mediapipe-rs/mediapipe/modules/selfie_segmentation/selfie_segmentation_cpu.pbtxt
2022-06-11 12:25:48 -07:00

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# 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"
}
# 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: "CUSTOM_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"
}