Blurs an image using the box filter

dst = cv.boxFilter(src)
dst = cv.boxFilter(src, 'OptionName',optionValue, ...)

Input

• src input image.

Output

• dst output image of the same size and type as src.

Options

• DDepth the output image depth (-1 to use class(src)). Default -1. See cv.filter2D for details.
• KSize blurring kernel size. Default [5,5]
• Anchor anchor point [x,y]; default value [-1,-1] means that the anchor is at the kernel center.
• Normalize flag, specifying whether the kernel is normalized by its area or not. default true
• BorderType border mode used to extrapolate pixels outside of the image. See cv.copyMakeBorder. Default 'Default'

The function smooths an image using the kernel:

K = alpha * ones(KSize)

where:

        | 1/prod(KSize)  when Normalize=true
alpha = |
        | 1              otherwise

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 cv.integral.