class_docstr: Class that performs pose landmarks detection on images.
class_docstr_tok: ["Class","that","performs","pose","landmarks","detection","on","images","."]
docstr: 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 landmarker 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.
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code: 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 landmarker 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),
          }
      )
  
imports: 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
  from mediapipe.python._framework_bindings import packet
  from mediapipe.tasks.cc.vision.pose_landmarker.proto import pose_landmarker_graph_options_pb2
  from mediapipe.tasks.python.components.containers import landmark
  from mediapipe.tasks.python.core import base_options
  from mediapipe.tasks.python.core import task_info
  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
  from mediapipe.tasks.python.vision.core import vision_task_running_mode
label_desc: Use image node_id 5 for calling the PoseLandmarker obj's underlying member method code with example usage: obj.detect_async(image, timestamp_ms, image_processing_options) without return types