Average Filter with OpenCV - c++

Question

I'm trying to implement an Averaging filter with a 5x5 kernel, although there is a function within OpenCV for this, I need to do it without it.
There is something wrong and I think that are the variables uchar, but I tried int, float and double and the image resulting it's not correct. I use an image with a padding of 7.
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
#include "filter.h"
#include <iostream>
#include <fstream>
using namespace std;
using namespace cv;
cv::Mat filter::mean_filter(cv::Mat& image_in){
int centro = 7;
float total = 0.0;
double window[25];
double mean= 0.0;
int final=0;
int nlines, ncols;
cv::Mat kernel = cv::Mat::ones(5, 5, CV_32S);
nlines=image_in.size().height;
ncols=image_in.size().width;
cv::Mat image_out = cv::Mat::zeros(nlines,ncols,CV_32S);
for (unsigned int j=centro; j<nlines - centro; j++){
for (unsigned int z=centro; z<ncols - centro; z++){
window[0]=image_in.at<uchar>(j-2,z-2);
window[1]=image_in.at<uchar>(j-1,z-2);
window[2]=image_in.at<uchar>(j ,z-2);
window[3]=image_in.at<uchar>(j+1,z-2);
window[4]=image_in.at<uchar>(j+2,z-2);
window[5]=image_in.at<uchar>(j-2,z-1);
window[6]=image_in.at<uchar>(j-1,z-1);
window[7]=image_in.at<uchar>(j ,z-1);
window[8]=image_in.at<uchar>(j+1,z-1);
window[9]=image_in.at<uchar>(j+2,z-1);
window[10]=image_in.at<uchar>(j-2,z);
window[11]=image_in.at<uchar>(j-1,z);
window[12]=image_in.at<uchar>(j ,z);
window[13]=image_in.at<uchar>(j+1,z);
window[14]=image_in.at<uchar>(j+2,z);
window[15]=image_in.at<uchar>(j-2,z+2);
window[16]=image_in.at<uchar>(j-1,z+2);
window[17]=image_in.at<uchar>(j ,z+2);
window[18]=image_in.at<uchar>(j+1,z+2);
window[19]=image_in.at<uchar>(j+2,z+2);
window[20]=image_in.at<uchar>(j-2,z+1);
window[21]=image_in.at<uchar>(j-1,z+1);
window[22]=image_in.at<uchar>(j ,z+1);
window[23]=image_in.at<uchar>(j+1,z+1);
window[24]=image_in.at<uchar>(j+2,z+1);
mean=0.0;
final=0;
for (unsigned int k=0; k<25; k++){
mean+=window[k];
}
mean=mean/25;
final=round(mean);
image_out.at<int>(j,z)=final;
}
}
return image_out;
}

Answer 1

I changed your code a bit and have a working solution. It is a quite primitiv approach but it works.
Possible improvements could be to reuse some of the already accumulated pixel-values by tracking which pixels leave the kernel area and which pixels enter it.
Another possibility for improvement is to parallelise the loop over the image.
cv::Mat mean_filter(cv::Mat& image_in, int kernel)
{
// Make sure you get a grayscale image.
assert(image_in.type() == CV_8UC1);
// Make sure your kernel is an uneven number
assert(kernel % 2 == 1);
// Make sure your kernel is bigger than 1
assert(kernel >= 1);
// for padding calculate the border needed
int padding = (kernel - 1) / 2;
int mean = 0.0;
int final = 0;
int nlines, ncols;
cv::Mat img_temp;
nlines = image_in.size().height;
ncols = image_in.size().width;
// Make propper padding. Here it is done with 0. Padding describes the adding of a border to the image in order to avoid a cropping by applying a filter-mask.
copyMakeBorder(image_in, img_temp, padding, padding, padding, padding, BORDER_CONSTANT, 0);
// allocate the output image as grayscale as the input is grayscale as well
cv::Mat image_out = cv::Mat::zeros(nlines, ncols, CV_8UC1);
// loop over whole image
for (unsigned int j = padding; j<nlines + padding; j++){
for (unsigned int z = padding; z<ncols + padding; z++){
mean = 0.0;
// loop over kernel area
for (int x = -padding; x <= padding; x++){
for (int y = -padding; y <= padding; y++){
// accumulate all pixel-values
mean += img_temp.at<uchar>(j + x, z + y);
}
}
mean = mean / (kernel * kernel);
final = round(mean);
// cast result to uchar and set pixel in output image
image_out.at<uchar>(j - padding, z - padding) = (uchar)final;
}
}
return image_out;
}