opencv查找轮廓---cvFindContours && cvDrawCountours 用法及例子
1、找到轮廓;
2、每个连通域的均值中心;
3、求连通域半径(平均);
4、相似度——最小半径/最大半径;
5、根据相似度阈值、半径阈值来判断是否是圆
#include <iostream> #include <opencv2/imgproc/imgproc.hpp> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> using namespace cv; using namespace std; int main() { Mat q_MatImage; Mat q_MatImageGray; Mat q_MatImageShow; Mat q_MatImageShow2; q_MatImage=imread("1.png");//读入一张图片 q_MatImage.copyTo(q_MatImageShow); q_MatImage.copyTo(q_MatImageShow2); cvtColor(q_MatImage,q_MatImageGray,CV_RGB2GRAY); double q_dEpsilon = 10E-9; unsigned int q_iReturn=0; int q_iX,q_iY,q_iWidth,q_iHeight; q_iX=20; q_iY=40; q_iWidth=600; q_iHeight=420; double q_dThresholdSimilarity=60; double q_dThresholdMin=35; double q_dThresholdMax=75; // Rect q_RectROI = Rect(q_iX,q_iY,q_iWidth,q_iHeight); // Mat q_MatROI = q_MatImageGray(q_RectROI); // // threshold(q_MatROI, q_MatROI, 150, 255, CV_THRESH_BINARY); threshold(q_MatImageGray, q_MatImageGray, 150, 255, CV_THRESH_BINARY); namedWindow("Test1"); //创建一个名为Test窗口 imshow("Test1",q_MatImageGray); //窗口中显示图像 vector<vector<Point>> q_vPointContours; //findContours(q_MatROI, q_vPointContours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE,Point(q_iX,q_iY)); findContours(q_MatImageGray, q_vPointContours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE,Point(0,0)); size_t q_iAmountContours = q_vPointContours.size(); for (size_t i = 0;i < q_iAmountContours; i++) { size_t q_perNum = q_vPointContours[i].size(); for (size_t j = 0;j < q_iAmountContours; j++) { circle( q_MatImageGray, q_vPointContours[i][j] ,3 , CV_RGB(0,255,0),1, 8, 3 ); } } namedWindow("findContours"); imshow("findContours",q_MatImageGray); std::vector<cv::Point2f> q_vPointCentersContours(q_iAmountContours); std::vector<double> q_vdRadiusesContours(q_iAmountContours); std::vector<double> q_vdSimilarityContours(q_iAmountContours); std::vector<bool> q_vbFlagCircles(q_iAmountContours); std::vector<double> q_vdRadiusesContour; double q_dRadiusMax,q_dRadiusMin; double q_dSumX,q_dSumY; size_t q_iAmountPoints; for(size_t q_iCycleContours=0;q_iCycleContours<q_iAmountContours;q_iCycleContours++) { q_dSumX=0.0; q_dSumY=0.0; q_iAmountPoints=q_vPointContours[q_iCycleContours].size(); if(0>=q_iAmountPoints) { continue; } for(size_t q_iCyclePoints=0;q_iCyclePoints<q_iAmountPoints;q_iCyclePoints++) { q_dSumX+=q_vPointContours[q_iCycleContours].at(q_iCyclePoints).x; q_dSumY+=q_vPointContours[q_iCycleContours].at(q_iCyclePoints).y; } q_vPointCentersContours[q_iCycleContours].x=(float)(q_dSumX/q_iAmountPoints);//均值中心点X</span> q_vPointCentersContours[q_iCycleContours].y=(float)(q_dSumY/q_iAmountPoints);//均值中心点Y</span> q_vdRadiusesContour.resize(q_iAmountPoints); double q_dDifferenceX,q_dDifferenceY; double q_dSumRadius=0.0; q_dRadiusMax=0.0; q_dRadiusMin=DBL_MAX;; for(size_t q_iCyclePoints=0;q_iCyclePoints<q_iAmountPoints;q_iCyclePoints++) { q_dDifferenceX=q_vPointCentersContours[q_iCycleContours].x-q_vPointContours[q_iCycleContours].at(q_iCyclePoints).x; q_dDifferenceY=q_vPointCentersContours[q_iCycleContours].y-q_vPointContours[q_iCycleContours].at(q_iCyclePoints).y; q_vdRadiusesContour[q_iCyclePoints]=sqrt(q_dDifferenceX*q_dDifferenceX+q_dDifferenceY*q_dDifferenceY); if(q_vdRadiusesContour[q_iCyclePoints]>q_dRadiusMax) { q_dRadiusMax=q_vdRadiusesContour[q_iCyclePoints]; } if(q_vdRadiusesContour[q_iCyclePoints]<q_dRadiusMin) { q_dRadiusMin=q_vdRadiusesContour[q_iCyclePoints]; } q_dSumRadius+=q_vdRadiusesContour[q_iCyclePoints]; } q_vdRadiusesContours[q_iCycleContours]=q_dSumRadius/q_iAmountPoints; //均值半径 q_vdSimilarityContours[q_iCycleContours]=100.0*q_dRadiusMin/q_dRadiusMax; //相似度 if((q_dThresholdSimilarity<q_vdSimilarityContours[q_iCycleContours])&& (q_dThresholdMin<q_vdRadiusesContours[q_iCycleContours])&& (q_dThresholdMax>q_vdRadiusesContours[q_iCycleContours])) //判断是否是圆 { q_vbFlagCircles[q_iCycleContours]=true; } else { q_vbFlagCircles[q_iCycleContours]=false; } } if(q_dEpsilon < 10) { cv::Point q_PointCenterCurrent; for(size_t q_iCycleContours=0;q_iCycleContours<q_iAmountContours;q_iCycleContours++) { if(q_vbFlagCircles[q_iCycleContours]) { q_PointCenterCurrent.x=(int)(q_vPointCentersContours[q_iCycleContours].x); q_PointCenterCurrent.y=(int)(q_vPointCentersContours[q_iCycleContours].y); circle(q_MatImageShow,q_PointCenterCurrent,3,Scalar(0.0,0.0,255.0),0); } } } int q_iIndexResultBegin=4; int q_iAmountCircleResult=4; int q_iIndexCiecleCurrent; int q_iCountCircles=0; for(size_t q_iCycleContours=0;q_iCycleContours<q_iAmountContours;q_iCycleContours++) { if(q_vbFlagCircles[q_iCycleContours]) { q_iIndexCiecleCurrent=q_iIndexResultBegin+q_iAmountCircleResult*q_iCountCircles; // match_result[q_iIndexCiecleCurrent]=(float)(q_vdSimilarityContours[q_iCycleContours]); // match_result[q_iIndexCiecleCurrent+1]=(float)(q_vdRadiusesContours[q_iCycleContours]); // match_result[q_iIndexCiecleCurrent+2]=(float)(q_vPointCentersContours[q_iCycleContours].x); // match_result[q_iIndexCiecleCurrent+3]=(float)(q_vPointCentersContours[q_iCycleContours].y); q_iCountCircles++; } } cout << "总共找到 " << q_iCountCircles << "个圆!" << endl; namedWindow("Test"); //创建一个名为Test窗口 imshow("Test",q_MatImageShow);//窗口中显示图像 waitKey(); //等待5000ms后窗口自动关闭 }
大律算法otsu:
int thresh = Otsu(q_MatImageGray); threshold(q_MatImageGray, q_MatImageGray, thresh, 255, CV_THRESH_BINARY); for(int i=0; i < q_MatImageGray.rows; i++) { for(int j = 0; j < q_MatImageGray.cols; j++) { q_MatImageGray.at<uchar>(i,j) = 255 -q_MatImageGray.at<uchar>(i,j); } }
int Otsu(Mat src) { int height=src.rows; int width =src.cols; //histogram float histogram[256] = {0}; for(int i=0; i < height; i++) { unsigned char* p=(unsigned char*)src.ptr<uchar>(i); for(int j = 0; j < width; j++) { histogram[*p++]++; } } //normalize histogram int size = height * width; for(int i = 0; i < 256; i++) { histogram[i] = histogram[i] / size; } //average pixel value float avgValue=0; for(int i=0; i < 256; i++) { avgValue += i * histogram[i]; //整幅图像的平均灰度 } int threshold; float maxVariance=0; float w = 0, u = 0; for(int i = 0; i < 256; i++) { w += histogram[i]; //假设当前灰度i为阈值, 0~i 灰度的像素(假设像素值在此范围的像素叫做前景像素) 所占整幅图像的比例 u += i * histogram[i]; // 灰度i 之前的像素(0~i)的平均灰度值: 前景像素的平均灰度值 float t = avgValue * w - u; float variance = t * t / (w * (1 - w) ); if(variance > maxVariance) { maxVariance = variance; threshold = i; } } return threshold; }
int Otsu2(Mat src) { int height=src.rows; int width =src.cols; int x=0,y=0; int pixelCount[256]; float pixelPro[256]; int i, j, pixelSum = width * height, threshold = 0; //初始化 for(i = 0; i < 256; i++) { pixelCount[i] = 0; pixelPro[i] = 0; } //统计灰度级中每个像素在整幅图像中的个数 for(i = y; i < height; i++) { for(j = x;j <width;j++) { pixelCount[src.at<uchar>(i,j)]++; } } //计算每个像素在整幅图像中的比例 for(i = 0; i < 256; i++) { pixelPro[i] = (float)(pixelCount[i]) / (float)(pixelSum); } //经典ostu算法,得到前景和背景的分割 //遍历灰度级[0,255],计算出方差最大的灰度值,为最佳阈值 float w0, w1, u0tmp, u1tmp, u0, u1, u,deltaTmp, deltaMax = 0; for(i = 0; i < 256; i++) { w0 = w1 = u0tmp = u1tmp = u0 = u1 = u = deltaTmp = 0; for(j = 0; j < 256; j++) { if(j <= i) //背景部分 { //以i为阈值分类,第一类总的概率 w0 += pixelPro[j]; u0tmp += j * pixelPro[j]; } else //前景部分 { //以i为阈值分类,第二类总的概率 w1 += pixelPro[j]; u1tmp += j * pixelPro[j]; } } u0 = u0tmp / w0; //第一类的平均灰度 u1 = u1tmp / w1; //第二类的平均灰度 u = u0tmp + u1tmp; //整幅图像的平均灰度 //计算类间方差 deltaTmp = w0 * (u0 - u)*(u0 - u) + w1 * (u1 - u)*(u1 - u); //找出最大类间方差以及对应的阈值 if(deltaTmp > deltaMax) { deltaMax = deltaTmp; threshold = i; } } //返回最佳阈值; return threshold; }