135 lines
3.7 KiB
Plaintext
135 lines
3.7 KiB
Plaintext
# MediaPipe Objectron object detection CPU subgraph.
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type: "ObjectDetectionOidV4Subgraph"
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input_stream: "IMAGE:input_video"
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input_side_packet: "LABELS_CSV:allowed_labels"
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output_stream: "DETECTIONS:detections"
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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:input_video"
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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: 300
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output_tensor_height: 300
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keep_aspect_ratio: false
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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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}
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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:image_tensor"
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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/objectron/object_detection_ssd_mobilenetv2_oidv4_fp16.tflite"
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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: 6
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min_scale: 0.2
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max_scale: 0.95
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input_size_height: 300
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input_size_width: 300
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anchor_offset_x: 0.5
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anchor_offset_y: 0.5
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strides: 16
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strides: 32
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strides: 64
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strides: 128
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strides: 256
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strides: 512
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aspect_ratios: 1.0
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aspect_ratios: 2.0
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aspect_ratios: 0.5
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aspect_ratios: 3.0
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aspect_ratios: 0.3333
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reduce_boxes_in_lowest_layer: 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:all_detections"
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options: {
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[mediapipe.TensorsToDetectionsCalculatorOptions.ext] {
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num_classes: 24
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num_boxes: 1917
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num_coords: 4
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ignore_classes: 0
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sigmoid_score: true
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apply_exponential_on_box_size: true
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x_scale: 10.0
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y_scale: 10.0
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h_scale: 5.0
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w_scale: 5.0
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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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# Maps detection label IDs to the corresponding label text. The label map is
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# provided in the label_map_path option.
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node {
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calculator: "DetectionLabelIdToTextCalculator"
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input_stream: "all_detections"
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output_stream: "labeled_detections"
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options: {
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[mediapipe.DetectionLabelIdToTextCalculatorOptions.ext] {
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label_map_path: "mediapipe/modules/objectron/object_detection_oidv4_labelmap.txt"
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}
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}
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}
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# Filters the detections to only those with valid scores
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# for the specified allowed labels.
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node {
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calculator: "FilterDetectionCalculator"
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input_stream: "DETECTIONS:labeled_detections"
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output_stream: "DETECTIONS:filtered_detections"
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input_side_packet: "LABELS_CSV:allowed_labels"
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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: "filtered_detections"
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output_stream: "detections"
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options: {
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[mediapipe.NonMaxSuppressionCalculatorOptions.ext] {
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min_suppression_threshold: 0.5
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max_num_detections: 100
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overlap_type: INTERSECTION_OVER_UNION
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return_empty_detections: true
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}
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}
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}
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