dnn_NMSBoxesRotated

Deep neural networksfunctionOpenCV 5.0.0

import { dnn_NMSBoxesRotated } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

ARGUMENTSbboxes, scores, score_threshold, nms_threshold

FUNCTIONdnn_NMSBoxesRotated

RETURN TYPEvoid

Call structure. A void return can still write to destination arguments. The parameter descriptions define inputs, outputs and ownership.

Performs non maximum suppression given boxes and corresponding scores.

dnn_NMSBoxesRotated(bboxes: RotatedRectVector, scores: FloatVector, score_threshold: number, nms_threshold: number, indices: IntVector, eta: number, top_k: number): void;

3 available overloads

dnn_NMSBoxesRotated(bboxes: RotatedRectVector, scores: FloatVector, score_threshold: number, nms_threshold: number, indices: IntVector): void;

dnn_NMSBoxesRotated(bboxes: RotatedRectVector, scores: FloatVector, score_threshold: number, nms_threshold: number, indices: IntVector, eta: number): void;

dnn_NMSBoxesRotated(bboxes: RotatedRectVector, scores: FloatVector, score_threshold: number, nms_threshold: number, indices: IntVector, eta: number, top_k: number): void;

bboxes: a set of bounding boxes to apply NMS.
scores: a set of corresponding confidences.
score_threshold: a threshold used to filter boxes by score.
nms_threshold: a threshold used in non maximum suppression.
indices: Output destination, filled by the native operation. the kept indices of bboxes after NMS.
eta: a coefficient in adaptive threshold formula: nms\_threshold_{i+1}=eta\cdot nms\_threshold_i.
top_k: if >0, keep at most top_k picked indices.

Upstream declaration ↗

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