python利用opencv实现SIFT特征提取与匹配

pip uninstall opencv-python
pip uninstall opencv-contrib-python

pip install opencv_python==3.4.2.16 
pip install opencv-contrib-python==3.4.2.16

#!/usr/bin/env python3
# -*- coding:utf-8 -*-
u'''
Created on 2019年6月14日
@author: wuluo
'''
__author__ = 'wuluo'
__version__ = '1.0.0'
__company__ = u'重庆交大'
__updated__ = '2019-06-14'
from matplotlib import pyplot as plt
from imagedt.decorator import time_cost
import cv2
print('cv version: ', cv2.__version__)

def bgr_rgb(img):
 (r, g, b) = cv2.split(img)
 return cv2.merge([b, g, r])

def orb_detect(image_a, image_b):
 # feature match
 orb = cv2.ORB_create()
 kp1, des1 = orb.detectAndCompute(image_a, None)
 kp2, des2 = orb.detectAndCompute(image_b, None)
 # create BFMatcher object
 bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
 # Match descriptors.
 matches = bf.match(des1, des2)
 # Sort them in the order of their distance.
 matches = sorted(matches, key=lambda x: x.distance)
 # Draw first 10 matches.
 img3 = cv2.drawMatches(image_a, kp1, image_b, kp2,
    matches[:100], None, flags=2)
 return bgr_rgb(img3)

@time_cost
def sift_detect(img1, img2, detector='surf'):
 if detector.startswith('si'):
 print("sift detector......")
 sift = cv2.xfeatures2d.SURF_create()
 else:
 print("surf detector......")
 sift = cv2.xfeatures2d.SURF_create()
 # find the keypoints and descriptors with SIFT
 kp1, des1 = sift.detectAndCompute(img1, None)
 kp2, des2 = sift.detectAndCompute(img2, None)
 # BFMatcher with default params
 bf = cv2.BFMatcher()
 matches = bf.knnMatch(des1, des2, k=2)
 # Apply ratio test
 good = [[m] for m, n in matches if m.distance < 0.5 * n.distance]
 # cv2.drawMatchesKnn expects list of lists as matches.
 img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2)
 return bgr_rgb(img3)

if __name__ == "__main__":
 # load image
 image_a = cv2.imread('G:/2018and2019two/qianrushi/wuluo1.jpg')#绝对路径
 image_b = cv2.imread('G:/2018and2019two/qianrushi/wuluo2.jpg')
 # ORB
 # img = orb_detect(image_a, image_b)
 # SIFT or SURF
 img = sift_detect(image_a, image_b)
 plt.imshow(img)
 plt.show()