1 | initial version |
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
sift = cv2.SIFT()
kp1, des1 = sift.detectAndCompute(img1,None) kp2, des2 = sift.detectAndCompute(img2,None)
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)
matchesMask = [[0,0] for i in xrange(len(matches))]
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()
2 | No.2 Revision |
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.imshow(img3,),plt.show()
3 | No.3 Revision |
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()