import cv2 import numpy as np chessboard_size = (7,10) # 棋盘格行列数 objp = np.zeros((np.prod(chessboard_size), 3), np.float32) # 内部点的坐标 objp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2) img = cv2.imread('C:/yingxiang/biaoding.png') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None) if ret == True: cv2.drawChessboardCorners(img, chessboard_size, corners, ret) ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera([objp], [corners], gray.shape[::-1], None, None) # 打印相机内部参数和畸变系数 print("相机内部参数：") print(mtx) print("畸变系数：") print(dist) # 打印外部参数 print("旋转向量：") print(rvecs) print("平移向量：") print(tvecs)这段代码怎么改可以打印出内部参数和畸变参数 以及旋转向量和平移向量

这段代码已经可以打印出相机内部参数和畸变系数了，只需要加上打印旋转向量和平移向量的代码即可。修改后的代码如下所示：

import cv2 
import numpy as np 

chessboard_size = (7,10) # 棋盘格行列数 
objp = np.zeros((np.prod(chessboard_size), 3), np.float32) # 内部点的坐标 
objp[:, :2] = np.mgrid[0:chessboard_size[0], 0:chessboard_size[1]].T.reshape(-1, 2) 

img = cv2.imread('C:/yingxiang/biaoding.png') 
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
ret, corners = cv2.findChessboardCorners(gray, chessboard_size, None) 

if ret == True: 
    cv2.drawChessboardCorners(img, chessboard_size, corners, ret) 
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera([objp], [corners], gray.shape[::-1], None, None) 

    # 打印相机内部参数和畸变系数 
    print("相机内部参数：") 
    print(mtx) 
    print("畸变系数：") 
    print(dist) 

    # 打印外部参数 
    print("旋转向量：") 
    print(rvecs) 
    print("平移向量：") 
    print(tvecs)

注意，需要确保棋盘格被成功检测到，才能正确计算相机的内部参数、畸变系数、旋转向量和平移向量。