准备
1.运行环境：VS2010与VS2017均可，opencv2.9与opencv2.9以上均可。
2.拍十五张标定板图片
3.放到calibdata.txt目录下
4.代码
#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(6,8); /* 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(5,5)); //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;
/************************************************************************
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="";
StrStm<<i+1;
StrStm>>imageFileName;
filePath+=imageFileName;
filePath+=".jpg";
Mat imageSource = imread(filePath);
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);
imshow("原始图像",imageSource);
imshow("矫正后图像",newimage);
waitKey();
StrStm.clear();
filePath.clear();
StrStm<<i+1;
StrStm>>imageFileName;
imageFileName += "_d.jpg";
imwrite(imageFileName,newimage);
}
std::cout<<"保存结束"<<endl;
return ;
}
划重点
右键工程文件的属性，找到配置属性，找到常规，找到MFC的使用，将使用标准Windows库改为在静态库中使用MFC。
