OpenCV-Python实战|相机校准全解:棋盘格 vs 圆形网格标定(原理+完整代码)

pythonimport cv2import numpy as npimport glob# 1. 配置棋盘格参数（内角点行列数）w, h = 9, 6# 物理坐标：假设每个方格边长20mmobjp = np.zeros((w*h,3), np.float32)objp[:,:2] = np.mgrid[0:w,0:h].T.reshape(-1,2) * 20# 存储3D物理点、2D图像点obj_points = []img_points = []# 读取所有标定图片images = glob.glob("chessboard/*.jpg")for fname in images:img = cv2.imread(fname)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 检测棋盘格内角点ret, corners = cv2.findChessboardCorners(gray, (w,h), None)if ret:obj_points.append(objp)# 亚像素精确化，提升精度criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)corners2 = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria)img_points.append(corners2)# 绘制角点可视化cv2.drawChessboardCorners(img, (w,h), corners2, ret)cv2.imshow("chessboard", img)cv2.waitKey(200)cv2.destroyAllWindows()# 2. 相机标定，求解内参、畸变系数ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, gray.shape[::-1], None, None)print("相机内参矩阵：")print(mtx)print("畸变系数：")print(dist)# 3. 畸变矫正img = cv2.imread("test.jpg")h, w = img.shape[:2]new_mtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))dst = cv2.undistort(img, mtx, dist, None, new_mtx)# 裁剪有效区域x,y,w,h = roidst = dst[y:y+h, x:x+w]cv2.imwrite("undistort_result.jpg", dst)cv2.imshow("origin", img)cv2.imshow("undistort", dst)cv2.waitKey(0)cv2.destroyAllWindows()

pythonimport cv2import numpy as npimport glob# 1. 圆形网格参数：11行4列非对称圆点w, h = 11, 4objp = np.zeros((w*h,3), np.float32)objp[:,:2] = np.mgrid[0:w,0:h].T.reshape(-1,2) * 15obj_points = []img_points = []images = glob.glob("circles/*.jpg")for fname in images:img = cv2.imread(fname)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 非对称圆形网格检测，高精度标定ret, corners = cv2.findCirclesGrid(gray, (w,h), flags=cv2.CALIB_CB_ASYMMETRIC_GRID)if ret:obj_points.append(objp)# 亚像素优化criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)corners2 = cv2.cornerSubPix(gray, corners, (5,5), (-1,-1), criteria)img_points.append(corners2)cv2.drawChessboardCorners(img, (w,h), corners2, ret)cv2.imshow("circles_grid", img)cv2.waitKey(200)cv2.destroyAllWindows()# 2. 标定求解参数ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, gray.shape[::-1], None, None)print("高精度标定-内参矩阵：")print(mtx)print("高精度标定-畸变系数：")print(dist)# 3. 畸变矫正img = cv2.imread("test.jpg")h, w = img.shape[:2]new_mtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))dst = cv2.undistort(img, mtx, dist, None, new_mtx)x,y,w,h = roidst = dst[y:y+h, x:x+w]cv2.imwrite("circles_undistort.jpg", dst)cv2.imshow("origin", img)cv2.imshow("circles_undistort", dst)cv2.waitKey(0)cv2.destroyAllWindows()