opencv相机标定

读图，检测交点，物点计算，相机矩阵求解

#include "opencv.hpp"

#include 
#include  
#include   
#include   
#include

using namespace cv;
using namespace std;

/*
@param File_Directory 为文件夹目录
@param FileType 为需要查找的文件类型
@param FilesName 为存放文件名的容器
*/



void m_calibration(String pattern, Size board_size, Size square_size, Mat& cameraMatrix, Mat& distCoeffs, vector<Mat>& rvecsMat, vector<Mat>& tvecsMat)
{
    ofstream fout("caliberation_result.txt");                       // 保存标定结果的文件 

    cout << "开始提取角点………………" << endl;
    int image_count = 0;                                            // 图像数量 
    Size image_size;                                                // 图像的尺寸 

    vector<Point2f> image_points;                                   // 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

    vector<cv::String> fn;
    glob(pattern, fn, false);
    
    
    for (int i = 0; i < fn.size(); i++)
    {
        image_count++;

        // 用于观察检验输出
        cout << "image_count = " << image_count << endl;
        Mat imageInput = imread(fn[i]);
        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 */
        bool ok = findChessboardCorners(imageInput, board_size, image_points, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + CALIB_CB_FAST_CHECK);
        if (0 == ok)
        {
            cout << "第" << image_count << "张照片提取角点失败，请删除后，重新标定！" << endl; //找不到角点
            imshow("失败照片", imageInput);
            waitKey(0);
        }
        else
        {
            Mat view_gray;
            cout << "imageInput.channels()=" << imageInput.channels() << endl;
            cvtColor(imageInput, view_gray, COLOR_BGR2GRAY);

            /* 亚像素精确化 */
            //find4QuadCornerSubpix(view_gray, image_points, Size(5, 5)); //对粗提取的角点进行精确化
            cv::cornerSubPix(view_gray, image_points, cv::Size(11, 11), cv::Size(-1, -1), TermCriteria(cv::TermCriteria::MAX_ITER + cv::TermCriteria::EPS, 30, 0.1));

            image_points_seq.push_back(image_points);  //保存亚像素角点

            /* 在图像上显示角点位置 */
            drawChessboardCorners(view_gray, board_size, image_points, true);

            //imshow("Camera Calibration", view_gray);//显示图片
            //waitKey(100);//暂停0.1S     
        }
    }
    cout << "角点提取完成！！！" << endl;


    /*棋盘三维信息*/
    vector<vector<Point3f>> object_points_seq;                     // 保存标定板上角点的三维坐标

    for (int t = 0; t < image_count; t++)
    {
        vector<Point3f> object_points;
        for (int i = 0; i < board_size.height; i++)
        {
            for (int 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;
                object_points.push_back(realPoint);
            }
        }
        object_points_seq.push_back(object_points);
    }

    /* 运行标定函数 */
    double err_first = calibrateCamera(object_points_seq, image_points_seq, image_size, cameraMatrix, distCoeffs, rvecsMat, tvecsMat,0);
    fout << "重投影误差1：" << err_first << "像素" << endl << endl;
    cout << "标定完成！！！" << endl;


    cout << "开始评价标定结果………………";
    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
    double totalErr = 0.0;
    double totalPoints = 0.0;
    vector<Point2f> image_points_pro;     // 保存重新计算得到的投影点

    for (int i = 0; i < image_count; i++)
    {

        projectPoints(object_points_seq[i], rvecsMat[i], tvecsMat[i], cameraMatrix, distCoeffs, image_points_pro);   //通过得到的摄像机内外参数，对角点的空间三维坐标进行重新投影计算

        err = norm(Mat(image_points_seq[i]), Mat(image_points_pro), NORM_L2);

        totalErr += err * err;
        totalPoints += object_points_seq[i].size();

        err /= object_points_seq[i].size();
        //fout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;
        total_err += err;
    }
    fout << "重投影误差2：" << sqrt(totalErr / totalPoints) << "像素" << endl << endl;
    fout << "重投影误差3：" << total_err / image_count << "像素" << endl << endl;


    //保存定标结果    
    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 << rvecsMat[i] << endl;

        /* 将旋转向量转换为相对应的旋转矩阵 */
        Rodrigues(rvecsMat[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 << tvecsMat[i] << endl << endl;
    }
    cout << "定标结果完成保存！！！" << endl;
    fout << endl;
}

void m_undistort(String pattern, Size image_size, Mat& cameraMatrix, Mat& distCoeffs)
{

    Mat mapx = Mat(image_size, CV_32FC1);   //X 坐标重映射参数
    Mat mapy = Mat(image_size, CV_32FC1);   //Y 坐标重映射参数
    Mat R = Mat::eye(3, 3, CV_32F);
    cout << "保存矫正图像" << endl;
    string imageFileName;                  //校正后图像的保存路径
    stringstream StrStm;
    string temp;
    vector<cv::String> fn;
    glob(pattern, fn, false);
    for (int i = 0; i < fn.size(); i++)
    {
        Mat imageSource = imread(fn[i]);

        Mat newimage = imageSource.clone();

        //方法一：使用initUndistortRectifyMap和remap两个函数配合实现
        //initUndistortRectifyMap(cameraMatrix,distCoeffs,R, Mat(),image_size,CV_32FC1,mapx,mapy);
        //  remap(imageSource,newimage,mapx, mapy, INTER_LINEAR);

        //方法二：不需要转换矩阵的方式，使用undistort函数实现
        undistort(imageSource, newimage, cameraMatrix, distCoeffs);

        StrStm << i + 1;
        StrStm >> temp;
        imageFileName = "矫正后图像//" + temp + "_d.jpg";
        imwrite(imageFileName, newimage);

        StrStm.clear();
        imageFileName.clear();
    }
    std::cout << "保存结束" << endl;
}

void main()
{
    string File_Directory1 = "C:/Users/lenovo/Desktop/opencv/相机标定/201";   //文件夹目录1

    //string FileType = ".jpg";    // 需要查找的文件类型

   // vectorFilesName1;    //存放文件名的容器

    //getFilesName(File_Directory1, FileType, FilesName1);   // 标定所用图像文件的路径


    Size board_size = Size(14, 19);                         // 标定板上每行、列的角点数 
    Size square_size = Size(10, 10);                       // 实际测量得到的标定板上每个棋盘格的物理尺寸，单位mm

    Mat cameraMatrix = Mat(3, 3, CV_32FC1, Scalar::all(0));        // 摄像机内参数矩阵
    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> rvecsMat;                                          // 存放所有图像的旋转向量，每一副图像的旋转向量为一个mat
    vector<Mat> tvecsMat;                                          // 存放所有图像的平移向量，每一副图像的平移向量为一个mat

    m_calibration(File_Directory1, board_size, square_size, cameraMatrix, distCoeffs, rvecsMat, tvecsMat);

    //m_undistort(File_Directory1, image_size, cameraMatrix, distCoeffs);

    return;
}