image_style refactoring
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# 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: -1
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max: 1
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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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# MediaPipe graph that performs face mesh with TensorFlow Lite on CPU.
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# Input image. (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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# Transforms the input image on CPU to a 320x320 image. To scale the image, by
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# default it uses the STRETCH scale mode that maps the entire input image to the
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# entire transformed image. As a result, image aspect ratio may be changed and
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# objects in the image may be deformed (stretched or squeezed), but the object
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# detection model used in this graph is agnostic to that deformation.
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node: {
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calculator: "ImageTransformationCalculator"
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input_stream: "IMAGE:throttled_input_video"
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output_stream: "IMAGE: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 CPU into an image tensor as a
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# TfLiteTensor. The zero_center option is set to true to normalize the
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# pixel values to [-1.f, 1.f] as opposed to [0.f, 1.f].
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node {
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calculator: "TfLiteConverterCalculator"
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input_stream: "IMAGE:transformed_input_video"
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output_stream: "TENSORS:input_tensors"
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options {
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[mediapipe.TfLiteConverterCalculatorOptions.ext] {
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zero_center: false
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max_num_channels: 3
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output_tensor_float_range {
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min: 0.0
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max: 255.0
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}
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}
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}
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}
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# Runs a TensorFlow Lite model on CPU that takes an image tensor and outputs a
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# vector of tensors representing, for instance, detection boxes/keypoints and
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# scores.
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node {
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calculator: "TfLiteInferenceCalculator"
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input_stream: "TENSORS:input_tensors"
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output_stream: "TENSORS:output_tensors"
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node_options: {
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[type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
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model_path: "mediapipe/models/model_float32.tflite"
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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:output_tensors"
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output_stream: "MASK:output_video"
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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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