【算法学习】高斯模糊算法

/// <summary>  /// 程序功能：c语言实现纯高斯模糊/// 系统Ubuntu 15.10，GCC开发环境,编程语言C,最新整理时间 whd 2016.9.2。  /// 参考代码：https://github.com/Duwaz/Gaussian_Blur/// <remarks> 1: 能处理24位BMP格式图像。</remarks>    /// <remarks> 2: 主程序无参数,默认处理工程目录下的input.bmp,处理后的结果为output.bmp。</remarks>    /// <remarks> 3: 处理效果由高斯模糊半径决定:GaussBlur(bmp_t*, double)函数的第二个参数</remarks> /// </summary> #include<stdio.h>  #include<stdlib.h>#include<malloc.h>#include <math.h>#define SQRT_2PI 2.506628274631//自定义数据类型typedef unsigned long       DWORD; //四个字节typedef int                 BOOL;typedef unsigned char       BYTE; //一个字节typedef unsigned short      WORD; //一个字节//位图信息头结构体定义typedef struct tagBITMAPINFOHEADER{   WORD   bfType;  //两个字节   DWORD  bfileSize;   DWORD  bfReserved;   DWORD  bOffBits;   DWORD  biSize;   //4个字节   long   biWidth;  //4个字节   long   biHeight;   WORD   biPlanes;   WORD   biBitCount;   DWORD  biCompression;   DWORD  biSizeImage;   long   biXPelsPerMeter;   long   biYPelsPerMeter;   DWORD biClrUsed;   DWORD biClrImportant;} __attribute__((packed))BITMAPINFOHEADER,*PBITMAPINFOHEADER;  //字节对齐typedef struct{BITMAPINFOHEADER header;  //信息头char *data;//图像数据} bmp_t;int win_size(double);  double Gauss(double, double);void bmp_free(bmp_t *);bmp_t *bmp_open(FILE *);int bmp_write(bmp_t*, FILE*);bmp_t *GaussBlur(bmp_t*, double);int main(void)  {          char *InputName, *OutputName;  //输入输出图像文件名变量FILE *InputFile, *OutputFile;  //输入输出图像文件bmp_t *bmp = NULL, *blur = NULL;        InputName = "input.bmp"; OutputName = "output.bmp";        if(!(InputFile=fopen(InputName, "r")))  //图像文件打开函数,打不开返回,提示找不到文件{printf("File not found\n");return 1;}        bmp = bmp_open(InputFile);   //图像文件读取操作,前面必须有打开的操作才能读取文件数据fclose(InputFile);//关闭文件        OutputFile = fopen(OutputName, "wb"); //打开并创建输出文件        if (OutputFile == NULL)         {printf("Can't open %s\n", OutputName);return 1;}        blur = GaussBlur(bmp, 4.0); //模糊处理并返回处理后数据        bmp_write(blur, OutputFile);//将处理后数据存放到新建的文件中         fclose(OutputFile); //关闭文件        bmp_free(bmp);//释放存放读取数据的内存        free(blur);//释放处理后图像数据的内存        return 0;}  /// <summary>  /// 函数功能:给定一个BMP图像文件,将其中的数据读取出来,并返回图像数据  /// 函数返回:图像的数据(信息头和数据)/// 知识点:如何读取一个图像文件的数据 /// </summary>  /// <param name="f">图像文件存放位置地址。</param>  bmp_t *bmp_open(FILE *f) {bmp_t *bmp;bmp = (bmp_t *)malloc(sizeof(bmp_t));bmp->data = NULL;if (fread(&(bmp->header), sizeof(BITMAPINFOHEADER), 1, f)) {bmp->data = (char*)malloc(bmp->header.biSizeImage);if (fread(bmp->data, bmp->header.biSizeImage, 1, f))                 printf("图像读取成功\n");                 printf("Width: %ld\n", bmp->header.biWidth);                 printf("Height: %ld\n", bmp->header.biHeight);                 printf("BitCount: %d\n\n", (int)bmp->header.biBitCount);return bmp;}fprintf(stderr, "Error reading file");bmp_free(bmp);return NULL;}/// <summary>  /// 函数功能:释放存放图像数据的内存  /// 函数返回:无/// 知识点:释放图像数据内存 /// </summary>  /// <param name="bmp">图像数据变量。</param>  void bmp_free(bmp_t *bmp){if (bmp == NULL) return;if (bmp->data != NULL) free(bmp->data);free(bmp);}/// <summary>  /// 函数功能:将图像数据写入到图像文件中  /// 函数返回:写入数据的个数/// 知识点:将数据写入文件中(将数值从内存写入文件中)/// </summary>  /// <param name="bmp">图像数据变量。</param>  /// <param name="out">文件流。</param>  int bmp_write(bmp_t *bmp, FILE *out) {return fwrite(&(bmp->header), sizeof(BITMAPINFOHEADER), 1, out)&& fwrite(bmp->data, bmp->header.biSizeImage, 1, out);}/// <summary>  /// 函数功能:模糊窗的大小(根据高斯半径和模糊半径满足3sigma原则) /// 函数返回:模糊窗的大小,即长或宽(长=宽)/// 知识点:高斯模糊的3*sigma原则/// </summary>  /// <param name="sigma">高斯核函数的参数sigma</param>  int win_size(double sigma){return (1 + (((int)ceil(3 * sigma)) * 2)); }/// <summary>  /// 函数功能:单像素点计算高斯系数 /// 函数返回:高斯系数/// 知识点:高斯系数计算公式/// </summary>  /// <param name="sigma">高斯核函数的参数sigma</param>  /// <param name="x">当前像素距离模糊窗中心的距离</param>  double Gauss(double sigma, double x){return exp(-(x * x) / (2.0 * sigma * sigma)) / (sigma * SQRT_2PI);}/// <summary>  /// 函数功能:计算高斯模糊窗下的每个像素对应的权值,计算一半就够了,因为是权值是对称的(一维高斯)/// 函数返回:模糊窗下每个像素的权值/// 知识点:模糊窗下的各点的高斯系数(一维数组)/// </summary>  /// <param name="sigma">高斯核函数的参数sigma</param>  /// <param name="win_size">模糊窗的大小</param>  double* GaussAlgorithm(int win_size, double sigma){int wincenter, x;double *kern, sum = 0.0;wincenter = win_size / 2;kern = (double*)calloc(win_size, sizeof(double));for (x = 0; x < wincenter + 1; x++){kern[wincenter - x] = kern[wincenter + x] = Gauss(sigma, x);sum += kern[wincenter - x] + ((x != 0) ? kern[wincenter + x] : 0.0);}for (x = 0; x < win_size; x++)kern[x] /= sum;return kern;}/// <summary>  /// 函数功能:高斯模糊实现函数/// 函数返回:模糊后的图像/// 知识点:模糊窗下的各点的高斯系数(数组)/// </summary>  /// <param name="src">待模糊的图像</param>  /// <param name="sigma">模糊半径(高斯核函数参数)</param>  /// <remarks> rgb三通道分别处理</remarks> bmp_t *GaussBlur(bmp_t *src, double sigma) {introw, col, col_r, col_g, col_b, winsize, halfsize, k, count, rows, count1, count2, count3;int width, height;double  row_g, row_b, row_r, col_all;unsigned char  r_r, r_b, r_g, c_all;char *tmp;double *algorithm;count=0;width = 3*src->header.biWidth; height = src->header.biHeight;if ((width % 4) != 0) width += (4 - (width % 4)); bmp_t *blur;blur = (bmp_t*)malloc(sizeof(bmp_t));blur->header = src->header;blur->header.biWidth = src->header.biWidth;blur->header.biHeight = src->header.biHeight;blur->header.biSizeImage = width * blur->header.biHeight;blur->data = (char*)malloc(blur->header.biSizeImage);winsize = win_size(sigma);algorithm = GaussAlgorithm(winsize, sigma); winsize *= 3; halfsize = winsize / 2;tmp = (char*)calloc(width * height, sizeof(char)); for (row = 0; row < height; row++){col_r = 0;col_g = 1;col_b = 2;for (rows = 0; rows < width; rows += 3){row_r = row_g = row_b = 0.0;count1 = count2 = count3 = 0;for (k = 1; k < winsize; k += 3){if ((k + col_r - halfsize >= 0) && (k + col_r - halfsize < width)){r_r = *(src->data + row * width + col_r + k - halfsize);row_r += (int)(r_r)* algorithm[count1];count1++;}if ((k + col_g - halfsize >= 0) && (k + col_g - halfsize < width)){r_g = *(src->data + row * width + col_g + k - halfsize);row_g += (int)(r_g)* algorithm[count2];count2++;}if ((k + col_b - halfsize >= 0) && (k + col_b - halfsize < width)){r_b = *(src->data + row * width + col_b + k - halfsize);row_b += (int)(r_b)* algorithm[count3];count3++;}}*(tmp + row * width + col_r) = (unsigned char)(ceil(row_r));*(tmp + row * width + col_g) = (unsigned char)(ceil(row_g));*(tmp + row * width + col_b) = (unsigned char)(ceil(row_b));col_r += 3;col_g += 3;col_b += 3;}}winsize /= 3;halfsize = winsize / 2;for (col = 0; col < width; col++)for (row = 0; row < height; row++){col_all = 0.0;for (k = 0; k < winsize; k++)if ((k + row - halfsize >= 0) && (k + row - halfsize < height)){c_all = *(tmp + (row + k - halfsize) * width + col);col_all += ((int)c_all) * algorithm[k];}*(blur->data + row * width + col) = (unsigned char)(ceil(col_all));}free(tmp);free(algorithm);return blur;}

算法处理效果:sigma=4.0