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