system_g = system_h.colPivHouseholderQr();
-  auto solution = system_g.solve(v).eval();
-  transformation_.topLeftCorner<3, 4>() = solution.transpose();
-  Update();
-}
-
-template void Box::Fit<Vector3f>(const std::vector<Vector3f>&);
-template void Box::Fit<Map<Vector3f>>(const std::vector<Map<Vector3f>>&);
-template void Box::Fit<Map<const Vector3f>>(
-    const std::vector<Map<const Vector3f>>&);
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/box.h b/mediapipe/modules/objectron/calculators/box.h
deleted file mode 100644
index 17218f7..0000000
--- a/mediapipe/modules/objectron/calculators/box.h
+++ /dev/null
@@ -1,132 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_H_
-#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_H_
-
-#include <vector>
-
-#include "mediapipe/modules/objectron/calculators/model.h"
-
-namespace mediapipe {
-
-// Model for the bounding box in 3D
-// The box has 9 degrees of freedom, which uniquely defines 8 keypoints in the
-// fixed world-coordinate system.
-//
-// The 8 keypoints are defined as follows
-//
-//  kp-id  axis
-//  0      000    ---
-//  1      001    --+
-//  2      010    -+-
-//  3      011    -++
-//  4      100    +--
-//  5      101    +-+
-//  6      110    ++-
-//  7      111    +++
-//
-// where xyz means positive or negative vector along the axis where the center
-// of the box is the origin. The resulting bounding box is
-//
-//              x                              x
-//      0 + + + + + + + + 4                 .-------
-//      +\                +\                |\
-//      + \ y             + \             z | \ y
-//      +  \              +  \              |  \
-//      +   2 + + + + + + + + 6
-//    z +   +             +   +
-//      +   +             +   +
-//      +   +     C       +   +
-//      +   +             +   +
-//      1 + + + + + + + + 5   +
-//       \  +              \  +
-//        \ +               \ +
-//         \+                \+
-//          3 + + + + + + + + 7
-//
-// World coordinate system: +y is up (aligned with gravity),
-// +z is toward the user, +x follows right hand rule.
-// The front face is defined as +z axis on xy plane.
-// The top face is defined as +y axis on xz plane.
-//
-
-class Box : public Model {
- public:
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
-
-  explicit Box(const std::string& category);
-  ~Box() override = default;
-
-  bool InsideTest(const Vector3f& point, int check_axis) const;
-
-  const std::vector<Face>& GetFaces() const { return faces_; }
-  const Face& GetFace(size_t face_id) const { return faces_[face_id]; }
-
-  const std::vector<std::array<int, 2>>& GetEdges() const { return edges_; }
-  const std::array<int, 2>& GetEdge(size_t edge_id) const {
-    return edges_[edge_id];
-  }
-
-  // Returns the keypoints for the front face of the box.
-  // The front face is defind as a face with +z normal vector on xy plane
-  // In Box's c'tor, the top face is set to {1, 3, 7, 5}
-  const Face& GetFrontFace() const;
-
-  // Returns the keypoints for the top face of the box.
-  // The top face is defind as a face with +z normal vector on xy plane
-  // In Box's c'tor, the top face is set to {1, 3, 7, 5}
-  const Face& GetTopFace() const;
-
-  void Update() override;
-  void Adjust(const std::vector<float>& variables) override;
-  float* GetVertex(size_t vertex_id) override;
-  const float* GetVertex(size_t vertex_id) const override;
-  void Deserialize(const Object& obj) override;
-  void Serialize(Object* obj) override;
-
-  // Computes the plane center and the normal vector for the plane the object
-  // is sitting on in the world cooordinate system. The normal vector is roughly
-  // aligned with gravity.
-  std::pair<Vector3f, Vector3f> GetGroundPlane() const;
-
-  // Estimates a box 9-dof parameters from the given vertices. Directly computes
-  // the scale of the box, then solves for orientation and translation.
-  // Expects a std::vector of size 9 of a Eigen::Vector3f or mapped Vector3f.
-  // If mapping proto messages, we recommend to use the Map<const Vector3f>.
-  // For example:
-  //
-  // using T = Map<const Vector3f>;
-  // std::vector<T> vertices;
-  // for (const auto& point : message) { // point is a repeated float message.
-  //   T p(point.data());
-  //   vertices.emplace_back(p);
-  // }
-  // box.Fit<T>(vertices);
-  //
-  // The Points must be arranged as 1 + 8 (center keypoint followed by 8 box
-  // vertices) vector. This function will overwrite the scale and transformation
-  // properties of the class.
-  template <typename T = Eigen::Map<const Vector3f>>
-  void Fit(const std::vector<T>& vertices);
-
- private:
-  std::vector<Face> faces_;
-  std::vector<std::array<int, 2>> edges_;
-  std::vector<Vector3f> bounding_box_;
-};
-
-}  // namespace mediapipe
-
-#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_H_
diff --git a/mediapipe/modules/objectron/calculators/box_util.cc b/mediapipe/modules/objectron/calculators/box_util.cc
deleted file mode 100644
index 0663b5b..0000000
--- a/mediapipe/modules/objectron/calculators/box_util.cc
+++ /dev/null
@@ -1,153 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/objectron/calculators/box_util.h"
-
-#include <math.h>
-
-#include "mediapipe/framework/port/logging.h"
-#include "mediapipe/framework/port/opencv_core_inc.h"
-#include "mediapipe/framework/port/opencv_imgproc_inc.h"
-#include "mediapipe/util/tracking/box_tracker.pb.h"
-
-namespace mediapipe {
-void ComputeBoundingRect(const std::vector<cv::Point2f>& points,
-                         mediapipe::TimedBoxProto* box) {
-  CHECK(box != nullptr);
-  float top = 1.0f;
-  float bottom = 0.0f;
-  float left = 1.0f;
-  float right = 0.0f;
-  for (const auto& point : points) {
-    top = std::min(top, point.y);
-    bottom = std::max(bottom, point.y);
-    left = std::min(left, point.x);
-    right = std::max(right, point.x);
-  }
-  box->set_top(top);
-  box->set_bottom(bottom);
-  box->set_left(left);
-  box->set_right(right);
-  // We are currently only doing axis aligned bounding box. If we need to
-  // compute rotated bounding box, then we need the original image aspect ratio,
-  // map back to original image space, compute cv::convexHull, then for each
-  // edge of the hull, rotate according to edge orientation, find the box.
-  box->set_rotation(0.0f);
-}
-
-float ComputeBoxIoU(const TimedBoxProto& box1, const TimedBoxProto& box2) {
-  cv::Point2f box1_center((box1.left() + box1.right()) * 0.5f,
-                          (box1.top() + box1.bottom()) * 0.5f);
-  cv::Size2f box1_size(box1.right() - box1.left(), box1.bottom() - box1.top());
-  cv::RotatedRect rect1(box1_center, box1_size,
-                        -box1.rotation() * 180.0f / M_PI);
-  cv::Point2f box2_center((box2.left() + box2.right()) * 0.5f,
-                          (box2.top() + box2.bottom()) * 0.5f);
-  cv::Size2f box2_size(box2.right() - box2.left(), box2.bottom() - box2.top());
-  cv::RotatedRect rect2(box2_center, box2_size,
-                        -box2.rotation() * 180.0f / M_PI);
-  std::vector<cv::Point2f> intersections_unsorted;
-  std::vector<cv::Point2f> intersections;
-  cv::rotatedRectangleIntersection(rect1, rect2, intersections_unsorted);
-  if (intersections_unsorted.size() < 3) {
-    return 0.0f;
-  }
-  cv::convexHull(intersections_unsorted, intersections);
-
-  // We use Shoelace formula to compute area of polygons.
-  float intersection_area = 0.0f;
-  for (int i = 0; i < intersections.size(); ++i) {
-    const auto& curr_pt = intersections[i];
-    const int i_next = (i + 1) == intersections.size() ? 0 : (i + 1);
-    const auto& next_pt = intersections[i_next];
-    intersection_area += (curr_pt.x * next_pt.y - next_pt.x * curr_pt.y);
-  }
-  intersection_area = std::abs(intersection_area) * 0.5f;
-
-  // Compute union area
-  const float union_area =
-      rect1.size.area() + rect2.size.area() - intersection_area + 1e-5f;
-
-  const float iou = intersection_area / union_area;
-  return iou;
-}
-
-std::vector<cv::Point2f> ComputeBoxCorners(const TimedBoxProto& box,
-                                           float width, float height) {
-  // Rotate 4 corner w.r.t. center.
-  const cv::Point2f center(0.5f * (box.left() + box.right()) * width,
-                           0.5f * (box.top() + box.bottom()) * height);
-  const std::vector<cv::Point2f> corners{
-      cv::Point2f(box.left() * width, box.top() * height),
-      cv::Point2f(box.left() * width, box.bottom() * height),
-      cv::Point2f(box.right() * width, box.bottom() * height),
-      cv::Point2f(box.right() * width, box.top() * height)};
-
-  const float cos_a = std::cos(box.rotation());
-  const float sin_a = std::sin(box.rotation());
-  std::vector<cv::Point2f> transformed_corners(4);
-  for (int k = 0; k < 4; ++k) {
-    // Scale and rotate w.r.t. center.
-    const cv::Point2f rad = corners[k] - center;
-    const cv::Point2f rot_rad(cos_a * rad.x - sin_a * rad.y,
-                              sin_a * rad.x + cos_a * rad.y);
-    transformed_corners[k] = center + rot_rad;
-    transformed_corners[k].x /= width;
-    transformed_corners[k].y /= height;
-  }
-  return transformed_corners;
-}
-
-cv::Mat PerspectiveTransformBetweenBoxes(const TimedBoxProto& src_box,
-                                         const TimedBoxProto& dst_box,
-                                         const float aspect_ratio) {
-  std::vector<cv::Point2f> box1_corners =
-      ComputeBoxCorners(src_box, /*width*/ aspect_ratio, /*height*/ 1.0f);
-  std::vector<cv::Point2f> box2_corners =
-      ComputeBoxCorners(dst_box, /*width*/ aspect_ratio, /*height*/ 1.0f);
-  cv::Mat affine_transform = cv::getPerspectiveTransform(
-      /*src*/ box1_corners, /*dst*/ box2_corners);
-  cv::Mat output_affine;
-  affine_transform.convertTo(output_affine, CV_32FC1);
-  return output_affine;
-}
-
-cv::Point2f MapPoint(const TimedBoxProto& src_box, const TimedBoxProto& dst_box,
-                     const cv::Point2f& src_point, float width, float height) {
-  const cv::Point2f src_center(
-      0.5f * (src_box.left() + src_box.right()) * width,
-      0.5f * (src_box.top() + src_box.bottom()) * height);
-  const cv::Point2f dst_center(
-      0.5f * (dst_box.left() + dst_box.right()) * width,
-      0.5f * (dst_box.top() + dst_box.bottom()) * height);
-  const float scale_x =
-      (dst_box.right() - dst_box.left()) / (src_box.right() - src_box.left());
-  const float scale_y =
-      (dst_box.bottom() - dst_box.top()) / (src_box.bottom() - src_box.top());
-  const float rotation = dst_box.rotation() - src_box.rotation();
-  const cv::Point2f rad =
-      cv::Point2f(src_point.x * width, src_point.y * height) - src_center;
-  const float rad_x = rad.x * scale_x;
-  const float rad_y = rad.y * scale_y;
-  const float cos_a = std::cos(rotation);
-  const float sin_a = std::sin(rotation);
-  const cv::Point2f rot_rad(cos_a * rad_x - sin_a * rad_y,
-                            sin_a * rad_x + cos_a * rad_y);
-  const cv::Point2f dst_point_image = dst_center + rot_rad;
-  const cv::Point2f dst_point(dst_point_image.x / width,
-                              dst_point_image.y / height);
-  return dst_point;
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/box_util.h b/mediapipe/modules/objectron/calculators/box_util.h
deleted file mode 100644
index fed21c0..0000000
--- a/mediapipe/modules/objectron/calculators/box_util.h
+++ /dev/null
@@ -1,50 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_UTIL_H_
-#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_UTIL_H_
-
-#include "mediapipe/framework/port/opencv_core_inc.h"
-#include "mediapipe/util/tracking/box_tracker.pb.h"
-
-namespace mediapipe {
-
-// This function fills the geometry of the TimedBoxProto. Id, timestamp etc.
-// need to be set outside this function.
-void ComputeBoundingRect(const std::vector<cv::Point2f>& points,
-                         mediapipe::TimedBoxProto* box);
-
-// This function computes the intersection over union between two boxes.
-float ComputeBoxIoU(const TimedBoxProto& box1, const TimedBoxProto& box2);
-
-// Computes corners of the box.
-// width and height are image width and height, which is typically
-// needed since the box is in normalized coordinates.
-std::vector<cv::Point2f> ComputeBoxCorners(const TimedBoxProto& box,
-                                           float width, float height);
-
-// Computes the perspective transform from box1 to box2.
-// The input argument aspect_ratio is width / height of the image.
-// The returned matrix should be a 3x3 matrix.
-cv::Mat PerspectiveTransformBetweenBoxes(const TimedBoxProto& src_box,
-                                         const TimedBoxProto& dst_box,
-                                         const float aspect_ratio);
-
-// Map point according to source and destination box location.
-cv::Point2f MapPoint(const TimedBoxProto& src_box, const TimedBoxProto& dst_box,
-                     const cv::Point2f& src_point, float width, float height);
-
-}  // namespace mediapipe
-
-#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_BOX_UTIL_H_
diff --git a/mediapipe/modules/objectron/calculators/box_util_test.cc b/mediapipe/modules/objectron/calculators/box_util_test.cc
deleted file mode 100644
index 2a3895f..0000000
--- a/mediapipe/modules/objectron/calculators/box_util_test.cc
+++ /dev/null
@@ -1,123 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/objectron/calculators/box_util.h"
-
-#include "mediapipe/framework/port/gmock.h"
-#include "mediapipe/framework/port/gtest.h"
-#include "mediapipe/framework/port/opencv_core_inc.h"
-#include "mediapipe/util/tracking/box_tracker.pb.h"
-
-namespace mediapipe {
-namespace {
-
-TEST(BoxUtilTest, TestComputeBoundingRect) {
-  std::vector<cv::Point2f> points{
-      cv::Point2f(0.35f, 0.25f), cv::Point2f(0.3f, 0.3f),
-      cv::Point2f(0.2f, 0.4f),   cv::Point2f(0.3f, 0.1f),
-      cv::Point2f(0.2f, 0.2f),   cv::Point2f(0.5f, 0.3f),
-      cv::Point2f(0.4f, 0.4f),   cv::Point2f(0.5f, 0.1f),
-      cv::Point2f(0.4f, 0.2f)};
-  TimedBoxProto box;
-  ComputeBoundingRect(points, &box);
-  EXPECT_FLOAT_EQ(0.1f, box.top());
-  EXPECT_FLOAT_EQ(0.4f, box.bottom());
-  EXPECT_FLOAT_EQ(0.2f, box.left());
-  EXPECT_FLOAT_EQ(0.5f, box.right());
-}
-
-TEST(BoxUtilTest, TestComputeBoxIoU) {
-  TimedBoxProto box1;
-  box1.set_top(0.2f);
-  box1.set_bottom(0.6f);
-  box1.set_left(0.1f);
-  box1.set_right(0.3f);
-  box1.set_rotation(0.0f);
-  TimedBoxProto box2 = box1;
-  box2.set_rotation(/*pi/2*/ 1.570796f);
-  const float box_area =
-      (box1.bottom() - box1.top()) * (box1.right() - box1.left());
-  const float box_intersection =
-      (box1.right() - box1.left()) * (box1.right() - box1.left());
-  const float expected_iou =
-      box_intersection / (box_area * 2 - box_intersection);
-  EXPECT_NEAR(expected_iou, ComputeBoxIoU(box1, box2), 3e-5f);
-
-  TimedBoxProto box3;
-  box3.set_top(0.2f);
-  box3.set_bottom(0.6f);
-  box3.set_left(0.5f);
-  box3.set_right(0.7f);
-  EXPECT_NEAR(0.0f, ComputeBoxIoU(box1, box3), 3e-5f);
-}
-
-TEST(BoxUtilTest, TestPerspectiveTransformBetweenBoxes) {
-  TimedBoxProto box1;
-  const float height = 4.0f;
-  const float width = 3.0f;
-  box1.set_top(1.0f / height);
-  box1.set_bottom(2.0f / height);
-  box1.set_left(1.0f / width);
-  box1.set_right(2.0f / width);
-  TimedBoxProto box2;
-  box2.set_top(1.0f / height);
-  box2.set_bottom(2.0f / height);
-  box2.set_left(1.0f / width);
-  box2.set_right(2.0f / width);
-  box2.set_rotation(/*pi/4*/ -0.785398f);
-  cv::Mat transform =
-      PerspectiveTransformBetweenBoxes(box1, box2, width / height);
-  const float kTolerence = 1e-5f;
-  const cv::Vec3f original_position(1.5f / width, 1.0f / height, 1.0f);
-  const cv::Mat transformed_position = transform * cv::Mat(original_position);
-  EXPECT_NEAR(
-      (1.5f - 0.5f * std::sqrt(2) / 2.0f) / width,
-      transformed_position.at<float>(0) / transformed_position.at<float>(2),
-      kTolerence);
-  EXPECT_NEAR(
-      (1.5f - 0.5f * std::sqrt(2) / 2.0f) / height,
-      transformed_position.at<float>(1) / transformed_position.at<float>(2),
-      kTolerence);
-}
-
-TEST(BoxUtilTest, TestMapPoint) {
-  const float height = 4.0f;
-  const float width = 3.0f;
-  TimedBoxProto box1;
-  box1.set_top(1.0f / height);
-  box1.set_bottom(2.0f / height);
-  box1.set_left(1.0f / width);
-  box1.set_right(2.0f / width);
-  TimedBoxProto box2;
-  box2.set_top(1.0f / height);
-  box2.set_bottom(2.0f / height);
-  box2.set_left(1.0f / width);
-  box2.set_right(2.0f / width);
-  box2.set_rotation(/*pi/4*/ -0.785398f);
-
-  cv::Point2f src_point1(1.2f / width, 1.4f / height);
-  cv::Point2f src_point2(1.3f / width, 1.8f / height);
-  const float distance1 = std::sqrt(0.1 * 0.1 + 0.4 * 0.4);
-  cv::Point2f dst_point1 = MapPoint(box1, box2, src_point1, width, height);
-  cv::Point2f dst_point2 = MapPoint(box1, box2, src_point2, width, height);
-  const float distance2 =
-      std::sqrt((dst_point1.x * width - dst_point2.x * width) *
-                    (dst_point1.x * width - dst_point2.x * width) +
-                (dst_point1.y * height - dst_point2.y * height) *
-                    (dst_point1.y * height - dst_point2.y * height));
-  EXPECT_NEAR(distance1, distance2, 1e-5f);
-}
-
-}  // namespace
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/camera_parameters.proto b/mediapipe/modules/objectron/calculators/camera_parameters.proto
deleted file mode 100644
index f5c843b..0000000
--- a/mediapipe/modules/objectron/calculators/camera_parameters.proto
+++ /dev/null
@@ -1,47 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-message CameraParametersProto {
-  // This number is non-negative, it represents camera height above ground
-  // normalized by focal length.
-  optional float height_above_ground = 1 [default = 100.0];
-  // Width of image in portrait orientation normalized by focal length
-  optional float portrait_width = 2 [default = 1.0103];
-  // Height of image in portrait orientation normalized by focal length
-  optional float portrait_height = 3 [default = 1.3435];
-  enum ImageOrientation {
-    PORTRAIT_ORIENTATION = 0;
-    LANDSCAPE_ORIENTATION = 1;
-  }
-  // The input image orientation
-  optional ImageOrientation image_orientation = 4
-      [default = PORTRAIT_ORIENTATION];
-
-  // This defines the projection method from 2D screen to 3D.
-  enum ProjectionMode {
-    UNSPECIFIED = 0;
-    // Projects 2D point to ground plane (horizontal plane).
-    GROUND_PLANE = 1;
-    // Projects 2D point to sphere.
-    SPHERE = 2;
-  }
-  optional ProjectionMode projection_mode = 5 [default = GROUND_PLANE];
-  // Radius of sphere when using the SPHERE projection mode above.
-  // The value is normalized by focal length.
-  optional float projection_sphere_radius = 6 [default = 100.0];
-}
diff --git a/mediapipe/modules/objectron/calculators/decoder.cc b/mediapipe/modules/objectron/calculators/decoder.cc
deleted file mode 100644
index 0af3458..0000000
--- a/mediapipe/modules/objectron/calculators/decoder.cc
+++ /dev/null
@@ -1,252 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/objectron/calculators/decoder.h"
-
-#include <limits>
-#include <vector>
-
-#include "Eigen/Core"
-#include "Eigen/Dense"
-#include "absl/status/status.h"
-#include "mediapipe/framework/port/canonical_errors.h"
-#include "mediapipe/framework/port/logging.h"
-#include "mediapipe/framework/port/opencv_imgproc_inc.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/box.h"
-#include "mediapipe/modules/objectron/calculators/epnp.h"
-#include "mediapipe/modules/objectron/calculators/types.h"
-
-namespace mediapipe {
-
-constexpr int Decoder::kNumOffsetmaps = 16;
-constexpr int kNumKeypoints = 9;
-
-namespace {
-
-inline void SetPoint3d(const Eigen::Vector3f& point_vec, Point3D* point_3d) {
-  point_3d->set_x(point_vec.x());
-  point_3d->set_y(point_vec.y());
-  point_3d->set_z(point_vec.z());
-}
-
-}  // namespace
-
-FrameAnnotation Decoder::DecodeBoundingBoxKeypoints(
-    const cv::Mat& heatmap, const cv::Mat& offsetmap) const {
-  CHECK_EQ(1, heatmap.channels());
-  CHECK_EQ(kNumOffsetmaps, offsetmap.channels());
-  CHECK_EQ(heatmap.cols, offsetmap.cols);
-  CHECK_EQ(heatmap.rows, offsetmap.rows);
-
-  const float offset_scale = std::min(offsetmap.cols, offsetmap.rows);
-  const std::vector<cv::Point> center_points = ExtractCenterKeypoints(heatmap);
-  std::vector<BeliefBox> boxes;
-  for (const auto& center_point : center_points) {
-    BeliefBox box;
-    box.box_2d.emplace_back(center_point.x, center_point.y);
-    const int center_x = static_cast<int>(std::round(center_point.x));
-    const int center_y = static_cast<int>(std::round(center_point.y));
-    box.belief = heatmap.at<float>(center_y, center_x);
-    if (config_.voting_radius() > 1) {
-      DecodeByVoting(heatmap, offsetmap, center_x, center_y, offset_scale,
-                     offset_scale, &box);
-    } else {
-      DecodeByPeak(offsetmap, center_x, center_y, offset_scale, offset_scale,
-                   &box);
-    }
-    if (IsNewBox(&boxes, &box)) {
-      boxes.push_back(std::move(box));
-    }
-  }
-
-  const float x_scale = 1.0f / offsetmap.cols;
-  const float y_scale = 1.0f / offsetmap.rows;
-  FrameAnnotation frame_annotations;
-  for (const auto& box : boxes) {
-    auto* object = frame_annotations.add_annotations();
-    for (const auto& point : box.box_2d) {
-      auto* point2d = object->add_keypoints()->mutable_point_2d();
-      point2d->set_x(point.first * x_scale);
-      point2d->set_y(point.second * y_scale);
-    }
-  }
-  return frame_annotations;
-}
-
-void Decoder::DecodeByPeak(const cv::Mat& offsetmap, int center_x, int center_y,
-                           float offset_scale_x, float offset_scale_y,
-                           BeliefBox* box) const {
-  const auto& offset = offsetmap.at<cv::Vec<float, kNumOffsetmaps>>(
-      /*row*/ center_y, /*col*/ center_x);
-  for (int i = 0; i < kNumOffsetmaps / 2; ++i) {
-    const float x_offset = offset[2 * i] * offset_scale_x;
-    const float y_offset = offset[2 * i + 1] * offset_scale_y;
-    box->box_2d.emplace_back(center_x + x_offset, center_y + y_offset);
-  }
-}
-
-void Decoder::DecodeByVoting(const cv::Mat& heatmap, const cv::Mat& offsetmap,
-                             int center_x, int center_y, float offset_scale_x,
-                             float offset_scale_y, BeliefBox* box) const {
-  // Votes at the center.
-  const auto& center_offset = offsetmap.at<cv::Vec<float, kNumOffsetmaps>>(
-      /*row*/ center_y, /*col*/ center_x);
-  std::vector<float> center_votes(kNumOffsetmaps, 0.f);
-  for (int i = 0; i < kNumOffsetmaps / 2; ++i) {
-    center_votes[2 * i] = center_x + center_offset[2 * i] * offset_scale_x;
-    center_votes[2 * i + 1] =
-        center_y + center_offset[2 * i + 1] * offset_scale_y;
-  }
-
-  // Find voting window.
-  int x_min = std::max(0, center_x - config_.voting_radius());
-  int y_min = std::max(0, center_y - config_.voting_radius());
-  int width = std::min(heatmap.cols - x_min, config_.voting_radius() * 2 + 1);
-  int height = std::min(heatmap.rows - y_min, config_.voting_radius() * 2 + 1);
-  cv::Rect rect(x_min, y_min, width, height);
-  cv::Mat heat = heatmap(rect);
-  cv::Mat offset = offsetmap(rect);
-
-  for (int i = 0; i < kNumOffsetmaps / 2; ++i) {
-    float x_sum = 0.f;
-    float y_sum = 0.f;
-    float votes = 0.f;
-    for (int r = 0; r < heat.rows; ++r) {
-      for (int c = 0; c < heat.cols; ++c) {
-        const float belief = heat.at<float>(r, c);
-        if (belief < config_.voting_threshold()) {
-          continue;
-        }
-        float offset_x =
-            offset.at<cv::Vec<float, kNumOffsetmaps>>(r, c)[2 * i] *
-            offset_scale_x;
-        float offset_y =
-            offset.at<cv::Vec<float, kNumOffsetmaps>>(r, c)[2 * i + 1] *
-            offset_scale_y;
-        float vote_x = c + rect.x + offset_x;
-        float vote_y = r + rect.y + offset_y;
-        float x_diff = std::abs(vote_x - center_votes[2 * i]);
-        float y_diff = std::abs(vote_y - center_votes[2 * i + 1]);
-        if (x_diff > config_.voting_allowance() ||
-            y_diff > config_.voting_allowance()) {
-          continue;
-        }
-        x_sum += vote_x * belief;
-        y_sum += vote_y * belief;
-        votes += belief;
-      }
-    }
-    box->box_2d.emplace_back(x_sum / votes, y_sum / votes);
-  }
-}
-
-bool Decoder::IsNewBox(std::vector<BeliefBox>* boxes, BeliefBox* box) const {
-  for (auto& b : *boxes) {
-    if (IsIdentical(b, *box)) {
-      if (b.belief < box->belief) {
-        std::swap(b, *box);
-      }
-      return false;
-    }
-  }
-  return true;
-}
-
-bool Decoder::IsIdentical(const BeliefBox& box_1,
-                          const BeliefBox& box_2) const {
-  // Skip the center point.
-  for (int i = 1; i < box_1.box_2d.size(); ++i) {
-    const float x_diff =
-        std::abs(box_1.box_2d[i].first - box_2.box_2d[i].first);
-    const float y_diff =
-        std::abs(box_1.box_2d[i].second - box_2.box_2d[i].second);
-    if (x_diff > config_.voting_allowance() ||
-        y_diff > config_.voting_allowance()) {
-      return false;
-    }
-  }
-  return true;
-}
-
-std::vector<cv::Point> Decoder::ExtractCenterKeypoints(
-    const cv::Mat& center_heatmap) const {
-  cv::Mat max_filtered_heatmap(center_heatmap.rows, center_heatmap.cols,
-                               center_heatmap.type());
-  const int kernel_size =
-      static_cast<int>(config_.local_max_distance() * 2 + 1 + 0.5f);
-  const cv::Size morph_size(kernel_size, kernel_size);
-  cv::dilate(center_heatmap, max_filtered_heatmap,
-             cv::getStructuringElement(cv::MORPH_RECT, morph_size));
-  cv::Mat peak_map;
-  cv::bitwise_and((center_heatmap >= max_filtered_heatmap),
-                  (center_heatmap >= config_.heatmap_threshold()), peak_map);
-  std::vector<cv::Point> locations;  // output, locations of non-zero pixels
-  cv::findNonZero(peak_map, locations);
-  return locations;
-}
-
-absl::Status Decoder::Lift2DTo3D(
-    const Eigen::Matrix<float, 4, 4, Eigen::RowMajor>& projection_matrix,
-    bool portrait, FrameAnnotation* estimated_box) const {
-  CHECK(estimated_box != nullptr);
-
-  for (auto& annotation : *estimated_box->mutable_annotations()) {
-    CHECK_EQ(kNumKeypoints, annotation.keypoints_size());
-
-    // Fill input 2D Points;
-    std::vector<Vector2f> input_points_2d;
-    input_points_2d.reserve(kNumKeypoints);
-    for (const auto& keypoint : annotation.keypoints()) {
-      input_points_2d.emplace_back(keypoint.point_2d().x(),
-                                   keypoint.point_2d().y());
-    }
-
-    // Run EPnP.
-    std::vector<Vector3f> output_points_3d;
-    output_points_3d.reserve(kNumKeypoints);
-    auto status = SolveEpnp(projection_matrix, portrait, input_points_2d,
-                            &output_points_3d);
-    if (!status.ok()) {
-      LOG(ERROR) << status;
-      return status;
-    }
-
-    // Fill 3D keypoints;
-    for (int i = 0; i < kNumKeypoints; ++i) {
-      SetPoint3d(output_points_3d[i],
-                 annotation.mutable_keypoints(i)->mutable_point_3d());
-    }
-
-    // Fit a box to the 3D points to get box scale, rotation, translation.
-    Box box("category");
-    box.Fit(output_points_3d);
-    const Eigen::Matrix<float, 3, 3, Eigen::RowMajor> rotation =
-        box.GetRotation();
-    const Eigen::Vector3f translation = box.GetTranslation();
-    const Eigen::Vector3f scale = box.GetScale();
-    // Fill box rotation.
-    *annotation.mutable_rotation() = {rotation.data(),
-                                      rotation.data() + rotation.size()};
-    // Fill box translation.
-    *annotation.mutable_translation() = {
-        translation.data(), translation.data() + translation.size()};
-    // Fill box scale.
-    *annotation.mutable_scale() = {scale.data(), scale.data() + scale.size()};
-  }
-  return absl::OkStatus();
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/decoder.h b/mediapipe/modules/objectron/calculators/decoder.h
deleted file mode 100644
index be69939..0000000
--- a/mediapipe/modules/objectron/calculators/decoder.h
+++ /dev/null
@@ -1,109 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_DECODER_H_
-#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_DECODER_H_
-
-#include <vector>
-
-#include "Eigen/Dense"
-#include "absl/status/status.h"
-#include "mediapipe/framework/port/opencv_core_inc.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/belief_decoder_config.pb.h"
-
-namespace mediapipe {
-
-// Decodes 3D bounding box from heatmaps and offset maps. In the future,
-// if we want to develop decoder for generic skeleton, then we need to
-// generalize this class, and make a few child classes.
-class Decoder {
- public:
-  static const int kNumOffsetmaps;
-
-  explicit Decoder(const BeliefDecoderConfig& config) : config_(config) {
-    epnp_alpha_ << 4.0f, -1.0f, -1.0f, -1.0f, 2.0f, -1.0f, -1.0f, 1.0f, 2.0f,
-        -1.0f, 1.0f, -1.0f, 0.0f, -1.0f, 1.0f, 1.0f, 2.0f, 1.0f, -1.0f, -1.0f,
-        0.0f, 1.0f, -1.0f, 1.0f, 0.0f, 1.0f, 1.0f, -1.0f, -2.0f, 1.0f, 1.0f,
-        1.0f;
-  }
-
-  // Decodes bounding boxes from predicted heatmap and offset maps.
-  // Input:
-  //   heatmap: a single channel cv::Mat representing center point heatmap
-  //   offsetmap: a 16 channel cv::Mat representing the 16 offset maps
-  //              (2 for each of the 8 vertices)
-  // Output:
-  //   Outputs 3D bounding boxes 2D vertices, represented by 'point_2d' field
-  //   in each 'keypoints' field of object annotations.
-  FrameAnnotation DecodeBoundingBoxKeypoints(const cv::Mat& heatmap,
-                                             const cv::Mat& offsetmap) const;
-
-  // Lifts the estimated 2D projections of bounding box vertices to 3D.
-  // This function uses the EPnP approach described in this paper:
-  // https://icwww.epfl.ch/~lepetit/papers/lepetit_ijcv08.pdf .
-  // Input:
-  //   projection_matrix: the projection matrix from 3D coordinate
-  //     to screen coordinate.
-  //     The 2D screen coordinate is defined as: u is along the long
-  //     edge of the device, pointing down; v is along the short edge
-  //     of the device, pointing right.
-  //   portrait: a boolen variable indicating whether our images are
-  //     obtained in portrait orientation or not.
-  //   estimated_box: annotation with point_2d field populated with
-  //     2d vertices.
-  // Output:
-  //   estimated_box: annotation with point_3d field populated with
-  //     3d vertices.
-  absl::Status Lift2DTo3D(
-      const Eigen::Matrix<float, 4, 4, Eigen::RowMajor>& projection_matrix,
-      bool portrait, FrameAnnotation* estimated_box) const;
-
- private:
-  struct BeliefBox {
-    float belief;
-    std::vector<std::pair<float, float>> box_2d;
-  };
-
-  std::vector<cv::Point> ExtractCenterKeypoints(
-      const cv::Mat& center_heatmap) const;
-
-  // Decodes 2D keypoints at the peak point.
-  void DecodeByPeak(const cv::Mat& offsetmap, int center_x, int center_y,
-                    float offset_scale_x, float offset_scale_y,
-                    BeliefBox* box) const;
-
-  // Decodes 2D keypoints by voting around the peak.
-  void DecodeByVoting(const cv::Mat& heatmap, const cv::Mat& offsetmap,
-                      int center_x, int center_y, float offset_scale_x,
-                      float offset_scale_y, BeliefBox* box) const;
-
-  // Returns true if it is a new box. Otherwise, it may replace an existing box
-  // if the new box's belief is higher.
-  bool IsNewBox(std::vector<BeliefBox>* boxes, BeliefBox* box) const;
-
-  // Returns true if the two boxes are identical.
-  bool IsIdentical(const BeliefBox& box_1, const BeliefBox& box_2) const;
-
-  BeliefDecoderConfig config_;
-  // Following equation (1) in this paper
-  // https://icwww.epfl.ch/~lepetit/papers/lepetit_ijcv08.pdf,
-  // this variable denotes the coefficients for the 4 control points
-  // for each of the 8 3D box vertices.
-  Eigen::Matrix<float, 8, 4, Eigen::RowMajor> epnp_alpha_;
-};
-
-}  // namespace mediapipe
-
-#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_DECODER_H_
diff --git a/mediapipe/modules/objectron/calculators/epnp.cc b/mediapipe/modules/objectron/calculators/epnp.cc
deleted file mode 100644
index 8bd7151..0000000
--- a/mediapipe/modules/objectron/calculators/epnp.cc
+++ /dev/null
@@ -1,167 +0,0 @@
-// Copyright 2021 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/objectron/calculators/epnp.h"
-
-namespace mediapipe {
-
-namespace {
-
-// NUmber of keypoints.
-constexpr int kNumKeypoints = 9;
-
-using Eigen::Map;
-using Eigen::Matrix;
-using Eigen::Matrix4f;
-using Eigen::Vector2f;
-using Eigen::Vector3f;
-
-}  // namespace
-
-absl::Status SolveEpnp(const float focal_x, const float focal_y,
-                       const float center_x, const float center_y,
-                       const bool portrait,
-                       const std::vector<Vector2f>& input_points_2d,
-                       std::vector<Vector3f>* output_points_3d) {
-  if (input_points_2d.size() != kNumKeypoints) {
-    return absl::InvalidArgumentError(
-        absl::StrFormat("Input must has %d 2D points.", kNumKeypoints));
-  }
-
-  if (output_points_3d == nullptr) {
-    return absl::InvalidArgumentError(
-        "Output pointer output_points_3d is Null.");
-  }
-
-  Matrix<float, (kNumKeypoints - 1) * 2, 12> m =
-      Matrix<float, (kNumKeypoints - 1) * 2, 12>::Zero();
-
-  Matrix<float, kNumKeypoints - 1, 4> epnp_alpha;
-  // The epnp_alpha is the Nx4 weight matrix from the EPnP paper, which is used
-  // to express the N box vertices as the weighted sum of 4 control points. The
-  // value of epnp_alpha is depedent on the set of control points been used.
-  // In our case we used the 4 control points as below (coordinates are in world
-  // coordinate system):
-  //     c0 = (0.0, 0.0, 0.0)  // Box center
-  //     c1 = (1.0, 0.0, 0.0)  // Right face center
-  //     c2 = (0.0, 1.0, 0.0)  // Top face center
-  //     c3 = (0.0, 0.0, 1.0)  // Front face center
-  //
-  //       3 + + + + + + + + 7
-  //       +\                +\          UP
-  //       + \               + \
-  //       +  \              +  \        |
-  //       +   4 + + + + + + + + 8       | y
-  //       +   +             +   +       |
-  //       +   +             +   +       |
-  //       +   +     (0)     +   +       .------- x
-  //       +   +             +   +        \
-  //       1 + + + + + + + + 5   +         \
-  //        \  +              \  +          \ z
-  //         \ +               \ +           \
-  //          \+                \+
-  //           2 + + + + + + + + 6
-  //
-  // For each box vertex shown above, we have the below weighted sum expression:
-  //   v1 = c0 - (c1 - c0) - (c2 - c0) - (c3 - c0) = 4*c0 - c1 - c2 - c3;
-  //   v2 = c0 - (c1 - c0) - (c2 - c0) + (c3 - c0) = 2*c0 - c1 - c2 + c3;
-  //   v3 = c0 - (c1 - c0) + (c2 - c0) - (c3 - c0) = 2*c0 - c1 + c2 - c3;
-  //   ...
-  // Thus we can determine the value of epnp_alpha as been used below.
-  //
-  // clang-format off
-  epnp_alpha << 4.0f, -1.0f, -1.0f, -1.0f,
-                2.0f, -1.0f, -1.0f,  1.0f,
-                2.0f, -1.0f,  1.0f, -1.0f,
-                0.0f, -1.0f,  1.0f,  1.0f,
-                2.0f,  1.0f, -1.0f, -1.0f,
-                0.0f,  1.0f, -1.0f,  1.0f,
-                0.0f,  1.0f,  1.0f, -1.0f,
-               -2.0f,  1.0f,  1.0f,  1.0f;
-  // clang-format on
-
-  for (int i = 0; i < input_points_2d.size() - 1; ++i) {
-    // Skip 0th landmark which is object center.
-    const auto& point_2d = input_points_2d[i + 1];
-
-    // Convert 2d point from `pixel coordinates` to `NDC coordinates`([-1, 1])
-    // following to the definitions in:
-    // https://google.github.io/mediapipe/solutions/objectron#ndc-space
-    // If portrait mode is been used, it's the caller's responsibility to
-    // convert the input 2d points' coordinates.
-    float x_ndc, y_ndc;
-    if (portrait) {
-      x_ndc = point_2d.y() * 2 - 1;
-      y_ndc = point_2d.x() * 2 - 1;
-    } else {
-      x_ndc = point_2d.x() * 2 - 1;
-      y_ndc = 1 - point_2d.y() * 2;
-    }
-
-    for (int j = 0; j < 4; ++j) {
-      // For each of the 4 control points, formulate two rows of the
-      // m matrix (two equations).
-      const float control_alpha = epnp_alpha(i, j);
-      m(i * 2, j * 3) = focal_x * control_alpha;
-      m(i * 2, j * 3 + 2) = (center_x + x_ndc) * control_alpha;
-      m(i * 2 + 1, j * 3 + 1) = focal_y * control_alpha;
-      m(i * 2 + 1, j * 3 + 2) = (center_y + y_ndc) * control_alpha;
-    }
-  }
-  // This is a self adjoint matrix. Use SelfAdjointEigenSolver for a fast
-  // and stable solution.
-  Matrix<float, 12, 12> mt_m = m.transpose() * m;
-  Eigen::SelfAdjointEigenSolver<Matrix<float, 12, 12>> eigen_solver(mt_m);
-  if (eigen_solver.info() != Eigen::Success) {
-    return absl::AbortedError("Eigen decomposition failed.");
-  }
-  CHECK_EQ(12, eigen_solver.eigenvalues().size());
-
-  // Eigenvalues are sorted in increasing order for SelfAdjointEigenSolver
-  // only! If you use other Eigen Solvers, it's not guaranteed to be in
-  // increasing order. Here, we just take the eigen vector corresponding
-  // to first/smallest eigen value, since we used SelfAdjointEigenSolver.
-  Eigen::VectorXf eigen_vec = eigen_solver.eigenvectors().col(0);
-  Map<Matrix<float, 4, 3, Eigen::RowMajor>> control_matrix(eigen_vec.data());
-
-  // All 3D points should be in front of camera (z < 0).
-  if (control_matrix(0, 2) > 0) {
-    control_matrix = -control_matrix;
-  }
-  Matrix<float, kNumKeypoints - 1, 3> vertices = epnp_alpha * control_matrix;
-
-  // Fill 0th 3D points.
-  output_points_3d->emplace_back(control_matrix(0, 0), control_matrix(0, 1),
-                                 control_matrix(0, 2));
-  // Fill the rest 3D points.
-  for (int i = 0; i < kNumKeypoints - 1; ++i) {
-    output_points_3d->emplace_back(vertices(i, 0), vertices(i, 1),
-                                   vertices(i, 2));
-  }
-  return absl::OkStatus();
-}
-
-absl::Status SolveEpnp(const Eigen::Matrix4f& projection_matrix,
-                       const bool portrait,
-                       const std::vector<Vector2f>& input_points_2d,
-                       std::vector<Vector3f>* output_points_3d) {
-  const float focal_x = projection_matrix(0, 0);
-  const float focal_y = projection_matrix(1, 1);
-  const float center_x = projection_matrix(0, 2);
-  const float center_y = projection_matrix(1, 2);
-  return SolveEpnp(focal_x, focal_y, center_x, center_y, portrait,
-                   input_points_2d, output_points_3d);
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/epnp.h b/mediapipe/modules/objectron/calculators/epnp.h
deleted file mode 100644
index 85be6f9..0000000
--- a/mediapipe/modules/objectron/calculators/epnp.h
+++ /dev/null
@@ -1,62 +0,0 @@
-// Copyright 2021 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_EPNP_H_
-#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_EPNP_H_
-
-#include <vector>
-
-#include "Eigen/Dense"
-#include "absl/status/status.h"
-#include "absl/strings/str_format.h"
-#include "mediapipe/framework/port/logging.h"
-
-namespace mediapipe {
-
-// This function performs EPnP algorithm, lifting normalized 2D points in pixel
-// space to 3D points in camera coordinate.
-//
-// Inputs:
-//   focal_x: camera focal length along x.
-//   focal_y: camera focal length along y.
-//   center_x: camera center along x.
-//   center_y: camera center along y.
-//   portrait: a boolen variable indicating whether our images are obtained in
-//     portrait orientation or not.
-//   input_points_2d: input 2D points to be lifted to 3D.
-//   output_points_3d: ouput 3D points in camera coordinate.
-absl::Status SolveEpnp(const float focal_x, const float focal_y,
-                       const float center_x, const float center_y,
-                       const bool portrait,
-                       const std::vector<Eigen::Vector2f>& input_points_2d,
-                       std::vector<Eigen::Vector3f>* output_points_3d);
-
-// This function performs EPnP algorithm, lifting normalized 2D points in pixel
-// space to 3D points in camera coordinate.
-//
-// Inputs:
-//   projection_matrix: the projection matrix from 3D coordinate
-//     to screen coordinate.
-//   portrait: a boolen variable indicating whether our images are obtained in
-//     portrait orientation or not.
-//   input_points_2d: input 2D points to be lifted to 3D.
-//   output_points_3d: ouput 3D points in camera coordinate.
-absl::Status SolveEpnp(const Eigen::Matrix4f& projection_matrix,
-                       const bool portrait,
-                       const std::vector<Eigen::Vector2f>& input_points_2d,
-                       std::vector<Eigen::Vector3f>* output_points_3d);
-
-}  // namespace mediapipe
-
-#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_EPNP_H_
diff --git a/mediapipe/modules/objectron/calculators/epnp_test.cc b/mediapipe/modules/objectron/calculators/epnp_test.cc
deleted file mode 100644
index 8cf218a..0000000
--- a/mediapipe/modules/objectron/calculators/epnp_test.cc
+++ /dev/null
@@ -1,169 +0,0 @@
-#include "mediapipe/modules/objectron/calculators/epnp.h"
-
-#include "mediapipe/framework/port/gmock.h"
-#include "mediapipe/framework/port/gtest.h"
-#include "mediapipe/framework/tool/test_util.h"
-
-namespace mediapipe {
-namespace {
-
-using Eigen::AngleAxisf;
-using Eigen::Map;
-using Eigen::Matrix;
-using Eigen::Matrix4f;
-using Eigen::RowMajor;
-using Eigen::Vector2f;
-using Eigen::Vector3f;
-using ::testing::HasSubstr;
-using ::testing::Test;
-using ::testing::status::StatusIs;
-using Matrix3f = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
-
-constexpr uint8_t kNumKeypoints = 9;
-
-// clang-format off
-constexpr float kUnitBox[] = { 0.0f,  0.0f,  0.0f,
-                              -0.5f, -0.5f, -0.5f,
-                              -0.5f, -0.5f,  0.5f,
-                              -0.5f,  0.5f, -0.5f,
-                              -0.5f,  0.5f,  0.5f,
-                               0.5f, -0.5f, -0.5f,
-                               0.5f, -0.5f,  0.5f,
-                               0.5f,  0.5f, -0.5f,
-                               0.5f,  0.5f,  0.5f, };
-// clang-format on
-
-constexpr float kFocalX = 1.0f;
-constexpr float kFocalY = 1.0f;
-constexpr float kCenterX = 0.0f;
-constexpr float kCenterY = 0.0f;
-
-constexpr float kAzimuth = 90.0f * M_PI / 180.0f;
-constexpr float kElevation = 45.0f * M_PI / 180.0f;
-constexpr float kTilt = 15.0f * M_PI / 180.0f;
-
-constexpr float kTranslationArray[] = {0.0f, 0.0f, -100.0f};
-
-constexpr float kScaleArray[] = {50.0f, 50.0f, 50.0f};
-
-class SolveEpnpTest : public Test {
- protected:
-  SolveEpnpTest() {}
-
-  void SetUp() override {
-    // Create vertices in world frame.
-    Map<const Matrix<float, kNumKeypoints, 3, RowMajor>> vertices_w(kUnitBox);
-
-    // Create Pose.
-    Matrix3f rotation;
-    rotation = AngleAxisf(kTilt, Vector3f::UnitZ()) *
-               AngleAxisf(kElevation, Vector3f::UnitX()) *
-               AngleAxisf(kAzimuth, Vector3f::UnitY());
-    Map<const Vector3f> translation(kTranslationArray);
-    Map<const Vector3f> scale(kScaleArray);
-
-    // Generate 3d vertices in camera frame.
-    const auto vertices_c =
-        ((rotation * scale.asDiagonal() * vertices_w.transpose()).colwise() +
-         translation)
-            .transpose();
-
-    // Generate input 2d points.
-    std::vector<Vector2f> input_2d_points;
-    std::vector<Vector3f> expected_3d_points;
-    for (int i = 0; i < kNumKeypoints; ++i) {
-      const auto x = vertices_c(i, 0);
-      const auto y = vertices_c(i, 1);
-      const auto z = vertices_c(i, 2);
-
-      const float x_ndc = -kFocalX * x / z + kCenterX;
-      const float y_ndc = -kFocalY * y / z + kCenterY;
-
-      const float x_pixel = (1.0f + x_ndc) / 2.0f;
-      const float y_pixel = (1.0f - y_ndc) / 2.0f;
-
-      expected_3d_points_.emplace_back(x, y, z);
-      input_2d_points_.emplace_back(x_pixel, y_pixel);
-    }
-  }
-
-  void VerifyOutput3dPoints(const std::vector<Vector3f>& output_3d_points) {
-    EXPECT_EQ(kNumKeypoints, output_3d_points.size());
-    const float scale = output_3d_points[0].z() / expected_3d_points_[0].z();
-    for (int i = 0; i < kNumKeypoints; ++i) {
-      EXPECT_NEAR(output_3d_points[i].x(), expected_3d_points_[i].x() * scale,
-                  2.e-6f);
-      EXPECT_NEAR(output_3d_points[i].y(), expected_3d_points_[i].y() * scale,
-                  2.e-6f);
-      EXPECT_NEAR(output_3d_points[i].z(), expected_3d_points_[i].z() * scale,
-                  2.e-6f);
-    }
-  }
-
-  std::vector<Vector2f> input_2d_points_;
-  std::vector<Vector3f> expected_3d_points_;
-};
-
-TEST_F(SolveEpnpTest, SolveEpnp) {
-  std::vector<Vector3f> output_3d_points;
-  MP_ASSERT_OK(SolveEpnp(kFocalX, kFocalY, kCenterX, kCenterY,
-                         /*portrait*/ false, input_2d_points_,
-                         &output_3d_points));
-  // Test output 3D points.
-  VerifyOutput3dPoints(output_3d_points);
-}
-
-TEST_F(SolveEpnpTest, SolveEpnppPortrait) {
-  std::vector<Vector3f> output_3d_points;
-  MP_ASSERT_OK(SolveEpnp(kFocalX, kFocalY, kCenterX, kCenterY,
-                         /*portrait*/ true, input_2d_points_,
-                         &output_3d_points));
-  // Test output 3D points.
-  for (auto& point_3d : output_3d_points) {
-    const auto x = point_3d.x();
-    const auto y = point_3d.y();
-    // Convert from portrait mode to normal mode, y => x, x => -y.
-    point_3d.x() = y;
-    point_3d.y() = -x;
-  }
-  VerifyOutput3dPoints(output_3d_points);
-}
-
-TEST_F(SolveEpnpTest, SolveEpnpProjectionMatrix) {
-  Matrix4f projection_matrix;
-  // clang-format off
-  projection_matrix << kFocalX,    0.0f, kCenterX, 0.0f,
-                          0.0f, kFocalY, kCenterY, 0.0f,
-                          0.0f,    0.0f,    -1.0f, 0.0f,
-                          0.0f,    0.0f,    -1.0f, 0.0f;
-  // clang-format on
-
-  std::vector<Vector3f> output_3d_points;
-  MP_ASSERT_OK(SolveEpnp(projection_matrix, /*portrait*/ false,
-                         input_2d_points_, &output_3d_points));
-
-  // Test output 3D points.
-  VerifyOutput3dPoints(output_3d_points);
-}
-
-TEST_F(SolveEpnpTest, BadInput2dPoints) {
-  // Generate empty input 2D points.
-  std::vector<Vector2f> input_2d_points;
-  std::vector<Vector3f> output_3d_points;
-  EXPECT_THAT(SolveEpnp(kFocalX, kFocalY, kCenterX, kCenterY,
-                        /*portrait*/ false, input_2d_points, &output_3d_points),
-              StatusIs(absl::StatusCode::kInvalidArgument,
-                       HasSubstr("Input must has")));
-}
-
-TEST_F(SolveEpnpTest, BadOutput3dPoints) {
-  // Generate null output 3D points.
-  std::vector<Vector3f>* output_3d_points = nullptr;
-  EXPECT_THAT(SolveEpnp(kFocalX, kFocalY, kCenterX, kCenterY,
-                        /*portrait*/ false, input_2d_points_, output_3d_points),
-              StatusIs(absl::StatusCode::kInvalidArgument,
-                       "Output pointer output_points_3d is Null."));
-}
-
-}  // namespace
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/filter_detection_calculator.cc b/mediapipe/modules/objectron/calculators/filter_detection_calculator.cc
deleted file mode 100644
index 0f29f9c..0000000
--- a/mediapipe/modules/objectron/calculators/filter_detection_calculator.cc
+++ /dev/null
@@ -1,262 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <memory>
-#include <string>
-#include <vector>
-
-#include "absl/container/node_hash_set.h"
-#include "absl/strings/str_split.h"
-#include "absl/strings/string_view.h"
-#include "absl/strings/strip.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/formats/detection.pb.h"
-#include "mediapipe/framework/formats/location_data.pb.h"
-#include "mediapipe/framework/port/logging.h"
-#include "mediapipe/framework/port/map_util.h"
-#include "mediapipe/framework/port/re2.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/modules/objectron/calculators/filter_detection_calculator.pb.h"
-
-namespace mediapipe {
-
-namespace {
-
-constexpr char kDetectionTag[] = "DETECTION";
-constexpr char kDetectionsTag[] = "DETECTIONS";
-constexpr char kLabelsTag[] = "LABELS";
-constexpr char kLabelsCsvTag[] = "LABELS_CSV";
-
-using mediapipe::RE2;
-using Detections = std::vector<Detection>;
-using Strings = std::vector<std::string>;
-
-}  // namespace
-
-// Filters the entries in a Detection to only those with valid scores
-// for the specified allowed labels. Allowed labels are provided as a
-// vector<std::string> in an optional input side packet. Allowed labels can
-// contain simple strings or regular expressions. The valid score range
-// can be set in the options.The allowed labels can be provided as
-// vector<std::string> (LABELS) or CSV std::string (LABELS_CSV) containing class
-// names of allowed labels. Note: Providing an empty vector in the input side
-// packet Packet causes this calculator to act as a sink if
-// empty_allowed_labels_means_allow_everything is set to false (default value).
-// To allow all labels, use the calculator with no input side packet stream, or
-// set empty_allowed_labels_means_allow_everything to true.
-//
-// Example config:
-// node {
-//   calculator: "FilterDetectionCalculator"
-//   input_stream: "DETECTIONS:detections"
-//   output_stream: "DETECTIONS:filtered_detections"
-//   input_side_packet: "LABELS:allowed_labels"
-//   options: {
-//     [mediapipe.FilterDetectionCalculatorOptions.ext]: {
-//       min_score: 0.5
-//     }
-//   }
-// }
-
-struct FirstGreaterComparator {
-  bool operator()(const std::pair<float, int>& a,
-                  const std::pair<float, int>& b) const {
-    return a.first > b.first;
-  }
-};
-
-absl::Status SortLabelsByDecreasingScore(const Detection& detection,
-                                         Detection* sorted_detection) {
-  RET_CHECK(sorted_detection);
-  RET_CHECK_EQ(detection.score_size(), detection.label_size());
-  if (!detection.label_id().empty()) {
-    RET_CHECK_EQ(detection.score_size(), detection.label_id_size());
-  }
-  // Copies input to keep all fields unchanged, and to reserve space for
-  // repeated fields. Repeated fields (score, label, and label_id) will be
-  // overwritten.
-  *sorted_detection = detection;
-
-  std::vector<std::pair<float, int>> scores_and_indices(detection.score_size());
-  for (int i = 0; i < detection.score_size(); ++i) {
-    scores_and_indices[i].first = detection.score(i);
-    scores_and_indices[i].second = i;
-  }
-
-  std::sort(scores_and_indices.begin(), scores_and_indices.end(),
-            FirstGreaterComparator());
-
-  for (int i = 0; i < detection.score_size(); ++i) {
-    const int index = scores_and_indices[i].second;
-    sorted_detection->set_score(i, detection.score(index));
-    sorted_detection->set_label(i, detection.label(index));
-  }
-
-  if (!detection.label_id().empty()) {
-    for (int i = 0; i < detection.score_size(); ++i) {
-      const int index = scores_and_indices[i].second;
-      sorted_detection->set_label_id(i, detection.label_id(index));
-    }
-  }
-  return absl::OkStatus();
-}
-
-class FilterDetectionCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc);
-  absl::Status Open(CalculatorContext* cc) override;
-  absl::Status Process(CalculatorContext* cc) override;
-
- private:
-  bool IsValidLabel(const std::string& label);
-  bool IsValidScore(float score);
-  // Stores numeric limits for filtering on the score.
-  FilterDetectionCalculatorOptions options_;
-  // We use the next two fields to possibly filter to a limited set of
-  // classes.  The hash_set will be empty in two cases: 1) if no input
-  // side packet stream is provided (not filtering on labels), or 2)
-  // if the input side packet contains an empty vector (no labels are
-  // allowed). We use limit_labels_ to distinguish between the two cases.
-  bool limit_labels_ = true;
-  absl::node_hash_set<std::string> allowed_labels_;
-};
-REGISTER_CALCULATOR(FilterDetectionCalculator);
-
-absl::Status FilterDetectionCalculator::GetContract(CalculatorContract* cc) {
-  RET_CHECK(!cc->Inputs().GetTags().empty());
-  RET_CHECK(!cc->Outputs().GetTags().empty());
-
-  if (cc->Inputs().HasTag(kDetectionTag)) {
-    cc->Inputs().Tag(kDetectionTag).Set<Detection>();
-    cc->Outputs().Tag(kDetectionTag).Set<Detection>();
-  }
-  if (cc->Inputs().HasTag(kDetectionsTag)) {
-    cc->Inputs().Tag(kDetectionsTag).Set<Detections>();
-    cc->Outputs().Tag(kDetectionsTag).Set<Detections>();
-  }
-  if (cc->InputSidePackets().HasTag(kLabelsTag)) {
-    cc->InputSidePackets().Tag(kLabelsTag).Set<Strings>();
-  }
-  if (cc->InputSidePackets().HasTag(kLabelsCsvTag)) {
-    cc->InputSidePackets().Tag(kLabelsCsvTag).Set<std::string>();
-  }
-  return absl::OkStatus();
-}
-
-absl::Status FilterDetectionCalculator::Open(CalculatorContext* cc) {
-  cc->SetOffset(TimestampDiff(0));
-  options_ = cc->Options<FilterDetectionCalculatorOptions>();
-  limit_labels_ = cc->InputSidePackets().HasTag(kLabelsTag) ||
-                  cc->InputSidePackets().HasTag(kLabelsCsvTag);
-  if (limit_labels_) {
-    Strings allowlist_labels;
-    if (cc->InputSidePackets().HasTag(kLabelsCsvTag)) {
-      allowlist_labels = absl::StrSplit(
-          cc->InputSidePackets().Tag(kLabelsCsvTag).Get<std::string>(), ',',
-          absl::SkipWhitespace());
-      for (auto& e : allowlist_labels) {
-        absl::StripAsciiWhitespace(&e);
-      }
-    } else {
-      allowlist_labels = cc->InputSidePackets().Tag(kLabelsTag).Get<Strings>();
-    }
-    allowed_labels_.insert(allowlist_labels.begin(), allowlist_labels.end());
-  }
-  if (limit_labels_ && allowed_labels_.empty()) {
-    if (options_.fail_on_empty_labels()) {
-      cc->GetCounter("VideosWithEmptyLabelsAllowlist")->Increment();
-      return tool::StatusFail(
-          "FilterDetectionCalculator received empty allowlist with "
-          "fail_on_empty_labels = true.");
-    }
-    if (options_.empty_allowed_labels_means_allow_everything()) {
-      // Continue as if side_input was not provided, i.e. pass all labels.
-      limit_labels_ = false;
-    }
-  }
-  return absl::OkStatus();
-}
-
-absl::Status FilterDetectionCalculator::Process(CalculatorContext* cc) {
-  if (limit_labels_ && allowed_labels_.empty()) {
-    return absl::OkStatus();
-  }
-  Detections detections;
-  if (cc->Inputs().HasTag(kDetectionsTag)) {
-    detections = cc->Inputs().Tag(kDetectionsTag).Get<Detections>();
-  } else if (cc->Inputs().HasTag(kDetectionTag)) {
-    detections.emplace_back(cc->Inputs().Tag(kDetectionsTag).Get<Detection>());
-  }
-  std::unique_ptr<Detections> outputs(new Detections);
-  for (const auto& input : detections) {
-    Detection output;
-    for (int i = 0; i < input.label_size(); ++i) {
-      const std::string& label = input.label(i);
-      const float score = input.score(i);
-      if (IsValidLabel(label) && IsValidScore(score)) {
-        output.add_label(label);
-        output.add_score(score);
-      }
-    }
-    if (output.label_size() > 0) {
-      if (input.has_location_data()) {
-        *output.mutable_location_data() = input.location_data();
-      }
-      Detection output_sorted;
-      if (!SortLabelsByDecreasingScore(output, &output_sorted).ok()) {
-        // Uses the orginal output if fails to sort.
-        cc->GetCounter("FailedToSortLabelsInDetection")->Increment();
-        output_sorted = output;
-      }
-      outputs->emplace_back(output_sorted);
-    }
-  }
-
-  if (cc->Outputs().HasTag(kDetectionsTag)) {
-    cc->Outputs()
-        .Tag(kDetectionsTag)
-        .Add(outputs.release(), cc->InputTimestamp());
-  } else if (!outputs->empty()) {
-    cc->Outputs()
-        .Tag(kDetectionsTag)
-        .Add(new Detection((*outputs)[0]), cc->InputTimestamp());
-  }
-  return absl::OkStatus();
-}
-
-bool FilterDetectionCalculator::IsValidLabel(const std::string& label) {
-  bool match = !limit_labels_ || allowed_labels_.contains(label);
-  if (!match) {
-    // If no exact match is found, check for regular expression
-    // comparions in the allowed_labels.
-    for (const auto& label_regexp : allowed_labels_) {
-      match = match || RE2::FullMatch(label, RE2(label_regexp));
-    }
-  }
-  return match;
-}
-
-bool FilterDetectionCalculator::IsValidScore(float score) {
-  if (options_.has_min_score() && score < options_.min_score()) {
-    LOG(ERROR) << "Filter out detection with low score " << score;
-    return false;
-  }
-  if (options_.has_max_score() && score > options_.max_score()) {
-    LOG(ERROR) << "Filter out detection with high score " << score;
-    return false;
-  }
-  return true;
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/filter_detection_calculator.proto b/mediapipe/modules/objectron/calculators/filter_detection_calculator.proto
deleted file mode 100644
index ea79b8d..0000000
--- a/mediapipe/modules/objectron/calculators/filter_detection_calculator.proto
+++ /dev/null
@@ -1,45 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-
-message FilterDetectionCalculatorOptions {
-  extend CalculatorOptions {
-    optional FilterDetectionCalculatorOptions ext = 339582987;
-  }
-  optional float min_score = 1;
-  optional float max_score = 2;
-  // Setting fail_on_empty_labels to true will cause the calculator to return a
-  // failure status on Open() if an empty list is provided on the external
-  // input, immediately terminating the graph run.
-  optional bool fail_on_empty_labels = 3 [default = false];
-  // If fail_on_empty_labels is set to false setting
-  // empty_allowed_labels_means_allow_everything to
-  // false will cause the calculator to close output stream and ignore remaining
-  // inputs if an empty list is provided. If
-  // empty_allowed_labels_means_allow_everything is set to true this will force
-  // calculator to pass all labels.
-  optional bool empty_allowed_labels_means_allow_everything = 6
-      [default = false];
-  // Determines whether the input format is a vector<Detection> (use-case object
-  // detectors) or Detection (use-case classifiers).
-  optional bool use_detection_vector = 4 [deprecated = true];
-  // Determines whether the input side packet format is a vector of labels, or
-  // a string with comma separated labels.
-  optional bool use_allowed_labels_csv = 5 [deprecated = true];
-}
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.cc b/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.cc
deleted file mode 100644
index 476f8cb..0000000
--- a/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.cc
+++ /dev/null
@@ -1,177 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-
-#include <cmath>
-#include <vector>
-
-#include "Eigen/Dense"
-#include "absl/memory/memory.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/formats/rect.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.pb.h"
-
-namespace mediapipe {
-
-using Matrix3fRM = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
-using Eigen::Vector2f;
-using Eigen::Vector3f;
-
-namespace {
-
-constexpr char kInputFrameAnnotationTag[] = "FRAME_ANNOTATION";
-constexpr char kOutputNormRectsTag[] = "NORM_RECTS";
-
-}  // namespace
-
-// A calculator that converts FrameAnnotation proto to NormalizedRect.
-// The rotation angle of the NormalizedRect is derived from object's 3d pose.
-// The angle is calculated such that after rotation the 2d projection of y-axis.
-// on the image plane is always vertical.
-class FrameAnnotationToRectCalculator : public CalculatorBase {
- public:
-  enum ViewStatus {
-    TOP_VIEW_ON,
-    TOP_VIEW_OFF,
-  };
-
-  static absl::Status GetContract(CalculatorContract* cc);
-  absl::Status Open(CalculatorContext* cc) override;
-  absl::Status Process(CalculatorContext* cc) override;
-
- private:
-  void AddAnnotationToRect(const ObjectAnnotation& annotation,
-                           std::vector<NormalizedRect>* rect);
-  float RotationAngleFromAnnotation(const ObjectAnnotation& annotation);
-
-  float RotationAngleFromPose(const Matrix3fRM& rotation,
-                              const Vector3f& translation, const Vector3f& vec);
-  ViewStatus status_;
-  float off_threshold_;
-  float on_threshold_;
-};
-REGISTER_CALCULATOR(FrameAnnotationToRectCalculator);
-
-absl::Status FrameAnnotationToRectCalculator::GetContract(
-    CalculatorContract* cc) {
-  RET_CHECK(!cc->Inputs().GetTags().empty());
-  RET_CHECK(!cc->Outputs().GetTags().empty());
-
-  if (cc->Inputs().HasTag(kInputFrameAnnotationTag)) {
-    cc->Inputs().Tag(kInputFrameAnnotationTag).Set<FrameAnnotation>();
-  }
-
-  if (cc->Outputs().HasTag(kOutputNormRectsTag)) {
-    cc->Outputs().Tag(kOutputNormRectsTag).Set<std::vector<NormalizedRect>>();
-  }
-  return absl::OkStatus();
-}
-
-absl::Status FrameAnnotationToRectCalculator::Open(CalculatorContext* cc) {
-  cc->SetOffset(TimestampDiff(0));
-  status_ = TOP_VIEW_OFF;
-  const auto& options = cc->Options<FrameAnnotationToRectCalculatorOptions>();
-  off_threshold_ = options.off_threshold();
-  on_threshold_ = options.on_threshold();
-  RET_CHECK(off_threshold_ <= on_threshold_);
-  return absl::OkStatus();
-}
-
-absl::Status FrameAnnotationToRectCalculator::Process(CalculatorContext* cc) {
-  if (cc->Inputs().Tag(kInputFrameAnnotationTag).IsEmpty()) {
-    return absl::OkStatus();
-  }
-  auto output_rects = absl::make_unique<std::vector<NormalizedRect>>();
-  const auto& frame_annotation =
-      cc->Inputs().Tag(kInputFrameAnnotationTag).Get<FrameAnnotation>();
-  for (const auto& object_annotation : frame_annotation.annotations()) {
-    AddAnnotationToRect(object_annotation, output_rects.get());
-  }
-
-  // Output.
-  cc->Outputs()
-      .Tag(kOutputNormRectsTag)
-      .Add(output_rects.release(), cc->InputTimestamp());
-  return absl::OkStatus();
-}
-
-void FrameAnnotationToRectCalculator::AddAnnotationToRect(
-    const ObjectAnnotation& annotation, std::vector<NormalizedRect>* rects) {
-  float x_min = std::numeric_limits<float>::max();
-  float x_max = std::numeric_limits<float>::min();
-  float y_min = std::numeric_limits<float>::max();
-  float y_max = std::numeric_limits<float>::min();
-  for (const auto& keypoint : annotation.keypoints()) {
-    const auto& point_2d = keypoint.point_2d();
-    x_min = std::min(x_min, point_2d.x());
-    x_max = std::max(x_max, point_2d.x());
-    y_min = std::min(y_min, point_2d.y());
-    y_max = std::max(y_max, point_2d.y());
-  }
-  NormalizedRect new_rect;
-  new_rect.set_x_center((x_min + x_max) / 2);
-  new_rect.set_y_center((y_min + y_max) / 2);
-  new_rect.set_width(x_max - x_min);
-  new_rect.set_height(y_max - y_min);
-  new_rect.set_rotation(RotationAngleFromAnnotation(annotation));
-  rects->push_back(new_rect);
-}
-
-float FrameAnnotationToRectCalculator::RotationAngleFromAnnotation(
-    const ObjectAnnotation& annotation) {
-  // Get box rotation and translation from annotation.
-  const auto box_rotation =
-      Eigen::Map<const Matrix3fRM>(annotation.rotation().data());
-  const auto box_translation =
-      Eigen::Map<const Vector3f>(annotation.translation().data());
-
-  // Rotation angle to use when top-view is on(top-view on),
-  // Which will make z-axis upright after the rotation.
-  const float angle_on =
-      RotationAngleFromPose(box_rotation, box_translation, Vector3f::UnitZ());
-  // Rotation angle to use when side-view is on(top-view off),
-  // Which will make y-axis upright after the rotation.
-  const float angle_off =
-      RotationAngleFromPose(box_rotation, box_translation, Vector3f::UnitY());
-
-  // Calculate angle between z-axis and viewing ray in degrees.
-  const float view_to_z_angle = std::acos(box_rotation(2, 1)) * 180 / M_PI;
-
-  // Determine threshold based on current status,
-  // on_threshold_ is used for TOP_VIEW_ON -> TOP_VIEW_OFF transition,
-  // off_threshold_ is used for TOP_VIEW_OFF -> TOP_VIEW_ON transition.
-  const float thresh =
-      (status_ == TOP_VIEW_ON) ? on_threshold_ : off_threshold_;
-
-  // If view_to_z_angle is smaller than threshold, then top-view is on;
-  // Otherwise top-view is off.
-  status_ = (view_to_z_angle < thresh) ? TOP_VIEW_ON : TOP_VIEW_OFF;
-
-  // Determine which angle to used based on current status_.
-  float angle_to_rotate = (status_ == TOP_VIEW_ON) ? angle_on : angle_off;
-  return angle_to_rotate;
-}
-
-float FrameAnnotationToRectCalculator::RotationAngleFromPose(
-    const Matrix3fRM& rotation, const Vector3f& translation,
-    const Vector3f& vec) {
-  auto p1 = rotation * vec + translation;
-  auto p2 = -rotation * vec + translation;
-  const float dy = p2[2] * p2[1] - p1[2] * p1[1];
-  const float dx = p2[2] * p2[0] - p1[2] * p1[0];
-  return M_PI / 2 - std::atan2(dy, dx);
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.proto b/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.proto
deleted file mode 100644
index 8959cb8..0000000
--- a/mediapipe/modules/objectron/calculators/frame_annotation_to_rect_calculator.proto
+++ /dev/null
@@ -1,31 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-
-message FrameAnnotationToRectCalculatorOptions {
-  extend CalculatorOptions {
-    optional FrameAnnotationToRectCalculatorOptions ext = 338119067;
-  }
-
-  // The threshold to use when top-view is off,to enable hysteresis,
-  // It's required that off_threshold <= on_threshold.
-  optional float off_threshold = 1 [default = 40.0];
-  // The threshold to use when top-view is on.
-  optional float on_threshold = 2 [default = 41.0];
-}
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_to_timed_box_list_calculator.cc b/mediapipe/modules/objectron/calculators/frame_annotation_to_timed_box_list_calculator.cc
deleted file mode 100644
index 7467880..0000000
--- a/mediapipe/modules/objectron/calculators/frame_annotation_to_timed_box_list_calculator.cc
+++ /dev/null
@@ -1,115 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <utility>
-
-#include "absl/memory/memory.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/port/opencv_core_inc.h"
-#include "mediapipe/framework/port/opencv_imgproc_inc.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/box_util.h"
-#include "mediapipe/util/tracking/box_tracker.pb.h"
-
-namespace {
-constexpr char kInputStreamTag[] = "FRAME_ANNOTATION";
-constexpr char kOutputStreamTag[] = "BOXES";
-}  // namespace
-
-namespace mediapipe {
-
-// Convert FrameAnnotation 3d bounding box detections to TimedBoxListProto
-// 2d bounding boxes.
-//
-// Input:
-//  FRAME_ANNOTATION - 3d bounding box annotation.
-// Output:
-//  BOXES - 2d bounding box enclosing the projection of 3d box.
-//
-// Usage example:
-// node {
-//   calculator: "FrameAnnotationToTimedBoxListCalculator"
-//   input_stream: "FRAME_ANNOTATION:frame_annotation"
-//   output_stream: "BOXES:boxes"
-// }
-class FrameAnnotationToTimedBoxListCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc);
-
-  absl::Status Open(CalculatorContext* cc) override;
-  absl::Status Process(CalculatorContext* cc) override;
-  absl::Status Close(CalculatorContext* cc) override;
-};
-REGISTER_CALCULATOR(FrameAnnotationToTimedBoxListCalculator);
-
-absl::Status FrameAnnotationToTimedBoxListCalculator::GetContract(
-    CalculatorContract* cc) {
-  RET_CHECK(!cc->Inputs().GetTags().empty());
-  RET_CHECK(!cc->Outputs().GetTags().empty());
-
-  if (cc->Inputs().HasTag(kInputStreamTag)) {
-    cc->Inputs().Tag(kInputStreamTag).Set<FrameAnnotation>();
-  }
-
-  if (cc->Outputs().HasTag(kOutputStreamTag)) {
-    cc->Outputs().Tag(kOutputStreamTag).Set<TimedBoxProtoList>();
-  }
-  return absl::OkStatus();
-}
-
-absl::Status FrameAnnotationToTimedBoxListCalculator::Open(
-    CalculatorContext* cc) {
-  return absl::OkStatus();
-}
-
-absl::Status FrameAnnotationToTimedBoxListCalculator::Process(
-    CalculatorContext* cc) {
-  if (cc->Inputs().HasTag(kInputStreamTag) &&
-      !cc->Inputs().Tag(kInputStreamTag).IsEmpty()) {
-    const auto& frame_annotation =
-        cc->Inputs().Tag(kInputStreamTag).Get<FrameAnnotation>();
-    auto output_objects = absl::make_unique<TimedBoxProtoList>();
-    for (const auto& annotation : frame_annotation.annotations()) {
-      std::vector<cv::Point2f> key_points;
-      for (const auto& keypoint : annotation.keypoints()) {
-        key_points.push_back(
-            cv::Point2f(keypoint.point_2d().x(), keypoint.point_2d().y()));
-      }
-      TimedBoxProto* added_box = output_objects->add_box();
-      ComputeBoundingRect(key_points, added_box);
-      added_box->set_id(annotation.object_id());
-      const int64 time_msec =
-          static_cast<int64>(std::round(frame_annotation.timestamp() / 1000));
-      added_box->set_time_msec(time_msec);
-    }
-
-    // Output
-    if (cc->Outputs().HasTag(kOutputStreamTag)) {
-      cc->Outputs()
-          .Tag(kOutputStreamTag)
-          .Add(output_objects.release(), cc->InputTimestamp());
-    }
-  }
-
-  return absl::OkStatus();
-}
-
-absl::Status FrameAnnotationToTimedBoxListCalculator::Close(
-    CalculatorContext* cc) {
-  return absl::OkStatus();
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_tracker.cc b/mediapipe/modules/objectron/calculators/frame_annotation_tracker.cc
deleted file mode 100644
index eebf885..0000000
--- a/mediapipe/modules/objectron/calculators/frame_annotation_tracker.cc
+++ /dev/null
@@ -1,102 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/objectron/calculators/frame_annotation_tracker.h"
-
-#include "absl/container/flat_hash_set.h"
-#include "mediapipe/framework/port/logging.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/box_util.h"
-#include "mediapipe/util/tracking/box_tracker.pb.h"
-
-namespace mediapipe {
-
-void FrameAnnotationTracker::AddDetectionResult(
-    const FrameAnnotation& frame_annotation) {
-  const int64 time_us =
-      static_cast<int64>(std::round(frame_annotation.timestamp()));
-  for (const auto& object_annotation : frame_annotation.annotations()) {
-    detected_objects_[time_us + object_annotation.object_id()] =
-        object_annotation;
-  }
-}
-
-FrameAnnotation FrameAnnotationTracker::ConsolidateTrackingResult(
-    const TimedBoxProtoList& tracked_boxes,
-    absl::flat_hash_set<int>* cancel_object_ids) {
-  CHECK(cancel_object_ids != nullptr);
-  FrameAnnotation frame_annotation;
-  std::vector<int64> keys_to_be_deleted;
-  for (const auto& detected_obj : detected_objects_) {
-    const int object_id = detected_obj.second.object_id();
-    if (cancel_object_ids->contains(object_id)) {
-      // Remember duplicated detections' keys.
-      keys_to_be_deleted.push_back(detected_obj.first);
-      continue;
-    }
-    TimedBoxProto ref_box;
-    for (const auto& box : tracked_boxes.box()) {
-      if (box.id() == object_id) {
-        ref_box = box;
-        break;
-      }
-    }
-    if (!ref_box.has_id() || ref_box.id() < 0) {
-      LOG(ERROR) << "Can't find matching tracked box for object id: "
-                 << object_id << ". Likely lost tracking of it.";
-      keys_to_be_deleted.push_back(detected_obj.first);
-      continue;
-    }
-
-    // Find duplicated boxes
-    for (const auto& box : tracked_boxes.box()) {
-      if (box.id() != object_id) {
-        if (ComputeBoxIoU(ref_box, box) > iou_threshold_) {
-          cancel_object_ids->insert(box.id());
-        }
-      }
-    }
-
-    // Map ObjectAnnotation from detection to tracked time.
-    // First, gather all keypoints from source detection.
-    std::vector<cv::Point2f> key_points;
-    for (const auto& keypoint : detected_obj.second.keypoints()) {
-      key_points.push_back(
-          cv::Point2f(keypoint.point_2d().x(), keypoint.point_2d().y()));
-    }
-    // Second, find source box.
-    TimedBoxProto src_box;
-    ComputeBoundingRect(key_points, &src_box);
-    ObjectAnnotation* tracked_obj = frame_annotation.add_annotations();
-    tracked_obj->set_object_id(ref_box.id());
-    // Finally, map all keypoints in the source detection to tracked location.
-    for (const auto& keypoint : detected_obj.second.keypoints()) {
-      cv::Point2f dst = MapPoint(
-          src_box, ref_box,
-          cv::Point2f(keypoint.point_2d().x(), keypoint.point_2d().y()),
-          img_width_, img_height_);
-      auto* dst_point = tracked_obj->add_keypoints()->mutable_point_2d();
-      dst_point->set_x(dst.x);
-      dst_point->set_y(dst.y);
-    }
-  }
-
-  for (const auto& key : keys_to_be_deleted) {
-    detected_objects_.erase(key);
-  }
-
-  return frame_annotation;
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_tracker.h b/mediapipe/modules/objectron/calculators/frame_annotation_tracker.h
deleted file mode 100644
index 11a469c..0000000
--- a/mediapipe/modules/objectron/calculators/frame_annotation_tracker.h
+++ /dev/null
@@ -1,62 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_FRAME_ANNOTATION_TRACKER_H_
-#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_FRAME_ANNOTATION_TRACKER_H_
-
-#include <functional>
-
-#include "absl/container/btree_map.h"
-#include "absl/container/flat_hash_set.h"
-#include "mediapipe/framework/port/integral_types.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/util/tracking/box_tracker.pb.h"
-
-namespace mediapipe {
-
-class FrameAnnotationTracker {
- public:
-  // If two bounding boxes have IoU over iou_threshold, then we consider them
-  // describing the same object.
-  FrameAnnotationTracker(float iou_threshold, float img_width, float img_height)
-      : iou_threshold_(iou_threshold),
-        img_width_(img_width),
-        img_height_(img_height) {}
-
-  // Adds detection results from an external detector.
-  void AddDetectionResult(const FrameAnnotation& frame_annotation);
-
-  // Consolidates tracking result from an external tracker, associates with
-  // the detection result by the object id, and produces the corresponding
-  // result in FrameAnnotation. When there are duplicates, output the ids that
-  // need to be cancelled in cancel_object_ids.
-  // Note that the returned FrameAnnotation is missing timestamp. Need to fill
-  // that field.
-  FrameAnnotation ConsolidateTrackingResult(
-      const TimedBoxProtoList& tracked_boxes,
-      absl::flat_hash_set<int>* cancel_object_ids);
-
- private:
-  float iou_threshold_;
-  float img_width_;
-  float img_height_;
-  // Cached detection results over time.
-  // Key is timestamp_us + object_id.
-  absl::btree_map<int64, ObjectAnnotation, std::greater<int64>>
-      detected_objects_;
-};
-
-}  // namespace mediapipe
-
-#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_FRAME_ANNOTATION_TRACKER_H_
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.cc b/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.cc
deleted file mode 100644
index 9079b9a..0000000
--- a/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.cc
+++ /dev/null
@@ -1,134 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "absl/container/flat_hash_set.h"
-#include "absl/memory/memory.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/frame_annotation_tracker.h"
-#include "mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.pb.h"
-#include "mediapipe/util/tracking/box_tracker.pb.h"
-
-namespace {
-constexpr char kInputFrameAnnotationTag[] = "FRAME_ANNOTATION";
-constexpr char kInputTrackedBoxesTag[] = "TRACKED_BOXES";
-constexpr char kOutputTrackedFrameAnnotationTag[] = "TRACKED_FRAME_ANNOTATION";
-constexpr char kOutputCancelObjectIdTag[] = "CANCEL_OBJECT_ID";
-}  // namespace
-
-namespace mediapipe {
-
-// Tracks frame annotations seeded/updated by FRAME_ANNOTATION input_stream.
-// When using this calculator, make sure FRAME_ANNOTATION and TRACKED_BOXES
-// are in different sync set.
-//
-// Input:
-//  FRAME_ANNOTATION - frame annotation.
-//  TRACKED_BOXES - 2d box tracking result
-// Output:
-//  TRACKED_FRAME_ANNOTATION - annotation inferred from 2d tracking result.
-//  CANCEL_OBJECT_ID - object id that needs to be cancelled from the tracker.
-//
-// Usage example:
-// node {
-//   calculator: "FrameAnnotationTrackerCalculator"
-//   input_stream: "FRAME_ANNOTATION:frame_annotation"
-//   input_stream: "TRACKED_BOXES:tracked_boxes"
-//   output_stream: "TRACKED_FRAME_ANNOTATION:tracked_frame_annotation"
-//   output_stream: "CANCEL_OBJECT_ID:cancel_object_id"
-// }
-class FrameAnnotationTrackerCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc);
-
-  absl::Status Open(CalculatorContext* cc) override;
-  absl::Status Process(CalculatorContext* cc) override;
-  absl::Status Close(CalculatorContext* cc) override;
-
- private:
-  std::unique_ptr<FrameAnnotationTracker> frame_annotation_tracker_;
-};
-REGISTER_CALCULATOR(FrameAnnotationTrackerCalculator);
-
-absl::Status FrameAnnotationTrackerCalculator::GetContract(
-    CalculatorContract* cc) {
-  RET_CHECK(!cc->Inputs().GetTags().empty());
-  RET_CHECK(!cc->Outputs().GetTags().empty());
-
-  if (cc->Inputs().HasTag(kInputFrameAnnotationTag)) {
-    cc->Inputs().Tag(kInputFrameAnnotationTag).Set<FrameAnnotation>();
-  }
-  if (cc->Inputs().HasTag(kInputTrackedBoxesTag)) {
-    cc->Inputs().Tag(kInputTrackedBoxesTag).Set<TimedBoxProtoList>();
-  }
-  if (cc->Outputs().HasTag(kOutputTrackedFrameAnnotationTag)) {
-    cc->Outputs().Tag(kOutputTrackedFrameAnnotationTag).Set<FrameAnnotation>();
-  }
-  if (cc->Outputs().HasTag(kOutputCancelObjectIdTag)) {
-    cc->Outputs().Tag(kOutputCancelObjectIdTag).Set<int>();
-  }
-  return absl::OkStatus();
-}
-
-absl::Status FrameAnnotationTrackerCalculator::Open(CalculatorContext* cc) {
-  const auto& options = cc->Options<FrameAnnotationTrackerCalculatorOptions>();
-  frame_annotation_tracker_ = absl::make_unique<FrameAnnotationTracker>(
-      options.iou_threshold(), options.img_width(), options.img_height());
-  return absl::OkStatus();
-}
-
-absl::Status FrameAnnotationTrackerCalculator::Process(CalculatorContext* cc) {
-  if (cc->Inputs().HasTag(kInputFrameAnnotationTag) &&
-      !cc->Inputs().Tag(kInputFrameAnnotationTag).IsEmpty()) {
-    frame_annotation_tracker_->AddDetectionResult(
-        cc->Inputs().Tag(kInputFrameAnnotationTag).Get<FrameAnnotation>());
-  }
-  if (cc->Inputs().HasTag(kInputTrackedBoxesTag) &&
-      !cc->Inputs().Tag(kInputTrackedBoxesTag).IsEmpty() &&
-      cc->Outputs().HasTag(kOutputTrackedFrameAnnotationTag)) {
-    absl::flat_hash_set<int> cancel_object_ids;
-    auto output_frame_annotation = absl::make_unique<FrameAnnotation>();
-    *output_frame_annotation =
-        frame_annotation_tracker_->ConsolidateTrackingResult(
-            cc->Inputs().Tag(kInputTrackedBoxesTag).Get<TimedBoxProtoList>(),
-            &cancel_object_ids);
-    output_frame_annotation->set_timestamp(cc->InputTimestamp().Microseconds());
-
-    cc->Outputs()
-        .Tag(kOutputTrackedFrameAnnotationTag)
-        .Add(output_frame_annotation.release(), cc->InputTimestamp());
-
-    if (cc->Outputs().HasTag(kOutputCancelObjectIdTag)) {
-      auto packet_timestamp = cc->InputTimestamp();
-      for (const auto& id : cancel_object_ids) {
-        // The timestamp is incremented (by 1 us) because currently the box
-        // tracker calculator only accepts one cancel object ID for any given
-        // timestamp.
-        cc->Outputs()
-            .Tag(kOutputCancelObjectIdTag)
-            .AddPacket(mediapipe::MakePacket<int>(id).At(packet_timestamp++));
-      }
-    }
-  }
-
-  return absl::OkStatus();
-}
-
-absl::Status FrameAnnotationTrackerCalculator::Close(CalculatorContext* cc) {
-  return absl::OkStatus();
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.proto b/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.proto
deleted file mode 100644
index f37308a..0000000
--- a/mediapipe/modules/objectron/calculators/frame_annotation_tracker_calculator.proto
+++ /dev/null
@@ -1,36 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// The option proto for the FrameAnnotationTrackerCalculatorOptions.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-
-message FrameAnnotationTrackerCalculatorOptions {
-  extend CalculatorOptions {
-    optional FrameAnnotationTrackerCalculatorOptions ext = 291291253;
-  }
-
-  // The threshold on intersection-over-union (IoU). We consider
-  // boxes with IoU larger than this threshold to be the duplicates.
-  optional float iou_threshold = 1 [default = 0.5];
-
-  // We need image dimension to properly compute annotation locations.
-  optional float img_width = 2;
-
-  optional float img_height = 3;
-}
diff --git a/mediapipe/modules/objectron/calculators/frame_annotation_tracker_test.cc b/mediapipe/modules/objectron/calculators/frame_annotation_tracker_test.cc
deleted file mode 100644
index d155f8e..0000000
--- a/mediapipe/modules/objectron/calculators/frame_annotation_tracker_test.cc
+++ /dev/null
@@ -1,143 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/objectron/calculators/frame_annotation_tracker.h"
-
-#include "absl/container/flat_hash_set.h"
-#include "mediapipe/framework/port/gmock.h"
-#include "mediapipe/framework/port/gtest.h"
-#include "mediapipe/framework/port/logging.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/util/tracking/box_tracker.pb.h"
-
-namespace mediapipe {
-namespace {
-
-// Create a new object annotation by shifting a reference
-// object annotation.
-ObjectAnnotation ShiftObject2d(const ObjectAnnotation& ref_obj, float dx,
-                               float dy) {
-  ObjectAnnotation obj = ref_obj;
-  for (auto& keypoint : *(obj.mutable_keypoints())) {
-    const float ref_x = keypoint.point_2d().x();
-    const float ref_y = keypoint.point_2d().y();
-    keypoint.mutable_point_2d()->set_x(ref_x + dx);
-    keypoint.mutable_point_2d()->set_y(ref_y + dy);
-  }
-  return obj;
-}
-
-TimedBoxProto ShiftBox(const TimedBoxProto& ref_box, float dx, float dy) {
-  TimedBoxProto box = ref_box;
-  box.set_top(ref_box.top() + dy);
-  box.set_bottom(ref_box.bottom() + dy);
-  box.set_left(ref_box.left() + dx);
-  box.set_right(ref_box.right() + dx);
-  return box;
-}
-
-// Constructs a fixed ObjectAnnotation.
-ObjectAnnotation ConstructFixedObject(
-    const std::vector<std::vector<float>>& points) {
-  ObjectAnnotation obj;
-  for (const auto& point : points) {
-    auto* keypoint = obj.add_keypoints();
-    CHECK_EQ(2, point.size());
-    keypoint->mutable_point_2d()->set_x(point[0]);
-    keypoint->mutable_point_2d()->set_y(point[1]);
-  }
-  return obj;
-}
-
-TEST(FrameAnnotationTrackerTest, TestConsolidation) {
-  // Add 4 detections represented by FrameAnnotation, of which 3 correspond
-  // to the same object.
-  ObjectAnnotation object1, object2, object3, object4;
-  // The bounding rectangle for these object keypoints is:
-  // x: [0.2, 0.5], y: [0.1, 0.4]
-  object3 = ConstructFixedObject({{0.35f, 0.25f},
-                                  {0.3f, 0.3f},
-                                  {0.2f, 0.4f},
-                                  {0.3f, 0.1f},
-                                  {0.2f, 0.2f},
-                                  {0.5f, 0.3f},
-                                  {0.4f, 0.4f},
-                                  {0.5f, 0.1f},
-                                  {0.4f, 0.2f}});
-  object3.set_object_id(3);
-  object1 = ShiftObject2d(object3, -0.05f, -0.05f);
-  object1.set_object_id(1);
-  object2 = ShiftObject2d(object3, 0.05f, 0.05f);
-  object2.set_object_id(2);
-  object4 = ShiftObject2d(object3, 0.2f, 0.2f);
-  object4.set_object_id(4);
-  FrameAnnotation frame_annotation_1;
-  frame_annotation_1.set_timestamp(30 * 1000);  // 30ms
-  *(frame_annotation_1.add_annotations()) = object1;
-  *(frame_annotation_1.add_annotations()) = object4;
-  FrameAnnotation frame_annotation_2;
-  frame_annotation_2.set_timestamp(60 * 1000);  // 60ms
-  *(frame_annotation_2.add_annotations()) = object2;
-  FrameAnnotation frame_annotation_3;
-  frame_annotation_3.set_timestamp(90 * 1000);  // 90ms
-  *(frame_annotation_3.add_annotations()) = object3;
-
-  FrameAnnotationTracker frame_annotation_tracker(/*iou_threshold*/ 0.5f, 1.0f,
-                                                  1.0f);
-  frame_annotation_tracker.AddDetectionResult(frame_annotation_1);
-  frame_annotation_tracker.AddDetectionResult(frame_annotation_2);
-  frame_annotation_tracker.AddDetectionResult(frame_annotation_3);
-
-  TimedBoxProtoList timed_box_proto_list;
-  TimedBoxProto* timed_box_proto = timed_box_proto_list.add_box();
-  timed_box_proto->set_top(0.4f);
-  timed_box_proto->set_bottom(0.7f);
-  timed_box_proto->set_left(0.6f);
-  timed_box_proto->set_right(0.9f);
-  timed_box_proto->set_id(3);
-  timed_box_proto->set_time_msec(150);
-  timed_box_proto = timed_box_proto_list.add_box();
-  *timed_box_proto = ShiftBox(timed_box_proto_list.box(0), 0.01f, 0.01f);
-  timed_box_proto->set_id(1);
-  timed_box_proto->set_time_msec(150);
-  timed_box_proto = timed_box_proto_list.add_box();
-  *timed_box_proto = ShiftBox(timed_box_proto_list.box(0), -0.01f, -0.01f);
-  timed_box_proto->set_id(2);
-  timed_box_proto->set_time_msec(150);
-  absl::flat_hash_set<int> cancel_object_ids;
-  FrameAnnotation tracked_detection =
-      frame_annotation_tracker.ConsolidateTrackingResult(timed_box_proto_list,
-                                                         &cancel_object_ids);
-  EXPECT_EQ(2, cancel_object_ids.size());
-  EXPECT_EQ(1, cancel_object_ids.count(1));
-  EXPECT_EQ(1, cancel_object_ids.count(2));
-  EXPECT_EQ(1, tracked_detection.annotations_size());
-  EXPECT_EQ(3, tracked_detection.annotations(0).object_id());
-  EXPECT_EQ(object3.keypoints_size(),
-            tracked_detection.annotations(0).keypoints_size());
-  const float x_offset = 0.4f;
-  const float y_offset = 0.3f;
-  const float tolerance = 1e-5f;
-  for (int i = 0; i < object3.keypoints_size(); ++i) {
-    const auto& point_2d =
-        tracked_detection.annotations(0).keypoints(i).point_2d();
-    EXPECT_NEAR(point_2d.x(), object3.keypoints(i).point_2d().x() + x_offset,
-                tolerance);
-    EXPECT_NEAR(point_2d.y(), object3.keypoints(i).point_2d().y() + y_offset,
-                tolerance);
-  }
-}
-
-}  // namespace
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/landmarks_to_frame_annotation_calculator.cc b/mediapipe/modules/objectron/calculators/landmarks_to_frame_annotation_calculator.cc
deleted file mode 100644
index 60c4876..0000000
--- a/mediapipe/modules/objectron/calculators/landmarks_to_frame_annotation_calculator.cc
+++ /dev/null
@@ -1,112 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-
-#include "absl/memory/memory.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/formats/landmark.pb.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/framework/port/status.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-
-namespace mediapipe {
-
-namespace {
-
-constexpr char kInputLandmarksTag[] = "LANDMARKS";
-constexpr char kInputMultiLandmarksTag[] = "MULTI_LANDMARKS";
-constexpr char kOutputFrameAnnotationTag[] = "FRAME_ANNOTATION";
-
-}  // namespace
-
-// A calculator that converts NormalizedLandmarkList to FrameAnnotation proto.
-class LandmarksToFrameAnnotationCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc);
-  absl::Status Open(CalculatorContext* cc) override;
-  absl::Status Process(CalculatorContext* cc) override;
-
- private:
-  void AddLandmarksToFrameAnnotation(const NormalizedLandmarkList& landmarks,
-                                     FrameAnnotation* frame_annotation);
-};
-REGISTER_CALCULATOR(LandmarksToFrameAnnotationCalculator);
-
-absl::Status LandmarksToFrameAnnotationCalculator::GetContract(
-    CalculatorContract* cc) {
-  RET_CHECK(!cc->Inputs().GetTags().empty());
-  RET_CHECK(!cc->Outputs().GetTags().empty());
-
-  if (cc->Inputs().HasTag(kInputLandmarksTag)) {
-    cc->Inputs().Tag(kInputLandmarksTag).Set<NormalizedLandmarkList>();
-  }
-  if (cc->Inputs().HasTag(kInputMultiLandmarksTag)) {
-    cc->Inputs()
-        .Tag(kInputMultiLandmarksTag)
-        .Set<std::vector<NormalizedLandmarkList>>();
-  }
-  if (cc->Outputs().HasTag(kOutputFrameAnnotationTag)) {
-    cc->Outputs().Tag(kOutputFrameAnnotationTag).Set<FrameAnnotation>();
-  }
-  return absl::OkStatus();
-}
-
-absl::Status LandmarksToFrameAnnotationCalculator::Open(CalculatorContext* cc) {
-  cc->SetOffset(TimestampDiff(0));
-  return absl::OkStatus();
-}
-
-absl::Status LandmarksToFrameAnnotationCalculator::Process(
-    CalculatorContext* cc) {
-  auto frame_annotation = absl::make_unique<FrameAnnotation>();
-
-  // Handle the case when input has only one NormalizedLandmarkList.
-  if (cc->Inputs().HasTag(kInputLandmarksTag) &&
-      !cc->Inputs().Tag(kInputLandmarksTag).IsEmpty()) {
-    const auto& landmarks =
-        cc->Inputs().Tag(kInputMultiLandmarksTag).Get<NormalizedLandmarkList>();
-    AddLandmarksToFrameAnnotation(landmarks, frame_annotation.get());
-  }
-
-  // Handle the case when input has muliple NormalizedLandmarkList.
-  if (cc->Inputs().HasTag(kInputMultiLandmarksTag) &&
-      !cc->Inputs().Tag(kInputMultiLandmarksTag).IsEmpty()) {
-    const auto& landmarks_list =
-        cc->Inputs()
-            .Tag(kInputMultiLandmarksTag)
-            .Get<std::vector<NormalizedLandmarkList>>();
-    for (const auto& landmarks : landmarks_list) {
-      AddLandmarksToFrameAnnotation(landmarks, frame_annotation.get());
-    }
-  }
-
-  // Output
-  if (cc->Outputs().HasTag(kOutputFrameAnnotationTag)) {
-    cc->Outputs()
-        .Tag(kOutputFrameAnnotationTag)
-        .Add(frame_annotation.release(), cc->InputTimestamp());
-  }
-  return absl::OkStatus();
-}
-
-void LandmarksToFrameAnnotationCalculator::AddLandmarksToFrameAnnotation(
-    const NormalizedLandmarkList& landmarks,
-    FrameAnnotation* frame_annotation) {
-  auto* new_annotation = frame_annotation->add_annotations();
-  for (const auto& landmark : landmarks.landmark()) {
-    auto* point2d = new_annotation->add_keypoints()->mutable_point_2d();
-    point2d->set_x(landmark.x());
-    point2d->set_y(landmark.y());
-  }
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.cc b/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.cc
deleted file mode 100644
index 1405e5a..0000000
--- a/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.cc
+++ /dev/null
@@ -1,169 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <memory>
-#include <unordered_map>
-#include <vector>
-
-#include "Eigen/Dense"
-#include "absl/memory/memory.h"
-#include "absl/strings/str_format.h"
-#include "absl/types/span.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/deps/file_path.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/decoder.h"
-#include "mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.pb.h"
-#include "mediapipe/modules/objectron/calculators/tensor_util.h"
-
-namespace {
-constexpr char kInputStreamTag[] = "FRAME_ANNOTATION";
-constexpr char kOutputStreamTag[] = "LIFTED_FRAME_ANNOTATION";
-
-// Each detection object will be assigned an unique id that starts from 1.
-static int object_id = 0;
-
-inline int GetNextObjectId() { return ++object_id; }
-}  // namespace
-
-namespace mediapipe {
-
-// Lifted the 2D points in a tracked frame annotation to 3D.
-//
-// Input:
-//  FRAME_ANNOTATIONS - Frame annotations with detected 2D points
-// Output:
-//  LIFTED_FRAME_ANNOTATIONS - Result FrameAnnotation with lifted 3D points.
-//
-// Usage example:
-// node {
-//   calculator: "Lift2DFrameAnnotationTo3DCalculator"
-//   input_stream: "FRAME_ANNOTATIONS:tracked_annotations"
-//   output_stream: "LIFTED_FRAME_ANNOTATIONS:lifted_3d_annotations"
-// }
-class Lift2DFrameAnnotationTo3DCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc);
-
-  absl::Status Open(CalculatorContext* cc) override;
-  absl::Status Process(CalculatorContext* cc) override;
-  absl::Status Close(CalculatorContext* cc) override;
-
- private:
-  absl::Status ProcessCPU(CalculatorContext* cc,
-                          FrameAnnotation* output_objects);
-  absl::Status LoadOptions(CalculatorContext* cc);
-
-  // Increment and assign object ID for each detected object.
-  // In a single MediaPipe session, the IDs are unique.
-  // Also assign timestamp for the FrameAnnotation to be the input packet
-  // timestamp.
-  void AssignObjectIdAndTimestamp(int64 timestamp_us,
-                                  FrameAnnotation* annotation);
-  std::unique_ptr<Decoder> decoder_;
-  Lift2DFrameAnnotationTo3DCalculatorOptions options_;
-  Eigen::Matrix<float, 4, 4, Eigen::RowMajor> projection_matrix_;
-};
-REGISTER_CALCULATOR(Lift2DFrameAnnotationTo3DCalculator);
-
-absl::Status Lift2DFrameAnnotationTo3DCalculator::GetContract(
-    CalculatorContract* cc) {
-  RET_CHECK(cc->Inputs().HasTag(kInputStreamTag));
-  RET_CHECK(cc->Outputs().HasTag(kOutputStreamTag));
-  cc->Inputs().Tag(kInputStreamTag).Set<FrameAnnotation>();
-  cc->Outputs().Tag(kOutputStreamTag).Set<FrameAnnotation>();
-
-  return absl::OkStatus();
-}
-
-absl::Status Lift2DFrameAnnotationTo3DCalculator::Open(CalculatorContext* cc) {
-  cc->SetOffset(TimestampDiff(0));
-  MP_RETURN_IF_ERROR(LoadOptions(cc));
-  // Load camera intrinsic matrix.
-  const float fx = options_.normalized_focal_x();
-  const float fy = options_.normalized_focal_y();
-  const float px = options_.normalized_principal_point_x();
-  const float py = options_.normalized_principal_point_y();
-  // clang-format off
-  projection_matrix_ << fx, 0.,  px, 0.,
-                        0., fy,  py, 0.,
-                        0., 0., -1., 0.,
-                        0., 0., -1., 0.;
-  // clang-format on
-  decoder_ = absl::make_unique<Decoder>(
-      BeliefDecoderConfig(options_.decoder_config()));
-  return absl::OkStatus();
-}
-
-absl::Status Lift2DFrameAnnotationTo3DCalculator::Process(
-    CalculatorContext* cc) {
-  if (cc->Inputs().Tag(kInputStreamTag).IsEmpty()) {
-    return absl::OkStatus();
-  }
-
-  auto output_objects = absl::make_unique<FrameAnnotation>();
-
-  MP_RETURN_IF_ERROR(ProcessCPU(cc, output_objects.get()));
-
-  // Output
-  if (cc->Outputs().HasTag(kOutputStreamTag)) {
-    cc->Outputs()
-        .Tag(kOutputStreamTag)
-        .Add(output_objects.release(), cc->InputTimestamp());
-  }
-
-  return absl::OkStatus();
-}
-
-absl::Status Lift2DFrameAnnotationTo3DCalculator::ProcessCPU(
-    CalculatorContext* cc, FrameAnnotation* output_objects) {
-  const auto& input_frame_annotations =
-      cc->Inputs().Tag(kInputStreamTag).Get<FrameAnnotation>();
-  // Copy the input frame annotation to the output
-  *output_objects = input_frame_annotations;
-
-  auto status = decoder_->Lift2DTo3D(projection_matrix_, /*portrait*/ false,
-                                     output_objects);
-  if (!status.ok()) {
-    LOG(ERROR) << status;
-    return status;
-  }
-  AssignObjectIdAndTimestamp(cc->InputTimestamp().Microseconds(),
-                             output_objects);
-
-  return absl::OkStatus();
-}
-
-absl::Status Lift2DFrameAnnotationTo3DCalculator::Close(CalculatorContext* cc) {
-  return absl::OkStatus();
-}
-
-absl::Status Lift2DFrameAnnotationTo3DCalculator::LoadOptions(
-    CalculatorContext* cc) {
-  // Get calculator options specified in the graph.
-  options_ = cc->Options<Lift2DFrameAnnotationTo3DCalculatorOptions>();
-
-  return absl::OkStatus();
-}
-
-void Lift2DFrameAnnotationTo3DCalculator::AssignObjectIdAndTimestamp(
-    int64 timestamp_us, FrameAnnotation* annotation) {
-  for (auto& ann : *annotation->mutable_annotations()) {
-    ann.set_object_id(GetNextObjectId());
-  }
-  annotation->set_timestamp(timestamp_us);
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.proto b/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.proto
deleted file mode 100644
index a3005c1..0000000
--- a/mediapipe/modules/objectron/calculators/lift_2d_frame_annotation_to_3d_calculator.proto
+++ /dev/null
@@ -1,42 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// The option proto for the Lift2DFrameAnnotationTo3DCalculatorOptions.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-import "mediapipe/modules/objectron/calculators/belief_decoder_config.proto";
-
-message Lift2DFrameAnnotationTo3DCalculatorOptions {
-  extend CalculatorOptions {
-    optional Lift2DFrameAnnotationTo3DCalculatorOptions ext = 290166284;
-  }
-
-  optional BeliefDecoderConfig decoder_config = 1;
-
-  // Camera focal length along x, normalized by width/2.
-  optional float normalized_focal_x = 2 [default = 1.0];
-
-  // Camera focal length along y, normalized by height/2.
-  optional float normalized_focal_y = 3 [default = 1.0];
-
-  // Camera principle point x, normalized by width/2, origin is image center.
-  optional float normalized_principal_point_x = 4 [default = 0.0];
-
-  // Camera principle point y, normalized by height/2, origin is image center.
-  optional float normalized_principal_point_y = 5 [default = 0.0];
-}
diff --git a/mediapipe/modules/objectron/calculators/model.cc b/mediapipe/modules/objectron/calculators/model.cc
deleted file mode 100644
index 40aca39..0000000
--- a/mediapipe/modules/objectron/calculators/model.cc
+++ /dev/null
@@ -1,101 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/objectron/calculators/model.h"
-
-#include "mediapipe/framework/port/logging.h"
-
-namespace mediapipe {
-
-void Model::SetTransformation(const Eigen::Matrix4f& transform) {
-  transformation_ = transform;
-}
-
-void Model::SetTranslation(const Eigen::Vector3f& translation) {
-  transformation_.col(3).template head<3>() = translation;
-}
-
-void Model::SetRotation(float roll, float pitch, float yaw) {
-  // In our coordinate system, Y is up. We first rotate the object around Y
-  // (yaw), then around Z (pitch), and finally around X (roll).
-  Eigen::Matrix3f r;
-  r = Eigen::AngleAxisf(yaw, Eigen::Vector3f::UnitY()) *
-      Eigen::AngleAxisf(pitch, Eigen::Vector3f::UnitZ()) *
-      Eigen::AngleAxisf(roll, Eigen::Vector3f::UnitX());
-  transformation_.topLeftCorner<3, 3>() = r;
-}
-
-void Model::SetRotation(const Eigen::Matrix3f& rotation) {
-  transformation_.topLeftCorner<3, 3>() = rotation;
-}
-
-void Model::SetScale(const Eigen::Vector3f& scale) { scale_ = scale; }
-
-void Model::SetCategory(const std::string& category) { category_ = category; }
-
-const Eigen::Vector3f Model::GetRotationAngles() const {
-  Vector3f ypr = transformation_.topLeftCorner<3, 3>().eulerAngles(1, 2, 0);
-  return Vector3f(ypr(2), ypr(1), ypr(0));  // swap YPR with RPY
-}
-
-const Eigen::Matrix4f& Model::GetTransformation() const {
-  return transformation_;
-}
-
-const Eigen::Vector3f& Model::GetScale() const { return scale_; }
-
-const Eigen::Ref<const Eigen::Vector3f> Model::GetTranslation() const {
-  return transformation_.col(3).template head<3>();
-}
-
-const Eigen::Ref<const Eigen::Matrix3f> Model::GetRotation() const {
-  return transformation_.template topLeftCorner<3, 3>();
-}
-
-const std::string& Model::GetCategory() const { return category_; }
-
-void Model::Deserialize(const Object& obj) {
-  CHECK_EQ(obj.rotation_size(), 9);
-  CHECK_EQ(obj.translation_size(), 3);
-  CHECK_EQ(obj.scale_size(), 3);
-  category_ = obj.category();
-
-  using RotationMatrix = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
-  transformation_.setIdentity();
-  transformation_.topLeftCorner<3, 3>() =
-      Eigen::Map<const RotationMatrix>(obj.rotation().data());
-  transformation_.col(3).head<3>() =
-      Eigen::Map<const Eigen::Vector3f>(obj.translation().data());
-  scale_ = Eigen::Map<const Eigen::Vector3f>(obj.scale().data());
-  Update();
-}
-
-void Model::Serialize(Object* obj) {
-  obj->set_category(category_);
-  for (int i = 0; i < 3; ++i) {
-    for (int j = 0; j < 3; ++j) {
-      obj->add_rotation(transformation_(i, j));
-    }
-  }
-
-  for (int i = 0; i < 3; ++i) {
-    obj->add_translation(transformation_(i, 3));
-  }
-
-  for (int i = 0; i < 3; ++i) {
-    obj->add_scale(scale_[i]);
-  }
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/model.h b/mediapipe/modules/objectron/calculators/model.h
deleted file mode 100644
index 72b5eb2..0000000
--- a/mediapipe/modules/objectron/calculators/model.h
+++ /dev/null
@@ -1,92 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_MODEL_H_
-#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_MODEL_H_
-
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/object.pb.h"
-#include "mediapipe/modules/objectron/calculators/types.h"
-
-namespace mediapipe {
-
-class Model {
- public:
-  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
-
-  enum Type {
-    kVisualizationOnly = 0,
-    kBoundingBox,
-    kSkeleton,
-    kShape,  // Shape is a virtual object.
-    kNumModes,
-  };
-
-  virtual ~Model() = default;
-
-  virtual void SetTransformation(const Eigen::Matrix4f& transform);
-  virtual void SetTranslation(const Eigen::Vector3f& translation);
-
-  // Compute the rotation matrix from these angles and update the transformation
-  // matrix accordingly
-  virtual void SetRotation(float roll, float pitch, float yaw);
-  virtual void SetRotation(const Eigen::Matrix3f& rotation);
-  virtual void SetScale(const Eigen::Vector3f& scale);
-  virtual void SetCategory(const std::string& category);
-  virtual size_t GetNumberKeypoints() const { return number_keypoints_; }
-
-  // Gets Euler angles in the order of roll, pitch, yaw.
-  virtual const Eigen::Vector3f GetRotationAngles() const;
-  virtual const Eigen::Matrix4f& GetTransformation() const;
-  virtual const Eigen::Vector3f& GetScale() const;
-  virtual const Eigen::Ref<const Eigen::Vector3f> GetTranslation() const;
-  virtual const Eigen::Ref<const Eigen::Matrix3f> GetRotation() const;
-  virtual const std::string& GetCategory() const;
-
-  // Update the model's keypoints in the world-coordinate system.
-  // The update includes transforming the model to the world-coordinate system
-  // as well as scaling the model.
-  // The user is expected to call this function after Setting the rotation,
-  // orientation or the scale of the model to get an updated model.
-  virtual void Update() = 0;
-
-  // Update the model's parameters (orientation, position, and scale) from the
-  // user-provided variables.
-  virtual void Adjust(const std::vector<float>& variables) = 0;
-
-  // Returns a pointer to the model's keypoints.
-  // Use Eigen::Map to cast the pointer back to Vector3 or Vector4
-  virtual const float* GetVertex(size_t id) const = 0;
-  virtual float* GetVertex(size_t id) = 0;
-  virtual void Deserialize(const Object& obj);
-  virtual void Serialize(Object* obj);
-
-  // TODO: make member variables protected, and add public apis.
-  // 4x4 transformation matrix mapping the first keypoint to world coordinate
-  Eigen::Matrix4f transformation_;
-  Eigen::Vector3f scale_;  // width, height, depth
-  Type model_type_;
-  size_t number_keypoints_;
-  std::string category_;
-
- protected:
-  Model(Type type, size_t number_keypoints, const std::string& category)
-      : model_type_(type),
-        number_keypoints_(number_keypoints),
-        category_(category) {}
-};
-
-}  // namespace mediapipe
-
-#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_MODEL_H_
diff --git a/mediapipe/modules/objectron/calculators/object.proto b/mediapipe/modules/objectron/calculators/object.proto
deleted file mode 100644
index a07e83f..0000000
--- a/mediapipe/modules/objectron/calculators/object.proto
+++ /dev/null
@@ -1,124 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-syntax = "proto3";
-
-package mediapipe;
-
-message KeyPoint {
-  // The position of the keypoint in the local coordinate system of the rigid
-  // object.
-  float x = 1;
-  float y = 2;
-  float z = 3;
-
-  // Sphere around the keypoint, indiciating annotator's confidence of the
-  // position in meters.
-  float confidence_radius = 4;
-
-  // The name of the keypoint (e.g. legs, head, etc.).
-  // Does not have to be unique.
-  string name = 5;
-
-  // Indicates whether the keypoint is hidden or not.
-  bool hidden = 6;
-}
-
-message Object {
-  // Unique object id through a sequence. There might be multiple objects of
-  // the same label in this sequence.
-  int32 id = 1;
-
-  // Describes what category an object is. E.g. object class, attribute,
-  // instance or person identity. This provides additional context for the
-  // object type.
-  string category = 2;
-
-  enum Type {
-    UNDEFINED_TYPE = 0;
-    BOUNDING_BOX = 1;
-    SKELETON = 2;
-  }
-
-  Type type = 3;
-
-  // 3x3 row-major rotation matrix describing the orientation of the rigid
-  // object's frame of reference in the world-coordinate system.
-  repeated float rotation = 4;
-
-  // 3x1 vector describing the translation of the rigid object's frame of
-  // reference in the world-coordinate system in meters.
-  repeated float translation = 5;
-
-  // 3x1 vector describing the scale of the rigid object's frame of reference in
-  // the world-coordinate system in meters.
-  repeated float scale = 6;
-
-  // List of all the key points associated with this object in the object
-  // coordinate system.
-  // The first keypoint is always the object's frame of reference,
-  // e.g. the centroid of the box.
-  // E.g. bounding box with its center as frame of reference, the 9 keypoints :
-  // {0., 0., 0.},
-  // {-.5, -.5, -.5}, {-.5, -.5, +.5}, {-.5, +.5, -.5}, {-.5, +.5, +.5},
-  // {+.5, -.5, -.5}, {+.5, -.5, +.5}, {+.5, +.5, -.5}, {+.5, +.5, +.5}
-  // To get the bounding box in the world-coordinate system, we first scale the
-  // box then transform the scaled box.
-  // For example, bounding box in the world coordinate system is
-  // rotation * scale * keypoints + translation
-  repeated KeyPoint keypoints = 7;
-
-  // Enum to reflect how this object is created.
-  enum Method {
-    UNKNOWN_METHOD = 0;
-    ANNOTATION = 1;    // Created by data annotation.
-    AUGMENTATION = 2;  // Created by data augmentation.
-  }
-  Method method = 8;
-}
-
-// The edge connecting two keypoints together
-message Edge {
-  // keypoint id of the edge's source
-  int32 source = 1;
-
-  // keypoint id of the edge's sink
-  int32 sink = 2;
-}
-
-// The skeleton template for different objects (e.g. humans, chairs, hands, etc)
-// The annotation tool reads the skeleton template dictionary.
-message Skeleton {
-  // The origin keypoint in the object coordinate system. (i.e. Point 0, 0, 0)
-  int32 reference_keypoint = 1;
-
-  // The skeleton's category (e.g. human, chair, hand.). Should be unique in the
-  // dictionary.
-  string category = 2;
-
-  // Initialization value for all the keypoints in the skeleton in the object's
-  // local coordinate system. Pursuit will transform these points using object's
-  // transformation to get the keypoint in the world-cooridnate.
-  repeated KeyPoint keypoints = 3;
-
-  // List of edges connecting keypoints
-  repeated Edge edges = 4;
-}
-
-// The list of all the modeled skeletons in our library. These models can be
-// objects (chairs, desks, etc), humans (full pose, hands, faces, etc), or box.
-// We can have multiple skeletons in the same file.
-message Skeletons {
-  repeated Skeleton object = 1;
-}
diff --git a/mediapipe/modules/objectron/calculators/tensor_util.cc b/mediapipe/modules/objectron/calculators/tensor_util.cc
deleted file mode 100644
index 0004edd..0000000
--- a/mediapipe/modules/objectron/calculators/tensor_util.cc
+++ /dev/null
@@ -1,48 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include "mediapipe/modules/objectron/calculators/tensor_util.h"
-
-#include "mediapipe/framework/port/logging.h"
-
-namespace mediapipe {
-
-cv::Mat ConvertTfliteTensorToCvMat(const TfLiteTensor& tensor) {
-  // Check tensor is BxCxWxH (size = 4) and the batch size is one(data[0] = 1)
-  CHECK(tensor.dims->size == 4 && tensor.dims->data[0] == 1);
-  CHECK_EQ(kTfLiteFloat32, tensor.type) << "tflite_tensor type is not float";
-
-  const size_t num_output_channels = tensor.dims->data[3];
-  const int dims = 2;
-  const int sizes[] = {tensor.dims->data[1], tensor.dims->data[2]};
-  const int type = CV_MAKETYPE(CV_32F, num_output_channels);
-  return cv::Mat(dims, sizes, type, reinterpret_cast<void*>(tensor.data.f));
-}
-
-cv::Mat ConvertTensorToCvMat(const mediapipe::Tensor& tensor) {
-  // Check tensor is BxCxWxH (size = 4) and the batch size is one(data[0] = 1)
-  CHECK(tensor.shape().dims.size() == 4 && tensor.shape().dims[0] == 1);
-  CHECK_EQ(mediapipe::Tensor::ElementType::kFloat32 == tensor.element_type(),
-           true)
-      << "tensor type is not float";
-
-  const size_t num_output_channels = tensor.shape().dims[3];
-  const int dims = 2;
-  const int sizes[] = {tensor.shape().dims[1], tensor.shape().dims[2]};
-  const int type = CV_MAKETYPE(CV_32F, num_output_channels);
-  auto cpu_view = tensor.GetCpuReadView();
-  return cv::Mat(dims, sizes, type, const_cast<void*>(cpu_view.buffer<void>()));
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/tensor_util.h b/mediapipe/modules/objectron/calculators/tensor_util.h
deleted file mode 100644
index 0b26209..0000000
--- a/mediapipe/modules/objectron/calculators/tensor_util.h
+++ /dev/null
@@ -1,31 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TENSOR_UTIL_H_
-#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TENSOR_UTIL_H_
-
-#include "mediapipe/framework/formats/tensor.h"
-#include "mediapipe/framework/port/opencv_core_inc.h"
-#include "tensorflow/lite/interpreter.h"
-
-namespace mediapipe {
-
-// Converts a single channel tflite tensor to a grayscale image
-cv::Mat ConvertTfliteTensorToCvMat(const TfLiteTensor& tensor);
-
-// Converts a single channel tensor to grayscale image
-cv::Mat ConvertTensorToCvMat(const mediapipe::Tensor& tensor);
-}  // namespace mediapipe
-
-#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TENSOR_UTIL_H_
diff --git a/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.cc b/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.cc
deleted file mode 100644
index 6989c34..0000000
--- a/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.cc
+++ /dev/null
@@ -1,209 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <memory>
-#include <unordered_map>
-#include <vector>
-
-#include "Eigen/Dense"
-#include "absl/memory/memory.h"
-#include "absl/strings/str_format.h"
-#include "absl/types/span.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/deps/file_path.h"
-#include "mediapipe/framework/formats/tensor.h"
-#include "mediapipe/framework/port/opencv_core_inc.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/belief_decoder_config.pb.h"
-#include "mediapipe/modules/objectron/calculators/decoder.h"
-#include "mediapipe/modules/objectron/calculators/tensor_util.h"
-#include "mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.pb.h"
-
-namespace {
-constexpr char kInputStreamTag[] = "TENSORS";
-constexpr char kOutputStreamTag[] = "ANNOTATIONS";
-
-// Each detection object will be assigned an unique id that starts from 1.
-static int object_id = 0;
-
-inline int GetNextObjectId() { return ++object_id; }
-}  // namespace
-
-namespace mediapipe {
-
-// Convert result Tensors from deep pursuit 3d model into FrameAnnotation.
-//
-// Input:
-//  TENSORS - Vector of Tensor of type kFloat32.
-// Output:
-//  ANNOTATIONS - Result FrameAnnotation.
-//
-// Usage example:
-// node {
-//   calculator: "TensorsToObjectsCalculator"
-//   input_stream: "TENSORS:tensors"
-//   output_stream: "ANNOTATIONS:annotations"
-// }
-class TensorsToObjectsCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc);
-
-  absl::Status Open(CalculatorContext* cc) override;
-  absl::Status Process(CalculatorContext* cc) override;
-  absl::Status Close(CalculatorContext* cc) override;
-
- private:
-  absl::Status ProcessCPU(CalculatorContext* cc,
-                          FrameAnnotation* output_objects);
-  absl::Status LoadOptions(CalculatorContext* cc);
-  // Takes point_3d in FrameAnnotation, projects to 2D, and overwrite the
-  // point_2d field with the projection.
-  void Project3DTo2D(bool portrait, FrameAnnotation* annotation) const;
-  // Increment and assign object ID for each detected object.
-  // In a single MediaPipe session, the IDs are unique.
-  // Also assign timestamp for the FrameAnnotation to be the input packet
-  // timestamp.
-  void AssignObjectIdAndTimestamp(int64 timestamp_us,
-                                  FrameAnnotation* annotation);
-
-  int num_classes_ = 0;
-  int num_keypoints_ = 0;
-
-  ::mediapipe::TensorsToObjectsCalculatorOptions options_;
-  std::unique_ptr<Decoder> decoder_;
-  Eigen::Matrix<float, 4, 4, Eigen::RowMajor> projection_matrix_;
-};
-REGISTER_CALCULATOR(TensorsToObjectsCalculator);
-
-absl::Status TensorsToObjectsCalculator::GetContract(CalculatorContract* cc) {
-  RET_CHECK(!cc->Inputs().GetTags().empty());
-  RET_CHECK(!cc->Outputs().GetTags().empty());
-
-  if (cc->Inputs().HasTag(kInputStreamTag)) {
-    cc->Inputs().Tag(kInputStreamTag).Set<std::vector<mediapipe::Tensor>>();
-  }
-
-  if (cc->Outputs().HasTag(kOutputStreamTag)) {
-    cc->Outputs().Tag(kOutputStreamTag).Set<FrameAnnotation>();
-  }
-  return absl::OkStatus();
-}
-
-absl::Status TensorsToObjectsCalculator::Open(CalculatorContext* cc) {
-  MP_RETURN_IF_ERROR(LoadOptions(cc));
-  // clang-format off
-  projection_matrix_ <<
-      1.5731,   0,       0, 0,
-      0,   2.0975,       0, 0,
-      0,        0, -1.0002, -0.2,
-      0,        0,      -1, 0;
-  // clang-format on
-  decoder_ = absl::make_unique<Decoder>(
-      BeliefDecoderConfig(options_.decoder_config()));
-
-  return absl::OkStatus();
-}
-
-absl::Status TensorsToObjectsCalculator::Process(CalculatorContext* cc) {
-  if (cc->Inputs().Tag(kInputStreamTag).IsEmpty()) {
-    return absl::OkStatus();
-  }
-
-  auto output_objects = absl::make_unique<FrameAnnotation>();
-
-  MP_RETURN_IF_ERROR(ProcessCPU(cc, output_objects.get()));
-
-  // Output
-  if (cc->Outputs().HasTag(kOutputStreamTag)) {
-    cc->Outputs()
-        .Tag(kOutputStreamTag)
-        .Add(output_objects.release(), cc->InputTimestamp());
-  }
-
-  return absl::OkStatus();
-}
-
-absl::Status TensorsToObjectsCalculator::ProcessCPU(
-    CalculatorContext* cc, FrameAnnotation* output_objects) {
-  const auto& input_tensors =
-      cc->Inputs().Tag(kInputStreamTag).Get<std::vector<mediapipe::Tensor>>();
-
-  cv::Mat prediction_heatmap = ConvertTensorToCvMat(input_tensors[0]);
-  cv::Mat offsetmap = ConvertTensorToCvMat(input_tensors[1]);
-
-  *output_objects =
-      decoder_->DecodeBoundingBoxKeypoints(prediction_heatmap, offsetmap);
-  auto status = decoder_->Lift2DTo3D(projection_matrix_, /*portrait*/ true,
-                                     output_objects);
-  if (!status.ok()) {
-    LOG(ERROR) << status;
-    return status;
-  }
-  Project3DTo2D(/*portrait*/ true, output_objects);
-  AssignObjectIdAndTimestamp(cc->InputTimestamp().Microseconds(),
-                             output_objects);
-
-  return absl::OkStatus();
-}
-
-absl::Status TensorsToObjectsCalculator::Close(CalculatorContext* cc) {
-  return absl::OkStatus();
-}
-
-absl::Status TensorsToObjectsCalculator::LoadOptions(CalculatorContext* cc) {
-  // Get calculator options specified in the graph.
-  options_ = cc->Options<::mediapipe::TensorsToObjectsCalculatorOptions>();
-
-  num_classes_ = options_.num_classes();
-  num_keypoints_ = options_.num_keypoints();
-
-  // Currently only support 2D when num_values_per_keypoint equals to 2.
-  CHECK_EQ(options_.num_values_per_keypoint(), 2);
-
-  return absl::OkStatus();
-}
-
-void TensorsToObjectsCalculator::Project3DTo2D(
-    bool portrait, FrameAnnotation* annotation) const {
-  for (auto& ann : *annotation->mutable_annotations()) {
-    for (auto& key_point : *ann.mutable_keypoints()) {
-      Eigen::Vector4f point3d;
-      point3d << key_point.point_3d().x(), key_point.point_3d().y(),
-          key_point.point_3d().z(), 1.0f;
-      Eigen::Vector4f point3d_projection = projection_matrix_ * point3d;
-      float u, v;
-      const float inv_w = 1.0f / point3d_projection(3);
-      if (portrait) {
-        u = (point3d_projection(1) * inv_w + 1.0f) * 0.5f;
-        v = (point3d_projection(0) * inv_w + 1.0f) * 0.5f;
-      } else {
-        u = (point3d_projection(0) * inv_w + 1.0f) * 0.5f;
-        v = (1.0f - point3d_projection(1) * inv_w) * 0.5f;
-      }
-      key_point.mutable_point_2d()->set_x(u);
-      key_point.mutable_point_2d()->set_y(v);
-    }
-  }
-}
-
-void TensorsToObjectsCalculator::AssignObjectIdAndTimestamp(
-    int64 timestamp_us, FrameAnnotation* annotation) {
-  for (auto& ann : *annotation->mutable_annotations()) {
-    ann.set_object_id(GetNextObjectId());
-  }
-  annotation->set_timestamp(timestamp_us);
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.proto b/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.proto
deleted file mode 100644
index 8d46fce..0000000
--- a/mediapipe/modules/objectron/calculators/tensors_to_objects_calculator.proto
+++ /dev/null
@@ -1,39 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// The option proto for the TensorsToObjectsCalculatorOptions.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-import "mediapipe/modules/objectron/calculators/belief_decoder_config.proto";
-
-message TensorsToObjectsCalculatorOptions {
-  extend CalculatorOptions {
-    optional TensorsToObjectsCalculatorOptions ext = 334361940;
-  }
-
-  // The number of output classes predicted by the detection model.
-  optional int32 num_classes = 1;
-
-  // The number of predicted keypoints.
-  optional int32 num_keypoints = 2;
-  // The dimension of each keypoint, e.g. number of values predicted for each
-  // keypoint.
-  optional int32 num_values_per_keypoint = 3 [default = 2];
-
-  optional BeliefDecoderConfig decoder_config = 4;
-}
diff --git a/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.cc b/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.cc
deleted file mode 100644
index e3686f6..0000000
--- a/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.cc
+++ /dev/null
@@ -1,217 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#include <memory>
-#include <unordered_map>
-#include <vector>
-
-#include "Eigen/Dense"
-#include "absl/memory/memory.h"
-#include "absl/strings/str_format.h"
-#include "absl/types/span.h"
-#include "mediapipe/framework/calculator_framework.h"
-#include "mediapipe/framework/deps/file_path.h"
-#include "mediapipe/framework/port/opencv_core_inc.h"
-#include "mediapipe/framework/port/ret_check.h"
-#include "mediapipe/modules/objectron/calculators/annotation_data.pb.h"
-#include "mediapipe/modules/objectron/calculators/belief_decoder_config.pb.h"
-#include "mediapipe/modules/objectron/calculators/decoder.h"
-#include "mediapipe/modules/objectron/calculators/tensor_util.h"
-#include "mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.pb.h"
-#include "tensorflow/lite/interpreter.h"
-
-namespace {
-constexpr char kInputStreamTag[] = "TENSORS";
-constexpr char kOutputStreamTag[] = "ANNOTATIONS";
-
-// Each detection object will be assigned an unique id that starts from 1.
-static int object_id = 0;
-
-inline int GetNextObjectId() { return ++object_id; }
-}  // namespace
-
-namespace mediapipe {
-
-// Convert result TFLite tensors from deep pursuit 3d model into
-// FrameAnnotation.
-//
-// Input:
-//  TENSORS - Vector of TfLiteTensor of type kTfLiteFloat32.
-// Output:
-//  ANNOTATIONS - Result FrameAnnotation.
-//
-// Usage example:
-// node {
-//   calculator: "TfLiteTensorsToObjectsCalculator"
-//   input_stream: "TENSORS:tensors"
-//   output_stream: "ANNOTATIONS:annotations"
-// }
-class TfLiteTensorsToObjectsCalculator : public CalculatorBase {
- public:
-  static absl::Status GetContract(CalculatorContract* cc);
-
-  absl::Status Open(CalculatorContext* cc) override;
-  absl::Status Process(CalculatorContext* cc) override;
-  absl::Status Close(CalculatorContext* cc) override;
-
- private:
-  absl::Status ProcessCPU(CalculatorContext* cc,
-                          FrameAnnotation* output_objects);
-  absl::Status LoadOptions(CalculatorContext* cc);
-  // Takes point_3d in FrameAnnotation, projects to 2D, and overwrite the
-  // point_2d field with the projection.
-  void Project3DTo2D(bool portrait, FrameAnnotation* annotation) const;
-  // Increment and assign object ID for each detected object.
-  // In a single MediaPipe session, the IDs are unique.
-  // Also assign timestamp for the FrameAnnotation to be the input packet
-  // timestamp.
-  void AssignObjectIdAndTimestamp(int64 timestamp_us,
-                                  FrameAnnotation* annotation);
-
-  int num_classes_ = 0;
-  int num_keypoints_ = 0;
-
-  ::mediapipe::TfLiteTensorsToObjectsCalculatorOptions options_;
-  std::unique_ptr<Decoder> decoder_;
-  Eigen::Matrix<float, 4, 4, Eigen::RowMajor> projection_matrix_;
-};
-REGISTER_CALCULATOR(TfLiteTensorsToObjectsCalculator);
-
-absl::Status TfLiteTensorsToObjectsCalculator::GetContract(
-    CalculatorContract* cc) {
-  RET_CHECK(!cc->Inputs().GetTags().empty());
-  RET_CHECK(!cc->Outputs().GetTags().empty());
-
-  if (cc->Inputs().HasTag(kInputStreamTag)) {
-    cc->Inputs().Tag(kInputStreamTag).Set<std::vector<TfLiteTensor>>();
-  }
-
-  if (cc->Outputs().HasTag(kOutputStreamTag)) {
-    cc->Outputs().Tag(kOutputStreamTag).Set<FrameAnnotation>();
-  }
-  return absl::OkStatus();
-}
-
-absl::Status TfLiteTensorsToObjectsCalculator::Open(CalculatorContext* cc) {
-  MP_RETURN_IF_ERROR(LoadOptions(cc));
-  // Load camera intrinsic matrix.
-  const float fx = options_.normalized_focal_x();
-  const float fy = options_.normalized_focal_y();
-  const float px = options_.normalized_principal_point_x();
-  const float py = options_.normalized_principal_point_y();
-  // clang-format off
-  projection_matrix_ << fx, 0.,  px, 0.,
-                        0., fy,  py, 0.,
-                        0., 0., -1., 0.,
-                        0., 0., -1., 0.;
-  // clang-format on
-  decoder_ = absl::make_unique<Decoder>(
-      BeliefDecoderConfig(options_.decoder_config()));
-
-  return absl::OkStatus();
-}
-
-absl::Status TfLiteTensorsToObjectsCalculator::Process(CalculatorContext* cc) {
-  if (cc->Inputs().Tag(kInputStreamTag).IsEmpty()) {
-    return absl::OkStatus();
-  }
-
-  auto output_objects = absl::make_unique<FrameAnnotation>();
-
-  MP_RETURN_IF_ERROR(ProcessCPU(cc, output_objects.get()));
-
-  // Output
-  if (cc->Outputs().HasTag(kOutputStreamTag)) {
-    cc->Outputs()
-        .Tag(kOutputStreamTag)
-        .Add(output_objects.release(), cc->InputTimestamp());
-  }
-
-  return absl::OkStatus();
-}
-
-absl::Status TfLiteTensorsToObjectsCalculator::ProcessCPU(
-    CalculatorContext* cc, FrameAnnotation* output_objects) {
-  const auto& input_tensors =
-      cc->Inputs().Tag(kInputStreamTag).Get<std::vector<TfLiteTensor>>();
-
-  cv::Mat prediction_heatmap = ConvertTfliteTensorToCvMat(input_tensors[0]);
-  cv::Mat offsetmap = ConvertTfliteTensorToCvMat(input_tensors[1]);
-
-  *output_objects =
-      decoder_->DecodeBoundingBoxKeypoints(prediction_heatmap, offsetmap);
-  auto status = decoder_->Lift2DTo3D(projection_matrix_, /*portrait*/ true,
-                                     output_objects);
-  if (!status.ok()) {
-    LOG(ERROR) << status;
-    return status;
-  }
-  Project3DTo2D(/*portrait*/ true, output_objects);
-  AssignObjectIdAndTimestamp(cc->InputTimestamp().Microseconds(),
-                             output_objects);
-
-  return absl::OkStatus();
-}
-
-absl::Status TfLiteTensorsToObjectsCalculator::Close(CalculatorContext* cc) {
-  return absl::OkStatus();
-}
-
-absl::Status TfLiteTensorsToObjectsCalculator::LoadOptions(
-    CalculatorContext* cc) {
-  // Get calculator options specified in the graph.
-  options_ =
-      cc->Options<::mediapipe::TfLiteTensorsToObjectsCalculatorOptions>();
-
-  num_classes_ = options_.num_classes();
-  num_keypoints_ = options_.num_keypoints();
-
-  // Currently only support 2D when num_values_per_keypoint equals to 2.
-  CHECK_EQ(options_.num_values_per_keypoint(), 2);
-
-  return absl::OkStatus();
-}
-
-void TfLiteTensorsToObjectsCalculator::Project3DTo2D(
-    bool portrait, FrameAnnotation* annotation) const {
-  for (auto& ann : *annotation->mutable_annotations()) {
-    for (auto& key_point : *ann.mutable_keypoints()) {
-      Eigen::Vector4f point3d;
-      point3d << key_point.point_3d().x(), key_point.point_3d().y(),
-          key_point.point_3d().z(), 1.0f;
-      Eigen::Vector4f point3d_projection = projection_matrix_ * point3d;
-      float u, v;
-      const float inv_w = 1.0f / point3d_projection(3);
-      if (portrait) {
-        u = (point3d_projection(1) * inv_w + 1.0f) * 0.5f;
-        v = (point3d_projection(0) * inv_w + 1.0f) * 0.5f;
-      } else {
-        u = (point3d_projection(0) * inv_w + 1.0f) * 0.5f;
-        v = (1.0f - point3d_projection(1) * inv_w) * 0.5f;
-      }
-      key_point.mutable_point_2d()->set_x(u);
-      key_point.mutable_point_2d()->set_y(v);
-    }
-  }
-}
-
-void TfLiteTensorsToObjectsCalculator::AssignObjectIdAndTimestamp(
-    int64 timestamp_us, FrameAnnotation* annotation) {
-  for (auto& ann : *annotation->mutable_annotations()) {
-    ann.set_object_id(GetNextObjectId());
-  }
-  annotation->set_timestamp(timestamp_us);
-}
-
-}  // namespace mediapipe
diff --git a/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.proto b/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.proto
deleted file mode 100644
index 32520d9..0000000
--- a/mediapipe/modules/objectron/calculators/tflite_tensors_to_objects_calculator.proto
+++ /dev/null
@@ -1,51 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-// The option proto for the TfLiteTensorsToObjectsCalculatorOptions.
-
-syntax = "proto2";
-
-package mediapipe;
-
-import "mediapipe/framework/calculator.proto";
-import "mediapipe/modules/objectron/calculators/belief_decoder_config.proto";
-
-message TfLiteTensorsToObjectsCalculatorOptions {
-  extend CalculatorOptions {
-    optional TfLiteTensorsToObjectsCalculatorOptions ext = 263667646;
-  }
-
-  // The number of output classes predicted by the detection model.
-  optional int32 num_classes = 1;
-
-  // The number of predicted keypoints.
-  optional int32 num_keypoints = 2;
-  // The dimension of each keypoint, e.g. number of values predicted for each
-  // keypoint.
-  optional int32 num_values_per_keypoint = 3 [default = 2];
-
-  optional BeliefDecoderConfig decoder_config = 4;
-
-  // Camera focal length along x, normalized by width/2.
-  optional float normalized_focal_x = 5 [default = 1.0];
-
-  // Camera focal length along y, normalized by height/2.
-  optional float normalized_focal_y = 6 [default = 1.0];
-
-  // Camera principle point x, normalized by width/2, origin is image center.
-  optional float normalized_principal_point_x = 7 [default = 0.0];
-
-  // Camera principle point y, normalized by height/2, origin is image center.
-  optional float normalized_principal_point_y = 8 [default = 0.0];
-}
diff --git a/mediapipe/modules/objectron/calculators/types.h b/mediapipe/modules/objectron/calculators/types.h
deleted file mode 100644
index dcc477d..0000000
--- a/mediapipe/modules/objectron/calculators/types.h
+++ /dev/null
@@ -1,56 +0,0 @@
-// Copyright 2020 The MediaPipe Authors.
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//      http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TYPES_H_
-#define MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TYPES_H_
-
-#include <array>
-
-#include "Eigen/Geometry"
-
-namespace mediapipe {
-
-using Eigen::Map;
-using Eigen::Vector2f;
-using Eigen::Vector3f;
-using Eigen::Vector4f;
-using Matrix4f_RM = Eigen::Matrix<float, 4, 4, Eigen::RowMajor>;
-using Matrix3f_RM = Eigen::Matrix<float, 3, 3, Eigen::RowMajor>;
-
-using Face = std::array<int, 4>;
-
-struct SuperPoint {
-  enum PointSourceType { kPointCloud = 0, kBoundingBox = 1, kSkeleton = 2 };
-  // The id of the point in the point-cloud
-  int reference_point;
-  // The source of the
-  PointSourceType source;
-  // The id of the point in set of points in current frame
-  int id;
-  // If source is kBoundingBox or kSkeleton, object_id stores the id of which \
-  // object this point belongs to.
-  int object_id;
-  // projected u-v value
-  Vector2f uv;
-  Vector2f pixel;
-  // the 3D point
-  Vector3f point_3d;
-  // Color
-  Eigen::Matrix<unsigned char, 4, 1> color;
-  bool rendered;
-};
-
-}  // namespace mediapipe
-
-#endif  // MEDIAPIPE_MODULES_OBJECTRON_CALCULATORS_TYPES_H_
diff --git a/mediapipe/modules/objectron/object_detection_3d_camera.tflite b/mediapipe/modules/objectron/object_detection_3d_camera.tflite
deleted file mode 100644
index 14cb826..0000000
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diff --git a/mediapipe/modules/objectron/object_detection_3d_chair.tflite b/mediapipe/modules/objectron/object_detection_3d_chair.tflite
deleted file mode 100644
index 3a23dfd..0000000
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diff --git a/mediapipe/modules/objectron/object_detection_3d_chair_1stage.tflite b/mediapipe/modules/objectron/object_detection_3d_chair_1stage.tflite
deleted file mode 100644
index 718dc97..0000000
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diff --git a/mediapipe/modules/objectron/object_detection_3d_cup.tflite b/mediapipe/modules/objectron/object_detection_3d_cup.tflite
deleted file mode 100644
index 1a7a5d3..0000000
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diff --git a/mediapipe/modules/objectron/object_detection_3d_sneakers.tflite b/mediapipe/modules/objectron/object_detection_3d_sneakers.tflite
deleted file mode 100644
index d64234d..0000000
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diff --git a/mediapipe/modules/objectron/object_detection_3d_sneakers_1stage.tflite b/mediapipe/modules/objectron/object_detection_3d_sneakers_1stage.tflite
deleted file mode 100644
index 2077114..0000000
Binary files a/mediapipe/modules/objectron/object_detection_3d_sneakers_1stage.tflite and /dev/null differ
diff --git a/mediapipe/modules/objectron/object_detection_oid_v4_cpu.pbtxt b/mediapipe/modules/objectron/object_detection_oid_v4_cpu.pbtxt
deleted file mode 100644
index f7a09fc..0000000
--- a/mediapipe/modules/objectron/object_detection_oid_v4_cpu.pbtxt
+++ /dev/null
@@ -1,134 +0,0 @@
-# MediaPipe Objectron object detection CPU subgraph.
-
-type: "ObjectDetectionOidV4Subgraph"
-
-input_stream: "IMAGE:input_video"
-input_side_packet: "LABELS_CSV:allowed_labels"
-output_stream: "DETECTIONS:detections"
-
-# Crops, resizes, and converts the input video into tensor.
-# Preserves aspect ratio of the images.
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:input_video"
-  output_stream: "TENSORS:image_tensor"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 300
-      output_tensor_height: 300
-      keep_aspect_ratio: false
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite"
-      delegate { xnnpack {} }
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  options: {
-    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
-      num_layers: 6
-      min_scale: 0.2
-      max_scale: 0.95
-      input_size_height: 300
-      input_size_width: 300
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 16
-      strides: 32
-      strides: 64
-      strides: 128
-      strides: 256
-      strides: 512
-      aspect_ratios: 1.0
-      aspect_ratios: 2.0
-      aspect_ratios: 0.5
-      aspect_ratios: 3.0
-      aspect_ratios: 0.3333
-      reduce_boxes_in_lowest_layer: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:all_detections"
-  options: {
-    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
-      num_classes: 24
-      num_boxes: 1917
-      num_coords: 4
-      ignore_classes: 0
-      sigmoid_score: true
-      apply_exponential_on_box_size: true
-      x_scale: 10.0
-      y_scale: 10.0
-      h_scale: 5.0
-      w_scale: 5.0
-      min_score_thresh: 0.5
-    }
-  }
-}
-
-# Maps detection label IDs to the corresponding label text. The label map is
-# provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "all_detections"
-  output_stream: "labeled_detections"
-  options: {
-    [mediapipe.DetectionLabelIdToTextCalculatorOptions.ext] {
-      label_map_path: "mediapipe/modules/objectron/object_detection_oidv4_labelmap.txt"
-    }
-  }
-}
-
-# Filters the detections to only those with valid scores
-# for the specified allowed labels.
-node {
-  calculator: "FilterDetectionCalculator"
-  input_stream: "DETECTIONS:labeled_detections"
-  output_stream: "DETECTIONS:filtered_detections"
-  input_side_packet: "LABELS_CSV:allowed_labels"
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "detections"
-  options: {
-    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
-      min_suppression_threshold: 0.5
-      max_num_detections: 100
-      overlap_type: INTERSECTION_OVER_UNION
-      return_empty_detections: true
-    }
-  }
-}
diff --git a/mediapipe/modules/objectron/object_detection_oid_v4_gpu.pbtxt b/mediapipe/modules/objectron/object_detection_oid_v4_gpu.pbtxt
deleted file mode 100644
index 7873e80..0000000
--- a/mediapipe/modules/objectron/object_detection_oid_v4_gpu.pbtxt
+++ /dev/null
@@ -1,136 +0,0 @@
-# MediaPipe Objectron object detection GPU subgraph.
-
-type: "ObjectDetectionOidV4Subgraph"
-
-input_stream: "IMAGE_GPU:input_video"
-input_side_packet: "LABELS_CSV:allowed_labels"
-output_stream: "DETECTIONS:detections"
-
-# Crops, resizes, and converts the input video into tensor.
-# Preserves aspect ratio of the images.
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE_GPU:input_video"
-  output_stream: "TENSORS:image_tensor"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 300
-      output_tensor_height: 300
-      keep_aspect_ratio: false
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:image_tensor"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite"
-      delegate { gpu {} }
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  options: {
-    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
-      num_layers: 6
-      min_scale: 0.2
-      max_scale: 0.95
-      input_size_height: 300
-      input_size_width: 300
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 16
-      strides: 32
-      strides: 64
-      strides: 128
-      strides: 256
-      strides: 512
-      aspect_ratios: 1.0
-      aspect_ratios: 2.0
-      aspect_ratios: 0.5
-      aspect_ratios: 3.0
-      aspect_ratios: 0.3333
-      reduce_boxes_in_lowest_layer: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:all_detections"
-  options: {
-    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
-      num_classes: 24
-      num_boxes: 1917
-      num_coords: 4
-      ignore_classes: 0
-      sigmoid_score: true
-      apply_exponential_on_box_size: true
-      x_scale: 10.0
-      y_scale: 10.0
-      h_scale: 5.0
-      w_scale: 5.0
-      min_score_thresh: 0.5
-    }
-  }
-}
-
-# Maps detection label IDs to the corresponding label text. The label map is
-# provided in the label_map_path option.
-node {
-  calculator: "DetectionLabelIdToTextCalculator"
-  input_stream: "all_detections"
-  output_stream: "labeled_detections"
-  options: {
-    [mediapipe.DetectionLabelIdToTextCalculatorOptions.ext] {
-      label_map_path: "object_detection_oidv4_labelmap.txt"
-    }
-  }
-}
-
-# Filters the detections to only those with valid scores
-# for the specified allowed labels.
-node {
-  calculator: "FilterDetectionCalculator"
-  input_stream: "DETECTIONS:labeled_detections"
-  output_stream: "DETECTIONS:filtered_detections"
-  input_side_packet: "LABELS_CSV:allowed_labels"
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "filtered_detections"
-  output_stream: "detections"
-  options: {
-    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
-      min_suppression_threshold: 0.5
-      max_num_detections: 100
-      overlap_type: INTERSECTION_OVER_UNION
-      return_empty_detections: true
-    }
-  }
-}
diff --git a/mediapipe/modules/objectron/object_detection_oidv4_labelmap.txt b/mediapipe/modules/objectron/object_detection_oidv4_labelmap.txt
deleted file mode 100644
index ef9032c..0000000
--- a/mediapipe/modules/objectron/object_detection_oidv4_labelmap.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-???
-Bicycle
-Boot
-Laptop
-Person
-Chair
-Cattle
-Desk
-Cat
-Computer mouse
-Computer monitor
-Box
-Mug
-Coffee cup
-Stationary bicycle
-Table
-Bottle
-High heels
-Vehicle
-Footwear
-Dog
-Book
-Camera
-Car
diff --git a/mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite b/mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite
deleted file mode 100644
index 3cb7291..0000000
Binary files a/mediapipe/modules/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite and /dev/null differ
diff --git a/mediapipe/modules/objectron/objectron_cpu.pbtxt b/mediapipe/modules/objectron/objectron_cpu.pbtxt
deleted file mode 100644
index 884da05..0000000
--- a/mediapipe/modules/objectron/objectron_cpu.pbtxt
+++ /dev/null
@@ -1,224 +0,0 @@
-# MediaPipe Objectron on CPU that produces 3D bounding boxes for objects.
-type: "ObjectronCpuSubgraph"
-
-# Input/Output streams and input side packets.
-input_stream: "IMAGE:image"
-# Path to TfLite model for 3D bounding box landmark prediction
-input_side_packet: "MODEL_PATH:box_landmark_model_path"
-# Allowed category labels, e.g. Footwear, Coffee cup, Mug, Chair, Camera
-input_side_packet: "LABELS_CSV:allowed_labels"
-# Max number of objects to detect/track. (int)
-input_side_packet: "MAX_NUM_OBJECTS:max_num_objects"
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-# Bounding box landmarks topology definition.
-# The numbers are indices in the box_landmarks list.
-#
-#       3 + + + + + + + + 7
-#       +\                +\          UP
-#       + \               + \
-#       +  \              +  \        |
-#       +   4 + + + + + + + + 8       | y
-#       +   +             +   +       |
-#       +   +             +   +       |
-#       +   +     (0)     +   +       .------- x
-#       +   +             +   +        \
-#       1 + + + + + + + + 5   +         \
-#        \  +              \  +          \ z
-#         \ +               \ +           \
-#          \+                \+
-#           2 + + + + + + + + 6
-
-# Collection of detected 3D objects, represented as a FrameAnnotation.
-output_stream: "FRAME_ANNOTATION:detected_objects"
-# Collection of box landmarks. (NormalizedLandmarkList)
-output_stream: "MULTI_LANDMARKS:multi_box_landmarks"
-# Crop rectangles derived from bounding box landmarks.
-output_stream: "NORM_RECTS:multi_box_rects"
-
-# Loads the file in the specified path into a blob.
-node {
-  calculator: "LocalFileContentsCalculator"
-  input_side_packet: "FILE_PATH:0:box_landmark_model_path"
-  output_side_packet: "CONTENTS:0:box_landmark_model_blob"
-}
-
-# Converts the input blob into a TF Lite model.
-node {
-  calculator: "TfLiteModelCalculator"
-  input_side_packet: "MODEL_BLOB:box_landmark_model_blob"
-  output_side_packet: "MODEL:box_landmark_model"
-}
-
-# When the optional input side packet "use_prev_landmarks" is either absent or
-# set to true, uses the landmarks on the previous image to help localize
-# landmarks on the current image.
-node {
-  calculator: "GateCalculator"
-  input_side_packet: "ALLOW:use_prev_landmarks"
-  input_stream: "prev_box_rects_from_landmarks"
-  output_stream: "gated_prev_box_rects_from_landmarks"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      allow: true
-    }
-  }
-}
-
-# Determines if an input vector of NormalizedRect has a size greater than or
-# equal to the provided max_num_objects.
-node {
-  calculator: "NormalizedRectVectorHasMinSizeCalculator"
-  input_stream: "ITERABLE:gated_prev_box_rects_from_landmarks"
-  input_side_packet: "max_num_objects"
-  output_stream: "prev_has_enough_objects"
-}
-
-# Drops the incoming image if BoxLandmarkSubgraph was able to identify box
-# presence in the previous image. Otherwise, passes the incoming image through
-# to trigger a new round of box detection in ObjectDetectionOidV4Subgraph.
-node {
-  calculator: "GateCalculator"
-  input_stream: "image"
-  input_stream: "DISALLOW:prev_has_enough_objects"
-  output_stream: "detection_image"
-
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      empty_packets_as_allow: true
-    }
-  }
-}
-
-# Subgraph that performs 2D object detection.
-node {
-  calculator: "ObjectDetectionOidV4Subgraph"
-  input_stream: "IMAGE:detection_image"
-  input_side_packet: "LABELS_CSV:allowed_labels"
-  output_stream: "DETECTIONS:raw_detections"
-}
-
-# Makes sure there are no more detections than provided max_num_objects.
-node {
-  calculator: "ClipDetectionVectorSizeCalculator"
-  input_stream: "raw_detections"
-  output_stream: "detections"
-  input_side_packet: "max_num_objects"
-
-}
-
-# Extracts image size from the input images.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_CPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Converts results of box detection into rectangles (normalized by image size)
-# that encloses the box.
-node {
-  calculator: "DetectionsToRectsCalculator"
-  input_stream: "DETECTIONS:detections"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECTS:box_rects_from_detections"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      output_zero_rect_for_empty_detections: false
-    }
-  }
-}
-
-# Performs association between NormalizedRect vector elements from previous
-# image and rects based on object detections from the current image. This
-# calculator ensures that the output box_rects vector doesn't contain
-# overlapping regions based on the specified min_similarity_threshold.
-node {
-  calculator: "AssociationNormRectCalculator"
-  input_stream: "box_rects_from_detections"
-  input_stream: "gated_prev_box_rects_from_landmarks"
-  output_stream: "multi_box_rects"
-  options: {
-    [mediapipe.AssociationCalculatorOptions.ext] {
-      min_similarity_threshold: 0.2
-    }
-  }
-}
-
-# Outputs each element of box_rects at a fake timestamp for the rest of the
-# graph to process. Clones image and image size packets for each
-# single_box_rect at the fake timestamp. At the end of the loop, outputs the
-# BATCH_END timestamp for downstream calculators to inform them that all
-# elements in the vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedRectCalculator"
-  input_stream: "ITERABLE:multi_box_rects"
-  input_stream: "CLONE:image"
-  output_stream: "ITEM:single_box_rect"
-  output_stream: "CLONE:landmarks_image"
-  output_stream: "BATCH_END:box_rects_timestamp"
-}
-
-# Subgraph that localizes box landmarks.
-node {
-  calculator: "BoxLandmarkSubgraph"
-  input_stream: "IMAGE:landmarks_image"
-  input_side_packet: "MODEL:box_landmark_model"
-  input_stream: "NORM_RECT:single_box_rect"
-  output_stream: "NORM_LANDMARKS:single_box_landmarks"
-}
-
-# Collects a set of landmarks for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITEM:single_box_landmarks"
-  input_stream: "BATCH_END:box_rects_timestamp"
-  output_stream: "ITERABLE:multi_box_landmarks"
-}
-
-# Convert box landmarks to frame annotations.
-node {
-  calculator: "LandmarksToFrameAnnotationCalculator"
-  input_stream: "MULTI_LANDMARKS:multi_box_landmarks"
-  output_stream: "FRAME_ANNOTATION:box_annotations"
-}
-
-# Lift the 2D landmarks to 3D using EPnP algorithm.
-node {
-  name: "Lift2DFrameAnnotationTo3DCalculator"
-  calculator: "Lift2DFrameAnnotationTo3DCalculator"
-  input_stream: "FRAME_ANNOTATION:box_annotations"
-  output_stream: "LIFTED_FRAME_ANNOTATION:detected_objects"
-  options: {
-    [mediapipe.Lift2DFrameAnnotationTo3DCalculatorOptions.ext] {
-      normalized_focal_x: 1.0
-      normalized_focal_y: 1.0
-    }
-  }
-}
-
-# Get rotated rectangle from detected box.
-node {
-  calculator: "FrameAnnotationToRectCalculator"
-  input_stream: "FRAME_ANNOTATION:detected_objects"
-  output_stream: "NORM_RECTS:box_rects_from_landmarks"
-}
-
-# Caches a box rectangle fed back from boxLandmarkSubgraph, and upon the
-# arrival of the next input image sends out the cached rectangle with the
-# timestamp replaced by that of the input image, essentially generating a packet
-# that carries the previous box rectangle. Note that upon the arrival of the
-# very first input image, an empty packet is sent out to jump start the
-# feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image"
-  input_stream: "LOOP:box_rects_from_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_box_rects_from_landmarks"
-}
diff --git a/mediapipe/modules/objectron/objectron_detection_1stage_gpu.pbtxt b/mediapipe/modules/objectron/objectron_detection_1stage_gpu.pbtxt
deleted file mode 100644
index 290b120..0000000
--- a/mediapipe/modules/objectron/objectron_detection_1stage_gpu.pbtxt
+++ /dev/null
@@ -1,83 +0,0 @@
-# MediaPipe Objectron detection gpu subgraph
-
-type: "ObjectronDetectionSubgraphGpu"
-
-input_stream: "IMAGE_GPU:input_video"
-output_stream: "ANNOTATIONS:objects"
-
-# Transforms the input image on GPU to a 480x640 image. To scale the input
-# image, the scale_mode option is set to FIT to preserve the aspect ratio,
-# resulting in potential letterboxing in the transformed image.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:input_video"
-  output_stream: "IMAGE_GPU:transformed_input_video"
-  options: {
-    [mediapipe.ImageTransformationCalculatorOptions.ext] {
-      output_width: 480
-      output_height: 640
-      scale_mode: FIT
-    }
-  }
-}
-
-# Converts the transformed input image on GPU into an image tensor stored as a
-# TfLiteTensor.
-node {
-  calculator: "TfLiteConverterCalculator"
-  input_stream: "IMAGE_GPU:transformed_input_video"
-  output_stream: "TENSORS_GPU:image_tensor"
-}
-
-# Generates a single side packet containing a TensorFlow Lite op resolver that
-# supports custom ops needed by the model used in this graph.
-node {
-  calculator: "TfLiteCustomOpResolverCalculator"
-  output_side_packet: "opresolver"
-  options: {
-    [mediapipe.TfLiteCustomOpResolverCalculatorOptions.ext] {
-      use_gpu: true
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "TfLiteInferenceCalculator"
-  input_stream: "TENSORS_GPU:image_tensor"
-  output_stream: "TENSORS:detection_tensors"
-  input_side_packet: "CUSTOM_OP_RESOLVER:opresolver"
-  options: {
-    [mediapipe.TfLiteInferenceCalculatorOptions.ext] {
-      model_path: "object_detection_3d.tflite"
-    }
-  }
-}
-
-# Decodes the model's output tensor (the heatmap and the distance fields) to 2D
-# keypoints. There are nine 2D keypoints: one center keypoint and eight vertices
-# for the 3D bounding box. The calculator parameters determine's the decoder's
-# sensitivity.
-node {
-  calculator: "TfLiteTensorsToObjectsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  output_stream: "ANNOTATIONS:objects"
-  options: {
-    [mediapipe.TfLiteTensorsToObjectsCalculatorOptions.ext] {
-      num_classes: 1
-      num_keypoints: 9
-      decoder_config {
-        heatmap_threshold: 0.6
-        local_max_distance: 2
-        offset_scale_coef: 1.0
-        voting_radius: 2
-        voting_allowance: 1
-        voting_threshold: 0.2
-      }
-      normalized_focal_x: 2.0975
-      normalized_focal_y: 1.5731
-    }
-  }
-}
diff --git a/mediapipe/modules/objectron/objectron_gpu.pbtxt b/mediapipe/modules/objectron/objectron_gpu.pbtxt
deleted file mode 100644
index 7ef2b67..0000000
--- a/mediapipe/modules/objectron/objectron_gpu.pbtxt
+++ /dev/null
@@ -1,186 +0,0 @@
-# MediaPipe Objectron on GPU that produces 3D bounding boxes for objects.
-type: "ObjectronGpuSubgraph"
-
-# Input/Output streams and input side packets.
-# Note that the input image is assumed to have aspect ratio 3:4 (width:height).
-input_stream: "IMAGE_GPU:image"
-# Allowed category labels, e.g. Footwear, Coffee cup, Mug, Chair, Camera
-input_side_packet: "LABELS_CSV:allowed_labels"
-# Max number of objects to detect/track. (int)
-input_side_packet: "MAX_NUM_OBJECTS:max_num_objects"
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Collection of detected 3D objects, represented as a FrameAnnotation.
-output_stream: "FRAME_ANNOTATION:detected_objects"
-
-# When the optional input side packet "use_prev_landmarks" is either absent or
-# set to true, uses the landmarks on the previous image to help localize
-# landmarks on the current image.
-node {
-  calculator: "GateCalculator"
-  input_side_packet: "ALLOW:use_prev_landmarks"
-  input_stream: "prev_box_rects_from_landmarks"
-  output_stream: "gated_prev_box_rects_from_landmarks"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      allow: true
-    }
-  }
-}
-
-# Determines if an input vector of NormalizedRect has a size greater than or
-# equal to the provided max_num_objects.
-node {
-  calculator: "NormalizedRectVectorHasMinSizeCalculator"
-  input_stream: "ITERABLE:gated_prev_box_rects_from_landmarks"
-  input_side_packet: "max_num_objects"
-  output_stream: "prev_has_enough_objects"
-}
-
-# Drops the incoming image if BoxLandmarkSubgraph was able to identify box
-# presence in the previous image. Otherwise, passes the incoming image through
-# to trigger a new round of box detection in ObjectDetectionOidV4Subgraph.
-node {
-  calculator: "GateCalculator"
-  input_stream: "image"
-  input_stream: "DISALLOW:prev_has_enough_objects"
-  output_stream: "detection_image"
-
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      empty_packets_as_allow: true
-    }
-  }
-}
-
-# Subgraph that performs 2D object detection.
-node {
-  calculator: "ObjectDetectionOidV4Subgraph"
-  input_stream: "IMAGE_GPU:detection_image"
-  input_side_packet: "LABELS_CSV:allowed_labels"
-  output_stream: "DETECTIONS:raw_detections"
-}
-
-# Makes sure there are no more detections than provided max_num_objects.
-node {
-  calculator: "ClipDetectionVectorSizeCalculator"
-  input_stream: "raw_detections"
-  output_stream: "detections"
-  input_side_packet: "max_num_objects"
-
-}
-
-# Extracts image size from the input images.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Converts results of box detection into rectangles (normalized by image size)
-# that encloses the box.
-node {
-  calculator: "DetectionsToRectsCalculator"
-  input_stream: "DETECTIONS:detections"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECTS:box_rects_from_detections"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      output_zero_rect_for_empty_detections: false
-    }
-  }
-}
-
-# Performs association between NormalizedRect vector elements from previous
-# image and rects based on object detections from the current image. This
-# calculator ensures that the output box_rects vector doesn't contain
-# overlapping regions based on the specified min_similarity_threshold.
-node {
-  calculator: "AssociationNormRectCalculator"
-  input_stream: "box_rects_from_detections"
-  input_stream: "gated_prev_box_rects_from_landmarks"
-  output_stream: "box_rects"
-  options: {
-    [mediapipe.AssociationCalculatorOptions.ext] {
-      min_similarity_threshold: 0.2
-    }
-  }
-}
-
-# Outputs each element of box_rects at a fake timestamp for the rest of the
-# graph to process. Clones image and image size packets for each
-# single_box_rect at the fake timestamp. At the end of the loop, outputs the
-# BATCH_END timestamp for downstream calculators to inform them that all
-# elements in the vector have been processed.
-node {
-  calculator: "BeginLoopNormalizedRectCalculator"
-  input_stream: "ITERABLE:box_rects"
-  input_stream: "CLONE:image"
-  output_stream: "ITEM:single_box_rect"
-  output_stream: "CLONE:landmarks_image"
-  output_stream: "BATCH_END:box_rects_timestamp"
-}
-
-# Subgraph that localizes box landmarks.
-node {
-  calculator: "BoxLandmarkSubgraph"
-  input_stream: "IMAGE:landmarks_image"
-  input_stream: "NORM_RECT:single_box_rect"
-  output_stream: "NORM_LANDMARKS:single_box_landmarks"
-}
-
-# Collects a set of landmarks for each hand into a vector. Upon receiving the
-# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
-# timestamp.
-node {
-  calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
-  input_stream: "ITEM:single_box_landmarks"
-  input_stream: "BATCH_END:box_rects_timestamp"
-  output_stream: "ITERABLE:multi_box_landmarks"
-}
-
-# Convert box landmarks to frame annotations.
-node {
-  calculator: "LandmarksToFrameAnnotationCalculator"
-  input_stream: "MULTI_LANDMARKS:multi_box_landmarks"
-  output_stream: "FRAME_ANNOTATION:box_annotations"
-}
-
-# Lift the 2D landmarks to 3D using EPnP algorithm.
-node {
-  calculator: "Lift2DFrameAnnotationTo3DCalculator"
-  input_stream: "FRAME_ANNOTATION:box_annotations"
-  output_stream: "LIFTED_FRAME_ANNOTATION:detected_objects"
-  options: {
-    [mediapipe.Lift2DFrameAnnotationTo3DCalculatorOptions.ext] {
-      normalized_focal_x: 2.0975
-      normalized_focal_y: 1.5731
-    }
-  }
-}
-
-# Get rotated rectangle from detected box.
-node {
-  calculator: "FrameAnnotationToRectCalculator"
-  input_stream: "FRAME_ANNOTATION:detected_objects"
-  output_stream: "NORM_RECTS:box_rects_from_landmarks"
-}
-
-# Caches a box rectangle fed back from boxLandmarkSubgraph, and upon the
-# arrival of the next input image sends out the cached rectangle with the
-# timestamp replaced by that of the input image, essentially generating a packet
-# that carries the previous box rectangle. Note that upon the arrival of the
-# very first input image, an empty packet is sent out to jump start the
-# feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image"
-  input_stream: "LOOP:box_rects_from_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_box_rects_from_landmarks"
-}
diff --git a/mediapipe/modules/objectron/objectron_tracking_1stage_gpu.pbtxt b/mediapipe/modules/objectron/objectron_tracking_1stage_gpu.pbtxt
deleted file mode 100644
index eb19a44..0000000
--- a/mediapipe/modules/objectron/objectron_tracking_1stage_gpu.pbtxt
+++ /dev/null
@@ -1,176 +0,0 @@
-# MediaPipe Objectron tracking gpu subgraph
-
-type: "ObjectronTrackingSubgraphGpu"
-
-input_stream: "FRAME_ANNOTATION:objects"
-input_stream: "IMAGE_GPU:input_video"
-output_stream: "LIFTED_FRAME_ANNOTATION:lifted_tracked_objects"
-
-
-# Converts the detected keypoints to Boxes, used by the tracking subgraph.
-node {
-  calculator: "FrameAnnotationToTimedBoxListCalculator"
-  input_stream: "FRAME_ANNOTATION:objects"
-  output_stream: "BOXES:start_pos"
-}
-
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:input_video"
-  output_stream: "IMAGE_GPU:downscaled_input_video"
-  options: {
-    [mediapipe.ImageTransformationCalculatorOptions.ext] {
-      output_width: 240
-      output_height: 320
-    }
-  }
-}
-
-# Converts GPU buffer to ImageFrame for processing tracking.
-node: {
-  calculator: "GpuBufferToImageFrameCalculator"
-  input_stream: "downscaled_input_video"
-  output_stream: "downscaled_input_video_cpu"
-}
-
-# Performs motion analysis on an incoming video stream.
-node: {
-  calculator: "MotionAnalysisCalculator"
-  input_stream: "VIDEO:downscaled_input_video_cpu"
-  output_stream: "CAMERA:camera_motion"
-  output_stream: "FLOW:region_flow"
-
-  options: {
-    [mediapipe.MotionAnalysisCalculatorOptions.ext]: {
-      analysis_options {
-        analysis_policy: ANALYSIS_POLICY_CAMERA_MOBILE
-        flow_options {
-          fast_estimation_min_block_size: 100
-          top_inlier_sets: 1
-          frac_inlier_error_threshold: 3e-3
-          downsample_mode: DOWNSAMPLE_TO_INPUT_SIZE
-          verification_distance: 5.0
-          verify_long_feature_acceleration: true
-          verify_long_feature_trigger_ratio: 0.1
-          tracking_options {
-            max_features: 500
-            adaptive_extraction_levels: 2
-            min_eig_val_settings {
-              adaptive_lowest_quality_level: 2e-4
-            }
-            klt_tracker_implementation: KLT_OPENCV
-          }
-        }
-      }
-    }
-  }
-}
-
-# Reads optical flow fields defined in
-# mediapipe/framework/formats/motion/optical_flow_field.h,
-# returns a VideoFrame with 2 channels (v_x and v_y), each channel is quantized
-# to 0-255.
-node: {
-  calculator: "FlowPackagerCalculator"
-  input_stream: "FLOW:region_flow"
-  input_stream: "CAMERA:camera_motion"
-  output_stream: "TRACKING:tracking_data"
-
-  options: {
-    [mediapipe.FlowPackagerCalculatorOptions.ext]: {
-      flow_packager_options: {
-        binary_tracking_data_support: false
-      }
-    }
-  }
-}
-
-# Tracks box positions over time.
-node: {
-  calculator: "BoxTrackerCalculator"
-  input_stream: "TRACKING:tracking_data"
-  input_stream: "TRACK_TIME:input_video"
-  input_stream: "START_POS:start_pos"
-  input_stream: "CANCEL_OBJECT_ID:cancel_object_id"
-  input_stream_info: {
-    tag_index: "CANCEL_OBJECT_ID"
-    back_edge: true
-  }
-  output_stream: "BOXES:boxes"
-
-  input_stream_handler {
-    input_stream_handler: "SyncSetInputStreamHandler"
-    options {
-      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
-        sync_set {
-          tag_index: "TRACKING"
-          tag_index: "TRACK_TIME"
-        }
-        sync_set {
-          tag_index: "START_POS"
-        }
-        sync_set {
-          tag_index: "CANCEL_OBJECT_ID"
-        }
-      }
-    }
-  }
-
-  options: {
-    [mediapipe.BoxTrackerCalculatorOptions.ext]: {
-      tracker_options: {
-        track_step_options {
-          track_object_and_camera: true
-          tracking_degrees: TRACKING_DEGREE_OBJECT_ROTATION_SCALE
-          inlier_spring_force: 0.0
-          static_motion_temporal_ratio: 3e-2
-        }
-      }
-      visualize_tracking_data: false
-      streaming_track_data_cache_size: 100
-    }
-  }
-}
-
-# Consolidates tracking and detection results.
-node {
-  calculator: "FrameAnnotationTrackerCalculator"
-  input_stream: "FRAME_ANNOTATION:objects"
-  input_stream: "TRACKED_BOXES:boxes"
-  output_stream: "TRACKED_FRAME_ANNOTATION:tracked_objects"
-  output_stream: "CANCEL_OBJECT_ID:cancel_object_id"
-  options: {
-    [mediapipe.FrameAnnotationTrackerCalculatorOptions.ext] {
-      img_width: 240
-      img_height: 320
-      iou_threshold: 0.1
-    }
-  }
-
-  input_stream_handler {
-    input_stream_handler: "SyncSetInputStreamHandler"
-    options {
-      [mediapipe.SyncSetInputStreamHandlerOptions.ext] {
-        sync_set {
-          tag_index: "FRAME_ANNOTATION"
-        }
-        sync_set {
-          tag_index: "TRACKED_BOXES"
-        }
-      }
-    }
-  }
-}
-
-# Lift the tracked 2D keypoints to 3D using EPnP algorithm.
-node {
-  calculator: "Lift2DFrameAnnotationTo3DCalculator"
-  input_stream: "FRAME_ANNOTATION:tracked_objects"
-  output_stream: "LIFTED_FRAME_ANNOTATION:lifted_tracked_objects"
-  options: {
-    [mediapipe.Lift2DFrameAnnotationTo3DCalculatorOptions.ext] {
-      normalized_focal_x: 2.0975
-      normalized_focal_y: 1.5731
-    }
-  }
-}
diff --git a/mediapipe/modules/palm_detection/BUILD b/mediapipe/modules/palm_detection/BUILD
deleted file mode 100644
index bed734b..0000000
--- a/mediapipe/modules/palm_detection/BUILD
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-exports_files([
-    "palm_detection_lite.tflite",
-    "palm_detection_full.tflite",
-])
-
-mediapipe_simple_subgraph(
-    name = "palm_detection_model_loader",
-    graph = "palm_detection_model_loader.pbtxt",
-    register_as = "PalmDetectionModelLoader",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/tflite:tflite_model_calculator",
-        "//mediapipe/calculators/util:local_file_contents_calculator",
-        "//mediapipe/framework/tool:switch_container",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "palm_detection_cpu",
-    graph = "palm_detection_cpu.pbtxt",
-    register_as = "PalmDetectionCpu",
-    deps = [
-        ":palm_detection_model_loader",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
-        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "palm_detection_gpu",
-    graph = "palm_detection_gpu.pbtxt",
-    register_as = "PalmDetectionGpu",
-    deps = [
-        ":palm_detection_model_loader",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
-        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-    ],
-)
diff --git a/mediapipe/modules/palm_detection/README.md b/mediapipe/modules/palm_detection/README.md
deleted file mode 100644
index c7fd610..0000000
--- a/mediapipe/modules/palm_detection/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# palm_detection
-
-Subgraphs|Details
-:--- | :---
-[`PalmDetectionCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_cpu.pbtxt)| Detects palms/hands. (CPU input.)
-[`PalmDetectionGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/palm_detection/palm_detection_gpu.pbtxt)| Detects palms/hands. (GPU input.)
-
diff --git a/mediapipe/modules/palm_detection/palm_detection_cpu.pbtxt b/mediapipe/modules/palm_detection/palm_detection_cpu.pbtxt
deleted file mode 100644
index 32b3927..0000000
--- a/mediapipe/modules/palm_detection/palm_detection_cpu.pbtxt
+++ /dev/null
@@ -1,147 +0,0 @@
-# MediaPipe graph to detect palms with TensorFlow Lite on CPU.
-
-type: "PalmDetectionCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# Complexity of the palm detection model: 0 or 1. Accuracy as well as inference
-# latency generally go up with the model complexity. If unspecified, functions
-# as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Detected palms. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of palms detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Transforms an image into a 128x128 tensor while keeping the aspect ratio, and
-# therefore may result in potential letterboxing.
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:image"
-  output_stream: "TENSORS:input_tensor"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 192
-      output_tensor_height: 192
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-    }
-  }
-}
-# Generates a single side packet containing a TensorFlow Lite op resolver that
-# supports custom ops needed by the model used in this graph.
-node {
-  calculator: "TfLiteCustomOpResolverCalculator"
-  output_side_packet: "opresolver"
-}
-
-# Loads the palm detection TF Lite model.
-node {
-  calculator: "PalmDetectionModelLoader"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  output_side_packet: "MODEL:model"
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensor"
-  output_stream: "TENSORS:detection_tensors"
-  input_side_packet: "CUSTOM_OP_RESOLVER:opresolver"
-  input_side_packet: "MODEL:model"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      delegate { xnnpack {} }
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  options: {
-    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
-      num_layers: 4
-      min_scale: 0.1484375
-      max_scale: 0.75
-      input_size_width: 192
-      input_size_height: 192
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 8
-      strides: 16
-      strides: 16
-      strides: 16
-      aspect_ratios: 1.0
-      fixed_anchor_size: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:unfiltered_detections"
-  options: {
-    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
-      num_classes: 1
-      num_boxes: 2016
-      num_coords: 18
-      box_coord_offset: 0
-      keypoint_coord_offset: 4
-      num_keypoints: 7
-      num_values_per_keypoint: 2
-      sigmoid_score: true
-      score_clipping_thresh: 100.0
-      reverse_output_order: true
-
-      x_scale: 192.0
-      y_scale: 192.0
-      w_scale: 192.0
-      h_scale: 192.0
-      min_score_thresh: 0.5
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "unfiltered_detections"
-  output_stream: "filtered_detections"
-  options: {
-    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
-      min_suppression_threshold: 0.3
-      overlap_type: INTERSECTION_OVER_UNION
-      algorithm: WEIGHTED
-    }
-  }
-}
-
-# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
-# letterboxed image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (the
-# input image to the graph before image transformation).
-node {
-  calculator: "DetectionLetterboxRemovalCalculator"
-  input_stream: "DETECTIONS:filtered_detections"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/palm_detection/palm_detection_full.tflite b/mediapipe/modules/palm_detection/palm_detection_full.tflite
deleted file mode 100755
index aee76a9..0000000
Binary files a/mediapipe/modules/palm_detection/palm_detection_full.tflite and /dev/null differ
diff --git a/mediapipe/modules/palm_detection/palm_detection_gpu.pbtxt b/mediapipe/modules/palm_detection/palm_detection_gpu.pbtxt
deleted file mode 100644
index 73e4127..0000000
--- a/mediapipe/modules/palm_detection/palm_detection_gpu.pbtxt
+++ /dev/null
@@ -1,153 +0,0 @@
-# MediaPipe graph to detect palms with TensorFlow Lite on GPU.
-
-type: "PalmDetectionGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# Complexity of the palm detection model: 0 or 1. Accuracy as well as inference
-# latency generally go up with the model complexity. If unspecified, functions
-# as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Detected palms. (std::vector<Detection>)
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of palms detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Transforms an image into a 256x256 tensor while keeping the aspect ratio, and
-# therefore may result in potential letterboxing.
-node {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "TENSORS:input_tensor"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 192
-      output_tensor_height: 192
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-# Generates a single side packet containing a TensorFlow Lite op resolver that
-# supports custom ops needed by the model used in this graph.
-node {
-  calculator: "TfLiteCustomOpResolverCalculator"
-  output_side_packet: "opresolver"
-  options: {
-    [mediapipe.TfLiteCustomOpResolverCalculatorOptions.ext] {
-      use_gpu: true
-    }
-  }
-}
-
-# Loads the palm detection TF Lite model.
-node {
-  calculator: "PalmDetectionModelLoader"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  output_side_packet: "MODEL:model"
-}
-
-# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensor"
-  output_stream: "TENSORS:detection_tensors"
-  input_side_packet: "CUSTOM_OP_RESOLVER:opresolver"
-  input_side_packet: "MODEL:model"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      delegate { gpu {} }
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  options: {
-    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
-      num_layers: 4
-      min_scale: 0.1484375
-      max_scale: 0.75
-      input_size_width: 192
-      input_size_height: 192
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 8
-      strides: 16
-      strides: 16
-      strides: 16
-      aspect_ratios: 1.0
-      fixed_anchor_size: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:unfiltered_detections"
-  options: {
-    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
-      num_classes: 1
-      num_boxes: 2016
-      num_coords: 18
-      box_coord_offset: 0
-      keypoint_coord_offset: 4
-      num_keypoints: 7
-      num_values_per_keypoint: 2
-      sigmoid_score: true
-      score_clipping_thresh: 100.0
-      reverse_output_order: true
-
-      x_scale: 192.0
-      y_scale: 192.0
-      w_scale: 192.0
-      h_scale: 192.0
-      min_score_thresh: 0.5
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "unfiltered_detections"
-  output_stream: "filtered_detections"
-  options: {
-    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
-      min_suppression_threshold: 0.3
-      overlap_type: INTERSECTION_OVER_UNION
-      algorithm: WEIGHTED
-    }
-  }
-}
-
-# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
-# letterboxed image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (the
-# input image to the graph before image transformation).
-node {
-  calculator: "DetectionLetterboxRemovalCalculator"
-  input_stream: "DETECTIONS:filtered_detections"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/palm_detection/palm_detection_lite.tflite b/mediapipe/modules/palm_detection/palm_detection_lite.tflite
deleted file mode 100755
index a19339a..0000000
Binary files a/mediapipe/modules/palm_detection/palm_detection_lite.tflite and /dev/null differ
diff --git a/mediapipe/modules/palm_detection/palm_detection_model_loader.pbtxt b/mediapipe/modules/palm_detection/palm_detection_model_loader.pbtxt
deleted file mode 100644
index f33a76e..0000000
--- a/mediapipe/modules/palm_detection/palm_detection_model_loader.pbtxt
+++ /dev/null
@@ -1,63 +0,0 @@
-# MediaPipe graph to load a selected palm detection TF Lite model.
-
-type: "PalmDetectionModelLoader"
-
-# Complexity of the palm detection model: 0 or 1. Accuracy as well as inference
-# latency generally go up with the model complexity. If unspecified, functions
-# as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# TF Lite model represented as a FlatBuffer.
-# (std::unique_ptr<tflite::FlatBufferModel, std::function<void(tflite::FlatBufferModel*)>>)
-output_side_packet: "MODEL:model"
-
-# Determines path to the desired pose landmark model file.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "SELECT:model_complexity"
-  output_side_packet: "PACKET:model_path"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      select: 1
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/palm_detection/palm_detection_lite.tflite"
-            }
-          }
-        }
-      }
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/palm_detection/palm_detection_full.tflite"
-            }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Loads the file in the specified path into a blob.
-node {
-  calculator: "LocalFileContentsCalculator"
-  input_side_packet: "FILE_PATH:model_path"
-  output_side_packet: "CONTENTS:model_blob"
-  options: {
-    [mediapipe.LocalFileContentsCalculatorOptions.ext]: {
-      text_mode: false
-    }
-  }
-}
-
-# Converts the input blob into a TF Lite model.
-node {
-  calculator: "TfLiteModelCalculator"
-  input_side_packet: "MODEL_BLOB:model_blob"
-  output_side_packet: "MODEL:model"
-}
diff --git a/mediapipe/modules/pose_detection/BUILD b/mediapipe/modules/pose_detection/BUILD
deleted file mode 100644
index f460300..0000000
--- a/mediapipe/modules/pose_detection/BUILD
+++ /dev/null
@@ -1,56 +0,0 @@
-# Copyright 2019 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "pose_detection_cpu",
-    graph = "pose_detection_cpu.pbtxt",
-    register_as = "PoseDetectionCpu",
-    deps = [
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_detection_gpu",
-    graph = "pose_detection_gpu.pbtxt",
-    register_as = "PoseDetectionGpu",
-    deps = [
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_detections_calculator",
-        "//mediapipe/calculators/tflite:ssd_anchors_calculator",
-        "//mediapipe/calculators/util:detection_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:non_max_suppression_calculator",
-    ],
-)
-
-exports_files(
-    srcs = [
-        "pose_detection.tflite",
-    ],
-)
diff --git a/mediapipe/modules/pose_detection/README.md b/mediapipe/modules/pose_detection/README.md
deleted file mode 100644
index e2e3b2f..0000000
--- a/mediapipe/modules/pose_detection/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# pose_detection
-
-Subgraphs|Details
-:--- | :---
-[`PoseDetectionCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt)| Detects poses. (CPU input, and inference is executed on CPU.)
-[`PoseDetectionGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt)| Detects poses. (GPU input, and inference is executed on GPU.)
-
diff --git a/mediapipe/modules/pose_detection/pose_detection.tflite b/mediapipe/modules/pose_detection/pose_detection.tflite
deleted file mode 100755
index 4f1c521..0000000
Binary files a/mediapipe/modules/pose_detection/pose_detection.tflite and /dev/null differ
diff --git a/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt b/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt
deleted file mode 100644
index 79ee1ac..0000000
--- a/mediapipe/modules/pose_detection/pose_detection_cpu.pbtxt
+++ /dev/null
@@ -1,159 +0,0 @@
-# MediaPipe graph to detect poses. (CPU input, and inference is executed on
-# CPU.)
-#
-# It is required that "pose_detection.tflite" is available at
-# "mediapipe/modules/pose_detection/pose_detection.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "PoseDetectionCpu"
-#     input_stream: "IMAGE:image"
-#     output_stream: "DETECTIONS:pose_detections"
-#   }
-
-type: "PoseDetectionCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# Detected poses. (std::vector<Detection>)
-# Bounding box in each pose detection is currently set to the bounding box of
-# the detected face. However, 4 additional key points are available in each
-# detection, which are used to further calculate a (rotated) bounding box that
-# encloses the body region of interest. Among the 4 key points, the first two
-# are for identifying the full-body region, and the second two for upper body
-# only:
-#
-# Key point 0 - mid hip center
-# Key point 1 - point that encodes size & rotation (for full body)
-# Key point 2 - mid shoulder center
-# Key point 3 - point that encodes size & rotation (for upper body)
-#
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of poses detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Transforms the input image into a 224x224 one while keeping the aspect ratio
-# (what is expected by the corresponding model), resulting in potential
-# letterboxing in the transformed image.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:image"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 224
-      output_tensor_height: 224
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-      # If this calculator truly operates in the CPU, then gpu_origin is
-      # ignored, but if some build switch insists on GPU inference, then we will
-      # still need to set this.
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/pose_detection/pose_detection.tflite"
-      delegate {
-        xnnpack {}
-      }
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  options: {
-    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
-      num_layers: 5
-      min_scale: 0.1484375
-      max_scale: 0.75
-      input_size_height: 224
-      input_size_width: 224
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 8
-      strides: 16
-      strides: 32
-      strides: 32
-      strides: 32
-      aspect_ratios: 1.0
-      fixed_anchor_size: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:unfiltered_detections"
-  options: {
-    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
-      num_classes: 1
-      num_boxes: 2254
-      num_coords: 12
-      box_coord_offset: 0
-      keypoint_coord_offset: 4
-      num_keypoints: 4
-      num_values_per_keypoint: 2
-      sigmoid_score: true
-      score_clipping_thresh: 100.0
-      reverse_output_order: true
-      x_scale: 224.0
-      y_scale: 224.0
-      h_scale: 224.0
-      w_scale: 224.0
-      min_score_thresh: 0.5
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "unfiltered_detections"
-  output_stream: "filtered_detections"
-  options: {
-    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
-      min_suppression_threshold: 0.3
-      overlap_type: INTERSECTION_OVER_UNION
-      algorithm: WEIGHTED
-    }
-  }
-}
-
-# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
-# letterboxed image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (the
-# input image to the graph before image transformation).
-node {
-  calculator: "DetectionLetterboxRemovalCalculator"
-  input_stream: "DETECTIONS:filtered_detections"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt b/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt
deleted file mode 100644
index b95a117..0000000
--- a/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt
+++ /dev/null
@@ -1,155 +0,0 @@
-# MediaPipe graph to detect poses. (GPU input, and inference is executed on
-# GPU.)
-#
-# It is required that "pose_detection.tflite" is available at
-# "mediapipe/modules/pose_detection/pose_detection.tflite"
-# path during execution.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "PoseDetectionGpu"
-#     input_stream: "IMAGE:image"
-#     output_stream: "DETECTIONS:pose_detections"
-#   }
-
-type: "PoseDetectionGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# Detected poses. (std::vector<Detection>)
-# Bounding box in each pose detection is currently set to the bounding box of
-# the detected face. However, 4 additional key points are available in each
-# detection, which are used to further calculate a (rotated) bounding box that
-# encloses the body region of interest. Among the 4 key points, the first two
-# are for identifying the full-body region, and the second two for upper body
-# only:
-#
-# Key point 0 - mid hip center
-# Key point 1 - point that encodes size & rotation (for full body)
-# Key point 2 - mid shoulder center
-# Key point 3 - point that encodes size & rotation (for upper body)
-#
-# NOTE: there will not be an output packet in the DETECTIONS stream for this
-# particular timestamp if none of poses detected. However, the MediaPipe
-# framework will internally inform the downstream calculators of the absence of
-# this packet so that they don't wait for it unnecessarily.
-output_stream: "DETECTIONS:detections"
-
-# Transforms the input image into a 224x224 one while keeping the aspect ratio
-# (what is expected by the corresponding model), resulting in potential
-# letterboxing in the transformed image.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 224
-      output_tensor_height: 224
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: -1.0
-        max: 1.0
-      }
-      border_mode: BORDER_ZERO
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-
-# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
-# vector of tensors representing, for instance, detection boxes/keypoints and
-# scores.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:detection_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      model_path: "mediapipe/modules/pose_detection/pose_detection.tflite"
-      #
-      delegate: { gpu { use_advanced_gpu_api: true } }
-    }
-  }
-}
-
-# Generates a single side packet containing a vector of SSD anchors based on
-# the specification in the options.
-node {
-  calculator: "SsdAnchorsCalculator"
-  output_side_packet: "anchors"
-  options: {
-    [mediapipe.SsdAnchorsCalculatorOptions.ext] {
-      num_layers: 5
-      min_scale: 0.1484375
-      max_scale: 0.75
-      input_size_height: 224
-      input_size_width: 224
-      anchor_offset_x: 0.5
-      anchor_offset_y: 0.5
-      strides: 8
-      strides: 16
-      strides: 32
-      strides: 32
-      strides: 32
-      aspect_ratios: 1.0
-      fixed_anchor_size: true
-    }
-  }
-}
-
-# Decodes the detection tensors generated by the TensorFlow Lite model, based on
-# the SSD anchors and the specification in the options, into a vector of
-# detections. Each detection describes a detected object.
-node {
-  calculator: "TensorsToDetectionsCalculator"
-  input_stream: "TENSORS:detection_tensors"
-  input_side_packet: "ANCHORS:anchors"
-  output_stream: "DETECTIONS:unfiltered_detections"
-  options: {
-    [mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
-      num_classes: 1
-      num_boxes: 2254
-      num_coords: 12
-      box_coord_offset: 0
-      keypoint_coord_offset: 4
-      num_keypoints: 4
-      num_values_per_keypoint: 2
-      sigmoid_score: true
-      score_clipping_thresh: 100.0
-      reverse_output_order: true
-      x_scale: 224.0
-      y_scale: 224.0
-      h_scale: 224.0
-      w_scale: 224.0
-      min_score_thresh: 0.5
-    }
-  }
-}
-
-# Performs non-max suppression to remove excessive detections.
-node {
-  calculator: "NonMaxSuppressionCalculator"
-  input_stream: "unfiltered_detections"
-  output_stream: "filtered_detections"
-  options: {
-    [mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
-      min_suppression_threshold: 0.3
-      overlap_type: INTERSECTION_OVER_UNION
-      algorithm: WEIGHTED
-    }
-  }
-}
-
-# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
-# letterboxed image (after image transformation with the FIT scale mode) to the
-# corresponding locations on the same image with the letterbox removed (the
-# input image to the graph before image transformation).
-node {
-  calculator: "DetectionLetterboxRemovalCalculator"
-  input_stream: "DETECTIONS:filtered_detections"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "DETECTIONS:detections"
-}
diff --git a/mediapipe/modules/pose_landmark/BUILD b/mediapipe/modules/pose_landmark/BUILD
deleted file mode 100644
index 787f0e2..0000000
--- a/mediapipe/modules/pose_landmark/BUILD
+++ /dev/null
@@ -1,189 +0,0 @@
-# Copyright 2020 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "pose_landmark_model_loader",
-    graph = "pose_landmark_model_loader.pbtxt",
-    register_as = "PoseLandmarkModelLoader",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/tflite:tflite_model_calculator",
-        "//mediapipe/calculators/util:local_file_contents_calculator",
-        "//mediapipe/framework/tool:switch_container",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_landmark_by_roi_gpu",
-    graph = "pose_landmark_by_roi_gpu.pbtxt",
-    register_as = "PoseLandmarkByRoiGpu",
-    deps = [
-        ":pose_landmark_model_loader",
-        ":pose_landmarks_and_segmentation_inverse_projection",
-        ":tensors_to_pose_landmarks_and_segmentation",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_landmark_by_roi_cpu",
-    graph = "pose_landmark_by_roi_cpu.pbtxt",
-    register_as = "PoseLandmarkByRoiCpu",
-    deps = [
-        ":pose_landmark_model_loader",
-        ":pose_landmarks_and_segmentation_inverse_projection",
-        ":tensors_to_pose_landmarks_and_segmentation",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "tensors_to_pose_landmarks_and_segmentation",
-    graph = "tensors_to_pose_landmarks_and_segmentation.pbtxt",
-    register_as = "TensorsToPoseLandmarksAndSegmentation",
-    deps = [
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:split_landmarks_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_floats_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_landmarks_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_segmentation_calculator",
-        "//mediapipe/calculators/util:refine_landmarks_from_heatmap_calculator",
-        "//mediapipe/calculators/util:thresholding_calculator",
-        "//mediapipe/calculators/util:visibility_copy_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_landmarks_and_segmentation_inverse_projection",
-    graph = "pose_landmarks_and_segmentation_inverse_projection.pbtxt",
-    register_as = "PoseLandmarksAndSegmentationInverseProjection",
-    deps = [
-        "//mediapipe/calculators/image:warp_affine_calculator",
-        "//mediapipe/calculators/util:inverse_matrix_calculator",
-        "//mediapipe/calculators/util:landmark_letterbox_removal_calculator",
-        "//mediapipe/calculators/util:landmark_projection_calculator",
-        "//mediapipe/calculators/util:world_landmark_projection_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_landmark_filtering",
-    graph = "pose_landmark_filtering.pbtxt",
-    register_as = "PoseLandmarkFiltering",
-    deps = [
-        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
-        "//mediapipe/calculators/util:landmarks_smoothing_calculator",
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:visibility_smoothing_calculator",
-        "//mediapipe/framework/tool:switch_container",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_segmentation_filtering",
-    graph = "pose_segmentation_filtering.pbtxt",
-    register_as = "PoseSegmentationFiltering",
-    deps = [
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/image:segmentation_smoothing_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_landmark_gpu",
-    graph = "pose_landmark_gpu.pbtxt",
-    register_as = "PoseLandmarkGpu",
-    deps = [
-        ":pose_detection_to_roi",
-        ":pose_landmark_by_roi_gpu",
-        ":pose_landmark_filtering",
-        ":pose_landmarks_to_roi",
-        ":pose_segmentation_filtering",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:merge_calculator",
-        "//mediapipe/calculators/core:packet_presence_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-        "//mediapipe/modules/pose_detection:pose_detection_gpu",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_landmark_cpu",
-    graph = "pose_landmark_cpu.pbtxt",
-    register_as = "PoseLandmarkCpu",
-    deps = [
-        ":pose_detection_to_roi",
-        ":pose_landmark_by_roi_cpu",
-        ":pose_landmark_filtering",
-        ":pose_landmarks_to_roi",
-        ":pose_segmentation_filtering",
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/core:gate_calculator",
-        "//mediapipe/calculators/core:merge_calculator",
-        "//mediapipe/calculators/core:packet_presence_calculator",
-        "//mediapipe/calculators/core:previous_loopback_calculator",
-        "//mediapipe/calculators/core:split_vector_calculator",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-        "//mediapipe/modules/pose_detection:pose_detection_cpu",
-    ],
-)
-
-exports_files(
-    srcs = [
-        "pose_landmark_full.tflite",
-        "pose_landmark_heavy.tflite",
-        "pose_landmark_lite.tflite",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_detection_to_roi",
-    graph = "pose_detection_to_roi.pbtxt",
-    register_as = "PoseDetectionToRoi",
-    deps = [
-        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "pose_landmarks_to_roi",
-    graph = "pose_landmarks_to_roi.pbtxt",
-    register_as = "PoseLandmarksToRoi",
-    deps = [
-        "//mediapipe/calculators/util:alignment_points_to_rects_calculator",
-        "//mediapipe/calculators/util:landmarks_to_detection_calculator",
-        "//mediapipe/calculators/util:rect_transformation_calculator",
-    ],
-)
diff --git a/mediapipe/modules/pose_landmark/README.md b/mediapipe/modules/pose_landmark/README.md
deleted file mode 100644
index 5752838..0000000
--- a/mediapipe/modules/pose_landmark/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# pose_landmark
-
-Subgraphs|Details
-:--- | :---
-[`PoseLandmarkByRoiCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_by_roi_cpu.pbtxt)| Detects landmarks of a single body pose. See landmarks (aka keypoints) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_topology.svg). (CPU input, and inference is executed on CPU.)
-[`PoseLandmarkByRoiGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_by_roi_gpu.pbtxt)| Detects landmarks of a single body pose. See landmarks (aka keypoints) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_topology.svg). (GPU input, and inference is executed on GPU)
-[`PoseLandmarkCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_cpu.pbtxt)| Detects landmarks of a single body pose. See landmarks (aka keypoints) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_topology.svg). (CPU input, and inference is executed on CPU)
-[`PoseLandmarkGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt)| Detects landmarks of a single body pose. See landmarks (aka keypoints) [scheme](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_topology.svg). (GPU input, and inference is executed on GPU.)
diff --git a/mediapipe/modules/pose_landmark/pose_detection_to_roi.pbtxt b/mediapipe/modules/pose_landmark/pose_detection_to_roi.pbtxt
deleted file mode 100644
index 47f82bb..0000000
--- a/mediapipe/modules/pose_landmark/pose_detection_to_roi.pbtxt
+++ /dev/null
@@ -1,45 +0,0 @@
-# MediaPipe graph to calculate pose region of interest (ROI) from a detection
-# provided by "PoseDetectionCpu" or "PoseDetectionGpu"
-#
-# NOTE: this graph is subject to change and should not be used directly.
-
-type: "PoseDetectionToRoi"
-
-# Pose detection. (Detection)
-input_stream: "DETECTION:detection"
-# Frame size (width and height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# ROI according to the first detection of input detections. (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Converts pose detection into a rectangle based on center and scale alignment
-# points.
-node {
-  calculator: "AlignmentPointsRectsCalculator"
-  input_stream: "DETECTION:detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:raw_roi"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 0
-      rotation_vector_end_keypoint_index: 1
-      rotation_vector_target_angle_degrees: 90
-    }
-  }
-}
-
-# Expands pose rect with marging used during training.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:raw_roi"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 1.25
-      scale_y: 1.25
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_by_roi_cpu.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_by_roi_cpu.pbtxt
deleted file mode 100644
index b674894..0000000
--- a/mediapipe/modules/pose_landmark/pose_landmark_by_roi_cpu.pbtxt
+++ /dev/null
@@ -1,178 +0,0 @@
-# MediaPipe graph to detect/predict pose landmarks and optionally segmentation
-# within an ROI. (CPU input, and inference is executed on CPU.)
-#
-# It is required that "pose_landmark_lite.tflite" or
-# "pose_landmark_full.tflite" or "pose_landmark_heavy.tflite" is available at
-# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
-# path respectively during execution, depending on the specification in the
-# MODEL_COMPLEXITY input side packet.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "PoseLandmarkByRoiCpu"
-#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-#     input_stream: "IMAGE:image"
-#     input_stream: "ROI:roi"
-#     output_stream: "LANDMARKS:landmarks"
-#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
-#   }
-
-type: "PoseLandmarkByRoiCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-# ROI (region of interest) within the given image where a pose is located.
-# (NormalizedRect)
-input_stream: "ROI:roi"
-
-# Whether to predict the segmentation mask. If unspecified, functions as set to
-# false. (bool)
-input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-
-# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
-# inference latency generally go up with the model complexity. If unspecified,
-# functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Pose landmarks within the given ROI. (NormalizedLandmarkList)
-# We have 33 landmarks (see pose_landmark_topology.svg) and there are other
-# auxiliary key points.
-# 0 - nose
-# 1 - left eye (inner)
-# 2 - left eye
-# 3 - left eye (outer)
-# 4 - right eye (inner)
-# 5 - right eye
-# 6 - right eye (outer)
-# 7 - left ear
-# 8 - right ear
-# 9 - mouth (left)
-# 10 - mouth (right)
-# 11 - left shoulder
-# 12 - right shoulder
-# 13 - left elbow
-# 14 - right elbow
-# 15 - left wrist
-# 16 - right wrist
-# 17 - left pinky
-# 18 - right pinky
-# 19 - left index
-# 20 - right index
-# 21 - left thumb
-# 22 - right thumb
-# 23 - left hip
-# 24 - right hip
-# 25 - left knee
-# 26 - right knee
-# 27 - left ankle
-# 28 - right ankle
-# 29 - left heel
-# 30 - right heel
-# 31 - left foot index
-# 32 - right foot index
-#
-# NOTE: If a pose is not present within the given ROI, for this particular
-# timestamp there will not be an output packet in the LANDMARKS stream. However,
-# the MediaPipe framework will internally inform the downstream calculators of
-# the absence of this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:landmarks"
-# Auxiliary landmarks for deriving the ROI in the subsequent image.
-# (NormalizedLandmarkList)
-output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
-
-# Pose world landmarks within the given ROI. (LandmarkList)
-# World landmarks are real-world 3D coordinates in meters with the origin at the
-# center between hips. WORLD_LANDMARKS shares the same landmark topology as
-# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
-# projected onto the 2D image surface, while WORLD_LANDMARKS provides
-# coordinates (in meters) of the 3D object itself.
-output_stream: "WORLD_LANDMARKS:world_landmarks"
-
-# Segmentation mask on CPU in ImageFormat::VEC32F1. (Image)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# Retrieves the image size.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_CPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Crops and transforms the specified ROI in the input image into an image patch
-# represented as a tensor of dimension expected by the corresponding ML model,
-# while maintaining the aspect ratio of the ROI (which can be different from
-# that of the image patch). Therefore, there can be letterboxing around the ROI
-# in the generated tensor representation.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE:image"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "MATRIX:transformation_matrix"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 256
-      output_tensor_height: 256
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-    }
-  }
-}
-
-# Loads the pose landmark TF Lite model.
-node {
-  calculator: "PoseLandmarkModelLoader"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  output_side_packet: "MODEL:model"
-}
-
-# Runs model inference on CPU.
-node {
-  calculator: "InferenceCalculator"
-  input_side_packet: "MODEL:model"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:output_tensors"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      delegate { xnnpack {} }
-    }
-  }
-}
-
-# Decodes the tensors into the corresponding landmark and segmentation mask
-# representation.
-node {
-  calculator: "TensorsToPoseLandmarksAndSegmentation"
-  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-  input_stream: "TENSORS:output_tensors"
-  output_stream: "LANDMARKS:roi_landmarks"
-  output_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
-  output_stream: "WORLD_LANDMARKS:roi_world_landmarks"
-  output_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
-}
-
-# Projects the landmarks and segmentation mask in the local coordinates of the
-# (potentially letterboxed) ROI back to the global coordinates of the full input
-# image.
-node {
-  calculator: "PoseLandmarksAndSegmentationInverseProjection"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "NORM_RECT:roi"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  input_stream: "MATRIX:transformation_matrix"
-  input_stream: "LANDMARKS:roi_landmarks"
-  input_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
-  input_stream: "WORLD_LANDMARKS:roi_world_landmarks"
-  input_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
-  output_stream: "LANDMARKS:landmarks"
-  output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
-  output_stream: "WORLD_LANDMARKS:world_landmarks"
-  output_stream: "SEGMENTATION_MASK:segmentation_mask"
-}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_by_roi_gpu.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_by_roi_gpu.pbtxt
deleted file mode 100644
index 7a2acce..0000000
--- a/mediapipe/modules/pose_landmark/pose_landmark_by_roi_gpu.pbtxt
+++ /dev/null
@@ -1,174 +0,0 @@
-# MediaPipe graph to detect/predict pose landmarks and optionally segmentation
-# within an ROI. (GPU input, and inference is executed on GPU.)
-#
-# It is required that "pose_landmark_lite.tflite" or
-# "pose_landmark_full.tflite" or "pose_landmark_heavy.tflite" is available at
-# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
-# path respectively during execution, depending on the specification in the
-# MODEL_COMPLEXITY input side packet.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "PoseLandmarkByRoiGpu"
-#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-#     input_stream: "IMAGE:image"
-#     input_stream: "ROI:roi"
-#     output_stream: "LANDMARKS:landmarks"
-#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
-#   }
-
-type: "PoseLandmarkByRoiGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-# ROI (region of interest) within the given image where a pose is located.
-# (NormalizedRect)
-input_stream: "ROI:roi"
-
-# Whether to predict the segmentation mask. If unspecified, functions as set to
-# false. (bool)
-input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-
-# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
-# inference latency generally go up with the model complexity. If unspecified,
-# functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Pose landmarks within the given ROI. (NormalizedLandmarkList)
-# We have 33 landmarks (see pose_landmark_topology.svg), and there are other
-# auxiliary key points.
-# 0 - nose
-# 1 - left eye (inner)
-# 2 - left eye
-# 3 - left eye (outer)
-# 4 - right eye (inner)
-# 5 - right eye
-# 6 - right eye (outer)
-# 7 - left ear
-# 8 - right ear
-# 9 - mouth (left)
-# 10 - mouth (right)
-# 11 - left shoulder
-# 12 - right shoulder
-# 13 - left elbow
-# 14 - right elbow
-# 15 - left wrist
-# 16 - right wrist
-# 17 - left pinky
-# 18 - right pinky
-# 19 - left index
-# 20 - right index
-# 21 - left thumb
-# 22 - right thumb
-# 23 - left hip
-# 24 - right hip
-# 25 - left knee
-# 26 - right knee
-# 27 - left ankle
-# 28 - right ankle
-# 29 - left heel
-# 30 - right heel
-# 31 - left foot index
-# 32 - right foot index
-#
-# NOTE: If a pose is not present within the given ROI, for this particular
-# timestamp there will not be an output packet in the LANDMARKS stream. However,
-# the MediaPipe framework will internally inform the downstream calculators of
-# the absence of this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:landmarks"
-# Auxiliary landmarks for deriving the ROI in the subsequent image.
-# (NormalizedLandmarkList)
-output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
-
-# Pose world landmarks within the given ROI. (LandmarkList)
-# World landmarks are real-world 3D coordinates in meters with the origin at the
-# center between hips. WORLD_LANDMARKS shares the same landmark topology as
-# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
-# projected onto the 2D image surface, while WORLD_LANDMARKS provides
-# coordinates (in meters) of the 3D object itself.
-output_stream: "WORLD_LANDMARKS:world_landmarks"
-
-# Segmentation mask on GPU in RGBA with the same mask values in R and A. (Image)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# Retrieves the image size.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Crops and transforms the specified ROI in the input image into an image patch
-# represented as a tensor of dimension expected by the corresponding ML model,
-# while maintaining the aspect ratio of the ROI (which can be different from
-# that of the image patch). Therefore, there can be letterboxing around the ROI
-# in the generated tensor representation.
-node: {
-  calculator: "ImageToTensorCalculator"
-  input_stream: "IMAGE_GPU:image"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "TENSORS:input_tensors"
-  output_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "MATRIX:transformation_matrix"
-  options: {
-    [mediapipe.ImageToTensorCalculatorOptions.ext] {
-      output_tensor_width: 256
-      output_tensor_height: 256
-      keep_aspect_ratio: true
-      output_tensor_float_range {
-        min: 0.0
-        max: 1.0
-      }
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-
-# Loads the pose landmark TF Lite model.
-node {
-  calculator: "PoseLandmarkModelLoader"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  output_side_packet: "MODEL:model"
-}
-
-# Runs model inference on GPU.
-node {
-  calculator: "InferenceCalculator"
-  input_side_packet: "MODEL:model"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:output_tensors"
-}
-
-# Decodes the tensors into the corresponding landmark and segmentation mask
-# representation.
-node {
-  calculator: "TensorsToPoseLandmarksAndSegmentation"
-  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-  input_stream: "TENSORS:output_tensors"
-  output_stream: "LANDMARKS:roi_landmarks"
-  output_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
-  output_stream: "WORLD_LANDMARKS:roi_world_landmarks"
-  output_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
-}
-
-# Projects the landmarks and segmentation mask in the local coordinates of the
-# (potentially letterboxed) ROI back to the global coordinates of the full input
-# image.
-node {
-  calculator: "PoseLandmarksAndSegmentationInverseProjection"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "NORM_RECT:roi"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  input_stream: "MATRIX:transformation_matrix"
-  input_stream: "LANDMARKS:roi_landmarks"
-  input_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
-  input_stream: "WORLD_LANDMARKS:roi_world_landmarks"
-  input_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
-  output_stream: "LANDMARKS:landmarks"
-  output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
-  output_stream: "WORLD_LANDMARKS:world_landmarks"
-  output_stream: "SEGMENTATION_MASK:segmentation_mask"
-}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_cpu.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_cpu.pbtxt
deleted file mode 100644
index 5faf08a..0000000
--- a/mediapipe/modules/pose_landmark/pose_landmark_cpu.pbtxt
+++ /dev/null
@@ -1,268 +0,0 @@
-# MediaPipe graph to detect/predict pose landmarks. (CPU input, and inference is
-# executed on CPU.) This graph tries to skip pose detection as much as possible
-# by using previously detected/predicted landmarks for new images.
-#
-# It is required that "pose_detection.tflite" is available at
-# "mediapipe/modules/pose_detection/pose_detection.tflite"
-# path during execution.
-#
-# It is required that "pose_landmark_lite.tflite" or
-# "pose_landmark_full.tflite" or "pose_landmark_heavy.tflite" is available at
-# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
-# path respectively during execution, depending on the specification in the
-# MODEL_COMPLEXITY input side packet.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "PoseLandmarkCpu"
-#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-#     input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
-#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-#     input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
-#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-#     input_stream: "IMAGE:image"
-#     output_stream: "LANDMARKS:pose_landmarks"
-#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
-#   }
-
-type: "PoseLandmarkCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# Whether to filter landmarks across different input images to reduce jitter.
-# If unspecified, functions as set to true. (bool)
-input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
-
-# Whether to predict the segmentation mask. If unspecified, functions as set to
-# false. (bool)
-input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-
-# Whether to filter segmentation mask across different input images to reduce
-# jitter. If unspecified, functions as set to true. (bool)
-input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
-
-# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
-# inference latency generally go up with the model complexity. If unspecified,
-# functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Pose landmarks. (NormalizedLandmarkList)
-# We have 33 landmarks (see pose_landmark_topology.svg), and there are other
-# auxiliary key points.
-# 0 - nose
-# 1 - left eye (inner)
-# 2 - left eye
-# 3 - left eye (outer)
-# 4 - right eye (inner)
-# 5 - right eye
-# 6 - right eye (outer)
-# 7 - left ear
-# 8 - right ear
-# 9 - mouth (left)
-# 10 - mouth (right)
-# 11 - left shoulder
-# 12 - right shoulder
-# 13 - left elbow
-# 14 - right elbow
-# 15 - left wrist
-# 16 - right wrist
-# 17 - left pinky
-# 18 - right pinky
-# 19 - left index
-# 20 - right index
-# 21 - left thumb
-# 22 - right thumb
-# 23 - left hip
-# 24 - right hip
-# 25 - left knee
-# 26 - right knee
-# 27 - left ankle
-# 28 - right ankle
-# 29 - left heel
-# 30 - right heel
-# 31 - left foot index
-# 32 - right foot index
-#
-# NOTE: if a pose is not present within the given ROI, for this particular
-# timestamp there will not be an output packet in the LANDMARKS stream. However,
-# the MediaPipe framework will internally inform the downstream calculators of
-# the absence of this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:pose_landmarks"
-
-# Pose world landmarks. (LandmarkList)
-# World landmarks are real-world 3D coordinates in meters with the origin at the
-# center between hips. WORLD_LANDMARKS shares the same landmark topology as
-# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
-# projected onto the 2D image surface, while WORLD_LANDMARKS provides
-# coordinates (in meters) of the 3D object itself.
-output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
-
-# Segmentation mask. (ImageFrame in ImageFormat::VEC32F1)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# Extra outputs (for debugging, for instance).
-# Detected poses. (Detection)
-output_stream: "DETECTION:pose_detection"
-# Regions of interest calculated based on landmarks. (NormalizedRect)
-output_stream: "ROI_FROM_LANDMARKS:pose_rect_from_landmarks"
-# Regions of interest calculated based on pose detections. (NormalizedRect)
-output_stream: "ROI_FROM_DETECTION:pose_rect_from_detection"
-
-# When the optional input side packet "use_prev_landmarks" is either absent or
-# set to true, uses the landmarks on the previous image to help localize
-# landmarks on the current image.
-node {
-  calculator: "GateCalculator"
-  input_side_packet: "ALLOW:use_prev_landmarks"
-  input_stream: "prev_pose_rect_from_landmarks"
-  output_stream: "gated_prev_pose_rect_from_landmarks"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      allow: true
-    }
-  }
-}
-
-# Checks if there's previous pose rect calculated from landmarks.
-node: {
-  calculator: "PacketPresenceCalculator"
-  input_stream: "PACKET:gated_prev_pose_rect_from_landmarks"
-  output_stream: "PRESENCE:prev_pose_rect_from_landmarks_is_present"
-}
-
-# Calculates size of the image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_CPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Drops the incoming image if the pose has already been identified from the
-# previous image. Otherwise, passes the incoming image through to trigger a new
-# round of pose detection.
-node {
-  calculator: "GateCalculator"
-  input_stream: "image"
-  input_stream: "image_size"
-  input_stream: "DISALLOW:prev_pose_rect_from_landmarks_is_present"
-  output_stream: "image_for_pose_detection"
-  output_stream: "image_size_for_pose_detection"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      empty_packets_as_allow: true
-    }
-  }
-}
-
-# Detects poses.
-node {
-  calculator: "PoseDetectionCpu"
-  input_stream: "IMAGE:image_for_pose_detection"
-  output_stream: "DETECTIONS:pose_detections"
-}
-
-# Gets the very first detection from "pose_detections" vector.
-node {
-  calculator: "SplitDetectionVectorCalculator"
-  input_stream: "pose_detections"
-  output_stream: "pose_detection"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Calculates region of interest based on pose detection, so that can be used
-# to detect landmarks.
-node {
-  calculator: "PoseDetectionToRoi"
-  input_stream: "DETECTION:pose_detection"
-  input_stream: "IMAGE_SIZE:image_size_for_pose_detection"
-  output_stream: "ROI:pose_rect_from_detection"
-}
-
-# Selects either pose rect (or ROI) calculated from detection or from previously
-# detected landmarks if available (in this case, calculation of pose rect from
-# detection is skipped).
-node {
-  calculator: "MergeCalculator"
-  input_stream: "pose_rect_from_detection"
-  input_stream: "gated_prev_pose_rect_from_landmarks"
-  output_stream: "pose_rect"
-}
-
-# Detects pose landmarks within specified region of interest of the image.
-node {
-  calculator: "PoseLandmarkByRoiCpu"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-  input_stream: "IMAGE:image"
-  input_stream: "ROI:pose_rect"
-  output_stream: "LANDMARKS:unfiltered_pose_landmarks"
-  output_stream: "AUXILIARY_LANDMARKS:unfiltered_auxiliary_landmarks"
-  output_stream: "WORLD_LANDMARKS:unfiltered_world_landmarks"
-  output_stream: "SEGMENTATION_MASK:unfiltered_segmentation_mask"
-}
-
-# Smoothes landmarks to reduce jitter.
-node {
-  calculator: "PoseLandmarkFiltering"
-  input_side_packet: "ENABLE:smooth_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "NORM_LANDMARKS:unfiltered_pose_landmarks"
-  input_stream: "AUX_NORM_LANDMARKS:unfiltered_auxiliary_landmarks"
-  input_stream: "WORLD_LANDMARKS:unfiltered_world_landmarks"
-  output_stream: "FILTERED_NORM_LANDMARKS:pose_landmarks"
-  output_stream: "FILTERED_AUX_NORM_LANDMARKS:auxiliary_landmarks"
-  output_stream: "FILTERED_WORLD_LANDMARKS:pose_world_landmarks"
-}
-
-# Calculates region of interest based on the auxiliary landmarks, to be used in
-# the subsequent image.
-node {
-  calculator: "PoseLandmarksToRoi"
-  input_stream: "LANDMARKS:auxiliary_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:pose_rect_from_landmarks"
-}
-
-# Caches pose rects calculated from landmarks, and upon the arrival of the next
-# input image, sends out the cached rects with timestamps replaced by that of
-# the input image, essentially generating a packet that carries the previous
-# pose rects. Note that upon the arrival of the very first input image, a
-# timestamp bound update occurs to jump start the feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image"
-  input_stream: "LOOP:pose_rect_from_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_pose_rect_from_landmarks"
-}
-
-# Smoothes segmentation to reduce jitter.
-node {
-  calculator: "PoseSegmentationFiltering"
-  input_side_packet: "ENABLE:smooth_segmentation"
-  input_stream: "SEGMENTATION_MASK:unfiltered_segmentation_mask"
-  output_stream: "FILTERED_SEGMENTATION_MASK:filtered_segmentation_mask"
-}
-
-# Converts the incoming segmentation mask represented as an Image into the
-# corresponding ImageFrame type.
-node: {
-  calculator: "FromImageCalculator"
-  input_stream: "IMAGE:filtered_segmentation_mask"
-  output_stream: "IMAGE_CPU:segmentation_mask"
-}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_filtering.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_filtering.pbtxt
deleted file mode 100644
index bb3665f..0000000
--- a/mediapipe/modules/pose_landmark/pose_landmark_filtering.pbtxt
+++ /dev/null
@@ -1,247 +0,0 @@
-# MediaPipe graph to filter landmarks temporally (across packets with
-# incremental timestamps) to reduce jitter.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "PoseLandmarkFiltering"
-#     input_side_packet: "ENABLE:enable"
-#     input_stream: "IMAGE_SIZE:image_size"
-#     input_stream: "NORM_LANDMARKS:landmarks"
-#     input_stream: "AUX_NORM_LANDMARKS:aux_landmarks"
-#     input_stream: "WORLD_LANDMARKS:world_landmarks"
-#     output_stream: "FILTERED_NORM_LANDMARKS:filtered_landmarks"
-#     output_stream: "FILTERED_AUX_NORM_LANDMARKS:filtered_aux_landmarks"
-#     output_stream: "FILTERED_WORLD_LANDMARKS:filtered_world_landmarks"
-#   }
-
-type: "PoseLandmarkFiltering"
-
-# Whether to enable filtering. If unspecified, functions as enabled. (bool)
-input_side_packet: "ENABLE:enable"
-
-# Size of the image (width & height) where the landmarks are estimated from.
-# (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-# Normalized landmarks. (NormalizedLandmarkList)
-input_stream: "NORM_LANDMARKS:landmarks"
-# Auxiliary set of normalized landmarks. (NormalizedLandmarkList)
-input_stream: "AUX_NORM_LANDMARKS:aux_landmarks"
-# World landmarks. (LandmarkList)
-input_stream: "WORLD_LANDMARKS:world_landmarks"
-# Filtered normalized landmarks. (NormalizedLandmarkList)
-output_stream: "FILTERED_NORM_LANDMARKS:filtered_landmarks"
-# Filtered auxiliary set of normalized landmarks. (NormalizedLandmarkList)
-output_stream: "FILTERED_AUX_NORM_LANDMARKS:filtered_aux_landmarks"
-# Filtered world landmarks. (LandmarkList)
-output_stream: "FILTERED_WORLD_LANDMARKS:filtered_world_landmarks"
-
-# Converts landmarks to a detection that tightly encloses all landmarks.
-node {
-  calculator: "LandmarksToDetectionCalculator"
-  input_stream: "NORM_LANDMARKS:aux_landmarks"
-  output_stream: "DETECTION:aux_detection"
-}
-
-# Converts detection into a rectangle based on center and scale alignment
-# points.
-node {
-  calculator: "AlignmentPointsRectsCalculator"
-  input_stream: "DETECTION:aux_detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:roi"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 0
-      rotation_vector_end_keypoint_index: 1
-      rotation_vector_target_angle_degrees: 90
-    }
-  }
-}
-
-# Smoothes pose landmark visibilities to reduce jitter.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "ENABLE:enable"
-  input_stream: "NORM_LANDMARKS:landmarks"
-  output_stream: "NORM_FILTERED_LANDMARKS:filtered_visibility"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      enable: true
-      contained_node: {
-        calculator: "VisibilitySmoothingCalculator"
-        options: {
-          [mediapipe.VisibilitySmoothingCalculatorOptions.ext] {
-            no_filter: {}
-          }
-        }
-      }
-      contained_node: {
-        calculator: "VisibilitySmoothingCalculator"
-        options: {
-          [mediapipe.VisibilitySmoothingCalculatorOptions.ext] {
-            low_pass_filter {
-              alpha: 0.1
-            }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Smoothes pose landmark coordinates to reduce jitter.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "ENABLE:enable"
-  input_stream: "NORM_LANDMARKS:filtered_visibility"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "OBJECT_SCALE_ROI:roi"
-  output_stream: "NORM_FILTERED_LANDMARKS:filtered_landmarks"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      enable: true
-      contained_node: {
-        calculator: "LandmarksSmoothingCalculator"
-        options: {
-          [mediapipe.LandmarksSmoothingCalculatorOptions.ext] {
-            no_filter: {}
-          }
-        }
-      }
-      contained_node: {
-        calculator: "LandmarksSmoothingCalculator"
-        options: {
-          [mediapipe.LandmarksSmoothingCalculatorOptions.ext] {
-            one_euro_filter {
-              # Min cutoff 0.1 results into ~0.01 alpha in landmark EMA filter
-              # when landmark is static.
-              min_cutoff: 0.05
-              # Beta 80.0 in combintation with min_cutoff 0.05 results into
-              # ~0.94 alpha in landmark EMA filter when landmark is moving fast.
-              beta: 80.0
-              # Derivative cutoff 1.0 results into ~0.17 alpha in landmark
-              # velocity EMA filter.
-              derivate_cutoff: 1.0
-            }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Smoothes world landmark visibilities to reduce jitter.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "ENABLE:enable"
-  input_stream: "LANDMARKS:world_landmarks"
-  output_stream: "FILTERED_LANDMARKS:filtered_world_visibility"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      enable: true
-      contained_node: {
-        calculator: "VisibilitySmoothingCalculator"
-        options: {
-          [mediapipe.VisibilitySmoothingCalculatorOptions.ext] {
-            no_filter: {}
-          }
-        }
-      }
-      contained_node: {
-        calculator: "VisibilitySmoothingCalculator"
-        options: {
-          [mediapipe.VisibilitySmoothingCalculatorOptions.ext] {
-            low_pass_filter {
-              alpha: 0.1
-            }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Smoothes world landmark coordinates to reduce jitter.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "ENABLE:enable"
-  input_stream: "LANDMARKS:filtered_world_visibility"
-  output_stream: "FILTERED_LANDMARKS:filtered_world_landmarks"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      enable: true
-      contained_node: {
-        calculator: "LandmarksSmoothingCalculator"
-        options: {
-          [mediapipe.LandmarksSmoothingCalculatorOptions.ext] {
-            no_filter: {}
-          }
-        }
-      }
-      contained_node: {
-        calculator: "LandmarksSmoothingCalculator"
-        options: {
-          [mediapipe.LandmarksSmoothingCalculatorOptions.ext] {
-            one_euro_filter {
-              # Min cutoff 0.1 results into ~ 0.02 alpha in landmark EMA filter
-              # when landmark is static.
-              min_cutoff: 0.1
-              # Beta 40.0 in combintation with min_cutoff 0.1 results into ~0.8
-              # alpha in landmark EMA filter when landmark is moving fast.
-              beta: 40.0
-              # Derivative cutoff 1.0 results into ~0.17 alpha in landmark
-              # velocity EMA filter.
-              derivate_cutoff: 1.0
-              # As world landmdarks are predicted in real world 3D coordintates
-              # in meters (rather than in pixels of input image) prediction
-              # scale does not depend on the pose size in the image.
-              disable_value_scaling: true
-            }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Smoothes auxiliary landmark visibilities to reduce jitter.
-node {
-  calculator: "VisibilitySmoothingCalculator"
-  input_stream: "NORM_LANDMARKS:aux_landmarks"
-  output_stream: "NORM_FILTERED_LANDMARKS:filtered_aux_visibility"
-  options: {
-    [mediapipe.VisibilitySmoothingCalculatorOptions.ext] {
-      low_pass_filter {
-        alpha: 0.1
-      }
-    }
-  }
-}
-
-# Smoothes auxiliary landmarks to reduce jitter.
-node {
-  calculator: "LandmarksSmoothingCalculator"
-  input_stream: "NORM_LANDMARKS:filtered_aux_visibility"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "OBJECT_SCALE_ROI:roi"
-  output_stream: "NORM_FILTERED_LANDMARKS:filtered_aux_landmarks"
-  options: {
-    [mediapipe.LandmarksSmoothingCalculatorOptions.ext] {
-      # Auxiliary landmarks are smoothed heavier than main landmarks to
-      # make ROI crop for pose landmarks prediction very stable when
-      # object is not moving but responsive enough in case of sudden
-      # movements.
-      one_euro_filter {
-        # Min cutoff 0.01 results into ~0.002 alpha in landmark EMA
-        # filter when landmark is static.
-        min_cutoff: 0.01
-        # Beta 10.0 in combintation with min_cutoff 0.01 results into ~0.68
-        # alpha in landmark EMA filter when landmark is moving fast.
-        beta: 10.0
-        # Derivative cutoff 1.0 results into ~0.17 alpha in landmark
-        # velocity EMA filter.
-        derivate_cutoff: 1.0
-      }
-    }
-  }
-}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_full.tflite b/mediapipe/modules/pose_landmark/pose_landmark_full.tflite
deleted file mode 100755
index e2ee84f..0000000
Binary files a/mediapipe/modules/pose_landmark/pose_landmark_full.tflite and /dev/null differ
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt
deleted file mode 100644
index 3ff9ac9..0000000
--- a/mediapipe/modules/pose_landmark/pose_landmark_gpu.pbtxt
+++ /dev/null
@@ -1,268 +0,0 @@
-# MediaPipe graph to detect/predict pose landmarks. (GPU input, and inference is
-# executed on GPU.) This graph tries to skip pose detection as much as possible
-# by using previously detected/predicted landmarks for new images.
-#
-# It is required that "pose_detection.tflite" is available at
-# "mediapipe/modules/pose_detection/pose_detection.tflite"
-# path during execution.
-#
-# It is required that "pose_landmark_lite.tflite" or
-# "pose_landmark_full.tflite" or "pose_landmark_heavy.tflite" is available at
-# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
-# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
-# path respectively during execution, depending on the specification in the
-# MODEL_COMPLEXITY input side packet.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "PoseLandmarkGpu"
-#     input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-#     input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
-#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-#     input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
-#     input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-#     input_stream: "IMAGE:image"
-#     output_stream: "LANDMARKS:pose_landmarks"
-#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
-#   }
-
-type: "PoseLandmarkGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# Whether to filter landmarks across different input images to reduce jitter.
-# If unspecified, functions as set to true. (bool)
-input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
-
-# Whether to predict the segmentation mask. If unspecified, functions as set to
-# false. (bool)
-input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-
-# Whether to filter segmentation mask across different input images to reduce
-# jitter. If unspecified, functions as set to true. (bool)
-input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
-
-# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
-# inference latency generally go up with the model complexity. If unspecified,
-# functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# Whether landmarks on the previous image should be used to help localize
-# landmarks on the current image. (bool)
-input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
-
-# Pose landmarks. (NormalizedLandmarkList)
-# We have 33 landmarks (see pose_landmark_topology.svg), and there are other
-# auxiliary key points.
-# 0 - nose
-# 1 - left eye (inner)
-# 2 - left eye
-# 3 - left eye (outer)
-# 4 - right eye (inner)
-# 5 - right eye
-# 6 - right eye (outer)
-# 7 - left ear
-# 8 - right ear
-# 9 - mouth (left)
-# 10 - mouth (right)
-# 11 - left shoulder
-# 12 - right shoulder
-# 13 - left elbow
-# 14 - right elbow
-# 15 - left wrist
-# 16 - right wrist
-# 17 - left pinky
-# 18 - right pinky
-# 19 - left index
-# 20 - right index
-# 21 - left thumb
-# 22 - right thumb
-# 23 - left hip
-# 24 - right hip
-# 25 - left knee
-# 26 - right knee
-# 27 - left ankle
-# 28 - right ankle
-# 29 - left heel
-# 30 - right heel
-# 31 - left foot index
-# 32 - right foot index
-#
-# NOTE: if a pose is not present within the given ROI, for this particular
-# timestamp there will not be an output packet in the LANDMARKS stream. However,
-# the MediaPipe framework will internally inform the downstream calculators of
-# the absence of this packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:pose_landmarks"
-
-# Pose world landmarks. (LandmarkList)
-# World landmarks are real-world 3D coordinates in meters with the origin at the
-# center between hips. WORLD_LANDMARKS shares the same landmark topology as
-# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
-# projected onto the 2D image surface, while WORLD_LANDMARKS provides
-# coordinates (in meters) of the 3D object itself.
-output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
-
-# Segmentation mask. (GpuBuffer in RGBA, with the same mask values in R and A)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# Extra outputs (for debugging, for instance).
-# Detected poses. (Detection)
-output_stream: "DETECTION:pose_detection"
-# Regions of interest calculated based on landmarks. (NormalizedRect)
-output_stream: "ROI_FROM_LANDMARKS:pose_rect_from_landmarks"
-# Regions of interest calculated based on pose detections. (NormalizedRect)
-output_stream: "ROI_FROM_DETECTION:pose_rect_from_detection"
-
-# When the optional input side packet "use_prev_landmarks" is either absent or
-# set to true, uses the landmarks on the previous image to help localize
-# landmarks on the current image.
-node {
-  calculator: "GateCalculator"
-  input_side_packet: "ALLOW:use_prev_landmarks"
-  input_stream: "prev_pose_rect_from_landmarks"
-  output_stream: "gated_prev_pose_rect_from_landmarks"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      allow: true
-    }
-  }
-}
-
-# Checks if there's previous pose rect calculated from landmarks.
-node: {
-  calculator: "PacketPresenceCalculator"
-  input_stream: "PACKET:gated_prev_pose_rect_from_landmarks"
-  output_stream: "PRESENCE:prev_pose_rect_from_landmarks_is_present"
-}
-
-# Calculates size of the image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "SIZE:image_size"
-}
-
-# Drops the incoming image if the pose has already been identified from the
-# previous image. Otherwise, passes the incoming image through to trigger a new
-# round of pose detection.
-node {
-  calculator: "GateCalculator"
-  input_stream: "image"
-  input_stream: "image_size"
-  input_stream: "DISALLOW:prev_pose_rect_from_landmarks_is_present"
-  output_stream: "image_for_pose_detection"
-  output_stream: "image_size_for_pose_detection"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      empty_packets_as_allow: true
-    }
-  }
-}
-
-# Detects poses.
-node {
-  calculator: "PoseDetectionGpu"
-  input_stream: "IMAGE:image_for_pose_detection"
-  output_stream: "DETECTIONS:pose_detections"
-}
-
-# Gets the very first detection from "pose_detections" vector.
-node {
-  calculator: "SplitDetectionVectorCalculator"
-  input_stream: "pose_detections"
-  output_stream: "pose_detection"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-      element_only: true
-    }
-  }
-}
-
-# Calculates region of interest based on pose detection, so that can be used
-# to detect landmarks.
-node {
-  calculator: "PoseDetectionToRoi"
-  input_stream: "DETECTION:pose_detection"
-  input_stream: "IMAGE_SIZE:image_size_for_pose_detection"
-  output_stream: "ROI:pose_rect_from_detection"
-}
-
-# Selects either pose rect (or ROI) calculated from detection or from previously
-# detected landmarks if available (in this case, calculation of pose rect from
-# detection is skipped).
-node {
-  calculator: "MergeCalculator"
-  input_stream: "pose_rect_from_detection"
-  input_stream: "gated_prev_pose_rect_from_landmarks"
-  output_stream: "pose_rect"
-}
-
-# Detects pose landmarks within specified region of interest of the image.
-node {
-  calculator: "PoseLandmarkByRoiGpu"
-  input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-  input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-  input_stream: "IMAGE:image"
-  input_stream: "ROI:pose_rect"
-  output_stream: "LANDMARKS:unfiltered_pose_landmarks"
-  output_stream: "AUXILIARY_LANDMARKS:unfiltered_auxiliary_landmarks"
-  output_stream: "WORLD_LANDMARKS:unfiltered_world_landmarks"
-  output_stream: "SEGMENTATION_MASK:unfiltered_segmentation_mask"
-}
-
-# Smoothes landmarks to reduce jitter.
-node {
-  calculator: "PoseLandmarkFiltering"
-  input_side_packet: "ENABLE:smooth_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  input_stream: "NORM_LANDMARKS:unfiltered_pose_landmarks"
-  input_stream: "AUX_NORM_LANDMARKS:unfiltered_auxiliary_landmarks"
-  input_stream: "WORLD_LANDMARKS:unfiltered_world_landmarks"
-  output_stream: "FILTERED_NORM_LANDMARKS:pose_landmarks"
-  output_stream: "FILTERED_AUX_NORM_LANDMARKS:auxiliary_landmarks"
-  output_stream: "FILTERED_WORLD_LANDMARKS:pose_world_landmarks"
-}
-
-# Calculates region of interest based on the auxiliary landmarks, to be used in
-# the subsequent image.
-node {
-  calculator: "PoseLandmarksToRoi"
-  input_stream: "LANDMARKS:auxiliary_landmarks"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "ROI:pose_rect_from_landmarks"
-}
-
-# Caches pose rects calculated from landmarks, and upon the arrival of the next
-# input image, sends out the cached rects with timestamps replaced by that of
-# the input image, essentially generating a packet that carries the previous
-# pose rects. Note that upon the arrival of the very first input image, a
-# timestamp bound update occurs to jump start the feedback loop.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:image"
-  input_stream: "LOOP:pose_rect_from_landmarks"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_pose_rect_from_landmarks"
-}
-
-# Smoothes segmentation to reduce jitter.
-node {
-  calculator: "PoseSegmentationFiltering"
-  input_side_packet: "ENABLE:smooth_segmentation"
-  input_stream: "SEGMENTATION_MASK:unfiltered_segmentation_mask"
-  output_stream: "FILTERED_SEGMENTATION_MASK:filtered_segmentation_mask"
-}
-
-# Converts the incoming segmentation mask represented as an Image into the
-# corresponding GpuBuffer type.
-node: {
-  calculator: "FromImageCalculator"
-  input_stream: "IMAGE:filtered_segmentation_mask"
-  output_stream: "IMAGE_GPU:segmentation_mask"
-}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite b/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite
deleted file mode 100755
index 9b767e7..0000000
Binary files a/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite and /dev/null differ
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_lite.tflite b/mediapipe/modules/pose_landmark/pose_landmark_lite.tflite
deleted file mode 100755
index 280cc72..0000000
Binary files a/mediapipe/modules/pose_landmark/pose_landmark_lite.tflite and /dev/null differ
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_model_loader.pbtxt b/mediapipe/modules/pose_landmark/pose_landmark_model_loader.pbtxt
deleted file mode 100644
index ce7036e..0000000
--- a/mediapipe/modules/pose_landmark/pose_landmark_model_loader.pbtxt
+++ /dev/null
@@ -1,73 +0,0 @@
-# MediaPipe graph to load a selected pose landmark TF Lite model.
-
-type: "PoseLandmarkModelLoader"
-
-# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
-# inference latency generally go up with the model complexity. If unspecified,
-# functions as set to 1. (int)
-input_side_packet: "MODEL_COMPLEXITY:model_complexity"
-
-# TF Lite model represented as a FlatBuffer.
-# (std::unique_ptr<tflite::FlatBufferModel, std::function<void(tflite::FlatBufferModel*)>>)
-output_side_packet: "MODEL:model"
-
-# Determines path to the desired pose landmark model file.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "SELECT:model_complexity"
-  output_side_packet: "PACKET:model_path"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      select: 1
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite"
-            }
-          }
-        }
-      }
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/pose_landmark/pose_landmark_full.tflite"
-            }
-          }
-        }
-      }
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
-            }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Loads the file in the specified path into a blob.
-node {
-  calculator: "LocalFileContentsCalculator"
-  input_side_packet: "FILE_PATH:model_path"
-  output_side_packet: "CONTENTS:model_blob"
-  options: {
-    [mediapipe.LocalFileContentsCalculatorOptions.ext]: {
-      text_mode: false
-    }
-  }
-}
-
-# Converts the input blob into a TF Lite model.
-node {
-  calculator: "TfLiteModelCalculator"
-  input_side_packet: "MODEL_BLOB:model_blob"
-  output_side_packet: "MODEL:model"
-}
diff --git a/mediapipe/modules/pose_landmark/pose_landmark_topology.svg b/mediapipe/modules/pose_landmark/pose_landmark_topology.svg
deleted file mode 100644
index a57269d..0000000
--- a/mediapipe/modules/pose_landmark/pose_landmark_topology.svg
+++ /dev/null
@@ -1,651 +0,0 @@
-<?xml version="1.0" encoding="UTF-8" standalone="no"?>
-<svg
-   xmlns:dc="http://purl.org/dc/elements/1.1/"
-   xmlns:cc="http://creativecommons.org/ns#"
-   xmlns:svg="http://www.w3.org/2000/svg"
-   xmlns="http://www.w3.org/2000/svg"
-   xmlns:xlink="http://www.w3.org/1999/xlink"
-   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
-   version="1.1"
-   id="svg901"
-   width="1000"
-   height="1400"
-   viewBox="0 0 1000 1400"
-   sodipodi:docname="pose_landmark_topology.svg">
-  <defs
-     id="defs905">
-    <marker
-       orient="auto"
-       refY="0"
-       refX="0"
-       id="DotL"
-       style="overflow:visible">
-      <path
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-         transform="matrix(0.8,0,0,0.8,5.92,0.8)" />
-    </marker>
-  </defs>
-  <sodipodi:namedview
-     pagecolor="#ffffff"
-     bordercolor="#666666"
-     borderopacity="1"
-     objecttolerance="10"
-     gridtolerance="10"
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-     showgrid="false" />
-  <g
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diff --git a/mediapipe/modules/pose_landmark/pose_landmarks_and_segmentation_inverse_projection.pbtxt b/mediapipe/modules/pose_landmark/pose_landmarks_and_segmentation_inverse_projection.pbtxt
deleted file mode 100644
index eec3b9b..0000000
--- a/mediapipe/modules/pose_landmark/pose_landmarks_and_segmentation_inverse_projection.pbtxt
+++ /dev/null
@@ -1,125 +0,0 @@
-# MediaPipe graph projecting the landmarks and segmentation mask defined in a
-# local coordinate system within a (potentially letterboxed) ROI back to the
-# global coordinate system of the full image that contains the ROI.
-#
-# EXAMPLE:
-# node {
-#   calculator: "PoseLandmarksAndSegmentationInverseProjection"
-#   input_stream: "IMAGE_SIZE:image_size"
-#   input_stream: "NORM_RECT:roi"
-#   input_stream: "LETTERBOX_PADDING:letterbox_padding"
-#   input_stream: "MATRIX:transformation_matrix"
-#   input_stream: "LANDMARKS:roi_landmarks"
-#   input_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
-#   input_stream: "WORLD_LANDMARKS:roi_world_landmarks"
-#   input_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
-#   output_stream: "LANDMARKS:landmarks"
-#   output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
-#   output_stream: "WORLD_LANDMARKS:world_landmarks"
-#   output_stream: "SEGMENTATION_MASK:segmentation_mask"
-# }
-
-type: "PoseLandmarksAndSegmentationInverseProjection"
-
-# Size of the full image (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-
-# ROI within the full image. (NormalizedRect)
-input_stream: "NORM_RECT:roi"
-
-# An array representing the letterbox padding around the ROI from the 4 sides:
-# [left, top, right, bottom]. The padding is normalized to [0.f, 1.f] by the
-# dimensions of the letterboxed/padded ROI. (std::array<float, 4>)
-input_stream: "LETTERBOX_PADDING:letterbox_padding"
-
-# An array representing a 4x4 row-major-order matrix that maps a point within
-# the ROI from the global coordinates of the full image to the local coordinates
-# within the letterboxed ROI. (std::array<float, 16>)
-input_stream: "MATRIX:transformation_matrix"
-
-# Input landmarks and segmentation mask in local coordinates within the
-# letterboxed ROI, and the corresponding outputs in global coordinates of the
-# full image.
-# LANDMARKS & AUXILIARY_LANDMARKS (NormalizedLandmarkList)
-# WORLD_LANDMARKS (LandmarkList)
-# SEGMENTATION_MASK (Image)
-input_stream: "LANDMARKS:roi_landmarks"
-input_stream: "AUXILIARY_LANDMARKS:roi_auxiliary_landmarks"
-input_stream: "WORLD_LANDMARKS:roi_world_landmarks"
-input_stream: "SEGMENTATION_MASK:roi_segmentation_mask"
-output_stream: "LANDMARKS:landmarks"
-output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
-output_stream: "WORLD_LANDMARKS:world_landmarks"
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# -----------------------------------------------------------------------------
-# LANDMARKS
-# -----------------------------------------------------------------------------
-
-# Adjusts landmarks (already normalized to [0.f, 1.f]) in the letterboxed ROI
-# to the corresponding coordinates with the letterbox removed.
-node {
-  calculator: "LandmarkLetterboxRemovalCalculator"
-  input_stream: "LANDMARKS:roi_landmarks"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "LANDMARKS:adjusted_landmarks"
-}
-node {
-  calculator: "LandmarkLetterboxRemovalCalculator"
-  input_stream: "LANDMARKS:roi_auxiliary_landmarks"
-  input_stream: "LETTERBOX_PADDING:letterbox_padding"
-  output_stream: "LANDMARKS:adjusted_auxiliary_landmarks"
-}
-
-# Projects the landmarks from the letterbox-removed ROI back to the full image.
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:adjusted_landmarks"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "NORM_LANDMARKS:landmarks"
-}
-node {
-  calculator: "LandmarkProjectionCalculator"
-  input_stream: "NORM_LANDMARKS:adjusted_auxiliary_landmarks"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "NORM_LANDMARKS:auxiliary_landmarks"
-}
-
-# -----------------------------------------------------------------------------
-# WORLD_LANDMARKS
-# -----------------------------------------------------------------------------
-
-# Projects the world landmarks from the letterboxed ROI to the full image.
-node {
-  calculator: "WorldLandmarkProjectionCalculator"
-  input_stream: "LANDMARKS:roi_world_landmarks"
-  input_stream: "NORM_RECT:roi"
-  output_stream: "LANDMARKS:world_landmarks"
-}
-
-# -----------------------------------------------------------------------------
-# SEGMENTATION_MASK
-# -----------------------------------------------------------------------------
-
-# Calculates the inverse transformation matrix.
-node {
-  calculator: "InverseMatrixCalculator"
-  input_stream: "MATRIX:transformation_matrix"
-  output_stream: "MATRIX:inverse_transformation_matrix"
-}
-
-# Projects the segmentation mask from the letterboxed ROI back to the full
-# image.
-node {
-  calculator: "WarpAffineCalculator"
-  input_stream: "IMAGE:roi_segmentation_mask"
-  input_stream: "MATRIX:inverse_transformation_matrix"
-  input_stream: "OUTPUT_SIZE:image_size"
-  output_stream: "IMAGE:segmentation_mask"
-  options: {
-    [mediapipe.WarpAffineCalculatorOptions.ext] {
-      border_mode: BORDER_ZERO
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
diff --git a/mediapipe/modules/pose_landmark/pose_landmarks_to_roi.pbtxt b/mediapipe/modules/pose_landmark/pose_landmarks_to_roi.pbtxt
deleted file mode 100644
index b1fe0e3..0000000
--- a/mediapipe/modules/pose_landmark/pose_landmarks_to_roi.pbtxt
+++ /dev/null
@@ -1,51 +0,0 @@
-# MediaPipe graph to calculate pose region of interest (ROI) from landmarks
-# detected by "PoseLandmarkByRoiCpu" or "PoseLandmarkByRoiGpu".
-#
-# NOTE: this graph is subject to change and should not be used directly.
-
-type: "PoseLandmarksToRoi"
-
-# Normalized landmarks. (NormalizedLandmarkList)
-input_stream: "LANDMARKS:landmarks"
-# Image size (width & height). (std::pair<int, int>)
-input_stream: "IMAGE_SIZE:image_size"
-# ROI according to landmarks. (NormalizedRect)
-output_stream: "ROI:roi"
-
-# Converts landmarks to a detection that tightly encloses all landmarks.
-node {
-  calculator: "LandmarksToDetectionCalculator"
-  input_stream: "NORM_LANDMARKS:landmarks"
-  output_stream: "DETECTION:detection"
-}
-
-# Converts detection into a rectangle based on center and scale alignment
-# points.
-node {
-  calculator: "AlignmentPointsRectsCalculator"
-  input_stream: "DETECTION:detection"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "NORM_RECT:raw_roi"
-  options: {
-    [mediapipe.DetectionsToRectsCalculatorOptions.ext] {
-      rotation_vector_start_keypoint_index: 0
-      rotation_vector_end_keypoint_index: 1
-      rotation_vector_target_angle_degrees: 90
-    }
-  }
-}
-
-# Expands pose rect with marging used during training.
-node {
-  calculator: "RectTransformationCalculator"
-  input_stream: "NORM_RECT:raw_roi"
-  input_stream: "IMAGE_SIZE:image_size"
-  output_stream: "roi"
-  options: {
-    [mediapipe.RectTransformationCalculatorOptions.ext] {
-      scale_x: 1.25
-      scale_y: 1.25
-      square_long: true
-    }
-  }
-}
diff --git a/mediapipe/modules/pose_landmark/pose_segmentation_filtering.pbtxt b/mediapipe/modules/pose_landmark/pose_segmentation_filtering.pbtxt
deleted file mode 100644
index c3882ad..0000000
--- a/mediapipe/modules/pose_landmark/pose_segmentation_filtering.pbtxt
+++ /dev/null
@@ -1,61 +0,0 @@
-# MediaPipe graph to filter segmentation masks temporally (across packets with
-# incremental timestamps) to reduce jitter.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "PoseSegmentationFiltering"
-#     input_side_packet: "ENABLE:enable"
-#     input_stream: "SEGMENTATION_MASK:segmentation_mask"
-#     output_stream: "FILTERED_SEGMENTATION_MASK:filtered_segmentation_mask"
-#   }
-
-type: "PoseSegmentationFiltering"
-
-# Whether to enable filtering. If unspecified, functions as enabled. (bool)
-input_side_packet: "ENABLE:enable"
-
-# Segmentation mask. (Image)
-input_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# Filtered segmentation mask. (Image)
-output_stream: "FILTERED_SEGMENTATION_MASK:filtered_segmentation_mask"
-
-# Drops the filtered segmentation mask from the previous frame if filtering is
-# not enabled. In that case, the downstream SegmentationSmoothingCalculator
-# becomes a simple passthrough.
-node {
-  calculator: "GateCalculator"
-  input_side_packet: "ALLOW:enable"
-  input_stream: "prev_filtered_segmentation_mask"
-  output_stream: "gated_prev_filtered_segmentation_mask"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      allow: true
-    }
-  }
-}
-
-# Smoothes segmentation to reduce jitter.
-node {
-  calculator: "SegmentationSmoothingCalculator"
-  input_stream: "MASK:segmentation_mask"
-  input_stream: "MASK_PREVIOUS:gated_prev_filtered_segmentation_mask"
-  output_stream: "MASK_SMOOTHED:filtered_segmentation_mask"
-  options {
-    [mediapipe.SegmentationSmoothingCalculatorOptions.ext] {
-      combine_with_previous_ratio: 0.7
-    }
-  }
-}
-
-# Caches the filtered segmentation mask, similar to above for the pose rect.
-node {
-  calculator: "PreviousLoopbackCalculator"
-  input_stream: "MAIN:segmentation_mask"
-  input_stream: "LOOP:filtered_segmentation_mask"
-  input_stream_info: {
-    tag_index: "LOOP"
-    back_edge: true
-  }
-  output_stream: "PREV_LOOP:prev_filtered_segmentation_mask"
-}
diff --git a/mediapipe/modules/pose_landmark/tensors_to_pose_landmarks_and_segmentation.pbtxt b/mediapipe/modules/pose_landmark/tensors_to_pose_landmarks_and_segmentation.pbtxt
deleted file mode 100644
index ac86233..0000000
--- a/mediapipe/modules/pose_landmark/tensors_to_pose_landmarks_and_segmentation.pbtxt
+++ /dev/null
@@ -1,265 +0,0 @@
-# MediaPipe graph performing tensor post processing to detect/predict pose
-# landmarks and segmenation mask.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "TensorsToPoseLandmarksAndSegmentation"
-#     input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-#     input_stream: "TENSORS:tensors"
-#     output_stream: "LANDMARKS:landmarks"
-#     output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
-#     output_stream: "WORLD_LANDMARKS:world_landmarks"
-#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
-#   }
-
-type: "TensorsToPoseLandmarksAndSegmentation"
-
-# Whether to predict segmentation mask. If unspecified, functions as set to
-# false. (bool)
-input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
-
-# Tensors from mode inference of
-# "mediapipe/modules/pose_landmark/pose_landmark_lite|full|heavy.tflite".
-# (std::vector<Tensor>)
-# tensors[0]: landmarks
-# tensors[1]: pose flag
-# tensors[2]: segmentation
-# tensors[3]: heatmap
-# tensors[4]: world landmarks
-input_stream: "TENSORS:tensors"
-
-# Pose landmarks. (NormalizedLandmarkList)
-# We have 33 landmarks (see pose_landmark_topology.svg) and there are other
-# auxiliary key points.
-# 0 - nose
-# 1 - left eye (inner)
-# 2 - left eye
-# 3 - left eye (outer)
-# 4 - right eye (inner)
-# 5 - right eye
-# 6 - right eye (outer)
-# 7 - left ear
-# 8 - right ear
-# 9 - mouth (left)
-# 10 - mouth (right)
-# 11 - left shoulder
-# 12 - right shoulder
-# 13 - left elbow
-# 14 - right elbow
-# 15 - left wrist
-# 16 - right wrist
-# 17 - left pinky
-# 18 - right pinky
-# 19 - left index
-# 20 - right index
-# 21 - left thumb
-# 22 - right thumb
-# 23 - left hip
-# 24 - right hip
-# 25 - left knee
-# 26 - right knee
-# 27 - left ankle
-# 28 - right ankle
-# 29 - left heel
-# 30 - right heel
-# 31 - left foot index
-# 32 - right foot index
-#
-# NOTE: If a pose is not present, for this particular timestamp there will not
-# be an output packet in the LANDMARKS stream. However, the MediaPipe framework
-# will internally inform the downstream calculators of the absence of this
-# packet so that they don't wait for it unnecessarily.
-output_stream: "LANDMARKS:landmarks"
-# Auxiliary landmarks (e.g., for deriving the ROI in the subsequent image).
-# (NormalizedLandmarkList)
-output_stream: "AUXILIARY_LANDMARKS:auxiliary_landmarks"
-
-# Pose world landmarks. (LandmarkList)
-# World landmarks are real-world 3D coordinates in meters with the origin at the
-# center between hips. WORLD_LANDMARKS shares the same landmark topology as
-# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
-# projected onto the 2D image surface, while WORLD_LANDMARKS provides
-# coordinates (in meters) of the 3D object itself.
-output_stream: "WORLD_LANDMARKS:world_landmarks"
-
-# Segmentation mask. (Image)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# Splits a vector of tensors to multiple vectors according to the ranges
-# specified in the option.
-node {
-  calculator: "SplitTensorVectorCalculator"
-  input_stream: "tensors"
-  output_stream: "landmark_tensor"
-  output_stream: "pose_flag_tensor"
-  output_stream: "segmentation_tensor"
-  output_stream: "heatmap_tensor"
-  output_stream: "world_landmark_tensor"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 1 }
-      ranges: { begin: 1 end: 2 }
-      ranges: { begin: 2 end: 3 }
-      ranges: { begin: 3 end: 4 }
-      ranges: { begin: 4 end: 5 }
-    }
-  }
-}
-
-# Converts the pose-flag tensor into a float that represents the confidence
-# score of pose presence.
-node {
-  calculator: "TensorsToFloatsCalculator"
-  input_stream: "TENSORS:pose_flag_tensor"
-  output_stream: "FLOAT:pose_presence_score"
-}
-
-# Applies a threshold to the confidence score to determine whether a pose is
-# present.
-node {
-  calculator: "ThresholdingCalculator"
-  input_stream: "FLOAT:pose_presence_score"
-  output_stream: "FLAG:pose_presence"
-  options: {
-    [mediapipe.ThresholdingCalculatorOptions.ext] {
-      threshold: 0.5
-    }
-  }
-}
-
-# Drops input tensors if pose is not present.
-node {
-  calculator: "GateCalculator"
-  input_stream: "landmark_tensor"
-  input_stream: "world_landmark_tensor"
-  input_stream: "segmentation_tensor"
-  input_stream: "heatmap_tensor"
-  input_stream: "ALLOW:pose_presence"
-  output_stream: "ensured_landmark_tensor"
-  output_stream: "ensured_world_landmark_tensor"
-  output_stream: "ensured_segmentation_tensor"
-  output_stream: "ensured_heatmap_tensor"
-}
-
-# -----------------------------------------------------------------------------
-# LANDMARKS
-# -----------------------------------------------------------------------------
-
-# Decodes the landmark tensors into a vector of landmarks, where the landmark
-# coordinates are normalized by the spatial dimensions of the tensor.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:ensured_landmark_tensor"
-  output_stream: "NORM_LANDMARKS:raw_landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 39
-      input_image_width: 256
-      input_image_height: 256
-      visibility_activation: SIGMOID
-      presence_activation: SIGMOID
-    }
-  }
-}
-
-# Refines landmarks with the heatmap tensor.
-node {
-  calculator: "RefineLandmarksFromHeatmapCalculator"
-  input_stream: "NORM_LANDMARKS:raw_landmarks"
-  input_stream: "TENSORS:ensured_heatmap_tensor"
-  output_stream: "NORM_LANDMARKS:all_landmarks"
-  options: {
-    [mediapipe.RefineLandmarksFromHeatmapCalculatorOptions.ext] {
-      kernel_size: 7
-    }
-  }
-}
-
-# Splits the landmarks into two sets: the actual pose landmarks and the
-# auxiliary landmarks.
-node {
-  calculator: "SplitNormalizedLandmarkListCalculator"
-  input_stream: "all_landmarks"
-  output_stream: "landmarks"
-  output_stream: "auxiliary_landmarks"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 33 }
-      ranges: { begin: 33 end: 35 }
-    }
-  }
-}
-
-# -----------------------------------------------------------------------------
-# WORLD_LANDMARKS
-# -----------------------------------------------------------------------------
-
-# Decodes the world-landmark tensors into a vector of world landmarks.
-node {
-  calculator: "TensorsToLandmarksCalculator"
-  input_stream: "TENSORS:ensured_world_landmark_tensor"
-  output_stream: "LANDMARKS:all_world_landmarks"
-  options: {
-    [mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
-      num_landmarks: 39
-    }
-  }
-}
-
-# Keeps only the actual world landmarks.
-node {
-  calculator: "SplitLandmarkListCalculator"
-  input_stream: "all_world_landmarks"
-  output_stream: "world_landmarks_without_visibility"
-  options: {
-    [mediapipe.SplitVectorCalculatorOptions.ext] {
-      ranges: { begin: 0 end: 33 }
-    }
-  }
-}
-
-# Reuses the visibility and presence field in pose landmarks for the world
-# landmarks.
-node {
-  calculator: "VisibilityCopyCalculator"
-  input_stream: "NORM_LANDMARKS_FROM:landmarks"
-  input_stream: "LANDMARKS_TO:world_landmarks_without_visibility"
-  output_stream: "LANDMARKS_TO:world_landmarks"
-  options: {
-    [mediapipe.VisibilityCopyCalculatorOptions.ext] {
-      copy_visibility: true
-      copy_presence: true
-    }
-  }
-}
-
-# -----------------------------------------------------------------------------
-# SEGMENTATION_MASK
-# -----------------------------------------------------------------------------
-
-# Drops segmentation tensors if segmentation is not enabled.
-node {
-  calculator: "GateCalculator"
-  input_side_packet: "ALLOW:enable_segmentation"
-  input_stream: "ensured_segmentation_tensor"
-  output_stream: "enabled_segmentation_tensor"
-  options: {
-    [mediapipe.GateCalculatorOptions.ext] {
-      allow: false
-    }
-  }
-}
-
-# Decodes the segmentation tensor into a mask image with pixel values in [0, 1]
-# (1 for person and 0 for background).
-node {
-  calculator: "TensorsToSegmentationCalculator"
-  input_stream: "TENSORS:enabled_segmentation_tensor"
-  output_stream: "MASK:segmentation_mask"
-  options: {
-    [mediapipe.TensorsToSegmentationCalculatorOptions.ext] {
-      activation: SIGMOID
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
diff --git a/mediapipe/modules/selfie_segmentation/BUILD b/mediapipe/modules/selfie_segmentation/BUILD
deleted file mode 100644
index 7fc271a..0000000
--- a/mediapipe/modules/selfie_segmentation/BUILD
+++ /dev/null
@@ -1,99 +0,0 @@
-# Copyright 2021 The MediaPipe Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-load(
-    "//mediapipe/framework/tool:mediapipe_graph.bzl",
-    "mediapipe_simple_subgraph",
-)
-
-licenses(["notice"])
-
-package(default_visibility = ["//visibility:public"])
-
-mediapipe_simple_subgraph(
-    name = "selfie_segmentation_model_loader",
-    graph = "selfie_segmentation_model_loader.pbtxt",
-    register_as = "SelfieSegmentationModelLoader",
-    deps = [
-        "//mediapipe/calculators/core:constant_side_packet_calculator",
-        "//mediapipe/calculators/tflite:tflite_model_calculator",
-        "//mediapipe/calculators/util:local_file_contents_calculator",
-        "//mediapipe/framework/tool:switch_container",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "selfie_segmentation_cpu",
-    graph = "selfie_segmentation_cpu.pbtxt",
-    register_as = "SelfieSegmentationCpu",
-    deps = [
-        ":selfie_segmentation_model_loader",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_segmentation_calculator",
-        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-        "//mediapipe/framework/tool:switch_container",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "selfie_segmentation_gpu",
-    graph = "selfie_segmentation_gpu.pbtxt",
-    register_as = "SelfieSegmentationGpu",
-    deps = [
-        ":selfie_segmentation_model_loader",
-        "//mediapipe/calculators/image:image_properties_calculator",
-        "//mediapipe/calculators/tensor:image_to_tensor_calculator",
-        "//mediapipe/calculators/tensor:inference_calculator",
-        "//mediapipe/calculators/tensor:tensors_to_segmentation_calculator",
-        "//mediapipe/calculators/tflite:tflite_custom_op_resolver_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-        "//mediapipe/framework/tool:switch_container",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "selfie_segmentation_cpu_image",
-    graph = "selfie_segmentation_cpu_image.pbtxt",
-    register_as = "SelfieSegmentationCpuImage",
-    deps = [
-        ":selfie_segmentation_cpu",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-        "//mediapipe/calculators/util:to_image_calculator",
-    ],
-)
-
-mediapipe_simple_subgraph(
-    name = "selfie_segmentation_gpu_image",
-    graph = "selfie_segmentation_gpu_image.pbtxt",
-    register_as = "SelfieSegmentationGpuImage",
-    deps = [
-        ":selfie_segmentation_gpu",
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/calculators/image:image_transformation_calculator",
-        "//mediapipe/calculators/util:from_image_calculator",
-        "//mediapipe/calculators/util:to_image_calculator",
-    ],
-)
-
-exports_files(
-    srcs = [
-        "selfie_segmentation.tflite",
-        "selfie_segmentation_landscape.tflite",
-    ],
-)
diff --git a/mediapipe/modules/selfie_segmentation/README.md b/mediapipe/modules/selfie_segmentation/README.md
deleted file mode 100644
index cd6c5e0..0000000
--- a/mediapipe/modules/selfie_segmentation/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-# selfie_segmentation
-
-Subgraphs|Details
-:--- | :---
-[`SelfieSegmentationCpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu.pbtxt)| Segments the person from background in a selfie image. (CPU input, and inference is executed on CPU.)
-[`SelfieSegmentationGpu`](https://github.com/google/mediapipe/tree/master/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu.pbtxt)| Segments the person from background in a selfie image. (GPU input, and inference is executed on GPU.)
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite b/mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite
deleted file mode 100644
index 374c072..0000000
Binary files a/mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite and /dev/null differ
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu.pbtxt b/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu.pbtxt
deleted file mode 100644
index 5918248..0000000
--- a/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu.pbtxt
+++ /dev/null
@@ -1,132 +0,0 @@
-# MediaPipe graph to perform selfie segmentation. (CPU input, and all processing
-# and inference are also performed on CPU)
-#
-# It is required that "selfie_segmentation.tflite" or
-# "selfie_segmentation_landscape.tflite" is available at
-# "mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite"
-# or
-# "mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite"
-# path respectively during execution, depending on the specification in the
-# MODEL_SELECTION input side packet.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "SelfieSegmentationCpu"
-#     input_side_packet: "MODEL_SELECTION:model_selection"
-#     input_stream: "IMAGE:image"
-#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
-#   }
-
-type: "SelfieSegmentationCpu"
-
-# CPU image. (ImageFrame)
-input_stream: "IMAGE:image"
-
-# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
-# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
-# optimized for landscape images. If unspecified, functions as set to 0. (int)
-input_side_packet: "MODEL_SELECTION:model_selection"
-
-# Segmentation mask. (ImageFrame in ImageFormat::VEC32F1)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# Resizes the input image into a tensor with a dimension desired by the model.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "SELECT:model_selection"
-  input_stream: "IMAGE:image"
-  output_stream: "TENSORS:input_tensors"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      select: 0
-      contained_node: {
-        calculator: "ImageToTensorCalculator"
-        options: {
-          [mediapipe.ImageToTensorCalculatorOptions.ext] {
-            output_tensor_width: 256
-            output_tensor_height: 256
-            keep_aspect_ratio: false
-            output_tensor_float_range {
-              min: 0.0
-              max: 1.0
-            }
-            border_mode: BORDER_ZERO
-          }
-        }
-      }
-      contained_node: {
-        calculator: "ImageToTensorCalculator"
-        options: {
-          [mediapipe.ImageToTensorCalculatorOptions.ext] {
-            output_tensor_width: 256
-            output_tensor_height: 144
-            keep_aspect_ratio: false
-            output_tensor_float_range {
-              min: 0.0
-              max: 1.0
-            }
-            border_mode: BORDER_ZERO
-          }
-        }
-      }
-    }
-  }
-}
-
-# Generates a single side packet containing a TensorFlow Lite op resolver that
-# supports custom ops needed by the model used in this graph.
-node {
-  calculator: "TfLiteCustomOpResolverCalculator"
-  output_side_packet: "op_resolver"
-}
-
-# Loads the selfie segmentation TF Lite model.
-node {
-  calculator: "SelfieSegmentationModelLoader"
-  input_side_packet: "MODEL_SELECTION:model_selection"
-  output_side_packet: "MODEL:model"
-}
-
-# Runs model inference on CPU.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:output_tensors"
-  input_side_packet: "MODEL:model"
-  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
-  options: {
-    [mediapipe.InferenceCalculatorOptions.ext] {
-      delegate {
-        xnnpack {}
-      }
-    }
-  }
-}
-
-# Retrieves the size of the input image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_CPU:image"
-  output_stream: "SIZE:input_size"
-}
-
-# Processes the output tensors into a segmentation mask that has the same size
-# as the input image into the graph.
-node {
-  calculator: "TensorsToSegmentationCalculator"
-  input_stream: "TENSORS:output_tensors"
-  input_stream: "OUTPUT_SIZE:input_size"
-  output_stream: "MASK:mask_image"
-  options: {
-    [mediapipe.TensorsToSegmentationCalculatorOptions.ext] {
-      activation: NONE
-    }
-  }
-}
-
-# Converts the incoming Image into the corresponding ImageFrame type.
-node: {
-  calculator: "FromImageCalculator"
-  input_stream: "IMAGE:mask_image"
-  output_stream: "IMAGE_CPU:segmentation_mask"
-}
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu_image.pbtxt b/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu_image.pbtxt
deleted file mode 100644
index a35ff0e..0000000
--- a/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu_image.pbtxt
+++ /dev/null
@@ -1,67 +0,0 @@
-# MediaPipe graph to perform selfie segmentation.
-
-type: "SelfieSegmentationCpuImage"
-
-# Input image. (Image)
-input_stream: "IMAGE:image"
-
-# The throttled input image. (Image)
-output_stream: "IMAGE:throttled_image"
-
-# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
-# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
-# optimized for landscape images. If unspecified, functions as set to 0. (int)
-input_side_packet: "MODEL_SELECTION:model_selection"
-
-# Segmentation mask. (Image)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "image"
-  input_stream: "FINISHED:segmentation_mask"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_image"
-  options: {
-    [mediapipe.FlowLimiterCalculatorOptions.ext] {
-      max_in_flight: 1
-      max_in_queue: 1
-    }
-  }
-}
-
-# Converts Image to ImageFrame for SelfieSegmentationCpu to consume.
-node {
-  calculator: "FromImageCalculator"
-  input_stream: "IMAGE:throttled_image"
-  output_stream: "IMAGE_CPU:raw_image_frame"
-  output_stream: "SOURCE_ON_GPU:is_gpu_image"
-}
-
-# TODO: Remove the extra flipping once adopting MlImage.
-# If the source images are on gpu, flip the data vertically before sending them
-# into SelfieSegmentationCpu. This maybe needed because OpenGL represents images
-# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
-# general assumes the image origin is at the top-left corner.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE:raw_image_frame"
-  input_stream: "FLIP_VERTICALLY:is_gpu_image"
-  output_stream: "IMAGE:image_frame"
-}
-
-node {
-  calculator: "SelfieSegmentationCpu"
-  input_side_packet: "MODEL_SELECTION:model_selection"
-  input_stream: "IMAGE:image_frame"
-  output_stream: "SEGMENTATION_MASK:segmentation_mask_image_frame"
-}
-
-node {
-  calculator: "ToImageCalculator"
-  input_stream: "IMAGE_CPU:segmentation_mask_image_frame"
-  output_stream: "IMAGE:segmentation_mask"
-}
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu.pbtxt b/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu.pbtxt
deleted file mode 100644
index 5f9e55e..0000000
--- a/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu.pbtxt
+++ /dev/null
@@ -1,133 +0,0 @@
-# MediaPipe graph to perform selfie segmentation. (GPU input, and all processing
-# and inference are also performed on GPU)
-#
-# It is required that "selfie_segmentation.tflite" or
-# "selfie_segmentation_landscape.tflite" is available at
-# "mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite"
-# or
-# "mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite"
-# path respectively during execution, depending on the specification in the
-# MODEL_SELECTION input side packet.
-#
-# EXAMPLE:
-#   node {
-#     calculator: "SelfieSegmentationGpu"
-#     input_side_packet: "MODEL_SELECTION:model_selection"
-#     input_stream: "IMAGE:image"
-#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
-#   }
-
-type: "SelfieSegmentationGpu"
-
-# GPU image. (GpuBuffer)
-input_stream: "IMAGE:image"
-
-# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
-# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
-# optimized for landscape images. If unspecified, functions as set to 0. (int)
-input_side_packet: "MODEL_SELECTION:model_selection"
-
-# Segmentation mask. (GpuBuffer in RGBA, with the same mask values in R and A)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-# Resizes the input image into a tensor with a dimension desired by the model.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "SELECT:model_selection"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "TENSORS:input_tensors"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      select: 0
-      contained_node: {
-        calculator: "ImageToTensorCalculator"
-        options: {
-          [mediapipe.ImageToTensorCalculatorOptions.ext] {
-            output_tensor_width: 256
-            output_tensor_height: 256
-            keep_aspect_ratio: false
-            output_tensor_float_range {
-              min: 0.0
-              max: 1.0
-            }
-            border_mode: BORDER_ZERO
-            gpu_origin: TOP_LEFT
-          }
-        }
-      }
-      contained_node: {
-        calculator: "ImageToTensorCalculator"
-        options: {
-          [mediapipe.ImageToTensorCalculatorOptions.ext] {
-            output_tensor_width: 256
-            output_tensor_height: 144
-            keep_aspect_ratio: false
-            output_tensor_float_range {
-              min: 0.0
-              max: 1.0
-            }
-            border_mode: BORDER_ZERO
-            gpu_origin: TOP_LEFT
-          }
-        }
-      }
-    }
-  }
-}
-
-# Generates a single side packet containing a TensorFlow Lite op resolver that
-# supports custom ops needed by the model used in this graph.
-node {
-  calculator: "TfLiteCustomOpResolverCalculator"
-  output_side_packet: "op_resolver"
-  options: {
-    [mediapipe.TfLiteCustomOpResolverCalculatorOptions.ext] {
-      use_gpu: true
-    }
-  }
-}
-
-# Loads the selfie segmentation TF Lite model.
-node {
-  calculator: "SelfieSegmentationModelLoader"
-  input_side_packet: "MODEL_SELECTION:model_selection"
-  output_side_packet: "MODEL:model"
-}
-
-# Runs model inference on GPU.
-node {
-  calculator: "InferenceCalculator"
-  input_stream: "TENSORS:input_tensors"
-  output_stream: "TENSORS:output_tensors"
-  input_side_packet: "MODEL:model"
-  input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
-}
-
-# Retrieves the size of the input image.
-node {
-  calculator: "ImagePropertiesCalculator"
-  input_stream: "IMAGE_GPU:image"
-  output_stream: "SIZE:input_size"
-}
-
-# Processes the output tensors into a segmentation mask that has the same size
-# as the input image into the graph.
-node {
-  calculator: "TensorsToSegmentationCalculator"
-  input_stream: "TENSORS:output_tensors"
-  input_stream: "OUTPUT_SIZE:input_size"
-  output_stream: "MASK:mask_image"
-  options: {
-    [mediapipe.TensorsToSegmentationCalculatorOptions.ext] {
-      activation: NONE
-      gpu_origin: TOP_LEFT
-    }
-  }
-}
-
-# Converts the incoming Image into the corresponding GpuBuffer type.
-node: {
-  calculator: "FromImageCalculator"
-  input_stream: "IMAGE:mask_image"
-  output_stream: "IMAGE_GPU:segmentation_mask"
-}
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu_image.pbtxt b/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu_image.pbtxt
deleted file mode 100644
index d5c0935..0000000
--- a/mediapipe/modules/selfie_segmentation/selfie_segmentation_gpu_image.pbtxt
+++ /dev/null
@@ -1,67 +0,0 @@
-# MediaPipe graph to perform selfie segmentation.
-
-type: "SelfieSegmentationGpuImage"
-
-# Input image. (Image)
-input_stream: "IMAGE:image"
-
-# The throttled input image. (Image)
-output_stream: "IMAGE:throttled_image"
-
-# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
-# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
-# optimized for landscape images. If unspecified, functions as set to 0. (int)
-input_side_packet: "MODEL_SELECTION:model_selection"
-
-# Segmentation mask. (Image)
-output_stream: "SEGMENTATION_MASK:segmentation_mask"
-
-node {
-  calculator: "FlowLimiterCalculator"
-  input_stream: "image"
-  input_stream: "FINISHED:segmentation_mask"
-  input_stream_info: {
-    tag_index: "FINISHED"
-    back_edge: true
-  }
-  output_stream: "throttled_image"
-  options: {
-    [mediapipe.FlowLimiterCalculatorOptions.ext] {
-      max_in_flight: 1
-      max_in_queue: 1
-    }
-  }
-}
-
-# Converts Image to ImageFrame for SelfieSegmentationGpu to consume.
-node {
-  calculator: "FromImageCalculator"
-  input_stream: "IMAGE:throttled_image"
-  output_stream: "IMAGE_GPU:raw_gpu_buffer"
-  output_stream: "SOURCE_ON_GPU:is_gpu_image"
-}
-
-# TODO: Remove the extra flipping once adopting MlImage.
-# If the source images are on gpu, flip the data vertically before sending them
-# into SelfieSegmentationGpu. This maybe needed because OpenGL represents images
-# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
-# general assumes the image origin is at the top-left corner.
-node: {
-  calculator: "ImageTransformationCalculator"
-  input_stream: "IMAGE_GPU:raw_gpu_buffer"
-  input_stream: "FLIP_VERTICALLY:is_gpu_image"
-  output_stream: "IMAGE_GPU:gpu_buffer"
-}
-
-node {
-  calculator: "SelfieSegmentationGpu"
-  input_side_packet: "MODEL_SELECTION:model_selection"
-  input_stream: "IMAGE:gpu_buffer"
-  output_stream: "SEGMENTATION_MASK:segmentation_mask_gpu_buffer"
-}
-
-node {
-  calculator: "ToImageCalculator"
-  input_stream: "IMAGE_GPU:segmentation_mask_gpu_buffer"
-  output_stream: "IMAGE:segmentation_mask"
-}
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite b/mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite
deleted file mode 100755
index 4ea3f8a..0000000
Binary files a/mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite and /dev/null differ
diff --git a/mediapipe/modules/selfie_segmentation/selfie_segmentation_model_loader.pbtxt b/mediapipe/modules/selfie_segmentation/selfie_segmentation_model_loader.pbtxt
deleted file mode 100644
index 39495f8..0000000
--- a/mediapipe/modules/selfie_segmentation/selfie_segmentation_model_loader.pbtxt
+++ /dev/null
@@ -1,63 +0,0 @@
-# MediaPipe graph to load a selected selfie segmentation TF Lite model.
-
-type: "SelfieSegmentationModelLoader"
-
-# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
-# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
-# optimized for landscape images. If unspecified, functions as set to 0. (int)
-input_side_packet: "MODEL_SELECTION:model_selection"
-
-# TF Lite model represented as a FlatBuffer.
-# (std::unique_ptr<tflite::FlatBufferModel, std::function<void(tflite::FlatBufferModel*)>>)
-output_side_packet: "MODEL:model"
-
-# Determines path to the desired pose landmark model file.
-node {
-  calculator: "SwitchContainer"
-  input_side_packet: "SELECT:model_selection"
-  output_side_packet: "PACKET:model_path"
-  options: {
-    [mediapipe.SwitchContainerOptions.ext] {
-      select: 0
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite"
-            }
-          }
-        }
-      }
-      contained_node: {
-        calculator: "ConstantSidePacketCalculator"
-        options: {
-          [mediapipe.ConstantSidePacketCalculatorOptions.ext]: {
-            packet {
-              string_value: "mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite"
-            }
-          }
-        }
-      }
-    }
-  }
-}
-
-# Loads the file in the specified path into a blob.
-node {
-  calculator: "LocalFileContentsCalculator"
-  input_side_packet: "FILE_PATH:model_path"
-  output_side_packet: "CONTENTS:model_blob"
-  options: {
-    [mediapipe.LocalFileContentsCalculatorOptions.ext]: {
-      text_mode: false
-    }
-  }
-}
-
-# Converts the input blob into a TF Lite model.
-node {
-  calculator: "TfLiteModelCalculator"
-  input_side_packet: "MODEL_BLOB:model_blob"
-  output_side_packet: "MODEL:model"
-}