--- name: camera-calibration description: OpenCV4 相机标定技能 - 单目/双目标定、畸变校正、立体匹配、深度估计、3D 重建 user-invocable: true argument-hint: 相机标定 OR 畸变校正 OR 立体匹配 OR 深度估计 OR 3D重建 OR 内参 OR 外参 --- # OpenCV4 Camera Calibration Skill > 相机标定与立体视觉完整指南 --- ## 何时使用 当需要以下帮助时使用此技能： - 单目相机标定（内参） - 双目相机标定（内外参） - 畸变校正 - 立体校正（极线校正） - 立体匹配和深度估计 - 3D 重建 --- ## 快速参考 ### 标定板生成 ```python import cv2 import numpy as np import glob # 生成棋盘格标定板 def generate_chessboard(size=(9, 6), square_size=1.0, save_path='chessboard.png'): objp = np.zeros((size[0]*size[1], 3), np.float32) objp[:, :2] = np.mgrid[0:size[0], 0:size[1]].T.reshape(-1, 2) * square_size fig, ax = plt.subplots(figsize=(8, 6)) ax.set_xlim(0, size[0]) ax.set_ylim(0, size[1]) ax.set_aspect('equal') ax.set_facecolor('white') for i in range(size[0] + 1): ax.axvline(i, color='black', linewidth=1) for i in range(size[1] + 1): ax.axhline(i, color='black', linewidth=1) plt.axis('off') plt.savefig(save_path, dpi=150, bbox_inches='tight', facecolor='white', edgecolor='none') plt.close() # 或使用 OpenCV 打印标定板 # 国际象棋：9x6 角点，80mm 格子 # 圆形标定板：4x11 圆点阵列 ``` ### 单目相机标定 ```python import cv2 import numpy as np import glob # 标定板参数 objp = np.zeros((6*9, 3), np.float32) objp[:, :2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2) objp *= 25 # 格子大小 mm # 存储所有图像的点 objpoints = [] # 3D 点 imgpoints = [] # 2D 点 images = glob.glob('calibration/*.jpg') img_size = None for fname in images: img = cv2.imread(fname) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img_size = gray.shape[::-1] # 查找棋盘格角点 ret, corners = cv2.findChessboardCorners(gray, (9, 6), None) if ret: objpoints.append(objp) # 亚像素精度优化 criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria) imgpoints.append(corners2) # 可视化 cv2.drawChessboardCorners(img, (9, 6), corners2, ret) cv2.imshow('Corners', img) cv2.waitKey(500) cv2.destroyAllWindows() # 标定 ret, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera( objpoints, imgpoints, img_size, None, None) print(f"Camera Matrix:\n{camera_matrix}") print(f"Distortion Coefficients:\n{dist_coeffs}") # 保存标定结果 np.savez('calibration.npz', camera_matrix=camera_matrix, dist_coeffs=dist_coeffs, rvecs=rvecs, tvecs=tvecs) ``` ### 畸变校正 ```python # 加载标定结果 data = np.load('calibration.npz') camera_matrix = data['camera_matrix'] dist_coeffs = data['dist_coeffs'] # 读取图像 img = cv2.imread('test.jpg') h, w = img.shape[:2] # 方法1：矫正映射 new_camera_matrix, roi = cv2.getOptimalNewCameraMatrix( camera_matrix, dist_coeffs, (w, h), 1, (w, h)) mapx, mapy = cv2.initUndistortRectifyMap( camera_matrix, dist_coeffs, None, new_camera_matrix, (w, h), 5) undistorted = cv2.remap(img, mapx, mapy, cv2.INTER_LINEAR) # 方法2：一步校正 undistorted = cv2.undistort(img, camera_matrix, dist_coeffs, None, new_camera_matrix) # 裁剪 ROI 区域 x, y, w, h = roi undistorted = undistorted[y:y+h, x:x+w] ``` ### 双目相机标定 ```python import cv2 import numpy as np import glob # 左右相机标定板点 objp = np.zeros((6*9, 3), np.float32) objp[:, :2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2) * 25 objpoints_l, imgpoints_l = [], [] objpoints_r, imgpoints_r = [], [] # 读取左右图像 images_l = sorted(glob.glob('stereo/left/*.jpg')) images_r = sorted(glob.glob('stereo/right/*.jpg')) for fname_l, fname_r in zip(images_l, images_r): img_l = cv2.imread(fname_l) img_r = cv2.imread(fname_r) gray_l = cv2.cvtColor(img_l, cv2.COLOR_BGR2GRAY) gray_r = cv2.cvtColor(img_r, cv2.COLOR_BGR2GRAY) ret_l, corners_l = cv2.findChessboardCorners(gray_l, (9, 6), None) ret_r, corners_r = cv2.findChessboardCorners(gray_r, (9, 6), None) if ret_l and ret_r: objpoints_l.append(objp) objpoints_r.append(objp) # 亚像素优化 corners_l = cv2.cornerSubPix(gray_l, corners_l, (11,11), (-1,-1), (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)) corners_r = cv2.cornerSubPix(gray_r, corners_r, (11,11), (-1,-1), (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)) imgpoints_l.append(corners_l) imgpoints_r.append(corners_r) # 分别标定左右相机 ret_l, mtx_l, dist_l, _, _ = cv2.calibrateCamera(objpoints_l, imgpoints_l, gray_l.shape[::-1], None, None) ret_r, mtx_r, dist_r, _, _ = cv2.calibrateCamera(objpoints_r, imgpoints_r, gray_r.shape[::-1], None, None) # 双目标定 ret, mtx_l, dist_l, mtx_r, dist_r, R, T, E, F = cv2.stereoCalibrate( objpoints_l, imgpoints_l, imgpoints_r, mtx_l, dist_l, mtx_r, dist_r, gray_l.shape[::-1], flags=cv2.CALIB_FIX_INTRINSIC) print(f"Rotation R:\n{R}") print(f"Translation T:\n{T}") ``` ### 立体校正和匹配 ```python # 立体校正 R1, R2, P1, P2, Q, _, _ = cv2.stereoRectify( mtx_l, dist_l, mtx_r, dist_r, gray_l.shape[::-1], R, T) # 计算映射表 map1_l, map2_l = cv2.initUndistortRectifyMap(mtx_l, dist_l, R1, P1, gray_l.shape[::-1], cv2.CV_32FC2) map1_r, map2_r = cv2.initUndistortRectifyMap(mtx_r, dist_r, R2, P2, gray_r.shape[::-1], cv2.CV_32FC2) # 校正图像 rect_l = cv2.remap(img_l, map1_l, map2_l, cv2.INTER_LINEAR) rect_r = cv2.remap(img_r, map1_r, map2_r, cv2.INTER_LINEAR) # SGBM 立体匹配 stereo = cv2.StereoSGBM_create(minDisparity=0, numDisparities=64, blockSize=9) disparity = stereo.compute(rect_l, rect_r) # 转换为深度图 depth = (mtx_l[0,0] * T[0,0]) / disparity ``` --- ## ROS2 集成 ```python # ROS2 相机标定 from rclpy.node import Node from sensor_msgs.msg import Image from cv_bridge import CvBridge import cv2 import numpy as np class CameraCalibrator(Node): def __init__(self): super().__init__('camera_calibrator') self.bridge = CvBridge() self.objpoints = [] self.imgpoints = [] self.objp = np.zeros((6*9, 3), np.float32) self.objp[:, :2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2) self.subscription = self.create_subscription( Image, '/camera/image_raw', self.calibration_callback, 10) def calibration_callback(self, msg): img = self.bridge.imgmsg_to_cv2(msg, 'bgr8') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, corners = cv2.findChessboardCorners(gray, (9, 6), None) if ret: corners2 = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)) self.objpoints.append(self.objp) self.imgpoints.append(corners2) cv2.drawChessboardCorners(img, (9, 6), corners2, ret) cv2.imshow('Calibration', img) cv2.waitKey(1) def calibrate(self, img_size): ret, mtx, dist, _, _ = cv2.calibrateCamera( self.objpoints, self.imgpoints, img_size, None, None) return mtx, dist ``` --- ## 最佳实践 1. **标定板准备**： - 打印在高精度纸上，或使用显示器显示 - 确保标定板平整无褶皱 - 格子尺寸精确（常用 25mm 或 30mm） 2. **标定图像采集**： - 至少 15-20 张图像 - 覆盖整个视野（中心和边缘） - 不同角度和距离（俯仰、偏航、滚动） - 光照均匀，避免反光 3. **标定质量评估**： - 重投影误差 < 0.5 像素 - 检查各图像的角点检测精度 - 剔除误差大的图像重新标定 4. **双目标定**： - 确保左右相机同步采集 - 两相机在同一平面上（简化标定） - 基线距离影响深度精度 --- ## 相关技能 - [opencv-image-processing](./image-processing) - 图像处理基础 - [opencv-depth-estimation](./depth-estimation) - 深度估计