opencv 区域提取三种算法
1.轮廓查找
findContours()函数，得到轮廓的点集集合
cv::vector<cv::vector<Point>> contours;threshold(roiMat,binImg,m_pPara.m_nMinGray,m_pPara.m_nMaxGray,THRESH_BINARY);//膨胀处理Mat dilaElement = getStructuringElement(MORPH_ELLIPSE, Size(m_pPara.m_nDilation, m_pPara.m_nDilation));dilate(binImg, binImg, dilaElement);//腐蚀处理Mat eroElement = getStructuringElement(MORPH_ELLIPSE, Size(m_pPara.m_nErosion, m_pPara.m_nErosion));erode(binImg, binImg, eroElement);//计算轮廓面积for (int k=0;k<contours.size();k++){//面积筛选dArea = contourArea(contours[k]);//轮廓填充绘制Mat imgBack = Mat::zeros(roiMat.size(), CV_8U);drawContours(imgBack, contours,k,255, -1);}//计算轮廓中心点Moments mu = moments(contours2[0]);Point2f center(mu.m10/mu.m00,mu.m01/mu.m00);//计算轮廓外接矩形cv::Rect bRect = boundingRect(contours2[0]);//计算轮廓旋转外接矩形 cv::RotatedRect rotRect= cv::minAreaRect(contours2[0]);Point2f pointsArr[4];rotRect.points(pointsArr);
检测结果如下:
2.斑点检测
SimpleBlobDetector函数
//区域检测参数设置 SimpleBlobDetector::Params params;//二值化阈值params.minThreshold = 100;params.maxThreshold = 255;//斑点颜色params.filterByColor =false;//斑点面积params.filterByArea = true;params.minArea = m_pPara.m_nMinArea;params.maxArea = m_pPara.m_nMaxArea;//斑点圆度params.filterByCircularity = true;params.minCircularity =m_pPara.m_dMinCir;//斑点惯性率params.filterByInertia = false;//斑点凸度比params.filterByConvexity = true;params.minCircularity = m_pPara.m_dMinCon;SimpleBlobDetector detector(params);//得到轮廓的关键点vector<KeyPoint> keypoints;detector.detect(roiMat, keypoints);
KeyPoint：
Point2f pt 关键点坐标
float size 点直径大小
float angle 关键点方向
检测结果如下:
3.角点检测
goodFeaturesToTrack（）函数
//存储角点位置vector<cv::Point2f> corners;//最小可接受的向量值double qualityLevel = 0.03;//两个角点之间的最小距离double minDistance = 6;//计算协方差矩阵的窗口大小int blockSize = 3;bool useHarris = false;double k = 0.04;//识别角点个数int num_corners = 3;cv::goodFeaturesToTrack(roiMat, corners, num_corners, qualityLevel, minDistance,cv::Mat(), blockSize, useHarris, k);//拟合亚像素角点位置//区域大小cv::Size winSize = cv::Size(3, 3);//类似于winSize,但总是有较小的范围， （-1，-1）表示忽略cv::Size zerozone = cv::Size(-1, -1);//迭代循环结束条件，三个参数，1：结束条件，2：最大迭代次数，3：阈值//以下TermCriteria表示同时到达最大迭代次数40与角点位置变化的最小值达到0.001则停止迭代cv::TermCriteria tc = cv::TermCriteria(cv::TermCriteria::EPS +cv::TermCriteria::MAX_ITER, 40, 0.001);//计算亚像素角点位置cv::cornerSubPix(roiMat, corners, winSize, zerozone, tc);
检测结果如下:
