先上传20张用摄像头拍摄的图
将这20张摄像头拍摄的图片放入同一个文件夹中，当然 你也可以自己把电脑作为屏幕拍摄不低于十张的图
import cv2 import numpy as np import glob from numpy import array as matrix, arange # 找棋盘格角点 # 阈值 criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # w h分别是棋盘格模板长边和短边规格（角点个数） w = 9 #注意这个是横向想的角点个数 你可以理解为第一行黑方块与第二行黑方块交点的个数 h = 6 #纵向同理可得 # 世界坐标系中的棋盘格点,例如(0,0,0), (1,0,0), (2,0,0) ....,(8,5,0)，去掉Z坐标，记为二维矩阵，认为在棋盘格这个平面上Z=0 objp = np.zeros((w * h, 3), np.float32) # 构造0矩阵，88行3列，用于存放角点的世界坐标 objp[:, :2] = np.mgrid[0:w, 0:h].T.reshape(-1, 2) # 三维网格坐标划分 # 储存棋盘格角点的世界坐标和图像坐标对 objpoints = [] # 在世界坐标系中的三维点 imgpoints = [] # 在图像平面的二维点 record = [] images = glob.glob('./CarND-Advanced-Lane-Lines-master/camera_cal/*.jpg') for fname in images: img = cv2.imread(fname) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 粗略找到棋盘格角点 这里找到的是这张图片中角点的亚像素点位置，共11×8 = 88个点，gray必须是8位灰度或者彩色图，（w,h）为角点规模 ret, corners = cv2.findChessboardCorners(gray, (w, h)) # 如果找到足够点对，将其存储起来 if ret == True: record.append(fname) # 精确找到角点坐标 corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria) # 将正确的objp点放入objpoints中 objpoints.append(objp) imgpoints.append(corners) # 将角点在图像上显示 cv2.drawChessboardCorners(img, (w, h), corners, ret) cv2.imshow('findCorners', img) cv2.waitKey() cv2.destroyAllWindows() # 标定 返回标定结果、相机的内参数矩阵、畸变系数、旋转矩阵和平移向量 ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None) print('ret:', ret) print('内参矩阵:', mtx) print('畸变系数:', dist) print('旋转矩阵:', rvecs) print('平移向量:', tvecs) # 去畸变 img2 = cv2.imread('./CarND-Advanced-Lane-Lines-master/camera_cal/calibration5.jpg') h, w = img2.shape[:2] newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h)) # 自由比例参数 dst = cv2.undistort(img2, mtx, dist, None, newcameramtx) # 根据前面ROI区域裁剪图片 # x, y, w, h = roi # dst = dst[y:y + h, x:x + w] cv2.imshow('fin', dst) cv2.imwrite('./fin.png', dst) cv2.waitKey() cv2.destroyAllWindows() # 反投影误差 total_error = 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 print("total error: ", total_error / len(objpoints))
