garbor_v[ start_i : stop_i ] = np.load(os.path.join(images_path, 'garbor_%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 GARBOR for file ', os.path.join(images_path, 'im%d.jpg' % (r + 1))
try:
start_time = time.time()
garbor_v[ start_i : stop_i ] = np.resize(garbor_features(img, kernels) , (1, len(kernels) * 2))
elapsed_time = time.time() - start_time
print 'It took %ds to calculate GARBOR...' % elapsed_time
np.save(os.path.join(images_path, 'garbor_%d.npy' % (r + 1)), garbor_v[ start_i : stop_i ])
except:
failures_garbor.append(r+1)
print 'Failures COARSENESS ', failures_coarseness
print 'Failures CONTRAST ', failures_contrast
print 'Failures DIRECTIONALITY ', failures_directionality
print 'Failures GARBOR ', failures_garbor
if not histogram_hasBeenCalculated:
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)
path_2 = "%s/arrays/level2_%d_%d" % (images_path, kCentroids, cIter)
images_index = np.load('%s/centroids_codebook_images_index_%d_%d_%d_%s_%s_%s_%s.npy' % (path_2, index[0] , kCentroids, cIter, useTamuraCoarseness, useTamuraContrast, useTamuraDirectionality, useGarbor))
centroids_codebook_histogram = np.load('%s/centroids_codebook_histogram_%d_%d_%d_%s_%s_%s_%s.npy' % (path_2, index[0] , kCentroids, cIter, useTamuraCoarseness, useTamuraContrast, useTamuraDirectionality, useGarbor))
vq_codes_obs_histogram = np.load('%s/vq_codes_obs_histogram_%d_%d_%d_%s_%s_%s_%s.npy' % (path_2, index[0] , kCentroids, cIter, useTamuraCoarseness, useTamuraContrast, useTamuraDirectionality, useGarbor))
standard_deviations_histogram = np.load('%s/standard_deviations_histogram_%d_%d_%d_%s_%s_%s_%s.npy' % (path_2, index[0] , kCentroids, cIter, useTamuraCoarseness, useTamuraContrast, useTamuraDirectionality, useGarbor))
