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added flann matching to find_obj.py

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This commit is contained in:
Alexander Mordvintsev 2011-08-17 02:00:52 +00:00
parent d3d55f3234
commit b3ae2c181a
1 changed files with 31 additions and 8 deletions
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39
samples/python2/find_obj.py
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@ -1,14 +1,19 @@
import numpy as np
import cv2
from common import anorm
from functools import partial
help_message = '''SURF image match
USAGE: findobj.py [ <image1> <image2> ]
'''
FLANN_INDEX_KDTREE = 1 # bug: flann enums are missing
def match(desc1, desc2, r_threshold = 0.75):
flann_params = dict(algorithm = FLANN_INDEX_KDTREE,
trees = 4)
def match_bruteforce(desc1, desc2, r_threshold = 0.75):
res = []
for i in xrange(len(desc1)):
dist = anorm( desc2 - desc1[i] )
@ -18,6 +23,14 @@ def match(desc1, desc2, r_threshold = 0.75):
res.append((i, n1))
return np.array(res)
def match_flann(desc1, desc2, r_threshold = 0.6):
flann = cv2.flann_Index(desc2, flann_params)
idx2, dist = flann.knnSearch(desc1, 2, params = {}) # bug: need to provide empty dict
mask = dist[:,0] / dist[:,1] < r_threshold
idx1 = np.arange(len(desc1))
pairs = np.int32( zip(idx1, idx2[:,0]) )
return pairs[mask]
def draw_match(img1, img2, p1, p2, status = None, H = None):
h1, w1 = img1.shape[:2]
h2, w2 = img2.shape[:2]
@ -50,6 +63,7 @@ def draw_match(img1, img2, p1, p2, status = None, H = None):
cv2.line(vis, (x2+w1-r, y2+r), (x2+w1+r, y2-r), col, thickness)
return vis
if __name__ == '__main__':
import sys
try: fn1, fn2 = sys.argv[1:3]
@ -68,12 +82,21 @@ if __name__ == '__main__':
desc2.shape = (-1, surf.descriptorSize())
print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
m = match(desc1, desc2)
matched_p1 = np.array([kp1[i].pt for i, j in m])
matched_p2 = np.array([kp2[j].pt for i, j in m])
H, status = cv2.findHomography(matched_p1, matched_p2, cv2.RANSAC, 10.0)
print '%d / %d inliers/matched' % (np.sum(status), len(status))
def match_and_draw(match, r_threshold):
m = match(desc1, desc2, r_threshold)
matched_p1 = np.array([kp1[i].pt for i, j in m])
matched_p2 = np.array([kp2[j].pt for i, j in m])
H, status = cv2.findHomography(matched_p1, matched_p2, cv2.RANSAC, 5.0)
print '%d / %d inliers/matched' % (np.sum(status), len(status))
vis = draw_match(img1, img2, matched_p1, matched_p2, status, H)
cv2.imshow('find_obj SURF', vis)
vis = draw_match(img1, img2, matched_p1, matched_p2, status, H)
return vis
print 'bruteforce match:',
vis_brute = match_and_draw( match_bruteforce, 0.75 )
print 'flann match:',
vis_flann = match_and_draw( match_flann, 0.6 ) # flann tends to find more distant second
# neighbours, so r_threshold is decreased
cv2.imshow('find_obj SURF', vis_brute)
cv2.imshow('find_obj SURF flann', vis_flann)
cv2.waitKey()