2017-5-28圆弧检测（opencv-qt）F:\学科、技能、编程\【编程-⽂件\proj\圆弧检测（opencv-qt）可以⾃动或者⼿动测量圆弧液柱的⾓度：使⽤说明：找圆⼼（最⼩⼆乘拟合）相关代码#include <iostream>#include <opencv2/opencv.hpp>using namespace std;using namespace cv;bool circleLeastFit(CvSeq* points, double ¢er_x, double ¢er_y, double &radius);//最⼩⼆乘法拟合函数int main(){const char* winname ="winname";//const char* winname1 ="winname1";//const char* winname2 ="winname2";//const char* winname3 ="winname3";char * picname = "P11.jpg";//加载原图IplImage * pImage = cvLoadImage(picname);//分量图像IplImage *pR = cvCreateImage(cvGetSize(pImage),IPL_DEPTH_8U,1);IplImage *pG = cvCreateImage(cvGetSize(pImage),IPL_DEPTH_8U,1);IplImage *pB = cvCreateImage(cvGetSize(pImage),IPL_DEPTH_8U,1);IplImage *temp = cvCreateImage(cvGetSize(pImage),IPL_DEPTH_8U,1);IplImage *binary = cvCreateImage(cvGetSize(pImage),IPL_DEPTH_8U,1);//trackbar的变量值 //对应各个通道int b_low =20;int b_high = 100;int g_low = 20;int g_high = 100;int r_low = 0;int r_high = 100;//轮廓相关CvMemStorage *storage = cvCreateMemStorage(0);CvSeq * seq = cvCreateSeq(CV_SEQ_ELTYPE_POINT, sizeof(CvSeq), sizeof(CvPoint), storage);//窗⼝cvNamedWindow(winname);cvShowImage(winname, pImage); //显⽰原图cvNamedWindow("r",2);cvNamedWindow("g",2);cvNamedWindow("b",2); //各个通道cvNamedWindow("binary",2);//⼆值化图//在相应的窗⼝建⽴滑动条cvCreateTrackbar( "b1","b", &b_low, 254, NULL); //H通道分量范围0-180cvSetTrackbarPos("b1","b",0 ); //设置默认位置cvCreateTrackbar( "b2","b", &b_high, 254, NULL);//H通道分量范围0-180cvSetTrackbarPos("b2","b",110 );cvCreateTrackbar( "g1","g", &g_low, 254, NULL);cvSetTrackbarPos("g1","g",0 );cvCreateTrackbar( "g2","g", &g_high, 254, NULL);cvSetTrackbarPos("g2","g",158 );cvCreateTrackbar( "r1","r", &r_low, 254, NULL);cvSetTrackbarPos("r1","r",68 );cvCreateTrackbar( "r2","r", &r_high, 254, NULL);cvSetTrackbarPos("r2","r",137);while(1){//各个通道分离cvSplit(pImage,pB,pG,pR,NULL);//阈值化cvThreshold(pB, temp,b_low , 255, CV_THRESH_BINARY);cvThreshold(pB, pB,b_high , 255, CV_THRESH_BINARY_INV);cvAnd(temp,pB,pB,NULL);//与操作，合成⼀张图cvThreshold(pG, temp,g_low , 255, CV_THRESH_BINARY);cvThreshold(pG, pG,g_high , 255, CV_THRESH_BINARY_INV);cvAnd(temp,pG,pG,NULL);//与操作，合成⼀张图cvThreshold(pR, temp,r_low , 255, CV_THRESH_BINARY);cvThreshold(pR, pR,r_high , 255, CV_THRESH_BINARY_INV);cvAnd(temp,pR,pR,NULL);//与操作，合成⼀张图//显⽰各个通道的图像cvShowImage("b",pB);cvShowImage("g",pG);cvShowImage("r",pR);//合成到⼀张图⾥cvAnd(pB, pG, binary, NULL);cvAnd(pR, binary, binary, NULL);//膨胀腐蚀操作去除⿊点//cvDilate(binary,binary);//cvErode(binary,binary);//显⽰合成的⼆值化图cvShowImage("binary",binary);//cvSaveImage("erzhitu.jpg",binary);// 处理轮廓int cnt = cvFindContours(binary,storage,&seq,sizeof(CvContour), CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);//返回轮廓的数⽬ CvSeq* _contour =seq;cout<<"number of contours "<<cnt<<endl;////////////////////_//找到长度最⼤轮廓；double maxarea=0;int ind_max = -1;int m=0;for( ; seq != 0; seq = seq->h_next ){m++;double tmparea = abs(cvArcLength(seq,CV_WHOLE_SEQ,-1));//double contArea = fabs(cvContourArea(pcontour,CV_WHOLE_SEQ));if(tmparea > maxarea){maxarea = tmparea;ind_max=m;}// cout<<"seqfor: "<<seq->total<<endl;}m=0;seq = _contour;for( ; seq != 0; seq = seq->h_next ){m++;if(m == ind_max){break;}}CvSeq* cur_cont = seq;cout<<"seq: "<<seq->total<<endl;cout<<"cur_cont: "<<cur_cont->total<<endl;//for (int i=0;i<cur_cont->total;++i)//{// CvPoint* p = CV_GET_SEQ_ELEM(CvPoint,cur_cont,i);//输出轮廓上点的坐标// printf("(%d,%d)\n",p->x,p->y);//}//cvWaitKey(0);//建⽴彩⾊输出图像IplImage *pOutlineImage = cvCreateImage(cvGetSize(pImage), IPL_DEPTH_8U, 3);cvCopy(pImage,pOutlineImage);//int nLevels = 5;//获取最⼤轮廓的凸包点集CvSeq* hull=NULL;hull = cvConvexHull2(cur_cont,0,CV_CLOCKWISE,0);cout<<"hull total points number:"<<hull->total<<endl;CvPoint pt0 = **(CvPoint**)cvGetSeqElem(hull,hull->total - 1);for(int i = 0;i<hull->total;++i){CvPoint pt1 = **(CvPoint**)cvGetSeqElem(hull,i);cvLine(pOutlineImage,pt0,pt1,CV_RGB(0,0,255));pt0 = pt1;}//最⼩⼆乘法拟合圆double center_x=0;double center_y=0;double radius=0;cout<<"nihe :"<<circleLeastFit(hull, center_x, center_y, radius);cout<<"canshu: "<<center_x<<endl<<center_y<<endl<<radius<<endl;//绘制圆cvCircle(pOutlineImage,Point2f(center_x,center_y),radius,CV_RGB(0,100,100));////////////////////////////////////////////////////////////////////////////绘制轮廓//cvDrawContours(pOutlineImage, cur_cont, CV_RGB(255,0,0), CV_RGB(0,255,0),0);//cvDrawContours(dst,contour,CV_RGB(255,0,0),CV_RGB(0,255,0),0);cvShowImage(winname, pOutlineImage); //显⽰原图jiangshang luokuoif (cvWaitKey(1000) == 27){cvSaveImage("tutu.jpg",pOutlineImage);break;}cvClearMemStorage( storage ); //清除轮廓所占⽤的内存cvReleaseImage(&pOutlineImage);//清除彩⾊输出图像}cvDestroyAllWindows();cvReleaseImage(&pImage);cvReleaseImage(&pR);cvReleaseImage(&pG);cvReleaseImage(&pB);cvReleaseImage(&temp);cvReleaseImage(&binary);return0;}//最⼩⼆乘法拟合，输出圆⼼的xy坐标值和半径⼤⼩；bool circleLeastFit(CvSeq* points, double ¢er_x, double ¢er_y, double &radius) {center_x = 0.0f;center_y = 0.0f;radius = 0.0f;if (points->total < 3){return false;}double sum_x = 0.0f, sum_y = 0.0f;double sum_x2 = 0.0f, sum_y2 = 0.0f;double sum_x3 = 0.0f, sum_y3 = 0.0f;double sum_xy = 0.0f, sum_x1y2 = 0.0f, sum_x2y1 = 0.0f;int N = points->total ;for (int i = 0; i < N; i++){CvPoint pt1 = **(CvPoint**)cvGetSeqElem(points,i);double x =pt1.x;double y = pt1.y ;double x2 = x * x;double y2 = y * y;sum_x += x;sum_y += y;sum_x2 += x2;sum_y2 += y2;sum_x3 += x2 * x;sum_y3 += y2 * y;sum_xy += x * y;sum_x1y2 += x * y2;sum_x2y1 += x2 * y;}double C, D, E, G, H;double a, b, c;C = N * sum_x2 - sum_x * sum_x;D = N * sum_xy - sum_x * sum_y;E = N * sum_x3 + N * sum_x1y2 - (sum_x2 + sum_y2) * sum_x;G = N * sum_y2 - sum_y * sum_y;H = N * sum_x2y1 + N * sum_y3 - (sum_x2 + sum_y2) * sum_y;a = (H * D - E * G) / (C * G - D * D);b = (H * C - E * D) / (D * D - G * C);c = -(a * sum_x + b * sum_y + sum_x2 + sum_y2) / N;center_x = a / (-2);center_y = b / (-2);radius = sqrt(a * a + b * b - 4 * c) / 2;return true;}标定相关代码#include "opencv2/core/core.hpp"#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/calib3d/calib3d.hpp"#include "opencv2/highgui/highgui.hpp"#include <iostream>#include <fstream>using namespace cv;using namespace std;#define MAX_GRAY_VALUE 255#define MIN_GRAY_VALUE 0using namespace cv;using namespace std;#include <opencv2/imgproc/imgproc.hpp>#include <iostream>#include <stdio.h>using namespace std;using namespace cv;bool check_line_state=false;IplImage* workImg;IplImage* imgshow;CvRect ROI_rect;IplImage* src=0;void on_mouse( int event, int x, int y, int flags, void* ustc){CvFont font;cvInitFont(&font, CV_FONT_HERSHEY_SIMPLEX, 0.5, 0.5, 0, 1, CV_AA);if( event == CV_EVENT_LBUTTONDOWN ){CvPoint pt = cvPoint(x,y);char temp[16];sprintf(temp,"(%d,%d)",pt.x,pt.y);cout<<"("<<pt.x<<","<<pt.y<<")"<<endl;x1=pt.x;y1=pt.y;cvPutText(src,temp, pt, &font, cvScalar(255, 255, 255, 0));cvCircle( src, pt, 2,cvScalar(255,0,0,0) ,CV_FILLED, CV_AA, 0 );cvShowImage( "src", src );}}int main(){//获取端点的坐标src=cvLoadImage("dst7.png",1);cvNamedWindow("src",1);cvSetMouseCallback( "src", on_mouse, 0 );cvShowImage("src",src);cvWaitKey(0);cvDestroyAllWindows();cvReleaseImage(&src);return0;}/*//寻找圆⼼坐标int main(int args,char** argv){ static double radius;static int i;Mat srcImage = imread("dst7.png");if (!srcImage.data)return -1;imshow("srcImage", srcImage);Mat srcGray;cvtColor(srcImage, srcGray, COLOR_BGR2GRAY);//⾼斯平滑滤波GaussianBlur(srcGray, srcGray, Size(9, 9), 2,2);static vector<Vec3f> circles;//Hough圆检测HoughCircles(srcGray, circles, CV_HOUGH_GRADIENT,1,srcGray.rows/8,200,16,0,0);//将得到的结果绘图for (size_t i = 0; i < circles.size(); i++){Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));cout<<center<<endl;radius = cvRound(circles[i][2]);cout<<radius<<endl;//检测圆中⼼circle(srcImage, center, 3, Scalar(0, 255, 0), -1, 8, 0);//检测圆轮廓circle(srcImage, center, radius, Scalar(120, 120, 120), 3, 8, 0);}imshow("HoughResult", srcImage);imwrite("HoughResult.jpg",srcImage);IplImage* image=cvLoadImage("dst5.png");int p=0;int q=0;for (int k=0;k<=360;k++){double m = 3.1415926535*2*k/360;CvScalar s;s=cvGet2D(image,cvRound(circles[i][0])+radius*cos(m),cvRound(circles[i][1])+radius*sin(m)); if(s.val[0]!=255){p++;q++;}elseq++;cout<<m<<p<<q<<endl;}waitKey(0);return 0;}//⼆值化部分//otsu函数计算最佳阈值int otsu(Mat dst){int i, j;int tmp;double u0, u1, w0, w1, u, uk;double cov;double maxcov = 0.0;int maxthread = 0;int hst[MAX_GRAY_VALUE] = { 0 };double pro_hst[MAX_GRAY_VALUE] = { 0.0 };int height = dst.cols;int width = dst.rows;//统计每个灰度的数量for (i = 0; i<width; i++){for (j = 0; j<height; j++){tmp = dst.at<uchar>(i, j);hst[tmp]++;}}//计算每个灰度级占图像中的概率for (i = MIN_GRAY_VALUE; i<MAX_GRAY_VALUE; i++) pro_hst[i] = (double)hst[i] / (double)(width*height);//计算全局平均灰度值u = 0.0;for (i = MIN_GRAY_VALUE; i<MAX_GRAY_VALUE; i++) u += i*pro_hst[i];//统计前景和背景的平均灰度值，并计算类间⽅差for (i = MIN_GRAY_VALUE; i<MAX_GRAY_VALUE; i++){ w0 = 0.0; w1 = 0.0; u0 = 0.0; u1 = 0.0; uk = 0.0;for (j = MIN_GRAY_VALUE; j < i; j++){uk += j*pro_hst[j];w0 += pro_hst[j];//前景占图像画幅⽐例}u0 = uk / w0;//前景平均灰度w1 = 1 - w0;//背景占图像画幅⽐例u1 = (u - uk) / (1 - w0);//背景平均灰度//计算类间⽅差cov = w0*w1*(u1 - u0)*(u1 - u0);if (cov > maxcov){maxcov = cov;maxthread = i;}}cout << maxthread << endl;return maxthread;}int main(){int width, height;int i, j;Mat obj = imread("22.jpg");Mat dst1;cvtColor(obj, dst1, CV_RGB2GRAY);//灰度化height = dst1.cols;//计算图像⾼度width = dst1.rows;//计算图像宽度int thd = otsu(dst1);imshow("原图", dst1);for (i = 0; i < width; i++)for (j = 0; j< height; j++)if (dst1.at<uchar>(i, j) > thd)dst1.at<uchar>(i, j) = MAX_GRAY_VALUE-1;elsedst1.at<uchar>(i, j) = MIN_GRAY_VALUE;imwrite("dst1.png",dst1);imshow("⼆值化", dst1);//膨胀（闭运算消除内部⽶粒）Mat dst2=imread("dst1.png");Mat dst3;Mat element = getStructuringElement(MORPH_RECT,Size(3,3));//imshow("闭运算消除内部空隙原图", dst2);dilate( dst2,dst3,element); //膨胀imshow("闭运算消除内部空隙效果图", dst3);imwrite("dst3.png",dst3);//腐蚀（开运算去除⽑刺）Mat dst4=imread("dst3.png");Mat dst5;//imshow("开运算去除⽑刺原图",dst4);erode(dst4,dst5,element);imshow("开运算去除⽑刺效果图",dst5);imwrite("dst5.png",dst5);//边缘检测Mat dst6=imread("dst5.png");Mat dst7;//imshow("边缘检测原图",dst6);Canny(dst6,dst7,150.100,3);imshow("边缘检测效果图",dst7);imwrite("dst7.png",dst7);waitKey(0);return 0;}int main(){cv::Mat image, Extractcorner;cv::vector<cv::Point2f> corners; //⽤来储存所有⾓点坐标cv::Size board_size = cv::Size(7, 7); //标定板每⾏，每列⾓点数image = cv::imread("2.jpg");Extractcorner = image.clone();cv::Mat imageGray;cv::cvtColor(image, imageGray, CV_RGB2GRAY);bool patternfound = cv::findChessboardCorners(image, board_size, corners, cv::CALIB_CB_FAST_CHECK + cv::CALIB_CB_ADAPTIVE_THRESH + cv::CALIB_CB_NORMALIZE_IMAGE);if (!patternfound){std::cout << "can not find chessboard corners!" << std::endl;exit(1);}else{//亚像素精确化cv::cornerSubPix(imageGray, corners, cv::Size(11, 11), cv::Size(-1, -1), cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));}//⾓点检测图像显⽰for (int i = 0; i < corners.size(); i++){cv::circle(Extractcorner, corners[i], 5, cv::Scalar(255, 0, 255), 2);}cv::imshow("Extractcorner", Extractcorner);cv::imwrite("Extractcorner.jpg", Extractcorner);//输出⾓点坐标cout<<corners<<endl;//参考图像cv::Mat image2, Extractcorner2;cv::vector<cv::Point2f> corners2; //⽤来储存所有⾓点坐标cv::Size board_size2 = cv::Size(7, 7); //标定板每⾏，每列⾓点数image2 = cv::imread("1.jpg");Extractcorner2 = image2.clone();cv::Mat imageGray2;cv::cvtColor(image2, imageGray2, CV_RGB2GRAY);bool patternfound2 = cv::findChessboardCorners(image2, board_size, corners2, cv::CALIB_CB_FAST_CHECK + cv::CALIB_CB_ADAPTIVE_THRESH + cv::CALIB_CB_NORMALIZE_IMAGE); if (!patternfound2){std::cout << "can not find chessboard corners!" << std::endl;exit(1);}else{//亚像素精确化cv::cornerSubPix(imageGray2, corners2, cv::Size(11, 11), cv::Size(-1, -1), cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));}//⾓点检测图像显⽰for (int i = 0; i < corners2.size(); i++){cv::circle(Extractcorner2, corners2[i], 5, cv::Scalar(255, 0, 255), 2);}cv::imshow("Extractcorner2", Extractcorner2);cv::imwrite("Extractcorner2.jpg", Extractcorner2);//输出⾓点坐标cout<<corners2<<endl;//仿射变换1Mat src = imread("2.jpg");Mat dst_warp;Point2f srcPoints[3];//原图中的三点Point2f dstPoints[3];//⽬标图中的三点//三个点对的值srcPoints[0] = Point2f(142.52786,115.98566);srcPoints[1] = Point2f(110.28358,409.29211);srcPoints[2] = Point2f(438.46851,126.58415);dstPoints[0] = Point2f(44.506947, 46.233685);dstPoints[1] = Point2f(44.496399, 325.76706);dstPoints[2] = Point2f(314.50659, 46.230354);Mat M1 = getAffineTransform(srcPoints,dstPoints);//由三个点对计算变换矩阵warpAffine(src,dst_warp,M1,src.size());//仿射变换imshow("src",src);imshow("dst_warp",dst_warp);imwrite("dst_warp.jpg",dst_warp);//⼀次变换后图像cv::Mat image3, Extractcorner3;cv::vector<cv::Point2f> corners3; //⽤来储存所有⾓点坐标cv::Size board_size3 = cv::Size(7, 7); //标定板每⾏，每列⾓点数image3 = cv::imread("dst_warp.jpg");Extractcorner3 = image3.clone();cv::Mat imageGray3;cv::cvtColor(image3, imageGray3, CV_RGB2GRAY);bool patternfound3 = cv::findChessboardCorners(image3, board_size, corners3, cv::CALIB_CB_FAST_CHECK + cv::CALIB_CB_ADAPTIVE_THRESH + cv::CALIB_CB_NORMALIZE_IMAGE); if (!patternfound3){std::cout << "can not find chessboard corners!" << std::endl;exit(1);}else{//亚像素精确化cv::cornerSubPix(imageGray3, corners3, cv::Size(11, 11), cv::Size(-1, -1), cv::TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));}//⾓点检测图像显⽰for (int i = 0; i < corners3.size(); i++){cv::circle(Extractcorner3, corners3[i], 5, cv::Scalar(255, 0, 255), 2);}cv::imshow("Extractcorner3", Extractcorner3);cv::imwrite("Extractcorner3.jpg", Extractcorner3);//输出⾓点坐标cout<<corners3<<endl;ofstream outfile;outfile.open("date.txt");outfile<<corners<<endl<<endl<<endl<<corners2<<corners3<<endl;outfile.close();//仿射变换2Mat src2 = imread("dst_warp.jpg");Mat dst_warp2;Point2f src2Points[3];//原图中的三点Point2f dst2Points[3];//⽬标图中的三点//三个点对的值src2Points[0] = Point2f(395.12207, 306.01523);src2Points[1] = Point2f(44.515686, 325.73227);src2Points[2] = Point2f(314.53482, 46.279831);dst2Points[0] = Point2f(314.49469, 325.76535);dst2Points[1] = Point2f(44.496399, 325.76706);dst2Points[2] = Point2f(314.50659, 46.230354);Mat M2 = getAffineTransform(src2Points,dst2Points);//由三个点对计算变换矩阵warpAffine(src2,dst_warp2,M2,src2.size());//仿射变换imshow("src2",src);imshow("dst_warp2",dst_warp2);imwrite("dst_warp2.jpg",dst_warp2);cv::waitKey(0);return 0;}*/。
