Added the PoseLandmarker Python API and a simple test

mediapipe/python/BUILD

@@ -92,6 +92,7 @@ cc_library(
         "//mediapipe/tasks/cc/audio/audio_embedder:audio_embedder_graph",
         "//mediapipe/tasks/cc/vision/face_detector:face_detector_graph",
         "//mediapipe/tasks/cc/vision/face_landmarker:face_landmarker_graph",
+        "//mediapipe/tasks/cc/vision/pose_landmarker:pose_landmarker_graph",
         "//mediapipe/tasks/cc/vision/face_stylizer:face_stylizer_graph",
         "//mediapipe/tasks/cc/vision/gesture_recognizer:gesture_recognizer_graph",
         "//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",

mediapipe/tasks/python/test/vision/BUILD

@@ -162,3 +162,26 @@ py_test(
         "@com_google_protobuf//:protobuf_python",
     ],
 )
+
+py_test(
+    name = "pose_landmarker_test",
+    srcs = ["pose_landmarker_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/vision:test_images",
+        "//mediapipe/tasks/testdata/vision:test_models",
+        "//mediapipe/tasks/testdata/vision:test_protos",
+    ],
+    deps = [
+        "//mediapipe/tasks/cc/components/containers/proto:landmarks_detection_result_py_pb2",
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/tasks/python/components/containers:landmark",
+        "//mediapipe/tasks/python/components/containers:landmark_detection_result",
+        "//mediapipe/tasks/python/components/containers:rect",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/vision:pose_landmarker",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+        "@com_google_protobuf//:protobuf_python",
+    ],
+)

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

@@ -0,0 +1,177 @@
+# 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 pose landmarker."""
+
+import enum
+from unittest import mock
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import numpy as np
+
+from google.protobuf import text_format
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.tasks.cc.components.containers.proto import landmarks_detection_result_pb2
+from mediapipe.tasks.python.components.containers import landmark as landmark_module
+from mediapipe.tasks.python.components.containers import landmark_detection_result as landmark_detection_result_module
+from mediapipe.tasks.python.components.containers import rect as rect_module
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.test import test_utils
+from mediapipe.tasks.python.vision import pose_landmarker
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+PoseLandmarkerResult = pose_landmarker.PoseLandmarkerResult
+_LandmarksDetectionResultProto = landmarks_detection_result_pb2.LandmarksDetectionResult
+_BaseOptions = base_options_module.BaseOptions
+_Rect = rect_module.Rect
+_Landmark = landmark_module.Landmark
+_NormalizedLandmark = landmark_module.NormalizedLandmark
+_LandmarksDetectionResult = landmark_detection_result_module.LandmarksDetectionResult
+_Image = image_module.Image
+_PoseLandmarker = pose_landmarker.PoseLandmarker
+_PoseLandmarkerOptions = pose_landmarker.PoseLandmarkerOptions
+_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+
+_POSE_LANDMARKER_BUNDLE_ASSET_FILE = 'pose_landmarker.task'
+_BURGER_IMAGE = 'burger.jpg'
+_POSE_IMAGE = 'pose.jpg'
+_POSE_LANDMARKS = 'pose_landmarks.pbtxt'
+_LANDMARKS_ERROR_TOLERANCE = 0.03
+_LANDMARKS_ON_VIDEO_ERROR_TOLERANCE = 0.03
+
+
+def _get_expected_pose_landmarker_result(
+    file_path: str) -> PoseLandmarkerResult:
+  landmarks_detection_result_file_path = test_utils.get_test_data_path(
+    file_path)
+  with open(landmarks_detection_result_file_path, 'rb') as f:
+    landmarks_detection_result_proto = _LandmarksDetectionResultProto()
+    # Use this if a .pb file is available.
+    # landmarks_detection_result_proto.ParseFromString(f.read())
+    text_format.Parse(f.read(), landmarks_detection_result_proto)
+    landmarks_detection_result = _LandmarksDetectionResult.create_from_pb2(
+      landmarks_detection_result_proto)
+  return PoseLandmarkerResult(
+      pose_landmarks=[landmarks_detection_result.landmarks],
+      pose_world_landmarks=[],
+      pose_auxiliary_landmarks=[]
+  )
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class PoseLandmarkerTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self.test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(_POSE_IMAGE))
+    self.model_path = test_utils.get_test_data_path(
+        _POSE_LANDMARKER_BUNDLE_ASSET_FILE)
+
+  def _expect_pose_landmarker_results_correct(
+      self, 
+      actual_result: PoseLandmarkerResult,
+      expected_result: PoseLandmarkerResult,
+      error_tolerance: float
+  ):
+    # Expects to have the same number of poses detected.
+    self.assertLen(actual_result.pose_landmarks,
+                   len(expected_result.pose_landmarks))
+    self.assertLen(actual_result.pose_world_landmarks,
+                   len(expected_result.pose_world_landmarks))
+    self.assertLen(actual_result.pose_auxiliary_landmarks,
+                   len(expected_result.pose_auxiliary_landmarks))
+    # Actual landmarks match expected landmarks.
+    actual_landmarks = actual_result.pose_landmarks[0]
+    expected_landmarks = expected_result.pose_landmarks[0]
+    for i, pose_landmark in enumerate(actual_landmarks):
+      self.assertAlmostEqual(
+          pose_landmark.x,
+          expected_landmarks[i].x,
+          delta=error_tolerance
+      )
+      self.assertAlmostEqual(
+          pose_landmark.y,
+          expected_landmarks[i].y,
+          delta=error_tolerance
+      )
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _PoseLandmarker.create_from_model_path(self.model_path) as landmarker:
+      self.assertIsInstance(landmarker, _PoseLandmarker)
+
+  def test_create_from_options_succeeds_with_valid_model_path(self):
+    # Creates with options containing model file successfully.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _PoseLandmarkerOptions(base_options=base_options)
+    with _PoseLandmarker.create_from_options(options) as landmarker:
+      self.assertIsInstance(landmarker, _PoseLandmarker)
+
+  def test_create_from_options_fails_with_invalid_model_path(self):
+    # Invalid empty model path.
+    with self.assertRaisesRegex(
+        RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
+      base_options = _BaseOptions(
+          model_asset_path='/path/to/invalid/model.tflite')
+      options = _PoseLandmarkerOptions(base_options=base_options)
+      _PoseLandmarker.create_from_options(options)
+
+  def test_create_from_options_succeeds_with_valid_model_content(self):
+    # Creates with options containing model content successfully.
+    with open(self.model_path, 'rb') as f:
+      base_options = _BaseOptions(model_asset_buffer=f.read())
+      options = _PoseLandmarkerOptions(base_options=base_options)
+      landmarker = _PoseLandmarker.create_from_options(options)
+      self.assertIsInstance(landmarker, _PoseLandmarker)
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME,
+       _get_expected_pose_landmarker_result(_POSE_LANDMARKS)),
+      (ModelFileType.FILE_CONTENT,
+       _get_expected_pose_landmarker_result(_POSE_LANDMARKS)))
+  def test_detect(self, model_file_type, expected_detection_result):
+    # Creates pose landmarker.
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=self.model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(self.model_path, 'rb') as f:
+        model_content = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_content)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    options = _PoseLandmarkerOptions(base_options=base_options)
+    landmarker = _PoseLandmarker.create_from_options(options)
+
+    # Performs pose landmarks detection on the input.
+    detection_result = landmarker.detect(self.test_image)
+    # Comparing results.
+    self._expect_pose_landmarker_results_correct(
+        detection_result, expected_detection_result, _LANDMARKS_ERROR_TOLERANCE
+    )
+    # Closes the pose landmarker explicitly when the pose landmarker is not used
+    # in a context.
+    landmarker.close()
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/tasks/python/vision/BUILD

@@ -179,6 +179,27 @@ py_library(
     ],
 )
 
+py_library(
+    name = "pose_landmarker",
+    srcs = [
+        "pose_landmarker.py",
+    ],
+    deps = [
+        "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/vision/pose_landmarker/proto:pose_landmarker_graph_options_py_pb2",
+        "//mediapipe/tasks/python/components/containers:landmark",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+        "//mediapipe/tasks/python/core:task_info",
+        "//mediapipe/tasks/python/vision/core:base_vision_task_api",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)
+
 py_library(
     name = "face_detector",
     srcs = [

mediapipe/tasks/python/vision/pose_landmarker.py

@@ -0,0 +1,428 @@
+# 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 pose landmarker task."""
+
+import dataclasses
+from typing import Callable, Mapping, Optional, List
+
+from mediapipe.framework.formats import landmark_pb2
+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.pose_landmarker.proto import pose_landmarker_graph_options_pb2
+from mediapipe.tasks.python.components.containers import landmark as landmark_module
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_BaseOptions = base_options_module.BaseOptions
+_PoseLandmarkerGraphOptionsProto = (
+    pose_landmarker_graph_options_pb2.PoseLandmarkerGraphOptions
+)
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+_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'
+_SEGMENTATION_MASK_STREAM_NAME = 'segmentation_mask'
+_SEGMENTATION_MASK_TAG = 'SEGMENTATION_MASK'
+_NORM_LANDMARKS_STREAM_NAME = 'norm_landmarks'
+_NORM_LANDMARKS_TAG = 'NORM_LANDMARKS'
+_POSE_WORLD_LANDMARKS_STREAM_NAME = 'world_landmarks'
+_POSE_WORLD_LANDMARKS_TAG = 'WORLD_LANDMARKS'
+_POSE_AUXILIARY_LANDMARKS_STREAM_NAME = 'auxiliary_landmarks'
+_POSE_AUXILIARY_LANDMARKS_TAG = 'AUXILIARY_LANDMARKS'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.pose_landmarker.PoseLandmarkerGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+@dataclasses.dataclass
+class PoseLandmarkerResult:
+  """The pose landmarks detection result from PoseLandmarker, where each vector element represents a single pose detected in the image.
+
+  Attributes:
+    pose_landmarks: Detected pose landmarks in normalized image coordinates.
+    pose_world_landmarks:  Detected pose landmarks in world coordinates.
+    pose_auxiliary_landmarks: Detected auxiliary landmarks, used for deriving
+      ROI for next frame.
+    segmentation_masks: Segmentation masks for pose.
+  """
+
+  pose_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  pose_world_landmarks: List[List[landmark_module.Landmark]]
+  pose_auxiliary_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  segmentation_masks: Optional[List[image_module.Image]] = None
+
+
+def _build_landmarker_result(
+    output_packets: Mapping[str, packet_module.Packet]
+) -> PoseLandmarkerResult:
+  """Constructs a `PoseLandmarkerResult` from output packets."""
+  pose_landmarker_result = PoseLandmarkerResult([], [], [], [])
+
+  if _SEGMENTATION_MASK_STREAM_NAME in output_packets:
+    pose_landmarker_result.segmentation_masks = packet_getter.get_image_list(
+      output_packets[_SEGMENTATION_MASK_STREAM_NAME]
+    )
+
+  pose_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_NORM_LANDMARKS_STREAM_NAME]
+  )
+  pose_world_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_POSE_WORLD_LANDMARKS_STREAM_NAME]
+  )
+  pose_auxiliary_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_POSE_AUXILIARY_LANDMARKS_STREAM_NAME]
+  )
+
+  for proto in pose_landmarks_proto_list:
+    pose_landmarks = landmark_pb2.NormalizedLandmarkList()
+    pose_landmarks.MergeFrom(proto)
+    pose_landmarks_list = []
+    for pose_landmark in pose_landmarks.landmark:
+      pose_landmarks_list.append(
+          landmark_module.NormalizedLandmark.create_from_pb2(pose_landmark)
+      )
+    pose_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)
+      )
+    pose_landmarker_result.pose_world_landmarks.append(
+        pose_world_landmarks_list
+    )
+
+  for proto in pose_auxiliary_landmarks_proto_list:
+    pose_auxiliary_landmarks = landmark_pb2.NormalizedLandmarkList()
+    pose_auxiliary_landmarks.MergeFrom(proto)
+    pose_auxiliary_landmarks_list = []
+    for pose_auxiliary_landmark in pose_auxiliary_landmarks.landmark:
+      pose_auxiliary_landmarks_list.append(
+        landmark_module.NormalizedLandmark.create_from_pb2(pose_auxiliary_landmark)
+      )
+    pose_landmarker_result.pose_auxiliary_landmarks.append(
+        pose_auxiliary_landmarks_list
+    )
+  return pose_landmarker_result
+
+
+@dataclasses.dataclass
+class PoseLandmarkerOptions:
+  """Options for the pose landmarker task.
+
+  Attributes:
+    base_options: Base options for the pose landmarker task.
+    running_mode: The running mode of the task. Default to the image mode.
+      HandLandmarker has three running modes: 1) The image mode for detecting
+      pose landmarks on single image inputs. 2) The video mode for detecting
+      pose landmarks on the decoded frames of a video. 3) The live stream mode
+      for detecting pose landmarks on the live stream of input data, such as
+      from camera. In this mode, the "result_callback" below must be specified
+      to receive the detection results asynchronously.
+    num_poses: The maximum number of poses can be detected by the PoseLandmarker.
+    min_pose_detection_confidence: The minimum confidence score for the pose
+      detection to be considered successful.
+    min_pose_presence_confidence: The minimum confidence score of pose presence
+      score in the pose landmark detection.
+    min_tracking_confidence: The minimum confidence score for the pose tracking
+      to be considered successful.
+    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.
+  """
+
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.IMAGE
+  num_poses: int = 1
+  min_pose_detection_confidence: float = 0.5
+  min_pose_presence_confidence: float = 0.5
+  min_tracking_confidence: float = 0.5
+  output_segmentation_masks: bool = False
+  result_callback: Optional[
+      Callable[[PoseLandmarkerResult, image_module.Image, int], None]
+  ] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _PoseLandmarkerGraphOptionsProto:
+    """Generates an PoseLandmarkerGraphOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = (
+        False if self.running_mode == _RunningMode.IMAGE else True
+    )
+
+    # Initialize the pose landmarker options from base options.
+    pose_landmarker_options_proto = _PoseLandmarkerGraphOptionsProto(
+        base_options=base_options_proto
+    )
+    pose_landmarker_options_proto.min_tracking_confidence = (
+        self.min_tracking_confidence
+    )
+    pose_landmarker_options_proto.pose_detector_graph_options.num_poses = (
+        self.num_poses
+    )
+    pose_landmarker_options_proto.pose_detector_graph_options.min_detection_confidence = (
+        self.min_pose_detection_confidence
+    )
+    pose_landmarker_options_proto.pose_landmarks_detector_graph_options.min_detection_confidence = (
+        self.min_pose_presence_confidence
+    )
+    return pose_landmarker_options_proto
+
+
+class PoseLandmarker(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs pose landmarks detection on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'PoseLandmarker':
+    """Creates an `PoseLandmarker` object from a TensorFlow Lite model and the default `PoseLandmarkerOptions`.
+
+    Note that the created `PoseLandmarker` instance is in image mode, for
+    detecting pose landmarks on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `PoseLandmarker` object that's created from the model file and the
+      default `PoseLandmarkerOptions`.
+
+    Raises:
+      ValueError: If failed to create `PoseLandmarker` object from the
+        provided file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = PoseLandmarkerOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE
+    )
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(
+      cls, options: PoseLandmarkerOptions
+  ) -> 'PoseLandmarker':
+    """Creates the `PoseLandmarker` object from pose landmarker options.
+
+    Args:
+      options: Options for the pose landmarker task.
+
+    Returns:
+      `PoseLandmarker` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `PoseLandmarker` object from
+        `PoseLandmarkerOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+
+      if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+        empty_packet = output_packets[_NORM_LANDMARKS_STREAM_NAME]
+        options.result_callback(
+            PoseLandmarkerResult([], [], []),
+            image,
+            empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
+        )
+        return
+
+      pose_landmarker_result = _build_landmarker_result(output_packets)
+      timestamp = output_packets[_NORM_LANDMARKS_STREAM_NAME].timestamp
+      options.result_callback(
+          pose_landmarker_result,
+          image,
+          timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
+      )
+
+    output_streams = [
+        ':'.join([_SEGMENTATION_MASK_TAG, _SEGMENTATION_MASK_STREAM_NAME]),
+        ':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
+        ':'.join([
+          _POSE_WORLD_LANDMARKS_TAG, _POSE_WORLD_LANDMARKS_STREAM_NAME
+        ]),
+        ':'.join([
+          _POSE_AUXILIARY_LANDMARKS_TAG,
+          _POSE_AUXILIARY_LANDMARKS_STREAM_NAME
+        ]),
+        ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
+    ]
+
+    if options.output_segmentation_masks:
+      output_streams.append(
+          ':'.join([_SEGMENTATION_MASK_TAG, _SEGMENTATION_MASK_STREAM_NAME])
+      )
+
+    task_info = _TaskInfo(
+        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,
+    )
+    return cls(
+        task_info.generate_graph_config(
+            enable_flow_limiting=options.running_mode
+            == _RunningMode.LIVE_STREAM
+        ),
+        options.running_mode,
+        packets_callback if options.result_callback else None,
+    )
+
+  def detect(
+      self,
+      image: image_module.Image,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> PoseLandmarkerResult:
+    """Performs pose landmarks detection on the given image.
+
+    Only use this method when the PoseLandmarker is created with the image
+    running mode.
+
+    Args:
+      image: MediaPipe Image.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The pose landmarker detection results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If pose 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[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+      return PoseLandmarkerResult([], [], [])
+
+    return _build_landmarker_result(output_packets)
+
+  def detect_for_video(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> PoseLandmarkerResult:
+    """Performs pose landmarks detection on the provided video frame.
+
+    Only use this method when the PoseLandmarker is created with the video
+    running mode.
+
+    Only use this method when the PoseLandmarker is created with the video
+    running mode. It's required to provide the video frame's timestamp (in
+    milliseconds) along with the video frame. The input timestamps should be
+    monotonically increasing for adjacent calls of this method.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input video frame in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The pose landmarks detection results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If pose 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[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+      return PoseLandmarkerResult([], [], [])
+
+    return _build_landmarker_result(output_packets)
+
+  def detect_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> None:
+    """Sends live image data to perform pose landmarks detection.
+
+    The results will be available via the "result_callback" provided in the
+    PoseLandmarkerOptions. Only use this method when the PoseLandmarker is
+    created with the live stream running mode.
+
+    Only use this method when the PoseLandmarker is created with the live
+    stream running mode. The input timestamps should be monotonically increasing
+    for adjacent calls of this method. This method will return immediately after
+    the input image is accepted. The results will be available via the
+    `result_callback` provided in the `PoseLandmarkerOptions`. The
+    `detect_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, pose landmarker may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` provides:
+      - The pose landmarks detection results.
+      - The input image that the pose landmarker runs on.
+      - The input timestamp in milliseconds.
+
+    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
+      pose 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),
+    })