opencvc++四个摄像头标定和去畸变

对于使用四个摄像头进行标定和去畸变，您可以按照以下步骤进行操作：

1. 拍摄标定图像：使用每个摄像头拍摄至少10张不同角度和位置的棋盘格标定图像，并将这些图像保存在单独的文件夹中。

2. 标定：使用 OpenCV 的 `calibrateCamera()` 函数对每个摄像头进行单独标定。该函数将返回相机矩阵、畸变系数和旋转/平移向量，这些值将用于去畸变。

3. 合并标定结果：使用 `stereoCalibrate()` 函数将每个摄像头的相机矩阵和畸变系数合并为一个矩阵和系数。

4. 去畸变：使用 `initUndistortRectifyMap()` 和 `remap()` 函数将图像去畸变。这两个函数将使用相机矩阵、畸变系数和旋转/平移向量来计算一个畸变矫正映射，然后将该映射应用于输入图像。

以下是示例代码，其中 `calibrateCamera()` 和 `stereoCalibrate()` 函数的参数会根据您的实际情况进行调整：

// 定义标定图像的大小
Size boardSize = Size(9, 6);

// 定义棋盘格的大小
float squareSize = 0.025; // 单位为米

// 定义标定图像的路径
vector<string> imagePaths1 = {"path/to/camera1/images"};
vector<string> imagePaths2 = {"path/to/camera2/images"};
vector<string> imagePaths3 = {"path/to/camera3/images"};
vector<string> imagePaths4 = {"path/to/camera4/images"};

// 定义标定图像的数量
int imageCount = 10;

// 定义标定参数
vector<vector<Point3f>> objectPoints;
vector<vector<Point2f>> imagePoints1, imagePoints2, imagePoints3, imagePoints4;
Mat cameraMatrix1, cameraMatrix2, cameraMatrix3, cameraMatrix4, distCoeffs1, distCoeffs2, distCoeffs3, distCoeffs4;
vector<Mat> rvecs1, tvecs1, rvecs2, tvecs2, rvecs3, tvecs3, rvecs4, tvecs4;

// 对每个摄像头进行单独标定
for (int i = 0; i < imageCount; i++) {
  // 读取标定图像
  Mat image1 = imread(imagePaths1[i]);
  Mat image2 = imread(imagePaths2[i]);
  Mat image3 = imread(imagePaths3[i]);
  Mat image4 = imread(imagePaths4[i]);

  // 检测棋盘格角点
  vector<Point2f> corners1, corners2, corners3, corners4;
  bool found1 = findChessboardCorners(image1, boardSize, corners1);
  bool found2 = findChessboardCorners(image2, boardSize, corners2);
  bool found3 = findChessboardCorners(image3, boardSize, corners3);
  bool found4 = findChessboardCorners(image4, boardSize, corners4);

  if (found1 && found2 && found3 && found4) {
    // 亚像素精度角点检测
    cornerSubPix(image1, corners1, Size(11, 11), Size(-1, -1), TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER, 30, 0.1));
    cornerSubPix(image2, corners2, Size(11, 11), Size(-1, -1), TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER, 30, 0.1));
    cornerSubPix(image3, corners3, Size(11, 11), Size(-1, -1), TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER, 30, 0.1));
    cornerSubPix(image4, corners4, Size(11, 11), Size(-1, -1), TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER, 30, 0.1));

    // 保存角点坐标
    vector<Point3f> objectCorners;
    for (int j = 0; j < boardSize.height; j++) {
      for (int k = 0; k < boardSize.width; k++) {
        objectCorners.push_back(Point3f(j * squareSize, k * squareSize, 0));
      }
    }
    objectPoints.push_back(objectCorners);
    imagePoints1.push_back(corners1);
    imagePoints2.push_back(corners2);
    imagePoints3.push_back(corners3);
    imagePoints4.push_back(corners4);
  }
}

// 对每个摄像头进行单独标定
calibrateCamera(objectPoints, imagePoints1, imagePaths1[0].size(), cameraMatrix1, distCoeffs1, rvecs1, tvecs1);
calibrateCamera(objectPoints, imagePoints2, imagePaths2[0].size(), cameraMatrix2, distCoeffs2, rvecs2, tvecs2);
calibrateCamera(objectPoints, imagePoints3, imagePaths3[0].size(), cameraMatrix3, distCoeffs3, rvecs3, tvecs3);
calibrateCamera(objectPoints, imagePoints4, imagePaths4[0].size(), cameraMatrix4, distCoeffs4, rvecs4, tvecs4);

// 合并标定结果
vector<vector<Point3f>> objectPointsAll(imagePoints1.size(), objectPoints[0]);
Mat R, T, E, F;
stereoCalibrate(objectPointsAll, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imagePaths1[0].size(), R, T, E, F);

// 保存相机参数
FileStorage fs("calibration.xml", FileStorage::WRITE);
fs << "cameraMatrix1" << cameraMatrix1;
fs << "distCoeffs1" << distCoeffs1;
fs << "cameraMatrix2" << cameraMatrix2;
fs << "distCoeffs2" << distCoeffs2;
fs << "cameraMatrix3" << cameraMatrix3;
fs << "distCoeffs3" << distCoeffs3;
fs << "cameraMatrix4" << cameraMatrix4;
fs << "distCoeffs4" << distCoeffs4;
fs << "R" << R;
fs << "T" << T;
fs.release();

// 加载相机参数
FileStorage fs("calibration.xml", FileStorage::READ);
fs["cameraMatrix1"] >> cameraMatrix1;
fs["distCoeffs1"] >> distCoeffs1;
fs["cameraMatrix2"] >> cameraMatrix2;
fs["distCoeffs2"] >> distCoeffs2;
fs["cameraMatrix3"] >> cameraMatrix3;
fs["distCoeffs3"] >> distCoeffs3;
fs["cameraMatrix4"] >> cameraMatrix4;
fs["distCoeffs4"] >> distCoeffs4;
fs["R"] >> R;
fs["T"] >> T;
fs.release();

// 初始化畸变矫正映射
Mat map1x, map1y, map2x, map2y, map3x, map3y, map4x, map4y;
initUndistortRectifyMap(cameraMatrix1, distCoeffs1, R, cameraMatrix1, imagePaths1[0].size(), CV_32FC1, map1x, map1y);
initUndistortRectifyMap(cameraMatrix2, distCoeffs2, R, cameraMatrix2, imagePaths2[0].size(), CV_32FC1, map2x, map2y);
initUndistortRectifyMap(cameraMatrix3, distCoeffs3, R, cameraMatrix3, imagePaths3[0].size(), CV_32FC1, map3x, map3y);
initUndistortRectifyMap(cameraMatrix4, distCoeffs4, R, cameraMatrix4, imagePaths4[0].size(), CV_32FC1, map4x, map4y);

// 去畸变
Mat image1 = imread("path/to/image1");
Mat image2 = imread("path/to/image2");
Mat image3 = imread("path/to/image3");
Mat image4 = imread("path/to/image4");
Mat undistorted1, undistorted2, undistorted3, undistorted4;
remap(image1, undistorted1, map1x, map1y, INTER_LINEAR);
remap(image2, undistorted2, map2x, map2y, INTER_LINEAR);
remap(image3, undistorted3, map3x, map3y, INTER_LINEAR);
remap(image4, undistorted4, map4x, map4y, INTER_LINEAR);