opencv3+python3 Feature Descriptor特徵檢測與匹配

OpenCV3:

import numpy as np
import cv2
from matplotlib import pyplot as plt


img1 = cv2.imread('1.jpg',0)          # queryImage
img2 = cv2.imread('2.jpg',0)  #trainImage


# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create()


# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)


# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50)   # or pass empty dictionary


flann = cv2.FlannBasedMatcher(index_params,search_params)


matches = flann.knnMatch(des1,des2,k=2)


# Need to draw only good matches, so create a mask
matchesMask = [[0,0] for i in range(len(matches))]


# ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches):
    if m.distance < 0.7*n.distance:
        matchesMask[i]=[1,0]


raw_params = dict(matchColor = (0,255,0),
                   singlePointColor = (255,0,0),
                   matchesMask = matchesMask,
                   flags = 0)       
                        
#img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)
img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None, flags=2)

plt.imshow(img3,),plt.show()