I never used the Python OpenCV interface but this tutorial shows how to use the ratio test with a FLANN based Matcher.

I copy / paste the tutorial code here:

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()

I never used the Python OpenCV interface but this tutorial shows how to use the ratio test with a FLANN based Matcher.

I copy / paste the tutorial code here:

import numpy as np
import cv2
from matplotlib import pyplot as plt
 
plt
img1 = cv2.imread('box.png',0) # queryImage
img2 = cv2.imread('box_in_scene.png',0) # trainImage
 
trainImage
# Initiate SIFT detector
 
detector
sift = cv2.SIFT()
 
cv2.SIFT()
# find the keypoints and descriptors with SIFT
 
SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
 
sift.detectAndCompute(img2,None)
# FLANN parameters
 
parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50) # or pass empty dictionary
 
dictionary
flann = cv2.FlannBasedMatcher(index_params,search_params)
 
cv2.FlannBasedMatcher(index_params,search_params)
matches = flann.knnMatch(des1,des2,k=2)
 
flann.knnMatch(des1,des2,k=2)
# Need to draw only good matches, so create a mask
 
mask
matchesMask = [[0,0] for i in xrange(len(matches))]
 
xrange(len(matches))]
# ratio test as per Lowe's paper
 
paper
for i,(m,n) in enumerate(matches):
if m.distance < 0.7*n.distance:
matchesMask[i]=[1,0]
 
matchesMask[i]=[1,0]
draw_params = dict(matchColor = (0,255,0),
singlePointColor = (255,0,0),
matchesMask = matchesMask,
flags = 0)
 
0)
img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)
 
plt.imshow(img3,),plt.show()
 cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)
plt.imshow(img3,),plt.show()

I never used the Python OpenCV interface but this tutorial shows how to use the ratio test with a FLANN based Matcher.Matcher.

I copy / paste the tutorial code code here:

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()