相机标定(张正友标定算法)解读与实战三

相机标定系列

  1. 相机标定(张正友标定算法)解读与实战一
  2. 相机标定(张正友标定算法)解读与实战二

前两篇文章偏重理论,介绍了针孔相机模型、镜头畸变模型和张氏标定的原理。今天主要讲解代码实现,虽然很多成熟视觉框架已经包含了相机标定,opencv 、matlab、halcon、ros, 为了更深入的结合原理,还是有必要自己码一遍。

《相机标定(张正友标定算法)解读与实战三》

这里数据采用OpenCV data 中提供的left 和right标定版图像,我这里用left的,共有13张,棋盘格BorderSize是 9×6,单元格大小SquareSize是 25×25。
用到的库:
OpenCV (图像库)
Eigen3 (矩阵库)
Ceres (算法优化库)
算法实现步骤:

  1. 提取图像的棋盘格角点 imagePoints
  2. 设置棋盘格角点对应的世界坐标点(z=0) objectPoints
  3. 计算imagePoints 与 objectPoints 对应的单应性矩阵 共有13个H
  4. 对H进行分解,构建Vb = 0, 计算B矩阵和内参矩阵K
  5. 根据K计算,每张棋盘格的外参R和t
  6. 把畸变系数也考虑进去构建最优化算法,求出最优K 和 畸变系数 k1、k2、k3、p1、p2。

1. 提取图像的棋盘格角点

// 准备数据 13张
 std::vector<std::string> files = { 
        "../data/images/left01.jpg",
        "../data/images/left02.jpg",
        "../data/images/left03.jpg",
        "../data/images/left04.jpg",
        "../data/images/left05.jpg",
        "../data/images/left06.jpg",
        "../data/images/left07.jpg",
        "../data/images/left08.jpg",
        "../data/images/left09.jpg",
        "../data/images/left11.jpg",
        "../data/images/left12.jpg",
        "../data/images/left13.jpg",
        "../data/images/left14.jpg",
    };
 // 2. 提取棋盘格角点
    std::vector<std::vector<Eigen::Vector2d>> imagePoints;
    std::vector<std::vector<Eigen::Vector3d>> objectPoints;
    cv::Size boardSize(9, 6); // 棋盘格大小
    cv::Size2f squareSize(25., 25.); // 单元格大小, 单位mm
    for(int i=0; i<files.size(); ++i) { 

        cv::Mat img =  cv::imread(files[i]);
        std::vector<cv::Point2f> corners;

        bool ok = cv::findChessboardCorners(img, boardSize, corners, cv::CALIB_CB_ADAPTIVE_THRESH | cv::CALIB_CB_FAST_CHECK | cv::CALIB_CB_NORMALIZE_IMAGE);
        if(ok) { 
            cv::Mat gray;
            cv::cvtColor(img, gray, CV_BGR2GRAY);
            cv::cornerSubPix(gray, corners, cv::Size(11, 11), cv::Size(-1, -1), cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::MAX_ITER, 30, 0.001));

           cv::drawChessboardCorners(img, boardSize, cv::Mat(corners), ok);
           cv::imshow("corners", img);
           cv::waitKey(100);
            std::vector<Eigen::Vector2d> _corners;
            for(auto& pt: corners){ 
                _corners.push_back(Eigen::Vector2d(pt.x, pt.y));
            }
            imagePoints.push_back(_corners);
        }
    }

2. 设置棋盘格角点对应的世界坐标点

 //3.0 设置世界坐标
    for(int i=0; i<imagePoints.size(); ++i){ 
        std::vector<Eigen::Vector3d> corners;
        getObjectPoints(boardSize, squareSize, corners);
        objectPoints.push_back(corners);
    }
void getObjectPoints(const cv::Size& borderSize, const cv::Size2f& squareSize, std::vector<Eigen::Vector3d>& objectPoints) { 
    for(int r=0; r<borderSize.height; ++r)
    { 
        for(int c=0; c<borderSize.width; ++c) { 
            objectPoints.push_back(Eigen::Vector3d(c*squareSize.width, r*squareSize.height, 0.));
        }
    }
}

3. 计算imagePoints 与 objectPoints 对应的单应性矩阵

之前已经写过关于单应性矩阵的求解DLP算法,具体代码中已经包含。这里直接贴出来OpenCV求解

bool findHomographyByOpenCV(std::vector<Eigen::Vector2d>& srcPoints, std::vector<Eigen::Vector2d>& dstPoints, Eigen::Matrix3d& H) { 

    std::vector<cv::Point2f> objectPoints, imagePoints;

    for(int i=0; i<srcPoints.size(); ++i) { 
        objectPoints.push_back(cv::Point2f(srcPoints[i](0), srcPoints[i](1)));
        imagePoints.push_back(cv::Point2f(dstPoints[i](0), dstPoints[i](1)));
    }

    cv::Mat hMat = findHomography(objectPoints, imagePoints, cv::RANSAC);

    cv::cv2eigen(hMat, H);

}

4. 对H进行分解,构建Vb = 0, 计算B矩阵并求解K内参矩阵

/** * Vij=[hi1hj1 hi1hj2+hi2hj1 hi2hj2 hi3hj1+hi1hj3 hi3hj2+hi2hj3 hi3hj3] * @param H * @param i * @param j * @return */
VectorXd getVector(const Matrix3d& H, int i, int j)
{ 
    i -= 1;
    j -= 1;
    VectorXd v(6);
    v << H(0, i)*H(0, j), H(0, i)*H(1, j) + H(1, i)*H(0, j), H(1, i)*H(1, j), H(2, i)*H(0, j) + H(0, i)*H(2, j), H(2, i)*H(1, j) + H(1, i)*H(2, j), H(2, i)*H(2, j);
    return v;
}
/** * * 计算相机内参初始值, 求解Vb =0; 并计算K矩阵 * */
Matrix3d solveInitCameraIntrinsic(std::vector<Matrix3d>& homos)
{ 
    int n = homos.size();
    // Vb = 0
    MatrixXd V(2*n, 6);
    for(int i=0; i<n; ++i)
    { 
        VectorXd v1 = getVector(homos[i], 1, 2);
        VectorXd v11 = getVector(homos[i], 1, 1);
        VectorXd v22 = getVector(homos[i], 2, 2);
        VectorXd v2 = v11 - v22;

        for(int j=0; j<6; ++j)
        { 
            V(2*i, j) = v1(j);
            V(2*i+1, j) = v2(j);
        }
    }

    //SVD 分解
    JacobiSVD<MatrixXd> svdSolver (V, ComputeThinV);
     MatrixXd v = svdSolver.matrixV();
    MatrixXd b = v.rightCols(1);

    std::cout <<"b = " << b << std::endl;


    // 求解内参 fx fy c uo v0
    double B11 = b(0), B12 = b(1), B22 = b(2), B13 = b(3), B23 = b(4), B33 = b(5);
    double v0 = (B12*B13-B11*B23) / (B11*B22-B12*B12);
    double s = B33-(B13*B13+v0*(B12*B13-B11*B23)) / B11;
    double fx = sqrt(s/B11);
    double fy = sqrt(s*B11 / (B11*B22-B12*B12));
    double c = -B12*fx*fx*fy/s;
    double u0 = c*v0/fx - B13*fx*fx/s;

    Matrix3d K;

    K << fx, c, u0,
    0, fy, v0,
    0, 0, 1;

    return K;
}

5. 根据K计算,每张棋盘格的外参R和t

/** * * 计算相机外参初始值 */
void solveInitCameraExtrinsic(std::vector<Matrix3d>& homos, Matrix3d& K, std::vector<Matrix3d>& RList, std::vector<Vector3d>& tList)
{ 

    int n = homos.size();
    Matrix3d kInv = K.inverse();
    for (int i=0; i<n; ++i)
    { 
        Vector3d r0, r1, r2;
        r0 = kInv*homos[i].col(0);
        r1 = kInv*homos[i].col(1);

        double s0 = sqrt(r0.dot(r0));
        double s1 = sqrt(r1.dot(r1));

        r0.array().col(0) /= s0;
        r1.array().col(0) /= s1;
        r2 = r0.cross(r1);

        Vector3d t = kInv*homos[i].col(2) / s0;

        Matrix3d R;
        R.array().col(0) = r0;
        R.array().col(1) = r1;
        R.array().col(2) = r2;

        std::cout <<"R " << R << std::endl;
        std::cout <<"t " << t.transpose() << std::endl;
        RList.push_back(R);
        tList.push_back(t);
    }
}

6. 构建最优化目标函数

求出最优K 和 畸变系数 k1、k2、k3、p1、p2以及外参。

上面计算出的K,和13个外参 R和t作为最优化的初始值。
优化框架采用ceres优化框架,首先要编写优化结构体 PROJECT_COST:

struct PROJECT_COST { 

    Eigen::Vector3d objPt;
    Eigen::Vector2d imgPt;

    PROJECT_COST(Eigen::Vector3d& objPt, Eigen::Vector2d& imgPt):objPt(objPt), imgPt(imgPt)
    { }


    template<typename T>
    bool operator()(
        const T *const k,
        const T *const r,
        const T *const t,
        T* residuals)const
    { 

        T pos3d[3] = { T(objPt(0)), T(objPt(1)), T(objPt(2))};
        T pos3d_proj[3];
        // 旋转
        ceres::AngleAxisRotatePoint(r, pos3d, pos3d_proj);
        // 平移
        pos3d_proj[0] += t[0];
        pos3d_proj[1] += t[1];
        pos3d_proj[2] += t[2];

        T xp = pos3d_proj[0] / pos3d_proj[2];
        T yp = pos3d_proj[1] / pos3d_proj[2];



        const T& fx = k[0];
        const T& fy = k[1];
        const T& cx = k[2];
        const T& cy = k[3];

        const T& k1 = k[4];
        const T& k2 = k[5];
        const T& k3 = k[6];

        const T& p1 = k[7];
        const T& p2 = k[8];
        
        T r_2 = xp*xp + yp*yp;
        
        /* // 径向畸变 T xdis = xp*(T(1.) + k1*r_2 + k2*r_2*r_2 + k3*r_2*r_2*r_2); T ydis = yp*(T(1.) + k1*r_2 + k2*r_2*r_2 + k3*r_2*r_2*r_2); // 切向畸变 xdis = xdis + T(2.)*p1*xp*yp + p2*(r_2 + T(2.)*xp*xp); ydis = ydis + p1*(r_2 + T(2.)*yp*yp) + T(2.)*p2*xp*yp; */
        
        // 径向畸变和切向畸变
        T xdis = xp*(T(1.) + k1*r_2 + k2*r_2*r_2 + k3*r_2*r_2*r_2) + T(2.)*p1*xp*yp + p2*(r_2 + T(2.)*xp*xp);
        T ydis = yp*(T(1.) + k1*r_2 + k2*r_2*r_2 + k3*r_2*r_2*r_2) + p1*(r_2 + T(2.)*yp*yp) + T(2.)*p2*xp*yp;
        

        // 像素距离
        T u = fx*xdis + cx;
        T v = fy*ydis + cy;
        
        residuals[0] = u - T(imgPt[0]);

        residuals[1] = v - T(imgPt[1]);
        
        return true;
    }

};

具体使用

 // 优化算法
    { 
        //
        ceres::Problem problem;
        double k[9] = { K(0,0), K(1,1), K(0,2), K(1,2), 0., 0., 0., 0., 0.};  // 设置内参初值


        for(int i=0; i<n; ++i) { 
            for(int j=0; j<imagePoints[i].size(); ++j) { 
            // 优化参数2->输出的残差数,表示x和y
            // 9 表示 内参4个 畸变系数5个
            // 3 外参,用旋转向量表示,输入需要把旋转矩阵转为旋转向量,再输入
            // 3 外参 平移向量
                ceres::CostFunction* costFunction=new ceres::AutoDiffCostFunction<PROJECT_COST, 2, 9, 3, 3>(
                    new PROJECT_COST(objectPoints[i][j], imagePoints[i][j]));

                problem.AddResidualBlock(costFunction,
                                         nullptr,
                                         k,
                                         rList[i].data(),
                                         tList[i].data()
                                        );

            }
        }
        std::cout << " solve Options ..." << std::endl;

        ceres::Solver::Options options;
        options.minimizer_progress_to_stdout = true;
        //options.linear_solver_type = ceres::DENSE_SCHUR;
        //options.trust_region_strategy_type = ceres::TrustRegionStrategyType::LEVENBERG_MARQUARDT;
        //options.preconditioner_type = ceres::JACOBI;
        //options.sparse_linear_algebra_library_type = ceres::EIGEN_SPARSE;
        ceres::Solver::Summary summary;
        ceres::Solve(options, &problem, &summary);
        std::cout << summary.BriefReport() << std::endl;


        if (!summary.IsSolutionUsable())
        { 
            std::cout << "Bundle Adjustment failed." << std::endl;
        }
        else
        { 
            //summary.num_
            // Display statistics about the minimization
            std::cout << std::endl
                      << "Bundle Adjustment statistics (approximated RMSE):\n"
                      << " #views: " << n << "\n"
                      << " #residuals: " << summary.num_residuals << "\n"
                      << " #num_parameters: " << summary.num_parameters << "\n"
                      << " #num_parameter_blocks: " << summary.num_parameter_blocks << "\n"
                      << " Initial RMSE: " << std::sqrt(summary.initial_cost / summary.num_residuals) << "\n"
                      << " Final RMSE: " << std::sqrt(summary.final_cost / summary.num_residuals) << "\n"
                      << " Time (s): " << summary.total_time_in_seconds << "\n"
                      << std::endl;

            for(auto& a: k) std::cout << a << " " ;

            //cv::Mat cameraMatrix, distCoeffs;
            //cameraMatrix = (cv::Mat_<double>(3, 3) << k[0], 0.0, k[2], 0, k[1], k[3], 0, 0, 1);
            //distCoeffs = (cv::Mat_<double>(1, 5) << k[4], k[5], k[7], k[8], k[6]);

			// 输出优化结果
            Eigen::Matrix3d cameraMatrix_;
            cameraMatrix_ << k[0], 0.0, k[2], 0, k[1], k[3], 0, 0, 1;
            Eigen::VectorXd  distCoeffs_(5);
            distCoeffs_ << k[4], k[5], k[7], k[8], k[6];
            
            // 评价投影误差
            std::vector<double> reprojErrs;
            double totalAvgErr = computeReprojectionErrors(objectPoints, imagePoints, rList, tList, cameraMatrix_, distCoeffs_, reprojErrs);
            std::cout << " avg re projection error = " << totalAvgErr << std::endl;
            for (size_t i = 0; i < reprojErrs.size(); i++)
            { 
                std::cout << i << " projection error = " << reprojErrs[i] << std::endl;
            }

            // Mat
            cv::eigen2cv(cameraMatrix_, cameraMatrix);
            cv::eigen2cv(distCoeffs_, distCoeffs);
        }
    }

最后的结果


536.073 536.016 342.371 235.536 -0.265091 -0.0467182 0.252215 0.00183296 -0.000314464  
avg re projection error = 0.234593
0 projection error = 0.16992
1 projection error = 0.846329
2 projection error = 0.159117
3 projection error = 0.176626
4 projection error = 0.141207
5 projection error = 0.162312
6 projection error = 0.18801
7 projection error = 0.214098
8 projection error = 0.22217
9 projection error = 0.153192
10 projection error = 0.177543
11 projection error = 0.28586
12 projection error = 0.15332

核心的代码和流程就这多,还有根据畸变系数对图像校正都在代码里,我把github地址也贴出来。
里面也包含鱼眼镜头的标定。代码结构如下:
《相机标定(张正友标定算法)解读与实战三》

附源码地址:
CameraCalibration
https://github.com/zhaitianyong/CameraCalibration

    原文作者:@way
    原文地址: https://blog.csdn.net/zhaitianyong/article/details/111059716
    本文转自网络文章,转载此文章仅为分享知识,如有侵权,请联系博主进行删除。
点赞