【摄像头标定】双目摄像头标定及矫正-opencv（python）
双目摄像头标定及矫正
- 棋盘格标定板
- 标定
- 矫正
棋盘格标定板
本文使用棋盘格标定板，可以到这篇博客中下载：https://blog.csdn.net/qq_39330520/article/details/107864568
标定
要进行标定首先需要双目拍的棋盘格图片，20张左右，由于本文的双目摄像头嵌入在开发板底板中，并且使用的是ros进行开发，所以对于大部分人拍照这里是没有参考价值的，对于也是使用ros开发的小伙伴，需要写一个节点发布双目摄像头的图像数据，然后再写一个节点订阅双目摄像头数据进行拍照保存。本文重点也不在拍照，对于其他小伙伴可以直接搜索一些适用的拍照方法，只要能获得到图片即可。
左摄像头图片如下：
右摄像头图片如下：
由于摄像头底层代码有问题，所以图像很暗，但不影响标定。
标定代码如下：
import cv2 import os import numpy as np import itertools import yaml # 定义文件夹路径 left_folder = "C:/new_pycharm_project/yolov10-main/shuangmu_left_pic" right_folder = "C:/new_pycharm_project/yolov10-main/shuangmu_right_pic" # 获取图像文件列表并排序 left_images = sorted(os.listdir(left_folder)) right_images = sorted(os.listdir(right_folder)) # 确保左右相机图像数量一致 assert len(left_images) == len(right_images), "左右相机图像数量不一致" # 加载两个摄像头图片文件夹并将里面的彩图转换为灰度图 def load_images(folder, images): img_list = [] for img_name in images: img_path = os.path.join(folder, img_name) frame = cv2.imread(img_path) if frame is not None: gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) img_list.append((frame, gray)) else: print(f"无法读取图像: {img_path}") return img_list # 检测棋盘格角点 def get_corners(imgs, pattern_size): corners = [] for frame, gray in imgs: ret, c = cv2.findChessboardCorners(gray, pattern_size) #ret 表示是否成功找到棋盘格角点，c 是一个数组，包含了检测到的角点的坐标 if not ret: print("未能检测到棋盘格角点") continue c = cv2.cornerSubPix(gray, c, (5, 5), (-1, -1), (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)) #cv2.cornerSubPix 函数用于提高棋盘格角点的精确度，对初始检测到的角点坐标 c 进行优化 corners.append(c) #将优化后的角点坐标 c 添加到 corners 列表中 # 绘制角点并显示 vis = frame.copy() cv2.drawChessboardCorners(vis, pattern_size, c, ret) new_size = (1280, 800) resized_img = cv2.resize(vis, new_size) cv2.imshow('Corners', resized_img) cv2.waitKey(150) return corners # 相机标定 def calibrate_camera(object_points, corners, imgsize): cm_input = np.eye(3, dtype=np.float32) ret = cv2.calibrateCamera(object_points, corners, imgsize, cm_input, None) return ret def save_calibration_to_yaml(file_path, cameraMatrix_l, distCoeffs_l, cameraMatrix_r, distCoeffs_r, R, T, E, F): data = { 'camera_matrix_left': { 'rows': 3, 'cols': 3, 'dt': 'd', 'data': cameraMatrix_l.flatten().tolist() }, 'dist_coeff_left': { 'rows': 1, 'cols': 5, 'dt': 'd', 'data': distCoeffs_l.flatten().tolist() }, 'camera_matrix_right': { 'rows': 3, 'cols': 3, 'dt': 'd', 'data': cameraMatrix_r.flatten().tolist() }, 'dist_coeff_right': { 'rows': 1, 'cols': 5, 'dt': 'd', 'data': distCoeffs_r.flatten().tolist() }, 'R': { 'rows': 3, 'cols': 3, 'dt': 'd', 'data': R.flatten().tolist() }, 'T': { 'rows': 3, 'cols': 1, 'dt': 'd', 'data': T.flatten().tolist() }, 'E': { 'rows': 3, 'cols': 3, 'dt': 'd', 'data': E.flatten().tolist() }, 'F': { 'rows': 3, 'cols': 3, 'dt': 'd', 'data': F.flatten().tolist() } } with open(file_path, 'w') as file: yaml.dump(data, file, default_flow_style=False) print(f"Calibration parameters saved to {file_path}") img_left = load_images(left_folder, left_images) #img_left是个列表，存放左摄像头所有的灰度图片。 img_right = load_images(right_folder, right_images) pattern_size = (8, 5) corners_left = get_corners(img_left, pattern_size) #corners_left的长度表示检测到棋盘格角点的图像数量。corners_left[i] 和 corners_right[i] 中存储了第 i 张图像检测到的棋盘格角点的二维坐标。 corners_right = get_corners(img_right, pattern_size) cv2.destroyAllWindows() # 断言，确保所有图像都检测到角点 assert len(corners_left) == len(img_left), "有图像未检测到左相机的角点" assert len(corners_right) == len(img_right), "有图像未检测到右相机的角点" # 准备标定所需数据 points = np.zeros((8 * 5, 3), dtype=np.float32) #创建40 行 3 列的零矩阵，用于存储棋盘格的三维坐标点。棋盘格的大小是 8 行 5 列，40 个角点。数据类型为 np.float32，这是一张图的，因为一个角点对应一个三维坐标 points[:, :2] = np.mgrid[0:8, 0:5].T.reshape(-1, 2) * 21 #给这些点赋予实际的物理坐标，* 21 是因为每个棋盘格的大小为 21mm object_points = [points] * len(corners_left) #包含了所有图像中棋盘格的三维物理坐标点 points。这里假设所有图像中棋盘格的物理坐标是相同的，因此用 points 复制 len(corners_left) 次。 imgsize = img_left[0][1].shape[::-1] #img_left[0] 是左相机图像列表中的第一张图像。img_left[0][1] 是该图像的灰度图像。shape[::-1] 取灰度图像的宽度和高度，并反转顺序，以符合 calibrateCamera 函数的要求。 print('开始左相机标定') ret_l = calibrate_camera(object_points, corners_left, imgsize) #object_points表示标定板上检测到的棋盘格角点的三维坐标；corners_left[i]表示棋盘格角点在图像中的二维坐标；imgsize表示图像大小 retval_l, cameraMatrix_l, distCoeffs_l, rvecs_l, tvecs_l = ret_l[:5] #返回值里就包含了标定的参数 print('开始右相机标定') ret_r = calibrate_camera(object_points, corners_right, imgsize) retval_r, cameraMatrix_r, distCoeffs_r, rvecs_r, tvecs_r = ret_r[:5] # 立体标定，得到左右相机的外参：旋转矩阵、平移矩阵、本质矩阵、基本矩阵 print('开始立体标定') criteria_stereo = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-5) ret_stereo = cv2.stereoCalibrate(object_points, corners_left, corners_right, cameraMatrix_l, distCoeffs_l, cameraMatrix_r, distCoeffs_r, imgsize, criteria=criteria_stereo, flags=cv2.CALIB_FIX_INTRINSIC) ret, _, _, _, _, R, T, E, F = ret_stereo # 输出结果 print("左相机内参:\n", cameraMatrix_l) print("左相机畸变系数:\n", distCoeffs_l) print("右相机内参:\n", cameraMatrix_r) print("右相机畸变系数:\n", distCoeffs_r) print("旋转矩阵 R:\n", R) print("平移向量 T:\n", T) print("本质矩阵 E:\n", E) print("基本矩阵 F:\n", F) print("标定完成") # 保存标定结果 save_calibration_to_yaml('calibration_parameters.yaml', cameraMatrix_l, distCoeffs_l, cameraMatrix_r, distCoeffs_r, R, T, E, F) # 计算重投影误差 def compute_reprojection_errors(objpoints, imgpoints, rvecs, tvecs, mtx, dist): total_error = 0 total_points = 0 for i in range(len(objpoints)): imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist) error = cv2.norm(imgpoints[i], imgpoints2, cv2.NORM_L2) / len(imgpoints2) total_error += error total_points += len(imgpoints2) mean_error = total_error / total_points return mean_error # 计算并打印左相机和右相机的重投影误差 print("左相机重投影误差: ", compute_reprojection_errors(object_points, corners_left, rvecs_l, tvecs_l, cameraMatrix_l, distCoeffs_l)) print("右相机重投影误差: ", compute_reprojection_errors(object_points, corners_right, rvecs_r, tvecs_r, cameraMatrix_r, distCoeffs_r)) # 立体矫正和显示 def stereo_rectify_and_display(img_l, img_r, cameraMatrix_l, distCoeffs_l, cameraMatrix_r, distCoeffs_r, R, T): img_size = img_l.shape[:2][::-1] # 立体校正 R1, R2, P1, P2, Q, _, _ = cv2.stereoRectify(cameraMatrix_l, distCoeffs_l, cameraMatrix_r, distCoeffs_r, img_size, R, T) map1x, map1y = cv2.initUndistortRectifyMap(cameraMatrix_l, distCoeffs_l, R1, P1, img_size, cv2.CV_32FC1) map2x, map2y = cv2.initUndistortRectifyMap(cameraMatrix_r, distCoeffs_r, R2, P2, img_size, cv2.CV_32FC1) # 图像矫正 rectified_img_l = cv2.remap(img_l, map1x, map1y, cv2.INTER_LINEAR) rectified_img_r = cv2.remap(img_r, map2x, map2y, cv2.INTER_LINEAR) # 显示矫正后的图像 combined_img = np.hstack((rectified_img_l, rectified_img_r)) cv2.imshow('Rectified Images', combined_img) cv2.imwrite("stereo_jiaozheng.png",combined_img) cv2.waitKey(0) cv2.destroyAllWindows() # 加载并矫正示例图像 example_idx = 0 img_l = img_left[example_idx][0] img_r = img_right[example_idx][0] stereo_rectify_and_display(img_l, img_r, cameraMatrix_l, distCoeffs_l, cameraMatrix_r, distCoeffs_r, R, T)标定完成后会显示一张矫正后的图像。代码重要的地方都给出了注释，主要流程就是分别对左右相机进行标定，然后对两个相机进行联合标定（立体标定），最后得到的参数会保存到yaml文件中：
--- camera_matrix_left: rows: 3 cols: 3 dt: d data: - 531.7200210313852 - 0 - 642.0170539101581 - 0 - 533.6471323984354 - 420.4033045027399 - 0 - 0 - 1 dist_coeff_left: rows: 1 cols: 5 dt: d data: - -0.1670007968198256 - 0.04560028196221921 - 0.0011938487550718078 - -0.000866537907860316 - -0.00805042100882671 camera_matrix_right: rows: 3 cols: 3 dt: d data: - 525.9058345430292 - 0 - 628.7761214904813 - 0 - 528.2078922687268 - 381.8575789135264 - 0 - 0 - 1 dist_coeff_right: rows: 1 cols: 5 dt: d data: - -0.15320688387351564 - 0.03439886104586617 - -0.0003732170677440928 - -0.0024909528446780153 - -0.005138400994014348 R: rows: 3 cols: 3 dt: d data: - 0.9999847004116569 - -0.00041406631566505544 - 0.005516112008926496 - 0.0003183979929468572 - 0.9998497209492369 - 0.017333036100216304 - -0.005522460079247196 - -0.017331014592906722 - 0.9998345554979852 T: rows: 3 cols: 1 dt: d data: - -55.849260376265015 - 2.1715925432988743 - 0.46949841441903933 E: rows: 3 cols: 3 dt: d data: - -0.012142020481601675 - -0.5070637607007459 - 2.1630954322858496 - 0.1610659204031652 - -0.9681187500627653 - 55.84261022903612 - -2.189341611238282 - -55.83996821910631 - -0.9800159939787676 F: rows: 3 cols: 3 dt: d data: - -2.4239149875305048e-8 - -0.0000010085973649868748 - 0.0027356495714066175 - 3.2013501988129346e-7 - -0.0000019172863951399893 - 0.05961765359743852 - -0.002405523166325036 - -0.057046539240958545 - 1分别是左相机的内参矩阵、畸变系数，右相机的内参矩阵和畸变系数，两个相机之间的旋转矩阵、平移矩阵、本质矩阵、基本矩阵。
矫正
import cv2 import yaml import numpy as np # 定义函数读取标定数据 def read_calibration_data(calibration_file): with open(calibration_file, 'r') as f: calib_data = yaml.safe_load(f) cameraMatrix_l = np.array(calib_data['camera_matrix_left']['data']).reshape(3, 3) distCoeffs_l = np.array(calib_data['dist_coeff_left']['data']) cameraMatrix_r = np.array(calib_data['camera_matrix_right']['data']).reshape(3, 3) distCoeffs_r = np.array(calib_data['dist_coeff_right']['data']) R = np.array(calib_data['R']['data']).reshape(3, 3) T = np.array(calib_data['T']['data']).reshape(3, 1) return cameraMatrix_l, distCoeffs_l, cameraMatrix_r, distCoeffs_r, R, T # 定义函数对图像进行矫正 def rectify_images(left_image_path, right_image_path, calibration_file): # 读取标定数据 cameraMatrix_l, distCoeffs_l, cameraMatrix_r, distCoeffs_r, R, T = read_calibration_data(calibration_file) # 读取左右图像 img_left = cv2.imread(left_image_path) img_right = cv2.imread(right_image_path) # 获取图像尺寸（假设左右图像尺寸相同） img_size = img_left.shape[:2][::-1] # 立体校正 R1, R2, P1, P2, Q, roi1, roi2 = cv2.stereoRectify(cameraMatrix_l, distCoeffs_l, cameraMatrix_r, distCoeffs_r, img_size, R, T) # 计算映射参数 map1_l, map2_l = cv2.initUndistortRectifyMap(cameraMatrix_l, distCoeffs_l, R1, P1, img_size, cv2.CV_32FC1) map1_r, map2_r = cv2.initUndistortRectifyMap(cameraMatrix_r, distCoeffs_r, R2, P2, img_size, cv2.CV_32FC1) # 应用映射并显示结果 rectified_img_l = cv2.remap(img_left, map1_l, map2_l, cv2.INTER_LINEAR) rectified_img_r = cv2.remap(img_right, map1_r, map2_r, cv2.INTER_LINEAR) # 合并图像显示 combined_img = np.hstack((rectified_img_l, rectified_img_r)) cv2.imshow('Rectified Images', combined_img) cv2.waitKey(0) cv2.destroyAllWindows() # 设置路径和文件名 left_image_path = "C:/new_pycharm_project/yolov10-main/shuangmu_left_pic/left_image0.png" right_image_path = "C:/new_pycharm_project/yolov10-main/shuangmu_right_pic/right_image0.png" calibration_file = "C:/new_pycharm_project/yolov10-main/calibration_parameters.yaml" # 调用函数进行图像矫正 rectify_images(left_image_path, right_image_path, calibration_file)结果对比：
第一张是矫正前的左右相机图像，第二张是矫正后的。可以看到去除了畸变，并且两图像基本出于同一水平线。
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