import cv2 import os import numpy as np import itertools import yaml # 定义文件夹路径 left_folder = "left" right_folder = "right" # 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,alpha=0) 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.jpg",combined_img) cv2.waitKey(0) cv2.destroyAllWindows() # 加载并矫正示例图像 example_idx = 3 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)