68 lines
2.5 KiB
Plaintext
68 lines
2.5 KiB
Plaintext
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# MediaPipe graph that performs face mesh with TensorFlow Lite on GPU.
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# GPU buffer. (GpuBuffer)
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input_stream: "input_video"
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# Max number of faces to detect/process. (int)
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input_side_packet: "num_faces"
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# Output image with rendered results. (GpuBuffer)
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output_stream: "output_video"
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# Collection of detected/processed faces, each represented as a list of
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# landmarks. (std::vector<NormalizedLandmarkList>)
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output_stream: "multi_face_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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# Defines side packets for further use in the graph.
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node {
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calculator: "ConstantSidePacketCalculator"
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output_side_packet: "PACKET:with_attention"
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node_options: {
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[type.googleapis.com/mediapipe.ConstantSidePacketCalculatorOptions]: {
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packet { bool_value: true }
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}
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}
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}
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# Subgraph that detects faces and corresponding landmarks.
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node {
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calculator: "FaceLandmarkFrontGpu"
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input_stream: "IMAGE:throttled_input_video"
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input_side_packet: "NUM_FACES:num_faces"
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input_side_packet: "WITH_ATTENTION:with_attention"
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output_stream: "LANDMARKS:multi_face_landmarks"
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output_stream: "ROIS_FROM_LANDMARKS:face_rects_from_landmarks"
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output_stream: "DETECTIONS:face_detections"
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output_stream: "ROIS_FROM_DETECTIONS:face_rects_from_detections"
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}
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# Subgraph that renders face-landmark annotation onto the input image.
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node {
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calculator: "FaceRendererGpu"
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input_stream: "IMAGE:throttled_input_video"
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input_stream: "LANDMARKS:multi_face_landmarks"
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input_stream: "NORM_RECTS:face_rects_from_landmarks"
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input_stream: "DETECTIONS:face_detections"
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output_stream: "IMAGE:output_video"
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}
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