Hello everyone, I'm moving my first steps with OpenCV in Python.
What I'd wish to do is given an image, find its "original" one from a collection of reference images. Just to be clear, the query image is a simple photo of the whole image (card), so it's not the scenario "find an object inside a photo", but "just" a similarity test.
My final database will be pretty large (about 25 000 images), but I started doing some tests on a smaller scale (only 270 images).
Recognition works perfectly, however it's pretty slow: it takes 8 seconds to iterate over all 270 images. I was able to speed up the job by saving the descriptors to disk and load them, instead of calculating them; anyway it's still slow.
So I started to work on FLANN: I get some results, but my main problem is to find the matching images. I get a whole array of points, but I don't know how to fetch the right image.
This is my code:
scanned = 'tests/temp_bw.png'
surf = cv2.xfeatures2d.SURF_create(400)
surf.setUpright(True)
img1 = cv2.imread(scanned, 0)
kp1, des1 = surf.detectAndCompute(img1, None)
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
des_all = None
for filename in os.listdir('images'):
img2 = cv2.imread('images/' + filename, 0)
kp2, des2 = surf.detectAndCompute(img2, None)
if des_all is None:
des_all = des2
else:
des_all = np.concatenate((des_all, des2))
flann = cv2.flann.Index()
print "Training..."
flann.build(des_all, index_params)
print "Matching..."
indexes, matches = flann.knnSearch(des1, 10)
# and now???
Any suggestions on how I can reference back the most similar image?
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