32.FLANN.py

# For SIFT /SURF

index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)

#for ORB

index_params= dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
	key_size = 12,
	# 20
	multi_probe_level = 1) #2

#Second dictionary is the SearchParams. It specifies the number of times the trees in the index should be recursively
#traversed. Higher values gives better precision, but also takes more time. If you want to change the value, pass
#search_params = dict(checks=100).

import numpy as np
import cv2
from matplotlib import pyplot as plt
img1 = cv2.imread('box.png',0)
# queryImage
img2 = cv2.imread('box_in_scene.png',0) # trainImage
# Initiate SIFT detector
sift = cv2.SIFT()
# 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 xrange(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]
draw_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)
plt.imshow(img3,),plt.show()