使用OpenCV和C++实现的分水岭算法(Watershed)

分水岭算法(watershed)是一种比较基本的数学形态学分割算法，其基本思想是将灰度图像转换为梯度图像，将梯度值看作高低起伏的山岭，将局部极小值及其邻域看作一个“集水盆”。设想一个个“集水盆”中存在积水，且水位不断升高，淹没梯度较低的地方，当水漫过程停止后，图像就可以被分割成几块连通区域。

分水岭算法有不同的实现方法。本文要实现的是通过人为标注一些种子点，将这些种子点看作集水盆的底部，利用区域增长的方法，完成图像的分割。试图实现OpenCV中cv::watershed函数的功能，经过测试，与OpenCV相比分割结果相似，但性能差很多。（前者32ms左右，后者8ms左右，原因可能是循环中使用了cv::mat来访问图像中的元素，改用指针速度可能会提高很多）。

OpenCV函数的运行结果：（OpenCV函数对分割边缘也做了处理，我写的那个程序没有）

程序运行结果：

参考：

http://wenku.baidu.com/view/d1fde240336c1eb91a375d95.html

http://blog.csdn.net/fdl19881/article/details/6749976

#include <opencv2/imgproc/imgproc.hpp>#include <opencv2/objdetect/objdetect.hpp>#include <opencv2/highgui/highgui.hpp>#include<vector>#include<iostream>#include<queue>#include<fstream>cv::Mat marker_mask;cv::Mat g_markers;cv::Mat img0, img, img_gray,wshed;cv::Point_<int> prev_pt(-1,-1);using std::vector;using std::queue;static void my_watershed(cv::Mat img,cv::Mat& markers,int comp_count);static void mouse_event(int event,int x, int y,int flags, void*){if(img.rows==0)return; if(event==CV_EVENT_LBUTTONUP||!(flags&CV_EVENT_FLAG_LBUTTON)) prev_pt=cv::Point_<int>(-1,-1); else if(event==CV_EVENT_LBUTTONDOWN) prev_pt=cv::Point2i(x,y); else if(event==CV_EVENT_MOUSEMOVE&&(flags&CV_EVENT_FLAG_LBUTTON)) { cv::Point2i pt(x,y); if(prev_pt.x<0) prev_pt=pt; cv::line(marker_mask,prev_pt,pt,cv::Scalar(255,255,255),1,8,0); cv::line(img,prev_pt,pt,cv::Scalar(255,255,255),1,8,0); prev_pt=pt; cv::imshow("image",img); }}int main(){img0=cv::imread("Lenna.png",1);img=img0.clone();CvRNG rng = cvRNG(-1); img_gray=img0.clone();wshed=img0.clone();marker_mask=cv::Mat(cv::Size(img0.cols,img0.rows),8,1);g_markers=cv::Mat(cv::Size(img0.cols,img0.rows),CV_32S,1);cv::cvtColor(img,marker_mask,CV_BGR2GRAY);cv::cvtColor(marker_mask,img_gray,CV_GRAY2BGR);for(int i=0;i<marker_mask.rows;i++)for(int j=0;j<marker_mask.cols;j++)marker_mask.at<unsigned char>(i,j)=0;for(int i=0;i<g_markers.rows;i++)for(int j=0;j<g_markers.cols;j++)g_markers.at<int>(i,j)=0;cv::imshow("image",img);cv::imshow("watershed transform",wshed);cv::setMouseCallback("image",mouse_event,0);for(;;){int c=cv::waitKey(0);if((char)c==27)break;if((char)c=='r'){for(int i=0;i<marker_mask.rows;i++)for(int j=0;j<marker_mask.cols;j++)marker_mask.at<unsigned char>(i,j)=0;img0.copyTo(img);cv::imshow("image",img);}if((char)c=='w'||(char)c==' '){vector<vector<cv::Point>> contours;CvMat* color_tab=0;int comp_count=0;cv::findContours(marker_mask,contours,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE,cv::Point(0,0));for(int i=0;i<g_markers.rows;i++)for(int j=0;j<g_markers.cols;j++)g_markers.at<int>(i,j)=0;vector<vector<cv::Point> >::iterator iter=contours.begin();for(int i=0;i<(int)contours.size();i++){cv::drawContours(g_markers,contours,i,cv::Scalar::all(comp_count+1),1,8,vector<cv::Vec4i>());comp_count++;}if(comp_count==0)continue;color_tab=cvCreateMat(1,comp_count,CV_8UC3);for(int i=0;i<comp_count;i++){uchar* ptr=color_tab->data.ptr+i*3;ptr[0]=(uchar)(cvRandInt(&rng)%180+50);ptr[1]=(uchar)(cvRandInt(&rng)%180+50);ptr[2]=(uchar)(cvRandInt(&rng)%180+50);}cv::Mat temp=g_markers.clone();double t=(double)cvGetTickCount();//my_watershed(img0,g_markers,comp_count);cv::watershed(img0,g_markers);t=(double)cvGetTickCount()-t;std::cout<<"exec time= "<<t/(cvGetTickFrequency()*1000.)<<std::endl;for(int i=0;i<g_markers.rows;i++)for(int j=0;j<g_markers.cols;j++){int idx=g_markers.at<int>(i,j);uchar* dst=&wshed.at<uchar>(i,j*3);if(idx==-1)dst[0]=dst[1]=dst[2]=(uchar)255;else if(idx<=0||idx>comp_count)dst[0]=dst[1]=dst[2]=(uchar)8;else{uchar* ptr=color_tab->data.ptr+(idx-1)*3;dst[0]=ptr[0];dst[1]=ptr[1];dst[2]=ptr[2];}}cv::addWeighted(wshed,0.5,img_gray,0.5,0,wshed);cv::imshow("watershed transform",wshed);cvReleaseMat(&color_tab);}}    return 0;}static void my_watershed(cv::Mat img0,cv::Mat& markers,int comp_count){cv::Mat gray=cv::Mat(cv::Size(img0.rows,img0.cols),8,1);cv::cvtColor(img0,gray,CV_BGR2GRAY);cv::Mat imge=cv::Mat(cv::Size(img0.rows,img0.cols),8,1);cv::Sobel(gray,imge,CV_8U,1,1);vector<queue<cv::Point2i>*>Labeleddata;//图像中各连通区域的点queue<cv::Point2i>* pque;//某连通区域已包含的点queue<cv::Point2i> quetem; //用于提取某连通区域中输入种子点中的初始种子点vector<int*> SeedCounts;int* Array;cv:: Point2i temp;int row=imge.rows,col=imge.cols;cv::Mat marker_saved=markers.clone();bool up,down,right,left,uplef,uprig,downlef,downrig;int m,n;for(int i=0;i<comp_count;i++){Array=new int[256];SeedCounts.push_back(Array);//统计某waterlevel的各个连通区域中种子点的个数pque=new queue<cv::Point2i>[256]; Labeleddata.push_back(pque);//存储该连通区域中种子生长所得的点}for(int i=0;i<row;i++)for(int j=0;j<col;j++){if(markers.at<int>(i,j)>0){temp.x=i;temp.y=j;quetem.push(temp);    int num=markers.at<int>(i,j);markers.at<int>(i,j)=-1;//该点已处理,其他种子点生长时将绕过该点while(!quetem.empty()){up=down=right=left=uplef=uprig=downlef=downrig=false;temp=quetem.front(); //提取出一个点，在该点的八连通区域内寻找可生长点m=temp.x;n=temp.y;quetem.pop();if(m-1>=0)//若上方可生长则添加为新种子{if(markers.at<int>(m-1,n)==num){temp.x=m-1;temp.y=n;quetem.push(temp);markers.at<int>(m-1,n)=-1;}else{up=true;}}if(m-1>=0&&n-1>=0){if(markers.at<int>(m-1,n-1)==num){temp.x=m-1;temp.y=n-1;quetem.push(temp);markers.at<int>(m-1,n-1)=-1;}else{uplef=true;}}if(m+1<=row-1){if(markers.at<int>(m+1,n)==num){temp.x=m+1;temp.y=n;quetem.push(temp);markers.at<int>(m+1,n)=-1;}else{down=true;}}if(m+1<=row-1&&n+1<=col-1){if(markers.at<int>(m+1,n+1)==num){temp.x=m+1;temp.y=n+1;quetem.push(temp);markers.at<int>(m+1,n+1)=-1;}else{downrig=true;}}if(n+1<=col-1){if(markers.at<int>(m,n+1)==num){temp.x=m;temp.y=n+1;quetem.push(temp);markers.at<int>(m,n+1)=-1;}else{right=true;}}if(m-1>=0&&n+1<=col-1){if(markers.at<int>(m-1,n+1)==num){temp.x=m-1;temp.y=n+1;quetem.push(temp);markers.at<int>(m-1,n+1)=-1;}else{uprig=true;}}if(n-1>=0){if(markers.at<int>(m,n-1)==num){temp.x=m;temp.y=n-1;quetem.push(temp);markers.at<int>(m,n-1)=-1;}else{left=true;}}if(m+1<=row-1&&n-1>=0){if(markers.at<int>(m+1,n-1)==num){temp.x=m+1;temp.y=n-1;quetem.push(temp);markers.at<int>(m+1,n-1)=-1;}else{downlef=true;}}//八连通区域中有未标记点，则该点属于初始种子点if(up||down||right||left||uplef||downlef||uprig||downrig){temp.x=m;temp.y=n;Labeleddata[comp_count-1][imge.at<uchar>(m,n)].push(temp);SeedCounts[comp_count-1][imge.at<uchar>(m,n)]++;}}}}bool active;int waterlevel;for(waterlevel=0;waterlevel<180;waterlevel++){active=true;while(active) //当1-count_com个连通区域都无可生长点时结束循环{active=false;for(int i=0;i<comp_count;i++)//将区域i中将waterlevel梯度以下的点用于区域增长{if(!Labeleddata[i][waterlevel].empty())//区域增长，经过多次迭代，直至该区域，该waterlevel无可生长点。{active=true;while(SeedCounts[i][waterlevel]>0) //SeedCount中保留了前一轮生长后各区域，各waterlevel的种子点个数，本轮生长结束后，将根据Labeleddata中的元素个数更新{SeedCounts[i][waterlevel]--;temp=Labeleddata[i][waterlevel].front();Labeleddata[i][waterlevel].pop();m=temp.x;n=temp.y;int num=marker_saved.at<int>(m,n);if(m-1>=0){if(!marker_saved.at<int>(m-1,n))//上方点未处理过{temp.x=m-1;temp.y=n;marker_saved.at<int>(m-1,n)=num;if(imge.at<uchar>(m-1,n)<=waterlevel)Labeleddata[i][waterlevel].push(temp);else{Labeleddata[i][imge.at<uchar>(m-1,n)].push(temp); //本次生长不处理，可能在waterlevel变化到某值时再用于生长SeedCounts[i][imge.at<uchar>(m-1,n)]++;}}}if(m+1<=row-1){if(!marker_saved.at<int>(m+1,n)){temp.x=m+1;temp.y=n;marker_saved.at<int>(m+1,n)=num;if(imge.at<uchar>(m+1,n)<=waterlevel)Labeleddata[i][waterlevel].push(temp);else{Labeleddata[i][imge.at<uchar>(m+1,n)].push(temp);SeedCounts[i][imge.at<uchar>(m+1,n)]++;}}}if(n+1<=col-1){if(!marker_saved.at<int>(m,n+1)){temp.x=m;temp.y=n+1;marker_saved.at<int>(m,n+1)=num;if(imge.at<uchar>(m,n+1)<=waterlevel)Labeleddata[i][waterlevel].push(temp);else{Labeleddata[i][imge.at<uchar>(m,n+1)].push(temp);SeedCounts[i][imge.at<uchar>(m,n+1)]++;}}}if(n-1>=0){if(!marker_saved.at<int>(m,n-1)){temp.x=m;temp.y=n-1;marker_saved.at<int>(m,n-1)=num;if(imge.at<uchar>(m,n-1)<=waterlevel)Labeleddata[i][waterlevel].push(temp);else{Labeleddata[i][imge.at<uchar>(m,n-1)].push(temp);SeedCounts[i][imge.at<uchar>(m,n-1)]++;}}}}SeedCounts[i][waterlevel]=Labeleddata[i][waterlevel].size();}}}}markers=marker_saved.clone();}