opencv摄像头标定程序实现

摄像头标定是计算机视觉中的一个重要步骤，用于确定摄像头的内参（如焦距、主点、畸变系数等）和外参（如旋转矩阵和平移向量）。OpenCV 提供了方便的工具来进行摄像头标定。下面分别给出 C++ 和 Python 的实现。
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1. C++ 实现

1.1 准备工作

准备一个棋盘格（如 9x6 的棋盘格）。
拍摄多张棋盘格图片（至少 10-15 张），确保棋盘格在不同位置和角度。

1.2 代码实现

#include <opencv2/opencv.hpp>
#include <iostream>
#include <vector>using namespace cv;
using namespace std;int main() {// 棋盘格的尺寸Size boardSize(9, 6);float squareSize = 1.0f; // 棋盘格方块的物理尺寸（单位：米）// 存储棋盘格角点的三维坐标vector<vector<Point3f>> objectPoints;// 存储棋盘格角点的二维图像坐标vector<vector<Point2f>> imagePoints;// 生成棋盘格的三维坐标vector<Point3f> obj;for (int i = 0; i < boardSize.height; i++) {for (int j = 0; j < boardSize.width; j++) {obj.push_back(Point3f(j * squareSize, i * squareSize, 0));}}// 读取图像vector<String> imagePaths;glob("path/to/calibration_images/*.jpg", imagePaths);for (size_t i = 0; i < imagePaths.size(); i++) {Mat image = imread(imagePaths[i]);Mat gray;cvtColor(image, gray, COLOR_BGR2GRAY);// 查找棋盘格角点vector<Point2f> corners;bool found = findChessboardCorners(gray, boardSize, corners);if (found) {// 亚像素精确化cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER, 30, 0.001));imagePoints.push_back(corners);objectPoints.push_back(obj);// 绘制并显示角点drawChessboardCorners(image, boardSize, Mat(corners), found);imshow("Corners", image);waitKey(500);}}// 标定摄像头Mat cameraMatrix, distCoeffs;vector<Mat> rvecs, tvecs;double rms = calibrateCamera(objectPoints, imagePoints, gray.size(), cameraMatrix, distCoeffs, rvecs, tvecs);cout << "RMS error: " << rms << endl;cout << "Camera matrix: " << cameraMatrix << endl;cout << "Distortion coefficients: " << distCoeffs << endl;// 保存标定结果FileStorage fs("camera_calibration.yml", FileStorage::WRITE);fs << "camera_matrix" << cameraMatrix;fs << "distortion_coefficients" << distCoeffs;fs.release();return 0;
}

1.3 运行说明

将棋盘格图片放在 path/to/calibration_images/ 目录下。
运行程序后，标定结果将保存在 camera_calibration.yml 文件中。

2. Python 实现

2.1 准备工作

准备一个棋盘格（如 9x6 的棋盘格）。
拍摄多张棋盘格图片（至少 10-15 张），确保棋盘格在不同位置和角度。

2.2 代码实现

import cv2
import numpy as np
import glob# 棋盘格的尺寸
board_size = (9, 6)
square_size = 1.0  # 棋盘格方块的物理尺寸（单位：米）# 存储棋盘格角点的三维坐标
object_points = []
# 存储棋盘格角点的二维图像坐标
image_points = []# 生成棋盘格的三维坐标
objp = np.zeros((board_size[0] * board_size[1], 3), np.float32)
objp[:, :2] = np.mgrid[0:board_size[0], 0:board_size[1]].T.reshape(-1, 2)
objp *= square_size# 读取图像
images = glob.glob('path/to/calibration_images/*.jpg')for fname in images:img = cv2.imread(fname)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 查找棋盘格角点ret, corners = cv2.findChessboardCorners(gray, board_size, None)if ret:# 亚像素精确化corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1),(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001))image_points.append(corners2)object_points.append(objp)# 绘制并显示角点cv2.drawChessboardCorners(img, board_size, corners2, ret)cv2.imshow('Corners', img)cv2.waitKey(500)# 标定摄像头
ret, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(object_points, image_points, gray.shape[::-1], None, None)print("RMS error:", ret)
print("Camera matrix:\n", camera_matrix)
print("Distortion coefficients:\n", dist_coeffs)# 保存标定结果
np.savez('camera_calibration.npz', camera_matrix=camera_matrix, dist_coeffs=dist_coeffs)cv2.destroyAllWindows()