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src/p/c/PCV-HEAD/examples/ch2_match_features.py   PCV(Download)
 
# read features and match
l1,d1 = sift.read_features_from_file(imname1+'.sift')
l2,d2 = sift.read_features_from_file(imname2+'.sift')
matchscores = sift.match_twosided(d1, d2)

src/p/c/PCV-HEAD/examples/ch4_ar_cube.py   PCV(Download)
# compute features
sift.process_image('../data/book_frontal.JPG','im0.sift')
l0,d0 = sift.read_features_from_file('im0.sift')
 
sift.process_image('../data/book_perspective.JPG','im1.sift')
l1,d1 = sift.read_features_from_file('im1.sift')

src/p/c/PCV-HEAD/examples/ch3_panorama.py   PCV(Download)
for i in range(5): 
    sift.process_image(imname[i],featname[i])
    l[i],d[i] = sift.read_features_from_file(featname[i])
 
matches = {}

src/p/c/PCV-HEAD/examples/ch2_matching_graph.py   PCV(Download)
    for j in range(i,nbr_images): # only compute upper triangle
        print 'comparing ', imlist[i], imlist[j]
        l1,d1 = sift.read_features_from_file(featlist[i]) 
        l2,d2 = sift.read_features_from_file(featlist[j])
        matches = sift.match_twosided(d1,d2)

src/p/c/PCV-HEAD/PCV/imagesearch/vocabulary.py   PCV(Download)
        # read the features from file
        descr = []
        descr.append(sift.read_features_from_file(featurefiles[0])[1])
        descriptors = descr[0] #stack all features for k-means
        for i in arange(1,nbr_images):
            descr.append(sift.read_features_from_file(featurefiles[i])[1])