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用OpenCV进行相机标定(张正友标定,有代码)

1 人参与  2023年03月23日 17:45  分类 : 《随便一记》  评论

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目录

1. 内参与畸变2. 用OpenCV标定相机程序3.画棋盘标定板4.OpenCV拍照

1. 内参与畸变

理论部分可以参考其他博客或者视觉slam十四讲
相机标定主要是为了获得相机的内参矩阵K和畸变参数

内参矩阵K
在这里插入图片描述

畸变系数:径向畸变(k1,k2,k3), 切向畸变(p1,p2)
在这里插入图片描述径向畸变公式
在这里插入图片描述切向畸变公式
在这里插入图片描述张正友标定方法能够提供一个比较好的初始解,用于后序的最优化.

这里用棋盘格进行标定,如果能够处理圆的偏心误差问题,用圆形图案标定板可能效果更好.

至少三张图片,一般用10-20张图片为最佳,要保证相机视野内各个角度,各个位置,各个方向都有图像.尽量多角度多位置.

最好用买的标定板,效果好,平.最好是背光板,能够保证足够的亮度和均匀度.

2. 用OpenCV标定相机程序

1,提取角点
2,亚像素角点
3,可视化提取角点(非必须)
4,标定
5,误差计算(重投影误差)

#include <iostream>#include <fstream>#include <string>#include <opencv2/opencv.hpp>using namespace std;int main(int argc, char **argv){    string dir = "/home/wfq/MyProjects/cal_images/";  //标定图片所在文件夹    ifstream fin(dir + "file_images.txt"); //读取标定图片的路径,与cpp程序在同一路径下    if (!fin)                              //检测是否读取到文件    {        cerr << "没有找到文件" << endl;        return -1;    }    ofstream fout(dir + "calibration_result.txt"); //输出结果保存在此文本文件下    //依次读取每一幅图片,从中提取角点    cout << "开始提取角点……" << endl;    int image_nums = 0;  //图片数量    cv::Size image_size; //图片尺寸    int points_per_row = 10;  //每行的内点数    int points_per_col = 7;   //每列的内点数    cv::Size corner_size = cv::Size(points_per_row, points_per_col); //标定板每行每列角点个数,共10*7个角点    vector<cv::Point2f> points_per_image;                            //缓存每幅图检测到的角点    vector<vector<cv::Point2f>> points_all_images;                   //用一个二维数组保存检测到的所有角点    string image_file_name;                                          //声明一个文件名的字符串    while (getline(fin, image_file_name)) //逐行读取,将行读入字符串    {        image_nums++;        //读入图片        cv::Mat image_raw = cv::imread(dir + image_file_name);        if (image_nums == 1)        {            // cout<<"channels = "<<image_raw.channels()<<endl;            // cout<<image_raw.type()<<endl;  //CV_8UC3            image_size.width = image_raw.cols;  //图像的宽对应着列数            image_size.height = image_raw.rows; //图像的高对应着行数            cout << "image_size.width = " << image_size.width << endl;            cout << "image_size.height = " << image_size.height << endl;        }        //角点检测        cv::Mat image_gray;                               //存储灰度图的矩阵        cv::cvtColor(image_raw, image_gray, CV_BGR2GRAY); //将BGR图转化为灰度图        // cout<<"image_gray.type() = "<<image_gray.type()<<endl;  //CV_8UC1        //step1 提取角点        bool success = cv::findChessboardCorners(image_gray, corner_size, points_per_image);        if (!success)        {            cout << "can not find the corners " << endl;            exit(1);        }        else        {            //亚像素精确化(两种方法)            //step2 亚像素角点            cv::find4QuadCornerSubpix(image_gray, points_per_image, cv::Size(5, 5));            // cornerSubPix(image_gray,points_per_image,Size(5,5));            points_all_images.push_back(points_per_image); //保存亚像素角点            //在图中画出角点位置            //step3 角点可视化            cv::drawChessboardCorners(image_raw, corner_size, points_per_image, success); //将角点连线            cv::imshow("Camera calibration", image_raw);            cv::waitKey(0); //等待按键输入        }    }    cv::destroyAllWindows();    //输出图像数目    int image_sum_nums = points_all_images.size();    cout << "image_sum_nums = " << image_sum_nums << endl;    //开始相机标定    cv::Size block_size(21, 21);                            //每个小方格实际大小, 只会影响最后求解的平移向量t    cv::Mat camera_K(3, 3, CV_32FC1, cv::Scalar::all(0));   //内参矩阵3*3    cv::Mat distCoeffs(1, 5, CV_32FC1, cv::Scalar::all(0)); //畸变矩阵1*5    vector<cv::Mat> rotationMat;                            //旋转矩阵    vector<cv::Mat> translationMat;                         //平移矩阵    //初始化角点三维坐标,从左到右,从上到下!!!    vector<cv::Point3f> points3D_per_image;    for (int i = 0; i < corner_size.height; i++)    {        for (int j = 0; j < corner_size.width; j++)        {            points3D_per_image.push_back(cv::Point3f(block_size.width * j, block_size.height * i, 0));        }    }    vector<vector<cv::Point3f>> points3D_all_images(image_nums,points3D_per_image);        //保存所有图像角点的三维坐标, z=0    int point_counts = corner_size.area(); //每张图片上角点个数    //!标定    /**     * points3D_all_images: 真实三维坐标     * points_all_images: 提取的角点     * image_size: 图像尺寸     * camera_K : 内参矩阵K     * distCoeffs: 畸变参数     * rotationMat: 每个图片的旋转向量     * translationMat: 每个图片的平移向量     * */    //step4 标定    cv::calibrateCamera(points3D_all_images, points_all_images, image_size, camera_K, distCoeffs, rotationMat, translationMat, 0);    //step5 对标定结果进行评价    double total_err = 0.0;               //所有图像平均误差总和    double err = 0.0;                     //每幅图像的平均误差    vector<cv::Point2f> points_reproject; //重投影点    cout << "\n\t每幅图像的标定误差:\n";    fout << "每幅图像的标定误差:\n";    for (int i = 0; i < image_nums; i++)    {        vector<cv::Point3f> points3D_per_image = points3D_all_images[i];        //通过之前标定得到的相机内外参,对三维点进行重投影        cv::projectPoints(points3D_per_image, rotationMat[i], translationMat[i], camera_K, distCoeffs, points_reproject);        //计算两者之间的误差        vector<cv::Point2f> detect_points = points_all_images[i];  //提取到的图像角点        cv::Mat detect_points_Mat = cv::Mat(1, detect_points.size(), CV_32FC2); //变为1*70的矩阵,2通道保存提取角点的像素坐标        cv::Mat points_reproject_Mat = cv::Mat(1, points_reproject.size(), CV_32FC2);  //2通道保存投影角点的像素坐标        for (int j = 0; j < detect_points.size(); j++)        {            detect_points_Mat.at<cv::Vec2f>(0, j) = cv::Vec2f(detect_points[j].x, detect_points[j].y);            points_reproject_Mat.at<cv::Vec2f>(0, j) = cv::Vec2f(points_reproject[j].x, points_reproject[j].y);        }        err = cv::norm(points_reproject_Mat, detect_points_Mat, cv::NormTypes::NORM_L2);        total_err += err /= point_counts;        cout << "第" << i + 1 << "幅图像的平均误差为: " << err << "像素" << endl;        fout << "第" << i + 1 << "幅图像的平均误差为: " << err << "像素" << endl;    }    cout << "总体平均误差为: " << total_err / image_nums << "像素" << endl;    fout << "总体平均误差为: " << total_err / image_nums << "像素" << endl;    cout << "评价完成!" << endl;    //将标定结果写入txt文件    cv::Mat rotate_Mat = cv::Mat(3, 3, CV_32FC1, cv::Scalar::all(0)); //保存旋转矩阵    cout << "\n相机内参数矩阵:" << endl;    cout << camera_K << endl<< endl;    fout << "\n相机内参数矩阵:" << endl;    fout << camera_K << endl<< endl;    cout << "畸变系数:\n";    cout << distCoeffs << endl<< endl<< endl;    fout << "畸变系数:\n";    fout << distCoeffs << endl<< endl<< endl;    for (int i = 0; i < image_nums; i++)    {        cv::Rodrigues(rotationMat[i], rotate_Mat); //将旋转向量通过罗德里格斯公式转换为旋转矩阵        fout << "第" << i + 1 << "幅图像的旋转矩阵为:" << endl;        fout << rotate_Mat << endl;        fout << "第" << i + 1 << "幅图像的平移向量为:" << endl;        fout << translationMat[i] << endl             << endl;    }    fout << endl;    fout.close();    return 0;}

3.画棋盘标定板

//函数声明,默认每行11个block, 没列8个block, block大小为75个像素. 也就是10*7个内点void drawChessBoard(int blocks_per_row=11, int blocks_per_col=8, int block_size = 75);// 11  8  75void drawChessBoard(int blocks_per_row, int blocks_per_col, int block_size){    //blocks_per_row=11 //每行11个格子,也就是10个点    //blocks_per_col=8  //每列8个格子,也就是7个点    //block_size=75     //每个格子的像素大小    cv::Size board_size = cv::Size(block_size * blocks_per_row, block_size * blocks_per_col);    cv::Mat chessboard = cv::Mat(board_size, CV_8UC1);    unsigned char color = 0;    for (int i = 0; i < blocks_per_row; i++)    {        color = ~color;        for (int j = 0; j < blocks_per_col; j++)        {            chessboard(cv::Rect(i * block_size, j * block_size, block_size, block_size)).setTo(color);            color = ~color;        }    }    cv::Mat chess_board = cv::Mat(board_size.height + 100, board_size.width + 100, CV_8UC1, cv::Scalar::all(256)); //上下左右留出50个像素空白    chessboard.copyTo(chess_board.rowRange(50, 50 + board_size.height).colRange(50, 50 + board_size.width));    cv::imshow("chess_board", chess_board);    cv::imwrite("chess_board.png", chess_board);    cv::waitKey(-1);    cv::destroyAllWindows();}

4.OpenCV拍照

#include <iostream>#include <opencv2/opencv.hpp>using namespace std;int main(int argc, char **argv){    cv::namedWindow("Camera",cv::WINDOW_AUTOSIZE);    cv::VideoCapture cap;    cap.open(0);    if(!cap.isOpened())    {        cout<<"camera open failed!\n";        return -1;    }    cv::Mat image;    int id=1;    char symbol;    while(id<=6)    {        cap>>image;        if(image.empty())            break;        cout<<"y or n"<<endl;        cin>>symbol;        if(symbol=='y')        {            cv::imwrite(to_string(id)+".png",image);            cout<<"第"<<id<<"张图片"<<endl;            id++;        }    }    return 0;}

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