def get_wordlist():
words = {}
with db.session_scope() as session:
for word in session.query(db.Word):
descriptor = np.frombuffer(word.descriptor, dtype=np.float32)
words[word.id - 1] = descriptor
return words
def _create_bag_of_words_for_image(self, image_id, size, wordlist):
with db.session_scope() as session:
image = session.query(db.Image).get(image_id)
method, width, height, features = load_features(
self._get_feature_file(image.path))
if method != self.method:
raise RuntimeError(
f"dataset uses '{feature_name(self.method)}' method but "
f"features were extracted with '{feature_name(method)}' "
"method")
words = self._classify_descriptors(
features['descriptor'], wordlist=wordlist)
feature_tree = feature_rtree(features)
word_histogram = partial(
self._word_histogram, size, words, feature_tree, width, height)
# total image
self._insert_bag_of_words(session, word_histogram, image, 1, 0, 0)
# rule of 3rds
for i in range(3):
for j in range(3):
self._insert_bag_of_words(
session, word_histogram, image, 3, i, j)
# rule of 5ths
for i in range(5):
for j in range(5):
self._insert_bag_of_words(
session, word_histogram, image, 5, i, j)
image.has_words = True
def get_keyword_counts():
keywords = {}
with db.session_scope() as session:
for keyword in session.query(db.Keyword):
keywords[keyword.name] = len(keyword.images)
return {k: v for (k,v) in
sorted(keywords.items(),
key=operator.itemgetter(1), reverse=True)}
def get_keywords_for_image(self, image):
image = self.relative_path(image)
with db.session_scope() as session:
image = session.query(db.Image).filter(
db.Image.path == str(image)).one_or_none()
if image:
return set([match.keyword.name for match in image.keywords])
return set()
def __init__(self, path=None):
if path is None:
path = find_dataset(Path.cwd())
self._path = path
db.connect(path / _DATASET_DIR / db._DATABASE_NAME)
with db.session_scope() as session:
extractor_id = int(session.query(db.KeyValue.value).filter(
db.KeyValue.key == 'method').first()[0])
self._feature_extractor = create_feature_extractor(extractor_id)
def index_keywords(self, *, progress=None):
with db.session_scope() as session:
image_ids = flatten(session.query(db.Image.id).filter(
~db.Image.has_keywords).all())
keywords = self.get_keywords(id=True)
if len(keywords) == 0:
return False
keywords_tree = self._get_keywords_rtree()
for id in get_progress(progress)(image_ids):
self._index_image_keywords(id, keywords_tree)
def index_words(self, *, progress=None):
with db.session_scope() as session:
image_ids = flatten(session.query(db.Image.id).filter(
~db.Image.has_words).all())
num_words = len(self.get_wordlist())
if num_words == 0:
raise RuntimeError('no wordlist set for dataset')
wordlist = self._get_wordlist_rtree()
for id in get_progress(progress)(image_ids):
self._create_bag_of_words_for_image(id, num_words, wordlist)
def _move_image_into(self, image_id, dest_dir):
dest_dir = self.absolute_path(dest_dir)
with db.session_scope() as session:
image = session.query(db.Image).get(image_id)
image_path = self.absolute_path(image.path)
dest_dir.mkdir(parents=True, exist_ok=True)
image_path.rename(dest_dir / image_path.name)
self._get_feature_file(image_path).rename(
self._get_feature_file(dest_dir / image_path.name))
image.path = str(self.relative_path(dest_dir / image_path.name))
def get_keywords(id=False):
keywords = {}
with db.session_scope() as session:
for keyword in session.query(db.Keyword):
themes = [np.frombuffer(theme.word_histogram, dtype=np.float32)
for theme in keyword.themes]
if id:
keywords[keyword.id] = np.vstack(themes)
else:
keywords[keyword.name] = np.vstack(themes)
return keywords
def get_images_from_keyword(keyword):
images = []
with db.session_scope() as session:
keyword = session.query(db.Keyword).filter(
db.Keyword.name == keyword).one_or_none()
if keyword is None:
return ValueError(
f"keyword '{keyword}' is not in this dataset")
for match in keyword.images:
images.append(match.image.path)
return images
def keyword_generator(self, paths):
images = set(str(self.relative_path(file)) for file in image_files(paths))
generator = KeywordGenerator()
with db.session_scope() as session:
query = session.query(db.BagOfWords.word_histogram).\
join(db.BagOfWords.image).\
filter(db.Image.path.in_(images)).\
filter(db.BagOfWords.divisions == 1)
for word_histogram in query:
generator.add_histogram(
np.frombuffer(word_histogram[0], dtype=np.float32))
return generator
def index_images(self, *, progress=None):
added = []
fs_images = []
with db.session_scope() as session:
for path in get_progress(progress)(image_files(self.path)):
relative_path = self.relative_path(path)
if self._index_image(session, path):
added.append(relative_path)
fs_images.append(relative_path)
removed = self._remove_missing_images(session, fs_images)
orphaned = self._remove_orphaned_features(fs_images)
return added, removed, orphaned
def init_dataset(path, method_id=None):
path = Path(path).absolute()
if path.is_dir():
ValueError(f"path '{path}' is not a directory")
dataset_path = path / _DATASET_DIR
dataset_path.mkdir(exist_ok=True)
if (dataset_path / Path(db._DATABASE_NAME)).is_file():
raise FileExistsError(f"existing dataset at '{dataset_path}'")
method_id = create_feature_extractor(method_id).id
db.init(dataset_path)
with db.session_scope() as session:
session.add(db.KeyValue(key='method', value=str(int(method_id))))
def set_wordlist(words):
with db.session_scope() as session:
# remove invalid data
session.query(db.Image).update(
{'has_words': False, 'has_keywords': False})
session.query(db.Word).delete()
session.query(db.BagOfWords).delete()
session.query(db.Keyword).delete()
session.query(db.KeywordMatch).delete()
# set wordlist
wordlist_hash = hashlib.sha1(words.tobytes()).hexdigest()
session.add(db.KeyValue(key='wordlist_hash', value=wordlist_hash))
for id, word in enumerate(words):
session.add(db.Word(id=(id + 1), descriptor=word.tobytes()))
def set_keywords(self, keywords):
with db.session_scope() as session:
# remove invalid data
session.query(db.Image).update({'has_keywords': False})
session.query(db.Keyword).delete()
session.query(db.KeywordTheme).delete()
session.query(db.KeywordMatch).delete()
# add keywords
for keyword, data in keywords.items():
keyword_ = db.Keyword(name=keyword)
session.add(keyword_)
for histogram in data:
session.add(db.KeywordTheme(
keyword=keyword_, word_histogram=histogram.tobytes()))
def create_clusterer(*, global_only=False, image_cohesion_factor=2):
if image_cohesion_factor < 0:
raise ValueError("'image_cohesion_factor' cannot be less than 0")
clusterere = Clusterer()
with db.session_scope() as session:
for bags_of_words in session.query(db.BagOfWords):
if global_only and bags_of_words.divisions > 1:
continue
histogram = np.frombuffer(
bags_of_words.word_histogram, dtype=np.float32)
weight = 1 / (bags_of_words.divisions ** image_cohesion_factor)
clusterere.add_histogram(
bags_of_words.image_id,
histogram, weight=weight)
return clusterere
def index_features(self, *, progress=None):
new_features = []
with db.session_scope() as session:
images = session.query(db.Image).filter(
~db.Image.has_features).all()
for image in get_progress(progress)(images):
path = self.absolute_path(image.path)
image_data = cv_image(path)
features = self._feature_extractor.extract(
image_data, max_features=_MAX_FEATURES_PER_IMAGE)
self._feature_extractor.save_features(
self._get_feature_file(path), image_data, features)
image.has_features = True
new_features.append(image.path)
return new_features
def _index_image_keywords(self, image_id, keywords_tree):
with db.session_scope() as session:
image = session.query(db.Image).get(image_id)
image_keyword_ids = {}
for bag_of_words in image.words:
divisions = bag_of_words.divisions
histogram = np.frombuffer(
bag_of_words.word_histogram, dtype=np.float32)
for id in keywords_tree.nearest(np.tile(histogram, 2), 1):
image_keyword_ids.setdefault(id, 0)
image_keyword_ids[id] += 1/(divisions**1.5)
image_keyword_ids = {
k: v for k, v in image_keyword_ids.items() if v >= 1}
for keyword_id in set(image_keyword_ids):
keyword = session.query(db.Keyword).get(keyword_id)
session.add(db.KeywordMatch(image=image, keyword=keyword))
image.has_keywords = True
def _get_feature_files(self):
with db.session_scope() as session:
return [self._get_feature_file(path[0])
for path in session.query(db.Image.path).all()]