for (int i=0;i<image_corners.size();i++){object_points.push_back(objects);}
时间: 2023-12-30 08:06:37 浏览: 24
这段代码的作用是将一个名为 `objects` 的对象添加到 `object_points` 向量中,循环执行的次数为 `image_corners.size()`。具体来说,它是用于在计算机视觉中进行相机标定时,将物体在三维空间中的坐标添加到 `object_points` 向量中,以便后续计算相机的内部参数和外部参数。其中,`image_corners` 是物体在图像中的角点坐标,而 `objects` 则是物体在三维空间中的坐标。
相关问题
opencv4.6.0版本的用c++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
下面是使用C++进行四个摄像头标定和去畸变的示例代码。该代码使用OpenCV 4.6.0版本,可以标定四个摄像头,并保存标定参数以及使用畸变图像。
```c++
#include <opencv2/opencv.hpp>
#include <iostream>
#include <vector>
using namespace cv;
using namespace std;
int main()
{
vector<string> img_names{"img1.jpg", "img2.jpg", "img3.jpg", "img4.jpg"};
Size board_size{9, 6};
vector<vector<Point2f>> corners_all;
vector<Mat> rvecs_all, tvecs_all;
vector<Mat> cameraMatrix_all, distCoeffs_all;
vector<vector<Point3f>> object_points;
// 初始化棋盘格三维点坐标
vector<Point3f> object_points_one;
for (int i = 0; i < board_size.height; ++i) {
for (int j = 0; j < board_size.width; ++j) {
object_points_one.emplace_back(j, i, 0);
}
}
for (int i = 0; i < img_names.size(); ++i) {
Mat img = imread(img_names[i]);
vector<Point2f> corners;
// 寻找角点
bool found = findChessboardCorners(img, board_size, corners, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE + CALIB_CB_FAST_CHECK);
if (found) {
Mat gray_img;
cvtColor(img, gray_img, COLOR_BGR2GRAY);
// 亚像素精确化
cornerSubPix(gray_img, corners, Size(5, 5), Size(-1, -1), TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 30, 0.1));
drawChessboardCorners(img, board_size, corners, found);
corners_all.emplace_back(corners);
object_points.emplace_back(object_points_one);
}
imshow("img", img);
waitKey();
}
// 标定
calibrateCameraRO(object_points, corners_all, Size(640, 480), 0, 0, rvecs_all, tvecs_all, CALIB_USE_INTRINSIC_GUESS | CALIB_FIX_K4 | CALIB_FIX_K5);
// 保存标定参数
for (int i = 0; i < img_names.size(); ++i) {
Mat cameraMatrix, distCoeffs;
FileStorage fs(img_names[i] + ".xml", FileStorage::WRITE);
fs << "camera_matrix" << rvecs_all[i];
fs << "distortion_coefficients" << tvecs_all[i];
fs.release();
cameraMatrix_all.push_back(cameraMatrix);
distCoeffs_all.push_back(distCoeffs);
}
// 去畸变并显示
for (int i = 0; i < img_names.size(); ++i) {
Mat img = imread(img_names[i]);
Mat cameraMatrix, distCoeffs;
FileStorage fs(img_names[i] + ".xml", FileStorage::READ);
fs["camera_matrix"] >> cameraMatrix;
fs["distortion_coefficients"] >> distCoeffs;
fs.release();
Mat img_undistorted;
undistort(img, img_undistorted, cameraMatrix, distCoeffs);
imshow("img_undistorted", img_undistorted);
waitKey();
}
return 0;
}
```
这段代码中,我们首先定义了待标定的图像文件名、棋盘格大小、角点坐标、相机内参、畸变系数等变量。然后,我们通过循环遍历所有图像,寻找角点并进行亚像素精确化。接着,我们使用 `calibrateCameraRO` 函数进行标定,该函数可以同时标定多个摄像头。最后,我们保存了每个摄像头的标定参数,并使用 `undistort` 函数将畸变图像进行去畸变并显示。
希望这段代码对您有所帮助。如果您有任何问题,请随时提出。
opencvc++四个摄像头标定和去畸变
标定和去畸变是相机应用中非常重要的一步,可以提高图像质量和测量精度。下面是一个使用OpenCV C++进行四个摄像头标定和去畸变的示例代码:
```c++
#include <iostream>
#include <opencv2/opencv.hpp>
int main()
{
// 标定所需的棋盘格参数
int board_width = 9; // 棋盘格宽度
int board_height = 6; // 棋盘格高度
int square_size = 27; // 棋盘格格子尺寸,单位mm
// 摄像头参数
int num_cameras = 4; // 摄像头数量
int image_width = 640; // 图像宽度
int image_height = 480; // 图像高度
// 读入标定所需的图像
std::vector<std::vector<cv::Mat>> image_points(num_cameras);
std::vector<std::vector<cv::Point3f>> object_points(num_cameras);
for (int i = 0; i < num_cameras; i++)
{
for (int j = 0; j < 20; j++)
{
cv::Mat image = cv::imread("camera_" + std::to_string(i) + "_" + std::to_string(j) + ".png");
if (image.empty())
{
std::cout << "Failed to read image: camera_" << i << "_" << j << ".png" << std::endl;
return -1;
}
// 寻找棋盘格角点
std::vector<cv::Point2f> corners;
bool found = cv::findChessboardCorners(image, cv::Size(board_width, board_height), corners);
// 如果找到了,将角点添加到标定数据中
if (found)
{
cv::Mat gray_image;
cv::cvtColor(image, gray_image, cv::COLOR_BGR2GRAY);
cv::cornerSubPix(gray_image, corners, cv::Size(11, 11), cv::Size(-1, -1), cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::MAX_ITER, 30, 0.1));
image_points[i].push_back(corners);
std::vector<cv::Point3f> object_pts;
for (int k = 0; k < board_height; k++)
{
for (int l = 0; l < board_width; l++)
{
object_pts.push_back(cv::Point3f(l * square_size, k * square_size, 0));
}
}
object_points[i].push_back(object_pts);
}
else
{
std::cout << "Failed to find corners in image: camera_" << i << "_" << j << ".png" << std::endl;
}
}
}
// 标定相机
std::vector<cv::Mat> camera_matrices(num_cameras);
std::vector<cv::Mat> dist_coeffs(num_cameras);
std::vector<std::vector<cv::Mat>> rvecs(num_cameras);
std::vector<std::vector<cv::Mat>> tvecs(num_cameras);
double rms_error = cv::calibrateMultiCamera(object_points, image_points, image_size, camera_matrices, dist_coeffs, rvecs, tvecs);
// 打印标定结果
std::cout << "RMS error: " << rms_error << std::endl;
for (int i = 0; i < num_cameras; i++)
{
std::cout << "Camera " << i << " matrix:" << std::endl << camera_matrices[i] << std::endl;
std::cout << "Camera " << i << " distortion coefficients:" << std::endl << dist_coeffs[i] << std::endl;
}
// 去除畸变
for (int i = 0; i < num_cameras; i++)
{
for (int j = 0; j < 20; j++)
{
cv::Mat image = cv::imread("camera_" + std::to_string(i) + "_" + std::to_string(j) + ".png");
if (image.empty())
{
std::cout << "Failed to read image: camera_" << i << "_" << j << ".png" << std::endl;
return -1;
}
cv::Mat undistorted_image;
cv::undistort(image, undistorted_image, camera_matrices[i], dist_coeffs[i]);
cv::imshow("Camera " + std::to_string(i), image);
cv::imshow("Camera " + std::to_string(i) + " undistorted", undistorted_image);
cv::waitKey(0);
}
}
return 0;
}
```
上述代码中,首先定义了标定所需的棋盘格参数、摄像头参数和图像文件名。然后,通过循环读入每个摄像头的标定图像,并在每张图像中寻找棋盘格角点。如果找到了,将角点添加到标定数据中。接着,使用`cv::calibrateMultiCamera`函数对所有摄像头进行标定,得到相机矩阵和畸变系数。最后,使用`cv::undistort`函数对每张图像进行去畸变处理。
注意:在实际应用中,需要根据棋盘格尺寸和相机距离合理设置`square_size`参数,并使用更多的标定图像来提高标定精度。同时,需要根据实际情况调整摄像头参数和图像文件名。
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)