66 lines
2.5 KiB
Plaintext
66 lines
2.5 KiB
Plaintext
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# MediaPipe graph that performs multi-hand tracking with TensorFlow Lite on GPU.
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# Used in the examples in
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# mediapipe/examples/android/src/java/com/mediapipe/apps/handtrackinggpu.
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# GPU image. (GpuBuffer)
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input_stream: "input_video"
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# Max number of hands to detect/process. (int)
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input_side_packet: "num_hands"
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# Model complexity (0 or 1). (int)
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input_side_packet: "model_complexity"
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# GPU image. (GpuBuffer)
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output_stream: "output_video"
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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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output_stream: "hand_landmarks"
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# Throttles the images flowing downstream for flow control. It passes through
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# the very first incoming image unaltered, and waits for downstream nodes
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# (calculators and subgraphs) in the graph to finish their tasks before it
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# passes through another image. All images that come in while waiting are
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# dropped, limiting the number of in-flight images in most part of the graph to
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# 1. This prevents the downstream nodes from queuing up incoming images and data
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# excessively, which leads to increased latency and memory usage, unwanted in
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# real-time mobile applications. It also eliminates unnecessarily computation,
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# e.g., the output produced by a node may get dropped downstream if the
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# subsequent nodes are still busy processing previous inputs.
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node {
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calculator: "FlowLimiterCalculator"
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input_stream: "input_video"
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input_stream: "FINISHED:output_video"
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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_input_video"
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}
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# Detects/tracks hand landmarks.
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node {
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calculator: "HandLandmarkTrackingGpu"
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input_stream: "IMAGE:throttled_input_video"
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input_side_packet: "MODEL_COMPLEXITY:model_complexity"
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input_side_packet: "NUM_HANDS:num_hands"
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output_stream: "LANDMARKS:hand_landmarks"
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output_stream: "HANDEDNESS:handedness"
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output_stream: "PALM_DETECTIONS:palm_detections"
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output_stream: "HAND_ROIS_FROM_LANDMARKS:hand_rects_from_landmarks"
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output_stream: "HAND_ROIS_FROM_PALM_DETECTIONS:hand_rects_from_palm_detections"
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}
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# Subgraph that renders annotations and overlays them on top of the input
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# images (see hand_renderer_gpu.pbtxt).
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node {
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calculator: "HandRendererSubgraph"
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input_stream: "IMAGE:throttled_input_video"
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input_stream: "DETECTIONS:palm_detections"
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input_stream: "LANDMARKS:hand_landmarks"
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input_stream: "HANDEDNESS:handedness"
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input_stream: "NORM_RECTS:0:hand_rects_from_palm_detections"
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input_stream: "NORM_RECTS:1:hand_rects_from_landmarks"
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output_stream: "IMAGE:output_video"
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}
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