基于opencv利用圆盘靶标的相机标定

#include <iostream> #include <sstream> #include <time.h> #include <stdio.h> #include <fstream> #include <opencv2/core/core.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <opencv2/calib3d/calib3d.hpp> #include <opencv2/highgui/highgui.hpp> using namespace cv; using namespace std; int main() { ifstream fin("calibdata.txt"); /* 标定所用图像文件的路径 */ ofstream fout("caliberation_result.txt"); /* 保存标定结果的文件 */ // 读取每一幅图像，从中提取出角点，然后对角点进行亚像素精确化 int image_count = 0; /* 图像数量 */ Size image_size; /* 图像的尺寸 */ Size board_size = Size(4, 11); /* 标定板上每行、列的角点数 */ vector<Point2f> image_points_buf; /* 缓存每幅图像上检测到的角点 */ vector<vector<Point2f>> image_points_seq; /* 保存检测到的所有角点 */ string filename; // 图片名 vector<string> filenames; while (getline(fin, filename)) { ++image_count; Mat imageInput = imread(filename); filenames.push_back(filename); // 读入第一张图片时获取图片大小 if (image_count == 1) { image_size.width = imageInput.cols; image_size.height = imageInput.rows; } /* 提取角点 */ if (0 == findCirclesGrid(imageInput, board_size, image_points_buf, cv::CALIB_CB_ASYMMETRIC_GRID)) // 0 == findCirclesGrid(imageInput, board_size, image_points, cv::CALIB_CB_ASYMMETRIC_GRID, blobDetector); { cout << "can not find chessboard corners!\n"; // 找不到角点 exit(1); } else { Mat view_gray; cvtColor(imageInput, view_gray, CV_RGB2GRAY); // 转灰度图 /* 亚像素精确化 */ // image_points_buf 初始的角点坐标向量，同时作为亚像素坐标位置的输出 // Size(5,5) 搜索窗口大小 // （-1，-1）表示没有死区 // TermCriteria 角点的迭代过程的终止条件, 可以为迭代次数和角点精度两者的组合 cornerSubPix(view_gray, image_points_buf, Size(5, 5), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1)); image_points_seq.push_back(image_points_buf); // 保存亚像素角点 /* 在图像上显示角点位置 */ drawChessboardCorners(view_gray, board_size, image_points_buf, false); // 用于在图片中标记角点 imshow("Camera Calibration", view_gray); // 显示图片 waitKey(500); //暂停0.5S } } int CornerNum = board_size.width * board_size.height; // 每张图片上总的角点数 //-------------以下是摄像机标定------------------ /*棋盘三维信息*/ float square_size = 17.2525; /* 实际测量得到的标定板上同一行相邻两个圆的半圆心距 */ vector<vector<Point3f>> object_points; /* 保存标定板上角点的三维坐标 */ /*内外参数*/ Mat cameraMatrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 摄像机内参数矩阵 */ vector<int> point_counts; // 每幅图像中角点的数量 Mat distCoeffs = Mat(1, 5, CV_32FC1, Scalar::all(0)); /* 摄像机的5个畸变系数：k1,k2,p1,p2,k3 */ vector<Mat> tvecsMat; /* 每幅图像的旋转向量 */ vector<Mat> rvecsMat; /* 每幅图像的平移向量 */ /* 初始化标定板上角点的三维坐标 */ int i, j, t; for (t = 0; t < image_count; t++) { vector<Point3f> tempPointSet; for (i = 0; i < board_size.height; i++) { for (j = 0; j < board_size.width; j++) { Point3f realPoint; /* 假设标定板放在世界坐标系中z=0的平面上 */ if (i % 2 == 0) { realPoint.x = i * square_size; realPoint.y = (2 * j) * square_size; realPoint.z = 0; } else { realPoint.x = i * square_size; realPoint.y = (2 * j + 1) * square_size; realPoint.z = 0; } tempPointSet.push_back(realPoint); } } object_points.push_back(tempPointSet); } /* 初始化每幅图像中的角点数量，假定每幅图像中都可以看到完整的标定板 */ for (i = 0; i < image_count; i++) { point_counts.push_back(board_size.width * board_size.height); } /* 开始标定 */ // object_points 世界坐标系中的角点的三维坐标 // image_points_seq 每一个内角点对应的图像坐标点 // image_size 图像的像素尺寸大小 // cameraMatrix 输出，内参矩阵 // distCoeffs 输出，畸变系数 // rvecsMat 输出，旋转向量 // tvecsMat 输出，位移向量 // 0 标定时所采用的算法 calibrateCamera(object_points, image_points_seq, image_size, cameraMatrix, distCoeffs, rvecsMat, tvecsMat, cv::CALIB_FIX_K3); //------------------------标定完成------------------------------------ // -------------------对标定结果进行评价------------------------------ double total_err = 0.0; /* 所有图像的平均误差的总和 */ double err = 0.0; /* 每幅图像的平均误差 */ vector<Point2f> image_points2; /* 保存重新计算得到的投影点 */ fout << "每幅图像的标定误差：\n"; for (i = 0; i < image_count; i++) { vector<Point3f> tempPointSet = object_points[i]; /* 通过得到的摄像机内外参数，对空间的三维点进行重新投影计算，得到新的投影点 */ projectPoints(tempPointSet, rvecsMat[i], tvecsMat[i], cameraMatrix, distCoeffs, image_points2); /* 计算新的投影点和旧的投影点之间的误差*/ vector<Point2f> tempImagePoint = image_points_seq[i]; Mat tempImagePointMat = Mat(1, tempImagePoint.size(), CV_32FC2); Mat image_points2Mat = Mat(1, image_points2.size(), CV_32FC2); for (int j = 0; j < tempImagePoint.size(); j++) { image_points2Mat.at<Vec2f>(0, j) = Vec2f(image_points2[j].x, image_points2[j].y); tempImagePointMat.at<Vec2f>(0, j) = Vec2f(tempImagePoint[j].x, tempImagePoint[j].y); } err = norm(image_points2Mat, tempImagePointMat, NORM_L2); total_err += err /= point_counts[i]; fout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl; } fout << "总体平均误差：" << total_err / image_count << "像素" << endl << endl; //-------------------------评价完成--------------------------------------------- //-----------------------保存定标结果------------------------------------------- Mat rotation_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 保存每幅图像的旋转矩阵 */ fout << "相机内参数矩阵：" << endl; fout << cameraMatrix << endl << endl; fout << "畸变系数：\n"; fout << distCoeffs << endl << endl << endl; for (int i = 0; i < image_count; i++) { fout << "第" << i + 1 << "幅图像的旋转向量：" << endl; fout << tvecsMat[i] << endl; /* 将旋转向量转换为相对应的旋转矩阵 */ Rodrigues(tvecsMat[i], rotation_matrix); fout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl; fout << rotation_matrix << endl; fout << "第" << i + 1 << "幅图像的平移向量：" << endl; fout << rvecsMat[i] << endl << endl; } fout << endl; //--------------------标定结果保存结束------------------------------- //----------------------显示定标结果-------------------------------- Mat mapx = Mat(image_size, CV_32FC1); Mat mapy = Mat(image_size, CV_32FC1); Mat R = Mat::eye(3, 3, CV_32F); string imageFileName; std::stringstream StrStm; for (int i = 0; i != image_count; i++) { initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, image_size, CV_32FC1, mapx, mapy); Mat imageSource = imread(filenames[i]); Mat newimage = imageSource.clone(); remap(imageSource, newimage, mapx, mapy, INTER_LINEAR); StrStm.clear(); imageFileName.clear(); StrStm << i + 1; StrStm >> imageFileName; imageFileName += "_d.jpg"; imwrite(imageFileName, newimage); } fin.close(); fout.close(); return 0; }