opencv手动对BGR彩图进行均值滤波

http://blog.csdn.net/hhygcy/article/details/4325304

这篇文章中实现了对灰度图的均值滤波，但相信我们平时做图像处理的时候还是要对原图进行滤波的

当然，我们可以用cvSplit()把原图分为B、G、R三部分，分别均值，之后再cvMerge，而我这里则是直接去操作

http://blog.csdn.net/xiaowei_cqu/article/details/7557063

通过上面这篇文章我们可以知道三通道图像的imgData分布为：

上文中还介绍了其它的获取图像像素点的方法，在我另一篇博文中也有提到，这里不做过多阐述

于是我们的代码就可以写出来了：

#include <iostream>
#include <cv.h>
#include <opencv2opencv.hpp>
#include <opencv2highguihighgui.hpp>
using namespace std;
using namespace cv;

int main() {
    IplImage* img = cvLoadImage("D:\other\video\437.jpg");

    uchar* data = (uchar*)img->imageData;
    int step = img->widthStep / sizeof(uchar);
    int channels = img->nChannels;
    uchar* b, *g, *r;
    uchar** pixelb = new uchar*[img->height], ** pixelg = new uchar*[img->height], ** pixelr = new uchar*[img->height];
    for (int i = 0; i < img->height; ++i) {
        pixelb[i] = new uchar[img->width];
        pixelg[i] = new uchar[img->width];
        pixelr[i] = new uchar[img->width];
    }

    for (int i = 1; i < img->height - 1; ++i){
        for (int j = 1; j < img->width; ++j) {
            pixelb[i][j] =
                (data[(i - 1)*step + (j - 1)*channels + 0] +
                data[(i - 1)*step + j*channels + 0] +
                data[(i - 1) * step + (j + 1)*channels + 0] +
                data[(i)*step + (j - 1)*channels + 0] +
                data[(i)*step + j*channels + 0] +
                data[(i)* step + (j + 1)*channels + 0] +
                data[(i + 1)*step + (j - 1)*channels + 0] +
                data[(i + 1)*step + j*channels + 0] +
                data[(i + 1) * step + (j + 1)*channels + 0]) / 9;

            pixelg[i][j] = 
                (data[(i - 1)*step + (j - 1)*channels + 1] +
                data[(i - 1)*step + j*channels + 1] +
                data[(i - 1) * step + (j + 1)*channels + 1] +
                data[(i)*step + (j - 1)*channels + 1] +
                data[(i)*step + j*channels + 1] +
                data[(i)* step + (j + 1)*channels + 1] +
                data[(i + 1)*step + (j - 1)*channels + 1] +
                data[(i + 1)*step + j*channels + 1] +
                data[(i + 1) * step + (j + 1)*channels + 1]) / 9;

            pixelr[i][j] = 
                (data[(i - 1)*step + (j - 1)*channels + 2] +
                data[(i - 1)*step + j*channels + 2] +
                data[(i - 1) * step + (j + 1)*channels + 2] +
                data[(i)*step + (j - 1)*channels + 2] +
                data[(i)*step + j*channels + 2] +
                data[(i)* step + (j + 1)*channels + 2] +
                data[(i + 1)*step + (j - 1)*channels + 2] +
                data[(i + 1)*step + j*channels + 2] +
                data[(i + 1) * step + (j + 1)*channels + 2]) / 9;
        }
    }

    for (int i = 1; i < img->height; ++i) {
        for (int j = 1; j < img->width; ++j) {
            data[i*step + j*channels + 0] = pixelb[i][j];
            data[i*step + j*channels + 1] = pixelg[i][j];
            data[i*step + j*channels + 2] = pixelr[i][j];
        }
    }
    cvNamedWindow("filter");
    cvShowImage("filter", img);
    cvWaitKey(0);
    cvReleaseImage(&img);
    return 0;
}

运行结果如图：

可见变得平滑很多：）