class_docstr: MediaPipe Pose.
  
  MediaPipe Pose processes an RGB image and returns pose landmarks on the most
  prominent person detected.
  
  Please refer to https://solutions.mediapipe.dev/pose#python-solution-api for
  usage examples.
class_docstr_tok: ["MediaPipe","Pose",".","MediaPipe","Pose","processes","an","RGB","image","and","returns","pose","landmarks","on","the","most","prominent","person","detected",".","Please","refer","to","https",":","\/\/solutions.mediapipe.dev\/pose","#","python-solution-api","for","usage","examples","."]
docstr: Initializes a MediaPipe Pose object.
  
  Args:
    static_image_mode: Whether to treat the input images as a batch of static
      and possibly unrelated images, or a video stream. See details in
      https://solutions.mediapipe.dev/pose#static_image_mode.
    model_complexity: Complexity of the pose landmark model: 0, 1 or 2. See
      details in https://solutions.mediapipe.dev/pose#model_complexity.
    smooth_landmarks: Whether to filter landmarks across different input
      images to reduce jitter. See details in
      https://solutions.mediapipe.dev/pose#smooth_landmarks.
    enable_segmentation: Whether to predict segmentation mask. See details in
      https://solutions.mediapipe.dev/pose#enable_segmentation.
    smooth_segmentation: Whether to filter segmentation across different input
      images to reduce jitter. See details in
      https://solutions.mediapipe.dev/pose#smooth_segmentation.
    min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for person
      detection to be considered successful. See details in
      https://solutions.mediapipe.dev/pose#min_detection_confidence.
    min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the
      pose landmarks to be considered tracked successfully. See details in
      https://solutions.mediapipe.dev/pose#min_tracking_confidence.
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code: def __init__(
      self,
      static_image_mode=False,
      model_complexity=1,
      smooth_landmarks=True,
      enable_segmentation=False,
      smooth_segmentation=True,
      min_detection_confidence=0.5,
      min_tracking_confidence=0.5,
  ):
      """Initializes a MediaPipe Pose object.
  
      Args:
        static_image_mode: Whether to treat the input images as a batch of static
          and possibly unrelated images, or a video stream. See details in
          https://solutions.mediapipe.dev/pose#static_image_mode.
        model_complexity: Complexity of the pose landmark model: 0, 1 or 2. See
          details in https://solutions.mediapipe.dev/pose#model_complexity.
        smooth_landmarks: Whether to filter landmarks across different input
          images to reduce jitter. See details in
          https://solutions.mediapipe.dev/pose#smooth_landmarks.
        enable_segmentation: Whether to predict segmentation mask. See details in
          https://solutions.mediapipe.dev/pose#enable_segmentation.
        smooth_segmentation: Whether to filter segmentation across different input
          images to reduce jitter. See details in
          https://solutions.mediapipe.dev/pose#smooth_segmentation.
        min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for person
          detection to be considered successful. See details in
          https://solutions.mediapipe.dev/pose#min_detection_confidence.
        min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the
          pose landmarks to be considered tracked successfully. See details in
          https://solutions.mediapipe.dev/pose#min_tracking_confidence.
      """
      _download_oss_pose_landmark_model(model_complexity)
      super().__init__(
          binary_graph_path=_BINARYPB_FILE_PATH,
          side_inputs={
              "model_complexity": model_complexity,
              "smooth_landmarks": smooth_landmarks
              and not static_image_mode,
              "enable_segmentation": enable_segmentation,
              "smooth_segmentation": smooth_segmentation
              and not static_image_mode,
              "use_prev_landmarks": not static_image_mode,
          },
          calculator_params={
              "posedetectioncpu__TensorsToDetectionsCalculator.min_score_thresh": min_detection_confidence,
              "poselandmarkbyroicpu__tensorstoposelandmarksandsegmentation__ThresholdingCalculator.threshold": min_tracking_confidence,
          },
          outputs=[
              "pose_landmarks",
              "pose_world_landmarks",
              "segmentation_mask",
          ],
      )
  
imports: import enum
  from typing import NamedTuple
  import numpy
  from mediapipe.calculators.core import constant_side_packet_calculator_pb2
  from mediapipe.calculators.core import gate_calculator_pb2
  from mediapipe.calculators.core import split_vector_calculator_pb2
  from mediapipe.calculators.image import warp_affine_calculator_pb2
  from mediapipe.calculators.tensor import image_to_tensor_calculator_pb2
  from mediapipe.calculators.tensor import inference_calculator_pb2
  from mediapipe.calculators.tensor import tensors_to_classification_calculator_pb2
  from mediapipe.calculators.tensor import tensors_to_detections_calculator_pb2
  from mediapipe.calculators.tensor import tensors_to_landmarks_calculator_pb2
  from mediapipe.calculators.tensor import tensors_to_segmentation_calculator_pb2
  from mediapipe.calculators.tflite import ssd_anchors_calculator_pb2
  from mediapipe.calculators.util import detections_to_rects_calculator_pb2
  from mediapipe.calculators.util import landmarks_smoothing_calculator_pb2
  from mediapipe.calculators.util import local_file_contents_calculator_pb2
  from mediapipe.calculators.util import logic_calculator_pb2
  from mediapipe.calculators.util import non_max_suppression_calculator_pb2
  from mediapipe.calculators.util import rect_transformation_calculator_pb2
  from mediapipe.calculators.util import thresholding_calculator_pb2
  from mediapipe.calculators.util import visibility_smoothing_calculator_pb2
  from mediapipe.framework.tool import switch_container_pb2
  from mediapipe.python.solution_base import SolutionBase
  from mediapipe.python.solutions import download_utils
  from mediapipe.python.solutions.pose_connections import POSE_CONNECTIONS
label_desc: Use image node_id 1 to create a new Pose object from inherited base classes: SolutionBase with example: obj = Pose(static_image_mode, model_complexity, smooth_landmarks, enable_segmentation, smooth_segmentation, min_detection_confidence, min_tracking_confidence)