海康相机SDK联合c++标定
本文出处：https://blog.csdn.net/qq_15029743/article/details/81133443
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<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 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<vector<Point3f>> 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<int> point_counts; // 每幅图像中角点的数量 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"; 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<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; 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;
