getDerivKernels

Image processingfunctionOpenCV 5.0.0

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

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

Explore visually: Sobel derivatives →

Measure image change along x or y while smoothing in the perpendicular direction.

VISUAL WALKTHROUGHEdges

REFERENCE INPUTIntensity image

Flat regions and sharp transitions respond very differently.

→

RESULTCombine x and y

Magnitude makes transitions visible regardless of sign. Display normalization is applied only to this illustration.

Computed teaching example on a 24 × 16 image. Small kernels and simplified settings keep each change visible; use the image laboratory for native OpenCV.

Returns filter coefficients for computing spatial image derivatives.

The function computes and returns the filter coefficients for spatial image derivatives. When ksize=FILTER_SCHARR, the Scharr 3 \times 3 kernels are generated (see #Scharr). Otherwise, Sobel kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to

getDerivKernels(kx: Mat, ky: Mat, dx: number, dy: number, ksize: number, normalize: boolean, ktype: number): void;

3 available overloads

getDerivKernels(kx: Mat, ky: Mat, dx: number, dy: number, ksize: number): void;

getDerivKernels(kx: Mat, ky: Mat, dx: number, dy: number, ksize: number, normalize: boolean): void;

getDerivKernels(kx: Mat, ky: Mat, dx: number, dy: number, ksize: number, normalize: boolean, ktype: number): void;

kx

Output destination, filled by the native operation. Output matrix of row filter coefficients. It has the type ktype .

ky

Output destination, filled by the native operation. Output matrix of column filter coefficients. It has the type ktype .

dx

Derivative order in respect of x.

dy

Derivative order in respect of y.

ksize

Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7.

normalize

Flag indicating whether to normalize (scale down) the filter coefficients or not. Theoretically, the coefficients should have the denominator =2^{ksize*2-dx-dy-2}. If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .

ktype

Type of filter coefficients. It can be CV_32f or CV_64F .

Upstream declaration ↗

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