张正友相机标定 Opencv实现

转载：http://blog.csdn.net/dcrmg/article/details/52929669

#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 (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(11,11)); //对粗提取的角点进行精确化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,true); //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;system("pause");return ;}