opencv学习_8 ( 图像细化)

深藏阁楼爱情的钟 2022-09-18 04:00 233阅读 0赞

图像细化——针对的是二值图像 或者用阀值处理的二值图像

(1)：例子 左边为输入图像 右边为细化的效果图

![SouthEast][]

(2)思想：

![SouthEast 1][]

公式: y = p0\*2^0 + p1\*2^1+ p2\*2^2 + p3\*2^3 + p4\*2^4 + p5\*2^5 + p6\*2^6 +p7\*2^7

前辈们对此作出了总结，得出每个点周围8领域的256种情况，放在一个char data\[256\]的数组中，不可以删除用0来表示，能被删除的用1来表示。然后对图像进行处理得到二值图像<0和1>，扫描图像，根据公式得出y，依次用data\[y\]判断该点是否可以被删除，直到所有的点都不可以被删除为止。

(3)算法步骤：

![SouthEast 2][]

(4)代码：

#include <stdlib.h>
    #include <string.h>
    
    #include "cv.h"
    #include "highgui.h"
    #include "cxcore.h"
    
    //基于索引表的细化细化算法
    //功能：对图象进行细化
    //参数：lpDIBBits：代表图象的一维数组
    //      lWidth：图象高度
    //      lHeight：图象宽度
    //      无返回值
    bool ThiningDIBSkeleton (unsigned char* lpDIBBits, int lWidth, int lHeight)
    {	
    	//循环变量
    	long i;
    	long j;
    	long lLength;
    
    	unsigned char deletemark[256] = {      // 这个即为前人据8领域总结的是否可以被删除的256种情况
    		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
    	};//索引表
    
    	unsigned char p0, p1, p2, p3, p4, p5, p6, p7;
    	unsigned char *pmid, *pmidtemp;    // pmid 用来指向二值图像  pmidtemp用来指向存放是否为边缘
    	unsigned char sum;
    	bool bStart = true;
    	lLength = lWidth * lHeight;
    	unsigned char *pTemp = new uchar[sizeof(unsigned char) * lWidth * lHeight]();  //动态创建数组 并且初始化
    	
    	//    P0 P1 P2
    	//    P7    P3
    	//    P6 P5 P4
    
    	while(bStart)
    	{
    		bStart = false;
    
    		//首先求边缘点
    		pmid = (unsigned char *)lpDIBBits + lWidth + 1;
    		memset(pTemp,  0, lLength);
    		pmidtemp = (unsigned char *)pTemp + lWidth + 1; //  如果是边缘点 则将其设为1
    		for(i = 1; i < lHeight -1; i++)     
    		{
    			for(j = 1; j < lWidth - 1; j++)
    			{
    				if( *pmid == 0)                   //是0 不是我们需要考虑的点
    				{
    					pmid++;
    					pmidtemp++;
    					continue;
    				}
    				p3 = *(pmid + 1);
    				p2 = *(pmid + 1 - lWidth);
    				p1 = *(pmid - lWidth);
    				p0 = *(pmid - lWidth -1);
    				p7 = *(pmid - 1);
    				p6 = *(pmid + lWidth - 1);
    				p5 = *(pmid + lWidth);
    				p4 = *(pmid + lWidth + 1);				
    				sum = p0 & p1 & p2 & p3 & p4 & p5 & p6 & p7;
    				if(sum == 0)
    				{
    					*pmidtemp = 1;       // 这样周围8个都是1的时候  pmidtemp==1 表明是边缘     					
    				}
    
    				pmid++;
    				pmidtemp++;
    			}
    			pmid++;
    			pmid++;
    			pmidtemp++;
    			pmidtemp++;
    		}
    		
    		//现在开始删除
    		pmid = (unsigned char *)lpDIBBits + lWidth + 1;
    		pmidtemp = (unsigned char *)pTemp + lWidth + 1;
    
    		for(i = 1; i < lHeight -1; i++)   // 不考虑图像第一行 第一列 最后一行 最后一列
    		{
    			for(j = 1; j < lWidth - 1; j++)
    			{
    				if( *pmidtemp == 0)     //1表明是边缘 0--周围8个都是1 即为中间点暂不予考虑
    				{
    					pmid++;
    					pmidtemp++;
    					continue;
    				}
    
    				p3 = *(pmid + 1);
    				p2 = *(pmid + 1 - lWidth);
    				p1 = *(pmid - lWidth);
    				p0 = *(pmid - lWidth -1);
    				p7 = *(pmid - 1);
    				p6 = *(pmid + lWidth - 1);
    				p5 = *(pmid + lWidth);
    				p4 = *(pmid + lWidth + 1);
    				
    				p1 *= 2;
    				p2 *= 4;
    				p3 *= 8;
    				p4 *= 16;
    				p5 *= 32;
    				p6 *= 64;
    				p7 *= 128;
    
    				sum = p0 | p1 | p2 | p3 | p4 | p5 | p6 | p7;
    			//	sum = p0 + p1 + p2 + p3 + p4 + p5 + p6 + p7;
    				if(deletemark[sum] == 1)
    				{
    					*pmid = 0;
    					bStart = true;      //  表明本次扫描进行了细化
    				}
    				pmid++;
    				pmidtemp++;
    			}
    
    			pmid++;
    			pmid++;
    			pmidtemp++;
    			pmidtemp++;
    		}
    	}
    	delete []pTemp;
    	return true;
    }
    
    int main(int argc, char* argv[])
    {
    	IplImage* src = cvLoadImage("E:\\study_opencv_video\\testthin\\char2.png",0);
    	cvThreshold(src,src,100,255,CV_THRESH_BINARY);
    	unsigned char* imagedata ;
    
    	cvNamedWindow("s",0);
    	cvShowImage("s" , src);
    	imagedata = new uchar[sizeof(char)*src->width*src->height]();
    	int x , y;
    	for(y=0;y<src->height;y++)
    	{
    		unsigned char* ptr  = (unsigned char*)(src->imageData + y*src->widthStep);
    		for(x=0;x<src->width;x++)
    		{
    			imagedata[y*src->width+x] = ptr[x] > 0 ? 1 : 0;
    		}
    	}
    	ThiningDIBSkeleton(imagedata,src->width,src->height);
    
    	for(y=0;y<src->height;y++)
    	{
    		unsigned char* ptr  = (unsigned char*)(src->imageData + y*src->widthStep);
    		for(x=0;x<src->width;x++)
    		{
    			ptr[x] = imagedata[y*src->width + x]>0? 255 : 0;
    		}
    
    	}
    	cvNamedWindow("src",0);
    	cvShowImage("src" , src);
    	cvWaitKey(0);
    	delete []imagedata;
    	return 0;
    }

作者：小村长  出处：http://blog.csdn.net/lu597203933 欢迎转载或分享，但请务必声明文章出处。 （新浪微博：小村长zack, 欢迎交流！）

[SouthEast]: /images/20220828/00f1b3aae4e24626b9a8dce9966a56aa.png
[SouthEast 1]: /images/20220828/c44f869ee60e4568873c13f5bff6a63d.png
[SouthEast 2]: /images/20220828/56fcd6d67bdf47d696ccdaa6b9eaa4bc.png