相机标定教程

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

using namespace cv;

int main() {
    // === 修改这里：匹配你的棋盘格 ===
    const int   board_w = 6, board_h = 4;              // 内角点数（7×5 格子 → 6×4 内角）
    const int   board_n = board_w * board_h;           // 总内角数 24
    const float square_w = 20.0f, square_h = 20.0f;    // 单格边长 20mm
    const int   img_count = 40;                        // 标定图片数量
    // ===============================

    Size board_size(board_w, board_h);
    std::vector<Point2f> corners_buf;
    std::vector<std::vector<Point2f>> image_points;

    Mat gray_img, drawn_img;
    Size img_size;
    int successes = 0;

    // 步骤 1：逐张检测角点 + 亚像素精化
    for (int i = 0; i < img_count; i++) {
        Mat src = imread(std::to_string(i) + ".jpg");
        if (src.empty()) continue;

        if (successes == 0) img_size = src.size();

        bool found = findChessboardCorners(src, board_size, corners_buf);
        if (found && (int)corners_buf.size() == board_n) {
            successes++;
            cvtColor(src, gray_img, COLOR_BGR2GRAY);
            // 亚像素精化
            find4QuadCornerSubpix(gray_img, corners_buf, Size(5, 5));
            image_points.push_back(corners_buf);

            // 可视化角点标注
            drawn_img = src.clone();
            drawChessboardCorners(drawn_img, board_size, corners_buf, found);
            imshow("corners", drawn_img);
            waitKey(50);
        } else {
            std::cout << "image " << i << " failed to find all corners" << std::endl;
        }
        corners_buf.clear();
    }
    std::cout << successes << " useful images" << std::endl;

    // 步骤 2：构造棋盘格 3D 物理坐标（所有 Z=0，格子依次排列）
    std::vector<std::vector<Point3f>> object_points;
    std::vector<Point3f> obj_buf;

    for (int i = 0; i < successes; i++) {
        for (int j = 0; j < board_h; j++) {
            for (int k = 0; k < board_w; k++) {
                obj_buf.push_back(Point3f(k * square_w, j * square_h, 0));
            }
        }
        object_points.push_back(obj_buf);
        obj_buf.clear();
    }

    // 步骤 3：标定
    Mat camera_matrix(3, 3, CV_32FC1, Scalar::all(0));
    Mat dist_coeffs(1, 5, CV_32FC1, Scalar::all(0));
    std::vector<Mat> rvecs, tvecs;

    double rms = calibrateCamera(object_points, image_points, img_size,
                                 camera_matrix, dist_coeffs, rvecs, tvecs);

    std::cout << "重投影误差(RMS): " << rms  << std::endl;
    std::cout << "相机内参矩阵:"    << std::endl << camera_matrix << std::endl;
    std::cout << "畸变系数:"        << std::endl << dist_coeffs   << std::endl;
    return 0;
}