一、原理

https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html
前提：摄像头固定因为摄像头一动，内参不变（畸变系数），但是外参（坐标变换）会变。
通过拍摄几张标定板的照片，然后得到畸变系数和相机内外参系数，然后每次读取摄像机图片时，将这些系数带进去，计算之后就可以得到矫正后的图片了。
效果如下：

畸变校正前

畸变校正后

显然上面图片四周直线都是弯曲的，被矫正后，变得效果不错了。

二、具体步骤

标定图：
程序在第三部分，具体步骤如下：
1、将第三步的代码复制到工程里
2、在工程目录下（主函数.cpp相同目录下）建立一个caliberation文件夹，采集10——20张照片（不同角度，方向，但是要把角点全部显示出来），将照片放入该文件夹下。

效果如下：

3、新建一个calibdata.txt文件，将步骤2的图片路径写进去格式如下：

./caliberation/1.jpg
./caliberation/2.jpg
./caliberation/3.jpg
./caliberation/4.jpg
./caliberation/5.jpg
./caliberation/6.jpg
./caliberation/7.jpg
./caliberation/8.jpg
./caliberation/9.jpg
./caliberation/10.jpg
./caliberation/11.jpg
./caliberation/12.jpg

4、新建一个chess文件夹（名字随便，记得在程序里改），用于保存畸变校正后的图片。
5、运行程序，会生成一个caliberation_result.txt文件，里面保存了内外参等一些参数。比如畸变系数，旋转矩阵，平移矩阵等。

三、参数获取程序代码

//2018.6.19：畸变校正#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(4, 6);    /* 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 (imageInput.empty()){cout << "can not open pic!\n";exit(-1);}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    namedWindow("Camera Calibration", 0);//创建窗口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(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 */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 = "chess";StrStm << i + 1;StrStm >> imageFileName;filePath += imageFileName;filePath += ".jpg";Mat imageSource = imread("1.jpg"); //读取畸变图片Mat newimage = imageSource.clone(); //校正后输出图片//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;
}

四、使用程序

这个部分就是将得到的参数，应用到具体的程序中，不用每次进行标定，只要摄像头位置不变，就可以将畸变参数带进去就可以矫正。


void InitMat(Mat& m, float* num)
{for (int i = 0; i<m.rows; i++)for (int j = 0; j<m.cols; j++)m.at<float>(i, j) = *(num + i * m.rows + j);
}int main()
{int i = 1000;int n = 1;Mat edges;Mat frame = imread("2.jpg"); //读取畸变图片Mat R = Mat::eye(3, 3, CV_32F);Size image_size;  /* 图像的尺寸 *///获取图像大小image_size.width = 1920;image_size.height = 1080;//cameraMatrix为 "相机内参数矩阵：" << endl;Mat mapx = Mat(image_size, CV_32FC1);Mat mapy = Mat(image_size, CV_32FC1);//参数矩阵float neican_data[] = { 9558.649257742036, 0, 959.3165310990756, 0, 9435.752651759443, 532.7507141910969, 0, 0, 1 };Mat cameraMatrix(3, 3, CV_32FC1);InitMat(cameraMatrix, neican_data);cout << "cameraMatrix= " << endl << " " << cameraMatrix << endl << endl;//测得的畸变系数float jibian_data[] = { -6.956561513881647, -68.83902522804168, -0.004834538444671919, 0.01471273691928269, -0.4916103704308509 };Mat distCoeffs(1, 5, CV_32FC1); /* 摄像机的5个畸变系数：k1,k2,p1,p2,k3 */InitMat(distCoeffs, jibian_data);cout << "distCoeffs= " << endl << " " << distCoeffs << endl << endl;i = 0;namedWindow("【原始图】", 0);//参数为零，则可以自由拖动imshow("【原始图】", frame);/********相机矫正*******************************************************************************/initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, image_size, CV_32FC1, mapx, mapy);Mat imageSource = frame; //读取畸变图片Mat newimage = imageSource.clone(); //校正后输出图片remap(imageSource, newimage, mapx, mapy, INTER_LINEAR);namedWindow("畸变校正后的图片", 0);//参数为零，则可以自由拖动imshow("畸变校正后的图片", newimage);}

上面只是矫正部分的代码

分界线

后续版本：

畸变矫正应用封装后，耗时6ms

/*****************************************************
2021.4.29：畸变校正
******************************************************/
#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;#define SRC_WIDTH  1920
#define SRC_HEIGHT 1080void InitMat(float* num , Mat& m)
{for (int i = 0; i<m.rows; i++)for (int j = 0; j<m.cols; j++)m.at<float>(i, j) = *(num + i * m.rows + j);
}Mat mapx = Mat(Size(SRC_WIDTH, SRC_HEIGHT), CV_32FC1);
Mat mapy = Mat(Size(SRC_WIDTH, SRC_HEIGHT), CV_32FC1);
Mat newimage ; //校正后输出图片void jiaozheng_init(void)
{int OK = 0;Mat R = Mat::eye(3, 3, CV_32F);//参数矩阵float neican_data[] = { 9558.649257742036, 0, 959.3165310990756, 0, 9435.752651759443, 532.7507141910969, 0, 0, 1 };Mat cameraMatrix(3, 3, CV_32FC1);InitMat(neican_data,cameraMatrix);cout << "cameraMatrix= " << endl << " " << cameraMatrix << endl << endl;//测得的畸变系数float jibian_data[] = { -6.956561513881647, -68.83902522804168, -0.004834538444671919, 0.01471273691928269, -0.4916103704308509 };Mat distCoeffs(1, 5, CV_32FC1); /* 摄像机的5个畸变系数：k1,k2,p1,p2,k3 */InitMat( jibian_data, distCoeffs);cout << "distCoeffs= " << endl << " " << distCoeffs << endl << endl;/********相机矫正*******************************************************************************///cout << "mapx= " << endl << " " << mapx << endl << endl;//cout << "mapy= " << endl << " " << mapy << endl << endl;initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, Size(SRC_WIDTH, SRC_HEIGHT), CV_32FC1, mapx, mapy);//cout << "mapx= " << endl << " " << mapx << endl << endl;//cout << "mapy= " << endl << " " << mapy << endl << endl;}int jibianjiaozheng(Mat src_picture, Mat & dst_picture)
{int OK = 0;static unsigned int INIT_MAT_OK = 1;if (INIT_MAT_OK){newimage = src_picture.clone(); //校正后输出图片INIT_MAT_OK = 0;}remap(src_picture, newimage, mapx, mapy, INTER_LINEAR);dst_picture= newimage+1;return OK;
}int main()
{# if 0//测试图片int hrst = 0;Mat frame = imread("yuantu.jpg"); //读取畸变图片Mat after;static unsigned int jiaozheng_init_flag = 0;if (!jiaozheng_init_flag){printf("初始化\n");jiaozheng_init();jiaozheng_init_flag = 1;}hrst = jibianjiaozheng(frame, after);if (hrst != 0){printf("畸变矫正失败\n");}namedWindow("畸变校正后的图片", 0);//参数为零，则可以自由拖动imshow("畸变校正后的图片", after);
#else//测试视频VideoCapture capture;capture.open("test_1.mp4");if (!capture.isOpened()){printf("文件打开失败");}Mat frame;Mat after;int hrst = 0;unsigned int jiaozheng_init_flag = 0;int timestart, timeEnd, duration;while (true){timestart = clock() * 1000 / CLOCKS_PER_SEC;capture >> frame;if (frame.data == NULL){printf("Image is empty\n");//writer.write(frame);break;//continue;}duration = clock() - timestart;printf("读图用时约: %d毫秒 \n", duration * 1000 / CLOCKS_PER_SEC);if (!jiaozheng_init_flag){printf("初始化\n");jiaozheng_init();jiaozheng_init_flag = 1;}hrst = jibianjiaozheng(frame, after);if (hrst != 0){printf("畸变矫正失败\n");}else{//printf("畸变矫正成功\n");}timeEnd = clock() * 1000 / CLOCKS_PER_SEC;duration = timeEnd- timestart;printf("畸变校正用时约: %d毫秒 \n", duration );printf("\n");//namedWindow("畸变校正后的图片", 0);//参数为零，则可以自由拖动//imshow("畸变校正后的图片", after);//waitKey(2);}#endifreturn 0;}//
//int main()
//{//  Mat frame = imread("yuantu.jpg"); //读取畸变图片
//  //cv::VideoWriter wrt("./test.avi", cv::VideoWriter::fourcc('M', 'J', 'P', 'G'), 30, cv::Size(1920, 1080));
//  cv::VideoWriter wrt("./test_1.mp4", cv::VideoWriter::fourcc('D', 'I', 'V', 'X'), 30, cv::Size(1920, 1080));
//
//  for(int i=0;i<600;i++)
//  {//
//      string fps_str = "frame_number: " + to_string(i);
//      Mat frame0 =frame.clone();
//      putText(frame0, fps_str, Point2f(10, 80), FONT_HERSHEY_COMPLEX_SMALL, 3.2, Scalar(50, 255, 0), 3);
//
//      namedWindow("畸变校正后的图片", 0);//参数为零，则可以自由拖动
//      imshow("畸变校正后的图片", frame0);
//      waitKey(33);
//      wrt << frame0; // where image should be cv::Mat class
//  }
//
//
//  return 0;
//}

参数采集第二版，添加了一些异常判断和提示

参数获取程序源码

/*****************************************************
2021.4.29：畸变校正标定过程，用于获取畸变矫正的几个参数根据不同的标定板需要修改;
Size board_size = Size(4, 6);    /* 标定板上每行、列的角点数
******************************************************/#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 <iostream>#include <stdlib.h> //srand()和rand()函数
#include<windows.h>using namespace cv;
using namespace std;/****************        打印相关组件 start !  ********************************************************************/#define  debug_show_picture        1 //是否显示部分调试图片，方便调试
#define  debug_save_shipin       0 //是否保存结果视频/*打印等级，修改后面的宏定义可以改变函数输出打印等级*/
#define ALG_PRTINT_LEVER PRINT_LEVEL_UNLIMIT#define ALG_PRTINT(...)  SAL_printf(__VA_ARGS__)
#define ALG_PRT(...)     ALG_PRTINT(__FUNCTION__, __LINE__, PRINT_LEVEL_UNLIMIT, __VA_ARGS__)
#define ALG_DBG(...)     ALG_PRTINT(__FUNCTION__, __LINE__, PRINT_LEVEL_DBG,     __VA_ARGS__)
#define ALG_WAR(...)     ALG_PRTINT(__FUNCTION__, __LINE__, PRINT_LEVEL_WRN,     __VA_ARGS__)
#define ALG_ERR(...)     ALG_PRTINT(__FUNCTION__, __LINE__, PRINT_LEVEL_ERR,     __VA_ARGS__)/***********************************************************************************************
* @enum     HAT_SAL_PRT_LEVEL_E
* @brief    打印输出的等级
***************************************************************************************************/
typedef enum _PRT_LEVEL_E_
{PRINT_LEVEL_ERR = 0,        /*错误打印信息*/PRINT_LEVEL_WRN = 1,        /*警告打印信息*/PRINT_LEVEL_DBG = 2,         /*调试打印信息*/PRINT_LEVEL_UNLIMIT = 3,    /*无限制打印信息*/PRINT_LEVEL_NOPRT = 4,      /*没有打印信息*/
} PRT_LEVEL_E;/*******************************************************************************
Function:   SAL_printf
Description: 该函数能够通过设置的打印等级ALG_PRTINT_LEVER，来控制是否输出相关语句
Input:
Output:
Return:
0:          Successful
ohters:     Failed
*******************************************************************************/
void SAL_printf(const char *pFun, UINT line, PRT_LEVEL_E levelParam, const char *fmt, ...)
{static INT8 g_printfInfo[4][16] = { "ERR", "WAR", "DBG", "INF" };va_list p;if (ALG_PRTINT_LEVER == PRINT_LEVEL_NOPRT || levelParam == PRINT_LEVEL_NOPRT){return;}if (levelParam <= ALG_PRTINT_LEVER){va_start(p, fmt);printf("[ALG][%s][%s][%4d] ", g_printfInfo[levelParam], pFun, line);vprintf(fmt, p);va_end(p);}
}
/******************************  打印相关组件 end   *************************************************************************/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………………\n";int image_count = 0;  /* 图像数量 */Size image_size;  /* 图像的尺寸 */Size board_size = Size(6, 9);    /* 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 << endl;Mat imageInput = imread(filename);if (imageInput.empty()){ALG_ERR("can not open pic,图片 %s 打开失败\n",filename.c_str());cout << "can not open pic!\n";Sleep(100000);//睡眠一下，不至于窗口跳出exit(-1);}Mat imageInput_copy = imageInput.clone(); //校正后输出图片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;}//printf("输出结果：%s\n", filename.c_str());/* 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ALG_ERR("图片： %s 找不到角点，请检查或者更换该图片\n",filename.c_str());Sleep(100000);//睡眠一下，不至于窗口跳出exit(1);}else{ALG_PRT("正在提取图片%s的角点，image_count=%d\n", filename.c_str(), image_count);Mat view_gray;cvtColor(imageInput, view_gray, COLOR_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    namedWindow("Camera Calibration", 0);//创建窗口imshow("Camera Calibration", view_gray);//显示图片    waitKey(5);//暂停0.5S //这种初始化点的方式也可以for (int i = 0;i < image_points_buf.size(); i++){Point p2;p2.x = image_points_buf[i].x;p2.y = image_points_buf[i].y;//画实心点circle(imageInput_copy, p2, 8, Scalar(0, 0, 255), -1); //第五个参数我设为-1，表明这是个实点。}namedWindow("imageInput_copy", 0);//创建窗口imshow("imageInput_copy", imageInput_copy);//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(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 */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 = "./caliberation";StrStm << i + 1;StrStm >> imageFileName;filePath += imageFileName;filePath += ".jpg";Mat imageSource = imread("./caliberation/01.jpg"); //读取畸变图片//Mat imageSource = imread(filePath); //读取畸变图片Mat newimage = imageSource.clone(); //校正后输出图片//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;
}

运行效果图

判断图像采集的是否合规

/*****************************************************
2021.6.8：2021.4.29：畸变校正标定过程，用于获取畸变矫正的几个参数根据不同的标定板需要修改;
Size board_size = Size(4, 6);    /* 标定板上每行、列的角点数
******************************************************/#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 <iostream>#include <stdlib.h> //srand()和rand()函数
#include<windows.h>using namespace cv;
using namespace std;/****************        打印相关组件 start !  ********************************************************************/#define  debug_show_picture        1 //是否显示部分调试图片，方便调试
#define  debug_save_shipin       0 //是否保存结果视频/*打印等级，修改后面的宏定义可以改变函数输出打印等级*/
#define ALG_PRTINT_LEVER PRINT_LEVEL_UNLIMIT#define ALG_PRTINT(...)  SAL_printf(__VA_ARGS__)
#define ALG_PRT(...)     ALG_PRTINT(__FUNCTION__, __LINE__, PRINT_LEVEL_UNLIMIT, __VA_ARGS__)
#define ALG_DBG(...)     ALG_PRTINT(__FUNCTION__, __LINE__, PRINT_LEVEL_DBG,     __VA_ARGS__)
#define ALG_WAR(...)     ALG_PRTINT(__FUNCTION__, __LINE__, PRINT_LEVEL_WRN,     __VA_ARGS__)
#define ALG_ERR(...)     ALG_PRTINT(__FUNCTION__, __LINE__, PRINT_LEVEL_ERR,     __VA_ARGS__)/***********************************************************************************************
* @enum     HAT_SAL_PRT_LEVEL_E
* @brief    打印输出的等级
***************************************************************************************************/
typedef enum _PRT_LEVEL_E_
{PRINT_LEVEL_ERR = 0,        /*错误打印信息*/PRINT_LEVEL_WRN = 1,        /*警告打印信息*/PRINT_LEVEL_DBG = 2,         /*调试打印信息*/PRINT_LEVEL_UNLIMIT = 3,    /*无限制打印信息*/PRINT_LEVEL_NOPRT = 4,      /*没有打印信息*/
} PRT_LEVEL_E;/*******************************************************************************
Function:   SAL_printf
Description: 该函数能够通过设置的打印等级ALG_PRTINT_LEVER，来控制是否输出相关语句
Input:
Output:
Return:
0:          Successful
ohters:     Failed
*******************************************************************************/
void SAL_printf(const char *pFun, UINT line, PRT_LEVEL_E levelParam, const char *fmt, ...)
{static INT8 g_printfInfo[4][16] = { "ERR", "WAR", "DBG", "INF" };va_list p;if (ALG_PRTINT_LEVER == PRINT_LEVEL_NOPRT || levelParam == PRINT_LEVEL_NOPRT){return;}if (levelParam <= ALG_PRTINT_LEVER){va_start(p, fmt);printf("[ALG][%s][%s][%4d] ", g_printfInfo[levelParam], pFun, line);vprintf(fmt, p);va_end(p);}
}
/******************************  打印相关组件 end   *************************************************************************/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………………\n";int image_count = 0;  /* 图像数量 */Size image_size;  /* 图像的尺寸 */Size board_size = Size(6, 9);    /* 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 << endl;Mat imageInput = imread(filename);if (imageInput.empty()){ALG_ERR("can not open pic,图片 %s 打开失败\n", filename.c_str());cout << "can not open pic!\n";Sleep(100000);//睡眠一下，不至于窗口跳出exit(-1);}Mat imageInput_copy = imageInput.clone(); //校正后输出图片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;}//printf("输出结果：%s\n", filename.c_str());/* 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ALG_ERR("图片： %s 找不到角点，请检查或者更换该图片\n", filename.c_str());continue;}else{ALG_PRT("正在提取图片%s的角点，image_count=%d\n", filename.c_str(), image_count);Mat view_gray;cvtColor(imageInput, view_gray, COLOR_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    namedWindow("Camera Calibration", 0);//创建窗口imshow("Camera Calibration", view_gray);//显示图片    waitKey(5);//暂停0.5S //这种初始化点的方式也可以for (int i = 0; i < image_points_buf.size(); i++){Point p2;p2.x = image_points_buf[i].x;p2.y = image_points_buf[i].y;//画实心点circle(imageInput_copy, p2, 8, Scalar(0, 0, 255), -1); //第五个参数我设为-1，表明这是个实点。}namedWindow("imageInput_copy", 0);//创建窗口imshow("imageInput_copy", imageInput_copy);//显示图片    waitKey(500);//暂停0.5S }}waitKey(5000000000000);//暂停0.5S
}

标定图像采集小技巧——使用代码按键采集

参考我的另外一篇文章：
opencv通过按键采集图片源码

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