【算法学习】纯高斯模糊算法处理灰度图片

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，VS2010开发环境,编程语言C++,OpenCV2.4.7最新整理时间 whd 2016.9.9。    /// 参考博客：http://www.cnblogs.com/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1/3    unsigned 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gaussKernPtr            int 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