Support both proto2 and proto3 in task subgraph options configuration, and revised the Holistic Landmarker API's implementation

mediapipe/tasks/python/core/BUILD

@@ -49,5 +49,6 @@ py_library(
         "//mediapipe/calculators/core:flow_limiter_calculator_py_pb2",
         "//mediapipe/framework:calculator_options_py_pb2",
         "//mediapipe/framework:calculator_py_pb2",
+        "@com_google_protobuf//:protobuf_python"
     ],
 )

mediapipe/tasks/python/core/task_info.py

@@ -21,6 +21,7 @@ from mediapipe.calculators.core import flow_limiter_calculator_pb2
 from mediapipe.framework import calculator_options_pb2
 from mediapipe.framework import calculator_pb2
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from google.protobuf.any_pb2 import Any
 
 
 @doc_controls.do_not_generate_docs
@@ -80,22 +81,31 @@ class TaskInfo:
       raise ValueError(
           '`task_options` doesn`t provide `to_pb2()` method to convert itself to be a protobuf object.'
       )
-    task_subgraph_options = calculator_options_pb2.CalculatorOptions()
+
     task_options_proto = self.task_options.to_pb2()
 
-    # For protobuf 2 compat.
+    node_config = calculator_pb2.CalculatorGraphConfig.Node(
+      calculator=self.task_graph,
+      input_stream=self.input_streams,
+      output_stream=self.output_streams
+    )
+
     if hasattr(task_options_proto, 'ext'):
+      # Use the extension mechanism for task_subgraph_options (proto2)
+      task_subgraph_options = calculator_options_pb2.CalculatorOptions()
       task_subgraph_options.Extensions[task_options_proto.ext].CopyFrom(
           task_options_proto)
+      node_config.options.CopyFrom(task_subgraph_options)
+    else:
+      # Use the Any type for task_subgraph_options (proto3)
+      task_subgraph_options = Any()
+      task_subgraph_options.Pack(self.task_options.to_pb2())
+      node_config.node_options.append(task_subgraph_options)
 
     if not enable_flow_limiting:
       return calculator_pb2.CalculatorGraphConfig(
           node=[
-              calculator_pb2.CalculatorGraphConfig.Node(
+            node_config
-                  calculator=self.task_graph,
-                  input_stream=self.input_streams,
-                  output_stream=self.output_streams,
-                  options=task_subgraph_options)
           ],
           input_stream=self.input_streams,
           output_stream=self.output_streams)
@@ -125,11 +135,7 @@ class TaskInfo:
         options=flow_limiter_options)
     config = calculator_pb2.CalculatorGraphConfig(
         node=[
-            calculator_pb2.CalculatorGraphConfig.Node(
+          node_config, flow_limiter
-                calculator=self.task_graph,
-                input_stream=task_subgraph_inputs,
-                output_stream=self.output_streams,
-                options=task_subgraph_options), flow_limiter
         ],
         input_stream=self.input_streams,
         output_stream=self.output_streams)

mediapipe/tasks/python/test/vision/BUILD

@@ -206,6 +206,7 @@ py_test(
     deps = [
         "//mediapipe/framework/formats:classification_py_pb2",
         "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/tasks/cc/vision/holistic_landmarker/proto:holistic_result_py_pb2",
         "//mediapipe/python:_framework_bindings",
         "//mediapipe/tasks/python/components/containers:category",
         "//mediapipe/tasks/python/components/containers:landmark",

mediapipe/tasks/python/test/vision/holistic_landmarker_test.py

@@ -14,6 +14,7 @@
 """Tests for holistic landmarker."""
 
 import enum
+from typing import List
 from unittest import mock
 
 from absl.testing import absltest
@@ -23,6 +24,7 @@ import numpy as np
 from google.protobuf import text_format
 from mediapipe.framework.formats import classification_pb2
 from mediapipe.framework.formats import landmark_pb2
+from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_result_pb2
 from mediapipe.python._framework_bindings import image as image_module
 from mediapipe.tasks.python.components.containers import category as category_module
 from mediapipe.tasks.python.components.containers import landmark as landmark_module
@@ -35,6 +37,7 @@ from mediapipe.tasks.python.vision.core import vision_task_running_mode as runni
 
 
 HolisticLandmarkerResult = holistic_landmarker.HolisticLandmarkerResult
+_HolisticResultProto = holistic_result_pb2.HolisticResult
 _BaseOptions = base_options_module.BaseOptions
 _Category = category_module.Category
 _Rect = rect_module.Rect
@@ -46,14 +49,31 @@ _HolisticLandmarkerOptions = holistic_landmarker.HolisticLandmarkerOptions
 _RUNNING_MODE = running_mode_module.VisionTaskRunningMode
 _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 
-_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE = 'face_landmarker.task'
+_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE = 'holistic_landmarker.task'
 _POSE_IMAGE = 'male_full_height_hands.jpg'
 _CAT_IMAGE = 'cat.jpg'
-_HOLISTIC_RESULT = "male_full_height_hands_result_cpu.pbtxt"
+_EXPECTED_HOLISTIC_RESULT = "male_full_height_hands_result_cpu.pbtxt"
 _LANDMARKS_MARGIN = 0.03
 _BLENDSHAPES_MARGIN = 0.13
 
 
+def _get_expected_holistic_landmarker_result(
+    file_path: str,
+) -> HolisticLandmarkerResult:
+  holistic_result_file_path = test_utils.get_test_data_path(
+    file_path
+  )
+  with open(holistic_result_file_path, 'rb') as f:
+    holistic_result_proto = _HolisticResultProto()
+    # Use this if a .pb file is available.
+    # holistic_result_proto.ParseFromString(f.read())
+    text_format.Parse(f.read(), holistic_result_proto)
+    holistic_landmarker_result = HolisticLandmarkerResult.create_from_pb2(
+      holistic_result_proto
+    )
+  return holistic_landmarker_result
+
+
 class ModelFileType(enum.Enum):
   FILE_CONTENT = 1
   FILE_NAME = 2
@@ -70,20 +90,77 @@ class HolisticLandmarkerTest(parameterized.TestCase):
         _HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
     )
 
+  def _expect_landmarks_correct(
+      self, actual_landmarks, expected_landmarks, margin
+  ):
+    # Expects to have the same number of poses detected.
+    self.assertLen(actual_landmarks, len(expected_landmarks))
+
+    for i, elem in enumerate(actual_landmarks):
+      self.assertAlmostEqual(elem.x, expected_landmarks[i].x, delta=margin)
+      self.assertAlmostEqual(elem.y, expected_landmarks[i].y, delta=margin)
+
+  def _expect_blendshapes_correct(
+      self, actual_blendshapes, expected_blendshapes, margin
+  ):
+    # Expects to have the same number of blendshapes.
+    self.assertLen(actual_blendshapes, len(expected_blendshapes))
+
+    for i, elem in enumerate(actual_blendshapes):
+      self.assertEqual(elem.index, expected_blendshapes[i].index)
+      self.assertAlmostEqual(
+        elem.score,
+        expected_blendshapes[i].score,
+        delta=margin,
+      )
+
+  def _expect_holistic_landmarker_results_correct(
+      self,
+      actual_result: HolisticLandmarkerResult,
+      expected_result: HolisticLandmarkerResult,
+      output_segmentation_masks: bool,
+      landmarks_margin: float,
+      blendshapes_margin: float,
+  ):
+    self._expect_landmarks_correct(
+      actual_result.pose_landmarks, expected_result.pose_landmarks,
+      landmarks_margin
+    )
+    self._expect_landmarks_correct(
+      actual_result.face_landmarks, expected_result.face_landmarks,
+      landmarks_margin
+    )
+    self._expect_blendshapes_correct(
+      actual_result.face_blendshapes, expected_result.face_blendshapes,
+      blendshapes_margin
+    )
+    if output_segmentation_masks:
+      self.assertIsInstance(actual_result.segmentation_masks, List)
+      for _, mask in enumerate(actual_result.segmentation_masks):
+        self.assertIsInstance(mask, _Image)
+    else:
+      self.assertIsNone(actual_result.segmentation_masks)
+
   @parameterized.parameters(
       (
           ModelFileType.FILE_NAME,
-          _HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
+          _HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
+          False,
+          _get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT)
       ),
       (
           ModelFileType.FILE_CONTENT,
-          _HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
+          _HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
+          False,
+          _get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT)
       ),
   )
   def test_detect(
       self,
       model_file_type,
-      model_name
+      model_name,
+      output_segmentation_masks,
+      expected_holistic_landmarker_result: HolisticLandmarkerResult
   ):
     # Creates holistic landmarker.
     model_path = test_utils.get_test_data_path(model_name)
@@ -98,15 +175,21 @@ class HolisticLandmarkerTest(parameterized.TestCase):
       raise ValueError('model_file_type is invalid.')
 
     options = _HolisticLandmarkerOptions(
-        base_options=base_options
+        base_options=base_options,
+        output_face_blendshapes=True
+        if expected_holistic_landmarker_result.face_blendshapes else False,
+        output_segmentation_masks=output_segmentation_masks,
     )
     landmarker = _HolisticLandmarker.create_from_options(options)
 
     # Performs holistic landmarks detection on the input.
     detection_result = landmarker.detect(self.test_image)
-
+    self._expect_holistic_landmarker_results_correct(
-    # Closes the holistic landmarker explicitly when the holistic landmarker is not used
+        detection_result, expected_holistic_landmarker_result,
-    # in a context.
+        output_segmentation_masks, _LANDMARKS_MARGIN, _BLENDSHAPES_MARGIN
+    )
+    # Closes the holistic landmarker explicitly when the holistic landmarker is
+    # not used in a context.
     landmarker.close()
 
 

mediapipe/tasks/python/vision/BUILD

@@ -254,6 +254,7 @@ py_library(
         "//mediapipe/python:_framework_bindings",
         "//mediapipe/python:packet_creator",
         "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/vision/holistic_landmarker/proto:holistic_result_py_pb2",
         "//mediapipe/tasks/cc/vision/holistic_landmarker/proto:holistic_landmarker_graph_options_py_pb2",
         "//mediapipe/tasks/python/components/containers:category",
         "//mediapipe/tasks/python/components/containers:landmark",

mediapipe/tasks/python/vision/holistic_landmarker.py

@@ -22,6 +22,7 @@ from mediapipe.python import packet_creator
 from mediapipe.python import packet_getter
 from mediapipe.python._framework_bindings import image as image_module
 from mediapipe.python._framework_bindings import packet as packet_module
+from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_result_pb2
 from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_landmarker_graph_options_pb2
 from mediapipe.tasks.python.components.containers import category as category_module
 from mediapipe.tasks.python.components.containers import landmark as landmark_module
@@ -33,6 +34,7 @@ from mediapipe.tasks.python.vision.core import image_processing_options as image
 from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
 
 _BaseOptions = base_options_module.BaseOptions
+_HolisticResultProto = holistic_result_pb2.HolisticResult
 _HolisticLandmarkerGraphOptionsProto = (
     holistic_landmarker_graph_options_pb2.HolisticLandmarkerGraphOptions
 )
@@ -43,9 +45,6 @@ _TaskInfo = task_info_module.TaskInfo
 _IMAGE_IN_STREAM_NAME = 'image_in'
 _IMAGE_OUT_STREAM_NAME = 'image_out'
 _IMAGE_TAG = 'IMAGE'
-_NORM_RECT_STREAM_NAME = 'norm_rect_in'
-_NORM_RECT_TAG = 'NORM_RECT'
-
 
 _POSE_LANDMARKS_STREAM_NAME = "pose_landmarks"
 _POSE_LANDMARKS_TAG_NAME = "POSE_LANDMARKS"
@@ -77,16 +76,64 @@ class HolisticLandmarkerResult:
   Attributes:
     TODO
   """
-  face_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  face_landmarks: List[landmark_module.NormalizedLandmark]
-  pose_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  pose_landmarks: List[landmark_module.NormalizedLandmark]
-  pose_world_landmarks: List[List[landmark_module.Landmark]]
+  pose_world_landmarks:List[landmark_module.Landmark]
-  left_hand_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  left_hand_landmarks: List[landmark_module.NormalizedLandmark]
-  left_hand_world_landmarks: List[List[landmark_module.Landmark]]
+  left_hand_world_landmarks: List[landmark_module.Landmark]
-  right_hand_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  right_hand_landmarks: List[landmark_module.NormalizedLandmark]
-  right_hand_world_landmarks: List[List[landmark_module.Landmark]]
+  right_hand_world_landmarks: List[landmark_module.Landmark]
-  face_blendshapes: Optional[List[List[category_module.Category]]] = None
+  face_blendshapes: Optional[List[category_module.Category]] = None
   segmentation_masks: Optional[List[image_module.Image]] = None
 
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls,
+      pb2_obj: _HolisticResultProto
+  ) -> 'HolisticLandmarkerResult':
+    """Creates a `HolisticLandmarkerResult` object from the given protobuf
+    object."""
+    return HolisticLandmarkerResult(
+      face_landmarks=[
+        landmark_module.NormalizedLandmark.create_from_pb2(landmark)
+        for landmark in pb2_obj.face_landmarks.landmark
+      ] if hasattr(pb2_obj, 'face_landmarks') else None,
+      pose_landmarks=[
+        landmark_module.NormalizedLandmark.create_from_pb2(landmark)
+        for landmark in pb2_obj.pose_landmarks.landmark
+      ] if hasattr(pb2_obj, 'pose_landmarks') else None,
+      pose_world_landmarks=[
+        landmark_module.Landmark.create_from_pb2(landmark)
+        for landmark in pb2_obj.pose_world_landmarks.landmark
+      ] if hasattr(pb2_obj, 'pose_world_landmarks') else None,
+      left_hand_landmarks=[
+        landmark_module.NormalizedLandmark.create_from_pb2(landmark)
+        for landmark in pb2_obj.left_hand_landmarks.landmark
+      ] if hasattr(pb2_obj, 'left_hand_landmarks') else None,
+      left_hand_world_landmarks=[
+        landmark_module.Landmark.create_from_pb2(landmark)
+        for landmark in pb2_obj.left_hand_world_landmarks.landmark
+      ] if hasattr(pb2_obj, 'left_hand_world_landmarks') else None,
+      right_hand_landmarks=[
+        landmark_module.NormalizedLandmark.create_from_pb2(landmark)
+        for landmark in pb2_obj.right_hand_landmarks.landmark
+      ] if hasattr(pb2_obj, 'right_hand_landmarks') else None,
+      right_hand_world_landmarks=[
+        landmark_module.Landmark.create_from_pb2(landmark)
+        for landmark in pb2_obj.right_hand_world_landmarks.landmark
+      ] if hasattr(pb2_obj, 'right_hand_world_landmarks') else None,
+      face_blendshapes=[
+        category_module.Category(
+          score=classification.score,
+          index=classification.index,
+          category_name=classification.label,
+          display_name=classification.display_name
+        )
+        for classification in pb2_obj.face_blendshapes.classification
+      ] if hasattr(pb2_obj, 'face_blendshapes') else None,
+    )
+
 
 def _build_landmarker_result(
     output_packets: Mapping[str, packet_module.Packet]
@@ -95,61 +142,92 @@ def _build_landmarker_result(
   holistic_landmarker_result = HolisticLandmarkerResult([], [], [], [], [], [],
                                                         [])
 
-  face_landmarks_proto_list = packet_getter.get_proto_list(
+  face_landmarks_proto_list = packet_getter.get_proto(
       output_packets[_FACE_LANDMARKS_STREAM_NAME]
     )
 
-  if _POSE_SEGMENTATION_MASK_STREAM_NAME in output_packets:
+  pose_landmarks_proto_list = packet_getter.get_proto(
-    holistic_landmarker_result.segmentation_masks = packet_getter.get_image_list(
-      output_packets[_POSE_SEGMENTATION_MASK_STREAM_NAME]
-    )
-
-  pose_landmarks_proto_list = packet_getter.get_proto_list(
     output_packets[_POSE_LANDMARKS_STREAM_NAME]
   )
 
-  pose_world_landmarks_proto_list = packet_getter.get_proto_list(
+  pose_world_landmarks_proto_list = packet_getter.get_proto(
     output_packets[_POSE_WORLD_LANDMARKS_STREAM_NAME]
   )
 
-  left_hand_landmarks_proto_list = packet_getter.get_proto_list(
+  left_hand_landmarks_proto_list = packet_getter.get_proto(
     output_packets[_LEFT_HAND_LANDMARKS_STREAM_NAME]
   )
 
-  left_hand_world_landmarks_proto_list = packet_getter.get_proto_list(
+  left_hand_world_landmarks_proto_list = packet_getter.get_proto(
     output_packets[_LEFT_HAND_WORLD_LANDMARKS_STREAM_NAME]
   )
 
-  right_hand_landmarks_proto_list = packet_getter.get_proto_list(
+  right_hand_landmarks_proto_list = packet_getter.get_proto(
     output_packets[_RIGHT_HAND_LANDMARKS_STREAM_NAME]
   )
 
-  right_hand_world_landmarks_proto_list = packet_getter.get_proto_list(
+  right_hand_world_landmarks_proto_list = packet_getter.get_proto(
     output_packets[_RIGHT_HAND_WORLD_LANDMARKS_STREAM_NAME]
   )
 
-  face_landmarks_results = []
-  for proto in face_landmarks_proto_list:
   face_landmarks = landmark_pb2.NormalizedLandmarkList()
-    face_landmarks.MergeFrom(proto)
+  face_landmarks.MergeFrom(face_landmarks_proto_list)
-    face_landmarks_list = []
   for face_landmark in face_landmarks.landmark:
-      face_landmarks_list.append(
+    holistic_landmarker_result.face_landmarks.append(
       landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
     )
-    face_landmarks_results.append(face_landmarks_list)
 
-  face_blendshapes_results = []
+  pose_landmarks = landmark_pb2.NormalizedLandmarkList()
+  pose_landmarks.MergeFrom(pose_landmarks_proto_list)
+  for pose_landmark in pose_landmarks.landmark:
+    holistic_landmarker_result.pose_landmarks.append(
+      landmark_module.NormalizedLandmark.create_from_pb2(pose_landmark)
+    )
+
+  pose_world_landmarks = landmark_pb2.LandmarkList()
+  pose_world_landmarks.MergeFrom(pose_world_landmarks_proto_list)
+  for pose_world_landmark in pose_world_landmarks.landmark:
+    holistic_landmarker_result.pose_world_landmarks.append(
+      landmark_module.Landmark.create_from_pb2(pose_world_landmark)
+    )
+
+  left_hand_landmarks = landmark_pb2.NormalizedLandmarkList()
+  left_hand_landmarks.MergeFrom(left_hand_landmarks_proto_list)
+  for hand_landmark in left_hand_landmarks.landmark:
+    holistic_landmarker_result.left_hand_landmarks.append(
+      landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
+    )
+
+  left_hand_world_landmarks = landmark_pb2.LandmarkList()
+  left_hand_world_landmarks.MergeFrom(left_hand_world_landmarks_proto_list)
+  for left_hand_world_landmark in left_hand_world_landmarks.landmark:
+    holistic_landmarker_result.left_hand_world_landmarks.append(
+      landmark_module.Landmark.create_from_pb2(left_hand_world_landmark)
+    )
+
+  right_hand_landmarks = landmark_pb2.NormalizedLandmarkList()
+  right_hand_landmarks.MergeFrom(right_hand_landmarks_proto_list)
+  for hand_landmark in right_hand_landmarks.landmark:
+    holistic_landmarker_result.right_hand_landmarks.append(
+      landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
+    )
+
+  right_hand_world_landmarks = landmark_pb2.LandmarkList()
+  right_hand_world_landmarks.MergeFrom(right_hand_world_landmarks_proto_list)
+  for right_hand_world_landmark in right_hand_world_landmarks.landmark:
+    holistic_landmarker_result.right_hand_world_landmarks.append(
+      landmark_module.Landmark.create_from_pb2(right_hand_world_landmark)
+    )
+
   if _FACE_BLENDSHAPES_STREAM_NAME in output_packets:
-    face_blendshapes_proto_list = packet_getter.get_proto_list(
+    face_blendshapes_proto_list = packet_getter.get_proto(
       output_packets[_FACE_BLENDSHAPES_STREAM_NAME]
     )
-    for proto in face_blendshapes_proto_list:
-      face_blendshapes_categories = []
     face_blendshapes_classifications = classification_pb2.ClassificationList()
-      face_blendshapes_classifications.MergeFrom(proto)
+    face_blendshapes_classifications.MergeFrom(face_blendshapes_proto_list)
+    holistic_landmarker_result.face_blendshapes = []
     for face_blendshapes in face_blendshapes_classifications.classification:
-        face_blendshapes_categories.append(
+      holistic_landmarker_result.face_blendshapes.append(
         category_module.Category(
           index=face_blendshapes.index,
           score=face_blendshapes.score,
@@ -157,76 +235,10 @@ def _build_landmarker_result(
           category_name=face_blendshapes.label,
         )
       )
-      face_blendshapes_results.append(face_blendshapes_categories)
 
-  for proto in pose_landmarks_proto_list:
+  if _POSE_SEGMENTATION_MASK_STREAM_NAME in output_packets:
-    pose_landmarks = landmark_pb2.NormalizedLandmarkList()
+    holistic_landmarker_result.segmentation_masks = packet_getter.get_image_list(
-    pose_landmarks.MergeFrom(proto)
+      output_packets[_POSE_SEGMENTATION_MASK_STREAM_NAME]
-    pose_landmarks_list = []
-    for pose_landmark in pose_landmarks.landmark:
-      pose_landmarks_list.append(
-        landmark_module.NormalizedLandmark.create_from_pb2(pose_landmark)
-      )
-    holistic_landmarker_result.pose_landmarks.append(pose_landmarks_list)
-
-  for proto in pose_world_landmarks_proto_list:
-    pose_world_landmarks = landmark_pb2.LandmarkList()
-    pose_world_landmarks.MergeFrom(proto)
-    pose_world_landmarks_list = []
-    for pose_world_landmark in pose_world_landmarks.landmark:
-      pose_world_landmarks_list.append(
-        landmark_module.Landmark.create_from_pb2(pose_world_landmark)
-      )
-    holistic_landmarker_result.pose_world_landmarks.append(
-      pose_world_landmarks_list
-    )
-
-  for proto in left_hand_landmarks_proto_list:
-    left_hand_landmarks = landmark_pb2.NormalizedLandmarkList()
-    left_hand_landmarks.MergeFrom(proto)
-    left_hand_landmarks_list = []
-    for hand_landmark in left_hand_landmarks.landmark:
-      left_hand_landmarks_list.append(
-        landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
-      )
-    holistic_landmarker_result.left_hand_landmarks.append(
-      left_hand_landmarks_list
-    )
-
-  for proto in left_hand_world_landmarks_proto_list:
-    left_hand_world_landmarks = landmark_pb2.LandmarkList()
-    left_hand_world_landmarks.MergeFrom(proto)
-    left_hand_world_landmarks_list = []
-    for left_hand_world_landmark in left_hand_world_landmarks.landmark:
-      left_hand_world_landmarks_list.append(
-        landmark_module.Landmark.create_from_pb2(left_hand_world_landmark)
-      )
-    holistic_landmarker_result.left_hand_world_landmarks.append(
-      left_hand_world_landmarks_list
-    )
-
-  for proto in right_hand_landmarks_proto_list:
-    right_hand_landmarks = landmark_pb2.NormalizedLandmarkList()
-    right_hand_landmarks.MergeFrom(proto)
-    right_hand_landmarks_list = []
-    for hand_landmark in right_hand_landmarks.landmark:
-      right_hand_landmarks_list.append(
-        landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
-      )
-    holistic_landmarker_result.right_hand_landmarks.append(
-      right_hand_landmarks_list
-    )
-
-  for proto in right_hand_world_landmarks_proto_list:
-    right_hand_world_landmarks = landmark_pb2.LandmarkList()
-    right_hand_world_landmarks.MergeFrom(proto)
-    right_hand_world_landmarks_list = []
-    for right_hand_world_landmark in right_hand_world_landmarks.landmark:
-      right_hand_world_landmarks_list.append(
-        landmark_module.Landmark.create_from_pb2(right_hand_world_landmark)
-      )
-    holistic_landmarker_result.right_hand_world_landmarks.append(
-      right_hand_world_landmarks_list
     )
 
   return holistic_landmarker_result
@@ -259,6 +271,9 @@ class HolisticLandmarkerOptions:
       landmark detection to be considered successful.
     min_hand_landmarks_confidence: The minimum confidence score for the hand
       landmark detection to be considered successful.
+    output_face_blendshapes: Whether FaceLandmarker outputs face blendshapes
+      classification. Face blendshapes are used for rendering the 3D face model.
+    output_segmentation_masks: whether to output segmentation masks.
     result_callback: The user-defined result callback for processing live stream
       data. The result callback should only be specified when the running mode
       is set to the live stream mode.
@@ -419,7 +434,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
         task_graph=_TASK_GRAPH_NAME,
         input_streams=[
             ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
-            ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
         ],
         output_streams=output_streams,
         task_options=options,
@@ -436,7 +450,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
   def detect(
       self,
       image: image_module.Image,
-      image_processing_options: Optional[_ImageProcessingOptions] = None,
   ) -> HolisticLandmarkerResult:
     """Performs holistic landmarks detection on the given image.
 
@@ -449,7 +462,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
 
     Args:
       image: MediaPipe Image.
-      image_processing_options: Options for image processing.
 
     Returns:
       The holistic landmarks detection results.
@@ -458,14 +470,8 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
       ValueError: If any of the input arguments is invalid.
       RuntimeError: If holistic landmarker detection failed to run.
     """
-    normalized_rect = self.convert_to_normalized_rect(
-        image_processing_options, image, roi_allowed=False
-    )
     output_packets = self._process_image_data({
         _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
-        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
-            normalized_rect.to_pb2()
-        ),
     })
 
     if output_packets[_FACE_LANDMARKS_STREAM_NAME].is_empty():
@@ -477,7 +483,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
       self,
       image: image_module.Image,
       timestamp_ms: int,
-      image_processing_options: Optional[_ImageProcessingOptions] = None,
   ) -> HolisticLandmarkerResult:
     """Performs holistic landmarks detection on the provided video frame.
 
@@ -492,7 +497,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
     Args:
       image: MediaPipe Image.
       timestamp_ms: The timestamp of the input video frame in milliseconds.
-      image_processing_options: Options for image processing.
 
     Returns:
       The holistic landmarks detection results.
@@ -501,16 +505,10 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
       ValueError: If any of the input arguments is invalid.
       RuntimeError: If holistic landmarker detection failed to run.
     """
-    normalized_rect = self.convert_to_normalized_rect(
-        image_processing_options, image, roi_allowed=False
-    )
     output_packets = self._process_video_data({
         _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
             timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
         ),
-        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
-            normalized_rect.to_pb2()
-        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
     })
 
     if output_packets[_FACE_LANDMARKS_STREAM_NAME].is_empty():
@@ -522,7 +520,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
       self,
       image: image_module.Image,
       timestamp_ms: int,
-      image_processing_options: Optional[_ImageProcessingOptions] = None,
   ) -> None:
     """Sends live image data to perform holistic landmarks detection.
 
@@ -548,20 +545,13 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
     Args:
       image: MediaPipe Image.
       timestamp_ms: The timestamp of the input image in milliseconds.
-      image_processing_options: Options for image processing.
 
     Raises:
       ValueError: If the current input timestamp is smaller than what the
       holistic landmarker has already processed.
     """
-    normalized_rect = self.convert_to_normalized_rect(
-        image_processing_options, image, roi_allowed=False
-    )
     self._send_live_stream_data({
         _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
             timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
         ),
-        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
-            normalized_rect.to_pb2()
-        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
     })