getGaussianKernel

Image processingfunctionOpenCV 5.0.0

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

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

Explore visually: Gaussian blur →

Replace each pixel with a weighted average that gives nearby pixels more influence.

VISUAL WALKTHROUGHFiltering

REFERENCE INPUTNoisy or uneven image

The same scene stays here while the working view changes.

→

RESULTApply the rule at every pixel

The whole image has now been processed. The highlighted input value 156 becomes 136.8.

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

imgproc_feature

Returns Gaussian filter coefficients.

The function computes and returns the \texttt{ksize} \times 1 matrix of Gaussian filter coefficients:

G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},

where i=0..\texttt{ksize}-1 and \alpha is the scale factor chosen so that \sum_i G_i=1.

Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level GaussianBlur.

See: sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur

getGaussianKernel(ksize: number, sigma: number, ktype: number): Mat;

2 available overloads

getGaussianKernel(ksize: number, sigma: number): Mat;

getGaussianKernel(ksize: number, sigma: number, ktype: number): Mat;

ksize

Aperture size. It should be odd ( \texttt{ksize} \mod 2 = 1 ) and positive.

sigma

Gaussian standard deviation. If it is non-positive, it is computed from ksize as sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8.

ktype

Type of filter coefficients. It can be CV_32F or CV_64F .

Returns

The Mat result. Release returned native handles with using or delete(), including handles nested in results.

Upstream declaration ↗

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