OpenCV学习(18) 细化算法(6)

本章我们在学习一下基于索引表的细化算法。

假设要处理的图像为二值图，前景值为1，背景值为0。

索引表细化算法使用下面的8邻域表示法：

一个像素的8邻域，我们可以用8位二进制表示，比如下面的8邻域，表示为00111000=0x38=56

我们可以枚举出各种情况下，当前像素能否删除的表，该表大小为256。它的索引即为8邻域表示的值，表中存的值为0或1，0表示当前像素不能删除，1表示可以删除。deletemark[256]

比如下图第一个表示，索引值为0，它表示孤立点，不能删除，所以deletemark[0]=0,第二个表示索引值为17，它表示端点，也不能删除，所以deletemark[17]=0，第三个表示索引为21，删除的话会改变连通域数量，所以deletemark[21]=0，第四个表示索引值为96，此时可以删除，所以deletemark[96]=1。

最终我们会定义一张完整的表来表示当前像素能否删除。

索引表细化算法描述很简单。

1.找到轮廓，其值用4表示

2.查找值为4的轮廓，查找索引表判断能否删除，能删除的话把它置为0。

循环迭代1，2直到再也没有可以删除的点为止。

下面的算法的代码：

void gThin::cvidxThin1(cv::Mat& src, cv::Mat& dst)        {

if(src.type()!=CV_8UC1)            {            printf("只能处理二值或灰度图像\n");return;            }//非原地操作时候，copy src到dstif(dst.data!=src.data)            {            src.copyTo(dst);            }

//    P0 P1 P2//    P7    P3//    P6 P5 P4unsigned char deletemark[256] = {            0,0,0,0,0,0,0,1,    0,0,1,1,0,0,1,1,            0,0,0,0,0,0,0,0,    0,0,1,1,1,0,1,1,            0,0,0,0,0,0,0,0,    1,0,0,0,1,0,1,1,            0,0,0,0,0,0,0,0,    1,0,1,1,1,0,1,1,            0,0,0,0,0,0,0,0,    0,0,0,0,0,0,0,0,            0,0,0,0,0,0,0,0,    0,0,0,0,0,0,0,0,            0,0,0,0,0,0,0,0,    1,0,0,0,1,0,1,1,            1,0,0,0,0,0,0,0,    1,0,1,1,1,0,1,1,            0,0,1,1,0,0,1,1,    0,0,0,1,0,0,1,1,            0,0,0,0,0,0,0,0,    0,0,0,1,0,0,1,1,            1,1,0,1,0,0,0,1,    0,0,0,0,0,0,0,0,            1,1,0,1,0,0,0,1,    1,1,0,0,1,0,0,0,            0,1,1,1,0,0,1,1,    0,0,0,1,0,0,1,1,            0,0,0,0,0,0,0,0,    0,0,0,0,0,1,1,1,            1,1,1,1,0,0,1,1,    1,1,0,0,1,1,0,0,            1,1,1,1,0,0,1,1,    1,1,0,0,1,1,0,0            };//索引int i, j;int width, height;//之所以减1，是方便处理8邻域，防止越界        width = src.cols -1;        height = src.rows -1;int step = src.step;int  p0, p1, p2,p3,p4,p5,p6,p7;        uchar* img;bool ifEnd;bool border = false; //交换删除的次序，防止从一边细化while(1)            {

border = !border;            img = dst.data;for(i = 1; i < height; i++)                {                img += step;for(j =1; j<width; j++)                    {                    uchar* p = img + j;//如果p点是背景点,继续循环if(p[0]==0) continue;                    p0 = p[-step-1]>0?1:0;                    p1 = p[-step]>0?1:0;                    p2 = p[-step+1]>0?1:0;                    p3 = p[1]>0?1:0;                    p4 = p[step+1]>0?1:0;                    p5 = p[step]>0?1:0;                    p6 = p[step-1]>0?1:0;                    p7 = p[-1]>0?1:0;

//如果sum等于0，则不是内部点，是轮廓点，设置其像素值为2int sum;                    sum =  p0 & p1 & p2 & p3 & p4 & p5 & p6 & p7;

//判断是否是邻接点或孤立点,0,1分别对于那个孤立点和端点if(sum==0)                        {                        dst.at<uchar>(i,j) = 4; //满足删除条件，设置当前像素为0                        }

}                }//printf("\n");//PrintMat(dst);//执行删除操作            ifEnd = false;

img = dst.data;for(i = 1; i < height; i++)                {                img += step;for(j =1; j<width; j++)                    {                    uchar* p = img + j;//如果p点是背景点,继续循环if(p[0]!=4) continue;                    p0 = p[-step-1]>0?1:0;                    p1 = p[-step]>0?1:0;                    p2 = p[-step+1]>0?1:0;                    p3 = p[1]>0?1:0;                    p4 = p[step+1]>0?1:0;                    p5 = p[step]>0?1:0;                    p6 = p[step-1]>0?1:0;                    p7 = p[-1]>0?1:0;

p1 = p1<<1;                    p2 = p2<<2;                    p3 = p3 <<3;                    p4 = p4<<4;                    p5 = p5<<5;                    p6 = p6 <<6;                    p7 = p7 << 7;

//求的8邻域在索引表中的索引int sum;                    sum = p0 | p1 | p2 | p3 | p4 | p5 | p6 | p7;

//判断是否是邻接点或孤立点,0,1分别对于那个孤立点和端点if(deletemark[sum] == 1)                        {                        dst.at<uchar>(i,j) = 0; //满足删除条件，设置当前像素为0                        ifEnd = true;                        }

}                }

//printf("\n");//PrintMat(dst);//printf("\n");

//已经没有可以细化的像素了，则退出迭代if(!ifEnd) break;            }

上面的算法可以看到细化后的轮廓偏右了，我们可以更改删除的循环条件，把循环拆分成三个，修改后的代码如下：

void gThin::cvidxThin(cv::Mat& src, cv::Mat& dst)    {

if(src.type()!=CV_8UC1)        {        printf("只能处理二值或灰度图像\n");return;        }//非原地操作时候，copy src到dstif(dst.data!=src.data)        {        src.copyTo(dst);        }

//    P0 P1 P2//    P7    P3//    P6 P5 P4unsigned char deletemark[256] = {        0,0,0,0,0,0,0,1,    0,0,1,1,0,0,1,1,        0,0,0,0,0,0,0,0,    0,0,1,1,1,0,1,1,        0,0,0,0,0,0,0,0,    1,0,0,0,1,0,1,1,        0,0,0,0,0,0,0,0,    1,0,1,1,1,0,1,1,        0,0,0,0,0,0,0,0,    0,0,0,0,0,0,0,0,        0,0,0,0,0,0,0,0,    0,0,0,0,0,0,0,0,        0,0,0,0,0,0,0,0,    1,0,0,0,1,0,1,1,        1,0,0,0,0,0,0,0,    1,0,1,1,1,0,1,1,        0,0,1,1,0,0,1,1,    0,0,0,1,0,0,1,1,        0,0,0,0,0,0,0,0,    0,0,0,1,0,0,1,1,        1,1,0,1,0,0,0,1,    0,0,0,0,0,0,0,0,        1,1,0,1,0,0,0,1,    1,1,0,0,1,0,0,0,        0,1,1,1,0,0,1,1,    0,0,0,1,0,0,1,1,        0,0,0,0,0,0,0,0,    0,0,0,0,0,1,1,1,        1,1,1,1,0,0,1,1,    1,1,0,0,1,1,0,0,        1,1,1,1,0,0,1,1,    1,1,0,0,1,1,0,0        };//索引int i, j;int width, height;//之所以减1，是方便处理8邻域，防止越界    width = src.cols -1;    height = src.rows -1;int step = src.step;int  p0, p1, p2,p3,p4,p5,p6,p7;    uchar* img;bool ifEnd;bool border = false; //交换删除的次序，防止从一边细化while(1)        {

border = !border;        img = dst.data;for(i = 1; i < height; i++)            {            img += step;for(j =1; j<width; j++)                {                uchar* p = img + j;//如果p点是背景点,继续循环if(p[0]==0) continue;                p0 = p[-step-1]>0?1:0;                p1 = p[-step]>0?1:0;                p2 = p[-step+1]>0?1:0;                p3 = p[1]>0?1:0;                p4 = p[step+1]>0?1:0;                p5 = p[step]>0?1:0;                p6 = p[step-1]>0?1:0;                p7 = p[-1]>0?1:0;

//如果sum等于0，则不是内部点，是轮廓点，设置其像素值为2int sum;                sum =  p0 & p1 & p2 & p3 & p4 & p5 & p6 & p7;

//判断是否是邻接点或孤立点,0,1分别对于那个孤立点和端点if(sum==0)                    {                    dst.at<uchar>(i,j) = 4; //满足删除条件，设置当前像素为0                    }

}            }//printf("\n");//PrintMat(dst);//执行删除操作        ifEnd = false;

img = dst.data;for(i = 1; i < height; i++)            {            img += step;for(j =1; j<width; j+=3)                {                uchar* p = img + j;//如果p点是背景点,继续循环if(p[0]!=4) continue;                p0 = p[-step-1]>0?1:0;                p1 = p[-step]>0?1:0;                p2 = p[-step+1]>0?1:0;                p3 = p[1]>0?1:0;                p4 = p[step+1]>0?1:0;                p5 = p[step]>0?1:0;                p6 = p[step-1]>0?1:0;                p7 = p[-1]>0?1:0;

p1 = p1<<1;                p2 = p2<<2;                p3 = p3 <<3;                p4 = p4<<4;                p5 = p5<<5;                p6 = p6 <<6;                p7 = p7 << 7;

//求的8邻域在索引表中的索引int sum;                sum = p0 | p1 | p2 | p3 | p4 | p5 | p6 | p7;

//判断是否是邻接点或孤立点,0,1分别对于那个孤立点和端点if(deletemark[sum] == 1)                    {                    dst.at<uchar>(i,j) = 0; //满足删除条件，设置当前像素为0                    ifEnd = true;                    }

}            }

img = dst.data;for(i = 1; i < height; i++)            {            img += step;for(j =2; j<width; j+=3)                {                uchar* p = img + j;//如果p点是背景点,继续循环if(p[0]!=4) continue;                p0 = p[-step-1]>0?1:0;                p1 = p[-step]>0?1:0;                p2 = p[-step+1]>0?1:0;                p3 = p[1]>0?1:0;                p4 = p[step+1]>0?1:0;                p5 = p[step]>0?1:0;                p6 = p[step-1]>0?1:0;                p7 = p[-1]>0?1:0;

p1 = p1<<1;                p2 = p2<<2;                p3 = p3 <<3;                p4 = p4<<4;                p5 = p5<<5;                p6 = p6 <<6;                p7 = p7 << 7;

//求的8邻域在索引表中的索引int sum;                sum = p0 | p1 | p2 | p3 | p4 | p5 | p6 | p7;

//判断是否是邻接点或孤立点,0,1分别对于那个孤立点和端点if(deletemark[sum] == 1)                    {                    dst.at<uchar>(i,j) = 0; //满足删除条件，设置当前像素为0                    ifEnd = true;                    }

}            }

img = dst.data;for(i = 1; i < height; i++)            {            img += step;for(j =3; j<width; j+=3)                {                uchar* p = img + j;//如果p点是背景点,继续循环if(p[0]!=4) continue;                p0 = p[-step-1]>0?1:0;                p1 = p[-step]>0?1:0;                p2 = p[-step+1]>0?1:0;                p3 = p[1]>0?1:0;                p4 = p[step+1]>0?1:0;                p5 = p[step]>0?1:0;                p6 = p[step-1]>0?1:0;                p7 = p[-1]>0?1:0;

p1 = p1<<1;                p2 = p2<<2;                p3 = p3 <<3;                p4 = p4<<4;                p5 = p5<<5;                p6 = p6 <<6;                p7 = p7 << 7;

//求的8邻域在索引表中的索引int sum;                sum = p0 | p1 | p2 | p3 | p4 | p5 | p6 | p7;

//判断是否是邻接点或孤立点,0,1分别对于那个孤立点和端点if(deletemark[sum] == 1)                    {                    dst.at<uchar>(i,j) = 0; //满足删除条件，设置当前像素为0                    ifEnd = true;                    }

}            }

//printf("\n");//PrintMat(dst);//printf("\n");

//已经没有可以细化的像素了，则退出迭代if(!ifEnd) break;        }

}

修改后的结果：

程序源代码：工程FirstOpenCV11