272 lines
10 KiB
Plaintext
272 lines
10 KiB
Plaintext
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# MediaPipe graph to detect/predict hand landmarks on CPU.
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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: "HandLandmarkTrackingCpu"
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# CPU image. (ImageFrame)
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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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# 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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# 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_hand_rects_from_landmarks"
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output_stream: "gated_prev_hand_rects_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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# Determines if an input vector of NormalizedRect has a size greater than or
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# equal to the provided num_hands.
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node {
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calculator: "NormalizedRectVectorHasMinSizeCalculator"
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input_stream: "ITERABLE:gated_prev_hand_rects_from_landmarks"
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input_side_packet: "num_hands"
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output_stream: "prev_has_enough_hands"
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}
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# Drops the incoming image if enough hands have 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 palm 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: "DISALLOW:prev_has_enough_hands"
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output_stream: "palm_detection_image"
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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 palms.
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node {
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calculator: "PalmDetectionCpu"
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input_side_packet: "MODEL_COMPLEXITY:model_complexity"
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input_stream: "IMAGE:palm_detection_image"
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output_stream: "DETECTIONS:all_palm_detections"
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}
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# Makes sure there are no more detections than the provided num_hands.
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node {
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calculator: "ClipDetectionVectorSizeCalculator"
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input_stream: "all_palm_detections"
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output_stream: "palm_detections"
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input_side_packet: "num_hands"
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}
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# Extracts image size.
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node {
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calculator: "ImagePropertiesCalculator"
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input_stream: "IMAGE:palm_detection_image"
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output_stream: "SIZE:palm_detection_image_size"
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}
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# Outputs each element of palm_detections at a fake timestamp for the rest of
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# the graph to process. Clones the image size packet for each palm_detection at
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# the fake timestamp. At the end of the loop, outputs the BATCH_END timestamp
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# for downstream calculators to inform them that all elements in the vector have
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# been processed.
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node {
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calculator: "BeginLoopDetectionCalculator"
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input_stream: "ITERABLE:palm_detections"
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input_stream: "CLONE:palm_detection_image_size"
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output_stream: "ITEM:palm_detection"
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output_stream: "CLONE:image_size_for_palms"
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output_stream: "BATCH_END:palm_detections_timestamp"
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}
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# Calculates region of interest (ROI) based on the specified palm.
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node {
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calculator: "PalmDetectionDetectionToRoi"
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input_stream: "DETECTION:palm_detection"
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input_stream: "IMAGE_SIZE:image_size_for_palms"
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output_stream: "ROI:hand_rect_from_palm_detection"
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}
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# Collects a NormalizedRect for each hand into a vector. Upon receiving the
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# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
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# timestamp.
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node {
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calculator: "EndLoopNormalizedRectCalculator"
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input_stream: "ITEM:hand_rect_from_palm_detection"
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input_stream: "BATCH_END:palm_detections_timestamp"
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output_stream: "ITERABLE:hand_rects_from_palm_detections"
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}
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# Performs association between NormalizedRect vector elements from previous
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# image and rects based on palm detections from the current image. This
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# calculator ensures that the output hand_rects vector doesn't contain
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# overlapping regions based on the specified min_similarity_threshold.
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node {
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calculator: "AssociationNormRectCalculator"
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input_stream: "hand_rects_from_palm_detections"
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input_stream: "gated_prev_hand_rects_from_landmarks"
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output_stream: "hand_rects"
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options: {
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[mediapipe.AssociationCalculatorOptions.ext] {
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min_similarity_threshold: 0.5
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}
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}
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}
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# Extracts image size.
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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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# Outputs each element of hand_rects at a fake timestamp for the rest of the
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# graph to process. Clones image and image size packets for each
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# single_hand_rect at the fake timestamp. At the end of the loop, outputs the
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# BATCH_END timestamp for downstream calculators to inform them that all
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# elements in the vector have been processed.
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node {
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calculator: "BeginLoopNormalizedRectCalculator"
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input_stream: "ITERABLE:hand_rects"
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input_stream: "CLONE:0:image"
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input_stream: "CLONE:1:image_size"
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output_stream: "ITEM:single_hand_rect"
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output_stream: "CLONE:0:image_for_landmarks"
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output_stream: "CLONE:1:image_size_for_landmarks"
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output_stream: "BATCH_END:hand_rects_timestamp"
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}
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# Detect hand landmarks for the specific hand rect.
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node {
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calculator: "HandLandmarkCpu"
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input_side_packet: "MODEL_COMPLEXITY:model_complexity"
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input_stream: "IMAGE:image_for_landmarks"
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input_stream: "ROI:single_hand_rect"
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output_stream: "LANDMARKS:single_hand_landmarks"
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output_stream: "WORLD_LANDMARKS:single_hand_world_landmarks"
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output_stream: "HANDEDNESS:single_handedness"
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}
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# Collects the handedness for each single hand into a vector. Upon receiving the
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# BATCH_END timestamp, outputs a vector of ClassificationList at the BATCH_END
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# timestamp.
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node {
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calculator: "EndLoopClassificationListCalculator"
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input_stream: "ITEM:single_handedness"
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input_stream: "BATCH_END:hand_rects_timestamp"
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output_stream: "ITERABLE:multi_handedness"
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}
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# Calculate region of interest (ROI) based on detected hand landmarks to reuse
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# on the subsequent runs of the graph.
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node {
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calculator: "HandLandmarkLandmarksToRoi"
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input_stream: "IMAGE_SIZE:image_size_for_landmarks"
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input_stream: "LANDMARKS:single_hand_landmarks"
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output_stream: "ROI:single_hand_rect_from_landmarks"
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}
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# Collects a set of landmarks for each hand into a vector. Upon receiving the
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# BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
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# timestamp.
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node {
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calculator: "EndLoopNormalizedLandmarkListVectorCalculator"
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input_stream: "ITEM:single_hand_landmarks"
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input_stream: "BATCH_END:hand_rects_timestamp"
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output_stream: "ITERABLE:multi_hand_landmarks"
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}
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# Collects a set of world landmarks for each hand into a vector. Upon receiving
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# the BATCH_END timestamp, outputs the vector of landmarks at the BATCH_END
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# timestamp.
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node {
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calculator: "EndLoopLandmarkListVectorCalculator"
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input_stream: "ITEM:single_hand_world_landmarks"
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input_stream: "BATCH_END:hand_rects_timestamp"
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output_stream: "ITERABLE:multi_hand_world_landmarks"
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}
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# Collects a NormalizedRect for each hand into a vector. Upon receiving the
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# BATCH_END timestamp, outputs the vector of NormalizedRect at the BATCH_END
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# timestamp.
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node {
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calculator: "EndLoopNormalizedRectCalculator"
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input_stream: "ITEM:single_hand_rect_from_landmarks"
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input_stream: "BATCH_END:hand_rects_timestamp"
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output_stream: "ITERABLE:hand_rects_from_landmarks"
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}
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# Caches hand 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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# hand 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:hand_rects_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_hand_rects_from_landmarks"
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}
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