53 lines
1.8 KiB
Plaintext
53 lines
1.8 KiB
Plaintext
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# MediaPipe graph that performs selfie segmentation with TensorFlow Lite on CPU.
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# CPU buffer. (ImageFrame)
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input_stream: "input_video"
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# Output image with rendered results. (ImageFrame)
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output_stream: "output_video"
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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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# Subgraph that performs selfie segmentation.
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node {
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calculator: "SelfieSegmentationCpu"
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input_stream: "IMAGE:throttled_input_video"
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output_stream: "SEGMENTATION_MASK:segmentation_mask"
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}
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# Colors the selfie segmentation with the color specified in the option.
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node {
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calculator: "RecolorCalculator"
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input_stream: "IMAGE:throttled_input_video"
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input_stream: "MASK:segmentation_mask"
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output_stream: "IMAGE:output_video"
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node_options: {
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[type.googleapis.com/mediapipe.RecolorCalculatorOptions] {
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color { r: 0 g: 0 b: 255 }
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mask_channel: RED
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invert_mask: true
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adjust_with_luminance: false
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}
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}
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}
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