网站模版怎么上传到空间,池州网站建设怎么样,建网站网,justnews wordpress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#xff0c;可以到这篇博客中下载#xff1a;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#xff0c;20张左右#xff0c;… 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)continuec 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, dtypenp.float32)ret cv2.calibrateCamera(object_points, corners, imgsize, cm_input, None)return retdef 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_styleFalse)print(fCalibration 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), dtypenp.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 21mmobject_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, criteriacriteria_stereo,flagscv2.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 0total_points 0for 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 errortotal_points len(imgpoints2)mean_error total_error / total_pointsreturn 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: 3cols: 3dt: ddata:- 531.7200210313852- 0- 642.0170539101581- 0- 533.6471323984354- 420.4033045027399- 0- 0- 1
dist_coeff_left:rows: 1cols: 5dt: ddata:- -0.1670007968198256- 0.04560028196221921- 0.0011938487550718078- -0.000866537907860316- -0.00805042100882671
camera_matrix_right:rows: 3cols: 3dt: ddata:- 525.9058345430292- 0- 628.7761214904813- 0- 528.2078922687268- 381.8575789135264- 0- 0- 1
dist_coeff_right:rows: 1cols: 5dt: ddata:- -0.15320688387351564- 0.03439886104586617- -0.0003732170677440928- -0.0024909528446780153- -0.005138400994014348
R:rows: 3cols: 3dt: ddata:- 0.9999847004116569- -0.00041406631566505544- 0.005516112008926496- 0.0003183979929468572- 0.9998497209492369- 0.017333036100216304- -0.005522460079247196- -0.017331014592906722- 0.9998345554979852
T:rows: 3cols: 1dt: ddata:- -55.849260376265015- 2.1715925432988743- 0.46949841441903933
E:rows: 3cols: 3dt: ddata:- -0.012142020481601675- -0.5070637607007459- 2.1630954322858496- 0.1610659204031652- -0.9681187500627653- 55.84261022903612- -2.189341611238282- -55.83996821910631- -0.9800159939787676
F:rows: 3cols: 3dt: ddata:- -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。
