mediapipe-rs/mediapipe/modules/objectron/box_landmark_gpu.pbtxt

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2022-03-01 13:04:01 +01:00
# MediaPipe Box landmark localization GPU subgraph.
type: "BoxLandmarkSubgraph"
input_stream: "IMAGE:image"
input_stream: "NORM_RECT:box_rect"
output_stream: "NORM_LANDMARKS:box_landmarks"
# Extracts image size from the input images.
node {
calculator: "ImagePropertiesCalculator"
input_stream: "IMAGE_GPU:image"
output_stream: "SIZE:image_size"
}
# Expands the rectangle that contain the box so that it's likely to cover the
# entire box.
node {
calculator: "RectTransformationCalculator"
input_stream: "NORM_RECT:box_rect"
input_stream: "IMAGE_SIZE:image_size"
output_stream: "box_rect_scaled"
options: {
[mediapipe.RectTransformationCalculatorOptions.ext] {
scale_x: 1.5
scale_y: 1.5
square_long: true
}
}
}
# Crops, resizes, and converts the input video into tensor.
# Preserves aspect ratio of the images.
node {
calculator: "ImageToTensorCalculator"
input_stream: "IMAGE_GPU:image"
input_stream: "NORM_RECT:box_rect_scaled"
output_stream: "TENSORS:image_tensor"
output_stream: "LETTERBOX_PADDING:letterbox_padding"
options {
[mediapipe.ImageToTensorCalculatorOptions.ext] {
output_tensor_width: 224
output_tensor_height: 224
keep_aspect_ratio: true
output_tensor_float_range {
min: 0.0
max: 1.0
}
gpu_origin: TOP_LEFT
border_mode: BORDER_REPLICATE
}
}
}
# Runs a TensorFlow Lite model on GPU that takes an image tensor and outputs a
# vector of tensors representing, for instance, detection boxes/keypoints and
# scores.
node {
calculator: "InferenceCalculator"
input_stream: "TENSORS:image_tensor"
output_stream: "TENSORS:output_tensors"
options: {
[mediapipe.InferenceCalculatorOptions.ext] {
model_path: "object_detection_3d.tflite"
delegate { gpu {} }
}
}
}
# Splits a vector of tensors to multiple vectors according to the ranges
# specified in option.
node {
calculator: "SplitTensorVectorCalculator"
input_stream: "output_tensors"
output_stream: "landmark_tensors"
output_stream: "box_flag_tensor"
options: {
[mediapipe.SplitVectorCalculatorOptions.ext] {
ranges: { begin: 0 end: 1 }
ranges: { begin: 1 end: 2 }
}
}
}
# Converts the box-flag tensor into a float that represents the confidence
# score of box presence.
node {
calculator: "TensorsToFloatsCalculator"
input_stream: "TENSORS:box_flag_tensor"
output_stream: "FLOAT:box_presence_score"
}
# Applies a threshold to the confidence score to determine whether a box is
# present.
node {
calculator: "ThresholdingCalculator"
input_stream: "FLOAT:box_presence_score"
output_stream: "FLAG:box_presence"
options: {
[mediapipe.ThresholdingCalculatorOptions.ext] {
threshold: 0.99
}
}
}
# Drops landmarks tensors if box is not present.
node {
calculator: "GateCalculator"
input_stream: "landmark_tensors"
input_stream: "ALLOW:box_presence"
output_stream: "gated_landmark_tensors"
}
# Decodes the landmark tensors into a list of landmarks, where the landmark
# coordinates are normalized by the size of the input image to the model.
node {
calculator: "TensorsToLandmarksCalculator"
input_stream: "TENSORS:gated_landmark_tensors"
output_stream: "NORM_LANDMARKS:landmarks"
options: {
[mediapipe.TensorsToLandmarksCalculatorOptions.ext] {
num_landmarks: 9
input_image_width: 224
input_image_height: 224
}
}
}
# Adjusts landmarks (already normalized to [0.f, 1.f]) on the letterboxed box
# image (after image transformation with the FIT scale mode) to the
# corresponding locations on the same image with the letterbox removed (box
# image before image transformation).
node {
calculator: "LandmarkLetterboxRemovalCalculator"
input_stream: "LANDMARKS:landmarks"
input_stream: "LETTERBOX_PADDING:letterbox_padding"
output_stream: "LANDMARKS:scaled_landmarks"
}
# Projects the landmarks from the cropped box image to the corresponding
# locations on the full image before cropping (input to the graph).
node {
calculator: "LandmarkProjectionCalculator"
input_stream: "NORM_LANDMARKS:scaled_landmarks"
input_stream: "NORM_RECT:box_rect_scaled"
output_stream: "NORM_LANDMARKS:box_landmarks"
}