快速高斯滤波函数[修正完善版]

原文地址：http://blog.csdn.net/markl22222/article/details/10313565

进行了修正和变量优化。原来作者的函数只支持2次方图片，这次做了修正（windows的bitmap行宽是4字节对齐的）。

（ 基本完善了，但是在某些条件下，Y方向的底边还是会出现偏差，一时找不到原因，暂且发表，希望有人能提醒一下。）

今天检查了一下代码，发现了一个低级的技术性错误，Y方向底边的颜色偏差已经修正，这个函数趋于完美，收工。

函数结构我规整了一下，很清晰，很好阅读。

int gauss_blur(byte_t* image,//位图数据int linebytes,//位图行字节数，BMP数据在windows中是4字节对齐的。否则在处理非二次幂的图像时会有偏差int width,//位图宽度int height,//位图高度int cbyte,//颜色通道数量float sigma//高斯系数){int x = 0, y = 0, n = 0;int channel = 0;int srcline = 0, dstline = 0;int channelsize = width*height;int bufsize = width > height ? width + 4 : height + 4;float *w1 = NULL, *w2 = NULL, *imgbuf = NULL;int time = 0;#if defined(_INC_WINDOWS)time = GetTickCount();#elif defined(_CLOCK_T)time  = clock();#endifw1 = (float*)malloc(bufsize * sizeof(float));if(!w1){return -1;}w2 = (float*)malloc(bufsize * sizeof(float));if(!w2){free(w1);return -1;}imgbuf = (float*)malloc(channelsize * sizeof(float));if(!imgbuf){free(w1);free(w2);return -1;}//----------------计算高斯核---------------------------------------//float q  = 0;float q2 = 0, q3 = 0;float b0 = 0, b1 = 0, b2 = 0, b3 = 0;float B  = 0;if (sigma >= 2.5f){q = 0.98711f * sigma - 0.96330f;}else if ((sigma >= 0.5f) && (sigma < 2.5f)){q = 3.97156f - 4.14554f * (float) sqrt (1.0f - 0.26891f * sigma);}else{q = 0.1147705018520355224609375f;}q2 = q * q;q3 = q * q2;b0 = (1.57825+ (2.44413f*q)+(1.4281f *q2)+(0.422205f*q3));b1 = (         (2.44413f*q)+(2.85619f*q2)+(1.26661f* q3));b2 = (                     -((1.4281f*q2)+(1.26661f* q3)));b3 = (                                    (0.422205f*q3));B = 1.0-((b1+b2+b3)/b0);b1 /= b0;b2 /= b0;b3 /= b0;//----------------计算高斯核结束---------------------------------------//// 处理图像的多个通道for (channel = 0; channel < cbyte; ++channel){// 获取一个通道的所有像素值，并预处理for(y=0; y<height; ++y){srcline = y*linebytes;dstline = y*width;for(x=0, n=channel; x<width; ++x, n+=cbyte){(imgbuf+dstline)[x] = float((image+srcline)[n]);}}for (int x=0; x<width; ++x){//横向处理w1[0] = (imgbuf + x)[0];w1[1] = (imgbuf + x)[0];w1[2] = (imgbuf + x)[0];for (y=0, n=0; y<height; ++y, n+=width){w1[y+3] = B*(imgbuf + x)[n] + (b1*w1[y+2] + b2*w1[y+1] + b3*w1[y+0]);}w2[height+0]= w1[height+2];w2[height+1]= w1[height+1];w2[height+2]= w1[height+0];for (int y=height-1, n=y*width; y>=0; --y, n-=width){//保存数据到缓存(imgbuf + x)[n] = w2[y] = B*w1[y+3] + (b1*w2[y+1] + b2*w2[y+2] + b3*w2[y+3]);}}//横向处理for (y=0, srcline=0, dstline=0; y<height; ++y, srcline+=width, dstline+=linebytes){//纵向处理//取当前行数据w1[0] = (imgbuf + srcline)[0];w1[1] = (imgbuf + srcline)[0];w1[2] = (imgbuf + srcline)[0];//正方向横向处理3个点的数据for (x=0; x<width ; ++x){w1[x+3] = B*(imgbuf + srcline)[x] + (b1*w1[x+2] + b2*w1[x+1] + b3*w1[x+0]);}w2[width+0]= w1[width+2];w2[width+1]= w1[width+1];w2[width+2]= w1[width+0];//反方向处理for (x=width-1, n=x*cbyte+channel; x>=0; --x, n-=cbyte){//处理保存数据到缓存//(imgbuf + dstline)[x] = w2[x] = B*w1[x+3] + (b1*w2[x+1] + b2*w2[x+2] + b3*w2[x+3]);//存储返回数据(image + dstline)[n] = w2[x] = B*w1[x+3] + (b1*w2[x+1] + b2*w2[x+2] + b3*w2[x+3]);}}//纵向处理/*//存储处理完毕的通道for(int y=0; y<height; y++){int dstline = y*linebytes;int srcline = y*width;for (int x=0; x<width; x++){(image + dstline)[x * cbyte + channel] = (imgbuf + srcline)[x];//byte_edge((imgbuf + srcline)[x]-1);}}//存储循环//*/}//通道循环free(w1);w1=NULL;free(w2);w2=NULL;free(imgbuf);imgbuf=NULL;#if defined(_INC_WINDOWS)return GetTickCount() - time;#elif defined(_CLOCK_T)return clock() - time;#elsereturn 0;#endif}

应用实例：

//打开一个24位BMP图像HBITMAP hbmp = (HBITMAP)LoadImage(NULL, "a.bmp", IMAGE_BITMAP, 0, 0,LR_DEFAULTSIZE|LR_CREATEDIBSECTION|LR_LOADFROMFILE);BITMAP bm;if(hbmp){HDC dc = CreateCompatibleDC(NULL);SelectObject(dc, hbmp);GetObject(Image1->Picture->Bitmap->Handle, sizeof(bm), &bm);//高斯图像处理gauss_blur((BYTE*)bm.bmBits, bm.bmWidthBytes, bm.bmWidth, bm.bmHeight, 3, 3.0);//复制到你的DC上BitBlt(hMyDC, 0, 0, bm.bmWidth, bm.bmHeight, dc, 0, 0, SRCCOPY);DeleteObject(hbmp);DeleteDC(dc);}