160 lines
5.0 KiB
Plaintext
160 lines
5.0 KiB
Plaintext
# MediaPipe graph to detect poses. (CPU input, and inference is executed on
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# CPU.)
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#
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# It is required that "pose_detection.tflite" is available at
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# "mediapipe/modules/pose_detection/pose_detection.tflite"
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# path during execution.
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#
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# EXAMPLE:
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# node {
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# calculator: "PoseDetectionCpu"
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# input_stream: "IMAGE:image"
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# output_stream: "DETECTIONS:pose_detections"
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# }
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type: "PoseDetectionCpu"
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# CPU image. (ImageFrame)
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input_stream: "IMAGE:image"
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# Detected poses. (std::vector<Detection>)
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# Bounding box in each pose detection is currently set to the bounding box of
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# the detected face. However, 4 additional key points are available in each
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# detection, which are used to further calculate a (rotated) bounding box that
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# encloses the body region of interest. Among the 4 key points, the first two
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# are for identifying the full-body region, and the second two for upper body
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# only:
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#
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# Key point 0 - mid hip center
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# Key point 1 - point that encodes size & rotation (for full body)
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# Key point 2 - mid shoulder center
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# Key point 3 - point that encodes size & rotation (for upper body)
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#
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# NOTE: there will not be an output packet in the DETECTIONS stream for this
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# particular timestamp if none of poses detected. However, the MediaPipe
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# framework will internally inform the downstream calculators of the absence of
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# this packet so that they don't wait for it unnecessarily.
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output_stream: "DETECTIONS:detections"
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# Transforms the input image into a 224x224 one while keeping the aspect ratio
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# (what is expected by the corresponding model), resulting in potential
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# letterboxing in the transformed image.
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node: {
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calculator: "ImageToTensorCalculator"
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input_stream: "IMAGE:image"
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output_stream: "TENSORS:input_tensors"
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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: -1.0
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max: 1.0
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}
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border_mode: BORDER_ZERO
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# If this calculator truly operates in the CPU, then gpu_origin is
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# ignored, but if some build switch insists on GPU inference, then we will
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# still need to set this.
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gpu_origin: TOP_LEFT
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}
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}
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}
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# Runs a TensorFlow Lite model on CPU 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:input_tensors"
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output_stream: "TENSORS:detection_tensors"
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options: {
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[mediapipe.InferenceCalculatorOptions.ext] {
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model_path: "mediapipe/modules/pose_detection/pose_detection.tflite"
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delegate {
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xnnpack {}
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}
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}
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}
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}
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# Generates a single side packet containing a vector of SSD anchors based on
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# the specification in the options.
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node {
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calculator: "SsdAnchorsCalculator"
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output_side_packet: "anchors"
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options: {
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[mediapipe.SsdAnchorsCalculatorOptions.ext] {
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num_layers: 5
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min_scale: 0.1484375
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max_scale: 0.75
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input_size_height: 224
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input_size_width: 224
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anchor_offset_x: 0.5
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anchor_offset_y: 0.5
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strides: 8
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strides: 16
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strides: 32
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strides: 32
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strides: 32
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aspect_ratios: 1.0
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fixed_anchor_size: true
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}
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}
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}
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# Decodes the detection tensors generated by the TensorFlow Lite model, based on
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# the SSD anchors and the specification in the options, into a vector of
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# detections. Each detection describes a detected object.
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node {
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calculator: "TensorsToDetectionsCalculator"
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input_stream: "TENSORS:detection_tensors"
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input_side_packet: "ANCHORS:anchors"
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output_stream: "DETECTIONS:unfiltered_detections"
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options: {
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[mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
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num_classes: 1
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num_boxes: 2254
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num_coords: 12
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box_coord_offset: 0
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keypoint_coord_offset: 4
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num_keypoints: 4
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num_values_per_keypoint: 2
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sigmoid_score: true
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score_clipping_thresh: 100.0
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reverse_output_order: true
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x_scale: 224.0
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y_scale: 224.0
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h_scale: 224.0
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w_scale: 224.0
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min_score_thresh: 0.5
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}
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}
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}
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# Performs non-max suppression to remove excessive detections.
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node {
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calculator: "NonMaxSuppressionCalculator"
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input_stream: "unfiltered_detections"
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output_stream: "filtered_detections"
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options: {
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[mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
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min_suppression_threshold: 0.3
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overlap_type: INTERSECTION_OVER_UNION
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algorithm: WEIGHTED
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}
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}
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}
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# Adjusts detection locations (already normalized to [0.f, 1.f]) on the
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# letterboxed 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 (the
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# input image to the graph before image transformation).
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node {
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calculator: "DetectionLetterboxRemovalCalculator"
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input_stream: "DETECTIONS:filtered_detections"
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input_stream: "LETTERBOX_PADDING:letterbox_padding"
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output_stream: "DETECTIONS:detections"
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}
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