116 lines
4.5 KiB
Plaintext
116 lines
4.5 KiB
Plaintext
# MediaPipe graph to detect/predict hand landmarks on GPU.
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#
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# The procedure is done in two steps:
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# - locate palms/hands
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# - detect landmarks for each palm/hand.
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# This graph tries to skip palm detection as much as possible by reusing
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# previously detected/predicted landmarks for new images.
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type: "HandLandmarkTrackingGpuImage"
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# Input image. (Image)
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input_stream: "IMAGE:image"
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# Max number of hands to detect/track. (int)
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input_side_packet: "NUM_HANDS:num_hands"
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# Complexity of hand landmark and palm detection models: 0 or 1. Accuracy as
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# well as inference latency generally go up with the model complexity. If
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# unspecified, 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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# Collection of detected/predicted hands, each represented as a list of
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# landmarks. (std::vector<NormalizedLandmarkList>)
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# NOTE: there will not be an output packet in the LANDMARKS stream for this
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# particular timestamp if none of hands detected. However, the MediaPipe
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# framework will internally inform the downstream calculators of the absence of
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# this packet so that they don't wait for it unnecessarily.
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output_stream: "LANDMARKS:multi_hand_landmarks"
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# Collection of detected/predicted hand world landmarks.
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# (std::vector<LandmarkList>)
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#
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# World landmarks are real-world 3D coordinates in meters with the origin in the
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# center of the hand bounding box calculated from the landmarks.
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#
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# WORLD_LANDMARKS shares the same landmark topology as LANDMARKS. However,
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# LANDMARKS provides coordinates (in pixels) of a 3D object projected onto the
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# 2D image surface, while WORLD_LANDMARKS provides coordinates (in meters) of
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# the 3D object itself.
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output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
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# Collection of handedness of the detected hands (i.e. is hand left or right),
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# each represented as a ClassificationList proto with a single Classification
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# entry. (std::vector<ClassificationList>)
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# Note that handedness is determined assuming the input image is mirrored,
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# i.e., taken with a front-facing/selfie camera with images flipped
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# horizontally.
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output_stream: "HANDEDNESS:multi_handedness"
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# The throttled input image. (Image)
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output_stream: "IMAGE:throttled_image"
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# Extra outputs (for debugging, for instance).
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# Detected palms. (std::vector<Detection>)
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output_stream: "PALM_DETECTIONS:palm_detections"
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# Regions of interest calculated based on landmarks.
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# (std::vector<NormalizedRect>)
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output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
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# Regions of interest calculated based on palm detections.
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# (std::vector<NormalizedRect>)
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output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
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node {
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calculator: "FlowLimiterCalculator"
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input_stream: "image"
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input_stream: "FINISHED:multi_hand_landmarks"
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input_stream_info: {
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tag_index: "FINISHED"
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back_edge: true
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}
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output_stream: "throttled_image"
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options: {
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[mediapipe.FlowLimiterCalculatorOptions.ext] {
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max_in_flight: 1
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max_in_queue: 1
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}
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}
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}
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# Converts Image to GpuBuffer for HandLandmarkTrackingGpu to consume.
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node {
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calculator: "FromImageCalculator"
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input_stream: "IMAGE:throttled_image"
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output_stream: "IMAGE_GPU:raw_gpu_buffer"
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output_stream: "SOURCE_ON_GPU:is_gpu_image"
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}
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# TODO: Remove the extra flipping once adopting MlImage.
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# If the source images are on gpu, flip the data vertically before sending them
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# into HandLandmarkTrackingGpu. This maybe needed because OpenGL represents
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# images assuming the image origin is at the bottom-left corner, whereas
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# MediaPipe in general assumes the image origin is at the top-left corner.
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node: {
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calculator: "ImageTransformationCalculator"
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input_stream: "IMAGE_GPU:raw_gpu_buffer"
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input_stream: "FLIP_VERTICALLY:is_gpu_image"
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output_stream: "IMAGE_GPU:gpu_buffer"
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}
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node {
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calculator: "HandLandmarkTrackingGpu"
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input_stream: "IMAGE:gpu_buffer"
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input_side_packet: "NUM_HANDS:num_hands"
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input_side_packet: "MODEL_COMPLEXITY:model_complexity"
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input_side_packet: "USE_PREV_LANDMARKS:use_prev_landmarks"
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output_stream: "LANDMARKS:multi_hand_landmarks"
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output_stream: "WORLD_LANDMARKS:multi_hand_world_landmarks"
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output_stream: "HANDEDNESS:multi_handedness"
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output_stream: "PALM_DETECTIONS:palm_detections"
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output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects"
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output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
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}
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