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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)

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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)

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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！")