129 lines
3.8 KiB
Plaintext
129 lines
3.8 KiB
Plaintext
# MediaPipe object detection subgraph.
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type: "ObjectDetectionSubgraphCpu"
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input_stream: "IMAGE:input_video"
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output_stream: "DETECTIONS:output_detections"
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# Transforms the input image on CPU to a 320x320 image. To scale the image, by
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# default it uses the STRETCH scale mode that maps the entire input image to the
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# entire transformed image. As a result, image aspect ratio may be changed and
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# objects in the image may be deformed (stretched or squeezed), but the object
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# detection model used in this graph is agnostic to that deformation.
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node: {
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calculator: "ImageTransformationCalculator"
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input_stream: "IMAGE:input_video"
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output_stream: "IMAGE:transformed_input_video"
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node_options: {
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[type.googleapis.com/mediapipe.ImageTransformationCalculatorOptions] {
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output_width: 320
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output_height: 320
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}
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}
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}
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# Converts the transformed input image on CPU into an image tensor stored as a
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# TfLiteTensor.
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node {
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calculator: "TfLiteConverterCalculator"
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input_stream: "IMAGE:transformed_input_video"
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output_stream: "TENSORS:image_tensor"
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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: "TfLiteInferenceCalculator"
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input_stream: "TENSORS:image_tensor"
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output_stream: "TENSORS:detection_tensors"
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node_options: {
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[type.googleapis.com/mediapipe.TfLiteInferenceCalculatorOptions] {
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model_path: "mediapipe/models/ssdlite_object_detection.tflite"
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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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node_options: {
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[type.googleapis.com/mediapipe.SsdAnchorsCalculatorOptions] {
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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: 320
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input_size_width: 320
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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: "TfLiteTensorsToDetectionsCalculator"
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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:detections"
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node_options: {
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[type.googleapis.com/mediapipe.TfLiteTensorsToDetectionsCalculatorOptions] {
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num_classes: 91
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num_boxes: 2034
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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: 0.6
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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: "detections"
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output_stream: "filtered_detections"
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node_options: {
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[type.googleapis.com/mediapipe.NonMaxSuppressionCalculatorOptions] {
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min_suppression_threshold: 0.4
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max_num_detections: 3
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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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# 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: "filtered_detections"
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output_stream: "output_detections"
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node_options: {
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[type.googleapis.com/mediapipe.DetectionLabelIdToTextCalculatorOptions] {
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label_map_path: "mediapipe/models/ssdlite_object_detection_labelmap.txt"
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}
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}
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}
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