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：

#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。