269 lines
9.3 KiB
Plaintext
269 lines
9.3 KiB
Plaintext
# MediaPipe graph to detect/predict pose landmarks. (CPU input, and inference is
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# executed on CPU.) This graph tries to skip pose detection as much as possible
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# by using previously detected/predicted landmarks for new images.
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#
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# It is required that "pose_detection.tflite" is available at
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# "mediapipe/modules/pose_detection/pose_detection.tflite"
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# path during execution.
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#
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# It is required that "pose_landmark_lite.tflite" or
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# "pose_landmark_full.tflite" or "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: "PoseLandmarkCpu"
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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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# input_stream: "IMAGE:image"
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# output_stream: "LANDMARKS:pose_landmarks"
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# output_stream: "SEGMENTATION_MASK:segmentation_mask"
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# }
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type: "PoseLandmarkCpu"
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# CPU image. (ImageFrame)
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input_stream: "IMAGE:image"
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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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# 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 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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# We have 33 landmarks (see pose_landmark_topology.svg), and there are other
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# auxiliary key points.
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# 0 - nose
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# 1 - left eye (inner)
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# 2 - left eye
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# 3 - left eye (outer)
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# 4 - right eye (inner)
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# 5 - right eye
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# 6 - right eye (outer)
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# 7 - left ear
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# 8 - right ear
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# 9 - mouth (left)
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# 10 - mouth (right)
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# 11 - left shoulder
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# 12 - right shoulder
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# 13 - left elbow
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# 14 - right elbow
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# 15 - left wrist
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# 16 - right wrist
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# 17 - left pinky
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# 18 - right pinky
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# 19 - left index
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# 20 - right index
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# 21 - left thumb
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# 22 - right thumb
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# 23 - left hip
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# 24 - right hip
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# 25 - left knee
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# 26 - right knee
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# 27 - left ankle
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# 28 - right ankle
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# 29 - left heel
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# 30 - right heel
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# 31 - left foot index
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# 32 - right foot index
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#
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# NOTE: if a pose is not present within the given ROI, for this particular
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# timestamp there will not be an output packet in the LANDMARKS stream. However,
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# the MediaPipe framework will internally inform the downstream calculators of
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# the absence of this packet so that they don't wait for it unnecessarily.
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output_stream: "LANDMARKS:pose_landmarks"
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# Pose world landmarks. (LandmarkList)
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# World landmarks are real-world 3D coordinates in meters with the origin at the
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# center between hips. WORLD_LANDMARKS shares the same landmark topology as
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# LANDMARKS. However, LANDMARKS provides coordinates (in pixels) of a 3D object
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# projected onto the 2D image surface, while WORLD_LANDMARKS provides
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# coordinates (in meters) of the 3D object itself.
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output_stream: "WORLD_LANDMARKS:pose_world_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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# Extra outputs (for debugging, for instance).
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# Detected poses. (Detection)
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output_stream: "DETECTION:pose_detection"
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# Regions of interest calculated based on landmarks. (NormalizedRect)
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output_stream: "ROI_FROM_LANDMARKS:pose_rect_from_landmarks"
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# Regions of interest calculated based on pose detections. (NormalizedRect)
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output_stream: "ROI_FROM_DETECTION:pose_rect_from_detection"
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# When the optional input side packet "use_prev_landmarks" is either absent or
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# set to true, uses the landmarks on the previous image to help localize
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# landmarks on the current image.
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node {
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calculator: "GateCalculator"
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input_side_packet: "ALLOW:use_prev_landmarks"
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input_stream: "prev_pose_rect_from_landmarks"
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output_stream: "gated_prev_pose_rect_from_landmarks"
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options: {
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[mediapipe.GateCalculatorOptions.ext] {
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allow: true
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}
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}
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}
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# Checks if there's previous pose rect calculated from landmarks.
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node: {
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calculator: "PacketPresenceCalculator"
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input_stream: "PACKET:gated_prev_pose_rect_from_landmarks"
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output_stream: "PRESENCE:prev_pose_rect_from_landmarks_is_present"
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}
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# Calculates size of the image.
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node {
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calculator: "ImagePropertiesCalculator"
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input_stream: "IMAGE_CPU:image"
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output_stream: "SIZE:image_size"
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}
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# Drops the incoming image if the pose has already been identified from the
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# previous image. Otherwise, passes the incoming image through to trigger a new
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# round of pose detection.
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node {
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calculator: "GateCalculator"
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input_stream: "image"
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input_stream: "image_size"
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input_stream: "DISALLOW:prev_pose_rect_from_landmarks_is_present"
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output_stream: "image_for_pose_detection"
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output_stream: "image_size_for_pose_detection"
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options: {
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[mediapipe.GateCalculatorOptions.ext] {
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empty_packets_as_allow: true
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}
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}
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}
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# Detects poses.
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node {
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calculator: "PoseDetectionCpu"
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input_stream: "IMAGE:image_for_pose_detection"
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output_stream: "DETECTIONS:pose_detections"
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}
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# Gets the very first detection from "pose_detections" vector.
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node {
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calculator: "SplitDetectionVectorCalculator"
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input_stream: "pose_detections"
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output_stream: "pose_detection"
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options: {
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[mediapipe.SplitVectorCalculatorOptions.ext] {
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ranges: { begin: 0 end: 1 }
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element_only: true
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}
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}
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}
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# Calculates region of interest based on pose detection, so that can be used
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# to detect landmarks.
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node {
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calculator: "PoseDetectionToRoi"
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input_stream: "DETECTION:pose_detection"
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input_stream: "IMAGE_SIZE:image_size_for_pose_detection"
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output_stream: "ROI:pose_rect_from_detection"
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}
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# Selects either pose rect (or ROI) calculated from detection or from previously
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# detected landmarks if available (in this case, calculation of pose rect from
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# detection is skipped).
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node {
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calculator: "MergeCalculator"
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input_stream: "pose_rect_from_detection"
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input_stream: "gated_prev_pose_rect_from_landmarks"
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output_stream: "pose_rect"
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}
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# Detects pose landmarks within specified region of interest of the image.
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node {
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calculator: "PoseLandmarkByRoiCpu"
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input_side_packet: "MODEL_COMPLEXITY:model_complexity"
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input_side_packet: "ENABLE_SEGMENTATION:enable_segmentation"
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input_stream: "IMAGE:image"
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input_stream: "ROI:pose_rect"
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output_stream: "LANDMARKS:unfiltered_pose_landmarks"
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output_stream: "AUXILIARY_LANDMARKS:unfiltered_auxiliary_landmarks"
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output_stream: "WORLD_LANDMARKS:unfiltered_world_landmarks"
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output_stream: "SEGMENTATION_MASK:unfiltered_segmentation_mask"
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}
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# Smoothes landmarks to reduce jitter.
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node {
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calculator: "PoseLandmarkFiltering"
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input_side_packet: "ENABLE:smooth_landmarks"
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input_stream: "IMAGE_SIZE:image_size"
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input_stream: "NORM_LANDMARKS:unfiltered_pose_landmarks"
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input_stream: "AUX_NORM_LANDMARKS:unfiltered_auxiliary_landmarks"
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input_stream: "WORLD_LANDMARKS:unfiltered_world_landmarks"
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output_stream: "FILTERED_NORM_LANDMARKS:pose_landmarks"
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output_stream: "FILTERED_AUX_NORM_LANDMARKS:auxiliary_landmarks"
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output_stream: "FILTERED_WORLD_LANDMARKS:pose_world_landmarks"
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}
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# Calculates region of interest based on the auxiliary landmarks, to be used in
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# the subsequent image.
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node {
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calculator: "PoseLandmarksToRoi"
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input_stream: "LANDMARKS:auxiliary_landmarks"
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input_stream: "IMAGE_SIZE:image_size"
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output_stream: "ROI:pose_rect_from_landmarks"
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}
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# Caches pose rects calculated from landmarks, and upon the arrival of the next
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# input image, sends out the cached rects with timestamps replaced by that of
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# the input image, essentially generating a packet that carries the previous
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# pose rects. Note that upon the arrival of the very first input image, a
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# timestamp bound update occurs to jump start the feedback loop.
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node {
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calculator: "PreviousLoopbackCalculator"
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input_stream: "MAIN:image"
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input_stream: "LOOP:pose_rect_from_landmarks"
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input_stream_info: {
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tag_index: "LOOP"
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back_edge: true
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}
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output_stream: "PREV_LOOP:prev_pose_rect_from_landmarks"
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}
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# Smoothes segmentation to reduce jitter.
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node {
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calculator: "PoseSegmentationFiltering"
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input_side_packet: "ENABLE:smooth_segmentation"
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input_stream: "SEGMENTATION_MASK:unfiltered_segmentation_mask"
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output_stream: "FILTERED_SEGMENTATION_MASK:filtered_segmentation_mask"
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}
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# Converts the incoming segmentation mask represented as an Image into the
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# corresponding ImageFrame type.
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node: {
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calculator: "FromImageCalculator"
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input_stream: "IMAGE:filtered_segmentation_mask"
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output_stream: "IMAGE_CPU:segmentation_mask"
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}
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