#include<opencv2/opencv.hpp>#include<vector>#include<iostream>intmain(){// 创建一个用于存储图像点的数组std::vector<std::vector<cv::Point2f>> imagePoints[2];// 假设我们提取了以下特征点std::vector<cv::Point2f> points1 = { {0.0f,0.0f},
cvui::printf(srcMat,0,24,1.0,0xFF0000,"count = %d", vectorPoint2fCorners.size());qDebug() << __FILE__ << __LINE__ << vectorPoint2fCorners.size();// 步骤五:绘制棋盘点cv::drawChessboardCorners(srcMat2, cv::Size(chessboardColCornerCount, chessboardRowCornerCount), vectorPoint2fCorner...
第二个参数corners,初始的角点坐标向量,同时作为亚像素坐标位置的输出,所以需要是浮点型数据,一般用元素是Pointf2f/Point2d的向量来表示:vector<Point2f/Point2d> iamgePointsBuf; 第三个参数winSize,大小为搜索窗口的一半; 第四个参数zeroZone,死区的一半尺寸,死区为不对搜索区的中央位置做求和运算的区域。它是用来...
vector< int > markerIds; vector< vector<Point2f> > markerCorners; cv::aruco::detectMarkers(inputImage, board.dictionary, markerCorners, markerIds); // if at least one marker detected if(markerIds.size() > 0) { std::vector<cv::Point2f> charucoCorners; std::vector<int> charucoIds; cv...
vector<Point2f> corners; goodFeaturesToTrack(gray, corners, 100, 0.01, 10, Mat(), 3, false, 0.04); cout << "角点数量" << corners.size() << endl; for (int i = 0; i<corners.size(); i++) { cout << "像素坐标:(" << corners[i].x << ", " << corners[i].y << ")" ...
vector<vector<Point>> contours; bitwise_not(binary, binary); findContours(binary, contours, hireachy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point); Mat result = img.clone; Point2f center; floatradius; // 轮廓分析 for(size_tt =0; t < contours.size; t++) { ...
// 步骤三:灰度化 cv::Mat grayMat; cv::cvtColor(srcMat, grayMat, cv::COLOR_BGR2GRAY); cv::imshow("3", grayMat); // cv::waitKey(0); // 步骤四:检测角点 std::vector<cv::Point2f> vectorPoint2fCorners; bool patternWasFound = false; patternWasFound = cv::findChessboardCorners(gray...
//-- Localize the objectstd::vector<Point2f>obj_pts;std::vector<Point2f>scene_pts;for(size_t i=0;i<goodMatches.size();i++){//-- Get the keypoints from the good matchesobj_pts.push_back(keypoints_obj[goodMatches[i].queryIdx].pt);scene_pts.push_back(keypoints_sence[goodMatches[...
(maxCorners, 1); vector<Point2f> corners; double qualityLevel = 0.01; double minDistance = 10; int blockSize = 3, gradientSize = 3; bool useHarrisDetector = false; double k = 0.04; Mat copy = src.clone(); goodFeaturesToTrack(src_gray, corners, maxCorners, qualityLevel, minDistance, ...
vector<vector<Point2f>> imagePoints; vector<vector<Point3f>> objectPoints; TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER,30,0.001); intnumCornersHor =7; intnumCornersVer =7; intnumSquares =50; vector<Point3f> obj; ...