https://blog.csdn.net/u012319493/article/details/77622053
#include
#include
#include <time.h>
#include <stdio.h>
#include
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/highgui/highgui.hpp>
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
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 image_points_buf; / 缓存每幅图像上检测到的角点 /
vector<vector> image_points_seq; / 保存检测到的所有角点 /
string filename; // 图片名
vector filenames;
while (getline(fin, filename))
{
++image_count;
Mat imageInput = imread(filename);
filenames.push_back(filename);
// 读入第一张图片时获取图片大小
if(image_count == 1)
{
image_size.width = imageInput.cols;
image_size.height = imageInput.rows;
}
/ 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); // 转灰度图
/ 亚像素精确化 /
// image_points_buf 初始的角点坐标向量，同时作为亚像素坐标位置的输出
// Size(5,5) 搜索窗口大小
// （-1，-1）表示没有死区
// TermCriteria 角点的迭代过程的终止条件, 可以为迭代次数和角点精度两者的组合
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 CornerNum = board_size.width * board_size.height; // 每张图片上总的角点数
//-------------以下是摄像机标定------------------
/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> 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 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 tvecsMat; / 每幅图像的旋转向量 /
vector 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 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 /
// object_points 世界坐标系中的角点的三维坐标
// image_points_seq 每一个内角点对应的图像坐标点
// image_size 图像的像素尺寸大小
// cameraMatrix 输出，内参矩阵
// distCoeffs 输出，畸变系数
// rvecsMat 输出，旋转向量
// tvecsMat 输出，位移向量
// 0 标定时所采用的算法
calibrateCamera(object_points, image_points_seq, image_size, cameraMatrix, distCoeffs, rvecsMat, tvecsMat, 0);
//------------------------标定完成------------------------------------
// -------------------对标定结果进行评价------------------------------
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 image_points2; / 保存重新计算得到的投影点 /
fout<<“每幅图像的标定误差：\n”;
for (i=0;i<image_count;i++)
{
vector 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 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(0,j) = Vec2f(image_points2[j].x, image_points2[j].y);
tempImagePointMat.at(0,j) = Vec2f(tempImagePoint[j].x, tempImagePoint[j].y);
}
err = norm(image_points2Mat, tempImagePointMat, NORM_L2);
total_err += err/= point_counts[i];
fout << “第” << i+1 << “幅图像的平均误差：” << err<< “像素” << endl;
}
fout << “总体平均误差：” << total_err/image_count << “像素” <<endl <<endl;
//-------------------------评价完成---------------------------------------------
//-----------------------保存定标结果-------------------------------------------
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;
}
fout<<endl;
//--------------------标定结果保存结束-------------------------------
//----------------------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);
string imageFileName;
std::stringstream StrStm;
for (int i = 0 ; i != image_count ; i++)
{
initUndistortRectifyMap(cameraMatrix, distCoeffs, R, cameraMatrix, image_size, CV_32FC1, mapx, mapy);
Mat imageSource = imread(filenames[i]);
Mat newimage = imageSource.clone();
remap(imageSource, newimage, mapx, mapy, INTER_LINEAR);
StrStm.clear();
imageFileName.clear();
StrStm << i+1;
StrStm >> imageFileName;
imageFileName += “_d.jpg”;
imwrite(imageFileName, newimage);
}
fin.close();
fout.close();
return ;
}*
来源：CSDN
作者：PD灬灰太狼
链接：https://blog.csdn.net/qq_42914355/article/details/90246237