Merge branch 'google:master' into image-classification-python-impl

docs/getting_started/cpp.md

@@ -54,7 +54,7 @@ Note: This currently works only on Linux, and please first follow
 
     ```bash
     GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/hand_tracking/hand_tracking_gpu \
-      --calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_mobile.pbtxt
+      --calculator_graph_config_file=mediapipe/graphs/hand_tracking/hand_tracking_desktop_live_gpu.pbtxt
     ```
 
     This will open up your webcam as long as it is connected and on. Any errors

mediapipe/calculators/image/BUILD

@@ -209,11 +209,18 @@ cc_library(
     alwayslink = 1,
 )
 
+mediapipe_proto_library(
+    name = "rotation_mode_proto",
+    srcs = ["rotation_mode.proto"],
+    visibility = ["//visibility:public"],
+)
+
 mediapipe_proto_library(
     name = "image_transformation_calculator_proto",
     srcs = ["image_transformation_calculator.proto"],
     visibility = ["//visibility:public"],
     deps = [
+        ":rotation_mode_proto",
         "//mediapipe/framework:calculator_options_proto",
         "//mediapipe/framework:calculator_proto",
         "//mediapipe/gpu:scale_mode_proto",
@@ -238,6 +245,7 @@ cc_library(
     }),
     visibility = ["//visibility:public"],
     deps = [
+        ":rotation_mode_cc_proto",
         ":image_transformation_calculator_cc_proto",
         "//mediapipe/framework:packet",
         "//mediapipe/framework:timestamp",

mediapipe/calculators/image/image_transformation_calculator.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "mediapipe/calculators/image/image_transformation_calculator.pb.h"
+#include "mediapipe/calculators/image/rotation_mode.pb.h"
 #include "mediapipe/framework/calculator_framework.h"
 #include "mediapipe/framework/formats/image_frame.h"
 #include "mediapipe/framework/formats/image_frame_opencv.h"

mediapipe/calculators/image/image_transformation_calculator.proto

@@ -16,20 +16,10 @@ syntax = "proto2";
 
 package mediapipe;
 
+import "mediapipe/calculators/image/rotation_mode.proto";
 import "mediapipe/framework/calculator.proto";
 import "mediapipe/gpu/scale_mode.proto";
 
-// Counterclockwise rotation.
-message RotationMode {
-  enum Mode {
-    UNKNOWN = 0;
-    ROTATION_0 = 1;
-    ROTATION_90 = 2;
-    ROTATION_180 = 3;
-    ROTATION_270 = 4;
-  }
-}
-
 message ImageTransformationCalculatorOptions {
   extend CalculatorOptions {
     optional ImageTransformationCalculatorOptions ext = 251952830;

mediapipe/calculators/image/rotation_mode.proto

@@ -0,0 +1,28 @@
+// 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;
+
+// Counterclockwise rotation.
+message RotationMode {
+  enum Mode {
+    UNKNOWN = 0;
+    ROTATION_0 = 1;
+    ROTATION_90 = 2;
+    ROTATION_180 = 3;
+    ROTATION_270 = 4;
+  }
+}

mediapipe/framework/api2/BUILD

@@ -14,15 +14,10 @@ cc_library(
     name = "builder",
     hdrs = ["builder.h"],
     deps = [
-        ":const_str",
-        ":contract",
-        ":node",
-        ":packet",
         ":port",
         "//mediapipe/framework:calculator_base",
         "//mediapipe/framework:calculator_contract",
         "@com_google_absl//absl/container:btree",
-        "@com_google_absl//absl/container:flat_hash_map",
         "@com_google_absl//absl/strings",
     ],
 )

mediapipe/framework/api2/builder.h

@@ -5,12 +5,7 @@
 #include <type_traits>
 
 #include "absl/container/btree_map.h"
-#include "absl/container/flat_hash_map.h"
 #include "absl/strings/string_view.h"
-#include "mediapipe/framework/api2/const_str.h"
-#include "mediapipe/framework/api2/contract.h"
-#include "mediapipe/framework/api2/node.h"
-#include "mediapipe/framework/api2/packet.h"
 #include "mediapipe/framework/api2/port.h"
 #include "mediapipe/framework/calculator_base.h"
 #include "mediapipe/framework/calculator_contract.h"
@@ -120,6 +115,9 @@ using AllowCast = std::integral_constant<bool, std::is_same_v<T, AnyType> &&
 
 }  // namespace internal_builder
 
+template <bool IsSide, typename T = internal::Generic>
+class SourceImpl;
+
 // These classes wrap references to the underlying source/destination
 // endpoints, adding type information and the user-visible API.
 template <bool IsSide, typename T = internal::Generic>
@@ -137,10 +135,14 @@ class DestinationImpl {
     return DestinationImpl<IsSide, U>(&base_);
   }
 
+ private:
   DestinationBase& base_;
+
+  template <bool Source_IsSide, typename Source_T>
+  friend class SourceImpl;
 };
 
-template <bool IsSide, typename T = internal::Generic>
+template <bool IsSide, typename T>
 class SourceImpl {
  public:
   using Base = SourceBase;
@@ -438,8 +440,9 @@ class Graph {
   // Creates a node of a specific type. Should be used for pure interfaces,
   // which do not have a built-in type string.
   template <class Calc>
-  Node<Calc>& AddNode(const std::string& type) {
+  Node<Calc>& AddNode(absl::string_view type) {
-    auto node = std::make_unique<Node<Calc>>(type);
+    auto node =
+        std::make_unique<Node<Calc>>(std::string(type.data(), type.size()));
     auto node_p = node.get();
     nodes_.emplace_back(std::move(node));
     return *node_p;
@@ -447,16 +450,18 @@ class Graph {
 
   // Creates a generic node, with no compile-time checking of inputs and
   // outputs. This can be used for calculators whose contract is not visible.
-  GenericNode& AddNode(const std::string& type) {
+  GenericNode& AddNode(absl::string_view type) {
-    auto node = std::make_unique<GenericNode>(type);
+    auto node =
+        std::make_unique<GenericNode>(std::string(type.data(), type.size()));
     auto node_p = node.get();
     nodes_.emplace_back(std::move(node));
     return *node_p;
   }
 
   // For legacy PacketGenerators.
-  PacketGenerator& AddPacketGenerator(const std::string& type) {
+  PacketGenerator& AddPacketGenerator(absl::string_view type) {
-    auto node = std::make_unique<PacketGenerator>(type);
+    auto node = std::make_unique<PacketGenerator>(
+        std::string(type.data(), type.size()));
     auto node_p = node.get();
     packet_gens_.emplace_back(std::move(node));
     return *node_p;

mediapipe/java/com/google/mediapipe/framework/image/BUILD

@@ -30,3 +30,10 @@ android_library(
         "@maven//:com_google_guava_guava",
     ],
 )
+
+# Expose the java source files for building mediapipe AAR.
+filegroup(
+    name = "java_src",
+    srcs = glob(["*.java"]),
+    visibility = ["//mediapipe:__subpackages__"],
+)

mediapipe/java/com/google/mediapipe/mediapipe_aar.bzl

@@ -164,7 +164,10 @@ EOF
         assets_dir = assets_dir,
     )
 
-    _aar_with_jni(name, name + "_android_lib")
+    mediapipe_build_aar_with_jni(
+        name = name,
+        android_library = name + "_android_lib",
+    )
 
 def _mediapipe_jni(name, gen_libmediapipe, calculators = []):
     """Generates MediaPipe jni library.
@@ -203,7 +206,14 @@ def _mediapipe_jni(name, gen_libmediapipe, calculators = []):
         alwayslink = 1,
     )
 
-def _aar_with_jni(name, android_library):
+def mediapipe_build_aar_with_jni(name, android_library):
+    """Builds MediaPipe AAR with jni.
+
+      Args:
+        name: The bazel target name.
+        android_library: the android library that contains jni.
+    """
+
     # Generates dummy AndroidManifest.xml for dummy apk usage
     # (dummy apk is generated by <name>_dummy_app target below)
     native.genrule(
@@ -214,7 +224,7 @@ cat > $(OUTS) <<EOF
 <manifest
   xmlns:android="http://schemas.android.com/apk/res/android"
   package="dummy.package.for.so">
-  <uses-sdk android:minSdkVersion="21"/>
+  <uses-sdk android:minSdkVersion="24"/>
 </manifest>
 EOF
 """,
@@ -241,6 +251,7 @@ chmod +w $(location :{}.aar)
 origdir=$$PWD
 cd $$(mktemp -d)
 unzip $$origdir/$(location :{}_dummy_app_unsigned.apk) "lib/*"
+find lib -name *_dummy_app.so -delete
 cp -r lib jni
 zip -r $$origdir/$(location :{}.aar) jni/*/*.so
 """.format(android_library, name, name, name, name),
@@ -287,6 +298,36 @@ def mediapipe_java_proto_srcs(name = ""):
         src_out = "com/google/mediapipe/proto/CalculatorProto.java",
     ))
 
+    proto_src_list.append(mediapipe_java_proto_src_extractor(
+        target = "//mediapipe/framework:calculator_options_java_proto_lite",
+        src_out = "com/google/mediapipe/proto/CalculatorOptionsProto.java",
+    ))
+
+    proto_src_list.append(mediapipe_java_proto_src_extractor(
+        target = "//mediapipe/framework:stream_handler_java_proto_lite",
+        src_out = "com/google/mediapipe/proto/StreamHandlerProto.java",
+    ))
+
+    proto_src_list.append(mediapipe_java_proto_src_extractor(
+        target = "//mediapipe/framework:packet_factory_java_proto_lite",
+        src_out = "com/google/mediapipe/proto/PacketFactoryProto.java",
+    ))
+
+    proto_src_list.append(mediapipe_java_proto_src_extractor(
+        target = "//mediapipe/framework:packet_generator_java_proto_lite",
+        src_out = "com/google/mediapipe/proto/PacketGeneratorProto.java",
+    ))
+
+    proto_src_list.append(mediapipe_java_proto_src_extractor(
+        target = "//mediapipe/framework:status_handler_java_proto_lite",
+        src_out = "com/google/mediapipe/proto/StatusHandlerProto.java",
+    ))
+
+    proto_src_list.append(mediapipe_java_proto_src_extractor(
+        target = "//mediapipe/framework:mediapipe_options_java_proto_lite",
+        src_out = "com/google/mediapipe/proto/MediaPipeOptionsProto.java",
+    ))
+
     proto_src_list.append(mediapipe_java_proto_src_extractor(
         target = "//mediapipe/framework/formats:landmark_java_proto_lite",
         src_out = "com/google/mediapipe/formats/proto/LandmarkProto.java",

mediapipe/model_maker/python/core/BUILD

@@ -12,8 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# Placeholder for internal Python strict library compatibility macro.
+
 package(
     default_visibility = ["//mediapipe:__subpackages__"],
 )
 
 licenses(["notice"])
+
+py_library(
+    name = "hyperparameters",
+    srcs = ["hyperparameters.py"],
+)

mediapipe/model_maker/python/core/hyperparameters.py

@@ -0,0 +1,68 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+"""Hyperparameters for training models. Shared across tasks."""
+
+import dataclasses
+import tempfile
+
+from typing import Optional
+
+
+# TODO: Integrate this class into ImageClassifier and other tasks.
+@dataclasses.dataclass
+class BaseHParams:
+  """Hyperparameters used for training models.
+
+  A common set of hyperparameters shared by the training jobs of all model
+  maker tasks.
+
+  Attributes:
+    learning_rate: The learning rate to use for gradient descent training.
+    batch_size: Batch size for training.
+    epochs: Number of training iterations over the dataset.
+    steps_per_epoch: An optional integer indicate the number of training steps
+      per epoch. If not set, the training pipeline calculates the default steps
+      per epoch as the training dataset size devided by batch size.
+    shuffle: True if the dataset is shuffled before training.
+    export_dir: The location of the model checkpoint files.
+    distribution_strategy: A string specifying which Distribution Strategy to
+      use. Accepted values are 'off', 'one_device', 'mirrored',
+      'parameter_server', 'multi_worker_mirrored', and 'tpu' -- case
+      insensitive. 'off' means not to use Distribution Strategy; 'tpu' means to
+      use TPUStrategy using `tpu_address`. See the tf.distribute.Strategy
+      documentation for more details:
+      https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy.
+    num_gpus: How many GPUs to use at each worker with the
+      DistributionStrategies API. The default is -1, which means utilize all
+      available GPUs.
+    tpu: The Cloud TPU to use for training. This should be either the name used
+      when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.
+  """
+
+  # Parameters for train configuration
+  learning_rate: float
+  batch_size: int
+  epochs: int
+  steps_per_epoch: Optional[int] = None
+
+  # Dataset-related parameters
+  shuffle: bool = False
+
+  # Parameters for model / checkpoint files
+  export_dir: str = tempfile.mkdtemp()
+
+  # Parameters for hardware acceleration
+  distribution_strategy: str = 'off'
+  num_gpus: int = -1  # default value of -1 means use all available GPUs
+  tpu: str = ''

mediapipe/model_maker/python/core/utils/model_util.py

@@ -26,6 +26,7 @@ from typing import Any, Callable, Dict, List, Optional, Text, Tuple, Union
 import numpy as np
 import tensorflow as tf
 
+# resources dependency
 from mediapipe.model_maker.python.core.data import dataset
 from mediapipe.model_maker.python.core.utils import quantization
 
@@ -33,6 +34,31 @@ DEFAULT_SCALE, DEFAULT_ZERO_POINT = 0, 0
 ESTIMITED_STEPS_PER_EPOCH = 1000
 
 
+def load_keras_model(model_path: str,
+                     compile_on_load: bool = False) -> tf.keras.Model:
+  """Loads a tensorflow Keras model from file and returns the Keras model.
+
+  Args:
+    model_path: Relative path to a directory containing model data, such as
+      <parent_path>/saved_model/.
+    compile_on_load: Whether the model should be compiled while loading. If
+      False, the model returned has to be compiled with the appropriate loss
+      function and custom metrics before running for inference on a test
+      dataset.
+
+  Returns:
+    A tensorflow Keras model.
+  """
+  # Extract the file path before mediapipe/ as the `base_dir`. By joining it
+  # with the `model_path` which defines the relative path under mediapipe/, it
+  # yields to the aboslution path of the model files directory.
+  cwd = os.path.dirname(__file__)
+  base_dir = cwd[:cwd.rfind('mediapipe')]
+  absolute_path = os.path.join(base_dir, model_path)
+  return tf.keras.models.load_model(
+      absolute_path, custom_objects={'tf': tf}, compile=compile_on_load)
+
+
 def get_steps_per_epoch(steps_per_epoch: Optional[int] = None,
                         batch_size: Optional[int] = None,
                         train_data: Optional[dataset.Dataset] = None) -> int:
@@ -68,7 +94,8 @@ def export_tflite(
     tflite_filepath: str,
     quantization_config: Optional[quantization.QuantizationConfig] = None,
     supported_ops: Tuple[tf.lite.OpsSet,
-                         ...] = (tf.lite.OpsSet.TFLITE_BUILTINS,)):
+                         ...] = (tf.lite.OpsSet.TFLITE_BUILTINS,),
+    preprocess: Optional[Callable[..., bool]] = None):
   """Converts the model to tflite format and saves it.
 
   Args:
@@ -76,6 +103,9 @@ def export_tflite(
     tflite_filepath: File path to save tflite model.
     quantization_config: Configuration for post-training quantization.
     supported_ops: A list of supported ops in the converted TFLite file.
+    preprocess: A callable to preprocess the representative dataset for
+        quantization. The callable takes three arguments in order: feature,
+        label, and is_training.
   """
   if tflite_filepath is None:
     raise ValueError(
@@ -87,7 +117,8 @@ def export_tflite(
     converter = tf.lite.TFLiteConverter.from_saved_model(save_path)
 
     if quantization_config:
-      converter = quantization_config.set_converter_with_quantization(converter)
+      converter = quantization_config.set_converter_with_quantization(
+          converter, preprocess=preprocess)
 
     converter.target_spec.supported_ops = supported_ops
     tflite_model = converter.convert()

mediapipe/model_maker/python/core/utils/model_util_test.py

@@ -15,7 +15,6 @@
 import os
 
 from absl.testing import parameterized
-import numpy as np
 import tensorflow as tf
 
 from mediapipe.model_maker.python.core.utils import model_util
@@ -25,6 +24,18 @@ from mediapipe.model_maker.python.core.utils import test_util
 
 class ModelUtilTest(tf.test.TestCase, parameterized.TestCase):
 
+  def test_load_model(self):
+    input_dim = 4
+    model = test_util.build_model(input_shape=[input_dim], num_classes=2)
+    saved_model_path = os.path.join(self.get_temp_dir(), 'saved_model')
+    model.save(saved_model_path)
+    loaded_model = model_util.load_keras_model(saved_model_path)
+
+    input_tensors = test_util.create_random_sample(size=[1, input_dim])
+    model_output = model.predict_on_batch(input_tensors)
+    loaded_model_output = loaded_model.predict_on_batch(input_tensors)
+    self.assertTrue((model_output == loaded_model_output).all())
+
   @parameterized.named_parameters(
       dict(
           testcase_name='input_only_steps_per_epoch',
@@ -124,9 +135,9 @@ class ModelUtilTest(tf.test.TestCase, parameterized.TestCase):
                    input_dim: int,
                    max_input_value: int = 1000,
                    atol: float = 1e-04):
-    np.random.seed(0)
+    random_input = test_util.create_random_sample(
-    random_input = np.random.uniform(
+        size=[1, input_dim], high=max_input_value)
-        low=0, high=max_input_value, size=(1, input_dim)).astype(np.float32)
+    random_input = tf.convert_to_tensor(random_input)
 
     self.assertTrue(
         test_util.is_same_output(

mediapipe/model_maker/python/core/utils/test_util.py

@@ -46,6 +46,24 @@ def create_dataset(data_size: int,
   return dataset
 
 
+def create_random_sample(size: Union[int, List[int]],
+                         low: float = 0,
+                         high: float = 1) -> np.ndarray:
+  """Creates and returns a random sample with floating point values.
+
+  Args:
+    size: Size of the output multi-dimensional array.
+    low: Lower boundary of the output values.
+    high: Higher boundary of the output values.
+
+  Returns:
+    1D array if the size is scalar. Otherwise, N-D array whose dimension equals
+    input size.
+  """
+  np.random.seed(0)
+  return np.random.uniform(low=low, high=high, size=size).astype(np.float32)
+
+
 def build_model(input_shape: List[int], num_classes: int) -> tf.keras.Model:
   """Builds a simple Keras model for test."""
   inputs = tf.keras.layers.Input(shape=input_shape)

mediapipe/tasks/cc/components/containers/proto/landmarks_detection_result.proto

@@ -21,6 +21,9 @@ import "mediapipe/framework/formats/classification.proto";
 import "mediapipe/framework/formats/landmark.proto";
 import "mediapipe/framework/formats/rect.proto";
 
+option java_package = "com.google.mediapipe.tasks.components.containers.proto";
+option java_outer_classname = "LandmarksDetectionResultProto";
+
 message LandmarksDetectionResult {
   optional mediapipe.NormalizedLandmarkList landmarks = 1;
   optional mediapipe.ClassificationList classifications = 2;

mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.cc

@@ -180,6 +180,15 @@ absl::StatusOr<std::unique_ptr<GestureRecognizer>> GestureRecognizer::Create(
       if (status_or_packets.value()[kImageOutStreamName].IsEmpty()) {
         return;
       }
+      Packet image_packet = status_or_packets.value()[kImageOutStreamName];
+      if (status_or_packets.value()[kHandGesturesStreamName].IsEmpty()) {
+        Packet empty_packet =
+            status_or_packets.value()[kHandGesturesStreamName];
+        result_callback(
+            {{{}, {}, {}, {}}}, image_packet.Get<Image>(),
+            empty_packet.Timestamp().Value() / kMicroSecondsPerMilliSecond);
+        return;
+      }
       Packet gesture_packet =
           status_or_packets.value()[kHandGesturesStreamName];
       Packet handedness_packet =
@@ -188,7 +197,6 @@ absl::StatusOr<std::unique_ptr<GestureRecognizer>> GestureRecognizer::Create(
           status_or_packets.value()[kHandLandmarksStreamName];
       Packet hand_world_landmarks_packet =
           status_or_packets.value()[kHandWorldLandmarksStreamName];
-      Packet image_packet = status_or_packets.value()[kImageOutStreamName];
       result_callback(
           {{gesture_packet.Get<std::vector<ClassificationList>>(),
             handedness_packet.Get<std::vector<ClassificationList>>(),
@@ -218,6 +226,9 @@ absl::StatusOr<GestureRecognitionResult> GestureRecognizer::Recognize(
   ASSIGN_OR_RETURN(auto output_packets,
                    ProcessImageData({{kImageInStreamName,
                                       MakePacket<Image>(std::move(image))}}));
+  if (output_packets[kHandGesturesStreamName].IsEmpty()) {
+    return {{{}, {}, {}, {}}};
+  }
   return {
       {/* gestures= */ {output_packets[kHandGesturesStreamName]
                             .Get<std::vector<ClassificationList>>()},
@@ -247,6 +258,9 @@ absl::StatusOr<GestureRecognitionResult> GestureRecognizer::RecognizeForVideo(
           {{kImageInStreamName,
             MakePacket<Image>(std::move(image))
                 .At(Timestamp(timestamp_ms * kMicroSecondsPerMilliSecond))}}));
+  if (output_packets[kHandGesturesStreamName].IsEmpty()) {
+    return {{{}, {}, {}, {}}};
+  }
   return {
       {/* gestures= */ {output_packets[kHandGesturesStreamName]
                             .Get<std::vector<ClassificationList>>()},

mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.h

@@ -87,9 +87,8 @@ struct GestureRecognizerOptions {
 // Performs hand gesture recognition on the given image.
 //
 // TODO  add the link to DevSite.
-// This API expects expects a pre-trained hand gesture model asset bundle, or a
+// This API expects a pre-trained hand gesture model asset bundle, or a custom
-// custom one created using Model Maker. See <link to the DevSite documentation
+// one created using Model Maker. See <link to the DevSite documentation page>.
-// page>.
 //
 // Inputs:
 //   Image

mediapipe/tasks/cc/vision/gesture_recognizer/proto/gesture_classifier_graph_options.proto

@@ -21,6 +21,9 @@ import "mediapipe/framework/calculator.proto";
 import "mediapipe/tasks/cc/components/processors/proto/classifier_options.proto";
 import "mediapipe/tasks/cc/core/proto/base_options.proto";
 
+option java_package = "com.google.mediapipe.tasks.vision.gesturerecognizer";
+option java_outer_classname = "GestureClassifierGraphOptionsProto";
+
 message GestureClassifierGraphOptions {
   extend mediapipe.CalculatorOptions {
     optional GestureClassifierGraphOptions ext = 478825465;

mediapipe/tasks/cc/vision/gesture_recognizer/proto/gesture_embedder_graph_options.proto

@@ -20,6 +20,9 @@ package mediapipe.tasks.vision.gesture_recognizer.proto;
 import "mediapipe/framework/calculator.proto";
 import "mediapipe/tasks/cc/core/proto/base_options.proto";
 
+option java_package = "com.google.mediapipe.tasks.vision.gesturerecognizer";
+option java_outer_classname = "GestureEmbedderGraphOptionsProto";
+
 message GestureEmbedderGraphOptions {
   extend mediapipe.CalculatorOptions {
     optional GestureEmbedderGraphOptions ext = 478825422;

mediapipe/tasks/cc/vision/gesture_recognizer/proto/hand_gesture_recognizer_graph_options.proto

@@ -23,6 +23,9 @@ import "mediapipe/tasks/cc/core/proto/base_options.proto";
 import "mediapipe/tasks/cc/vision/gesture_recognizer/proto/gesture_classifier_graph_options.proto";
 import "mediapipe/tasks/cc/vision/gesture_recognizer/proto/gesture_embedder_graph_options.proto";
 
+option java_package = "com.google.mediapipe.tasks.vision.gesturerecognizer";
+option java_outer_classname = "HandGestureRecognizerGraphOptionsProto";
+
 message HandGestureRecognizerGraphOptions {
   extend mediapipe.CalculatorOptions {
     optional HandGestureRecognizerGraphOptions ext = 463370452;

mediapipe/tasks/cc/vision/hand_landmarker/proto/hand_landmarker_graph_options.proto

@@ -22,6 +22,9 @@ import "mediapipe/tasks/cc/core/proto/base_options.proto";
 import "mediapipe/tasks/cc/vision/hand_detector/proto/hand_detector_graph_options.proto";
 import "mediapipe/tasks/cc/vision/hand_landmarker/proto/hand_landmarks_detector_graph_options.proto";
 
+option java_package = "com.google.mediapipe.tasks.vision.handlandmarker";
+option java_outer_classname = "HandLandmarkerGraphOptionsProto";
+
 message HandLandmarkerGraphOptions {
   extend mediapipe.CalculatorOptions {
     optional HandLandmarkerGraphOptions ext = 462713202;

mediapipe/tasks/cc/vision/hand_landmarker/proto/hand_landmarks_detector_graph_options.proto

@@ -20,6 +20,9 @@ package mediapipe.tasks.vision.hand_landmarker.proto;
 import "mediapipe/framework/calculator.proto";
 import "mediapipe/tasks/cc/core/proto/base_options.proto";
 
+option java_package = "com.google.mediapipe.tasks.vision.handlandmarker";
+option java_outer_classname = "HandLandmarksDetectorGraphOptionsProto";
+
 message HandLandmarksDetectorGraphOptions {
   extend mediapipe.CalculatorOptions {
     optional HandLandmarksDetectorGraphOptions ext = 474472470;

mediapipe/tasks/cc/vision/image_classifier/image_classifier.cc

@@ -59,14 +59,24 @@ constexpr int kMicroSecondsPerMilliSecond = 1000;
 using ::mediapipe::tasks::components::containers::proto::ClassificationResult;
 using ::mediapipe::tasks::core::PacketMap;
 
-// Builds a NormalizedRect covering the entire image.
+// Returns a NormalizedRect covering the full image if input is not present.
-NormalizedRect BuildFullImageNormRect() {
+// Otherwise, makes sure the x_center, y_center, width and height are set in
-  NormalizedRect norm_rect;
+// case only a rotation was provided in the input.
-  norm_rect.set_x_center(0.5);
+NormalizedRect FillNormalizedRect(
-  norm_rect.set_y_center(0.5);
+    std::optional<NormalizedRect> normalized_rect) {
-  norm_rect.set_width(1);
+  NormalizedRect result;
-  norm_rect.set_height(1);
+  if (normalized_rect.has_value()) {
-  return norm_rect;
+    result = *normalized_rect;
+  }
+  bool has_coordinates = result.has_x_center() || result.has_y_center() ||
+                         result.has_width() || result.has_height();
+  if (!has_coordinates) {
+    result.set_x_center(0.5);
+    result.set_y_center(0.5);
+    result.set_width(1);
+    result.set_height(1);
+  }
+  return result;
 }
 
 // Creates a MediaPipe graph config that contains a subgraph node of
@@ -154,15 +164,14 @@ absl::StatusOr<std::unique_ptr<ImageClassifier>> ImageClassifier::Create(
 }
 
 absl::StatusOr<ClassificationResult> ImageClassifier::Classify(
-    Image image, std::optional<NormalizedRect> roi) {
+    Image image, std::optional<NormalizedRect> image_processing_options) {
   if (image.UsesGpu()) {
     return CreateStatusWithPayload(
         absl::StatusCode::kInvalidArgument,
         "GPU input images are currently not supported.",
         MediaPipeTasksStatus::kRunnerUnexpectedInputError);
   }
-  NormalizedRect norm_rect =
+  NormalizedRect norm_rect = FillNormalizedRect(image_processing_options);
-      roi.has_value() ? roi.value() : BuildFullImageNormRect();
   ASSIGN_OR_RETURN(
       auto output_packets,
       ProcessImageData(
@@ -173,15 +182,15 @@ absl::StatusOr<ClassificationResult> ImageClassifier::Classify(
 }
 
 absl::StatusOr<ClassificationResult> ImageClassifier::ClassifyForVideo(
-    Image image, int64 timestamp_ms, std::optional<NormalizedRect> roi) {
+    Image image, int64 timestamp_ms,
+    std::optional<NormalizedRect> image_processing_options) {
   if (image.UsesGpu()) {
     return CreateStatusWithPayload(
         absl::StatusCode::kInvalidArgument,
         "GPU input images are currently not supported.",
         MediaPipeTasksStatus::kRunnerUnexpectedInputError);
   }
-  NormalizedRect norm_rect =
+  NormalizedRect norm_rect = FillNormalizedRect(image_processing_options);
-      roi.has_value() ? roi.value() : BuildFullImageNormRect();
   ASSIGN_OR_RETURN(
       auto output_packets,
       ProcessVideoData(
@@ -195,16 +204,16 @@ absl::StatusOr<ClassificationResult> ImageClassifier::ClassifyForVideo(
       .Get<ClassificationResult>();
 }
 
-absl::Status ImageClassifier::ClassifyAsync(Image image, int64 timestamp_ms,
+absl::Status ImageClassifier::ClassifyAsync(
-                                            std::optional<NormalizedRect> roi) {
+    Image image, int64 timestamp_ms,
+    std::optional<NormalizedRect> image_processing_options) {
   if (image.UsesGpu()) {
     return CreateStatusWithPayload(
         absl::StatusCode::kInvalidArgument,
         "GPU input images are currently not supported.",
         MediaPipeTasksStatus::kRunnerUnexpectedInputError);
   }
-  NormalizedRect norm_rect =
+  NormalizedRect norm_rect = FillNormalizedRect(image_processing_options);
-      roi.has_value() ? roi.value() : BuildFullImageNormRect();
   return SendLiveStreamData(
       {{kImageInStreamName,
         MakePacket<Image>(std::move(image))

mediapipe/tasks/cc/vision/image_classifier/image_classifier.h

@@ -105,9 +105,18 @@ class ImageClassifier : tasks::vision::core::BaseVisionTaskApi {
   static absl::StatusOr<std::unique_ptr<ImageClassifier>> Create(
       std::unique_ptr<ImageClassifierOptions> options);
 
-  // Performs image classification on the provided single image. Classification
+  // Performs image classification on the provided single image.
-  // is performed on the region of interest specified by the `roi` argument if
+  //
-  // provided, or on the entire image otherwise.
+  // The optional 'image_processing_options' parameter can be used to specify:
+  // - the rotation to apply to the image before performing classification, by
+  //   setting its 'rotation' field in radians (e.g. 'M_PI / 2' for a 90°
+  //   anti-clockwise rotation).
+  // and/or
+  // - the region-of-interest on which to perform classification, by setting its
+  //  'x_center', 'y_center', 'width' and 'height' fields. If none of these is
+  //  set, they will automatically be set to cover the full image.
+  // If both are specified, the crop around the region-of-interest is extracted
+  // first, then the specified rotation is applied to the crop.
   //
   // Only use this method when the ImageClassifier is created with the image
   // running mode.
@@ -117,11 +126,21 @@ class ImageClassifier : tasks::vision::core::BaseVisionTaskApi {
   // YUVToImageCalculator is integrated.
   absl::StatusOr<components::containers::proto::ClassificationResult> Classify(
       mediapipe::Image image,
-      std::optional<mediapipe::NormalizedRect> roi = std::nullopt);
+      std::optional<mediapipe::NormalizedRect> image_processing_options =
+          std::nullopt);
 
-  // Performs image classification on the provided video frame. Classification
+  // Performs image classification on the provided video frame.
-  // is performed on the region of interested specified by the `roi` argument if
+  //
-  // provided, or on the entire image otherwise.
+  // The optional 'image_processing_options' parameter can be used to specify:
+  // - the rotation to apply to the image before performing classification, by
+  //   setting its 'rotation' field in radians (e.g. 'M_PI / 2' for a 90°
+  //   anti-clockwise rotation).
+  // and/or
+  // - the region-of-interest on which to perform classification, by setting its
+  //  'x_center', 'y_center', 'width' and 'height' fields. If none of these is
+  //  set, they will automatically be set to cover the full image.
+  // If both are specified, the crop around the region-of-interest is extracted
+  // first, then the specified rotation is applied to the crop.
   //
   // Only use this method when the ImageClassifier is created with the video
   // running mode.
@@ -131,12 +150,22 @@ class ImageClassifier : tasks::vision::core::BaseVisionTaskApi {
   // must be monotonically increasing.
   absl::StatusOr<components::containers::proto::ClassificationResult>
   ClassifyForVideo(mediapipe::Image image, int64 timestamp_ms,
-                   std::optional<mediapipe::NormalizedRect> roi = std::nullopt);
+                   std::optional<mediapipe::NormalizedRect>
+                       image_processing_options = std::nullopt);
 
   // Sends live image data to image classification, and the results will be
   // available via the "result_callback" provided in the ImageClassifierOptions.
-  // Classification is performed on the region of interested specified by the
+  //
-  // `roi` argument if provided, or on the entire image otherwise.
+  // The optional 'image_processing_options' parameter can be used to specify:
+  // - the rotation to apply to the image before performing classification, by
+  //   setting its 'rotation' field in radians (e.g. 'M_PI / 2' for a 90°
+  //   anti-clockwise rotation).
+  // and/or
+  // - the region-of-interest on which to perform classification, by setting its
+  //  'x_center', 'y_center', 'width' and 'height' fields. If none of these is
+  //  set, they will automatically be set to cover the full image.
+  // If both are specified, the crop around the region-of-interest is extracted
+  // first, then the specified rotation is applied to the crop.
   //
   // Only use this method when the ImageClassifier is created with the live
   // stream running mode.
@@ -153,9 +182,9 @@ class ImageClassifier : tasks::vision::core::BaseVisionTaskApi {
   //     longer be valid when the callback returns. To access the image data
   //     outside of the callback, callers need to make a copy of the image.
   //   - The input timestamp in milliseconds.
-  absl::Status ClassifyAsync(
+  absl::Status ClassifyAsync(mediapipe::Image image, int64 timestamp_ms,
-      mediapipe::Image image, int64 timestamp_ms,
+                             std::optional<mediapipe::NormalizedRect>
-      std::optional<mediapipe::NormalizedRect> roi = std::nullopt);
+                                 image_processing_options = std::nullopt);
 
   // TODO: add Classify() variants taking a region of interest as
   // additional argument.

mediapipe/tasks/cc/vision/image_classifier/image_classifier_test.cc

@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "mediapipe/tasks/cc/vision/image_classifier/image_classifier.h"
 
+#include <cmath>
 #include <functional>
 #include <memory>
 #include <string>
@@ -546,18 +547,102 @@ TEST_F(ImageModeTest, SucceedsWithRegionOfInterest) {
   options->classifier_options.max_results = 1;
   MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<ImageClassifier> image_classifier,
                           ImageClassifier::Create(std::move(options)));
-  // NormalizedRect around the soccer ball.
+  // Crop around the soccer ball.
-  NormalizedRect roi;
+  NormalizedRect image_processing_options;
-  roi.set_x_center(0.532);
+  image_processing_options.set_x_center(0.532);
-  roi.set_y_center(0.521);
+  image_processing_options.set_y_center(0.521);
-  roi.set_width(0.164);
+  image_processing_options.set_width(0.164);
-  roi.set_height(0.427);
+  image_processing_options.set_height(0.427);
 
-  MP_ASSERT_OK_AND_ASSIGN(auto results, image_classifier->Classify(image, roi));
+  MP_ASSERT_OK_AND_ASSIGN(auto results, image_classifier->Classify(
+                                            image, image_processing_options));
 
   ExpectApproximatelyEqual(results, GenerateSoccerBallResults(0));
 }
 
+TEST_F(ImageModeTest, SucceedsWithRotation) {
+  MP_ASSERT_OK_AND_ASSIGN(Image image,
+                          DecodeImageFromFile(JoinPath("./", kTestDataDirectory,
+                                                       "burger_rotated.jpg")));
+  auto options = std::make_unique<ImageClassifierOptions>();
+  options->base_options.model_asset_path =
+      JoinPath("./", kTestDataDirectory, kMobileNetFloatWithMetadata);
+  options->classifier_options.max_results = 3;
+  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<ImageClassifier> image_classifier,
+                          ImageClassifier::Create(std::move(options)));
+
+  // Specify a 90° anti-clockwise rotation.
+  NormalizedRect image_processing_options;
+  image_processing_options.set_rotation(M_PI / 2.0);
+
+  MP_ASSERT_OK_AND_ASSIGN(auto results, image_classifier->Classify(
+                                            image, image_processing_options));
+
+  // Results differ slightly from the non-rotated image, but that's expected
+  // as models are very sensitive to the slightest numerical differences
+  // introduced by the rotation and JPG encoding.
+  ExpectApproximatelyEqual(results, ParseTextProtoOrDie<ClassificationResult>(
+                                        R"pb(classifications {
+                                               entries {
+                                                 categories {
+                                                   index: 934
+                                                   score: 0.6371766
+                                                   category_name: "cheeseburger"
+                                                 }
+                                                 categories {
+                                                   index: 963
+                                                   score: 0.049443405
+                                                   category_name: "meat loaf"
+                                                 }
+                                                 categories {
+                                                   index: 925
+                                                   score: 0.047918003
+                                                   category_name: "guacamole"
+                                                 }
+                                                 timestamp_ms: 0
+                                               }
+                                               head_index: 0
+                                               head_name: "probability"
+                                             })pb"));
+}
+
+TEST_F(ImageModeTest, SucceedsWithRegionOfInterestAndRotation) {
+  MP_ASSERT_OK_AND_ASSIGN(
+      Image image, DecodeImageFromFile(JoinPath("./", kTestDataDirectory,
+                                                "multi_objects_rotated.jpg")));
+  auto options = std::make_unique<ImageClassifierOptions>();
+  options->base_options.model_asset_path =
+      JoinPath("./", kTestDataDirectory, kMobileNetFloatWithMetadata);
+  options->classifier_options.max_results = 1;
+  MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<ImageClassifier> image_classifier,
+                          ImageClassifier::Create(std::move(options)));
+  // Crop around the chair, with 90° anti-clockwise rotation.
+  NormalizedRect image_processing_options;
+  image_processing_options.set_x_center(0.2821);
+  image_processing_options.set_y_center(0.2406);
+  image_processing_options.set_width(0.5642);
+  image_processing_options.set_height(0.1286);
+  image_processing_options.set_rotation(M_PI / 2.0);
+
+  MP_ASSERT_OK_AND_ASSIGN(auto results, image_classifier->Classify(
+                                            image, image_processing_options));
+
+  ExpectApproximatelyEqual(results,
+                           ParseTextProtoOrDie<ClassificationResult>(
+                               R"pb(classifications {
+                                      entries {
+                                        categories {
+                                          index: 560
+                                          score: 0.6800408
+                                          category_name: "folding chair"
+                                        }
+                                        timestamp_ms: 0
+                                      }
+                                      head_index: 0
+                                      head_name: "probability"
+                                    })pb"));
+}
+
 class VideoModeTest : public tflite_shims::testing::Test {};
 
 TEST_F(VideoModeTest, FailsWithCallingWrongMethod) {
@@ -646,16 +731,17 @@ TEST_F(VideoModeTest, SucceedsWithRegionOfInterest) {
   options->classifier_options.max_results = 1;
   MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<ImageClassifier> image_classifier,
                           ImageClassifier::Create(std::move(options)));
-  // NormalizedRect around the soccer ball.
+  // Crop around the soccer ball.
-  NormalizedRect roi;
+  NormalizedRect image_processing_options;
-  roi.set_x_center(0.532);
+  image_processing_options.set_x_center(0.532);
-  roi.set_y_center(0.521);
+  image_processing_options.set_y_center(0.521);
-  roi.set_width(0.164);
+  image_processing_options.set_width(0.164);
-  roi.set_height(0.427);
+  image_processing_options.set_height(0.427);
 
   for (int i = 0; i < iterations; ++i) {
-    MP_ASSERT_OK_AND_ASSIGN(auto results,
+    MP_ASSERT_OK_AND_ASSIGN(
-                            image_classifier->ClassifyForVideo(image, i, roi));
+        auto results,
+        image_classifier->ClassifyForVideo(image, i, image_processing_options));
     ExpectApproximatelyEqual(results, GenerateSoccerBallResults(i));
   }
   MP_ASSERT_OK(image_classifier->Close());
@@ -790,15 +876,16 @@ TEST_F(LiveStreamModeTest, SucceedsWithRegionOfInterest) {
       };
   MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr<ImageClassifier> image_classifier,
                           ImageClassifier::Create(std::move(options)));
-  // NormalizedRect around the soccer ball.
+  // Crop around the soccer ball.
-  NormalizedRect roi;
+  NormalizedRect image_processing_options;
-  roi.set_x_center(0.532);
+  image_processing_options.set_x_center(0.532);
-  roi.set_y_center(0.521);
+  image_processing_options.set_y_center(0.521);
-  roi.set_width(0.164);
+  image_processing_options.set_width(0.164);
-  roi.set_height(0.427);
+  image_processing_options.set_height(0.427);
 
   for (int i = 0; i < iterations; ++i) {
-    MP_ASSERT_OK(image_classifier->ClassifyAsync(image, i, roi));
+    MP_ASSERT_OK(
+        image_classifier->ClassifyAsync(image, i, image_processing_options));
   }
   MP_ASSERT_OK(image_classifier->Close());
 

mediapipe/tasks/examples/android/objectdetector/src/main/BUILD

@@ -34,8 +34,8 @@ android_binary(
         "//mediapipe/java/com/google/mediapipe/framework/image",
         "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:detection",
         "//mediapipe/tasks/java/com/google/mediapipe/tasks/core",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/vision/core",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/vision:core",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/vision/objectdetector",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/vision:objectdetector",
         "//third_party:androidx_appcompat",
         "//third_party:androidx_constraint_layout",
         "//third_party:opencv",

mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers/BUILD

@@ -63,3 +63,10 @@ android_library(
         "@maven//:com_google_guava_guava",
     ],
 )
+
+# Expose the java source files for building mediapipe tasks core AAR.
+filegroup(
+    name = "java_src",
+    srcs = glob(["*.java"]),
+    visibility = ["//mediapipe/tasks/java/com/google/mediapipe/tasks/core:__subpackages__"],
+)

mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors/BUILD

@@ -28,3 +28,10 @@ android_library(
         "@maven//:com_google_guava_guava",
     ],
 )
+
+# Expose the java source files for building mediapipe tasks core AAR.
+filegroup(
+    name = "java_src",
+    srcs = glob(["*.java"]),
+    visibility = ["//mediapipe/tasks/java/com/google/mediapipe/tasks/core:__subpackages__"],
+)

mediapipe/tasks/java/com/google/mediapipe/tasks/core/AndroidManifest.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.google.mediapipe.tasks.core">
+
+    <uses-sdk android:minSdkVersion="24"
+        android:targetSdkVersion="30" />
+
+</manifest>

mediapipe/tasks/java/com/google/mediapipe/tasks/core/BUILD

@@ -20,6 +20,7 @@ android_library(
     javacopts = [
         "-Xep:AndroidJdkLibsChecker:OFF",
     ],
+    manifest = "AndroidManifest.xml",
     deps = [
         "//mediapipe/calculators/core:flow_limiter_calculator_java_proto_lite",
         "//mediapipe/calculators/tensor:inference_calculator_java_proto_lite",

mediapipe/tasks/java/com/google/mediapipe/tasks/vision/BUILD

@@ -0,0 +1,142 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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 = ["//mediapipe/tasks:internal"])
+
+android_library(
+    name = "core",
+    srcs = glob(["core/*.java"]),
+    javacopts = [
+        "-Xep:AndroidJdkLibsChecker:OFF",
+    ],
+    deps = [
+        ":libmediapipe_tasks_vision_jni_lib",
+        "//mediapipe/framework/formats:rect_java_proto_lite",
+        "//mediapipe/java/com/google/mediapipe/framework:android_framework_no_mff",
+        "//mediapipe/java/com/google/mediapipe/framework/image",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/core",
+        "@maven//:com_google_guava_guava",
+    ],
+)
+
+# The native library of all MediaPipe vision tasks.
+cc_binary(
+    name = "libmediapipe_tasks_vision_jni.so",
+    linkshared = 1,
+    linkstatic = 1,
+    deps = [
+        "//mediapipe/calculators/core:flow_limiter_calculator",
+        "//mediapipe/java/com/google/mediapipe/framework/jni:mediapipe_framework_jni",
+        "//mediapipe/tasks/cc/vision/gesture_recognizer:gesture_recognizer_graph",
+        "//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
+        "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/core/jni:model_resources_cache_jni",
+    ],
+)
+
+cc_library(
+    name = "libmediapipe_tasks_vision_jni_lib",
+    srcs = [":libmediapipe_tasks_vision_jni.so"],
+    alwayslink = 1,
+)
+
+android_library(
+    name = "objectdetector",
+    srcs = [
+        "objectdetector/ObjectDetectionResult.java",
+        "objectdetector/ObjectDetector.java",
+    ],
+    javacopts = [
+        "-Xep:AndroidJdkLibsChecker:OFF",
+    ],
+    manifest = "objectdetector/AndroidManifest.xml",
+    deps = [
+        ":core",
+        "//mediapipe/framework:calculator_options_java_proto_lite",
+        "//mediapipe/framework/formats:detection_java_proto_lite",
+        "//mediapipe/framework/formats:location_data_java_proto_lite",
+        "//mediapipe/java/com/google/mediapipe/framework:android_framework",
+        "//mediapipe/java/com/google/mediapipe/framework/image",
+        "//mediapipe/tasks/cc/core/proto:base_options_java_proto_lite",
+        "//mediapipe/tasks/cc/vision/object_detector/proto:object_detector_options_java_proto_lite",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:category",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:detection",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/core",
+        "//third_party:autovalue",
+        "@maven//:com_google_guava_guava",
+    ],
+)
+
+android_library(
+    name = "imageclassifier",
+    srcs = [
+        "imageclassifier/ImageClassificationResult.java",
+        "imageclassifier/ImageClassifier.java",
+    ],
+    javacopts = [
+        "-Xep:AndroidJdkLibsChecker:OFF",
+    ],
+    manifest = "imageclassifier/AndroidManifest.xml",
+    deps = [
+        ":core",
+        "//mediapipe/framework:calculator_options_java_proto_lite",
+        "//mediapipe/java/com/google/mediapipe/framework:android_framework",
+        "//mediapipe/java/com/google/mediapipe/framework/image",
+        "//mediapipe/tasks/cc/components/containers/proto:category_java_proto_lite",
+        "//mediapipe/tasks/cc/components/containers/proto:classifications_java_proto_lite",
+        "//mediapipe/tasks/cc/core/proto:base_options_java_proto_lite",
+        "//mediapipe/tasks/cc/vision/image_classifier/proto:image_classifier_graph_options_java_proto_lite",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:category",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:classification_entry",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:classifications",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/processors:classifieroptions",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/core",
+        "//third_party:autovalue",
+        "@maven//:com_google_guava_guava",
+    ],
+)
+
+android_library(
+    name = "gesturerecognizer",
+    srcs = [
+        "gesturerecognizer/GestureRecognitionResult.java",
+        "gesturerecognizer/GestureRecognizer.java",
+    ],
+    javacopts = [
+        "-Xep:AndroidJdkLibsChecker:OFF",
+    ],
+    manifest = "gesturerecognizer/AndroidManifest.xml",
+    deps = [
+        ":core",
+        "//mediapipe/framework:calculator_options_java_proto_lite",
+        "//mediapipe/framework/formats:classification_java_proto_lite",
+        "//mediapipe/framework/formats:landmark_java_proto_lite",
+        "//mediapipe/java/com/google/mediapipe/framework:android_framework",
+        "//mediapipe/java/com/google/mediapipe/framework/image",
+        "//mediapipe/tasks/cc/components/processors/proto:classifier_options_java_proto_lite",
+        "//mediapipe/tasks/cc/core/proto:base_options_java_proto_lite",
+        "//mediapipe/tasks/cc/vision/gesture_recognizer/proto:gesture_recognizer_graph_options_java_proto_lite",
+        "//mediapipe/tasks/cc/vision/gesture_recognizer/proto:hand_gesture_recognizer_graph_options_java_proto_lite",
+        "//mediapipe/tasks/cc/vision/hand_detector/proto:hand_detector_graph_options_java_proto_lite",
+        "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarker_graph_options_java_proto_lite",
+        "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarks_detector_graph_options_java_proto_lite",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:category",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:landmark",
+        "//mediapipe/tasks/java/com/google/mediapipe/tasks/core",
+        "//third_party:autovalue",
+        "@maven//:com_google_guava_guava",
+    ],
+)

mediapipe/tasks/java/com/google/mediapipe/tasks/vision/core/BUILD

@@ -1,53 +0,0 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
-#
-# 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 = ["//mediapipe/tasks:internal"])
-
-android_library(
-    name = "core",
-    srcs = glob(["*.java"]),
-    javacopts = [
-        "-Xep:AndroidJdkLibsChecker:OFF",
-    ],
-    deps = [
-        ":libmediapipe_tasks_vision_jni_lib",
-        "//mediapipe/framework/formats:rect_java_proto_lite",
-        "//mediapipe/java/com/google/mediapipe/framework:android_framework_no_mff",
-        "//mediapipe/java/com/google/mediapipe/framework/image",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/core",
-        "@maven//:com_google_guava_guava",
-    ],
-)
-
-# The native library of all MediaPipe vision tasks.
-cc_binary(
-    name = "libmediapipe_tasks_vision_jni.so",
-    linkshared = 1,
-    linkstatic = 1,
-    deps = [
-        "//mediapipe/calculators/core:flow_limiter_calculator",
-        "//mediapipe/java/com/google/mediapipe/framework/jni:mediapipe_framework_jni",
-        "//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
-        "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/core/jni:model_resources_cache_jni",
-    ],
-)
-
-cc_library(
-    name = "libmediapipe_tasks_vision_jni_lib",
-    srcs = [":libmediapipe_tasks_vision_jni.so"],
-    alwayslink = 1,
-)

mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/BUILD

@@ -1,40 +0,0 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
-#
-# 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.
-
-package(default_visibility = ["//mediapipe/tasks:internal"])
-
-licenses(["notice"])
-
-android_library(
-    name = "gesturerecognizer",
-    srcs = [
-        "GestureRecognitionResult.java",
-    ],
-    javacopts = [
-        "-Xep:AndroidJdkLibsChecker:OFF",
-    ],
-    manifest = ":AndroidManifest.xml",
-    deps = [
-        "//mediapipe/framework:calculator_options_java_proto_lite",
-        "//mediapipe/framework/formats:classification_java_proto_lite",
-        "//mediapipe/framework/formats:landmark_java_proto_lite",
-        "//mediapipe/tasks/cc/core/proto:base_options_java_proto_lite",
-        "//mediapipe/tasks/cc/vision/gesture_recognizer/proto:gesture_recognizer_graph_options_java_proto_lite",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:category",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:landmark",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/core",
-        "//third_party:autovalue",
-        "@maven//:com_google_guava_guava",
-    ],
-)

mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizer.java

@@ -0,0 +1,466 @@
+// Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+//
+// 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.
+
+package com.google.mediapipe.tasks.vision.gesturerecognizer;
+
+import android.content.Context;
+import android.os.ParcelFileDescriptor;
+import com.google.auto.value.AutoValue;
+import com.google.mediapipe.formats.proto.LandmarkProto.LandmarkList;
+import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmarkList;
+import com.google.mediapipe.proto.CalculatorOptionsProto.CalculatorOptions;
+import com.google.mediapipe.formats.proto.ClassificationProto.ClassificationList;
+import com.google.mediapipe.framework.AndroidPacketGetter;
+import com.google.mediapipe.framework.Packet;
+import com.google.mediapipe.framework.PacketGetter;
+import com.google.mediapipe.framework.image.BitmapImageBuilder;
+import com.google.mediapipe.framework.image.Image;
+import com.google.mediapipe.tasks.components.processors.proto.ClassifierOptionsProto;
+import com.google.mediapipe.tasks.core.BaseOptions;
+import com.google.mediapipe.tasks.core.ErrorListener;
+import com.google.mediapipe.tasks.core.OutputHandler;
+import com.google.mediapipe.tasks.core.OutputHandler.ResultListener;
+import com.google.mediapipe.tasks.core.TaskInfo;
+import com.google.mediapipe.tasks.core.TaskOptions;
+import com.google.mediapipe.tasks.core.TaskRunner;
+import com.google.mediapipe.tasks.core.proto.BaseOptionsProto;
+import com.google.mediapipe.tasks.vision.core.BaseVisionTaskApi;
+import com.google.mediapipe.tasks.vision.core.RunningMode;
+import com.google.mediapipe.tasks.vision.handdetector.HandDetectorGraphOptionsProto;
+import com.google.mediapipe.tasks.vision.handlandmarker.HandLandmarkerGraphOptionsProto;
+import com.google.mediapipe.tasks.vision.handlandmarker.HandLandmarksDetectorGraphOptionsProto;
+import java.io.File;
+import java.io.IOException;
+import java.nio.ByteBuffer;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+import java.util.Optional;
+
+/**
+ * Performs gesture recognition on images.
+ *
+ * <p>This API expects a pre-trained hand gesture model asset bundle, or a custom one created using
+ * Model Maker. See <TODO link to the DevSite documentation page>.
+ *
+ * <ul>
+ *   <li>Input image {@link Image}
+ *       <ul>
+ *         <li>The image that gesture recognition runs on.
+ *       </ul>
+ *   <li>Output GestureRecognitionResult {@link GestureRecognitionResult}
+ *       <ul>
+ *         <li>A GestureRecognitionResult containing hand landmarks and recognized hand gestures.
+ *       </ul>
+ * </ul>
+ */
+public final class GestureRecognizer extends BaseVisionTaskApi {
+  private static final String TAG = GestureRecognizer.class.getSimpleName();
+  private static final String IMAGE_IN_STREAM_NAME = "image_in";
+  private static final List<String> INPUT_STREAMS =
+      Collections.unmodifiableList(Arrays.asList("IMAGE:" + IMAGE_IN_STREAM_NAME));
+  private static final List<String> OUTPUT_STREAMS =
+      Collections.unmodifiableList(
+          Arrays.asList(
+              "LANDMARKS:hand_landmarks",
+              "WORLD_LANDMARKS:world_hand_landmarks",
+              "HANDEDNESS:handedness",
+              "HAND_GESTURES:hand_gestures",
+              "IMAGE:image_out"));
+  private static final int LANDMARKS_OUT_STREAM_INDEX = 0;
+  private static final int WORLD_LANDMARKS_OUT_STREAM_INDEX = 1;
+  private static final int HANDEDNESS_OUT_STREAM_INDEX = 2;
+  private static final int HAND_GESTURES_OUT_STREAM_INDEX = 3;
+  private static final int IMAGE_OUT_STREAM_INDEX = 4;
+  private static final String TASK_GRAPH_NAME =
+      "mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerGraph";
+
+  /**
+   * Creates a {@link GestureRecognizer} instance from a model file and the default {@link
+   * GestureRecognizerOptions}.
+   *
+   * @param context an Android {@link Context}.
+   * @param modelPath path to the gesture recognition model with metadata in the assets.
+   * @throws MediaPipeException if there is an error during {@link GestureRecognizer} creation.
+   */
+  public static GestureRecognizer createFromFile(Context context, String modelPath) {
+    BaseOptions baseOptions = BaseOptions.builder().setModelAssetPath(modelPath).build();
+    return createFromOptions(
+        context, GestureRecognizerOptions.builder().setBaseOptions(baseOptions).build());
+  }
+
+  /**
+   * Creates a {@link GestureRecognizer} instance from a model file and the default {@link
+   * GestureRecognizerOptions}.
+   *
+   * @param context an Android {@link Context}.
+   * @param modelFile the gesture recognition model {@link File} instance.
+   * @throws IOException if an I/O error occurs when opening the tflite model file.
+   * @throws MediaPipeException if there is an error during {@link GestureRecognizer} creation.
+   */
+  public static GestureRecognizer createFromFile(Context context, File modelFile)
+      throws IOException {
+    try (ParcelFileDescriptor descriptor =
+        ParcelFileDescriptor.open(modelFile, ParcelFileDescriptor.MODE_READ_ONLY)) {
+      BaseOptions baseOptions =
+          BaseOptions.builder().setModelAssetFileDescriptor(descriptor.getFd()).build();
+      return createFromOptions(
+          context, GestureRecognizerOptions.builder().setBaseOptions(baseOptions).build());
+    }
+  }
+
+  /**
+   * Creates a {@link GestureRecognizer} instance from a model buffer and the default {@link
+   * GestureRecognizerOptions}.
+   *
+   * @param context an Android {@link Context}.
+   * @param modelBuffer a direct {@link ByteBuffer} or a {@link MappedByteBuffer} of the detection
+   *     model.
+   * @throws MediaPipeException if there is an error during {@link GestureRecognizer} creation.
+   */
+  public static GestureRecognizer createFromBuffer(Context context, final ByteBuffer modelBuffer) {
+    BaseOptions baseOptions = BaseOptions.builder().setModelAssetBuffer(modelBuffer).build();
+    return createFromOptions(
+        context, GestureRecognizerOptions.builder().setBaseOptions(baseOptions).build());
+  }
+
+  /**
+   * Creates a {@link GestureRecognizer} instance from a {@link GestureRecognizerOptions}.
+   *
+   * @param context an Android {@link Context}.
+   * @param recognizerOptions a {@link GestureRecognizerOptions} instance.
+   * @throws MediaPipeException if there is an error during {@link GestureRecognizer} creation.
+   */
+  public static GestureRecognizer createFromOptions(
+      Context context, GestureRecognizerOptions recognizerOptions) {
+    // TODO: Consolidate OutputHandler and TaskRunner.
+    OutputHandler<GestureRecognitionResult, Image> handler = new OutputHandler<>();
+    handler.setOutputPacketConverter(
+        new OutputHandler.OutputPacketConverter<GestureRecognitionResult, Image>() {
+          @Override
+          public GestureRecognitionResult convertToTaskResult(List<Packet> packets) {
+            // If there is no hands detected in the image, just returns empty lists.
+            if (packets.get(HAND_GESTURES_OUT_STREAM_INDEX).isEmpty()) {
+              return GestureRecognitionResult.create(
+                  new ArrayList<>(),
+                  new ArrayList<>(),
+                  new ArrayList<>(),
+                  new ArrayList<>(),
+                  packets.get(HAND_GESTURES_OUT_STREAM_INDEX).getTimestamp());
+            }
+            return GestureRecognitionResult.create(
+                PacketGetter.getProtoVector(
+                    packets.get(LANDMARKS_OUT_STREAM_INDEX), NormalizedLandmarkList.parser()),
+                PacketGetter.getProtoVector(
+                    packets.get(WORLD_LANDMARKS_OUT_STREAM_INDEX), LandmarkList.parser()),
+                PacketGetter.getProtoVector(
+                    packets.get(HANDEDNESS_OUT_STREAM_INDEX), ClassificationList.parser()),
+                PacketGetter.getProtoVector(
+                    packets.get(HAND_GESTURES_OUT_STREAM_INDEX), ClassificationList.parser()),
+                packets.get(HAND_GESTURES_OUT_STREAM_INDEX).getTimestamp());
+          }
+
+          @Override
+          public Image convertToTaskInput(List<Packet> packets) {
+            return new BitmapImageBuilder(
+                    AndroidPacketGetter.getBitmapFromRgb(packets.get(IMAGE_OUT_STREAM_INDEX)))
+                .build();
+          }
+        });
+    recognizerOptions.resultListener().ifPresent(handler::setResultListener);
+    recognizerOptions.errorListener().ifPresent(handler::setErrorListener);
+    TaskRunner runner =
+        TaskRunner.create(
+            context,
+            TaskInfo.<GestureRecognizerOptions>builder()
+                .setTaskGraphName(TASK_GRAPH_NAME)
+                .setInputStreams(INPUT_STREAMS)
+                .setOutputStreams(OUTPUT_STREAMS)
+                .setTaskOptions(recognizerOptions)
+                .setEnableFlowLimiting(recognizerOptions.runningMode() == RunningMode.LIVE_STREAM)
+                .build(),
+            handler);
+    return new GestureRecognizer(runner, recognizerOptions.runningMode());
+  }
+
+  /**
+   * Constructor to initialize an {@link GestureRecognizer} from a {@link TaskRunner} and a {@link
+   * RunningMode}.
+   *
+   * @param taskRunner a {@link TaskRunner}.
+   * @param runningMode a mediapipe vision task {@link RunningMode}.
+   */
+  private GestureRecognizer(TaskRunner taskRunner, RunningMode runningMode) {
+    super(taskRunner, runningMode, IMAGE_IN_STREAM_NAME);
+  }
+
+  /**
+   * Performs gesture recognition on the provided single image. Only use this method when the {@link
+   * GestureRecognizer} is created with {@link RunningMode.IMAGE}. TODO update java doc
+   * for input image format.
+   *
+   * <p>{@link GestureRecognizer} supports the following color space types:
+   *
+   * <ul>
+   *   <li>{@link Bitmap.Config.ARGB_8888}
+   * </ul>
+   *
+   * @param inputImage a MediaPipe {@link Image} object for processing.
+   * @throws MediaPipeException if there is an internal error.
+   */
+  public GestureRecognitionResult recognize(Image inputImage) {
+    return (GestureRecognitionResult) processImageData(inputImage);
+  }
+
+  /**
+   * Performs gesture recognition on the provided video frame. Only use this method when the {@link
+   * GestureRecognizer} is created with {@link RunningMode.VIDEO}.
+   *
+   * <p>It's required to provide the video frame's timestamp (in milliseconds). The input timestamps
+   * must be monotonically increasing.
+   *
+   * <p>{@link GestureRecognizer} supports the following color space types:
+   *
+   * <ul>
+   *   <li>{@link Bitmap.Config.ARGB_8888}
+   * </ul>
+   *
+   * @param inputImage a MediaPipe {@link Image} object for processing.
+   * @param inputTimestampMs the input timestamp (in milliseconds).
+   * @throws MediaPipeException if there is an internal error.
+   */
+  public GestureRecognitionResult recognizeForVideo(Image inputImage, long inputTimestampMs) {
+    return (GestureRecognitionResult) processVideoData(inputImage, inputTimestampMs);
+  }
+
+  /**
+   * Sends live image data to perform gesture recognition, and the results will be available via the
+   * {@link ResultListener} provided in the {@link GestureRecognizerOptions}. Only use this method
+   * when the {@link GestureRecognition} is created with {@link RunningMode.LIVE_STREAM}.
+   *
+   * <p>It's required to provide a timestamp (in milliseconds) to indicate when the input image is
+   * sent to the gesture recognizer. The input timestamps must be monotonically increasing.
+   *
+   * <p>{@link GestureRecognizer} supports the following color space types:
+   *
+   * <ul>
+   *   <li>{@link Bitmap.Config.ARGB_8888}
+   * </ul>
+   *
+   * @param inputImage a MediaPipe {@link Image} object for processing.
+   * @param inputTimestampMs the input timestamp (in milliseconds).
+   * @throws MediaPipeException if there is an internal error.
+   */
+  public void recognizeAsync(Image inputImage, long inputTimestampMs) {
+    sendLiveStreamData(inputImage, inputTimestampMs);
+  }
+
+  /** Options for setting up an {@link GestureRecognizer}. */
+  @AutoValue
+  public abstract static class GestureRecognizerOptions extends TaskOptions {
+
+    /** Builder for {@link GestureRecognizerOptions}. */
+    @AutoValue.Builder
+    public abstract static class Builder {
+      /** Sets the base options for the gesture recognizer task. */
+      public abstract Builder setBaseOptions(BaseOptions value);
+
+      /**
+       * Sets the running mode for the gesture recognizer task. Default to the image mode. Gesture
+       * recognizer has three modes:
+       *
+       * <ul>
+       *   <li>IMAGE: The mode for recognizing gestures on single image inputs.
+       *   <li>VIDEO: The mode for recognizing gestures on the decoded frames of a video.
+       *   <li>LIVE_STREAM: The mode for for recognizing gestures on a live stream of input data,
+       *       such as from camera. In this mode, {@code setResultListener} must be called to set up
+       *       a listener to receive the recognition results asynchronously.
+       * </ul>
+       */
+      public abstract Builder setRunningMode(RunningMode value);
+
+      // TODO: remove these. Temporary solutions before bundle asset is ready.
+      public abstract Builder setBaseOptionsHandDetector(BaseOptions value);
+
+      public abstract Builder setBaseOptionsHandLandmarker(BaseOptions value);
+
+      public abstract Builder setBaseOptionsGestureRecognizer(BaseOptions value);
+
+      /** Sets the maximum number of hands can be detected by the GestureRecognizer. */
+      public abstract Builder setNumHands(Integer value);
+
+      /** Sets minimum confidence score for the hand detection to be considered successfully */
+      public abstract Builder setMinHandDetectionConfidence(Float value);
+
+      /** Sets minimum confidence score of hand presence score in the hand landmark detection. */
+      public abstract Builder setMinHandPresenceConfidence(Float value);
+
+      /** Sets the minimum confidence score for the hand tracking to be considered successfully. */
+      public abstract Builder setMinTrackingConfidence(Float value);
+
+      /**
+       * Sets the minimum confidence score for the gestures to be considered successfully. If < 0,
+       * the gesture confidence threshold=0.5 for the model is used.
+       *
+       * <p>TODO  Note this option is subject to change, after scoring merging
+       * calculator is implemented.
+       */
+      public abstract Builder setMinGestureConfidence(Float value);
+
+      /**
+       * Sets the result listener to receive the detection results asynchronously when the gesture
+       * recognizer is in the live stream mode.
+       */
+      public abstract Builder setResultListener(
+          ResultListener<GestureRecognitionResult, Image> value);
+
+      /** Sets an optional error listener. */
+      public abstract Builder setErrorListener(ErrorListener value);
+
+      abstract GestureRecognizerOptions autoBuild();
+
+      /**
+       * Validates and builds the {@link GestureRecognizerOptions} instance.
+       *
+       * @throws IllegalArgumentException if the result listener and the running mode are not
+       *     properly configured. The result listener should only be set when the object detector is
+       *     in the live stream mode.
+       */
+      public final GestureRecognizerOptions build() {
+        GestureRecognizerOptions options = autoBuild();
+        if (options.runningMode() == RunningMode.LIVE_STREAM) {
+          if (!options.resultListener().isPresent()) {
+            throw new IllegalArgumentException(
+                "The gesture recognizer is in the live stream mode, a user-defined result listener"
+                    + " must be provided in GestureRecognizerOptions.");
+          }
+        } else if (options.resultListener().isPresent()) {
+          throw new IllegalArgumentException(
+              "The gesture recognizer is in the image or the video mode, a user-defined result"
+                  + " listener shouldn't be provided in GestureRecognizerOptions.");
+        }
+        return options;
+      }
+    }
+
+    abstract BaseOptions baseOptions();
+
+    // TODO: remove these. Temporary solutions before bundle asset is ready.
+    abstract BaseOptions baseOptionsHandDetector();
+
+    abstract BaseOptions baseOptionsHandLandmarker();
+
+    abstract BaseOptions baseOptionsGestureRecognizer();
+
+    abstract RunningMode runningMode();
+
+    abstract Optional<Integer> numHands();
+
+    abstract Optional<Float> minHandDetectionConfidence();
+
+    abstract Optional<Float> minHandPresenceConfidence();
+
+    abstract Optional<Float> minTrackingConfidence();
+
+    // TODO update gesture confidence options after score merging calculator is ready.
+    abstract Optional<Float> minGestureConfidence();
+
+    abstract Optional<ResultListener<GestureRecognitionResult, Image>> resultListener();
+
+    abstract Optional<ErrorListener> errorListener();
+
+    public static Builder builder() {
+      return new AutoValue_GestureRecognizer_GestureRecognizerOptions.Builder()
+          .setRunningMode(RunningMode.IMAGE)
+          .setNumHands(1)
+          .setMinHandDetectionConfidence(0.5f)
+          .setMinHandPresenceConfidence(0.5f)
+          .setMinTrackingConfidence(0.5f)
+          .setMinGestureConfidence(-1f);
+    }
+
+    /**
+     * Converts a {@link GestureRecognizerOptions} to a {@link CalculatorOptions} protobuf message.
+     */
+    @Override
+    public CalculatorOptions convertToCalculatorOptionsProto() {
+      BaseOptionsProto.BaseOptions.Builder baseOptionsBuilder =
+          BaseOptionsProto.BaseOptions.newBuilder()
+              .setUseStreamMode(runningMode() != RunningMode.IMAGE)
+              .mergeFrom(convertBaseOptionsToProto(baseOptions()));
+      GestureRecognizerGraphOptionsProto.GestureRecognizerGraphOptions.Builder taskOptionsBuilder =
+          GestureRecognizerGraphOptionsProto.GestureRecognizerGraphOptions.newBuilder()
+              .setBaseOptions(baseOptionsBuilder);
+
+      // Setup HandDetectorGraphOptions.
+      HandDetectorGraphOptionsProto.HandDetectorGraphOptions.Builder
+          handDetectorGraphOptionsBuilder =
+              HandDetectorGraphOptionsProto.HandDetectorGraphOptions.newBuilder()
+                  .setBaseOptions(
+                      BaseOptionsProto.BaseOptions.newBuilder()
+                          .setUseStreamMode(runningMode() != RunningMode.IMAGE)
+                          .mergeFrom(convertBaseOptionsToProto(baseOptionsHandDetector())));
+      numHands().ifPresent(handDetectorGraphOptionsBuilder::setNumHands);
+      minHandDetectionConfidence()
+          .ifPresent(handDetectorGraphOptionsBuilder::setMinDetectionConfidence);
+
+      // Setup HandLandmarkerGraphOptions.
+      HandLandmarksDetectorGraphOptionsProto.HandLandmarksDetectorGraphOptions.Builder
+          handLandmarksDetectorGraphOptionsBuilder =
+              HandLandmarksDetectorGraphOptionsProto.HandLandmarksDetectorGraphOptions.newBuilder()
+                  .setBaseOptions(
+                      BaseOptionsProto.BaseOptions.newBuilder()
+                          .setUseStreamMode(runningMode() != RunningMode.IMAGE)
+                          .mergeFrom(convertBaseOptionsToProto(baseOptionsHandLandmarker())));
+      minHandPresenceConfidence()
+          .ifPresent(handLandmarksDetectorGraphOptionsBuilder::setMinDetectionConfidence);
+      HandLandmarkerGraphOptionsProto.HandLandmarkerGraphOptions.Builder
+          handLandmarkerGraphOptionsBuilder =
+              HandLandmarkerGraphOptionsProto.HandLandmarkerGraphOptions.newBuilder()
+                  .setBaseOptions(
+                      BaseOptionsProto.BaseOptions.newBuilder()
+                          .setUseStreamMode(runningMode() != RunningMode.IMAGE)
+                          .mergeFrom(convertBaseOptionsToProto(baseOptionsHandLandmarker())));
+      minTrackingConfidence()
+          .ifPresent(handLandmarkerGraphOptionsBuilder::setMinTrackingConfidence);
+      handLandmarkerGraphOptionsBuilder
+          .setHandDetectorGraphOptions(handDetectorGraphOptionsBuilder.build())
+          .setHandLandmarksDetectorGraphOptions(handLandmarksDetectorGraphOptionsBuilder.build());
+
+      // Setup HandGestureRecognizerGraphOptions.
+      HandGestureRecognizerGraphOptionsProto.HandGestureRecognizerGraphOptions.Builder
+          handGestureRecognizerGraphOptionsBuilder =
+              HandGestureRecognizerGraphOptionsProto.HandGestureRecognizerGraphOptions.newBuilder()
+                  .setBaseOptions(
+                      BaseOptionsProto.BaseOptions.newBuilder()
+                          .setUseStreamMode(runningMode() != RunningMode.IMAGE)
+                          .mergeFrom(convertBaseOptionsToProto(baseOptionsGestureRecognizer())));
+      ClassifierOptionsProto.ClassifierOptions.Builder classifierOptionsBuilder =
+          ClassifierOptionsProto.ClassifierOptions.newBuilder();
+      minGestureConfidence().ifPresent(classifierOptionsBuilder::setScoreThreshold);
+      handGestureRecognizerGraphOptionsBuilder.setClassifierOptions(
+          classifierOptionsBuilder.build());
+
+      taskOptionsBuilder
+          .setHandLandmarkerGraphOptions(handLandmarkerGraphOptionsBuilder.build())
+          .setHandGestureRecognizerGraphOptions(handGestureRecognizerGraphOptionsBuilder.build());
+      return CalculatorOptions.newBuilder()
+          .setExtension(
+              GestureRecognizerGraphOptionsProto.GestureRecognizerGraphOptions.ext,
+              taskOptionsBuilder.build())
+          .build();
+    }
+  }
+}

mediapipe/tasks/java/com/google/mediapipe/tasks/vision/imageclassifier/AndroidManifest.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.google.mediapipe.tasks.vision.imageclassifier">
+
+    <uses-sdk android:minSdkVersion="24"
+        android:targetSdkVersion="30" />
+
+</manifest>

mediapipe/tasks/java/com/google/mediapipe/tasks/vision/imageclassifier/ImageClassificationResult.java

@@ -0,0 +1,102 @@
+// Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+//
+// 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.
+
+package com.google.mediapipe.tasks.vision.imageclassifier;
+
+import com.google.auto.value.AutoValue;
+import com.google.mediapipe.tasks.components.container.proto.CategoryProto;
+import com.google.mediapipe.tasks.components.container.proto.ClassificationsProto;
+import com.google.mediapipe.tasks.components.containers.Category;
+import com.google.mediapipe.tasks.components.containers.ClassificationEntry;
+import com.google.mediapipe.tasks.components.containers.Classifications;
+import com.google.mediapipe.tasks.core.TaskResult;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+
+/** Represents the classification results generated by {@link ImageClassifier}. */
+@AutoValue
+public abstract class ImageClassificationResult implements TaskResult {
+
+  /**
+   * Creates an {@link ImageClassificationResult} instance from a {@link
+   * ClassificationsProto.ClassificationResult} protobuf message.
+   *
+   * @param classificationResult a {@link ClassificationsProto.ClassificationResult} protobuf
+   *     message.
+   * @param timestampMs a timestamp for this result.
+   */
+  // TODO: consolidate output formats across platforms.
+  static ImageClassificationResult create(
+      ClassificationsProto.ClassificationResult classificationResult, long timestampMs) {
+    List<Classifications> classifications = new ArrayList<>();
+    for (ClassificationsProto.Classifications classificationsProto :
+        classificationResult.getClassificationsList()) {
+      classifications.add(classificationsFromProto(classificationsProto));
+    }
+    return new AutoValue_ImageClassificationResult(
+        timestampMs, Collections.unmodifiableList(classifications));
+  }
+
+  @Override
+  public abstract long timestampMs();
+
+  /** Contains one set of results per classifier head. */
+  public abstract List<Classifications> classifications();
+
+  /**
+   * Converts a {@link CategoryProto.Category} protobuf message to a {@link Category} object.
+   *
+   * @param category the {@link CategoryProto.Category} protobuf message to convert.
+   */
+  static Category categoryFromProto(CategoryProto.Category category) {
+    return Category.create(
+        category.getScore(),
+        category.getIndex(),
+        category.getCategoryName(),
+        category.getDisplayName());
+  }
+
+  /**
+   * Converts a {@link ClassificationsProto.ClassificationEntry} protobuf message to a {@link
+   * ClassificationEntry} object.
+   *
+   * @param entry the {@link ClassificationsProto.ClassificationEntry} protobuf message to convert.
+   */
+  static ClassificationEntry classificationEntryFromProto(
+      ClassificationsProto.ClassificationEntry entry) {
+    List<Category> categories = new ArrayList<>();
+    for (CategoryProto.Category category : entry.getCategoriesList()) {
+      categories.add(categoryFromProto(category));
+    }
+    return ClassificationEntry.create(categories, entry.getTimestampMs());
+  }
+
+  /**
+   * Converts a {@link ClassificationsProto.Classifications} protobuf message to a {@link
+   * Classifications} object.
+   *
+   * @param classifications the {@link ClassificationsProto.Classifications} protobuf message to
+   *     convert.
+   */
+  static Classifications classificationsFromProto(
+      ClassificationsProto.Classifications classifications) {
+    List<ClassificationEntry> entries = new ArrayList<>();
+    for (ClassificationsProto.ClassificationEntry entry : classifications.getEntriesList()) {
+      entries.add(classificationEntryFromProto(entry));
+    }
+    return Classifications.create(
+        entries, classifications.getHeadIndex(), classifications.getHeadName());
+  }
+}

mediapipe/tasks/java/com/google/mediapipe/tasks/vision/imageclassifier/ImageClassifier.java

@@ -0,0 +1,456 @@
+// Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+//
+// 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.
+
+package com.google.mediapipe.tasks.vision.imageclassifier;
+
+import android.content.Context;
+import android.graphics.RectF;
+import android.os.ParcelFileDescriptor;
+import com.google.auto.value.AutoValue;
+import com.google.mediapipe.proto.CalculatorOptionsProto.CalculatorOptions;
+import com.google.mediapipe.framework.AndroidPacketGetter;
+import com.google.mediapipe.framework.MediaPipeException;
+import com.google.mediapipe.framework.Packet;
+import com.google.mediapipe.framework.PacketGetter;
+import com.google.mediapipe.framework.ProtoUtil;
+import com.google.mediapipe.framework.image.BitmapImageBuilder;
+import com.google.mediapipe.framework.image.Image;
+import com.google.mediapipe.tasks.components.container.proto.ClassificationsProto;
+import com.google.mediapipe.tasks.components.processors.ClassifierOptions;
+import com.google.mediapipe.tasks.core.BaseOptions;
+import com.google.mediapipe.tasks.core.ErrorListener;
+import com.google.mediapipe.tasks.core.OutputHandler;
+import com.google.mediapipe.tasks.core.OutputHandler.ResultListener;
+import com.google.mediapipe.tasks.core.TaskInfo;
+import com.google.mediapipe.tasks.core.TaskOptions;
+import com.google.mediapipe.tasks.core.TaskRunner;
+import com.google.mediapipe.tasks.core.proto.BaseOptionsProto;
+import com.google.mediapipe.tasks.vision.core.BaseVisionTaskApi;
+import com.google.mediapipe.tasks.vision.core.RunningMode;
+import com.google.mediapipe.tasks.vision.imageclassifier.proto.ImageClassifierGraphOptionsProto;
+import com.google.protobuf.InvalidProtocolBufferException;
+import java.io.File;
+import java.io.IOException;
+import java.nio.ByteBuffer;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+import java.util.Optional;
+
+/**
+ * Performs classification on images.
+ *
+ * <p>The API expects a TFLite model with optional, but strongly recommended, <a
+ * href="https://www.tensorflow.org/lite/convert/metadata">TFLite Model Metadata.</a>.
+ *
+ * <p>The API supports models with one image input tensor and one or more output tensors. To be more
+ * specific, here are the requirements.
+ *
+ * <ul>
+ *   <li>Input image tensor ({@code kTfLiteUInt8}/{@code kTfLiteFloat32})
+ *       <ul>
+ *         <li>image input of size {@code [batch x height x width x channels]}.
+ *         <li>batch inference is not supported ({@code batch} is required to be 1).
+ *         <li>only RGB inputs are supported ({@code channels} is required to be 3).
+ *         <li>if type is kTfLiteFloat32, NormalizationOptions are required to be attached to the
+ *             metadata for input normalization.
+ *       </ul>
+ *   <li>At least one output tensor ({@code kTfLiteUInt8}/{@code kTfLiteFloat32}) with:
+ *       <ul>
+ *         <li>{@code N} classes and either 2 or 4 dimensions, i.e. {@code [1 x N]} or {@code [1 x 1
+ *             x 1 x N]}
+ *         <li>optional (but recommended) label map(s) as AssociatedFile-s with type
+ *             TENSOR_AXIS_LABELS, containing one label per line. The first such AssociatedFile (if
+ *             any) is used to fill the {@code class_name} field of the results. The {@code
+ *             display_name} field is filled from the AssociatedFile (if any) whose locale matches
+ *             the {@code display_names_locale} field of the {@code ImageClassifierOptions} used at
+ *             creation time ("en" by default, i.e. English). If none of these are available, only
+ *             the {@code index} field of the results will be filled.
+ *         <li>optional score calibration can be attached using ScoreCalibrationOptions and an
+ *             AssociatedFile with type TENSOR_AXIS_SCORE_CALIBRATION. See <a
+ *             href="https://github.com/google/mediapipe/blob/master/mediapipe/tasks/metadata/metadata_schema.fbs">
+ *             metadata_schema.fbs</a> for more details.
+ *       </ul>
+ * </ul>
+ *
+ * <p>An example of such model can be found <a
+ * href="https://tfhub.dev/bohemian-visual-recognition-alliance/lite-model/models/mushroom-identification_v1/1">
+ * TensorFlow Hub</a>.
+ */
+public final class ImageClassifier extends BaseVisionTaskApi {
+  private static final String TAG = ImageClassifier.class.getSimpleName();
+  private static final String IMAGE_IN_STREAM_NAME = "image_in";
+  private static final String NORM_RECT_IN_STREAM_NAME = "norm_rect_in";
+  private static final List<String> INPUT_STREAMS =
+      Collections.unmodifiableList(
+          Arrays.asList("IMAGE:" + IMAGE_IN_STREAM_NAME, "NORM_RECT:" + NORM_RECT_IN_STREAM_NAME));
+  private static final List<String> OUTPUT_STREAMS =
+      Collections.unmodifiableList(
+          Arrays.asList("CLASSIFICATION_RESULT:classification_result_out", "IMAGE:image_out"));
+  private static final int CLASSIFICATION_RESULT_OUT_STREAM_INDEX = 0;
+  private static final int IMAGE_OUT_STREAM_INDEX = 1;
+  private static final String TASK_GRAPH_NAME =
+      "mediapipe.tasks.vision.image_classifier.ImageClassifierGraph";
+
+  static {
+    ProtoUtil.registerTypeName(
+        ClassificationsProto.ClassificationResult.class,
+        "mediapipe.tasks.components.containers.proto.ClassificationResult");
+  }
+
+  /**
+   * Creates an {@link ImageClassifier} instance from a model file and default {@link
+   * ImageClassifierOptions}.
+   *
+   * @param context an Android {@link Context}.
+   * @param modelPath path to the classification model in the assets.
+   * @throws MediaPipeException if there is an error during {@link ImageClassifier} creation.
+   */
+  public static ImageClassifier createFromFile(Context context, String modelPath) {
+    BaseOptions baseOptions = BaseOptions.builder().setModelAssetPath(modelPath).build();
+    return createFromOptions(
+        context, ImageClassifierOptions.builder().setBaseOptions(baseOptions).build());
+  }
+
+  /**
+   * Creates an {@link ImageClassifier} instance from a model file and default {@link
+   * ImageClassifierOptions}.
+   *
+   * @param context an Android {@link Context}.
+   * @param modelFile the classification model {@link File} instance.
+   * @throws IOException if an I/O error occurs when opening the tflite model file.
+   * @throws MediaPipeException if there is an error during {@link ImageClassifier} creation.
+   */
+  public static ImageClassifier createFromFile(Context context, File modelFile) throws IOException {
+    try (ParcelFileDescriptor descriptor =
+        ParcelFileDescriptor.open(modelFile, ParcelFileDescriptor.MODE_READ_ONLY)) {
+      BaseOptions baseOptions =
+          BaseOptions.builder().setModelAssetFileDescriptor(descriptor.getFd()).build();
+      return createFromOptions(
+          context, ImageClassifierOptions.builder().setBaseOptions(baseOptions).build());
+    }
+  }
+
+  /**
+   * Creates an {@link ImageClassifier} instance from a model buffer and default {@link
+   * ImageClassifierOptions}.
+   *
+   * @param context an Android {@link Context}.
+   * @param modelBuffer a direct {@link ByteBuffer} or a {@link MappedByteBuffer} of the
+   *     classification model.
+   * @throws MediaPipeException if there is an error during {@link ImageClassifier} creation.
+   */
+  public static ImageClassifier createFromBuffer(Context context, final ByteBuffer modelBuffer) {
+    BaseOptions baseOptions = BaseOptions.builder().setModelAssetBuffer(modelBuffer).build();
+    return createFromOptions(
+        context, ImageClassifierOptions.builder().setBaseOptions(baseOptions).build());
+  }
+
+  /**
+   * Creates an {@link ImageClassifier} instance from an {@link ImageClassifierOptions} instance.
+   *
+   * @param context an Android {@link Context}.
+   * @param options an {@link ImageClassifierOptions} instance.
+   * @throws MediaPipeException if there is an error during {@link ImageClassifier} creation.
+   */
+  public static ImageClassifier createFromOptions(Context context, ImageClassifierOptions options) {
+    OutputHandler<ImageClassificationResult, Image> handler = new OutputHandler<>();
+    handler.setOutputPacketConverter(
+        new OutputHandler.OutputPacketConverter<ImageClassificationResult, Image>() {
+          @Override
+          public ImageClassificationResult convertToTaskResult(List<Packet> packets) {
+            try {
+              return ImageClassificationResult.create(
+                  PacketGetter.getProto(
+                      packets.get(CLASSIFICATION_RESULT_OUT_STREAM_INDEX),
+                      ClassificationsProto.ClassificationResult.getDefaultInstance()),
+                  packets.get(CLASSIFICATION_RESULT_OUT_STREAM_INDEX).getTimestamp());
+            } catch (InvalidProtocolBufferException e) {
+              throw new MediaPipeException(
+                  MediaPipeException.StatusCode.INTERNAL.ordinal(), e.getMessage());
+            }
+          }
+
+          @Override
+          public Image convertToTaskInput(List<Packet> packets) {
+            return new BitmapImageBuilder(
+                    AndroidPacketGetter.getBitmapFromRgb(packets.get(IMAGE_OUT_STREAM_INDEX)))
+                .build();
+          }
+        });
+    options.resultListener().ifPresent(handler::setResultListener);
+    options.errorListener().ifPresent(handler::setErrorListener);
+    TaskRunner runner =
+        TaskRunner.create(
+            context,
+            TaskInfo.<ImageClassifierOptions>builder()
+                .setTaskGraphName(TASK_GRAPH_NAME)
+                .setInputStreams(INPUT_STREAMS)
+                .setOutputStreams(OUTPUT_STREAMS)
+                .setTaskOptions(options)
+                .setEnableFlowLimiting(options.runningMode() == RunningMode.LIVE_STREAM)
+                .build(),
+            handler);
+    return new ImageClassifier(runner, options.runningMode());
+  }
+
+  /**
+   * Constructor to initialize an {@link ImageClassifier} from a {@link TaskRunner} and {@link
+   * RunningMode}.
+   *
+   * @param taskRunner a {@link TaskRunner}.
+   * @param runningMode a mediapipe vision task {@link RunningMode}.
+   */
+  private ImageClassifier(TaskRunner taskRunner, RunningMode runningMode) {
+    super(taskRunner, runningMode, IMAGE_IN_STREAM_NAME, NORM_RECT_IN_STREAM_NAME);
+  }
+
+  /**
+   * Performs classification on the provided single image. Only use this method when the {@link
+   * ImageClassifier} is created with {@link RunningMode.IMAGE}.
+   *
+   * <p>{@link ImageClassifier} supports the following color space types:
+   *
+   * <ul>
+   *   <li>{@link Bitmap.Config.ARGB_8888}
+   * </ul>
+   *
+   * @param inputImage a MediaPipe {@link Image} object for processing.
+   * @throws MediaPipeException if there is an internal error.
+   */
+  public ImageClassificationResult classify(Image inputImage) {
+    return (ImageClassificationResult) processImageData(inputImage, buildFullImageRectF());
+  }
+
+  /**
+   * Performs classification on the provided single image and region-of-interest. Only use this
+   * method when the {@link ImageClassifier} is created with {@link RunningMode.IMAGE}.
+   *
+   * <p>{@link ImageClassifier} supports the following color space types:
+   *
+   * <ul>
+   *   <li>{@link Bitmap.Config.ARGB_8888}
+   * </ul>
+   *
+   * @param inputImage a MediaPipe {@link Image} object for processing.
+   * @param roi a {@link RectF} specifying the region of interest on which to perform
+   *     classification. Coordinates are expected to be specified as normalized values in [0,1].
+   * @throws MediaPipeException if there is an internal error.
+   */
+  public ImageClassificationResult classify(Image inputImage, RectF roi) {
+    return (ImageClassificationResult) processImageData(inputImage, roi);
+  }
+
+  /**
+   * Performs classification on the provided video frame. Only use this method when the {@link
+   * ImageClassifier} is created with {@link RunningMode.VIDEO}.
+   *
+   * <p>It's required to provide the video frame's timestamp (in milliseconds). The input timestamps
+   * must be monotonically increasing.
+   *
+   * <p>{@link ImageClassifier} supports the following color space types:
+   *
+   * <ul>
+   *   <li>{@link Bitmap.Config.ARGB_8888}
+   * </ul>
+   *
+   * @param inputImage a MediaPipe {@link Image} object for processing.
+   * @param inputTimestampMs the input timestamp (in milliseconds).
+   * @throws MediaPipeException if there is an internal error.
+   */
+  public ImageClassificationResult classifyForVideo(Image inputImage, long inputTimestampMs) {
+    return (ImageClassificationResult)
+        processVideoData(inputImage, buildFullImageRectF(), inputTimestampMs);
+  }
+
+  /**
+   * Performs classification on the provided video frame with additional region-of-interest. Only
+   * use this method when the {@link ImageClassifier} is created with {@link RunningMode.VIDEO}.
+   *
+   * <p>It's required to provide the video frame's timestamp (in milliseconds). The input timestamps
+   * must be monotonically increasing.
+   *
+   * <p>{@link ImageClassifier} supports the following color space types:
+   *
+   * <ul>
+   *   <li>{@link Bitmap.Config.ARGB_8888}
+   * </ul>
+   *
+   * @param inputImage a MediaPipe {@link Image} object for processing.
+   * @param roi a {@link RectF} specifying the region of interest on which to perform
+   *     classification. Coordinates are expected to be specified as normalized values in [0,1].
+   * @param inputTimestampMs the input timestamp (in milliseconds).
+   * @throws MediaPipeException if there is an internal error.
+   */
+  public ImageClassificationResult classifyForVideo(
+      Image inputImage, RectF roi, long inputTimestampMs) {
+    return (ImageClassificationResult) processVideoData(inputImage, roi, inputTimestampMs);
+  }
+
+  /**
+   * Sends live image data to perform classification, and the results will be available via the
+   * {@link ResultListener} provided in the {@link ImageClassifierOptions}. Only use this method
+   * when the {@link ImageClassifier} is created with {@link RunningMode.LIVE_STREAM}.
+   *
+   * <p>It's required to provide a timestamp (in milliseconds) to indicate when the input image is
+   * sent to the object detector. The input timestamps must be monotonically increasing.
+   *
+   * <p>{@link ImageClassifier} supports the following color space types:
+   *
+   * <ul>
+   *   <li>{@link Bitmap.Config.ARGB_8888}
+   * </ul>
+   *
+   * @param inputImage a MediaPipe {@link Image} object for processing.
+   * @param inputTimestampMs the input timestamp (in milliseconds).
+   * @throws MediaPipeException if there is an internal error.
+   */
+  public void classifyAsync(Image inputImage, long inputTimestampMs) {
+    sendLiveStreamData(inputImage, buildFullImageRectF(), inputTimestampMs);
+  }
+
+  /**
+   * Sends live image data and additional region-of-interest to perform classification, and the
+   * results will be available via the {@link ResultListener} provided in the {@link
+   * ImageClassifierOptions}. Only use this method when the {@link ImageClassifier} is created with
+   * {@link RunningMode.LIVE_STREAM}.
+   *
+   * <p>It's required to provide a timestamp (in milliseconds) to indicate when the input image is
+   * sent to the object detector. The input timestamps must be monotonically increasing.
+   *
+   * <p>{@link ImageClassifier} supports the following color space types:
+   *
+   * <ul>
+   *   <li>{@link Bitmap.Config.ARGB_8888}
+   * </ul>
+   *
+   * @param inputImage a MediaPipe {@link Image} object for processing.
+   * @param roi a {@link RectF} specifying the region of interest on which to perform
+   *     classification. Coordinates are expected to be specified as normalized values in [0,1].
+   * @param inputTimestampMs the input timestamp (in milliseconds).
+   * @throws MediaPipeException if there is an internal error.
+   */
+  public void classifyAsync(Image inputImage, RectF roi, long inputTimestampMs) {
+    sendLiveStreamData(inputImage, roi, inputTimestampMs);
+  }
+
+  /** Options for setting up and {@link ImageClassifier}. */
+  @AutoValue
+  public abstract static class ImageClassifierOptions extends TaskOptions {
+
+    /** Builder for {@link ImageClassifierOptions}. */
+    @AutoValue.Builder
+    public abstract static class Builder {
+      /** Sets the {@link BaseOptions} for the image classifier task. */
+      public abstract Builder setBaseOptions(BaseOptions baseOptions);
+
+      /**
+       * Sets the {@link RunningMode} for the image classifier task. Default to the image mode.
+       * Image classifier has three modes:
+       *
+       * <ul>
+       *   <li>IMAGE: The mode for performing classification on single image inputs.
+       *   <li>VIDEO: The mode for performing classification on the decoded frames of a video.
+       *   <li>LIVE_STREAM: The mode for for performing classification on a live stream of input
+       *       data, such as from camera. In this mode, {@code setResultListener} must be called to
+       *       set up a listener to receive the classification results asynchronously.
+       * </ul>
+       */
+      public abstract Builder setRunningMode(RunningMode runningMode);
+
+      /**
+       * Sets the optional {@link ClassifierOptions} controling classification behavior, such as
+       * score threshold, number of results, etc.
+       */
+      public abstract Builder setClassifierOptions(ClassifierOptions classifierOptions);
+
+      /**
+       * Sets the {@link ResultListener} to receive the classification results asynchronously when
+       * the image classifier is in the live stream mode.
+       */
+      public abstract Builder setResultListener(
+          ResultListener<ImageClassificationResult, Image> resultListener);
+
+      /** Sets an optional {@link ErrorListener}. */
+      public abstract Builder setErrorListener(ErrorListener errorListener);
+
+      abstract ImageClassifierOptions autoBuild();
+
+      /**
+       * Validates and builds the {@link ImageClassifierOptions} instance. *
+       *
+       * @throws IllegalArgumentException if the result listener and the running mode are not
+       *     properly configured. The result listener should only be set when the image classifier
+       *     is in the live stream mode.
+       */
+      public final ImageClassifierOptions build() {
+        ImageClassifierOptions options = autoBuild();
+        if (options.runningMode() == RunningMode.LIVE_STREAM) {
+          if (!options.resultListener().isPresent()) {
+            throw new IllegalArgumentException(
+                "The image classifier is in the live stream mode, a user-defined result listener"
+                    + " must be provided in the ImageClassifierOptions.");
+          }
+        } else if (options.resultListener().isPresent()) {
+          throw new IllegalArgumentException(
+              "The image classifier is in the image or video mode, a user-defined result listener"
+                  + " shouldn't be provided in ImageClassifierOptions.");
+        }
+        return options;
+      }
+    }
+
+    abstract BaseOptions baseOptions();
+
+    abstract RunningMode runningMode();
+
+    abstract Optional<ClassifierOptions> classifierOptions();
+
+    abstract Optional<ResultListener<ImageClassificationResult, Image>> resultListener();
+
+    abstract Optional<ErrorListener> errorListener();
+
+    public static Builder builder() {
+      return new AutoValue_ImageClassifier_ImageClassifierOptions.Builder()
+          .setRunningMode(RunningMode.IMAGE);
+    }
+
+    /**
+     * Converts a {@link ImageClassifierOptions} to a {@link CalculatorOptions} protobuf message.
+     */
+    @Override
+    public CalculatorOptions convertToCalculatorOptionsProto() {
+      BaseOptionsProto.BaseOptions.Builder baseOptionsBuilder =
+          BaseOptionsProto.BaseOptions.newBuilder();
+      baseOptionsBuilder.setUseStreamMode(runningMode() != RunningMode.IMAGE);
+      baseOptionsBuilder.mergeFrom(convertBaseOptionsToProto(baseOptions()));
+      ImageClassifierGraphOptionsProto.ImageClassifierGraphOptions.Builder taskOptionsBuilder =
+          ImageClassifierGraphOptionsProto.ImageClassifierGraphOptions.newBuilder()
+              .setBaseOptions(baseOptionsBuilder);
+      if (classifierOptions().isPresent()) {
+        taskOptionsBuilder.setClassifierOptions(classifierOptions().get().convertToProto());
+      }
+      return CalculatorOptions.newBuilder()
+          .setExtension(
+              ImageClassifierGraphOptionsProto.ImageClassifierGraphOptions.ext,
+              taskOptionsBuilder.build())
+          .build();
+    }
+  }
+
+  /** Creates a RectF covering the full image. */
+  private static RectF buildFullImageRectF() {
+    return new RectF(0, 0, 1, 1);
+  }
+}

mediapipe/tasks/java/com/google/mediapipe/tasks/vision/objectdetector/BUILD

@@ -1,44 +0,0 @@
-# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
-#
-# 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.
-
-package(default_visibility = ["//mediapipe/tasks:internal"])
-
-licenses(["notice"])
-
-android_library(
-    name = "objectdetector",
-    srcs = [
-        "ObjectDetectionResult.java",
-        "ObjectDetector.java",
-    ],
-    javacopts = [
-        "-Xep:AndroidJdkLibsChecker:OFF",
-    ],
-    manifest = ":AndroidManifest.xml",
-    deps = [
-        "//mediapipe/framework:calculator_options_java_proto_lite",
-        "//mediapipe/framework/formats:detection_java_proto_lite",
-        "//mediapipe/framework/formats:location_data_java_proto_lite",
-        "//mediapipe/java/com/google/mediapipe/framework:android_framework",
-        "//mediapipe/java/com/google/mediapipe/framework/image",
-        "//mediapipe/tasks/cc/core/proto:base_options_java_proto_lite",
-        "//mediapipe/tasks/cc/vision/object_detector/proto:object_detector_options_java_proto_lite",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:category",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:detection",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/core",
-        "//mediapipe/tasks/java/com/google/mediapipe/tasks/vision/core",
-        "//third_party:autovalue",
-        "@maven//:com_google_guava_guava",
-    ],
-)

mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/AndroidManifest.xml

@@ -0,0 +1,24 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.google.mediapipe.tasks.vision.gesturerecognizertest"
+    android:versionCode="1"
+    android:versionName="1.0" >
+
+    <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
+
+    <uses-sdk android:minSdkVersion="24"
+        android:targetSdkVersion="30" />
+
+    <application
+        android:label="gesturerecognizertest"
+        android:name="android.support.multidex.MultiDexApplication"
+        android:taskAffinity="">
+        <uses-library android:name="android.test.runner" />
+    </application>
+
+    <instrumentation
+        android:name="com.google.android.apps.common.testing.testrunner.GoogleInstrumentationTestRunner"
+        android:targetPackage="com.google.mediapipe.tasks.vision.gesturerecognizertest" />
+
+</manifest>

mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/BUILD

@@ -0,0 +1,19 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+# TODO: Enable this in OSS

mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerTest.java

@@ -0,0 +1,495 @@
+// Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+//
+// 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.
+
+package com.google.mediapipe.tasks.vision.gesturerecognizer;
+
+import static com.google.common.truth.Truth.assertThat;
+import static org.junit.Assert.assertThrows;
+
+import android.content.res.AssetManager;
+import android.graphics.BitmapFactory;
+import androidx.test.core.app.ApplicationProvider;
+import androidx.test.ext.junit.runners.AndroidJUnit4;
+import com.google.common.truth.Correspondence;
+import com.google.mediapipe.formats.proto.ClassificationProto;
+import com.google.mediapipe.framework.MediaPipeException;
+import com.google.mediapipe.framework.image.BitmapImageBuilder;
+import com.google.mediapipe.framework.image.Image;
+import com.google.mediapipe.tasks.components.containers.Category;
+import com.google.mediapipe.tasks.components.containers.Landmark;
+import com.google.mediapipe.tasks.components.containers.proto.LandmarksDetectionResultProto.LandmarksDetectionResult;
+import com.google.mediapipe.tasks.core.BaseOptions;
+import com.google.mediapipe.tasks.vision.core.RunningMode;
+import com.google.mediapipe.tasks.vision.gesturerecognizer.GestureRecognizer.GestureRecognizerOptions;
+import java.io.InputStream;
+import java.util.Arrays;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.Suite;
+import org.junit.runners.Suite.SuiteClasses;
+
+/** Test for {@link GestureRecognizer}. */
+@RunWith(Suite.class)
+@SuiteClasses({GestureRecognizerTest.General.class, GestureRecognizerTest.RunningModeTest.class})
+public class GestureRecognizerTest {
+  private static final String HAND_DETECTOR_MODEL_FILE = "palm_detection_full.tflite";
+  private static final String HAND_LANDMARKER_MODEL_FILE = "hand_landmark_full.tflite";
+  private static final String GESTURE_RECOGNIZER_MODEL_FILE =
+      "cg_classifier_screen3d_landmark_features_nn_2022_08_04_base_simple_model.tflite";
+  private static final String TWO_HANDS_IMAGE = "right_hands.jpg";
+  private static final String THUMB_UP_IMAGE = "thumb_up.jpg";
+  private static final String NO_HANDS_IMAGE = "cats_and_dogs.jpg";
+  private static final String THUMB_UP_LANDMARKS = "thumb_up_landmarks.pb";
+  private static final String TAG = "Gesture Recognizer Test";
+  private static final String THUMB_UP_LABEL = "Thumb_Up";
+  private static final int THUMB_UP_INDEX = 5;
+  private static final float LANDMARKS_ERROR_TOLERANCE = 0.03f;
+  private static final int IMAGE_WIDTH = 382;
+  private static final int IMAGE_HEIGHT = 406;
+
+  @RunWith(AndroidJUnit4.class)
+  public static final class General extends GestureRecognizerTest {
+
+    @Test
+    public void recognize_successWithValidModels() throws Exception {
+      GestureRecognizerOptions options =
+          GestureRecognizerOptions.builder()
+              .setBaseOptions(
+                  BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+              .setBaseOptionsHandDetector(
+                  BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+              .setBaseOptionsHandLandmarker(
+                  BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+              .setBaseOptionsGestureRecognizer(
+                  BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+              .build();
+      GestureRecognizer gestureRecognizer =
+          GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      GestureRecognitionResult actualResult =
+          gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE));
+      GestureRecognitionResult expectedResult =
+          getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+      assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult);
+    }
+
+    @Test
+    public void recognize_successWithEmptyResult() throws Exception {
+      GestureRecognizerOptions options =
+          GestureRecognizerOptions.builder()
+              .setBaseOptions(
+                  BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+              .setBaseOptionsHandDetector(
+                  BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+              .setBaseOptionsHandLandmarker(
+                  BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+              .setBaseOptionsGestureRecognizer(
+                  BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+              .build();
+      GestureRecognizer gestureRecognizer =
+          GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      GestureRecognitionResult actualResult =
+          gestureRecognizer.recognize(getImageFromAsset(NO_HANDS_IMAGE));
+      assertThat(actualResult.landmarks()).isEmpty();
+      assertThat(actualResult.worldLandmarks()).isEmpty();
+      assertThat(actualResult.handednesses()).isEmpty();
+      assertThat(actualResult.gestures()).isEmpty();
+    }
+
+    @Test
+    public void recognize_successWithMinGestureConfidence() throws Exception {
+      GestureRecognizerOptions options =
+          GestureRecognizerOptions.builder()
+              .setBaseOptions(
+                  BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+              .setBaseOptionsHandDetector(
+                  BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+              .setBaseOptionsHandLandmarker(
+                  BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+              .setBaseOptionsGestureRecognizer(
+                  BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+              // TODO update the confidence to be in range [0,1] after embedding model
+              // and scoring calculator is integrated.
+              .setMinGestureConfidence(3.0f)
+              .build();
+      GestureRecognizer gestureRecognizer =
+          GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      GestureRecognitionResult actualResult =
+          gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE));
+      GestureRecognitionResult expectedResult =
+          getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+      // Only contains one top scoring gesture.
+      assertThat(actualResult.gestures().get(0)).hasSize(1);
+      assertActualGestureEqualExpectedGesture(
+          actualResult.gestures().get(0).get(0), expectedResult.gestures().get(0).get(0));
+    }
+
+    @Test
+    public void recognize_successWithNumHands() throws Exception {
+      GestureRecognizerOptions options =
+          GestureRecognizerOptions.builder()
+              .setBaseOptions(
+                  BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+              .setBaseOptionsHandDetector(
+                  BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+              .setBaseOptionsHandLandmarker(
+                  BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+              .setBaseOptionsGestureRecognizer(
+                  BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+              .setNumHands(2)
+              .build();
+      GestureRecognizer gestureRecognizer =
+          GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      GestureRecognitionResult actualResult =
+          gestureRecognizer.recognize(getImageFromAsset(TWO_HANDS_IMAGE));
+      assertThat(actualResult.handednesses()).hasSize(2);
+    }
+  }
+
+  @RunWith(AndroidJUnit4.class)
+  public static final class RunningModeTest extends GestureRecognizerTest {
+    @Test
+    public void create_failsWithIllegalResultListenerInNonLiveStreamMode() throws Exception {
+      for (RunningMode mode : new RunningMode[] {RunningMode.IMAGE, RunningMode.VIDEO}) {
+        IllegalArgumentException exception =
+            assertThrows(
+                IllegalArgumentException.class,
+                () ->
+                    GestureRecognizerOptions.builder()
+                        .setBaseOptions(
+                            BaseOptions.builder()
+                                .setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE)
+                                .build())
+                        .setBaseOptionsHandDetector(
+                            BaseOptions.builder()
+                                .setModelAssetPath(HAND_DETECTOR_MODEL_FILE)
+                                .build())
+                        .setBaseOptionsHandLandmarker(
+                            BaseOptions.builder()
+                                .setModelAssetPath(HAND_LANDMARKER_MODEL_FILE)
+                                .build())
+                        .setBaseOptionsGestureRecognizer(
+                            BaseOptions.builder()
+                                .setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE)
+                                .build())
+                        .setRunningMode(mode)
+                        .setResultListener((gestureRecognitionResult, inputImage) -> {})
+                        .build());
+        assertThat(exception)
+            .hasMessageThat()
+            .contains("a user-defined result listener shouldn't be provided");
+      }
+    }
+  }
+
+  @Test
+  public void create_failsWithMissingResultListenerInLiveSteamMode() throws Exception {
+    IllegalArgumentException exception =
+        assertThrows(
+            IllegalArgumentException.class,
+            () ->
+                GestureRecognizerOptions.builder()
+                    .setBaseOptions(
+                        BaseOptions.builder()
+                            .setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE)
+                            .build())
+                    .setBaseOptionsHandDetector(
+                        BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+                    .setBaseOptionsHandLandmarker(
+                        BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+                    .setBaseOptionsGestureRecognizer(
+                        BaseOptions.builder()
+                            .setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE)
+                            .build())
+                    .setRunningMode(RunningMode.LIVE_STREAM)
+                    .build());
+    assertThat(exception)
+        .hasMessageThat()
+        .contains("a user-defined result listener must be provided");
+  }
+
+  @Test
+  public void recognize_failsWithCallingWrongApiInImageMode() throws Exception {
+    GestureRecognizerOptions options =
+        GestureRecognizerOptions.builder()
+            .setBaseOptions(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setBaseOptionsHandDetector(
+                BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+            .setBaseOptionsHandLandmarker(
+                BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+            .setBaseOptionsGestureRecognizer(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setRunningMode(RunningMode.IMAGE)
+            .build();
+
+    GestureRecognizer gestureRecognizer =
+        GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+    MediaPipeException exception =
+        assertThrows(
+            MediaPipeException.class,
+            () -> gestureRecognizer.recognizeForVideo(getImageFromAsset(THUMB_UP_IMAGE), 0));
+    assertThat(exception).hasMessageThat().contains("not initialized with the video mode");
+    exception =
+        assertThrows(
+            MediaPipeException.class,
+            () -> gestureRecognizer.recognizeAsync(getImageFromAsset(THUMB_UP_IMAGE), 0));
+    assertThat(exception).hasMessageThat().contains("not initialized with the live stream mode");
+  }
+
+  @Test
+  public void recognize_failsWithCallingWrongApiInVideoMode() throws Exception {
+    GestureRecognizerOptions options =
+        GestureRecognizerOptions.builder()
+            .setBaseOptions(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setBaseOptionsHandDetector(
+                BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+            .setBaseOptionsHandLandmarker(
+                BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+            .setBaseOptionsGestureRecognizer(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setRunningMode(RunningMode.VIDEO)
+            .build();
+
+    GestureRecognizer gestureRecognizer =
+        GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+    MediaPipeException exception =
+        assertThrows(
+            MediaPipeException.class,
+            () -> gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE)));
+    assertThat(exception).hasMessageThat().contains("not initialized with the image mode");
+    exception =
+        assertThrows(
+            MediaPipeException.class,
+            () -> gestureRecognizer.recognizeAsync(getImageFromAsset(THUMB_UP_IMAGE), 0));
+    assertThat(exception).hasMessageThat().contains("not initialized with the live stream mode");
+  }
+
+  @Test
+  public void recognize_failsWithCallingWrongApiInLiveSteamMode() throws Exception {
+    GestureRecognizerOptions options =
+        GestureRecognizerOptions.builder()
+            .setBaseOptions(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setBaseOptionsHandDetector(
+                BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+            .setBaseOptionsHandLandmarker(
+                BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+            .setBaseOptionsGestureRecognizer(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setRunningMode(RunningMode.LIVE_STREAM)
+            .setResultListener((gestureRecognitionResult, inputImage) -> {})
+            .build();
+
+    GestureRecognizer gestureRecognizer =
+        GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+    MediaPipeException exception =
+        assertThrows(
+            MediaPipeException.class,
+            () -> gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE)));
+    assertThat(exception).hasMessageThat().contains("not initialized with the image mode");
+    exception =
+        assertThrows(
+            MediaPipeException.class,
+            () -> gestureRecognizer.recognizeForVideo(getImageFromAsset(THUMB_UP_IMAGE), 0));
+    assertThat(exception).hasMessageThat().contains("not initialized with the video mode");
+  }
+
+  @Test
+  public void recognize_successWithImageMode() throws Exception {
+    GestureRecognizerOptions options =
+        GestureRecognizerOptions.builder()
+            .setBaseOptions(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setBaseOptionsHandDetector(
+                BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+            .setBaseOptionsHandLandmarker(
+                BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+            .setBaseOptionsGestureRecognizer(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setRunningMode(RunningMode.IMAGE)
+            .build();
+
+    GestureRecognizer gestureRecognizer =
+        GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+    GestureRecognitionResult actualResult =
+        gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE));
+    GestureRecognitionResult expectedResult =
+        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+    assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult);
+  }
+
+  @Test
+  public void recognize_successWithVideoMode() throws Exception {
+    GestureRecognizerOptions options =
+        GestureRecognizerOptions.builder()
+            .setBaseOptions(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setBaseOptionsHandDetector(
+                BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+            .setBaseOptionsHandLandmarker(
+                BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+            .setBaseOptionsGestureRecognizer(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setRunningMode(RunningMode.VIDEO)
+            .build();
+    GestureRecognizer gestureRecognizer =
+        GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+    GestureRecognitionResult expectedResult =
+        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+    for (int i = 0; i < 3; i++) {
+      GestureRecognitionResult actualResult =
+          gestureRecognizer.recognizeForVideo(getImageFromAsset(THUMB_UP_IMAGE), i);
+      assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult);
+    }
+  }
+
+  @Test
+  public void recognize_failsWithOutOfOrderInputTimestamps() throws Exception {
+    Image image = getImageFromAsset(THUMB_UP_IMAGE);
+    GestureRecognitionResult expectedResult =
+        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+    GestureRecognizerOptions options =
+        GestureRecognizerOptions.builder()
+            .setBaseOptions(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setBaseOptionsHandDetector(
+                BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+            .setBaseOptionsHandLandmarker(
+                BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+            .setBaseOptionsGestureRecognizer(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setRunningMode(RunningMode.LIVE_STREAM)
+            .setResultListener(
+                (actualResult, inputImage) -> {
+                  assertActualResultApproximatelyEqualsToExpectedResult(
+                      actualResult, expectedResult);
+                  assertImageSizeIsExpected(inputImage);
+                })
+            .build();
+    try (GestureRecognizer gestureRecognizer =
+        GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options)) {
+      gestureRecognizer.recognizeAsync(image, 1);
+      MediaPipeException exception =
+          assertThrows(MediaPipeException.class, () -> gestureRecognizer.recognizeAsync(image, 0));
+      assertThat(exception)
+          .hasMessageThat()
+          .contains("having a smaller timestamp than the processed timestamp");
+    }
+  }
+
+  @Test
+  public void recognize_successWithLiveSteamMode() throws Exception {
+    Image image = getImageFromAsset(THUMB_UP_IMAGE);
+    GestureRecognitionResult expectedResult =
+        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+    GestureRecognizerOptions options =
+        GestureRecognizerOptions.builder()
+            .setBaseOptions(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setBaseOptionsHandDetector(
+                BaseOptions.builder().setModelAssetPath(HAND_DETECTOR_MODEL_FILE).build())
+            .setBaseOptionsHandLandmarker(
+                BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_MODEL_FILE).build())
+            .setBaseOptionsGestureRecognizer(
+                BaseOptions.builder().setModelAssetPath(GESTURE_RECOGNIZER_MODEL_FILE).build())
+            .setRunningMode(RunningMode.LIVE_STREAM)
+            .setResultListener(
+                (actualResult, inputImage) -> {
+                  assertActualResultApproximatelyEqualsToExpectedResult(
+                      actualResult, expectedResult);
+                  assertImageSizeIsExpected(inputImage);
+                })
+            .build();
+    try (GestureRecognizer gestureRecognizer =
+        GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options)) {
+      for (int i = 0; i < 3; i++) {
+        gestureRecognizer.recognizeAsync(image, i);
+      }
+    }
+  }
+
+  private static Image getImageFromAsset(String filePath) throws Exception {
+    AssetManager assetManager = ApplicationProvider.getApplicationContext().getAssets();
+    InputStream istr = assetManager.open(filePath);
+    return new BitmapImageBuilder(BitmapFactory.decodeStream(istr)).build();
+  }
+
+  private static GestureRecognitionResult getExpectedGestureRecognitionResult(
+      String filePath, String gestureLabel, int gestureIndex) throws Exception {
+    AssetManager assetManager = ApplicationProvider.getApplicationContext().getAssets();
+    InputStream istr = assetManager.open(filePath);
+    LandmarksDetectionResult landmarksDetectionResultProto =
+        LandmarksDetectionResult.parser().parseFrom(istr);
+    ClassificationProto.ClassificationList gesturesProto =
+        ClassificationProto.ClassificationList.newBuilder()
+            .addClassification(
+                ClassificationProto.Classification.newBuilder()
+                    .setLabel(gestureLabel)
+                    .setIndex(gestureIndex))
+            .build();
+    return GestureRecognitionResult.create(
+        Arrays.asList(landmarksDetectionResultProto.getLandmarks()),
+        Arrays.asList(landmarksDetectionResultProto.getWorldLandmarks()),
+        Arrays.asList(landmarksDetectionResultProto.getClassifications()),
+        Arrays.asList(gesturesProto),
+        /*timestampMs=*/ 0);
+  }
+
+  private static void assertActualResultApproximatelyEqualsToExpectedResult(
+      GestureRecognitionResult actualResult, GestureRecognitionResult expectedResult) {
+    // Expects to have the same number of hands detected.
+    assertThat(actualResult.landmarks()).hasSize(expectedResult.landmarks().size());
+    assertThat(actualResult.worldLandmarks()).hasSize(expectedResult.worldLandmarks().size());
+    assertThat(actualResult.handednesses()).hasSize(expectedResult.handednesses().size());
+    assertThat(actualResult.gestures()).hasSize(expectedResult.gestures().size());
+
+    // Actual landmarks match expected landmarks.
+    assertThat(actualResult.landmarks().get(0))
+        .comparingElementsUsing(
+            Correspondence.from(
+                (Correspondence.BinaryPredicate<Landmark, Landmark>)
+                    (actual, expected) -> {
+                      return Correspondence.tolerance(LANDMARKS_ERROR_TOLERANCE)
+                              .compare(actual.x(), expected.x())
+                          && Correspondence.tolerance(LANDMARKS_ERROR_TOLERANCE)
+                              .compare(actual.y(), expected.y());
+                    },
+                "landmarks approximately equal to"))
+        .containsExactlyElementsIn(expectedResult.landmarks().get(0));
+
+    // Actual handedness matches expected handedness.
+    Category actualTopHandedness = actualResult.handednesses().get(0).get(0);
+    Category expectedTopHandedness = expectedResult.handednesses().get(0).get(0);
+    assertThat(actualTopHandedness.index()).isEqualTo(expectedTopHandedness.index());
+    assertThat(actualTopHandedness.categoryName()).isEqualTo(expectedTopHandedness.categoryName());
+
+    // Actual gesture with top score matches expected gesture.
+    Category actualTopGesture = actualResult.gestures().get(0).get(0);
+    Category expectedTopGesture = expectedResult.gestures().get(0).get(0);
+    assertActualGestureEqualExpectedGesture(actualTopGesture, expectedTopGesture);
+  }
+
+  private static void assertActualGestureEqualExpectedGesture(
+      Category actualGesture, Category expectedGesture) {
+    assertThat(actualGesture.index()).isEqualTo(actualGesture.index());
+    assertThat(expectedGesture.categoryName()).isEqualTo(expectedGesture.categoryName());
+  }
+
+  private static void assertImageSizeIsExpected(Image inputImage) {
+    assertThat(inputImage).isNotNull();
+    assertThat(inputImage.getWidth()).isEqualTo(IMAGE_WIDTH);
+    assertThat(inputImage.getHeight()).isEqualTo(IMAGE_HEIGHT);
+  }
+}

mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/imageclassifier/AndroidManifest.xml

@@ -0,0 +1,24 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.google.mediapipe.tasks.vision.imageclassifiertest"
+    android:versionCode="1"
+    android:versionName="1.0" >
+
+    <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE"/>
+    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE"/>
+
+    <uses-sdk android:minSdkVersion="24"
+        android:targetSdkVersion="30" />
+
+    <application
+        android:label="imageclassifiertest"
+        android:name="android.support.multidex.MultiDexApplication"
+        android:taskAffinity="">
+        <uses-library android:name="android.test.runner" />
+    </application>
+
+    <instrumentation
+        android:name="com.google.android.apps.common.testing.testrunner.GoogleInstrumentationTestRunner"
+        android:targetPackage="com.google.mediapipe.tasks.vision.imageclassifiertest" />
+
+</manifest>

mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/imageclassifier/BUILD

@@ -0,0 +1,19 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+# TODO: Enable this in OSS

mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/imageclassifier/ImageClassifierTest.java

@@ -0,0 +1,445 @@
+// Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+//
+// 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.
+
+package com.google.mediapipe.tasks.vision.imageclassifier;
+
+import static com.google.common.truth.Truth.assertThat;
+import static org.junit.Assert.assertThrows;
+
+import android.content.res.AssetManager;
+import android.graphics.BitmapFactory;
+import android.graphics.RectF;
+import androidx.test.core.app.ApplicationProvider;
+import androidx.test.ext.junit.runners.AndroidJUnit4;
+import com.google.mediapipe.framework.MediaPipeException;
+import com.google.mediapipe.framework.image.BitmapImageBuilder;
+import com.google.mediapipe.framework.image.Image;
+import com.google.mediapipe.tasks.components.containers.Category;
+import com.google.mediapipe.tasks.components.processors.ClassifierOptions;
+import com.google.mediapipe.tasks.core.BaseOptions;
+import com.google.mediapipe.tasks.core.TestUtils;
+import com.google.mediapipe.tasks.vision.core.RunningMode;
+import com.google.mediapipe.tasks.vision.imageclassifier.ImageClassifier.ImageClassifierOptions;
+import java.io.InputStream;
+import java.nio.ByteBuffer;
+import java.util.Arrays;
+import java.util.List;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.Suite;
+import org.junit.runners.Suite.SuiteClasses;
+
+/** Test for {@link ImageClassifier}/ */
+@RunWith(Suite.class)
+@SuiteClasses({ImageClassifierTest.General.class, ImageClassifierTest.RunningModeTest.class})
+public class ImageClassifierTest {
+  private static final String FLOAT_MODEL_FILE = "mobilenet_v2_1.0_224.tflite";
+  private static final String QUANTIZED_MODEL_FILE = "mobilenet_v1_0.25_224_quant.tflite";
+  private static final String BURGER_IMAGE = "burger.jpg";
+  private static final String MULTI_OBJECTS_IMAGE = "multi_objects.jpg";
+
+  @RunWith(AndroidJUnit4.class)
+  public static final class General extends ImageClassifierTest {
+
+    @Test
+    public void create_failsWithMissingModel() throws Exception {
+      String nonExistentFile = "/path/to/non/existent/file";
+      MediaPipeException exception =
+          assertThrows(
+              MediaPipeException.class,
+              () ->
+                  ImageClassifier.createFromFile(
+                      ApplicationProvider.getApplicationContext(), nonExistentFile));
+      assertThat(exception).hasMessageThat().contains(nonExistentFile);
+    }
+
+    @Test
+    public void create_failsWithInvalidModelBuffer() throws Exception {
+      // Create a non-direct model ByteBuffer.
+      ByteBuffer modelBuffer =
+          TestUtils.loadToNonDirectByteBuffer(
+              ApplicationProvider.getApplicationContext(), FLOAT_MODEL_FILE);
+
+      IllegalArgumentException exception =
+          assertThrows(
+              IllegalArgumentException.class,
+              () ->
+                  ImageClassifier.createFromBuffer(
+                      ApplicationProvider.getApplicationContext(), modelBuffer));
+
+      assertThat(exception)
+          .hasMessageThat()
+          .contains("The model buffer should be either a direct ByteBuffer or a MappedByteBuffer.");
+    }
+
+    @Test
+    public void classify_succeedsWithNoOptions() throws Exception {
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromFile(
+              ApplicationProvider.getApplicationContext(), FLOAT_MODEL_FILE);
+      ImageClassificationResult results = imageClassifier.classify(getImageFromAsset(BURGER_IMAGE));
+
+      assertHasOneHeadAndOneTimestamp(results, 0);
+      assertThat(results.classifications().get(0).entries().get(0).categories()).hasSize(1001);
+      assertThat(results.classifications().get(0).entries().get(0).categories().get(0))
+          .isEqualTo(Category.create(0.7952058f, 934, "cheeseburger", ""));
+    }
+
+    @Test
+    public void classify_succeedsWithFloatModel() throws Exception {
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setClassifierOptions(ClassifierOptions.builder().setMaxResults(3).build())
+              .build();
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      ImageClassificationResult results = imageClassifier.classify(getImageFromAsset(BURGER_IMAGE));
+
+      assertHasOneHeadAndOneTimestamp(results, 0);
+      assertCategoriesAre(
+          results,
+          Arrays.asList(
+              Category.create(0.7952058f, 934, "cheeseburger", ""),
+              Category.create(0.027329788f, 932, "bagel", ""),
+              Category.create(0.019334773f, 925, "guacamole", "")));
+    }
+
+    @Test
+    public void classify_succeedsWithQuantizedModel() throws Exception {
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(QUANTIZED_MODEL_FILE).build())
+              .setClassifierOptions(ClassifierOptions.builder().setMaxResults(1).build())
+              .build();
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      ImageClassificationResult results = imageClassifier.classify(getImageFromAsset(BURGER_IMAGE));
+
+      assertHasOneHeadAndOneTimestamp(results, 0);
+      assertCategoriesAre(
+          results, Arrays.asList(Category.create(0.97265625f, 934, "cheeseburger", "")));
+    }
+
+    @Test
+    public void classify_succeedsWithScoreThreshold() throws Exception {
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setClassifierOptions(ClassifierOptions.builder().setScoreThreshold(0.02f).build())
+              .build();
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      ImageClassificationResult results = imageClassifier.classify(getImageFromAsset(BURGER_IMAGE));
+
+      assertHasOneHeadAndOneTimestamp(results, 0);
+      assertCategoriesAre(
+          results,
+          Arrays.asList(
+              Category.create(0.7952058f, 934, "cheeseburger", ""),
+              Category.create(0.027329788f, 932, "bagel", "")));
+    }
+
+    @Test
+    public void classify_succeedsWithAllowlist() throws Exception {
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setClassifierOptions(
+                  ClassifierOptions.builder()
+                      .setCategoryAllowlist(Arrays.asList("cheeseburger", "guacamole", "meat loaf"))
+                      .build())
+              .build();
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      ImageClassificationResult results = imageClassifier.classify(getImageFromAsset(BURGER_IMAGE));
+
+      assertHasOneHeadAndOneTimestamp(results, 0);
+      assertCategoriesAre(
+          results,
+          Arrays.asList(
+              Category.create(0.7952058f, 934, "cheeseburger", ""),
+              Category.create(0.019334773f, 925, "guacamole", ""),
+              Category.create(0.006279315f, 963, "meat loaf", "")));
+    }
+
+    @Test
+    public void classify_succeedsWithDenylist() throws Exception {
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setClassifierOptions(
+                  ClassifierOptions.builder()
+                      .setMaxResults(3)
+                      .setCategoryDenylist(Arrays.asList("bagel"))
+                      .build())
+              .build();
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      ImageClassificationResult results = imageClassifier.classify(getImageFromAsset(BURGER_IMAGE));
+
+      assertHasOneHeadAndOneTimestamp(results, 0);
+      assertCategoriesAre(
+          results,
+          Arrays.asList(
+              Category.create(0.7952058f, 934, "cheeseburger", ""),
+              Category.create(0.019334773f, 925, "guacamole", ""),
+              Category.create(0.006279315f, 963, "meat loaf", "")));
+    }
+
+    @Test
+    public void classify_succeedsWithRegionOfInterest() throws Exception {
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setClassifierOptions(ClassifierOptions.builder().setMaxResults(1).build())
+              .build();
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      // RectF around the soccer ball.
+      RectF roi = new RectF(0.450f, 0.308f, 0.614f, 0.734f);
+      ImageClassificationResult results =
+          imageClassifier.classify(getImageFromAsset(MULTI_OBJECTS_IMAGE), roi);
+
+      assertHasOneHeadAndOneTimestamp(results, 0);
+      assertCategoriesAre(
+          results, Arrays.asList(Category.create(0.9969325f, 806, "soccer ball", "")));
+    }
+  }
+
+  @RunWith(AndroidJUnit4.class)
+  public static final class RunningModeTest extends ImageClassifierTest {
+
+    @Test
+    public void create_failsWithIllegalResultListenerInNonLiveStreamMode() throws Exception {
+      for (RunningMode mode : new RunningMode[] {RunningMode.IMAGE, RunningMode.VIDEO}) {
+        IllegalArgumentException exception =
+            assertThrows(
+                IllegalArgumentException.class,
+                () ->
+                    ImageClassifierOptions.builder()
+                        .setBaseOptions(
+                            BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+                        .setRunningMode(mode)
+                        .setResultListener((imageClassificationResult, inputImage) -> {})
+                        .build());
+        assertThat(exception)
+            .hasMessageThat()
+            .contains("a user-defined result listener shouldn't be provided");
+      }
+    }
+
+    @Test
+    public void create_failsWithMissingResultListenerInLiveSteamMode() throws Exception {
+      IllegalArgumentException exception =
+          assertThrows(
+              IllegalArgumentException.class,
+              () ->
+                  ImageClassifierOptions.builder()
+                      .setBaseOptions(
+                          BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+                      .setRunningMode(RunningMode.LIVE_STREAM)
+                      .build());
+      assertThat(exception)
+          .hasMessageThat()
+          .contains("a user-defined result listener must be provided");
+    }
+
+    @Test
+    public void classify_failsWithCallingWrongApiInImageMode() throws Exception {
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setRunningMode(RunningMode.IMAGE)
+              .build();
+
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      MediaPipeException exception =
+          assertThrows(
+              MediaPipeException.class,
+              () -> imageClassifier.classifyForVideo(getImageFromAsset(BURGER_IMAGE), 0));
+      assertThat(exception).hasMessageThat().contains("not initialized with the video mode");
+      exception =
+          assertThrows(
+              MediaPipeException.class,
+              () -> imageClassifier.classifyAsync(getImageFromAsset(BURGER_IMAGE), 0));
+      assertThat(exception).hasMessageThat().contains("not initialized with the live stream mode");
+    }
+
+    @Test
+    public void classify_failsWithCallingWrongApiInVideoMode() throws Exception {
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setRunningMode(RunningMode.VIDEO)
+              .build();
+
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      MediaPipeException exception =
+          assertThrows(
+              MediaPipeException.class,
+              () -> imageClassifier.classify(getImageFromAsset(BURGER_IMAGE)));
+      assertThat(exception).hasMessageThat().contains("not initialized with the image mode");
+      exception =
+          assertThrows(
+              MediaPipeException.class,
+              () -> imageClassifier.classifyAsync(getImageFromAsset(BURGER_IMAGE), 0));
+      assertThat(exception).hasMessageThat().contains("not initialized with the live stream mode");
+    }
+
+    @Test
+    public void classify_failsWithCallingWrongApiInLiveSteamMode() throws Exception {
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setRunningMode(RunningMode.LIVE_STREAM)
+              .setResultListener((imageClassificationResult, inputImage) -> {})
+              .build();
+
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+
+      MediaPipeException exception =
+          assertThrows(
+              MediaPipeException.class,
+              () -> imageClassifier.classify(getImageFromAsset(BURGER_IMAGE)));
+      assertThat(exception).hasMessageThat().contains("not initialized with the image mode");
+      exception =
+          assertThrows(
+              MediaPipeException.class,
+              () -> imageClassifier.classifyForVideo(getImageFromAsset(BURGER_IMAGE), 0));
+      assertThat(exception).hasMessageThat().contains("not initialized with the video mode");
+    }
+
+    @Test
+    public void classify_succeedsWithImageMode() throws Exception {
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setClassifierOptions(ClassifierOptions.builder().setMaxResults(1).build())
+              .build();
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      ImageClassificationResult results = imageClassifier.classify(getImageFromAsset(BURGER_IMAGE));
+
+      assertHasOneHeadAndOneTimestamp(results, 0);
+      assertCategoriesAre(
+          results, Arrays.asList(Category.create(0.7952058f, 934, "cheeseburger", "")));
+    }
+
+    @Test
+    public void classify_succeedsWithVideoMode() throws Exception {
+      Image image = getImageFromAsset(BURGER_IMAGE);
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setClassifierOptions(ClassifierOptions.builder().setMaxResults(1).build())
+              .setRunningMode(RunningMode.VIDEO)
+              .build();
+      ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      for (int i = 0; i < 3; i++) {
+        ImageClassificationResult results = imageClassifier.classifyForVideo(image, i);
+        assertHasOneHeadAndOneTimestamp(results, i);
+        assertCategoriesAre(
+            results, Arrays.asList(Category.create(0.7952058f, 934, "cheeseburger", "")));
+      }
+    }
+
+    @Test
+    public void classify_failsWithOutOfOrderInputTimestamps() throws Exception {
+      Image image = getImageFromAsset(BURGER_IMAGE);
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setClassifierOptions(ClassifierOptions.builder().setMaxResults(1).build())
+              .setRunningMode(RunningMode.LIVE_STREAM)
+              .setResultListener(
+                  (imageClassificationResult, inputImage) -> {
+                    assertCategoriesAre(
+                        imageClassificationResult,
+                        Arrays.asList(Category.create(0.7952058f, 934, "cheeseburger", "")));
+                    assertImageSizeIsExpected(inputImage);
+                  })
+              .build();
+      try (ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options)) {
+        imageClassifier.classifyAsync(getImageFromAsset(BURGER_IMAGE), 1);
+        MediaPipeException exception =
+            assertThrows(MediaPipeException.class, () -> imageClassifier.classifyAsync(image, 0));
+        assertThat(exception)
+            .hasMessageThat()
+            .contains("having a smaller timestamp than the processed timestamp");
+      }
+    }
+
+    @Test
+    public void classify_succeedsWithLiveStreamMode() throws Exception {
+      Image image = getImageFromAsset(BURGER_IMAGE);
+      ImageClassifierOptions options =
+          ImageClassifierOptions.builder()
+              .setBaseOptions(BaseOptions.builder().setModelAssetPath(FLOAT_MODEL_FILE).build())
+              .setClassifierOptions(ClassifierOptions.builder().setMaxResults(1).build())
+              .setRunningMode(RunningMode.LIVE_STREAM)
+              .setResultListener(
+                  (imageClassificationResult, inputImage) -> {
+                    assertCategoriesAre(
+                        imageClassificationResult,
+                        Arrays.asList(Category.create(0.7952058f, 934, "cheeseburger", "")));
+                    assertImageSizeIsExpected(inputImage);
+                  })
+              .build();
+      try (ImageClassifier imageClassifier =
+          ImageClassifier.createFromOptions(ApplicationProvider.getApplicationContext(), options)) {
+        for (int i = 0; i < 3; ++i) {
+          imageClassifier.classifyAsync(image, i);
+        }
+      }
+    }
+  }
+
+  private static Image getImageFromAsset(String filePath) throws Exception {
+    AssetManager assetManager = ApplicationProvider.getApplicationContext().getAssets();
+    InputStream istr = assetManager.open(filePath);
+    return new BitmapImageBuilder(BitmapFactory.decodeStream(istr)).build();
+  }
+
+  private static void assertHasOneHeadAndOneTimestamp(
+      ImageClassificationResult results, long timestampMs) {
+    assertThat(results.classifications()).hasSize(1);
+    assertThat(results.classifications().get(0).headIndex()).isEqualTo(0);
+    assertThat(results.classifications().get(0).headName()).isEqualTo("probability");
+    assertThat(results.classifications().get(0).entries()).hasSize(1);
+    assertThat(results.classifications().get(0).entries().get(0).timestampMs())
+        .isEqualTo(timestampMs);
+  }
+
+  private static void assertCategoriesAre(
+      ImageClassificationResult results, List<Category> categories) {
+    assertThat(results.classifications().get(0).entries().get(0).categories())
+        .hasSize(categories.size());
+    for (int i = 0; i < categories.size(); i++) {
+      assertThat(results.classifications().get(0).entries().get(0).categories().get(i))
+          .isEqualTo(categories.get(i));
+    }
+  }
+
+  private static void assertImageSizeIsExpected(Image inputImage) {
+    assertThat(inputImage).isNotNull();
+    assertThat(inputImage.getWidth()).isEqualTo(480);
+    assertThat(inputImage.getHeight()).isEqualTo(325);
+  }
+}

mediapipe/tasks/python/metadata/metadata_writers/BUILD

@@ -0,0 +1,21 @@
+# Placeholder for internal Python strict library compatibility macro.
+
+package(
+    default_visibility = [
+        "//visibility:public",
+    ],
+    licenses = ["notice"],
+)
+
+py_library(
+    name = "metadata_info",
+    srcs = [
+        "metadata_info.py",
+    ],
+    srcs_version = "PY3",
+    visibility = ["//visibility:public"],
+    deps = [
+        "//mediapipe/tasks/metadata:metadata_schema_py",
+        "//mediapipe/tasks/metadata:schema_py",
+    ],
+)

mediapipe/tasks/python/metadata/metadata_writers/__init__.py

@@ -0,0 +1,13 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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/tasks/python/metadata/metadata_writers/metadata_info.py

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+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+"""Helper classes for common model metadata information."""
+
+import csv
+import os
+from typing import List, Optional, Type
+
+from mediapipe.tasks.metadata import metadata_schema_py_generated as _metadata_fb
+from mediapipe.tasks.metadata import schema_py_generated as _schema_fb
+
+# Min and max values for UINT8 tensors.
+_MIN_UINT8 = 0
+_MAX_UINT8 = 255
+
+# Default description for vocabulary files.
+_VOCAB_FILE_DESCRIPTION = ("Vocabulary file to convert natural language "
+                           "words to embedding vectors.")
+
+
+class GeneralMd:
+  """A container for common metadata information of a model.
+
+  Attributes:
+    name: name of the model.
+    version: version of the model.
+    description: description of what the model does.
+    author: author of the model.
+    licenses: licenses of the model.
+  """
+
+  def __init__(self,
+               name: Optional[str] = None,
+               version: Optional[str] = None,
+               description: Optional[str] = None,
+               author: Optional[str] = None,
+               licenses: Optional[str] = None) -> None:
+    self.name = name
+    self.version = version
+    self.description = description
+    self.author = author
+    self.licenses = licenses
+
+  def create_metadata(self) -> _metadata_fb.ModelMetadataT:
+    """Creates the model metadata based on the general model information.
+
+    Returns:
+      A Flatbuffers Python object of the model metadata.
+    """
+    model_metadata = _metadata_fb.ModelMetadataT()
+    model_metadata.name = self.name
+    model_metadata.version = self.version
+    model_metadata.description = self.description
+    model_metadata.author = self.author
+    model_metadata.license = self.licenses
+    return model_metadata
+
+
+class AssociatedFileMd:
+  """A container for common associated file metadata information.
+
+  Attributes:
+    file_path: path to the associated file.
+    description: description of the associated file.
+    file_type: file type of the associated file [1].
+    locale: locale of the associated file [2].
+    [1]:
+      https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L77
+    [2]:
+      https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L176
+  """
+
+  def __init__(
+      self,
+      file_path: str,
+      description: Optional[str] = None,
+      file_type: Optional[int] = _metadata_fb.AssociatedFileType.UNKNOWN,
+      locale: Optional[str] = None) -> None:
+    self.file_path = file_path
+    self.description = description
+    self.file_type = file_type
+    self.locale = locale
+
+  def create_metadata(self) -> _metadata_fb.AssociatedFileT:
+    """Creates the associated file metadata.
+
+    Returns:
+      A Flatbuffers Python object of the associated file metadata.
+    """
+    file_metadata = _metadata_fb.AssociatedFileT()
+    file_metadata.name = os.path.basename(self.file_path)
+    file_metadata.description = self.description
+    file_metadata.type = self.file_type
+    file_metadata.locale = self.locale
+    return file_metadata
+
+
+class LabelFileMd(AssociatedFileMd):
+  """A container for label file metadata information."""
+
+  _LABEL_FILE_DESCRIPTION = ("Labels for categories that the model can "
+                             "recognize.")
+  _FILE_TYPE = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
+
+  def __init__(self, file_path: str, locale: Optional[str] = None) -> None:
+    """Creates a LabelFileMd object.
+
+    Args:
+      file_path: file_path of the label file.
+      locale: locale of the label file [1].
+      [1]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L176
+    """
+    super().__init__(file_path, self._LABEL_FILE_DESCRIPTION, self._FILE_TYPE,
+                     locale)
+
+
+class ScoreCalibrationMd:
+  """A container for score calibration [1] metadata information.
+
+  [1]:
+    https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
+  """
+
+  _SCORE_CALIBRATION_FILE_DESCRIPTION = (
+      "Contains sigmoid-based score calibration parameters. The main purposes "
+      "of score calibration is to make scores across classes comparable, so "
+      "that a common threshold can be used for all output classes.")
+  _FILE_TYPE = _metadata_fb.AssociatedFileType.TENSOR_AXIS_SCORE_CALIBRATION
+
+  def __init__(self,
+               score_transformation_type: _metadata_fb.ScoreTransformationType,
+               default_score: float, file_path: str) -> None:
+    """Creates a ScoreCalibrationMd object.
+
+    Args:
+      score_transformation_type: type of the function used for transforming the
+        uncalibrated score before applying score calibration.
+      default_score: the default calibrated score to apply if the uncalibrated
+        score is below min_score or if no parameters were specified for a given
+        index.
+      file_path: file_path of the score calibration file [1].
+      [1]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L133
+
+    Raises:
+      ValueError: if the score_calibration file is malformed.
+    """
+    self._score_transformation_type = score_transformation_type
+    self._default_score = default_score
+    self._file_path = file_path
+
+    # Sanity check the score calibration file.
+    with open(self._file_path) as calibration_file:
+      csv_reader = csv.reader(calibration_file, delimiter=",")
+      for row in csv_reader:
+        if row and len(row) != 3 and len(row) != 4:
+          raise ValueError(
+              f"Expected empty lines or 3 or 4 parameters per line in score"
+              f" calibration file, but got {len(row)}.")
+
+        if row and float(row[0]) < 0:
+          raise ValueError(
+              f"Expected scale to be a non-negative value, but got "
+              f"{float(row[0])}.")
+
+  def create_metadata(self) -> _metadata_fb.ProcessUnitT:
+    """Creates the score calibration metadata based on the information.
+
+    Returns:
+      A Flatbuffers Python object of the score calibration metadata.
+    """
+    score_calibration = _metadata_fb.ProcessUnitT()
+    score_calibration.optionsType = (
+        _metadata_fb.ProcessUnitOptions.ScoreCalibrationOptions)
+    options = _metadata_fb.ScoreCalibrationOptionsT()
+    options.scoreTransformation = self._score_transformation_type
+    options.defaultScore = self._default_score
+    score_calibration.options = options
+    return score_calibration
+
+  def create_score_calibration_file_md(self) -> AssociatedFileMd:
+    return AssociatedFileMd(self._file_path,
+                            self._SCORE_CALIBRATION_FILE_DESCRIPTION,
+                            self._FILE_TYPE)
+
+
+class TensorMd:
+  """A container for common tensor metadata information.
+
+  Attributes:
+    name: name of the tensor.
+    description: description of what the tensor is.
+    min_values: per-channel minimum value of the tensor.
+    max_values: per-channel maximum value of the tensor.
+    content_type: content_type of the tensor.
+    associated_files: information of the associated files in the tensor.
+    tensor_name: name of the corresponding tensor [1] in the TFLite model. It is
+      used to locate the corresponding tensor and decide the order of the tensor
+      metadata [2] when populating model metadata.
+    [1]:
+      https://github.com/tensorflow/tensorflow/blob/cb67fef35567298b40ac166b0581cd8ad68e5a3a/tensorflow/lite/schema/schema.fbs#L1129-L1136
+    [2]:
+      https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L623-L640
+  """
+
+  def __init__(
+      self,
+      name: Optional[str] = None,
+      description: Optional[str] = None,
+      min_values: Optional[List[float]] = None,
+      max_values: Optional[List[float]] = None,
+      content_type: int = _metadata_fb.ContentProperties.FeatureProperties,
+      associated_files: Optional[List[Type[AssociatedFileMd]]] = None,
+      tensor_name: Optional[str] = None) -> None:
+    self.name = name
+    self.description = description
+    self.min_values = min_values
+    self.max_values = max_values
+    self.content_type = content_type
+    self.associated_files = associated_files
+    self.tensor_name = tensor_name
+
+  def create_metadata(self) -> _metadata_fb.TensorMetadataT:
+    """Creates the input tensor metadata based on the information.
+
+    Returns:
+      A Flatbuffers Python object of the input metadata.
+    """
+    tensor_metadata = _metadata_fb.TensorMetadataT()
+    tensor_metadata.name = self.name
+    tensor_metadata.description = self.description
+
+    # Create min and max values
+    stats = _metadata_fb.StatsT()
+    stats.max = self.max_values
+    stats.min = self.min_values
+    tensor_metadata.stats = stats
+
+    # Create content properties
+    content = _metadata_fb.ContentT()
+    if self.content_type is _metadata_fb.ContentProperties.FeatureProperties:
+      content.contentProperties = _metadata_fb.FeaturePropertiesT()
+    elif self.content_type is _metadata_fb.ContentProperties.ImageProperties:
+      content.contentProperties = _metadata_fb.ImagePropertiesT()
+    elif self.content_type is (
+        _metadata_fb.ContentProperties.BoundingBoxProperties):
+      content.contentProperties = _metadata_fb.BoundingBoxPropertiesT()
+    elif self.content_type is _metadata_fb.ContentProperties.AudioProperties:
+      content.contentProperties = _metadata_fb.AudioPropertiesT()
+
+    content.contentPropertiesType = self.content_type
+    tensor_metadata.content = content
+
+    # TODO: check if multiple label files have populated locale.
+    # Create associated files
+    if self.associated_files:
+      tensor_metadata.associatedFiles = [
+          file.create_metadata() for file in self.associated_files
+      ]
+    return tensor_metadata
+
+
+class InputImageTensorMd(TensorMd):
+  """A container for input image tensor metadata information.
+
+  Attributes:
+    norm_mean: the mean value used in tensor normalization [1].
+    norm_std: the std value used in the tensor normalization [1]. norm_mean and
+      norm_std must have the same dimension.
+    color_space_type: the color space type of the input image [2].
+    [1]:
+      https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L389
+    [2]:
+      https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L198
+  """
+
+  # Min and max float values for image pixels.
+  _MIN_PIXEL = 0.0
+  _MAX_PIXEL = 255.0
+
+  def __init__(
+      self,
+      name: Optional[str] = None,
+      description: Optional[str] = None,
+      norm_mean: Optional[List[float]] = None,
+      norm_std: Optional[List[float]] = None,
+      color_space_type: Optional[int] = _metadata_fb.ColorSpaceType.UNKNOWN,
+      tensor_type: Optional["_schema_fb.TensorType"] = None) -> None:
+    """Initializes the instance of InputImageTensorMd.
+
+    Args:
+      name: name of the tensor.
+      description: description of what the tensor is.
+      norm_mean: the mean value used in tensor normalization [1].
+      norm_std: the std value used in the tensor normalization [1]. norm_mean
+        and norm_std must have the same dimension.
+      color_space_type: the color space type of the input image [2].
+      tensor_type: data type of the tensor.
+      [1]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L389
+      [2]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L198
+
+    Raises:
+      ValueError: if norm_mean and norm_std have different dimensions.
+    """
+    if norm_std and norm_mean and len(norm_std) != len(norm_mean):
+      raise ValueError(
+          f"norm_mean and norm_std are expected to be the same dim. But got "
+          f"{len(norm_mean)} and {len(norm_std)}")
+
+    if tensor_type is _schema_fb.TensorType.UINT8:
+      min_values = [_MIN_UINT8]
+      max_values = [_MAX_UINT8]
+    elif tensor_type is _schema_fb.TensorType.FLOAT32 and norm_std and norm_mean:
+      min_values = [
+          float(self._MIN_PIXEL - mean) / std
+          for mean, std in zip(norm_mean, norm_std)
+      ]
+      max_values = [
+          float(self._MAX_PIXEL - mean) / std
+          for mean, std in zip(norm_mean, norm_std)
+      ]
+    else:
+      # Uint8 and Float32 are the two major types currently. And Task library
+      # doesn't support other types so far.
+      min_values = None
+      max_values = None
+
+    super().__init__(name, description, min_values, max_values,
+                     _metadata_fb.ContentProperties.ImageProperties)
+    self.norm_mean = norm_mean
+    self.norm_std = norm_std
+    self.color_space_type = color_space_type
+
+  def create_metadata(self) -> _metadata_fb.TensorMetadataT:
+    """Creates the input image metadata based on the information.
+
+    Returns:
+      A Flatbuffers Python object of the input image metadata.
+    """
+    tensor_metadata = super().create_metadata()
+    tensor_metadata.content.contentProperties.colorSpace = self.color_space_type
+    # Create normalization parameters
+    if self.norm_mean and self.norm_std:
+      normalization = _metadata_fb.ProcessUnitT()
+      normalization.optionsType = (
+          _metadata_fb.ProcessUnitOptions.NormalizationOptions)
+      normalization.options = _metadata_fb.NormalizationOptionsT()
+      normalization.options.mean = self.norm_mean
+      normalization.options.std = self.norm_std
+      tensor_metadata.processUnits = [normalization]
+    return tensor_metadata
+
+
+class ClassificationTensorMd(TensorMd):
+  """A container for the classification tensor metadata information.
+
+  Attributes:
+    label_files: information of the label files [1] in the classification
+      tensor.
+    score_calibration_md: information of the score calibration operation [2] in
+      the classification tensor.
+    [1]:
+      https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99
+    [2]:
+      https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
+  """
+
+  # Min and max float values for classification results.
+  _MIN_FLOAT = 0.0
+  _MAX_FLOAT = 1.0
+
+  def __init__(self,
+               name: Optional[str] = None,
+               description: Optional[str] = None,
+               label_files: Optional[List[LabelFileMd]] = None,
+               tensor_type: Optional[int] = None,
+               score_calibration_md: Optional[ScoreCalibrationMd] = None,
+               tensor_name: Optional[str] = None) -> None:
+    """Initializes the instance of ClassificationTensorMd.
+
+    Args:
+      name: name of the tensor.
+      description: description of what the tensor is.
+      label_files: information of the label files [1] in the classification
+        tensor.
+      tensor_type: data type of the tensor.
+      score_calibration_md: information of the score calibration files operation
+        [2] in the classification tensor.
+      tensor_name: name of the corresponding tensor [3] in the TFLite model. It
+        is used to locate the corresponding classification tensor and decide the
+        order of the tensor metadata [4] when populating model metadata.
+      [1]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99
+      [2]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L456
+      [3]:
+        https://github.com/tensorflow/tensorflow/blob/cb67fef35567298b40ac166b0581cd8ad68e5a3a/tensorflow/lite/schema/schema.fbs#L1129-L1136
+      [4]:
+        https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L623-L640
+    """
+    self.score_calibration_md = score_calibration_md
+
+    if tensor_type is _schema_fb.TensorType.UINT8:
+      min_values = [_MIN_UINT8]
+      max_values = [_MAX_UINT8]
+    elif tensor_type is _schema_fb.TensorType.FLOAT32:
+      min_values = [self._MIN_FLOAT]
+      max_values = [self._MAX_FLOAT]
+    else:
+      # Uint8 and Float32 are the two major types currently. And Task library
+      # doesn't support other types so far.
+      min_values = None
+      max_values = None
+
+    associated_files = label_files or []
+    if self.score_calibration_md:
+      associated_files.append(
+          score_calibration_md.create_score_calibration_file_md())
+
+    super().__init__(name, description, min_values, max_values,
+                     _metadata_fb.ContentProperties.FeatureProperties,
+                     associated_files, tensor_name)
+
+  def create_metadata(self) -> _metadata_fb.TensorMetadataT:
+    """Creates the classification tensor metadata based on the information."""
+    tensor_metadata = super().create_metadata()
+    if self.score_calibration_md:
+      tensor_metadata.processUnits = [
+          self.score_calibration_md.create_metadata()
+      ]
+    return tensor_metadata

mediapipe/tasks/python/test/metadata/metadata_writers/BUILD

@@ -0,0 +1,26 @@
+# Placeholder for internal Python strict test compatibility macro.
+
+package(
+    default_visibility = [
+        "//visibility:public",
+    ],
+    licenses = ["notice"],  # Apache 2.0
+)
+
+py_test(
+    name = "metadata_info_test",
+    srcs = ["metadata_info_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/metadata:data_files",
+    ],
+    python_version = "PY3",
+    srcs_version = "PY3",
+    deps = [
+        "//mediapipe/tasks/metadata:metadata_schema_py",
+        "//mediapipe/tasks/metadata:schema_py",
+        "//mediapipe/tasks/python/metadata",
+        "//mediapipe/tasks/python/metadata/metadata_writers:metadata_info",
+        "//mediapipe/tasks/python/test:test_utils",
+        "@flatbuffers//:runtime_py",
+    ],
+)

mediapipe/tasks/python/test/metadata/metadata_writers/metadata_info_test.py

@@ -0,0 +1,343 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+"""Tests for metadata info classes."""
+
+import tempfile
+
+from absl.testing import absltest
+from absl.testing import parameterized
+
+import flatbuffers
+from mediapipe.tasks.metadata import metadata_schema_py_generated as _metadata_fb
+from mediapipe.tasks.metadata import schema_py_generated as _schema_fb
+from mediapipe.tasks.python.metadata import metadata as _metadata
+from mediapipe.tasks.python.metadata.metadata_writers import metadata_info
+from mediapipe.tasks.python.test import test_utils
+
+_SCORE_CALIBRATION_FILE = test_utils.get_test_data_path("score_calibration.txt")
+
+
+class GeneralMdTest(absltest.TestCase):
+
+  _EXPECTED_GENERAL_META_JSON = test_utils.get_test_data_path(
+      "general_meta.json")
+
+  def test_create_metadata_should_succeed(self):
+    general_md = metadata_info.GeneralMd(
+        name="model",
+        version="v1",
+        description="A ML model.",
+        author="MediaPipe",
+        licenses="Apache")
+    general_metadata = general_md.create_metadata()
+
+    # Create the Flatbuffers object and convert it to the json format.
+    builder = flatbuffers.Builder(0)
+    builder.Finish(
+        general_metadata.Pack(builder),
+        _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
+    metadata_json = _metadata.convert_to_json(bytes(builder.Output()))
+
+    with open(self._EXPECTED_GENERAL_META_JSON, "r") as f:
+      expected_json = f.read()
+
+    self.assertEqual(metadata_json, expected_json)
+
+
+class AssociatedFileMdTest(absltest.TestCase):
+
+  _EXPECTED_META_JSON = test_utils.get_test_data_path(
+      "associated_file_meta.json")
+
+  def test_create_metadata_should_succeed(self):
+    file_md = metadata_info.AssociatedFileMd(
+        file_path="label.txt",
+        description="The label file.",
+        file_type=_metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS,
+        locale="en")
+    file_metadata = file_md.create_metadata()
+
+    # Create the Flatbuffers object and convert it to the json format.
+    model_metadata = _metadata_fb.ModelMetadataT()
+    model_metadata.associatedFiles = [file_metadata]
+    builder = flatbuffers.Builder(0)
+    builder.Finish(
+        model_metadata.Pack(builder),
+        _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
+    metadata_json = _metadata.convert_to_json(bytes(builder.Output()))
+
+    with open(self._EXPECTED_META_JSON, "r") as f:
+      expected_json = f.read()
+    self.assertEqual(metadata_json, expected_json)
+
+
+class TensorMdTest(parameterized.TestCase):
+
+  _TENSOR_NAME = "input"
+  _TENSOR_DESCRIPTION = "The input tensor."
+  _TENSOR_MIN = 0
+  _TENSOR_MAX = 1
+  _LABEL_FILE_EN = "labels.txt"
+  _LABEL_FILE_CN = "labels_cn.txt"  # Locale label file in Chinese.
+  _EXPECTED_FEATURE_TENSOR_JSON = test_utils.get_test_data_path(
+      "feature_tensor_meta.json")
+  _EXPECTED_IMAGE_TENSOR_JSON = test_utils.get_test_data_path(
+      "image_tensor_meta.json")
+  _EXPECTED_BOUNDING_BOX_TENSOR_JSON = test_utils.get_test_data_path(
+      "bounding_box_tensor_meta.json")
+
+  @parameterized.named_parameters(
+      {
+          "testcase_name": "feature_tensor",
+          "content_type": _metadata_fb.ContentProperties.FeatureProperties,
+          "golden_json": _EXPECTED_FEATURE_TENSOR_JSON
+      }, {
+          "testcase_name": "image_tensor",
+          "content_type": _metadata_fb.ContentProperties.ImageProperties,
+          "golden_json": _EXPECTED_IMAGE_TENSOR_JSON
+      }, {
+          "testcase_name": "bounding_box_tensor",
+          "content_type": _metadata_fb.ContentProperties.BoundingBoxProperties,
+          "golden_json": _EXPECTED_BOUNDING_BOX_TENSOR_JSON
+      })
+  def test_create_metadata_should_succeed(self, content_type, golden_json):
+    associated_file1 = metadata_info.AssociatedFileMd(
+        file_path=self._LABEL_FILE_EN, locale="en")
+    associated_file2 = metadata_info.AssociatedFileMd(
+        file_path=self._LABEL_FILE_CN, locale="cn")
+
+    tensor_md = metadata_info.TensorMd(
+        name=self._TENSOR_NAME,
+        description=self._TENSOR_DESCRIPTION,
+        min_values=[self._TENSOR_MIN],
+        max_values=[self._TENSOR_MAX],
+        content_type=content_type,
+        associated_files=[associated_file1, associated_file2])
+    tensor_metadata = tensor_md.create_metadata()
+
+    metadata_json = _metadata.convert_to_json(
+        _create_dummy_model_metadata_with_tensor(tensor_metadata))
+    with open(golden_json, "r") as f:
+      expected_json = f.read()
+    self.assertEqual(metadata_json, expected_json)
+
+
+class InputImageTensorMdTest(parameterized.TestCase):
+
+  _NAME = "image"
+  _DESCRIPTION = "The input image."
+  _NORM_MEAN = (0, 127.5, 255)
+  _NORM_STD = (127.5, 127.5, 127.5)
+  _COLOR_SPACE_TYPE = _metadata_fb.ColorSpaceType.RGB
+  _EXPECTED_FLOAT_TENSOR_JSON = test_utils.get_test_data_path(
+      "input_image_tensor_float_meta.json")
+  _EXPECTED_UINT8_TENSOR_JSON = test_utils.get_test_data_path(
+      "input_image_tensor_uint8_meta.json")
+  _EXPECTED_UNSUPPORTED_TENSOR_JSON = test_utils.get_test_data_path(
+      "input_image_tensor_unsupported_meta.json")
+
+  @parameterized.named_parameters(
+      {
+          "testcase_name": "float",
+          "tensor_type": _schema_fb.TensorType.FLOAT32,
+          "golden_json": _EXPECTED_FLOAT_TENSOR_JSON
+      }, {
+          "testcase_name": "uint8",
+          "tensor_type": _schema_fb.TensorType.UINT8,
+          "golden_json": _EXPECTED_UINT8_TENSOR_JSON
+      }, {
+          "testcase_name": "unsupported_tensor_type",
+          "tensor_type": _schema_fb.TensorType.INT16,
+          "golden_json": _EXPECTED_UNSUPPORTED_TENSOR_JSON
+      })
+  def test_create_metadata_should_succeed(self, tensor_type, golden_json):
+    tesnor_md = metadata_info.InputImageTensorMd(
+        name=self._NAME,
+        description=self._DESCRIPTION,
+        norm_mean=list(self._NORM_MEAN),
+        norm_std=list(self._NORM_STD),
+        color_space_type=self._COLOR_SPACE_TYPE,
+        tensor_type=tensor_type)
+    tensor_metadata = tesnor_md.create_metadata()
+
+    metadata_json = _metadata.convert_to_json(
+        _create_dummy_model_metadata_with_tensor(tensor_metadata))
+    with open(golden_json, "r") as f:
+      expected_json = f.read()
+    self.assertEqual(metadata_json, expected_json)
+
+  def test_init_should_throw_exception_with_incompatible_mean_and_std(self):
+    norm_mean = [0]
+    norm_std = [1, 2]
+    with self.assertRaises(ValueError) as error:
+      metadata_info.InputImageTensorMd(norm_mean=norm_mean, norm_std=norm_std)
+    self.assertEqual(
+        f"norm_mean and norm_std are expected to be the same dim. But got "
+        f"{len(norm_mean)} and {len(norm_std)}", str(error.exception))
+
+
+class ClassificationTensorMdTest(parameterized.TestCase):
+
+  _NAME = "probability"
+  _DESCRIPTION = "The classification result tensor."
+  _LABEL_FILE_EN = "labels.txt"
+  _LABEL_FILE_CN = "labels_cn.txt"  # Locale label file in Chinese.
+  _CALIBRATION_DEFAULT_SCORE = 0.2
+  _EXPECTED_FLOAT_TENSOR_JSON = test_utils.get_test_data_path(
+      "classification_tensor_float_meta.json")
+  _EXPECTED_UINT8_TENSOR_JSON = test_utils.get_test_data_path(
+      "classification_tensor_uint8_meta.json")
+  _EXPECTED_UNSUPPORTED_TENSOR_JSON = test_utils.get_test_data_path(
+      "classification_tensor_unsupported_meta.json")
+
+  @parameterized.named_parameters(
+      {
+          "testcase_name": "float",
+          "tensor_type": _schema_fb.TensorType.FLOAT32,
+          "golden_json": _EXPECTED_FLOAT_TENSOR_JSON
+      }, {
+          "testcase_name": "uint8",
+          "tensor_type": _schema_fb.TensorType.UINT8,
+          "golden_json": _EXPECTED_UINT8_TENSOR_JSON
+      }, {
+          "testcase_name": "unsupported_tensor_type",
+          "tensor_type": _schema_fb.TensorType.INT16,
+          "golden_json": _EXPECTED_UNSUPPORTED_TENSOR_JSON
+      })
+  def test_create_metadata_should_succeed(self, tensor_type, golden_json):
+    label_file_en = metadata_info.LabelFileMd(
+        file_path=self._LABEL_FILE_EN, locale="en")
+    label_file_cn = metadata_info.LabelFileMd(
+        file_path=self._LABEL_FILE_CN, locale="cn")
+    score_calibration_md = metadata_info.ScoreCalibrationMd(
+        _metadata_fb.ScoreTransformationType.IDENTITY,
+        self._CALIBRATION_DEFAULT_SCORE, _SCORE_CALIBRATION_FILE)
+
+    tesnor_md = metadata_info.ClassificationTensorMd(
+        name=self._NAME,
+        description=self._DESCRIPTION,
+        label_files=[label_file_en, label_file_cn],
+        tensor_type=tensor_type,
+        score_calibration_md=score_calibration_md)
+    tensor_metadata = tesnor_md.create_metadata()
+
+    metadata_json = _metadata.convert_to_json(
+        _create_dummy_model_metadata_with_tensor(tensor_metadata))
+    with open(golden_json, "r") as f:
+      expected_json = f.read()
+    self.assertEqual(metadata_json, expected_json)
+
+
+class ScoreCalibrationMdTest(absltest.TestCase):
+  _DEFAULT_VALUE = 0.2
+  _EXPECTED_TENSOR_JSON = test_utils.get_test_data_path(
+      "score_calibration_tensor_meta.json")
+  _EXPECTED_MODEL_META_JSON = test_utils.get_test_data_path(
+      "score_calibration_file_meta.json")
+
+  def test_create_metadata_should_succeed(self):
+    score_calibration_md = metadata_info.ScoreCalibrationMd(
+        _metadata_fb.ScoreTransformationType.LOG, self._DEFAULT_VALUE,
+        _SCORE_CALIBRATION_FILE)
+    score_calibration_metadata = score_calibration_md.create_metadata()
+
+    metadata_json = _metadata.convert_to_json(
+        _create_dummy_model_metadata_with_process_uint(
+            score_calibration_metadata))
+    with open(self._EXPECTED_TENSOR_JSON, "r") as f:
+      expected_json = f.read()
+    self.assertEqual(metadata_json, expected_json)
+
+  def test_create_score_calibration_file_md_should_succeed(self):
+    score_calibration_md = metadata_info.ScoreCalibrationMd(
+        _metadata_fb.ScoreTransformationType.LOG, self._DEFAULT_VALUE,
+        _SCORE_CALIBRATION_FILE)
+    score_calibration_file_md = (
+        score_calibration_md.create_score_calibration_file_md())
+    file_metadata = score_calibration_file_md.create_metadata()
+
+    # Create the Flatbuffers object and convert it to the json format.
+    model_metadata = _metadata_fb.ModelMetadataT()
+    model_metadata.associatedFiles = [file_metadata]
+    builder = flatbuffers.Builder(0)
+    builder.Finish(
+        model_metadata.Pack(builder),
+        _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
+    metadata_json = _metadata.convert_to_json(bytes(builder.Output()))
+
+    with open(self._EXPECTED_MODEL_META_JSON, "r") as f:
+      expected_json = f.read()
+    self.assertEqual(metadata_json, expected_json)
+
+  def test_create_score_calibration_file_fails_with_less_colunms(self):
+    with tempfile.TemporaryDirectory() as temp_dir:
+      malformed_calibration_file = test_utils.create_calibration_file(
+          temp_dir, content="1.0,0.2")
+
+      with self.assertRaisesRegex(
+          ValueError,
+          "Expected empty lines or 3 or 4 parameters per line in score" +
+          " calibration file, but got 2."):
+        metadata_info.ScoreCalibrationMd(
+            _metadata_fb.ScoreTransformationType.LOG, self._DEFAULT_VALUE,
+            malformed_calibration_file)
+
+  def test_create_score_calibration_file_fails_with_negative_scale(self):
+    with tempfile.TemporaryDirectory() as temp_dir:
+      malformed_calibration_file = test_utils.create_calibration_file(
+          temp_dir, content="-1.0,0.2,0.1")
+
+      with self.assertRaisesRegex(
+          ValueError,
+          "Expected scale to be a non-negative value, but got -1.0."):
+        metadata_info.ScoreCalibrationMd(
+            _metadata_fb.ScoreTransformationType.LOG, self._DEFAULT_VALUE,
+            malformed_calibration_file)
+
+
+def _create_dummy_model_metadata_with_tensor(
+    tensor_metadata: _metadata_fb.TensorMetadataT) -> bytes:
+  # Create a dummy model using the tensor metadata.
+  subgraph_metadata = _metadata_fb.SubGraphMetadataT()
+  subgraph_metadata.inputTensorMetadata = [tensor_metadata]
+  model_metadata = _metadata_fb.ModelMetadataT()
+  model_metadata.subgraphMetadata = [subgraph_metadata]
+
+  # Create the Flatbuffers object and convert it to the json format.
+  builder = flatbuffers.Builder(0)
+  builder.Finish(
+      model_metadata.Pack(builder),
+      _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
+  return bytes(builder.Output())
+
+
+def _create_dummy_model_metadata_with_process_uint(
+    process_unit_metadata: _metadata_fb.ProcessUnitT) -> bytes:
+  # Create a dummy model using the tensor metadata.
+  subgraph_metadata = _metadata_fb.SubGraphMetadataT()
+  subgraph_metadata.inputProcessUnits = [process_unit_metadata]
+  model_metadata = _metadata_fb.ModelMetadataT()
+  model_metadata.subgraphMetadata = [subgraph_metadata]
+
+  # Create the Flatbuffers object and convert it to the json format.
+  builder = flatbuffers.Builder(0)
+  builder.Finish(
+      model_metadata.Pack(builder),
+      _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
+  return bytes(builder.Output())
+
+
+if __name__ == "__main__":
+  absltest.main()

mediapipe/tasks/python/test/test_utils.py

@@ -43,3 +43,13 @@ def get_test_data_path(file_or_dirname: str) -> str:
       if f.endswith(file_or_dirname):
         return os.path.join(directory, f)
   raise ValueError("No %s in test directory" % file_or_dirname)
+
+
+def create_calibration_file(file_dir: str,
+                            file_name: str = "score_calibration.txt",
+                            content: str = "1.0,2.0,3.0,4.0") -> str:
+  """Creates the calibration file."""
+  calibration_file = os.path.join(file_dir, file_name)
+  with open(calibration_file, mode="w") as file:
+    file.write(content)
+  return calibration_file

mediapipe/tasks/testdata/metadata/BUILD

@@ -33,7 +33,21 @@ mediapipe_files(srcs = [
 
 exports_files([
     "external_file",
+    "general_meta.json",
     "golden_json.json",
+    "associated_file_meta.json",
+    "bounding_box_tensor_meta.json",
+    "classification_tensor_float_meta.json",
+    "classification_tensor_uint8_meta.json",
+    "classification_tensor_unsupported_meta.json",
+    "feature_tensor_meta.json",
+    "image_tensor_meta.json",
+    "input_image_tensor_float_meta.json",
+    "input_image_tensor_uint8_meta.json",
+    "input_image_tensor_unsupported_meta.json",
+    "score_calibration.txt",
+    "score_calibration_file_meta.json",
+    "score_calibration_tensor_meta.json",
 ])
 
 filegroup(
@@ -51,7 +65,21 @@ filegroup(
 filegroup(
     name = "data_files",
     srcs = [
+        "associated_file_meta.json",
+        "bounding_box_tensor_meta.json",
+        "classification_tensor_float_meta.json",
+        "classification_tensor_uint8_meta.json",
+        "classification_tensor_unsupported_meta.json",
         "external_file",
+        "feature_tensor_meta.json",
+        "general_meta.json",
         "golden_json.json",
+        "image_tensor_meta.json",
+        "input_image_tensor_float_meta.json",
+        "input_image_tensor_uint8_meta.json",
+        "input_image_tensor_unsupported_meta.json",
+        "score_calibration.txt",
+        "score_calibration_file_meta.json",
+        "score_calibration_tensor_meta.json",
     ],
 )

mediapipe/tasks/testdata/metadata/associated_file_meta.json

@@ -0,0 +1,10 @@
+{
+  "associated_files": [
+    {
+      "name": "label.txt",
+      "description": "The label file.",
+      "type": "TENSOR_AXIS_LABELS",
+      "locale": "en"
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/bounding_box_tensor_meta.json

@@ -0,0 +1,35 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "input",
+          "description": "The input tensor.",
+          "content": {
+            "content_properties_type": "BoundingBoxProperties",
+            "content_properties": {
+            }
+          },
+          "stats": {
+            "max": [
+              1.0
+            ],
+            "min": [
+              0.0
+            ]
+          },
+          "associated_files": [
+            {
+              "name": "labels.txt",
+              "locale": "en"
+            },
+            {
+              "name": "labels_cn.txt",
+              "locale": "cn"
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/classification_tensor_float_meta.json

@@ -0,0 +1,52 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "probability",
+          "description": "The classification result tensor.",
+          "content": {
+            "content_properties_type": "FeatureProperties",
+            "content_properties": {
+            }
+          },
+          "process_units": [
+            {
+              "options_type": "ScoreCalibrationOptions",
+              "options": {
+                "default_score": 0.2
+              }
+            }
+          ],
+          "stats": {
+            "max": [
+              1.0
+            ],
+            "min": [
+              0.0
+            ]
+          },
+          "associated_files": [
+            {
+              "name": "labels.txt",
+              "description": "Labels for categories that the model can recognize.",
+              "type": "TENSOR_AXIS_LABELS",
+              "locale": "en"
+            },
+            {
+              "name": "labels_cn.txt",
+              "description": "Labels for categories that the model can recognize.",
+              "type": "TENSOR_AXIS_LABELS",
+              "locale": "cn"
+            },
+            {
+              "name": "score_calibration.txt",
+              "description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
+              "type": "TENSOR_AXIS_SCORE_CALIBRATION"
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/classification_tensor_uint8_meta.json

@@ -0,0 +1,52 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "probability",
+          "description": "The classification result tensor.",
+          "content": {
+            "content_properties_type": "FeatureProperties",
+            "content_properties": {
+            }
+          },
+          "process_units": [
+            {
+              "options_type": "ScoreCalibrationOptions",
+              "options": {
+                "default_score": 0.2
+              }
+            }
+          ],
+          "stats": {
+            "max": [
+              255.0
+            ],
+            "min": [
+              0.0
+            ]
+          },
+          "associated_files": [
+            {
+              "name": "labels.txt",
+              "description": "Labels for categories that the model can recognize.",
+              "type": "TENSOR_AXIS_LABELS",
+              "locale": "en"
+            },
+            {
+              "name": "labels_cn.txt",
+              "description": "Labels for categories that the model can recognize.",
+              "type": "TENSOR_AXIS_LABELS",
+              "locale": "cn"
+            },
+            {
+              "name": "score_calibration.txt",
+              "description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
+              "type": "TENSOR_AXIS_SCORE_CALIBRATION"
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/classification_tensor_unsupported_meta.json

@@ -0,0 +1,46 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "probability",
+          "description": "The classification result tensor.",
+          "content": {
+            "content_properties_type": "FeatureProperties",
+            "content_properties": {
+            }
+          },
+          "process_units": [
+            {
+              "options_type": "ScoreCalibrationOptions",
+              "options": {
+                "default_score": 0.2
+              }
+            }
+          ],
+          "stats": {
+          },
+          "associated_files": [
+            {
+              "name": "labels.txt",
+              "description": "Labels for categories that the model can recognize.",
+              "type": "TENSOR_AXIS_LABELS",
+              "locale": "en"
+            },
+            {
+              "name": "labels_cn.txt",
+              "description": "Labels for categories that the model can recognize.",
+              "type": "TENSOR_AXIS_LABELS",
+              "locale": "cn"
+            },
+            {
+              "name": "score_calibration.txt",
+              "description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
+              "type": "TENSOR_AXIS_SCORE_CALIBRATION"
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/feature_tensor_meta.json

@@ -0,0 +1,35 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "input",
+          "description": "The input tensor.",
+          "content": {
+            "content_properties_type": "FeatureProperties",
+            "content_properties": {
+            }
+          },
+          "stats": {
+            "max": [
+              1.0
+            ],
+            "min": [
+              0.0
+            ]
+          },
+          "associated_files": [
+            {
+              "name": "labels.txt",
+              "locale": "en"
+            },
+            {
+              "name": "labels_cn.txt",
+              "locale": "cn"
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/general_meta.json

@@ -0,0 +1,7 @@
+{
+  "name": "model",
+  "description": "A ML model.",
+  "version": "v1",
+  "author": "MediaPipe",
+  "license": "Apache"
+}

mediapipe/tasks/testdata/metadata/image_tensor_meta.json

@@ -0,0 +1,35 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "input",
+          "description": "The input tensor.",
+          "content": {
+            "content_properties_type": "ImageProperties",
+            "content_properties": {
+            }
+          },
+          "stats": {
+            "max": [
+              1.0
+            ],
+            "min": [
+              0.0
+            ]
+          },
+          "associated_files": [
+            {
+              "name": "labels.txt",
+              "locale": "en"
+            },
+            {
+              "name": "labels_cn.txt",
+              "locale": "cn"
+            }
+          ]
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/input_image_tensor_float_meta.json

@@ -0,0 +1,47 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "image",
+          "description": "The input image.",
+          "content": {
+            "content_properties_type": "ImageProperties",
+            "content_properties": {
+              "color_space": "RGB"
+            }
+          },
+          "process_units": [
+            {
+              "options_type": "NormalizationOptions",
+              "options": {
+                "mean": [
+                  0.0,
+                  127.5,
+                  255.0
+                ],
+                "std": [
+                  127.5,
+                  127.5,
+                  127.5
+                ]
+              }
+            }
+          ],
+          "stats": {
+            "max": [
+              2.0,
+              1.0,
+              0.0
+            ],
+            "min": [
+              0.0,
+              -1.0,
+              -2.0
+            ]
+          }
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/input_image_tensor_uint8_meta.json

@@ -0,0 +1,43 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "image",
+          "description": "The input image.",
+          "content": {
+            "content_properties_type": "ImageProperties",
+            "content_properties": {
+              "color_space": "RGB"
+            }
+          },
+          "process_units": [
+            {
+              "options_type": "NormalizationOptions",
+              "options": {
+                "mean": [
+                  0.0,
+                  127.5,
+                  255.0
+                ],
+                "std": [
+                  127.5,
+                  127.5,
+                  127.5
+                ]
+              }
+            }
+          ],
+          "stats": {
+            "max": [
+              255.0
+            ],
+            "min": [
+              0.0
+            ]
+          }
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/input_image_tensor_unsupported_meta.json

@@ -0,0 +1,37 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_tensor_metadata": [
+        {
+          "name": "image",
+          "description": "The input image.",
+          "content": {
+            "content_properties_type": "ImageProperties",
+            "content_properties": {
+              "color_space": "RGB"
+            }
+          },
+          "process_units": [
+            {
+              "options_type": "NormalizationOptions",
+              "options": {
+                "mean": [
+                  0.0,
+                  127.5,
+                  255.0
+                ],
+                "std": [
+                  127.5,
+                  127.5,
+                  127.5
+                ]
+              }
+            }
+          ],
+          "stats": {
+          }
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/score_calibration.txt

@@ -0,0 +1,511 @@
+
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+0.9901729226112366,0.8561913371086121,0.8783953189849854,0.5859375

mediapipe/tasks/testdata/metadata/score_calibration_file_meta.json

@@ -0,0 +1,9 @@
+{
+  "associated_files": [
+    {
+      "name": "score_calibration.txt",
+      "description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
+      "type": "TENSOR_AXIS_SCORE_CALIBRATION"
+    }
+  ]
+}

mediapipe/tasks/testdata/metadata/score_calibration_tensor_meta.json

@@ -0,0 +1,15 @@
+{
+  "subgraph_metadata": [
+    {
+      "input_process_units": [
+        {
+          "options_type": "ScoreCalibrationOptions",
+          "options": {
+            "score_transformation": "LOG",
+            "default_score": 0.2
+          }
+        }
+      ]
+    }
+  ]
+}

mediapipe/tasks/testdata/vision/BUILD

@@ -25,6 +25,7 @@ package(
 mediapipe_files(srcs = [
     "burger.jpg",
     "burger_crop.jpg",
+    "burger_rotated.jpg",
     "cat.jpg",
     "cat_mask.jpg",
     "cats_and_dogs.jpg",
@@ -46,6 +47,7 @@ mediapipe_files(srcs = [
     "mobilenet_v3_small_100_224_embedder.tflite",
     "mozart_square.jpg",
     "multi_objects.jpg",
+    "multi_objects_rotated.jpg",
     "palm_detection_full.tflite",
     "pointing_up.jpg",
     "right_hands.jpg",
@@ -72,6 +74,7 @@ filegroup(
     srcs = [
         "burger.jpg",
         "burger_crop.jpg",
+        "burger_rotated.jpg",
         "cat.jpg",
         "cat_mask.jpg",
         "cats_and_dogs.jpg",
@@ -81,6 +84,7 @@ filegroup(
         "left_hands.jpg",
         "mozart_square.jpg",
         "multi_objects.jpg",
+        "multi_objects_rotated.jpg",
         "pointing_up.jpg",
         "right_hands.jpg",
         "segmentation_golden_rotation0.png",

third_party/external_files.bzl

@@ -22,12 +22,24 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/albert_with_metadata.tflite?generation=1661875651648830"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_associated_file_meta_json",
+        sha256 = "5b2cba11ae893e1226af6570813955889e9f171d6d2c67b3e96ecb6b96d8c681",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/associated_file_meta.json?generation=1665422792304395"],
+    )
+
     http_file(
         name = "com_google_mediapipe_bert_text_classifier_tflite",
         sha256 = "1e5a550c09bff0a13e61858bcfac7654d7fcc6d42106b4f15e11117695069600",
         urls = ["https://storage.googleapis.com/mediapipe-assets/bert_text_classifier.tflite?generation=1663009542017720"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_bounding_box_tensor_meta_json",
+        sha256 = "cc019cee86529955a24a3d43ca3d778fa366bcb90d67c8eaf55696789833841a",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/bounding_box_tensor_meta.json?generation=1665422797529909"],
+    )
+
     http_file(
         name = "com_google_mediapipe_BUILD",
         sha256 = "d2b2a8346202691d7f831887c84e9642e974f64ed67851d9a58cf15c94b1f6b3",
@@ -46,6 +58,12 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/burger.jpg?generation=1661875667922678"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_burger_rotated_jpg",
+        sha256 = "b7bb5e59ef778f3ce6b3e616c511908a53d513b83a56aae58b7453e14b0a4b2a",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/burger_rotated.jpg?generation=1665065843774448"],
+    )
+
     http_file(
         name = "com_google_mediapipe_cat_jpg",
         sha256 = "2533197401eebe9410ea4d063f86c43fbd2666f3e8165a38aca155c0d09c21be",
@@ -70,6 +88,24 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/cats_and_dogs_no_resizing.jpg?generation=1661875687251296"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_classification_tensor_float_meta_json",
+        sha256 = "1d10b1c9c87eabac330651136804074ddc134779e94a73cf783207c3aa2a5619",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/classification_tensor_float_meta.json?generation=1665422803073223"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_classification_tensor_uint8_meta_json",
+        sha256 = "74f4d64ee0017d11e0fdc975a88d974d73b72b889fd4d67992356052edde0f1e",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/classification_tensor_uint8_meta.json?generation=1665422808178685"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_classification_tensor_unsupported_meta_json",
+        sha256 = "4810ad8a00f0078c6a693114d00f692aa70ff2d61030a6e516db1e654707e208",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/classification_tensor_unsupported_meta.json?generation=1665422813312699"],
+    )
+
     http_file(
         name = "com_google_mediapipe_coco_efficientdet_lite0_v1_1_0_quant_2021_09_06_tflite",
         sha256 = "dee1b4af055a644804d5594442300ecc9e4f7080c25b7c044c98f527eeabb6cf",
@@ -166,6 +202,18 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/face_landmark_with_attention.tflite?generation=1661875751615925"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_feature_tensor_meta_json",
+        sha256 = "b2c30ddfd495956ce81085f8a143422f4310b002cfbf1c594ff2ee0576e29d6f",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/feature_tensor_meta.json?generation=1665422818797346"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_general_meta_json",
+        sha256 = "b95363e4bae89b9c2af484498312aaad4efc7ff57c7eadcc4e5e7adca641445f",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/general_meta.json?generation=1665422822603848"],
+    )
+
     http_file(
         name = "com_google_mediapipe_golden_json_json",
         sha256 = "55c0c88748d099aa379930504df62c6c8f1d8874ea52d2f8a925f352c4c7f09c",
@@ -208,6 +256,30 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/hand_recrop.tflite?generation=1661875770633070"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_image_tensor_meta_json",
+        sha256 = "aad86fde3defb379c82ff7ee48e50493a58529cdc0623cf0d7bf135c3577060e",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/image_tensor_meta.json?generation=1665422826106636"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_input_image_tensor_float_meta_json",
+        sha256 = "426ecf5c3ace61db3936b950c3709daece15827ea21905ddbcdc81b1c6e70232",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/input_image_tensor_float_meta.json?generation=1665422829230563"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_input_image_tensor_uint8_meta_json",
+        sha256 = "dc7ff86b606641e480c7d154b5f467e1f8c895f85733c73ba47a259a66ed187b",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/input_image_tensor_uint8_meta.json?generation=1665422832572887"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_input_image_tensor_unsupported_meta_json",
+        sha256 = "443d436c2068df8201b9822c35e724acfd8004a788d388e7d74c38a2425c55df",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/input_image_tensor_unsupported_meta.json?generation=1665422835757143"],
+    )
+
     http_file(
         name = "com_google_mediapipe_iris_and_gaze_tflite",
         sha256 = "b6dcb860a92a3c7264a8e50786f46cecb529672cdafc17d39c78931257da661d",
@@ -370,6 +442,12 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/multi_objects.jpg?generation=1663251779213308"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_multi_objects_rotated_jpg",
+        sha256 = "175f6c572ffbab6554e382fd5056d09720eef931ccc4ed79481bdc47a8443911",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/multi_objects_rotated.jpg?generation=1665065847969523"],
+    )
+
     http_file(
         name = "com_google_mediapipe_object_detection_3d_camera_tflite",
         sha256 = "f66e92e81ed3f4698f74d565a7668e016e2288ea92fb42938e33b778bd1e110d",
@@ -472,6 +550,24 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/right_hands.jpg?generation=1661875908672404"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_score_calibration_file_meta_json",
+        sha256 = "6a3c305620371f662419a496f75be5a10caebca7803b1e99d8d5d22ba51cda94",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/score_calibration_file_meta.json?generation=1665422841236117"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_score_calibration_tensor_meta_json",
+        sha256 = "24cbde7f76dd6a09a55d07f30493c2f254d61154eb2e8d18ed947ff56781186d",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/score_calibration_tensor_meta.json?generation=1665422844327992"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_score_calibration_txt",
+        sha256 = "34b0c51a8c79b4515bdd24e440c4b76a9f0fd01ef6385b36af983036e7be6271",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/score_calibration.txt?generation=1665422847392804"],
+    )
+
     http_file(
         name = "com_google_mediapipe_segmentation_golden_rotation0_png",
         sha256 = "9ee993919b753118928ba2d14f7c5c83a6cfc23355e6943dac4ad81eedd73069",