OpenCV单目相机内参标定C++

基于OpenCV 实现单目相机内参标定：

a.使用OpenCV库实现内参标定过程。通过角点检测、亚像素角点定位、角点存储与三维坐标生成和摄像机标定分别获取左右相机的内参。

b.具体地，使用库函数检测两组图像（左右相机拍摄图像）中棋盘格的角点；对检测到的角点进行亚像素级别的精确化，以提高角点位置的准确性；根据左右相机拍摄的棋盘格图像及角点位置，计算出两个相机的内参矩阵及畸变系数；生成畸变校正和图像校正的映射表。

c.内参标定模块的输出是左右相机的内参矩阵和畸变系数，以及每幅图像的旋转向量和平移向量，最后计算并输出标定结果的总体平均误差，以评估标定过程的准确性。

#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<sstream>
#include<opencv2/imgproc/types_c.h> 
#include <opencv2/core/utils/logger.hpp> //隐藏日志using namespace std;
using namespace cv;
int main()
{cv::utils::logging::setLogLevel(utils::logging::LOG_LEVEL_SILENT);//不再输出日志//或//utils::logging::setLogLevel(utils::logging::LOG_LEVEL_ERROR);//只输出错误日志int image_count = 20;//检测的图片数量Size image_size;Size board_size = Size(11, 8);//图片上棋盘格（标定板）的内角点个数（行、列的角点数）vector<Point2f> image_corners;//缓存每幅图像上检测到的角点vector<vector<Point2f>> corners_Seq;//保存检测到的所有角点vector<Mat> image_Seq;int count = 0;/*********************读入图像，检测角点********************************************/for (int i = 0; i < image_count; i++){//读入一系列图片string imageFileName, imageFileName1;stringstream StrStm;StrStm << "left";//StrStm << "F:/vsprojects2022/ConsoleApplication6/ConsoleApplication6/20241022/right";StrStm << i + 1;StrStm >> imageFileName;StrStm.str("");//清除数据流，以便下次使用imageFileName += ".bmp";cout << imageFileName << endl;Mat image = imread(imageFileName);image_size = image.size();Mat image_gray;Mat dstImage1;//cvtColor(image, image_gray, CV_RGB2GRAY); // opencv4版本中应改为 COLOR_RGB2GRAYcvtColor(image, image_gray, COLOR_RGB2GRAY);/*imshow("1",image_gray);waitKey(0);*///检测标定板的角点bool patternfound = findChessboardCorners(image, board_size, image_corners, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE);//+ CALIB_CB_FAST_CHECK);if (!patternfound){cout << "Can not find chessboard corners!" << endl;return -1;}else{/* 亚像素精确化 */cornerSubPix(image_gray, image_corners, Size(11, 11), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));/* 绘制检测到的角点并保存 *//*drawChessboardCorners(image, Size(7, 7), image_corners, patternfound);//红色为先检测的点resize(image, dstImage1, Size(image.cols / 5, image.rows / 5), 0, 0, INTER_LINEAR);imshow("1", dstImage1);waitKey(0);*/Mat imageTemp = image.clone();for (int j = 0; j < image_corners.size(); j++){circle(imageTemp, image_corners[j], 3, Scalar(0, 0, 255), -1, 8, 0);}string imageFileName;std::stringstream StrStm;StrStm << i + 1;StrStm >> imageFileName;imageFileName += "_corner.jpg";imwrite(imageFileName, imageTemp);//保存角点检测结果图count = count + image_corners.size();corners_Seq.push_back(image_corners);}image_Seq.push_back(image);}/****************************摄像机标定****************************************/Size square_size = Size(50, 50);                                      /**** 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   ****/Mat image_points = Mat(1, count, CV_32FC2, Scalar::all(0));          /*****   保存提取的所有角点   *****/vector<int>  point_counts;                                          /*****    每幅图像中角点的数量    ****/Mat intrinsic_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0));                /*****    摄像机内参数矩阵    ****///Mat distortion_coeffs = Mat(1, 4, CV_32FC1, Scalar::all(0));            /* 摄像机的4个畸变系数：k1,k2,p1,p2 */Mat distortion_coeffs = Mat(1, 5, CV_32FC1, Scalar::all(0));            /* 摄像机的4个畸变系数：k1,k2,p1,p2 */vector<cv::Mat> rotation_vectors;                                      /* 每幅图像的旋转向量 */vector<cv::Mat> translation_vectors;                                  /* 每幅图像的平移向量 *//* 初始化标定板上角点的三维坐标 */for (int t = 0; t < image_count; t++){vector<Point3f> tempPointSet;for (int i = 0; i < board_size.height; i++){for (int j = 0; j < board_size.width; j++){/* 假设标定板放在世界坐标系中z=0的平面上 */Point3f tempPoint;tempPoint.x = i * square_size.width;tempPoint.y = j * square_size.height;tempPoint.z = 0;tempPointSet.push_back(tempPoint);}}object_Points.push_back(tempPointSet);}/* 初始化每幅图像中的角点数量，这里我们假设每幅图像中都可以看到完整的标定板 */for (int i = 0; i < image_count; i++){point_counts.push_back(board_size.width * board_size.height);}/*************************开始标定 *******************************///根据角点的世界坐标和像素坐标求参数calibrateCamera(object_Points, corners_Seq, image_size, intrinsic_matrix, distortion_coeffs, rotation_vectors, translation_vectors, 0);/***********************显示标定结果***************************/Mat rotation_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); /* 保存每幅图像的旋转矩阵 */cout << "相机的内参矩阵" << intrinsic_matrix << endl;cout << "相机的畸变系数" << distortion_coeffs << endl;ofstream outfile;outfile.open("内参标定结果.txt");outfile << "相机的内参矩阵为：" << endl << intrinsic_matrix << endl;outfile << "相机的畸变系数为：" << endl << distortion_coeffs << endl;outfile.close();cout << "输出每幅图的旋转向量和平移向量" << endl;for (int i = 0; i < image_count; i++){cout << "第" << i + 1 << "幅图的旋转向量" << rotation_vectors[i] << endl;/* 将旋转向量转换为相对应的旋转矩阵 */Rodrigues(rotation_vectors[i], rotation_matrix);cout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl;cout << rotation_matrix << endl;cout << "第" << i + 1 << "幅图像的平移向量：" << endl;cout << translation_vectors[i] << endl;}/******************对标定结果进行评价**********************************/cout << "开始评价标定结果………………" << endl;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    ****/for (int 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, rotation_vectors[i], translation_vectors[i], intrinsic_matrix, distortion_coeffs, image_points2);/* 计算新的投影点和旧的投影点之间的误差*/vector<Point2f> tempImagePoint = corners_Seq[i];Mat tempImagePointMat = Mat(1, tempImagePoint.size(), CV_32FC2);Mat image_points2Mat = Mat(1, image_points2.size(), CV_32FC2);for (size_t i = 0; i != tempImagePoint.size(); i++){image_points2Mat.at<Vec2f>(0, i) = Vec2f(image_points2[i].x, image_points2[i].y);tempImagePointMat.at<Vec2f>(0, i) = Vec2f(tempImagePoint[i].x, tempImagePoint[i].y);}err = norm(image_points2Mat, tempImagePointMat, NORM_L2);total_err += err /= point_counts[i];cout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;}cout << "总体平均误差：" << total_err / image_count << "像素" << endl;Mat mapx = Mat(image_size, CV_32FC1);Mat mapy = Mat(image_size, CV_32FC1);Mat R = Mat::eye(3, 3, CV_32F);cout << "保存校正图像" << endl;for (int i = 0; i != image_count; i++){Mat newCameraMatrix = Mat(3, 3, CV_32FC1, Scalar::all(0));//根据标定结果进行图像的修正initUndistortRectifyMap(intrinsic_matrix, distortion_coeffs, R, getOptimalNewCameraMatrix(intrinsic_matrix, distortion_coeffs, image_size, 1, image_size, 0), image_size, CV_32FC1, mapx, mapy);Mat t = image_Seq[i].clone();cv::remap(image_Seq[i], t, mapx, mapy, INTER_LINEAR);string imageFileName;std::stringstream StrStm;StrStm << i + 1;StrStm >> imageFileName;imageFileName += "_校正后图像.jpg";imwrite(imageFileName, t);//保存畸变校正结果图}cout << "保存结束" << endl;//waitKey(0);system("pause");return 0;
}

代码运行结果如下：