148 lines
4.4 KiB
Plaintext
148 lines
4.4 KiB
Plaintext
# MediaPipe graph to detect palms with TensorFlow Lite on CPU.
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type: "PalmDetectionCpu"
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# CPU image. (ImageFrame)
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input_stream: "IMAGE:image"
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# Complexity of the palm detection model: 0 or 1. Accuracy as well as inference
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# latency generally go up with the model complexity. If unspecified, functions
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# as set to 1. (int)
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input_side_packet: "MODEL_COMPLEXITY:model_complexity"
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# Detected palms. (std::vector<Detection>)
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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 palms 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 an image into a 128x128 tensor while keeping the aspect ratio, and
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# therefore may result in potential letterboxing.
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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_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: 192
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output_tensor_height: 192
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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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border_mode: BORDER_ZERO
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}
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}
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}
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# Generates a single side packet containing a TensorFlow Lite op resolver that
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# supports custom ops needed by the model used in this graph.
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node {
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calculator: "TfLiteCustomOpResolverCalculator"
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output_side_packet: "opresolver"
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}
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# Loads the palm detection TF Lite model.
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node {
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calculator: "PalmDetectionModelLoader"
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input_side_packet: "MODEL_COMPLEXITY:model_complexity"
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output_side_packet: "MODEL:model"
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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_tensor"
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output_stream: "TENSORS:detection_tensors"
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input_side_packet: "CUSTOM_OP_RESOLVER:opresolver"
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input_side_packet: "MODEL:model"
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options: {
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[mediapipe.InferenceCalculatorOptions.ext] {
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delegate { xnnpack {} }
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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: 4
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min_scale: 0.1484375
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max_scale: 0.75
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input_size_width: 192
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input_size_height: 192
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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: 16
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strides: 16
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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: 2016
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num_coords: 18
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box_coord_offset: 0
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keypoint_coord_offset: 4
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num_keypoints: 7
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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: 192.0
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y_scale: 192.0
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w_scale: 192.0
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h_scale: 192.0
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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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