147 lines
5.7 KiB
Plaintext
147 lines
5.7 KiB
Plaintext
# Predicts pose + left/right hand + face landmarks.
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#
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# It is required that:
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# - "face_detection_short_range.tflite" is available at
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# "mediapipe/modules/face_detection/face_detection_short_range.tflite"
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#
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# - "face_landmark.tflite" is available at
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# "mediapipe/modules/face_landmark/face_landmark.tflite"
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#
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# - "hand_landmark_full.tflite" is available at
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# "mediapipe/modules/hand_landmark/hand_landmark_full.tflite"
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#
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# - "hand_recrop.tflite" is available at
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# "mediapipe/modules/holistic_landmark/hand_recrop.tflite"
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#
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# - "handedness.txt" is available at
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# "mediapipe/modules/hand_landmark/handedness.txt"
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#
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# - "pose_detection.tflite" is available at
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# "mediapipe/modules/pose_detection/pose_detection.tflite"
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#
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# - "pose_landmark_lite.tflite" or "pose_landmark_full.tflite" or
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# "pose_landmark_heavy.tflite" is available at
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# "mediapipe/modules/pose_landmark/pose_landmark_lite.tflite" or
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# "mediapipe/modules/pose_landmark/pose_landmark_full.tflite" or
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# "mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite"
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# path respectively during execution, depending on the specification in the
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# MODEL_COMPLEXITY input side packet.
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#
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# EXAMPLE:
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# node {
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# calculator: "HolisticLandmarkCpu"
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# input_stream: "IMAGE:input_video"
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# input_side_packet: "MODEL_COMPLEXITY:model_complexity"
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# input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
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# input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
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# input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
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# input_side_packet: "REFINE_FACE_LANDMARKS:refine_face_landmarks"
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# input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
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# output_stream: "POSE_LANDMARKS:pose_landmarks"
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# output_stream: "FACE_LANDMARKS:face_landmarks"
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# output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
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# output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
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# }
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#
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# NOTE: if a pose/hand/face output is not present in the image, for this
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# particular timestamp there will not be an output packet in the corresponding
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# output stream below. However, the MediaPipe framework will internally inform
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# the downstream calculators of the absence of this packet so that they don't
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# wait for it unnecessarily.
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type: "HolisticLandmarkCpu"
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# CPU image. (ImageFrame)
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input_stream: "IMAGE:image"
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# Complexity of the pose landmark model: 0, 1 or 2. Landmark accuracy as well as
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# inference latency generally go up with the model complexity. If unspecified,
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# functions as set to 1. (int)
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input_side_packet: "MODEL_COMPLEXITY:model_complexity"
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# Whether to filter landmarks across different input images to reduce jitter.
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# If unspecified, functions as set to true. (bool)
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input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
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# Whether to predict the segmentation mask. If unspecified, functions as set to
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# false. (bool)
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input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
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# Whether to filter segmentation mask across different input images to reduce
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# jitter. If unspecified, functions as set to true. (bool)
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input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
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# Whether to run the face landmark model with attention on lips and eyes to
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# provide more accuracy, and additionally output iris landmarks. If unspecified,
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# functions as set to false. (bool)
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input_side_packet: "REFINE_FACE_LANDMARKS:refine_face_landmarks"
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# Whether landmarks on the previous image should be used to help localize
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# landmarks on the current image. (bool)
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input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
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# Pose landmarks. (NormalizedLandmarkList)
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# 33 pose landmarks.
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output_stream: "POSE_LANDMARKS:pose_landmarks"
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# 33 pose world landmarks. (LandmarkList)
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output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
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# 21 left hand landmarks. (NormalizedLandmarkList)
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output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
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# 21 right hand landmarks. (NormalizedLandmarkList)
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output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
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# 468 face landmarks. (NormalizedLandmarkList)
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output_stream: "FACE_LANDMARKS:face_landmarks"
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# Segmentation mask. (ImageFrame in ImageFormat::VEC32F1)
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output_stream: "SEGMENTATION_MASK:segmentation_mask"
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# Debug outputs
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output_stream: "POSE_ROI:pose_landmarks_roi"
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output_stream: "POSE_DETECTION:pose_detection"
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# Predicts pose landmarks.
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node {
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calculator: "PoseLandmarkCpu"
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input_stream: "IMAGE:image"
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input_side_packet: "MODEL_COMPLEXITY:model_complexity"
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input_side_packet: "SMOOTH_LANDMARKS:smooth_landmarks"
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input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
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input_side_packet: "SMOOTH_SEGMENTATION:smooth_segmentation"
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input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
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output_stream: "LANDMARKS:pose_landmarks"
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output_stream: "WORLD_LANDMARKS:pose_world_landmarks"
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output_stream: "SEGMENTATION_MASK:segmentation_mask"
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output_stream: "ROI_FROM_LANDMARKS:pose_landmarks_roi"
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output_stream: "DETECTION:pose_detection"
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}
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# Predicts left and right hand landmarks based on the initial pose landmarks.
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node {
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calculator: "HandLandmarksLeftAndRightCpu"
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input_stream: "IMAGE:image"
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input_stream: "POSE_LANDMARKS:pose_landmarks"
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output_stream: "LEFT_HAND_LANDMARKS:left_hand_landmarks"
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output_stream: "RIGHT_HAND_LANDMARKS:right_hand_landmarks"
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}
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# Extracts face-related pose landmarks.
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node {
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calculator: "SplitNormalizedLandmarkListCalculator"
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input_stream: "pose_landmarks"
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output_stream: "face_landmarks_from_pose"
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options: {
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[mediapipe.SplitVectorCalculatorOptions.ext] {
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ranges: { begin: 0 end: 11 }
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}
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}
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}
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# Predicts face landmarks based on the initial pose landmarks.
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node {
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calculator: "FaceLandmarksFromPoseCpu"
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input_stream: "IMAGE:image"
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input_stream: "FACE_LANDMARKS_FROM_POSE:face_landmarks_from_pose"
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input_side_packet: "REFINE_LANDMARKS:refine_face_landmarks"
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output_stream: "FACE_LANDMARKS:face_landmarks"
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}
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