matlab中的Sobel算子C程序源码

第一个：




/**************************************************

*   Sobel算子边缘检测

*   parameter: srcData - 原始图像数据指针

*              dstData - 存储处理后的图像

*              lWidth,lHeight - 图像的宽和高

*              dLineBites - 单行图像的字节数(4的倍数)

**************************************************/

void WINAPI SobelEdgeDetect(LPBYTE srcData,LPBYTE dstData,LONG lWidth,LONG lHeight,DWORD dLineBites)

{

    int KERNEL[4][9] = {{-1,0,1,-2,0,2,-1,0,1},

    {-1,-2,-1,0,0,0,1,2,1},

    {-2,-1,0,-1,0,1,0,1,2},

    {0,-1,-2,1,0,-1,2,1,0}};

    int i,j,k,kernel_sum,total_sum = 0,nMax = 0;

    unsigned char* pTmpSrcData = NULL;

    unsigned char* pTmpDstData = NULL;

    for (i = 1;i < lHeight - 1;i++)

    {

        pTmpSrcData = srcData + dLineBites * i;

        pTmpDstData = dstData + dLineBites * i;

        for (j = 1;j < lWidth - 1; ++j)

        {

            total_sum = 0;

            nMax = 0;

            for (k = 0;k < 4;k++)

            {

                kernel_sum = *(pTmpSrcData + j - dLineBites - 1) * KERNEL[k][0]+

                    *(pTmpSrcData + j - dLineBites ) * KERNEL[k][1]+

                    *(pTmpSrcData + j - dLineBites + 1) * KERNEL[k][2]+

                    *(pTmpSrcData + j - 1) * KERNEL[k][3]+

                    *(pTmpSrcData + j ) * KERNEL[k][4]+

                    *(pTmpSrcData + j + 1) * KERNEL[k][5]+

                    *(pTmpSrcData + j + dLineBites - 1) * KERNEL[k][6]+

                    *(pTmpSrcData + j + dLineBites ) * KERNEL[k][7]+

                    *(pTmpSrcData + j + dLineBites + 1) * KERNEL[k][8];

                kernel_sum = abs(kernel_sum);

                //total_sum += abs(kernel_sum);

                if(nMax < kernel_sum)

                    nMax = kernel_sum;

 

            }

            if(nMax > 255)

                nMax = 255;

            *(pTmpDstData + j) = (unsigned char)nMax;

        }

    }

}
















第二个：




/***********************************************************************

* Sobel边缘检测 （scale=0.5）

* 参数: image0为原图形，image1为边缘检测结果，w、h为图像的宽和高

* 当type为true时，差分结果取水平和垂直方向差分中较大者，否则取平均值

************************************************************************/

void SideSobel(BYTE* image0, BYTE* image1, unsigned int w, unsigned int h, bool type)

{

     int x, y, a, aHr, aHg, aHb, aVr, aVg, aVb, aH, aV;

     long n;

     double scale = 0.2;              // 该值是动态的,

     //依次处理每个像素

     for(y = 1; y < h-1; y++)

         for(x = 1; x < w-1; x++)

         {

              //计算像素的偏移位置

              n = (y*w+x)*4;

              //计算红色分量水平灰度差

              aHr = abs( (image0[n-w*4-4]+image0[n-4]*2+image0[n+w*4-4])

                   - (image0[n-w*4+4]+image0[n+4]*2+image0[n+w*4+4]) );

              //计算红色分量垂直灰度差

              aVr = abs( (image0[n-w*4-4]+image0[n-w*4]*2+image0[n-w*4+4])

                   - (image0[n+w*4-4]+image0[n+w*4]*2+image0[n+w*4+4]) );

              //计算绿色分量水平灰度差

              aHg = abs( (image0[n-w*4-4+1]+image0[n-4+1]*2+image0[n+w*4-4+1])

                   - (image0[n-w*4+4+1]+image0[n+4+1]*2+image0[n+w*4+4+1]) );

              //计算绿色分量垂直灰度差

              aVg = abs( (image0[n-w*4-4+1]+image0[n-w*4+1]*2+image0[n-w*4+4+1])

                   - (image0[n+w*4-4+1]+image0[n+w*4+1]*2+image0[n+w*4+4+1]) );

              //计算蓝色分量水平灰度差

              aHb = abs( (image0[n-w*4-4+2]+image0[n-4+2]*2+image0[n+w*4-4+2])

                   - (image0[n-w*4+4+2]+image0[n+4+2]*2+image0[n+w*4+4+2]) );

              //计算蓝色分量垂直灰度差

              aVb = abs( (image0[n-w*4-4+2]+image0[n-w*4+2]*2+image0[n-w*4+4+2])

                   - (image0[n+w*4-4+2]+image0[n+w*4+2]*2+image0[n+w*4+4+2]) );

 

              //计算水平综合灰度差

              aH = aHr + aHg + aHb;

              //计算垂直综合灰度差

              aV = aVr + aVg + aVb;

 

              if(type)

              {

                   //取水平和垂直方向差分中较大者

                   if(aH > aV) a = aH;

                   else a = aV;

              }

              else

              {

                   //取水平和垂直方向差分的平均值

                   a = (aH + aV)/2;

              }

 

               a = a *scale;

 

              a = a>255?255:a;

              //生成边缘扫描结果

              SetPixel(image1,n,a);

         }

}