相机标定 calib3d 学习笔记

来源：互联网发布：网络枪支案件判刑编辑：程序博客网时间：2024/06/05 16:20
opencv给的官方代码利用xml读取文件，不如简单的读取txt文本的格式，便于编辑。这份代码有三个要注意的地方。
1.txt文件要标好照片
2.Size board_size = Size(7, 8);我用的是7*8（内角点）的标定板
3.Size square_size = Size(10, 10);一般情况下应该是这个10*10。
#include <opencv2/opencv.hpp>#include <iostream>  #include <fstream>  using namespace cv;using namespace std;int main(){    ifstream fin("t.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(7, 8);    /* 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。      std::cout << "开始提取角点………………" << endl;    Mat imageInput[6];    while (getline(fin, filename))    {        /* 输出检验*/        int i=image_count++;        // 用于观察检验输出          imageInput[i] = imread(filename);        if (image_count == 1)  //读入第一张图片时获取图像宽高信息          {            image_size = imageInput[i].size();            std::cout << "the size of images are : "<<image_size << endl;        }        std::cout << "current image_count : " << image_count << endl;        /* 提取角点 */        if (0 == findChessboardCorners(imageInput[i], board_size, image_points_buf))        {            std::cout << "can not find chessboard corners!\n"; //找不到角点              exit(1);        }        else        {            Mat view_gray;            cvtColor(imageInput[i], view_gray, CV_RGB2GRAY);            /* 亚像素精确化 */            find4QuadCornerSubpix(view_gray, image_points_buf, Size(7, 7)); //对粗提取的角点进行精确化               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, true); //用于在图片中标记角点              imshow("Camera Calibration", view_gray);//显示图片              waitKey(100);//暂停0.1S                 }    }    int total = image_points_seq.size();    std::cout << "total = " << total << endl;    int CornerNum = board_size.width*board_size.height;  //每张图片上总的角点数      for (int i = 0; i<total; i++)    {        // 便于控制台查看          std::cout << std::endl;        int j = i + 1;        std::cout << "----> 第 " << j << "张图片的角点坐标  : " << endl;;        //输出所有的角点          for (int j = 0; j < CornerNum; j++)        {            std::cout << " ( " << image_points_seq[i][j].x;            std::cout << " ," << image_points_seq[i][j].y <<" )"<< endl;        }    }    std::cout << "角点提取完成！\n";    //以下是摄像机标定      std::cout << "开始标定………………" << endl;    /*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);    std::cout << "标定完成！\n";    //对标定结果进行评价      std::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 */    std::cout << "\t每幅图像的标定误差：\n\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 << endl;        fout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl << endl;    }    std::cout << "总体平均误差：" << total_err / image_count << "像素" << endl << 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;    Mat dst;    undistort(imageInput[0], dst, cameraMatrix, distCoeffs);    imshow("result_ex",dst);    waitKey(500);    Mat map1, map2;    initUndistortRectifyMap(        cameraMatrix, distCoeffs, Mat(),        getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, image_size, 1, image_size, 0), image_size,        CV_16SC2, map1, map2);    remap(imageInput[0], imageInput[0], map1, map2, INTER_LINEAR);    imshow("result_ex2", imageInput[0]);    waitKey(500);    return 0;}
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