68 lines
2.0 KiB
Plaintext
68 lines
2.0 KiB
Plaintext
# MediaPipe graph to perform selfie segmentation.
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type: "SelfieSegmentationCpuImage"
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# Input image. (Image)
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input_stream: "IMAGE:image"
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# The throttled input image. (Image)
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output_stream: "IMAGE:throttled_image"
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# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
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# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
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# optimized for landscape images. If unspecified, functions as set to 0. (int)
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input_side_packet: "MODEL_SELECTION:model_selection"
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# Segmentation mask. (Image)
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output_stream: "SEGMENTATION_MASK:segmentation_mask"
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node {
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calculator: "FlowLimiterCalculator"
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input_stream: "image"
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input_stream: "FINISHED:segmentation_mask"
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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_image"
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options: {
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[mediapipe.FlowLimiterCalculatorOptions.ext] {
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max_in_flight: 1
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max_in_queue: 1
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}
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}
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}
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# Converts Image to ImageFrame for SelfieSegmentationCpu to consume.
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node {
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calculator: "FromImageCalculator"
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input_stream: "IMAGE:throttled_image"
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output_stream: "IMAGE_CPU:raw_image_frame"
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output_stream: "SOURCE_ON_GPU:is_gpu_image"
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}
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# TODO: Remove the extra flipping once adopting MlImage.
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# If the source images are on gpu, flip the data vertically before sending them
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# into SelfieSegmentationCpu. This maybe needed because OpenGL represents images
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# assuming the image origin is at the bottom-left corner, whereas MediaPipe in
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# general assumes the image origin is at the top-left corner.
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node: {
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calculator: "ImageTransformationCalculator"
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input_stream: "IMAGE:raw_image_frame"
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input_stream: "FLIP_VERTICALLY:is_gpu_image"
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output_stream: "IMAGE:image_frame"
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}
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node {
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calculator: "SelfieSegmentationCpu"
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input_side_packet: "MODEL_SELECTION:model_selection"
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input_stream: "IMAGE:image_frame"
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output_stream: "SEGMENTATION_MASK:segmentation_mask_image_frame"
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}
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node {
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calculator: "ToImageCalculator"
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input_stream: "IMAGE_CPU:segmentation_mask_image_frame"
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output_stream: "IMAGE:segmentation_mask"
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}
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