boxFilter

Image processingfunctionOpenCV 5.0.0

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

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

Blurs an image using the box filter.

The function smooths an image using the kernel:

\texttt{K} =  \alpha \begin{bmatrix} 1 & 1 & 1 &  \cdots & 1 & 1  \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \hdotsfor{6} \\ 1 & 1 & 1 &  \cdots & 1 & 1 \end{bmatrix}

where

\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true}  \\1 & \texttt{otherwise}\end{cases}

Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral.

See: blur, bilateralFilter, GaussianBlur, medianBlur, integral

boxFilter(src: Mat, dst: Mat, ddepth: number, ksize: Size, anchor: Point, normalize: boolean, borderType: number): void;

4 available overloads

boxFilter(src: Mat, dst: Mat, ddepth: number, ksize: Size): void;

boxFilter(src: Mat, dst: Mat, ddepth: number, ksize: Size, anchor: Point): void;

boxFilter(src: Mat, dst: Mat, ddepth: number, ksize: Size, anchor: Point, normalize: boolean): void;

boxFilter(src: Mat, dst: Mat, ddepth: number, ksize: Size, anchor: Point, normalize: boolean, borderType: number): void;

src: input image.
dst: Output destination, filled by the native operation. output image of the same size and type as src.
ddepth: the output image depth (-1 to use src.depth()).
ksize: blurring kernel size.
anchor: anchor point; default value Point(-1,-1) means that the anchor is at the kernel center.
normalize: flag, specifying whether the kernel is normalized by its area or not.
borderType: border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

Upstream declaration ↗

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