def create (images, computed_feature_vectors, scaler):
feature_vectors = []
step_mod = len(images) * 0.05 # print progress in steps of 5 percent
done = 0
print('[K-MEANS++] SIFT feature extraction:\n[K-MEANS++] 0%', end = '')
sys.stdout.flush()
for image in images:
if image in computed_feature_vectors:
descriptors = computed_feature_vectors[image]
else:
try:
descriptors = fe.extract(settings.images_dir + image, settings.scale_size)
except Exception as e:
print('Error while computing SIFT for image {}.'.format(image))
continue
computed_feature_vectors[image] = descriptors
fe.unroll_and_append_descriptors(descriptors, feature_vectors)
done += 1
if done % step_mod == 0:
print(' {}%'.format((100 * done) / len(images)), end = '')
sys.stdout.flush()
if done < len(images):
print(' 100%')
print('[K-MEANS++] Number of \'SIFTed\' images: {}'.format(done))
print('[K-MEANS++] Total number of sift descriptors: {}'
.format(len(feature_vectors))
)
sys.stdout.flush()
# do k-means clustering (512 clusters, k-means++, 4 cores, show information)
kmeans = cluster.KMeans(n_clusters = settings.class_count, init = 'k-means++',
n_jobs = 4
)
# 10 SIFT descriptors per images (approximately)
kmeans.fit(scaler.transform(
random.sample(feature_vectors, int(10 * done))
))
# dump k-means clustering for further usage
with open(settings.filepaths['k-means++'], 'wb') as f:
pe.dump(kmeans, f)
return kmeans
def create (images, computed_feature_vectors):
feature_vectors = []
step_mod = len(images) * 0.05 # print progress in steps of 5 percent
done = 0
print('[SCALER] SIFT feature extraction:\n[SCALER] 0%', end = '')
sys.stdout.flush()
for image in images:
if image in computed_feature_vectors:
descriptors = computed_feature_vectors[image]
else:
try:
descriptors = fe.extract(settings.images_dir + image, settings.scale_size)
except Exception as e:
print('Error while computing SIFT for image {}.'.format(image))
continue
computed_feature_vectors[image] = descriptors
fe.unroll_and_append_descriptors(descriptors, feature_vectors)
done += 1
if done % step_mod == 0:
print(' {}%'.format((100 * done) / len(images)), end = '')
sys.stdout.flush()
if done < len(images):
print(' 100%')
print('[SCALER] Number of \'SIFTed\' images: {}'.format(done))
print('[SCALER] Total number of SIFT descriptors: {}'
.format(len(feature_vectors))
)
sys.stdout.flush()
# calibrate min-max-scaler
min_max_scaler = preprocessing.MinMaxScaler()
# 10 SIFT descriptors per image (approximately)
min_max_scaler = min_max_scaler.fit(
random.sample(feature_vectors, int(10 * done))
)
# dump min-max-scaler for further usage
with open(settings.filepaths['scaler'], 'wb') as f:
pe.dump(min_max_scaler, f)
return min_max_scaler
