goodFeaturesToTrackWithQuality

Features and matchingfunctionOpenCV 5.0.0

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

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

ARGUMENTSimage, corners, maxCorners, qualityLevel

FUNCTIONgoodFeaturesToTrackWithQuality

RETURN TYPEvoid

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

Same as above, but returns also quality measure of the detected corners.

goodFeaturesToTrackWithQuality(image: Mat, corners: Mat, maxCorners: number, qualityLevel: number, minDistance: number, mask: Mat, cornersQuality: Mat, blockSize: number, gradientSize: number, useHarrisDetector: boolean, k: number): void;

5 available overloads

goodFeaturesToTrackWithQuality(image: Mat, corners: Mat, maxCorners: number, qualityLevel: number, minDistance: number, mask: Mat, cornersQuality: Mat): void;

goodFeaturesToTrackWithQuality(image: Mat, corners: Mat, maxCorners: number, qualityLevel: number, minDistance: number, mask: Mat, cornersQuality: Mat, blockSize: number): void;

goodFeaturesToTrackWithQuality(image: Mat, corners: Mat, maxCorners: number, qualityLevel: number, minDistance: number, mask: Mat, cornersQuality: Mat, blockSize: number, gradientSize: number): void;

goodFeaturesToTrackWithQuality(image: Mat, corners: Mat, maxCorners: number, qualityLevel: number, minDistance: number, mask: Mat, cornersQuality: Mat, blockSize: number, gradientSize: number, useHarrisDetector: boolean): void;

goodFeaturesToTrackWithQuality(image: Mat, corners: Mat, maxCorners: number, qualityLevel: number, minDistance: number, mask: Mat, cornersQuality: Mat, blockSize: number, gradientSize: number, useHarrisDetector: boolean, k: number): void;

image: Input 8-bit or floating-point 32-bit, single-channel image.
corners: Output destination, filled by the native operation. Output vector of detected corners.
maxCorners: Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance: Minimum possible Euclidean distance between the returned corners.
mask: Region of interest. If the image is not empty (it needs to have the type CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
cornersQuality: Output destination, filled by the native operation. Output vector of quality measure of the detected corners.
blockSize: Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs .
gradientSize: Aperture parameter for the Sobel operator used for derivatives computation. See cornerEigenValsAndVecs .
useHarrisDetector: Parameter indicating whether to use a Harris detector (see #cornerHarris) or #cornerMinEigenVal.
k: Free parameter of the Harris detector.

Upstream declaration ↗

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