丁莹渐晕标定代码c++

丁莹渐晕标定是一种基于图像的相机标定方法，可以校准相机的内部参数和外部参数。以下是一个简单的 C++ 实现：

#include <iostream>
#include <vector>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

// 丁莹渐晕标定函数
void calibration(vector<vector<Point2f>> imagePoints,
                 vector<vector<Point3f>> objectPoints,
                 Size imageSize, Mat &cameraMatrix, Mat &distCoeffs) {
    // 初始化相机参数估计器
    vector<Mat> rvecs, tvecs;
    cameraMatrix = Mat::eye(3, 3, CV_64F);
    distCoeffs = Mat::zeros(8, 1, CV_64F);

    // 进行标定
    double rms = calibrateCamera(objectPoints, imagePoints, imageSize,
                                 cameraMatrix, distCoeffs, rvecs, tvecs);

    // 输出标定结果
    cout << "RMS error: " << rms << endl;
    cout << "Camera matrix: " << endl << cameraMatrix << endl;
    cout << "Distortion coefficients: " << endl << distCoeffs << endl;
}

int main() {
    // 读取标定图像
    vector<vector<Point2f>> imagePoints;
    vector<vector<Point3f>> objectPoints;
    Size boardSize(6, 9);
    float squareSize = 1.f;
    vector<String> filenames;
    glob("calibration_images/*.jpg", filenames);
    for (int i = 0; i < filenames.size(); ++i) {
        Mat image = imread(filenames[i]);
        if (image.empty()) continue;
        vector<Point2f> corners;
        bool found = findChessboardCorners(image, boardSize, corners);
        if (found) {
            Mat gray;
            cvtColor(image, gray, COLOR_BGR2GRAY);
            cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
                         TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER, 30, 0.1));
            drawChessboardCorners(image, boardSize, corners, found);
            imshow("image", image);
            waitKey(0);
            imagePoints.push_back(corners);
            vector<Point3f> obj;
            for (int j = 0; j < boardSize.height; ++j)
                for (int k = 0; k < boardSize.width; ++k)
                    obj.push_back(Point3f(k * squareSize, j * squareSize, 0));
            objectPoints.push_back(obj);
        }
    }

    // 进行标定
    Mat cameraMatrix, distCoeffs;
    calibration(imagePoints, objectPoints, Size(1920, 1080), cameraMatrix, distCoeffs);

    return 0;
}

在这个实现中，我们首先使用 `glob` 函数读取标定图像，然后使用 OpenCV 的 `findChessboardCorners` 函数检测棋盘格角点，在检测到角点的情况下，使用 `cornerSubPix` 函数对角点进行亚像素精度的优化，并将优化后的角点和对应的物理坐标保存到 `imagePoints` 和 `objectPoints` 中。最后，调用 `calibration` 函数进行相机标定，并输出标定结果。