133 lines
3.9 KiB
Plaintext
133 lines
3.9 KiB
Plaintext
# MediaPipe graph to perform selfie segmentation. (CPU input, and all processing
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# and inference are also performed on CPU)
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#
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# It is required that "selfie_segmentation.tflite" or
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# "selfie_segmentation_landscape.tflite" is available at
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# "mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite"
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# or
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# "mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite"
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# path respectively during execution, depending on the specification in the
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# MODEL_SELECTION input side packet.
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#
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# EXAMPLE:
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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"
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# output_stream: "SEGMENTATION_MASK:segmentation_mask"
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# }
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type: "SelfieSegmentationCpu"
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# CPU image. (ImageFrame)
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input_stream: "IMAGE: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. (ImageFrame in ImageFormat::VEC32F1)
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output_stream: "SEGMENTATION_MASK:segmentation_mask"
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# Resizes the input image into a tensor with a dimension desired by the model.
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node {
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calculator: "SwitchContainer"
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input_side_packet: "SELECT:model_selection"
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input_stream: "IMAGE:image"
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output_stream: "TENSORS:input_tensors"
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options: {
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[mediapipe.SwitchContainerOptions.ext] {
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select: 0
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contained_node: {
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calculator: "ImageToTensorCalculator"
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options: {
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[mediapipe.ImageToTensorCalculatorOptions.ext] {
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output_tensor_width: 256
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output_tensor_height: 256
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keep_aspect_ratio: false
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output_tensor_float_range {
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min: 0.0
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max: 1.0
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}
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border_mode: BORDER_ZERO
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}
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}
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}
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contained_node: {
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calculator: "ImageToTensorCalculator"
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options: {
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[mediapipe.ImageToTensorCalculatorOptions.ext] {
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output_tensor_width: 256
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output_tensor_height: 144
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keep_aspect_ratio: false
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output_tensor_float_range {
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min: 0.0
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max: 1.0
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}
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border_mode: BORDER_ZERO
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}
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}
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}
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}
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}
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}
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# Generates a single side packet containing a TensorFlow Lite op resolver that
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# supports custom ops needed by the model used in this graph.
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node {
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calculator: "TfLiteCustomOpResolverCalculator"
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output_side_packet: "op_resolver"
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}
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# Loads the selfie segmentation TF Lite model.
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node {
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calculator: "SelfieSegmentationModelLoader"
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input_side_packet: "MODEL_SELECTION:model_selection"
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output_side_packet: "MODEL:model"
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}
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# Runs model inference on CPU.
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node {
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calculator: "InferenceCalculator"
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input_stream: "TENSORS:input_tensors"
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output_stream: "TENSORS:output_tensors"
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input_side_packet: "MODEL:model"
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input_side_packet: "CUSTOM_OP_RESOLVER:op_resolver"
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options: {
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[mediapipe.InferenceCalculatorOptions.ext] {
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delegate {
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xnnpack {}
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}
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}
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}
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}
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# Retrieves the size of the input image.
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node {
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calculator: "ImagePropertiesCalculator"
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input_stream: "IMAGE_CPU:image"
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output_stream: "SIZE:input_size"
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}
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# Processes the output tensors into a segmentation mask that has the same size
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# as the input image into the graph.
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node {
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calculator: "TensorsToSegmentationCalculator"
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input_stream: "TENSORS:output_tensors"
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input_stream: "OUTPUT_SIZE:input_size"
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output_stream: "MASK:mask_image"
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options: {
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[mediapipe.TensorsToSegmentationCalculatorOptions.ext] {
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activation: NONE
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}
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}
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}
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# Converts the incoming Image into the corresponding ImageFrame type.
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node: {
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calculator: "FromImageCalculator"
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input_stream: "IMAGE:mask_image"
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output_stream: "IMAGE_CPU:segmentation_mask"
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}
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