104 lines
3.1 KiB
Plaintext
104 lines
3.1 KiB
Plaintext
# MediaPipe graph performing common processing to detect faces, currently
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# consisting of tensor post processing.
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#
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# EXAMPLE:
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# node {
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# calculator: "FaceDetectionFrontCommon"
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# input_stream: "TENSORS:detection_tensors"
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# input_stream: "MATRIX:transform_matrix"
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# output_stream: "DETECTIONS:detections"
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# }
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type: "FaceDetectionFrontCommon"
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# Detection tensors. (std::vector<Tensor>)
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input_stream: "TENSORS:detection_tensors"
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# A 4x4 row-major-order matrix that maps a point represented in the detection
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# tensors to a desired coordinate system, e.g., in the original input image
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# before scaling/cropping. (std::array<float, 16>)
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input_stream: "MATRIX:transform_matrix"
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# Detected faces. (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 faces 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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# 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_height: 128
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input_size_width: 128
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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: 896
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num_coords: 16
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box_coord_offset: 0
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keypoint_coord_offset: 4
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num_keypoints: 6
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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: 128.0
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y_scale: 128.0
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h_scale: 128.0
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w_scale: 128.0
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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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# Projects the detections from input tensor to the corresponding locations on
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# the original image (input to the graph).
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node {
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calculator: "DetectionProjectionCalculator"
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input_stream: "DETECTIONS:filtered_detections"
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input_stream: "PROJECTION_MATRIX:transform_matrix"
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output_stream: "DETECTIONS:detections"
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}
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