opencvc++四个摄像头标定和去畸变
时间: 2023-07-24 14:55:06 浏览: 40
您好!针对您的问题,我可以给出以下的回答:
1. 标定四个摄像头
要对四个摄像头进行标定,需要使用相应的标定板和标定算法。一般来说,标定板使用的是棋盘格,可以通过 OpenCV 中的函数 `cv::findChessboardCorners` 找到棋盘格的角点,再使用 `cv::calibrateCamera` 函数进行标定。对于四个摄像头,需要分别对每个摄像头进行标定,得到每个摄像头的内参和外参。
2. 去畸变
在标定完成后,可以使用 `cv::undistort` 函数对图像进行去畸变。这个函数需要输入原始图像、相机内参和畸变系数,输出去畸变后的图像。如果需要同时对多个摄像头进行去畸变,可以分别对每个摄像头的图像进行处理。
希望能够对您有所帮助!
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:
```c++
// 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:
```c++
// 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:
```c++
// 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:
```c++
// 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。
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