# 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"
}