Algoritmo de calibración de cámara-OpenCV

#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
    std::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(6, 4);               /* 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;
        std::cout<<"image_count = "<<image_count<<std::endl;
        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;
            std::cout<<"image_size.width = "<<image_size.width<<std::endl;
            std::cout<<"image_size.height = "<<image_size.height<<std::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);  // 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*/
    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 */
    // 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, 0);

    //------------------------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;
}