OpenCV Filters - boxFilter

Blurs an image using the box filter.

C++: void boxFilter(InputArray src, OutputArray dst, int ddepth, Size ksize, Point anchor=Point(-1,-1), bool normalize=true, int borderType=BORDER_DEFAULT )

Python: cv2.boxFilter(src, ddepth, ksize[, dst[, anchor[, normalize[, borderType]]]]) → dst

Parameters:

src – input image. dst – 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.

The function smoothes an image using the kernel:

Where,

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().

Reference: OpenCV Documentation - boxFilter


Example
This is a sample code (C++) with images for opencv box filter.

 string imgFileName = "lena.jpg";

 cv::Mat src = cv::imread(imgFileName);
 if (!src.data){
    cout << "Unable to open file" << endl;
    getchar();
    return 1;
 }

 cv::Mat dst;
 cv::boxFilter(src, dst, -1, cv::Size(16, 16));

 cv::namedWindow("Source");
 cv::namedWindow("Filtered");

 cv::imshow("Source", src);
 cv::imshow("Filtered", dst);
 cv::waitKey(0);

 cv::imwrite("Box Filter.jpg", dst);

 return 0;

Filtered Image	Source Image

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