相机标定——单目、双目

双目标定

https://www.cnblogs.com/polly333/p/5013505.html

#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

#include <vector>
#include <string>
#include <algorithm>
#include <iostream>
#include <iterator>
#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>

using namespace cv;
using namespace std;



static void StereoCalib(const vector<string>& imagelist, Size boardSize, bool useCalibrated=true, bool showRectified=true)
{
    if( imagelist.size() % 2 != 0 )
    {
        cout << "Error: the image list contains odd (non-even) number of elements\n";
        return;
    }

    bool displayCorners = false;//true;
    const int maxScale = 2;
    const float squareSize = 1.f;  // Set this to your actual square size
    // ARRAY AND VECTOR STORAGE:
    //创建图像坐标和世界坐标系坐标矩阵
    vector<vector<Point2f> > imagePoints[2];
    vector<vector<Point3f> > objectPoints;
    Size imageSize;

    int i, j, k, nimages = (int)imagelist.size()/2;
    //确定左右视图矩阵的数量，比如10副图，左右矩阵分别为5个
    imagePoints[0].resize(nimages);
    imagePoints[1].resize(nimages);
    vector<string> goodImageList;

    for( i = j = 0; i < nimages; i++ )
    {
        for( k = 0; k < 2; k++ )
        {
            //逐个读取图片
            const string& filename = imagelist[i*2+k];
            Mat img = imread(filename, 0);
            if(img.empty())
                break;
            if( imageSize == Size() )
                imageSize = img.size();
            else if( img.size() != imageSize )
            {
                cout << "The image " << filename << " has the size different from the first image size. Skipping the pair\n";
                break;
            }
            bool found = false;
            //设置图像矩阵的引用，此时指向左右视图的矩阵首地址
            vector<Point2f>& corners = imagePoints[k][j];
            for( int scale = 1; scale <= maxScale; scale++ )
            {
                Mat timg;
                //图像是8bit的灰度或彩色图像
                if( scale == 1 )
                    timg = img;
                else
                    resize(img, timg, Size(), scale, scale);
                //boardSize为棋盘图的行、列数
                found = findChessboardCorners(timg, boardSize, corners,
                    CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_NORMALIZE_IMAGE);
                if( found )
                {
                    //如果为多通道图像
                    if( scale > 1 )
                    {
                        Mat cornersMat(corners);
                        cornersMat *= 1./scale;
                    }
                    break;
                }
            }
            if( displayCorners )
            {
                cout << filename << endl;
                Mat cimg, cimg1;
                cvtColor(img, cimg, COLOR_GRAY2BGR);
                drawChessboardCorners(cimg, boardSize, corners, found);
                double sf = 640./MAX(img.rows, img.cols);
                resize(cimg, cimg1, Size(), sf, sf);
                imshow("corners", cimg1);
                char c = (char)waitKey(500);
                if( c == 27 || c == 'q' || c == 'Q' ) //Allow ESC to quit
                    exit(-1);
            }
            else
                putchar('.');
            if( !found )
                break;
            cornerSubPix(img, corners, Size(11,11), Size(-1,-1),
                         TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS,
                                      30, 0.01));
        }
        if( k == 2 )
        {
            goodImageList.push_back(imagelist[i*2]);
            goodImageList.push_back(imagelist[i*2+1]);
            j++;
        }
    }
    cout << j << " pairs have been successfully detected.\n";
    nimages = j;
    if( nimages < 2 )
    {
        cout << "Error: too little pairs to run the calibration\n";
        return;
    }

    imagePoints[0].resize(nimages);
    imagePoints[1].resize(nimages);
    // 图像点的世界坐标系
    objectPoints.resize(nimages);

    for( i = 0; i < nimages; i++ )
    {
        for( j = 0; j < boardSize.height; j++ )
            for( k = 0; k < boardSize.width; k++ )
                //直接转为float类型，坐标为行、列
                objectPoints[i].push_back(Point3f(j*squareSize, k*squareSize, 0));
    }

    cout << "Running stereo calibration ...\n";
    //创建内参矩阵
    Mat cameraMatrix[2], distCoeffs[2];
    cameraMatrix[0] = Mat::eye(3, 3, CV_64F);
    cameraMatrix[1] = Mat::eye(3, 3, CV_64F);
    Mat R, T, E, F;
    //求解双目标定的参数
    double rms = stereoCalibrate(objectPoints, imagePoints[0], imagePoints[1],
                    cameraMatrix[0], distCoeffs[0],
                    cameraMatrix[1], distCoeffs[1],
                    imageSize, R, T, E, F,
                    TermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, 1e-5),
                    CV_CALIB_FIX_ASPECT_RATIO +
                    CV_CALIB_ZERO_TANGENT_DIST +
                    CV_CALIB_SAME_FOCAL_LENGTH +
                    CV_CALIB_RATIONAL_MODEL +
                    CV_CALIB_FIX_K3 + CV_CALIB_FIX_K4 + CV_CALIB_FIX_K5);
    cout << "done with RMS error=" << rms << endl;

// CALIBRATION QUALITY CHECK
// because the output fundamental matrix implicitly
// includes all the output information,
// we can check the quality of calibration using the
// epipolar geometry constraint: m2^t*F*m1=0
    //计算标定误差
    double err = 0;
    int npoints = 0;
    vector<Vec3f> lines[2];
    for( i = 0; i < nimages; i++ )
    {
        int npt = (int)imagePoints[0][i].size();
        Mat imgpt[2];
        for( k = 0; k < 2; k++ )
        {
            imgpt[k] = Mat(imagePoints[k][i]);
            //校正图像点坐标
            undistortPoints(imgpt[k], imgpt[k], cameraMatrix[k], distCoeffs[k], Mat(), cameraMatrix[k]);
            //求解对极线
            computeCorrespondEpilines(imgpt[k], k+1, F, lines[k]);
        }
        //计算求解点与实际点的误差
        for( j = 0; j < npt; j++ )
        {
            double errij = fabs(imagePoints[0][i][j].x*lines[1][j][0] +
                                imagePoints[0][i][j].y*lines[1][j][1] + lines[1][j][2]) +
                           fabs(imagePoints[1][i][j].x*lines[0][j][0] +
                                imagePoints[1][i][j].y*lines[0][j][1] + lines[0][j][2]);
            err += errij;
        }
        npoints += npt;
    }
    cout << "average reprojection err = " <<  err/npoints << endl;

    // save intrinsic parameters
    FileStorage fs("intrinsics.yml", CV_STORAGE_WRITE);
    if( fs.isOpened() )
    {
        fs << "M1" << cameraMatrix[0] << "D1" << distCoeffs[0] <<
            "M2" << cameraMatrix[1] << "D2" << distCoeffs[1];
        fs.release();
    }
    else
        cout << "Error: can not save the intrinsic parameters\n";

    Mat R1, R2, P1, P2, Q;
    Rect validRoi[2];
    //双目视觉校正，根据内参矩阵，两摄像机之间平移矩阵以及投射矩阵
    stereoRectify(cameraMatrix[0], distCoeffs[0],
                  cameraMatrix[1], distCoeffs[1],
                  imageSize, R, T, R1, R2, P1, P2, Q,
                  CALIB_ZERO_DISPARITY, 1, imageSize, &validRoi[0], &validRoi[1]);

    fs.open("extrinsics.yml", CV_STORAGE_WRITE);
    if( fs.isOpened() )
    {
        fs << "R" << R << "T" << T << "R1" << R1 << "R2" << R2 << "P1" << P1 << "P2" << P2 << "Q" << Q;
        fs.release();
    }
    else
        cout << "Error: can not save the intrinsic parameters\n";

    // OpenCV can handle left-right
    // or up-down camera arrangements
    bool isVerticalStereo = fabs(P2.at<double>(1, 3)) > fabs(P2.at<double>(0, 3));

// COMPUTE AND DISPLAY RECTIFICATION
    if( !showRectified )
        return;

    Mat rmap[2][2];
// IF BY CALIBRATED (CALIBRATE'S METHOD)
    //用标定的话，就不许计算左右相机的透射矩阵
    if( useCalibrated )
    {
        // we already computed everything
    }
// OR ELSE HARTLEY'S METHOD
    else
 // use intrinsic parameters of each camera, but
 // compute the rectification transformation directly
 // from the fundamental matrix
    {
        vector<Point2f> allimgpt[2];
        for( k = 0; k < 2; k++ )
        {
            for( i = 0; i < nimages; i++ )
                std::copy(imagePoints[k][i].begin(), imagePoints[k][i].end(), back_inserter(allimgpt[k]));
        }
        F = findFundamentalMat(Mat(allimgpt[0]), Mat(allimgpt[1]), FM_8POINT, 0, 0);
        Mat H1, H2;
        stereoRectifyUncalibrated(Mat(allimgpt[0]), Mat(allimgpt[1]), F, imageSize, H1, H2, 3);

        R1 = cameraMatrix[0].inv()*H1*cameraMatrix[0];
        R2 = cameraMatrix[1].inv()*H2*cameraMatrix[1];
        P1 = cameraMatrix[0];
        P2 = cameraMatrix[1];
    }

    //Precompute maps for cv::remap()
    //根据左右相机的投射矩阵，校正图像
    initUndistortRectifyMap(cameraMatrix[0], distCoeffs[0], R1, P1, imageSize, CV_16SC2, rmap[0][0], rmap[0][1]);
    initUndistortRectifyMap(cameraMatrix[1], distCoeffs[1], R2, P2, imageSize, CV_16SC2, rmap[1][0], rmap[1][1]);

    Mat canvas;
    double sf;
    int w, h;
    if( !isVerticalStereo )
    {
        sf = 600./MAX(imageSize.width, imageSize.height);
        w = cvRound(imageSize.width*sf);
        h = cvRound(imageSize.height*sf);
        canvas.create(h, w*2, CV_8UC3);
    }
    else
    {
        sf = 300./MAX(imageSize.width, imageSize.height);
        w = cvRound(imageSize.width*sf);
        h = cvRound(imageSize.height*sf);
        canvas.create(h*2, w, CV_8UC3);
    }

    for( i = 0; i < nimages; i++ )
    {
        for( k = 0; k < 2; k++ )
        {
            Mat img = imread(goodImageList[i*2+k], 0), rimg, cimg;
            remap(img, rimg, rmap[k][0], rmap[k][1], CV_INTER_LINEAR);
            cvtColor(rimg, cimg, COLOR_GRAY2BGR);
            Mat canvasPart = !isVerticalStereo ? canvas(Rect(w*k, 0, w, h)) : canvas(Rect(0, h*k, w, h));
            resize(cimg, canvasPart, canvasPart.size(), 0, 0, CV_INTER_AREA);
            if( useCalibrated )
            {
                Rect vroi(cvRound(validRoi[k].x*sf), cvRound(validRoi[k].y*sf),
                          cvRound(validRoi[k].width*sf), cvRound(validRoi[k].height*sf));
                rectangle(canvasPart, vroi, Scalar(0,0,255), 3, 8);
            }
        }

        if( !isVerticalStereo )
            for( j = 0; j < canvas.rows; j += 16 )
                line(canvas, Point(0, j), Point(canvas.cols, j), Scalar(0, 255, 0), 1, 8);
        else
            for( j = 0; j < canvas.cols; j += 16 )
                line(canvas, Point(j, 0), Point(j, canvas.rows), Scalar(0, 255, 0), 1, 8);
        imshow("rectified", canvas);
        char c = (char)waitKey();
        if( c == 27 || c == 'q' || c == 'Q' )
            break;
    }
}


static bool readStringList( const string& filename, vector<string>& l )
{
    l.resize(0);
    FileStorage fs(filename, FileStorage::READ);
    if( !fs.isOpened() )
        return false;
    FileNode n = fs.getFirstTopLevelNode();
    if( n.type() != FileNode::SEQ )
        return false;
    FileNodeIterator it = n.begin(), it_end = n.end();
    for( ; it != it_end; ++it )
        l.push_back((string)*it);
    return true;
}

int main(int argc, char** argv)
{
    Size boardSize;
    string imagelistfn;
    bool showRectified = true;

    for( int i = 1; i < argc; i++ )
    {
        if( string(argv[i]) == "-w" )
        {
            if( sscanf(argv[++i], "%d", &boardSize.width) != 1 || boardSize.width <= 0 )
            {
                cout << "invalid board width" << endl;
                return print_help();
            }
        }
        else if( string(argv[i]) == "-h" )
        {
            if( sscanf(argv[++i], "%d", &boardSize.height) != 1 || boardSize.height <= 0 )
            {
                cout << "invalid board height" << endl;
                return print_help();
            }
        }
        else if( string(argv[i]) == "-nr" )
            showRectified = false;
        else if( string(argv[i]) == "--help" )
            return print_help();
        else if( argv[i][0] == '-' )
        {
            cout << "invalid option " << argv[i] << endl;
            return 0;
        }
        else
            imagelistfn = argv[i];
    }

    if( imagelistfn == "" )
    {
        imagelistfn = "stereo_calib.xml";
        boardSize = Size(9, 6);
    }
    else if( boardSize.width <= 0 || boardSize.height <= 0 )
    {
        cout << "if you specified XML file with chessboards, you should also specify the board width and height (-w and -h options)" << endl;
        return 0;
    }

    vector<string> imagelist;
    bool ok = readStringList(imagelistfn, imagelist);
    if(!ok || imagelist.empty())
    {
        cout << "can not open " << imagelistfn << " or the string list is empty" << endl;
        return print_help();
    }

    StereoCalib(imagelist, boardSize, true, showRectified);
    return 0;
}

单目标定

https://www.cnblogs.com/zyly/p/9366080.html

/*************************************************************************************
*
*   Description：相机标定，张氏标定法  单目标定
*   Author     ：JNU
*   Data       ：2018.7.22
*
************************************************************************************/
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
#include <fstream>
#include <vector>

using namespace cv;
using namespace std;

void main(char *args)
{
    //保存文件名称
    std::vector<std::string>  filenames;

    //需要更改的参数
    //左相机标定，指定左相机图片路径，以及标定结果保存文件
    string infilename = "sample/left/filename.txt";        //如果是右相机把left改为right
    string outfilename = "sample/left/caliberation_result.txt";

    //标定所用图片文件的路径,每一行保存一个标定图片的路径  ifstream 是从硬盘读到内存
    ifstream fin(infilename);
    //保存标定的结果  ofstream 是从内存写到硬盘
    ofstream fout(outfilename);

    /*
    1.读取毎一幅图像，从中提取出角点，然后对角点进行亚像素精确化、获取每个角点在像素坐标系中的坐标
    像素坐标系的原点位于图像的左上角
    */
    std::cout << "开始提取角点......" << std::endl;;
    //图像数量
    int imageCount = 0;
    //图像尺寸
    cv::Size imageSize;
    //标定板上每行每列的角点数
    cv::Size boardSize = cv::Size(9, 6);
    //缓存每幅图像上检测到的角点
    std::vector<Point2f>  imagePointsBuf;
    //保存检测到的所有角点
    std::vector<std::vector<Point2f>> imagePointsSeq;
    char filename[100];
    if (fin.is_open())
    {
        //读取完毕？
        while (!fin.eof())
        {
            //一次读取一行
            fin.getline(filename, sizeof(filename) / sizeof(char));
            //保存文件名
            filenames.push_back(filename);
            //读取图片
            Mat imageInput = cv::imread(filename);
            //读入第一张图片时获取图宽高信息
            if (imageCount == 0)
            {
                imageSize.width = imageInput.cols;
                imageSize.height = imageInput.rows;
                std::cout << "imageSize.width = " << imageSize.width << std::endl;
                std::cout << "imageSize.height = " << imageSize.height << std::endl;
            }

            std::cout << "imageCount = " << imageCount << std::endl;
            imageCount++;

            //提取每一张图片的角点
            if (cv::findChessboardCorners(imageInput, boardSize, imagePointsBuf) == 0)
            {
                //找不到角点
                std::cout << "Can not find chessboard corners!" << std::endl;
                exit(1);
            }
            else
            {
                Mat viewGray;
                //转换为灰度图片
                cv::cvtColor(imageInput, viewGray, cv::COLOR_BGR2GRAY);
                //亚像素精确化   对粗提取的角点进行精确化
                cv::find4QuadCornerSubpix(viewGray, imagePointsBuf, cv::Size(5, 5));
                //保存亚像素点
                imagePointsSeq.push_back(imagePointsBuf);
                //在图像上显示角点位置
                cv::drawChessboardCorners(viewGray, boardSize, imagePointsBuf, true);
                //显示图片
                //cv::imshow("Camera Calibration", viewGray);
                cv::imwrite("test.jpg", viewGray);
                //等待0.5s
                //waitKey(500);
            }
        }        
        
        //计算每张图片上的角点数 54
        int cornerNum = boardSize.width * boardSize.height;

        //角点总数
        int total = imagePointsSeq.size()*cornerNum;
        std::cout << "total = " << total << std::endl;

        for (int i = 0; i < total; i++)
        {
            int num = i / cornerNum;
            int p = i%cornerNum;
            //cornerNum是每幅图片的角点个数，此判断语句是为了输出，便于调试
            if (p == 0)
            {                                        
                std::cout << "\n第 " << num+1 << "张图片的数据 -->: " << std::endl;
            }
            //输出所有的角点
            std::cout<<p+1<<":("<< imagePointsSeq[num][p].x;
            std::cout << imagePointsSeq[num][p].y<<")\t";
            if ((p+1) % 3 == 0)
            {
                std::cout << std::endl;
            }
        }

        std::cout << "角点提取完成!" << std::endl;

        /*
        2.摄像机标定 世界坐标系原点位于标定板左上角(第一个方格的左上角)
        */
        std::cout << "开始标定" << std::endl;
        //棋盘三维信息，设置棋盘在世界坐标系的坐标
        //实际测量得到标定板上每个棋盘格的大小
        cv::Size squareSize = cv::Size(26, 26);
        //毎幅图片角点数量
        std::vector<int> pointCounts;
        //保存标定板上角点的三维坐标
        std::vector<std::vector<cv::Point3f>> objectPoints;
        //摄像机内参数矩阵 M=[fx γ u0,0 fy v0,0 0 1]
        cv::Mat cameraMatrix = cv::Mat(3, 3, CV_64F, Scalar::all(0));
        //摄像机的5个畸变系数k1,k2,p1,p2,k3
        cv::Mat distCoeffs = cv::Mat(1, 5, CV_64F, Scalar::all(0));
        //每幅图片的旋转向量
        std::vector<cv::Mat> tvecsMat;
        //每幅图片的平移向量
        std::vector<cv::Mat> rvecsMat;

        //初始化标定板上角点的三维坐标
        int i, j, t;
        for (t = 0; t < imageCount; t++)
        {
            std::vector<cv::Point3f> tempPointSet;
            //行数
            for (i = 0; i < boardSize.height; i++)
            {
                //列数
                for (j = 0; j < boardSize.width; j++)
                {
                    cv::Point3f realPoint;
                    //假设标定板放在世界坐标系中z=0的平面上。
                    realPoint.x = i*squareSize.width;
                    realPoint.y = j*squareSize.height;
                    realPoint.z = 0;
                    tempPointSet.push_back(realPoint);
                }
            }
            objectPoints.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 < imageCount; i++)
        {
            pointCounts.push_back(boardSize.width*boardSize.height);
        }
        //开始标定
        cv::calibrateCamera(objectPoints, imagePointsSeq, imageSize, cameraMatrix, distCoeffs, rvecsMat, tvecsMat);
        std::cout << "标定完成" << std::endl;
        //对标定结果进行评价
        std::cout << "开始评价标定结果......" << std::endl;
        //所有图像的平均误差的总和
        double totalErr = 0.0;
        //每幅图像的平均误差
        double err = 0.0;
        //保存重新计算得到的投影点
        std::vector<cv::Point2f> imagePoints2;
        std::cout << "每幅图像的标定误差:" << std::endl;
        fout << "每幅图像的标定误差:" << std::endl;
        for (i = 0; i < imageCount; i++)
        {
            std::vector<cv::Point3f> tempPointSet = objectPoints[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imagePoints2（在像素坐标系下的点坐标）
            cv::projectPoints(tempPointSet, rvecsMat[i], tvecsMat[i], cameraMatrix, distCoeffs, imagePoints2);
            //计算新的投影点和旧的投影点之间的误差
            std::vector<cv::Point2f> tempImagePoint = imagePointsSeq[i];
            cv::Mat tempImagePointMat = cv::Mat(1, tempImagePoint.size(), CV_32FC2);
            cv::Mat imagePoints2Mat = cv::Mat(1, imagePoints2.size(), CV_32FC2);
            for (int j = 0; j < tempImagePoint.size(); j++)
            {
                imagePoints2Mat.at<cv::Vec2f>(0, j) = cv::Vec2f(imagePoints2[j].x, imagePoints2[j].y);
                tempImagePointMat.at<cv::Vec2f>(0, j) = cv::Vec2f(tempImagePoint[j].x, tempImagePoint[j].y);
            }
            //Calculates an absolute difference norm or a relative difference norm.
            err = cv::norm(imagePoints2Mat, tempImagePointMat, NORM_L2);
            totalErr += err /= pointCounts[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;

        }
        //每张图像的平均总误差
        std::cout << "  总体平均误差:" << totalErr / imageCount << "像素" << std::endl;
        fout << "总体平均误差:" << totalErr / imageCount << "像素" << std::endl;
        std::cout << "评价完成!" << std::endl;
        //保存标定结果
        std::cout << "开始保存标定结果....." << std::endl;
        //保存每张图像的旋转矩阵
        cv::Mat rotationMatrix = cv::Mat(3, 3, CV_32FC1, Scalar::all(0));
        fout << "相机内参数矩阵:" << std::endl;
        fout << cameraMatrix << std::endl << std::endl;
        fout << "畸变系数:" << std::endl;
        fout << distCoeffs << std::endl << std::endl;

        for (int i = 0; i < imageCount; i++)
        {
            fout << "第" << i + 1 << "幅图像的旋转向量:" << std::endl;
            fout << tvecsMat[i] << std::endl;
            //将旋转向量转换为相对应的旋转矩阵
            cv::Rodrigues(tvecsMat[i], rotationMatrix);
            fout << "第" << i + 1 << "幅图像的旋转矩阵:" << std::endl;
            fout << rotationMatrix << std::endl;
            fout << "第" << i + 1 << "幅图像的平移向量:" << std::endl;
            fout << rvecsMat[i] << std::endl;
        }
        std::cout << "保存完成" << std::endl;

        /************************************************************************
        显示定标结果
        *************************************************************************/
        cv::Mat mapx = cv::Mat(imageSize, CV_32FC1);
        cv::Mat mapy = cv::Mat(imageSize, CV_32FC1);
        cv::Mat R = cv::Mat::eye(3, 3, CV_32F);
        std::cout << "显示矫正图像" << endl;
        for (int i = 0; i != imageCount; i++)
        {
            std::cout << "Frame #" << i + 1 << "..." << endl;
            //计算图片畸变矫正的映射矩阵mapx、mapy(不进行立体校正、立体校正需要使用双摄)
            initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, imageSize, CV_32FC1, mapx, mapy);
            //读取一张图片
            Mat imageSource = imread(filenames[i]);
            Mat newimage = imageSource.clone();
            //另一种不需要转换矩阵的方式
            //undistort(imageSource,newimage,cameraMatrix,distCoeffs);
            //进行校正
            remap(imageSource, newimage, mapx, mapy, INTER_LINEAR);
            imshow("原始图像", imageSource);
            imshow("矫正后图像", newimage);
            waitKey();
        }

        //释放资源
        fin.close();
        fout.close();
        system("pause");        
    }
}