def _new_keyword(args):
dataset = Dataset()
generator = dataset.keyword_generator(args.image)
keyword = generator.generate(themes=args.themes)
if args.progress:
_print(
f'Keyword generation complete! ({keyword.shape[0]} themes found)')
save_keyword(args.keyword, keyword, dataset.method)
def _keywords(args):
dataset = Dataset()
if args.image:
for keyword in dataset.get_keywords_for_image(args.image):
print(keyword)
else:
for keyword, count in dataset.get_keyword_counts().items():
print(f'{count:3d} {keyword}')
def _keyword(args):
dataset = Dataset()
try:
images = dataset.get_images_from_keyword(args.keyword)
for image in images:
print(image)
except ValueError as err:
CommandLineError(str(err))
def _set_wordlist(args):
method, words = load_wordlist(args['file'])
dataset = Dataset()
if dataset.method != method:
raise CommandLineError(
f"dataset has method '{feature_name(dataset.method)}' but "
f"wordlist was built with method '{feature_name(method)}'")
if not args['yes']:
print('This will cause bags of words and keyword matches to be '
'deleted, you will need need to re-index the dataset '
'afterwards.')
if input('Do you wish to proceed (yes/NO): ').lower() != 'yes':
return
dataset.set_wordlist(words)
def _set_keywords(args):
dataset = Dataset()
# load keywords
keywords = {}
for file in files(args['file']):
method, keyword, keyword_data = load_keyword(file)
keywords[keyword] = keyword_data
if dataset.method != method:
raise CommandLineError(
f"dataset has method '{feature_name(dataset.method)}' but "
f"wordlist was built with method '{feature_name(method)}'")
# get confirmation
if not args['yes']:
print('This will cause keyword matches to be deleted, you will need'
'need to re-index the dataset afterwards.')
if input('Do you wish to proceed (yes/NO): ').lower() != 'yes':
return
# set keywords
dataset.set_keywords(keywords)
def _cluster(args):
_index(args)
dataset = Dataset()
if args.progress:
_print('Loading Bags of Visual Words...')
clusterere = dataset.create_clusterer(global_only=args.global_only,
image_cohesion_factor=args.cohesion)
if clusterere.size == 0:
raise CommandLineError(
"missing bags of words, re-run the 'index' command")
if args.progress:
_print('Using K-Means to cluster images...')
cluster_mapping = clusterere.cluster(
affinity=args.affinity,
progress_printer=_print if args.progress else None)
if args.progress:
_print('Rearranging images into clusters...')
dataset.cluster(cluster_mapping)
if args.progress:
_print('Clustering complete!')
def _new_wordlist(args):
if args.features and args.features < args.words:
raise CommandLineError(
"'--features' must be greater than the number of 'words'")
dataset = Dataset()
try:
generator = dataset.wordlist_generator(
progress=_progress(args.progress, 'Loading feature files'))
except FileNotFoundError:
raise CommandLineError(
"missing features files, re-run the 'index' command")
if args.progress:
cluster_method = 'K-Means' if args.kmeans else 'Mini Batch K-Means'
print(
f'Using {cluster_method} to generate wordlist from '
f'{generator.num_descriptors()} features...',
file=sys.stderr,
flush=True)
words = generator.generate(args.words,
max_features=args.features,
minibatch=(not args.kmeans))
if args.progress:
print('Wordlist complete!', file=sys.stderr, flush=True)
save_wordlist(args.file, words, generator.method)
def _index(args):
dataset = Dataset()
with warnings.catch_warnings(record=True) as w:
added, removed, orphaned = dataset.index_images(
progress=_progress(args.progress, 'Indexing images'))
if args.progress:
message = ''
if added:
message += f'{len(added)} new images'
if removed:
message += f' (removed {len(removed)})'
if orphaned:
message += f', {len(orphaned)} orphaned feature files removed'
if message:
_print(message)
dataset.index_features(
progress=_progress(args.progress, 'Extracting features'))
for warning in w:
if warning.category is ImageReadWarning:
_print(warning.message)
dataset.index_words(progress=_progress(
args.progress, 'Generating Bags of Visual Words'))
dataset.index_keywords(
progress=_progress(args.progress, 'Applying keywords to images'))