海康威视网络摄像头SDK中的相机标定（二次开发）

首先放上一张效果动图：如果你需要这样的Demo,请下载：海康威视标定Demo

软件配置环境：VS2013+OpenCV2.49+海康威视相关SDK导入，Release下编译运行

标定部分核心代码：

m_progress.SetPos(0);
	CString  PIC = "";
	CStdioFile picpath("calibdata.ini", CFile::modeRead);
	picpath.ReadString(PIC);
	picpath.Close();

	// TODO:  在此添加控件通知处理程序代码
	ifstream fin("calibdata.ini"); /* 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	
	m_progress.SetPos(20);
	cout << "开始提取角点………………";
	int image_count = 0;  /* 图像数量 */
	  /* 图像的尺寸 */
	Size board_size = Size(6, 8);    /* 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> 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
			imwrite("2.bmp", view_gray);
			CImage image;
			CString showJD = "2.bmp";
			int cx, cy;
			CRect   rect;
			//根据路径载入图片    
			//char strPicPath[] = PicName;
			image.Load(showJD);
			//获取图片的宽 高  
			cx = image.GetWidth();
			cy = image.GetHeight();

			CWnd *pWnd = NULL;
			pWnd = GetDlgItem(IDC_STATIC_JD);//获取控件句柄  
			//获取Picture Control控件的客户区  
			pWnd->GetClientRect(&rect);

			CDC *pDc = NULL;
			pDc = pWnd->GetDC();//获取picture control的DC    
			//设置指定设备环境中的位图拉伸模式  
			int ModeOld = SetStretchBltMode(pDc->m_hDC, STRETCH_HALFTONE);
			//从源矩形中复制一个位图到目标矩形，按目标设备设置的模式进行图像的拉伸或压缩  
			image.StretchBlt(pDc->m_hDC, rect, SRCCOPY);
			SetStretchBltMode(pDc->m_hDC, ModeOld);
			ReleaseDC(pDc);



			//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";
	m_progress.SetPos(50);
	//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> object_points; /* 保存标定板上角点的三维坐标 */
	/*内外参数*/
	/* 摄像机内参数矩阵 */
	vector point_counts;  // 每幅图像中角点的数量
	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 */
	calibrateCamera(object_points, image_points_seq, image_size, cameraMatrix, distCoeffs, rvecsMat, tvecsMat, 0);
	cout << "标定完成！\n";
	m_progress.SetPos(70);
	//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 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 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];
		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;
	m_progress.SetPos(80);
	fout << endl;
	/************************************************************************
	显示定标结果
	*************************************************************************/
	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);
		func(cameraMatrix, distCoeffs, R, image_size, mapx, mapy);
		StrStm.clear();
		imageFileName.clear();
		string filePath = PIC;
		/*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);
		/*imshow("原始图像", imageSource);
	imshow("矫正后图像", newimage);*/
		////
		CImage  image1;
		MatToCImage(newimage, image1);
		//PIC = PicName;
		CImage  image;
		int cx, cy;
		CRect   rect;
		//根据路径载入图片    
		//char strPicPath[] = PicName;
		image.Load(PIC);
		//获取图片的宽 高  
		cx = image1.GetWidth();
		cy = image1.GetHeight();

		CWnd *pWnd = NULL;
		pWnd = GetDlgItem(IDC_STATIC_JZ);//获取控件句柄  
		//获取Picture Control控件的客户区  
		pWnd->GetClientRect(&rect);

		CDC *pDc = NULL;
		pDc = pWnd->GetDC();//获取picture control的DC    
		//设置指定设备环境中的位图拉伸模式  
		int ModeOld = SetStretchBltMode(pDc->m_hDC, STRETCH_HALFTONE);
		//从源矩形中复制一个位图到目标矩形，按目标设备设置的模式进行图像的拉伸或压缩  
		image1.StretchBlt(pDc->m_hDC, rect, SRCCOPY);
		SetStretchBltMode(pDc->m_hDC, ModeOld);
		ReleaseDC(pDc);
		////

		waitKey();
		StrStm.clear();
		filePath.clear();
		CString str3 = "_calibrated";
		PIC.Insert(14, str3);
		imageFileName = PIC;
		imwrite(imageFileName, newimage);
		file.Open("calibrated.ini", CFile::modeCreate | CFile::modeNoTruncate | CFile::modeWrite);
		file.Write(PIC, strlen(PIC));
		file.Close();
	}
	std::cout << "保存结束" << endl;
	m_progress.SetPos(100);
	return;