1 import cv2 as cv
2 import numpy as np
3
4
5 # def draw_keypoints(img, keypoints):
6 # for kp in keypoints:
7 # x, y = kp.pt
8 # cv.circle(img, (int(x), int(y)), 2, (0, 255, 0))
9
10 # 两张键盘图片，用于特征匹配
11 img1 = cv.imread('../images/keyboard1.jpg')
12 img2 = cv.imread('../images/keyboard2.jpg')
13 gray1 = cv.cvtColor(img1, cv.COLOR_BGR2GRAY)
14 gray2 = cv.cvtColor(img2, cv.COLOR_BGR2GRAY)
15 orb = cv.ORB_create()
16 kp1, des1 = orb.detectAndCompute(gray1, None) # 计算特征点和描述符
17 kp2, des2 = orb.detectAndCompute(gray2, None)
18 bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True) # 建立匹配关系
19 matches = bf.match(des1, des2) # 匹配描述符
20 matches = sorted(matches, key=lambda x:x.distance) # 根据距离来排序
21 # img3 = cv.drawMatches(gray1, kp1, gray2, kp2, matches[:10], gray2, flags=2)
22 img3 = cv.drawMatches(img1, kp1, img2, kp2, matches[:10], img2, flags=2)
23 cv.imshow('img3', img3)
24 cv.waitKey(0)
25 cv.destroyAllWindows()
效果：
本来还想看看SIFT和SURF的效果，但是模块没有了，也有说安装opencv-contrib-python的，但是安装后好像还要操作一下，也没写清楚
参考链接：
OpenCV+Python特征提取算法与图像描述符之SIFT / SURF / ORB
OpenCV探索之路（二十三）：特征检测和特征匹配方法汇总