张正友相机标定Opencv实现以及标定流程&&标定结果评价&&图像矫正流程解析（附标定程序和棋盘图）

//! finds checkerboard pattern of the specified size in the image
CV_EXPORTS_W bool findChessboardCorners( InputArray image, Size patternSize,
                                         OutputArray corners,
                                         int flags=CALIB_CB_ADAPTIVE_THRESH+CALIB_CB_NORMALIZE_IMAGE );

//! adjusts the corner locations with sub-pixel accuracy to maximize the certain cornerness criteria
CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners,
                                Size winSize, Size zeroZone,
                                TermCriteria criteria );

//! finds subpixel-accurate positions of the chessboard corners
CV_EXPORTS bool find4QuadCornerSubpix(InputArray img, InputOutputArray corners, Size region_size);

//! draws the checkerboard pattern (found or partly found) in the image
CV_EXPORTS_W void drawChessboardCorners( InputOutputArray image, Size patternSize,
                                         InputArray corners, bool patternWasFound );

//! finds intrinsic and extrinsic camera parameters from several fews of a known calibration pattern.
CV_EXPORTS_W double calibrateCamera( InputArrayOfArrays objectPoints,
                                     InputArrayOfArrays imagePoints,
                                     Size imageSize,
                                     CV_OUT InputOutputArray cameraMatrix,
                                     CV_OUT InputOutputArray distCoeffs,
                                     OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
                                     int flags=0, TermCriteria criteria = TermCriteria(
                                         TermCriteria::COUNT+TermCriteria::EPS, 30, DBL_EPSILON) );

//! projects points from the model coordinate space to the image coordinates. Also computes derivatives of the image coordinates w.r.t the intrinsic and extrinsic camera parameters
CV_EXPORTS_W void projectPoints( InputArray objectPoints,
                                 InputArray rvec, InputArray tvec,
                                 InputArray cameraMatrix, InputArray distCoeffs,
                                 OutputArray imagePoints,
                                 OutputArray jacobian=noArray(),
                                 double aspectRatio=0 );

//! initializes maps for cv::remap() to correct lens distortion and optionally rectify the image
CV_EXPORTS_W void initUndistortRectifyMap( InputArray cameraMatrix, InputArray distCoeffs,
                           InputArray R, InputArray newCameraMatrix,
                           Size size, int m1type, OutputArray map1, OutputArray map2 );

//! warps the image using the precomputed maps. The maps are stored in either floating-point or integer fixed-point format
CV_EXPORTS_W void remap( InputArray src, OutputArray dst,
                         InputArray map1, InputArray map2,
                         int interpolation, int borderMode=BORDER_CONSTANT,
                         const Scalar& borderValue=Scalar());

//! corrects lens distortion for the given camera matrix and distortion coefficients
CV_EXPORTS_W void undistort( InputArray src, OutputArray dst,
                             InputArray cameraMatrix,
                             InputArray distCoeffs,
                             InputArray newCameraMatrix=noArray() );

#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(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(rvecsMat[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<<tvecsMat[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="chess";
		StrStm<<i+1;
		StrStm>>imageFileName;
		filePath+=imageFileName;
		filePath+=".bmp";
		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);		
		StrStm.clear();
		filePath.clear();
		StrStm<<i+1;
		StrStm>>imageFileName;
		imageFileName += "_d.jpg";
		imwrite(imageFileName,newimage);
	}
	std::cout<<"保存结束"<<endl;	
	return ;
}