// // 生成棋盘格(demo)
// void CreateGridironPattern()
// {
//     // 单位转换
//     int dot_per_inch = 108;
//     /*
//     * 这里以我惠普 光影精灵9的参数计算如下：
//     *  公式： DPI = 1920 / sqrt(15.6 ^ 2 + (1920 / 1080 * 15.6)^2)
//     *  sqrt(15.6 ^ 2 + (1920 / 1080 * 15.6)^2) ≈ 17.76
//     *///     double cm_to_inch = 0.3937;   // 1cm = 0.3937inch
//     double inch_to_cm = 2.54;    //  1inch = 2.54cm( 1 英寸 = 2.54 厘米 是一个国际公认的单位)
//     double inch_per_dot = 1.0 / 96.0;//     // 自定义标定板
//     double blockSize_cm = 1.5;  // 方格尺寸： 边长1.5cm的正方形
//     // 设置横列方框数目
//     int blockcol = 10;
//     int blockrow = 8;//     int blockSize = (int)(blockSize_cm / inch_to_cm * dot_per_inch);
//     cout << "标定板尺寸: " << blockSize << endl;//     int imageSizeCol = blockSize * blockrow;
//     int imageSizeRow = blockSize * blockcol;//     Mat chessBoard(imageSizeCol, imageSizeRow, CV_8UC3, Scalar::all(0));
//     unsigned char color = 0;//     for (int i = 0; i < imageSizeRow; i = i + blockSize)
//     {
//         color = ~color; // 将颜色值取反,如果开始为0，取反后为255(即黑白互换)
//         for (int j = 0; j < imageSizeCol; j = j + blockSize)
//         {
//             Mat ROI = chessBoard(Rect(i, j, blockSize, blockSize));
//             ROI.setTo(Scalar::all(color));
//             color = ~color;
//         }
//     }//     imshow("chess board", chessBoard);
//     imwrite("chessBard.jpg", chessBoard);//     waitKey(0);
//     return;
// }

#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;int main()
{// 读取文件std::vector<cv::String> images;std::string path = "./images/*.jpg";cv::glob(path, images);if(images.size() == 0){cout << "path is error" << endl;return 0;}// 设置变量int image_count = 0;                        // 图像数量Size image_size;                            // 图像的尺寸Size board_size = Size(9, 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// 读取文件并进行操作for (int i = 0; i < images.size(); i++){image_count++;cout << "image_count: " << image_count << endl;Mat imageInput = cv::imread(images[i]);if(imageInput.empty()){cout << "read error" << endl;return 0;}//读入第一张图片时获取图像宽高信息if (image_count == 1){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, 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;  /* 每幅图像的平移向量 */vector<Mat> rvecsMat; /* 每幅图像的旋转向量 *//* 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";cout << "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;cout << "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;cout << "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 */cout << "相机内参数矩阵：" << endl;cout << cameraMatrix << endl << endl;cout << "畸变系数：\n";cout << distCoeffs << endl << endl << endl;for (int i = 0; i < image_count; i++){cout << "第" << i + 1 << "幅图像的旋转向量：" << endl;cout << rvecsMat[i] << endl;/* 将旋转向量转换为相对应的旋转矩阵 */Rodrigues(rvecsMat[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 << tvecsMat[i] << endl << endl;}std::cout << "完成保存" << endl;cout << 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();cout << images[i] << endl;Mat imageSource = imread(images[i]);Mat newimage = imageSource.clone();//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();StrStm << i + 1;StrStm >> imageFileName;imageFileName += "_d.jpg";imwrite(imageFileName, newimage);}std::cout << "保存结束" << endl;
}