62 lines
1.8 KiB
Plaintext
62 lines
1.8 KiB
Plaintext
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# MediaPipe graph that performs hand detection on desktop with TensorFlow Lite
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# on CPU.
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# Used in the example in
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# mediapipie/examples/desktop/hand_tracking:hand_detection_tflite.
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# max_queue_size limits the number of packets enqueued on any input stream
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# by throttling inputs to the graph. This makes the graph only process one
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# frame per time.
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max_queue_size: 1
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# Decodes an input video file into images and a video header.
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node {
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calculator: "OpenCvVideoDecoderCalculator"
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input_side_packet: "INPUT_FILE_PATH:input_video_path"
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output_stream: "VIDEO:input_video"
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output_stream: "VIDEO_PRESTREAM:input_video_header"
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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_stream: "IMAGE:input_video"
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output_stream: "DETECTIONS:output_detections"
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}
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# Converts the detections to drawing primitives for annotation overlay.
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node {
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calculator: "DetectionsToRenderDataCalculator"
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input_stream: "DETECTIONS:output_detections"
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output_stream: "RENDER_DATA:render_data"
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node_options: {
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[type.googleapis.com/mediapipe.DetectionsToRenderDataCalculatorOptions] {
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thickness: 4.0
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color { r: 0 g: 255 b: 0 }
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}
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}
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}
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# Draws annotations and overlays them on top of the original image coming into
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# the graph.
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node {
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calculator: "AnnotationOverlayCalculator"
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input_stream: "IMAGE:input_video"
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input_stream: "render_data"
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output_stream: "IMAGE:output_video"
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}
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# Encodes the annotated images into a video file, adopting properties specified
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# in the input video header, e.g., video framerate.
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node {
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calculator: "OpenCvVideoEncoderCalculator"
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input_stream: "VIDEO:output_video"
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input_stream: "VIDEO_PRESTREAM:input_video_header"
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input_side_packet: "OUTPUT_FILE_PATH:output_video_path"
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node_options: {
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[type.googleapis.com/mediapipe.OpenCvVideoEncoderCalculatorOptions]: {
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codec: "avc1"
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video_format: "mp4"
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}
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}
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}
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