tamura_directionality_v[r] = np.load(os.path.join(images_path, 'tamura_directionality_%d.npy' % (r + 1)))
else :
if img is None:
img = misc.imread(os.path.join(images_path, 'im%d.jpg' % (r + 1) ))
if useGreyScale:
img = rgb2gray(img)
print 'Calculating Tamura DIRECTIONALITY for file ', os.path.join(images_path, 'im%d.jpg' % (r + 1))
try:
start_time = time.time()
tamura_directionality_v[r] = degreeDirect(img, threshold, neigh)
elapsed_time = time.time() - start_time
np.save(os.path.join(images_path, 'tamura_directionality_%d.npy' % (r + 1)),tamura_directionality_v[r])
except:
failures_directionality.append(r+1)
print 'It took %ds to calculate DIRECTIONALITY...' % elapsed_time
#print tamura_directionality_v[r]
if useGarbor:
vq_codes_obs_features = np.load('%s/vq_codes_obs_features_%d_%d_%s_%s_%s_%s.npy' % (path_1, kCentroids_features, cIter_features, useTamuraCoarseness, useTamuraContrast, useTamuraDirectionality, useGarbor))
standard_deviations_features = np.load('%s/standard_deviations_features_%d_%d_%s_%s_%s_%s.npy' % (path_1, kCentroids_features, cIter_features, useTamuraCoarseness, useTamuraContrast, useTamuraDirectionality, useGarbor))
learning_set_features_image = np.zeros(n_colums_features)
j = 0
if useTamuraCoarseness:
learning_set_features_image[j] = coarseness(img)
j = j + 1
if useTamuraContrast:
learning_set_features_image[j] = contrast(img)
j = j + 1
if useTamuraDirectionality:
learning_set_features_image[j] = degreeDirect(img, threshold, neigh)
j = j + 1
if useGarbor:
start_i = j
stop_i = start_i + n_kernels * 2
learning_set_features_image[start_i : stop_i] = np.resize(garbor_features(img, kernels) , (1, n_kernels * 2))
for i in range (n_colums_features):
learning_set_features_image[i] = learning_set_features_image[i] / standard_deviations_features[i]
# Images from 1o level Kmeans
(index, dist) = spy.vq.vq(np.array([learning_set_features_image]), centroids_codebook_features)