上传文件至 Code
This commit is contained in:
parent
433359be75
commit
bace0c9870
210
Code/biaoding.py
Normal file
210
Code/biaoding.py
Normal file
@ -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)
|
||||
55
Code/rectify.py
Normal file
55
Code/rectify.py
Normal file
@ -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)
|
||||
|
||||
48
Code/stereo.py
Normal file
48
Code/stereo.py
Normal file
@ -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！")
|
||||
Loading…
x
Reference in New Issue
Block a user