先新建工程,然后建立一个放置采集到的样本图片的文件夹,再建立一个txt格式文件,里面写上图片的路径,如果是相对路径,注意将图片文件和txt都放到工程目录下。
程序;
// Xiang_Ji_Biao_Ding.cpp: 定义控制台应用程序的入口点。 //相机标定与畸变校正 #include "stdafx.h" #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; void 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 cout << "开始提取角点………………"; int image_count = 0; /* 图像数量 */ Size image_size; /* 图像的尺寸 */ Size board_size = Size(4, 6); /* 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; int count = -1;//用于存储角点个数。 while (getline(fin, filename)) //将行读入字符串,fin为文件,filename为字符串 { image_count++; /* 图像数量++ */ // 用于观察检验输出 cout << "image_count = " << image_count << endl; /* 输出检验*/ cout << "-->count = " << count; Mat imageInput = imread(filename); if (image_count == 1) //读入第一张图片时获取图像宽高信息 { image_size.width = imageInput.cols; image_size.height = imageInput.rows; cout << "image_size.width = " << image_size.width << endl; cout << "image_size.height = " << image_size.height << endl; } /* 提取角点 */ if (0 == findChessboardCorners(imageInput, board_size, image_points_buf)) { cout << "can not find chessboard corners!\n"; //找不到角点 exit(1); } else { Mat view_gray; cvtColor(imageInput, view_gray, CV_RGB2GRAY); /* 亚像素精确化 */ find4QuadCornerSubpix(view_gray, image_points_buf, Size(5, 5)); //对粗提取的角点进行精确化 //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 total = image_points_seq.size(); cout << "total = " << total << endl; int CornerNum = board_size.width*board_size.height; //每张图片上总的角点数 for (int ii = 0; ii<total; ii++) { if (0 == ii % CornerNum)// 24 是每幅图片的角点个数。此判断语句是为了输出 图片号,便于控制台观看 { int i = -1; i = ii / CornerNum; int j = i + 1; cout << "--> 第 " << j << "图片的数据 --> : " << endl; } if (0 == ii % 3) // 此判断语句,格式化输出,便于控制台查看 { cout << endl; } else { cout.width(10); } //输出所有的角点 cout << " -->" << image_points_seq[ii][0].x; cout << " -->" << image_points_seq[ii][0].y; } cout << "角点提取完成!\n"; //以下是摄像机标定 cout << "开始标定………………"; /*棋盘三维信息*/ Size square_size = Size(10, 10); /* 实际测量得到的标定板上每个棋盘格的大小 */ 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 */ realPoint.x = i * square_size.width; realPoint.y = j * square_size.height; 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 */ calibrateCamera(object_points, image_points_seq, image_size, cameraMatrix, distCoeffs, rvecsMat, tvecsMat, 0); cout << "标定完成!\n"; //对标定结果进行评价 cout << "开始评价标定结果………………\n"; 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 */ cout << "\t每幅图像的标定误差:\n"; 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]; std::cout << "第" << i + 1 << "幅图像的平均误差:" << err << "像素" << endl; fout << "第" << i + 1 << "幅图像的平均误差:" << err << "像素" << endl; } std::cout << "总体平均误差:" << total_err / image_count << "像素" << endl; fout << "总体平均误差:" << total_err / image_count << "像素" << endl << endl; std::cout << "评价完成!" << endl; //保存定标结果 std::cout << "开始保存定标结果………………" << endl; 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; } std::cout << "完成保存" << endl; fout << endl; /************************************************************************ 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); std::cout << "保存矫正图像" << endl; string imageFileName; std::stringstream StrStm; for (int i = 0; i != image_count; i++) { std::cout << "Frame #" << i + 1 << "..." << endl; initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, image_size, CV_32FC1, mapx, mapy); StrStm.clear(); imageFileName.clear(); string filePath = "chess"; StrStm << i + 1; StrStm >> imageFileName; filePath += imageFileName; filePath += ".bmp"; Mat imageSource = imread(filePath); Mat newimage = imageSource.clone(); //另一种不需要转换矩阵的方式 //undistort(imageSource,newimage,cameraMatrix,distCoeffs); remap(imageSource, newimage, mapx, mapy, INTER_LINEAR); StrStm.clear(); filePath.clear(); StrStm << i + 1; StrStm >> imageFileName; imageFileName += "_d.jpg"; imwrite(imageFileName, newimage); } std::cout << "保存结束" << endl; return; }