opencvc++四个摄像头标定和去畸变

对于OpenCV C++四个摄像头的标定和去畸变，可以按照以下步骤进行：

1. 准备标定板：使用黑白棋盘格标定板，并对其进行拍照，得到多张不同角度下的标定板图像。

2. 提取角点：使用OpenCV的函数findChessboardCorners()，对标定板图像进行角点提取。

3. 标定相机：使用OpenCV的函数calibrateCamera()，对四个相机进行标定，得到相机的内参矩阵和畸变系数。

4. 保存参数：将相机的内参矩阵和畸变系数保存到文件中。

5. 去畸变：使用OpenCV的函数undistort()，对四个相机的图像进行去畸变处理。

需要注意的是，标定板的摆放位置和角度应该尽可能多样化，以便得到更加准确的标定结果。同时，标定板的大小也应该与实际应用场景中的物体大小相匹配。

相关问题

opencvc++四个摄像头标定和去畸变的C++代码

以下是使用OpenCV进行四个摄像头标定和去畸变的C++代码示例：

标定：

// 定义标定板的大小
Size boardSize = Size(8, 6);

// 定义标定板上每个角点的实际坐标
vector<Point3f> objectPoints;
for (int i = 0; i < boardSize.height; i++) {
    for (int j = 0; j < boardSize.width; j++) {
        objectPoints.push_back(Point3f(j, i, 0));
    }
}

// 读取图像和角点坐标
vector<vector<Point2f>> imagePoints;
vector<vector<Point3f>> objectPointsList;
for (int i = 0; i < 4; i++) {
    string filename = "camera" + to_string(i) + ".png";
    Mat image = imread(filename);
    vector<Point2f> corners;
    bool found = findChessboardCorners(image, boardSize, corners, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + CALIB_CB_FAST_CHECK);
    if (found) {
        Mat gray;
        cvtColor(image, gray, COLOR_BGR2GRAY);
        cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 30, 0.1));
        imagePoints.push_back(corners);
        objectPointsList.push_back(objectPoints);
    }
}

// 标定相机
Mat cameraMatrix[4], distCoeffs[4];
vector<Mat> rvecs[4], tvecs[4];
calibrateCamera(objectPointsList, imagePoints, Size(image.cols, image.rows), cameraMatrix, distCoeffs, rvecs, tvecs);

去畸变：

// 去畸变
Mat undistorted[4];
for (int i = 0; i < 4; i++) {
    string filename = "camera" + to_string(i) + ".png";
    Mat image = imread(filename);
    undistort(image, undistorted[i], cameraMatrix[i], distCoeffs[i]);
}

请注意，这只是一个示例代码，并且假定你已经拍摄了足够的图像来进行标定。在实际应用中，需要根据具体情况进行调整。

opencvc++四个摄像头标定和去畸变，并保存标定参数以及使用畸变图像的代码

好的，以下是使用OpenCV进行四个摄像头标定和去畸变，并保存标定参数和畸变图像的C++代码示例：

标定并保存标定参数：

// 定义标定板的大小
Size boardSize = Size(8, 6);

// 定义标定板上每个角点的实际坐标
vector<Point3f> objectPoints;
for (int i = 0; i < boardSize.height; i++) {
    for (int j = 0; j < boardSize.width; j++) {
        objectPoints.push_back(Point3f(j, i, 0));
    }
}

// 读取图像和角点坐标
vector<vector<Point2f>> imagePoints;
vector<vector<Point3f>> objectPointsList;
for (int i = 0; i < 4; i++) {
    string filename = "camera" + to_string(i) + ".png";
    Mat image = imread(filename);
    vector<Point2f> corners;
    bool found = findChessboardCorners(image, boardSize, corners, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + CALIB_CB_FAST_CHECK);
    if (found) {
        Mat gray;
        cvtColor(image, gray, COLOR_BGR2GRAY);
        cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1), TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 30, 0.1));
        imagePoints.push_back(corners);
        objectPointsList.push_back(objectPoints);
    }
}

// 标定相机
Mat cameraMatrix[4], distCoeffs[4];
vector<Mat> rvecs[4], tvecs[4];
calibrateCamera(objectPointsList, imagePoints, Size(image.cols, image.rows), cameraMatrix, distCoeffs, rvecs, tvecs);

// 保存标定参数
FileStorage fs("calibration.xml", FileStorage::WRITE);
fs << "cameraMatrix0" << cameraMatrix[0];
fs << "cameraMatrix1" << cameraMatrix[1];
fs << "cameraMatrix2" << cameraMatrix[2];
fs << "cameraMatrix3" << cameraMatrix[3];
fs << "distCoeffs0" << distCoeffs[0];
fs << "distCoeffs1" << distCoeffs[1];
fs << "distCoeffs2" << distCoeffs[2];
fs << "distCoeffs3" << distCoeffs[3];
fs.release();

去畸变并保存畸变图像：

// 读取标定参数
FileStorage fs("calibration.xml", FileStorage::READ);
Mat cameraMatrix[4], distCoeffs[4];
fs["cameraMatrix0"] >> cameraMatrix[0];
fs["cameraMatrix1"] >> cameraMatrix[1];
fs["cameraMatrix2"] >> cameraMatrix[2];
fs["cameraMatrix3"] >> cameraMatrix[3];
fs["distCoeffs0"] >> distCoeffs[0];
fs["distCoeffs1"] >> distCoeffs[1];
fs["distCoeffs2"] >> distCoeffs[2];
fs["distCoeffs3"] >> distCoeffs[3];
fs.release();

// 去畸变并保存畸变图像
Mat undistorted[4];
for (int i = 0; i < 4; i++) {
    string filename = "camera" + to_string(i) + ".png";
    Mat image = imread(filename);
    undistort(image, undistorted[i], cameraMatrix[i], distCoeffs[i]);
    string undistortedFilename = "undistorted" + to_string(i) + ".png";
    imwrite(undistortedFilename, undistorted[i]);
}

请注意，上述代码仅供参考，并且假设您已经拍摄了足够的图像来进行标定。在实际应用中，需要根据具体情况进行调整。