图像处理——（源）均值滤波（blue）、高斯滤波（GaussianBlue）函数编程实现

#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include<iostream>
#include<stdlib.h>
using namespace cv;
using namespace std;


void MeanFilter_my1(const Mat &src, Mat &dst, int ksize)//均值滤波
{
    CV_Assert(ksize % 2 == 1);

    int *kernel = new int[ksize*ksize];
    for (int i = 0; i < ksize*ksize; i++)
        kernel[i] = 1;
    Mat tmp;
    int len = ksize / 2;
    tmp.create(Size(src.cols + len, src.rows + len), src.type());//添加边框
    dst.create(Size(src.cols, src.rows), src.type());

    int channel = src.channels();
    uchar *ps = src.data;
    uchar *pt = tmp.data;

    for (int row = 0; row < tmp.rows; row++)//添加边框的过程
    {
        for (int col = 0; col < tmp.cols; col++)
        {
            for (int c = 0; c < channel; c++)
            {
                if (row >= len && row < tmp.rows - len && col >= len && col < tmp.cols - len)
                    pt[(tmp.cols * row + col)*channel + c] = ps[(src.cols * (row - len) + col - len) * channel + c];
                else
                    pt[(tmp.cols * row + col)*channel + c] = 0;
            }
        }
    }


    uchar *pd = dst.data;
    pt = tmp.data;
    for (int row = len; row < tmp.rows - len; row++)//卷积的过程
    {
        for (int col = len; col < tmp.cols - len; col++)
        {
            for (int c = 0; c < channel; c++)
            {
                short t = 0;
                for (int x = -len; x <= len; x++)
                {
                    for (int y = -len; y <= len; y++)
                    {
                        t += kernel[(len + x) * ksize + y + len] * pt[((row + x) * tmp.cols + col + y) * channel + c];
                    }
                }

                pd[(dst.cols * (row - len) + col - len) * channel + c] = saturate_cast<ushort> (t/(ksize*ksize));//防止数据溢出ushort是16为数据
            }
        }
    }
    delete[] kernel;
}
//方差可调节
void GaussFilter_my(const Mat &src, Mat &dst, int ksize, double sigmaX, double sigmaY = 0)
{
    CV_Assert(ksize % 2 == 1);

    if (fabs(sigmaY) < 1e-5)
        sigmaY = sigmaX;

    double *kernel = new double[ksize*ksize];
    int center = ksize / 2;
    double sum = 0;
    for (int i = 0; i < ksize; i++)
    {
        for (int j = 0; j < ksize; j++)
        {
            kernel[i * ksize + j] = exp(-(i - center)*(i - center) / (2 * sigmaX*sigmaX) - (j - center)*(j - center) / (2 * sigmaY*sigmaY));
            sum += kernel[i*ksize + j];
        }
    }
    for (int i = 0; i < ksize; i++)
    {
        for (int j = 0; j < ksize; j++)
        {
            kernel[i*ksize + j] /= sum;
        }
    }
    Mat tmp;
    int len = ksize / 2;
    tmp.create(Size(src.cols + len, src.rows + len), src.type());//添加边框
    dst.create(Size(src.cols, src.rows), src.type());


    int channel = src.channels();
    uchar *ps = src.data;
    uchar *pt = tmp.data;

    for (int row = 0; row < tmp.rows; row++)//添加边框的过程
    {
        for (int col = 0; col < tmp.cols; col++)
        {
            for (int c = 0; c < channel; c++)
            {
                if (row >= len && row < tmp.rows - len && col >= len && col < tmp.cols - len)
                    pt[(tmp.cols * row + col)*channel + c] = ps[(src.cols * (row - len) + col - len) * channel + c];
                else
                    pt[(tmp.cols * row + col)*channel + c] = 0;
            }
        }
    }

    uchar *pd = dst.data;
    pt = tmp.data;
    for (int row = len; row < tmp.rows - len; row++)//卷积的过程
    {
        for (int col = len; col < tmp.cols - len; col++)
        {
            for (int c = 0; c < channel; c++)
            {
                short t = 0;
                for (int x = -len; x <= len; x++)
                {
                    for (int y = -len; y <= len; y++)
                    {
                        t += kernel[(len + x) * ksize + y + len] * pt[((row + x) * tmp.cols + col + y) * channel + c];
                    }
                }

                pd[(dst.cols * (row - len) + col - len) * channel + c] = saturate_cast<ushort> (t);//防止数据溢出ushort是16为数据
            }
        }
    }
    delete[] kernel;
}

int main()
{
    Mat img=imread("E://lena.jpg");
    Mat dst=imread("E://heibai.jpg");    
    imshow("img",img);
    imshow("dst",dst);
    int k=7;
    double sigmaX=0.001;
    double sigmaY = 0.001;
    MeanFilter_my1(img,dst,k);
    imshow("dst1",dst);
    GaussFilter_my(img,dst,k,sigmaX,sigmaY );
    imshow("dst1",dst);

    
    waitKey(0);
}