Canny边缘检测原理与C++实现(2)实现部分-白红宇

Canny边缘检测原理与C++实现(2)实现部分

阅读量：4952 次

发布时间：2019-06-11

本文共 10063 字，大约阅读时间需要 33 分钟。

转载请说明出处：

http://blog.csdn.net/zhubaohua_bupt/article/details/73844187

本代码实现完全脱离opencv，如果需要显示，可以调用，以便观察检测效果。

首先，由于多次用到图像，所以定义图像数据结构，

DATA.h

#ifndef DATA_#define DATA_#include 
    
     #include
     
      #include"memory.h"using namespace std;typedef  unsigned char PIXUC1;typedef   float  PIXFC1;//单通道 类型图像template
      
       class IMGCH1{public:   IMGCH1(unsigned int HEIGHT_,unsigned int WIDTH_,unsigned char INITVALUE);   IMGCH1(unsigned int HEIGHT_,unsigned int WIDTH_,PIXVALUETYPE* dataPtr_);   ~IMGCH1();PIXVALUETYPE* dataPtr;unsigned int rows;unsigned int cols;};template
       
        IMGCH1
        
         ::IMGCH1(unsigned int HEIGHT_,unsigned int WIDTH_,unsigned char INITVALUE):rows(HEIGHT_),cols(WIDTH_){   dataPtr=new PIXVALUETYPE[rows*cols];   memset(dataPtr,INITVALUE,rows*cols);}template
         
          IMGCH1
          
           ::IMGCH1(unsigned int HEIGHT_,unsigned int WIDTH_,PIXVALUETYPE* dataPtr_):rows(HEIGHT_),cols(WIDTH_){ dataPtr=new PIXVALUETYPE[rows*cols]; long int datalength =rows*cols; //拷贝数据 PIXVALUETYPE*pt=dataPtr; PIXVALUETYPE*pt_=dataPtr_;for(int i=0;i
           
             IMGCH1
            
             :: ~IMGCH1(){ delete [] dataPtr; }#endif

MyCanny.h

#include "iostream"  #include "math.h"  #include"DATA.h"using namespace std;   class MyCanny{public:void operator()(const IMGCH1
    
     & srcimg,IMGCH1
     
      & CannyImg,int lowthread,int highthread,int gaussSize);	//******************灰度转换函数*************************  //第一个参数image输入的彩色RGB图像；  //第二个参数imageGray是转换后输出的灰度图像；  //*************************************************************  void ToUchar(const IMGCH1
      
        &floatImage,IMGCH1
       
         &imageUchar);//******************高斯卷积核生成函数*************************  // gaus是一个指向含有N个double类型数组的二维指针；  // size是高斯卷积核的尺寸大小；  // gausArray 是一个指向含有N个double类型数组的一维指针；  // sigma是卷积核的标准差  //*************************************************************  void GetGaussianKernel(float **gaus, float* gausArray,const int size,const float sigma);    //******************高斯滤波*************************  //imageSource是待滤波原始图像；  //imageGaussian是滤波后输出图像；  //gausArray是一个指向含有N个double类型数组的指针；  //size是滤波核的尺寸  //*************************************************************  void GaussianFilter(const IMGCH1
        
         & srcimg,IMGCH1
         
          &imageGaussian,float gausArray[],int size);//******************Sobel算子计算梯度和方向******************** //imageSourc原始灰度图像； //imageSobelXY是梯度图像； //pointDrection是梯度方向数组指针 //************************************************************* void SobelGradDirection(const IMGCH1
          
            &imageSource,IMGCH1
           
             &imageSobelXY,char *pointDrection); //******************局部极大值抑制************************* //imageInput输入的Sobel梯度图像；//imageOutPut是输出的局部极大值抑制图像；//pointDrection是图像上每个点的梯度方向数组指针 梯度方向角,简化为 0(水平) 45，-45,90(垂直)//************************************************************* void LocalMaxValue(const IMGCH1
            
              &imageInput, IMGCH1
             
               &imageOutput, char *pointDrection); //******************双阈值处理************************* //imageInput经过局部极大值抑制的梯度图 //lowThreshold是低阈值 //highThreshold是高阈值 //****************************************************** void DoubleThreshold( IMGCH1
              
                &imageIput,int lowThreshold,int highThreshold); //******************双阈值中间像素连接处理********************* //imageInput 经过处理的局部极大值抑制的梯度图 
               
                0 >highThreshold ->255//函数执行完后是Canny边缘检测图, 边缘(255)其他(0)//lowThreshold是低阈值 //highThreshold是高阈值 //************************************************************* void DoubleThresholdLink(IMGCH1
                
                  &imageInput,IMGCH1
                 
                   &CannyImg,int lowThreshold) ;//******************递归连接边缘********************* //imageInput 梯度幅值图像； // x,y 为要检测点的坐标//lowThreshold是低阈值 //************************************************************* void LinkEdge(IMGCH1
                  
                    &imageInput,int x,int y,int lowThreshold );void SHOW(const IMGCH1
                   
                     &imageInput);int width;int height;};

MyCanny.cpp

#include"MyCanny.h"//为了显示，可以把类里的show函数注释掉，就可以在实现部分不依赖于opencv  #include"opencv.hpp"using namespace cv;void MyCanny::operator()(const IMGCH1
    
     & srcimg_,IMGCH1
     
      & CannyImg_ ,int lowthread,int highthread,int size){   if(srcimg_.cols==0&&srcimg_.rows==0)    {          cerr<<"  imageSource is empty"<
      
       highthread)    {	   cerr<<"Fourth parameter must large than third parameter. "<
       
         imageGaussian_(height,width,PIXUC1(0));    GaussianFilter(srcimg_,imageGaussian_,gausArray,size);   //梯度   IMGCH1
        
          SobelGradAmpl_(height,width,PIXUC1(0));     char *pointDirection=new  char[srcimg_.cols*srcimg_.rows];  //定义梯度方向角数组    SobelGradDirection(imageGaussian_,SobelGradAmpl_,pointDirection);  //计算X、Y方向梯度和方向角 //局部非极大值抑制       IMGCH1
         
           imageLocalMax_(height,width,PIXUC1(0)); LocalMaxValue(SobelGradAmpl_,imageLocalMax_,pointDirection); //双阈值处理 DoubleThreshold(imageLocalMax_,lowthread,highthread); //双阈值中间阈值滤除及连接 DoubleThresholdLink(imageLocalMax_,CannyImg_,lowthread); delete []pointDirection; for(int i=0;i
          
           & imageSource, IMGCH1
           
             &imageGaussian,float *gausArray,int size){ //滤波 for(int _row=0;_row
            
             =imageSource.rows?imageSource.rows-1:row; col=col<0?0:col; col=col>=imageSource.cols?imageSource.cols-1:col; //卷积和 imageGaussian.dataPtr[_row*width+_col]+=gausArray[k]*imageSource.dataPtr[row*width+col]; k++; } } } } } //******************Sobel算子计算X、Y方向梯度和梯度方向角******************** void MyCanny::SobelGradDirection(const IMGCH1
             
              & imageSource, IMGCH1
              
               &SobelAmpXY, char *pointDrection){ for(int i=0;i<(imageSource.rows-1)*(imageSource.cols-1);i++) { pointDrection[i]=0; } IMGCH1
               
                 imageSobelX(height,width,PIXFC1(0)); IMGCH1
                
                  imageSobelY(height,width,PIXFC1(0)); PIXUC1 *P=imageSource.dataPtr; PIXFC1 *PX=imageSobelX.dataPtr; PIXFC1 *PY=imageSobelY.dataPtr; int k=0; for(int row=1;row<(imageSource.rows-1);row++) { for(int col=1;col<(imageSource.cols-1);col++) { pointDrection[k]=-1; //通过指针遍历图像上每一个像素 int gradY=P[(row-1)*width+col+1]+P[row*width+col+1]*2+P[(row+1)*width+col+1]-P[(row-1)*width+col-1]-P[row*width+col-1]*2-P[(row+1)*width+col-1]; PY[row*width+col]=abs(gradY); int gradX=P[(row+1)*width+col-1]+P[(row+1)*width+col]*2+P[(row+1)*width+col+1]-P[(row-1)*width+col-1]-P[(row-1)*width+col]*2-P[(row-1)*width+col+1]; PX[row*width+col]=abs(gradX); if(gradX==0) { gradX=0.01; //防止除数为0异常 } float gradDrection=atan2(gradY,gradX)*57.3;//弧度转换为度 if(gradDrection<=-67.5&&gradDrection<=-112.5||gradDrection>=67.5&&gradDrection<=-112.5) pointDrection[k]=90; else if(gradDrection>=22.5&&gradDrection<67.5||gradDrection>=-157.5&&gradDrection<-112.5) pointDrection[k]=45; else if(gradDrection>=-67.5&&gradDrection<22.5||gradDrection>=112.5&&gradDrection<157.5) pointDrection[k]=-45; else pointDrection[k]=0; k++; } } IMGCH1
                 
                   imageSobelXY(height,width,PIXFC1(0)); for(int row=0;row
                  
                    &imageInput, IMGCH1
                   
                     &imageOutput, char *pointDrection){ for(int row=0;row
                    
                      &imageIput,int lowThreshold,int highThreshold){ for(int row=0;row
                     
                      highThreshold) imageIput.dataPtr[row*width+col]=255; if(imageIput.dataPtr[row*width+col]
                      
                        &imageInput,IMGCH1
                       
                         &CannyImg,int lowThreshold){ for(int row=1;row
                        
                          &imageInput,int x,int y,int lowThreshold ){ int nextpoint_x=-1; int nextpoint_y=-1; if(x<1||x>width-1||y<1||y>height-1) return; if( imageInput.dataPtr[(y-1)*width+x-1]>=lowThreshold&& imageInput.dataPtr[(y-1)*width+x-1]!=255) { nextpoint_x=x-1; nextpoint_y=y-1; imageInput.dataPtr[nextpoint_y*width+nextpoint_x]=255; LinkEdge(imageInput,nextpoint_x,nextpoint_y,lowThreshold); } else if( imageInput.dataPtr[(y-1)*width+x]>=lowThreshold&&imageInput.dataPtr[(y-1)*width+x]!=255) { nextpoint_x=x; nextpoint_y=y-1; imageInput.dataPtr[nextpoint_y*width+nextpoint_x]=255; LinkEdge(imageInput,nextpoint_x,nextpoint_y,lowThreshold); } else if(imageInput.dataPtr[(y-1)*width+x+1]>=lowThreshold&&imageInput.dataPtr[(y-1)*width+x+1]!=255) { nextpoint_x=x+1; nextpoint_y=y-1; imageInput.dataPtr[nextpoint_y*width+nextpoint_x]=255; LinkEdge(imageInput,nextpoint_x,nextpoint_y,lowThreshold); } else if( imageInput.dataPtr[y*width+x-1]>=lowThreshold&&imageInput.dataPtr[y*width+x-1]!=255 ) { nextpoint_x=x-1; nextpoint_y=y; imageInput.dataPtr[nextpoint_y*width+nextpoint_x]=255; LinkEdge(imageInput,nextpoint_x,nextpoint_y,lowThreshold); } else if( imageInput.dataPtr[y*width+x+1]>=lowThreshold&&imageInput.dataPtr[y*width+x+1]!=255 ) { nextpoint_x=x+1; nextpoint_y=y; imageInput.dataPtr[nextpoint_y*width+nextpoint_x]=255; LinkEdge(imageInput,nextpoint_x,nextpoint_y,lowThreshold); } else if( imageInput.dataPtr[(y+1)*width+x-1]>=lowThreshold&&imageInput.dataPtr[(y+1)*width+x-1]!=255 ) { nextpoint_x=x-1; nextpoint_y=y+1; imageInput.dataPtr[nextpoint_y*width+nextpoint_x]=255; LinkEdge(imageInput,nextpoint_x,nextpoint_y,lowThreshold); } else if( imageInput.dataPtr[(y+1)*width+x]>=lowThreshold&& imageInput.dataPtr[(y+1)*width+x]!=255 ) { nextpoint_x=x; nextpoint_y=y+1; imageInput.dataPtr[nextpoint_y*width+nextpoint_x]=255; LinkEdge(imageInput,nextpoint_x,nextpoint_y,lowThreshold); } else if(imageInput.dataPtr[(y+1)*width+x+1]>=lowThreshold&&imageInput.dataPtr[(y+1)*width+x+1]!=255) { nextpoint_x=x+1; nextpoint_y=y+1; imageInput.dataPtr[nextpoint_y*width+nextpoint_x]=255; LinkEdge(imageInput,nextpoint_x,nextpoint_y,lowThreshold); }}void MyCanny::ToUchar(const IMGCH1
                         
                           &floatImage,IMGCH1
                          
                            &imageUchar){ for(int row=0;row
                           
                            255?255: floatImage.dataPtr[row*width+col];}void MyCanny::SHOW(const IMGCH1
                            
                              &imageInput){ Mat show=Mat::zeros(imageInput.rows,imageInput.cols,CV_8UC1); for(int row=0;row
                             
                              (row,col)=imageInput.dataPtr[row*imageInput.cols+col]; } imshow(" ",show); waitKey(0);}

测试

#include"opencv.hpp"using namespace cv;#include"MyCanny.h"using namespace std;int main(){		Mat src=imread("E:\\matchpic\\right_16488.jpg",0);		//转换数据结构	IMGCH1
    
      src_(src.rows,src.cols,PIXUC1(0));	for(int row=0;row
     
      (row,col);	Mat Cannyedge=Mat::zeros(src.rows,src.cols,CV_8UC1);	IMGCH1
      
        Cannyedge_(src.rows,src.cols,PIXUC1(0));		MyCanny MyCanny_;		MyCanny_(src_,Cannyedge_,10,100,3);//检测	//转换回Mat显示		for(int row=0;row
       
        (row,col)=Cannyedge_.dataPtr[row*src.cols+col];		imshow("Cannyedge ",Cannyedge);				waitKey(0); }