实现功能：
c++语言实现纯高斯模糊处理灰度图像，不受图片格式限制
算法实现：
/// <summary> /// 程序功能：c语言实现纯高斯模糊处理灰度图像 /// 系统win7，VS开发环境,编程语言C++,OpenCV2.4.7最新整理时间 whd .9.9。 /// 参考博客：/tntmonks/p/5123854.html /// </summary>/// <param name=" pixels">源图像数据在内存的起始地址。</param>/// <param name="width">源和目标图像的宽度。</param>/// <param name="height">源和目标图像的高度。</param>/// <param name=" channels">通道数，灰度图像cn=1,彩色图像cn=3</param>/// <param name="sigma">sigma的平方是高斯函数的方差</param>/// <remarks> 1: 能处理8位灰度和24位图像。需要分开进行，后面会合成一个程序</remarks>// 以下为参考函数实现的整个过程//（1）建立工程，复制粘贴博客代码。// (2) 添加malloc()和free()函数的头文件// (3) exp()函数的头文件// (4) 修改Gasussblur中形参int sigma为float sigma，更加符合实际情况// (5) 配置OpenCV// (6) 调用函数 #include "stdafx.h"#include<stdlib.h> //malloc(),free()函数需要的头文件#include<math.h>#include<windows.h> //包含时钟头文件#include <opencv2/opencv.hpp>using namespace std;using namespace cv;inline int* buildGaussKern(int winSize, int sigma){int wincenter, x;float sum = 0.0f;wincenter = winSize / 2;float *kern = (float*)malloc(winSize*sizeof(float));int *ikern = (int*)malloc(winSize*sizeof(int));float SQRT_2PI = 2.506628274631f;float sigmaMul2PI = 1.0f / (sigma * SQRT_2PI);float divSigmaPow2 = 1.0f / (2.0f * sigma * sigma);for (x = 0; x < wincenter + 1; x++){kern[wincenter - x] = kern[wincenter + x] = exp(-(x * x)* divSigmaPow2) * sigmaMul2PI;sum += kern[wincenter - x] + ((x != 0) ? kern[wincenter + x] : 0.0);}sum = 1.0f / sum;for (x = 0; x < winSize; x++){kern[x] *= sum;ikern[x] = kern[x] * 256.0f;}free(kern);return ikern;}void GaussBlur(unsigned char* pixels, unsigned int width, unsigned int height, unsigned int channels, float sigma){width = 3 * width;if ((width % 4) != 0) width += (4 - (width % 4));unsigned int winsize = (1 + (((int)ceil(3 * sigma)) * 2));int *gaussKern = buildGaussKern(winsize, sigma);winsize *= 3;unsigned int halfsize = winsize / 2;unsigned char *tmpBuffer = (unsigned char*)malloc(width * height* sizeof(unsigned char));for (unsigned int h = 0; h < height; h++){unsigned int rowWidth = h * width;for (unsigned int w = 0; w < width; w += channels){unsigned int rowR = 0;unsigned int rowG = 0;unsigned int rowB = 0;int * gaussKernPtr = gaussKern;int whalfsize = w + width - halfsize;unsigned int curPos = rowWidth + w;for (unsigned int k = 1; k < winsize; k += channels){unsigned int pos = rowWidth + ((k + whalfsize) % width);int fkern = *gaussKernPtr++;rowR += (pixels[pos] * fkern);rowG += (pixels[pos + 1] * fkern);rowB += (pixels[pos + 2] * fkern);}tmpBuffer[curPos] = ((unsigned char)(rowR >> 8));tmpBuffer[curPos + 1] = ((unsigned char)(rowG >> 8));tmpBuffer[curPos + 2] = ((unsigned char)(rowB >> 8));}}winsize /= 3;halfsize = winsize / 2;for (unsigned int w = 0; w < width; w++){for (unsigned int h = 0; h < height; h++){unsigned int col_all = 0;int hhalfsize = h + height - halfsize;for (unsigned int k = 0; k < winsize; k++){col_all += tmpBuffer[((k + hhalfsize) % height)* width + w] * gaussKern[k];}pixels[h * width + w] = (unsigned char)(col_all >> 8);}}free(tmpBuffer);free(gaussKern);}void GaussBlur1D(unsigned char* pixels,unsigned char* pixelsout, unsigned int width, unsigned int height, float sigma) //删掉unsigned int channels,因为是单通道没有用{width = 1 * width; //3修改为1，因为三个通道变为了1个通道，存储每行数据的宽度变为了原来的1/3.if ((width % 4) != 0) width += (4 - (width % 4));unsigned int winsize = (1 + (((int)ceil(3 * sigma)) * 2)); //窗的大小int *gaussKern = buildGaussKern(winsize, sigma); //构建高斯核，计算高斯系数winsize *= 1; //3改为1，高斯窗的宽度变为原来的1/3unsigned int halfsize = winsize / 2; //窗的边到中心的距离unsigned char *tmpBuffer = (unsigned char*)malloc(width * height* sizeof(unsigned char)); //开辟新的内存存储处理高斯模糊后的数据for (unsigned int h = 0; h < height; h++) //外层循环，图像的高度{unsigned int rowWidth = h * width;//当前行的宽度为图像的高度乘以每行图像的数据所占的宽度。因为是按行存储的数组。for (unsigned int w = 0; w < width; w++) //w+=channels，可以修改为w++，因为是单通道数据，而不是三通道数据{unsigned int rowR = 0; //存储r分量的数据int * gaussKernPtr = gaussKern;//将高斯系数赋值给gaussKernPtrint whalfsize = w + width - halfsize;unsigned int curPos = rowWidth + w; //当前位置for (unsigned int k = 1; k < winsize;k++) // k += channels修改为k++{unsigned int pos = rowWidth + ((k + whalfsize) % width);int fkern = *gaussKernPtr++;rowR += (pixels[pos] * fkern); //当前像素值乘以高斯系数，rowR这了泛指单通道的当前像素点高斯处理后的数 }tmpBuffer[curPos] = ((unsigned char)(rowR >> 8)); //除以256}}halfsize = winsize / 2;for (unsigned int w = 0; w < width; w++){for (unsigned int h = 0; h < height; h++){unsigned int col_all = 0;int hhalfsize = h + height - halfsize;for (unsigned int k = 0; k < winsize; k++){col_all += tmpBuffer[((k + hhalfsize) % height)* width + w] * gaussKern[k];}pixelsout[h * width + w] = (unsigned char)(col_all >> 8);}}free(tmpBuffer);free(gaussKern);}int _tmain(int argc, _TCHAR* argv[]){const char* imagename = "C:\\Users\\Administrator.IES7LSEJAZ1GGRL\\Desktop\\PureGaussian-master\\GaussianBlur\\GaussianBlur\\InputName.bmp";//从文件中读入图像Mat img = imread(imagename);Mat dst = imread(imagename);Mat gray_img;Mat gray_dst;cvtColor(img, gray_img, CV_BGR2GRAY);cvtColor(dst, gray_dst, CV_BGR2GRAY);//如果读入图像失败if(img.empty()){fprintf(stderr, "Can not load image %s\n", imagename);return -1;}LARGE_INTEGER m_nFreq;LARGE_INTEGER m_nBeginTime;LARGE_INTEGER nEndTime;QueryPerformanceFrequency(&m_nFreq); // 获取时钟周期QueryPerformanceCounter(&m_nBeginTime); // 获取时钟计数GaussBlur1D(gray_img.data,gray_dst.data,gray_img.cols,gray_img.rows,2);QueryPerformanceCounter(&nEndTime);cout << (nEndTime.QuadPart-m_nBeginTime.QuadPart)*100/m_nFreq.QuadPart << endl;//显示图像imshow("原图像",gray_img);imshow("模糊图像", gray_dst);//此函数等待按键，按键盘任意键就返回waitKey();return 0;}
算法实现效果：sigma=2.0
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