两张图片之间存在平移量，尝试找出平移量

import cv2 import numpy as np # 读取两张照片 image1 = cv2.imread('im1.jpg', 0) # 灰度图像 image2 = cv2.imread('im2.jpg', 0) # 使用ORB特征检测器和描述符来找到关键点和描述符 orb = cv2.ORB_create() keypoints1, descriptors1 = orb.detectAndCompute(image1, None) keypoints2, descriptors2 = orb.detectAndCompute(image2, None) # 使用暴力匹配器进行特征匹配 bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) matches = bf.match(descriptors1, descriptors2) # 根据匹配结果计算出两张图像之间的平移矩阵 if len(matches) > 4: # 至少需要4个匹配点 src_pts = np.float32([keypoints1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2) dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2) M, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) dx = M[0, 2] # 水平方向的平移量 dy = M[1, 2] # 垂直方向的平移量 print("水平方向的平移量:", dx) print("垂直方向的平移量:", dy) else: print("匹配点太少，无法计算平移量") # 绘制匹配的关键点 matched_image = cv2.drawMatches(image1, keypoints1, image2, keypoints2, matches[:10], None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS) cv2.namedWindow("Matches",0); cv2.resizeWindow("Matches", 640, 480); cv2.imshow("Matches", matched_image) cv2.waitKey(0) cv2.destroyAllWindows()