chromium/third_party/mediapipe/src/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu.pbtxt

# MediaPipe graph to perform selfie segmentation. (CPU input, and all processing
# and inference are also performed on CPU)
#
# It is required that "selfie_segmentation.tflite" or
# "selfie_segmentation_landscape.tflite" is available at
# "mediapipe/modules/selfie_segmentation/selfie_segmentation.tflite"
# or
# "mediapipe/modules/selfie_segmentation/selfie_segmentation_landscape.tflite"
# path respectively during execution, depending on the specification in the
# MODEL_SELECTION input side packet.
#
# EXAMPLE:
#   node {
#     calculator: "SelfieSegmentationCpu"
#     input_side_packet: "MODEL_SELECTION:model_selection"
#     input_stream: "IMAGE:image"
#     output_stream: "SEGMENTATION_MASK:segmentation_mask"
#   }

type: "SelfieSegmentationCpu"

# CPU image. (ImageFrame)
input_stream: "IMAGE:image"

# An integer 0 or 1. Use 0 to select a general-purpose model (operating on a
# 256x256 tensor), and 1 to select a model (operating on a 256x144 tensor) more
# optimized for landscape images. If unspecified, functions as set to 0. (int)
input_side_packet: "MODEL_SELECTION:model_selection"

# Segmentation mask. (ImageFrame in ImageFormat::VEC32F1)
output_stream: "SEGMENTATION_MASK:segmentation_mask"

# Resizes the input image into a tensor with a dimension desired by the model.
node {
  calculator: "SwitchContainer"
  input_side_packet: "SELECT:model_selection"
  input_stream: "IMAGE:image"
  output_stream: "TENSORS:input_tensors"
  options: {
    [mediapipe.SwitchContainerOptions.ext] {
      select: 0
      contained_node: {
        calculator: "ImageToTensorCalculator"
        options: {
          [mediapipe.ImageToTensorCalculatorOptions.ext] {
            output_tensor_width: 256
            output_tensor_height: 256
            keep_aspect_ratio: false
            output_tensor_float_range {
              min: 0.0
              max: 1.0
            }
            border_mode: BORDER_ZERO
          }
        }
      }
      contained_node: {
        calculator: "ImageToTensorCalculator"
        options: {
          [mediapipe.ImageToTensorCalculatorOptions.ext] {
            output_tensor_width: 256
            output_tensor_height: 144
            keep_aspect_ratio: false
            output_tensor_float_range {
              min: 0.0
              max: 1.0
            }
            border_mode: BORDER_ZERO
          }
        }
      }
    }
  }
}

# Generates a single side packet containing a TensorFlow Lite op resolver that
# supports custom ops needed by the model used in this graph.
node {
  calculator: "TfLiteCustomOpResolverCalculator"
  output_side_packet: "OP_RESOLVER:op_resolver"
}

# Loads the selfie segmentation TF Lite model.
node {
  calculator: "SelfieSegmentationModelLoader"
  input_side_packet: "MODEL_SELECTION:model_selection"
  output_side_packet: "MODEL:model"
}

# Runs model inference on CPU.
node {
  calculator: "InferenceCalculator"
  input_stream: "TENSORS:input_tensors"
  output_stream: "TENSORS:output_tensors"
  input_side_packet: "MODEL:model"
  input_side_packet: "OP_RESOLVER:op_resolver"
  options: {
    [mediapipe.InferenceCalculatorOptions.ext] {
      delegate {
        xnnpack {}
      }
    }
  }
}

# Retrieves the size of the input image.
node {
  calculator: "ImagePropertiesCalculator"
  input_stream: "IMAGE_CPU:image"
  output_stream: "SIZE:input_size"
}

# Processes the output tensors into a segmentation mask that has the same size
# as the input image into the graph.
node {
  calculator: "TensorsToSegmentationCalculator"
  input_stream: "TENSORS:output_tensors"
  input_stream: "OUTPUT_SIZE:input_size"
  output_stream: "MASK:mask_image"
  options: {
    [mediapipe.TensorsToSegmentationCalculatorOptions.ext] {
      activation: NONE
    }
  }
}

# Converts the incoming Image into the corresponding ImageFrame type.
node: {
  calculator: "FromImageCalculator"
  input_stream: "IMAGE:mask_image"
  output_stream: "IMAGE_CPU:segmentation_mask"
}