single.cpp

#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(6, 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。

    while (getline(fin, filename))

    {

        image_count++;

        // 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(28, 28);  /* 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 = "left";

        StrStm << i + 1;

        StrStm >> imageFileName;

        filePath += imageFileName;

        filePath += ".jpg";

        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;

}