OpenCV 圆与矩形识别的方法

HoughCircles(edges, circles, CV_HOUGH_GRADIENT, 1.5, 10, 200, 100, 0, 0);

cvThreshold(tgray, gray, 75, 250, CV_THRESH_BINARY);

#include <opencv2/opencv.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <WINSOCK2.H>#include<iostream>#include<thread>#include <winsock2.h>#include <stdio.h>#include<string>#include <windows.h>#pragma comment(lib,"ws2_32.lib")#include<vector> using namespace cv;     //////////////////////////////////////////////////////////////////   //函数功能：用向量来做COSα=两向量之积/两向量模的乘积求两条线段夹角   //输入：  线段3个点坐标pt1,pt2,pt0,最后一个参数为公共点   //输出：  线段夹角，单位为角度   //////////////////////////////////////////////////////////////////double angle(CvPoint* pt1, CvPoint* pt2, CvPoint* pt0){ double dx1 = pt1->x - pt0->x; double dy1 = pt1->y - pt0->y; double dx2 = pt2->x - pt0->x; double dy2 = pt2->y - pt0->y; double angle_line = (dx1*dx2 + dy1 * dy2) / sqrt((dx1*dx1 + dy1 * dy1)*(dx2*dx2 + dy2 * dy2) + 1e-10);//余弦值 return acos(angle_line) * 180 / 3.141592653;}////////////////////////////////////////////////////////////////////函数功能：采用多边形检测，通过约束条件寻找矩形//输入：  img 原图像//     storage 存储//     minarea，maxarea 检测矩形的最小/最大面积//     minangle,maxangle 检测矩形边夹角范围，单位为角度//输出：  矩形序列//////////////////////////////////////////////////////////////////CvSeq* findSquares4(IplImage* img, CvMemStorage* storage, int minarea, int maxarea, int minangle, int maxangle, int(&temp)[30]){ CvSeq* contours;//边缘 int N = 6; //阈值分级 CvSize sz = cvSize(img->width & -2, img->height & -2); IplImage* timg = cvCloneImage(img);//拷贝一次img IplImage* gray = cvCreateImage(sz, 8, 1); //img灰度图 IplImage* pyr = cvCreateImage(cvSize(sz.width / 2, sz.height / 2), 8, 3); //金字塔滤波3通道图像中间变量 IplImage* tgray = cvCreateImage(sz, 8, 1); ; CvSeq* result; double s, t; int sk = 0; CvSeq* squares = cvCreateSeq(0, sizeof(CvSeq), sizeof(CvPoint), storage);  cvSetImageROI(timg, cvRect(0, 0, sz.width, sz.height)); //金字塔滤波  cvPyrDown(timg, pyr, 7); cvPyrUp(pyr, timg, 7); //在3个通道中寻找矩形  for (int c = 0; c < 3; c++) //对3个通道分别进行处理  { cvSetImageCOI(timg, c + 1); cvCopy(timg, tgray, 0); //依次将BGR通道送入tgray      for (int l = 0; l < N; l++) {  //不同阈值下二值化  cvThreshold(tgray, gray, 75, 250, CV_THRESH_BINARY);  cvShowImage("111", gray);  cvFindContours(gray, storage, &contours, sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE, cvPoint(0, 0));  while (contours)  { //多边形逼近         result = cvApproxPoly(contours, sizeof(CvContour), storage, CV_POLY_APPROX_DP, cvContourPerimeter(contours)*0.02, 0);   //如果是凸四边形并且面积在范围内   if (result->total == 4 && fabs(cvContourArea(result, CV_WHOLE_SEQ)) > minarea && fabs(cvContourArea(result, CV_WHOLE_SEQ)) < maxarea && cvCheckContourConvexity(result))  {    s = 0;   //判断每一条边   for (int i = 0; i < 5; i++)   {   if (i >= 2)   {  //角度          t = fabs(angle((CvPoint*)cvGetSeqElem(result, i), (CvPoint*)cvGetSeqElem(result, i - 2), (CvPoint*)cvGetSeqElem(result, i - 1)));    s = s > t ? s : t;   }   }   //这里的S为直角判定条件 单位为角度   if (s > minangle && s < maxangle)   {   for (int i = 0; i < 4; i++)    cvSeqPush(squares, (CvPoint*)cvGetSeqElem(result, i));   CvRect rect = cvBoundingRect(contours, 1);    // 获取矩形边界框    CvPoint p1;   p1 = cvPoint(rect.x + rect.width / 2, rect.y + rect.height / 2);  //矩形中心坐标    std::cout << "X:" << p1.x << "Y：" << p1.y << std::endl;   }  }  contours = contours->h_next;  } } std::cout << "圆的数量是"<<sk << std::endl; temp[26] = sk;   sk = 0; } cvReleaseImage(&gray); cvReleaseImage(&pyr); cvReleaseImage(&tgray); cvReleaseImage(&timg);  return squares;}////////////////////////////////////////////////////////////////////函数功能：画出所有矩形//输入：  img 原图像//     squares 矩形序列//     wndname 窗口名称//输出：  图像中标记矩形//////////////////////////////////////////////////////////////////void drawSquares(IplImage* img, CvSeq* squares, const char* wndname){ CvSeqReader reader; IplImage* cpy = cvCloneImage(img); CvPoint pt[4]; int i; cvStartReadSeq(squares, &reader, 0); for (i = 0; i < squares->total; i += 4) { CvPoint* rect = pt; int count = 4; memcpy(pt, reader.ptr, squares->elem_size); CV_NEXT_SEQ_ELEM(squares->elem_size, reader); memcpy(pt + 1, reader.ptr, squares->elem_size); CV_NEXT_SEQ_ELEM(squares->elem_size, reader); memcpy(pt + 2, reader.ptr, squares->elem_size); CV_NEXT_SEQ_ELEM(squares->elem_size, reader); memcpy(pt + 3, reader.ptr, squares->elem_size); CV_NEXT_SEQ_ELEM(squares->elem_size, reader); //cvPolyLine( cpy, &rect, &count, 1, 1, CV_RGB(0,255,0), 3, CV_AA, 0 ); cvPolyLine(cpy, &rect, &count, 1, 1, CV_RGB(rand() & 255, rand() & 255, rand() & 255), 1, CV_AA, 0);//彩色绘制 } cvShowImage("22", cpy); cvReleaseImage(&cpy);} void SendMessageOne(){ //开起摄像头 VideoCapture capture; capture.open(0); Mat edges; //定义转化的灰度图 if (!capture.isOpened()) namedWindow("【效果图】", CV_WINDOW_NORMAL); const char* winn = "1111"; if (!capture.isOpened()) //namedWindow(winn, CV_WINDOW_NORMAL); CvMemStorage* storage = 0; CvMemStorage* storage = 0; storage = cvCreateMemStorage(0); while (1) { int Y=0, J=0; Mat frame; capture >> frame; IplImage img0 = frame; //drawSquares(&img0, findSquares4(&img0, storage, 100, 2000, 80, 100, a), winn); //cvClearMemStorage(storage); //清空存储 Mat E = frame(Range(1, 320), Range(1, 240)); cvtColor(frame, edges, CV_BGR2GRAY); //高斯滤波 GaussianBlur(edges, edges, Size(7, 7), 2, 2); std::vector<Vec3f> circles;//存储每个圆的位置信息      //霍夫圆 HoughCircles(edges, circles, CV_HOUGH_GRADIENT, 1.5, 10, 100, 100, 0, 50); for (size_t i = 0; i < circles.size(); i++) {  Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));  int radius = cvRound(circles[i][2]);  //std::cout << "圆的X是" << circles[i][0] << "圆的Y是" << circles[i][1] << std:: endl;  //绘制圆轮廓   circle(frame, center, radius, Scalar(155, 50, 255), 3, 8, 0);  int R = frame.at<Vec3b>(cvRound(circles[i][1]), cvRound(circles[i][0]))[2];//R  int G = frame.at<Vec3b>(cvRound(circles[i][1]), cvRound(circles[i][0]))[1];//G  int B = frame.at<Vec3b>(cvRound(circles[i][1]), cvRound(circles[i][0]))[0];//B  int num = R + G + B;  std::cout << "圆心颜色是" << num << std::endl;        }  imshow("【效果图】", frame); waitKey(30); }} int main(){ std::thread *a = new std::thread(SendMessageOne); a->join();  return 0;}