mediapipe/mediapipe/graphs/image_style/image_style.pbtxt
2022-06-24 23:59:54 +04:00

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