单目标定程序

// subpixel.cpp : 定义控制台应用程序的入口点。
//
/*
#include "stdafx.h"
#include<iostream>
#include <cmath>
#include<opencv2\opencv.hpp>

using namespace cv;
using namespace std;

*/
// opencv_test.cpp : 定义控制台应用程序的入口点。
//
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/highgui/highgui.hpp"

#include "stdafx.h"
#include <opencv2/opencv.hpp>
//#include <opencv2/highgui.hpp>
#include "cv.h"
#include <cv.hpp>
#include <iostream>

using namespace std;
using namespace cv;

int mmain();

const int boardWidth = 9;                               //横向的角点数目
const int boardHeight = 6;                              //纵向的角点数据
const int boardCorner = boardWidth * boardHeight;       //总的角点数据
const int frameNumber = 13;                             //相机标定时需要采用的图像帧数
const int squareSize = 20;                              //标定板黑白格子的大小 单位mm
const Size boardSize = Size(boardWidth, boardHeight);   //

Mat intrinsic;                                          //相机内参数
Mat distortion_coeff;                                   //相机畸变参数
vector<Mat> rvecs;                                      //旋转向量
vector<Mat> tvecs;                                      //平移向量
vector<vector<Point2f>> corners;                        //各个图像找到的角点的集合 和objRealPoint 一一对应
vector<vector<Point3f>> objRealPoint;                   //各副图像的角点的实际物理坐标集合

vector<Point2f> corner;                                 //某一副图像找到的角点

/*
int main()
{
int t;
//cout<<"OK!!"<<endl;

//Mat sd=imread("left1.jpg");
//imshow("lans",sd);
mmain();

waitKey();

return 0;
}*/

/*计算标定板上模块的实际物理坐标*/
void calRealPoint
4000
(vector<vector<Point3f>>& obj, int boardwidth,int boardheight, int imgNumber, int squaresize)
{
//  Mat imgpoint(boardheight, boardwidth, CV_32FC3,Scalar(0,0,0));
vector<Point3f> imgpoint;
for (int rowIndex = 0; rowIndex < boardheight; rowIndex++)
{
for (int colIndex = 0; colIndex < boardwidth; colIndex++)
{
//  imgpoint.at<Vec3f>(rowIndex, colIndex) = Vec3f(rowIndex * squaresize, colIndex*squaresize, 0);
imgpoint.push_back(Point3f(rowIndex * squaresize, colIndex * squaresize, 0));
}
}
for (int imgIndex = 0; imgIndex < imgNumber; imgIndex++)
{
obj.push_back(imgpoint);
}
}

/*设置相机的初始参数 也可以不估计*/
void guessCameraParam(void )
{
/*分配内存*/
intrinsic.create(3, 3, CV_64FC1);
distortion_coeff.create(5, 1, CV_64FC1);

/*
fx 0 cx
0 fy cy
0 0  1
*/
intrinsic.at<double>(0,0) = 256.8093262;   //fx
intrinsic.at<double>(0, 2) = 160.2826538;   //cx
intrinsic.at<double>(1, 1) = 254.7511139;   //fy
intrinsic.at<double>(1, 2) = 127.6264572;   //cy

intrinsic.at<double>(0, 1) = 0;
intrinsic.at<double>(1, 0) = 0;
intrinsic.at<double>(2, 0) = 0;
intrinsic.at<double>(2, 1) = 0;
intrinsic.at<double>(2, 2) = 1;

/*
k1 k2 p1 p2 p3
*/
distortion_coeff.at<double>(0, 0) = -0.193740;  //k1
distortion_coeff.at<double>(1, 0) = -0.378588;  //k2
distortion_coeff.at<double>(2, 0) = 0.028980;   //p1
distortion_coeff.at<double>(3, 0) = 0.008136;   //p2
distortion_coeff.at<double>(4, 0) = 0;        //p3
}

void outputCameraParam(void )
{
/*保存数据*/
//cvSave("cameraMatrix.xml", &intrinsic);
//cvSave("cameraDistoration.xml", &distortion_coeff);
//cvSave("rotatoVector.xml", &rvecs);
//cvSave("translationVector.xml", &tvecs);
/*输出数据*/
cout << "fx :" << intrinsic.at<double>(0, 0) << endl << "fy :" << intrinsic.at<double>(1, 1) << endl;
cout << "cx :" << intrinsic.at<double>(0, 2) << endl << "cy :" << intrinsic.at<double>(1, 2) << endl;

cout << "k1 :" << distortion_coeff.at<double>(0, 0) << endl;
cout << "k2 :" << distortion_coeff.at<double>(1, 0) << endl;
cout << "p1 :" << distortion_coeff.at<double>(2, 0) << endl;
cout << "p2 :" << distortion_coeff.at<double>(3, 0) << endl;
cout << "p3 :" << distortion_coeff.at<double>(4, 0) << endl;
}

int main()
{
int imageHeight;
int imageWidth;
int goodFrameCount = 0;

Mat img,rgbImage;
Mat tImage=imread("left1.jpg");
imageHeight = tImage.rows;
imageWidth = tImage.cols;
Mat grayImage(imageHeight,imageWidth,CV_8U);
while (goodFrameCount < frameNumber)
{
char filename[100];
sprintf_s(filename,"left%d.jpg", goodFrameCount + 1);
rgbImage = imread(filename);
cvtColor(rgbImage, grayImage, CV_BGR2GRAY);
imshow("Camera", grayImage);

bool isFind = findChessboardCorners(rgbImage, boardSize, corner,0);
//bool isFind = findChessboardCorners( rgbImage, boardSize, corner,
//CV_CALIB_CB_ADAPTIVE_THRESH | CV_CALIB_CB_FAST_CHECK | CV_CALIB_CB_NORMALIZE_IMAGE);

if (isFind == true) //所有角点都被找到 说明这幅图像是可行的
{
cornerSubPix(grayImage, corner, Size(5,5), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 20, 0.1));
drawChessboardCorners(rgbImage, boardSize, corner, isFind);
imshow("chessboard", rgbImage);
corners.push_back(corner);
goodFrameCount++;
cout << "The image"<<goodFrameCount<<" is good" << endl;
}
else
{
cout << "The image is bad please try again" << endl;
}

if (waitKey(10) == 'q')
{
break;
}

}

/*
图像采集完毕 接下来开始摄像头的校正
calibrateCamera()
输入参数 objectPoints  角点的实际物理坐标
imagePoints   角点的图像坐标
imageSize     图像的大小
输出参数
cameraMatrix  相机的内参矩阵
distCoeffs    相机的畸变参数
rvecs         旋转矢量(外参数)
tvecs         平移矢量(外参数）
*/

/*设置实际初始参数 根据calibrateCamera来 如果flag = 0 也可以不进行设置*/
guessCameraParam();
cout << "guess successful" << endl;
/*计算实际的校正点的三维坐标*/
calRealPoint(objRealPoint, boardWidth, boardHeight,frameNumber, squareSize);
cout << "cal real successful" << endl;
/*标定摄像头*/
calibrateCamera(objRealPoint, corners, Size(imageWidth, imageHeight), intrinsic, distortion_coeff, rvecs, tvecs, 0);
cout << "calibration successful" << endl;
/*保存并输出参数*/
outputCameraParam();
cout << "out successful" << endl;

/*显示畸变校正效果*/
Mat cImage;
undistort(rgbImage, cImage, intrinsic, distortion_coeff);
imshow("Corret Image", cImage);
cout << "Correct Image" << endl;
cout << "Wait for Key" << endl;
waitKey();

return 0;
}