From bace0c98703b5d3b39dec0343dd9f48384872a51 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9D=B3=E5=92=8C=E9=A2=9C?= <2584851718@qq.com> Date: Wed, 23 Oct 2024 20:39:19 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?= =?UTF-8?q?=20Code?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Code/biaoding.py | 210 +++++++++++++++++++++++++++++++++++++++++++++++ Code/rectify.py | 55 +++++++++++++ Code/stereo.py | 48 +++++++++++ 3 files changed, 313 insertions(+) create mode 100644 Code/biaoding.py create mode 100644 Code/rectify.py create mode 100644 Code/stereo.py diff --git a/Code/biaoding.py b/Code/biaoding.py new file mode 100644 index 0000000..44523af --- /dev/null +++ b/Code/biaoding.py @@ -0,0 +1,210 @@ +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) diff --git a/Code/rectify.py b/Code/rectify.py new file mode 100644 index 0000000..2755c5e --- /dev/null +++ b/Code/rectify.py @@ -0,0 +1,55 @@ +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 = "left/left_WIN_20241023_14_54_55_Pro.jpg" +right_image_path = "right/right_WIN_20241023_14_54_55_Pro.jpg" +calibration_file = "calibration_parameters.yaml" + +# 调用函数进行图像矫正 +rectify_images(left_image_path, right_image_path, calibration_file) + diff --git a/Code/stereo.py b/Code/stereo.py new file mode 100644 index 0000000..97a5c41 --- /dev/null +++ b/Code/stereo.py @@ -0,0 +1,48 @@ +import cv2 +import os + +# 定义输入文件夹路径和输出文件夹路径 +input_folder = 'images' # 替换为你的输入文件夹路径 +output_folder_left = 'left' +output_folder_right = 'right' + +# 创建输出文件夹，如果不存在则创建 +if not os.path.exists(output_folder_left): + os.makedirs(output_folder_left) +if not os.path.exists(output_folder_right): + os.makedirs(output_folder_right) + +# 遍历输入文件夹中的所有图片 +for filename in os.listdir(input_folder): + if filename.endswith(".png") or filename.endswith(".jpg"): + # 构建图片的完整路径 + img_path = os.path.join(input_folder, filename) + + # 读取图片 + image = cv2.imread(img_path) + + if image is None: + print(f"无法读取图像文件: {filename}") + continue + + # 获取图片的高度和宽度 + height, width, _ = image.shape + + # 计算左右图像的宽度 + half_width = width // 2 + + # 切割出左半部分和右半部分图像 + left_image = image[:, :half_width] + right_image = image[:, half_width:] + + # 构建保存路径 + left_image_path = os.path.join(output_folder_left, f"left_{filename}") + right_image_path = os.path.join(output_folder_right, f"right_{filename}") + + # 保存左右图像 + cv2.imwrite(left_image_path, left_image) + cv2.imwrite(right_image_path, right_image) + + print(f"已保存：{left_image_path} 和 {right_image_path}") + +print("所有图像已处理完成！")