85 lines
2.3 KiB
Plaintext
85 lines
2.3 KiB
Plaintext
# MediaPipe graph that performs hair segmentation with TensorFlow Lite on GPU.
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# Used in the example in
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# mediapipie/examples/android/src/java/com/mediapipe/apps/hairsegmentationgpu.
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# Images on GPU coming into and out of the graph.
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input_stream: "input_video"
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output_stream: "output_video"
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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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node: {
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calculator: "ImageTransformationCalculator"
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input_stream: "IMAGE_GPU:throttled_input_video"
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output_stream: "IMAGE_GPU:transformed_input_video"
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node_options: {
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[type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
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output_width: 256
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output_height: 256
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}
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}
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}
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# Converts the transformed input image on GPU into an image tensor stored in
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# tflite::gpu::GlBuffer. The zero_center option is set to false to normalize the
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# pixel values to [0.f, 1.f] as opposed to [-1.f, 1.f]. With the
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# max_num_channels option set to 4, all 4 RGBA channels are contained in the
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# image tensor.
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node {
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calculator: "TfLiteConverterCalculator"
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input_stream: "IMAGE_GPU:transformed_input_video"
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output_stream: "TENSORS_GPU:image_tensor"
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options {
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[mediapipe.TfLiteConverterCalculatorOptions.ext] {
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output_tensor_float_range {
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min: 0
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max: 255
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}
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}
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}
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}
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node {
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calculator: "TfLiteInferenceCalculator"
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input_stream: "TENSORS_GPU:image_tensor"
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output_stream: "TENSORS:stylized_tensor"
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node_options: {
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[type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
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model_path: "mediapipe/models/metaf-512-mobile3.tflite"
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use_gpu: true
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}
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}
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}
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node {
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calculator: "TfLiteTensorsToSegmentationCalculator"
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input_stream: "TENSORS:stylized_tensor"
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output_stream: "MASK:mask_image"
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node_options: {
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[type.googleapis.com/mediapipe.TfLiteTensorsToSegmentationCalculatorOptions] {
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tensor_width: 256
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tensor_height: 256
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tensor_channels: 3
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}
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}
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}
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# Transfers the annotated image from CPU back to GPU memory, to be sent out of
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# the graph.
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node: {
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calculator: "ImageFrameToGpuBufferCalculator"
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input_stream: "mask_image"
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output_stream: "output_video"
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}
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