def set_picture_label(embedding_id, new_label, cluster, cluster_dict):
# TODO: Refactor! Extract parts to DBManager?
# TODO: Don't accept label if it's the same as the old one!
new_cluster_id = DBManager.get_max_cluster_id() + 1
embedding = cluster.get_embedding(embedding_id)
cluster.remove_embedding_by_id(embedding_id)
new_cluster = Cluster(new_cluster_id, [embedding], [embedding_id], new_label)
cluster_dict.add_cluster(new_cluster)
if cluster.get_size() == 0:
cluster_dict.remove_cluster(cluster)
modified_clusters = ClusterDict([new_cluster])
else:
modified_clusters = ClusterDict([new_cluster, cluster])
def set_pic_label_worker(con):
if cluster.get_size() == 0:
# TODO: Remove cluster like that???
embeddings_row_dicts = DBManager.remove_cluster(cluster, con=con, close_connections=False)
emb_id_to_face_dict = make_emb_id_to_face_dict_from_row_dicts(embeddings_row_dicts)
emb_id_to_img_id_dict = make_emb_id_to_img_id_dict_from_row_dicts(embeddings_row_dicts)
else:
emb_id_to_face_dict = None
emb_id_to_img_id_dict = None
DBManager.store_clusters(modified_clusters, emb_id_to_face_dict=emb_id_to_face_dict,
emb_id_to_img_id_dict=emb_id_to_img_id_dict, con=con, close_connections=False)
DBManager.store_certain_labels(cluster=new_cluster, con=con, close_connections=False)
try:
DBManager.connection_wrapper(set_pic_label_worker)
except IncompleteDatabaseOperation:
cluster.add_embedding(embedding, embedding_id)
if cluster.get_size() == 0:
cluster_dict.add_cluster(cluster)
cluster_dict.remove_cluster(new_cluster)
raise
def cluster_embeddings_no_split(self,
embeddings,
embeddings_ids=None,
existing_clusters_dict=None,
should_reset_cluster_ids=False,
final_clusters_only=True):
"""
Build clusters from face embeddings stored in the given path using the specified classification threshold.
(Currently handled as: All embeddings closer than the distance given by the classification threshold are placed
in the same cluster. If cluster_save_path is set, store the resulting clusters as directories in the given path.
:param should_reset_cluster_ids:
:param embeddings: Iterable containing the embeddings. It embeddings_ids is None, must consist of
(id, embedding)-pairs
:param embeddings_ids: Ordered iterable with the embedding ids. Must be at least as long as embeddings.
:param existing_clusters_dict:
:param final_clusters_only: If true, only the final iterable of clusters is returned. Otherwise, return that
final iterable, as well as a list of modified/newly created and deleted clusters
:return:
"""
# TODO: Allow embeddings_ids to be none? Get next id via DB query?
# TODO: Allow embeddings_ids to be shorter than embeddings and 'fill up' remaining ids?
# embeddings = list(embeddings)
if not embeddings:
if final_clusters_only:
return ClusterDict()
return ClusterDict(), ClusterDict(), ClusterDict()
if embeddings_ids is None:
embeddings_with_ids = embeddings
else:
# if len(embeddings) > len(embeddings_ids):
# raise ValueError(f'Too few ids for embeddings ({len(embeddings_ids)} passed, but {len(embeddings)}'
# f' needed)')
embeddings_with_ids = zip(embeddings_ids, embeddings)
if existing_clusters_dict is None:
existing_clusters_dict = ClusterDict()
else:
# # Don't iterate over embeddings in existing clusters
# embeddings_with_ids = dict(embeddings_with_ids)
# existing_embeddings = existing_clusters_dict.get_embeddings()
# remove_multiple(embeddings_with_ids, existing_embeddings)
# embeddings_with_ids = embeddings_with_ids.items()
# Don't iterate over embeddings in existing clusters
def exists_in_any_cluster(emb_id, _):
return existing_clusters_dict.any_cluster_with_emb(emb_id)
embeddings_with_ids = starfilterfalse(exists_in_any_cluster,
embeddings_with_ids)
cluster_dict = existing_clusters_dict
if should_reset_cluster_ids:
cluster_dict.reset_ids()
next_cluster_id = cluster_dict.get_max_id() + 1
else:
max_existing_id = cluster_dict.get_max_id()
max_db_id = DBManager.get_max_cluster_id()
next_cluster_id = max(max_existing_id, max_db_id) + 1
embeddings_with_ids = list(embeddings_with_ids)
random.seed(0)
random.shuffle(embeddings_with_ids)
modified_clusters_ids, removed_clusters_ids = set(), set()
for counter, (embedding_id,
new_embedding) in enumerate(embeddings_with_ids,
start=1):
print_progress(counter, "embedding_id iteration")
closest_clusters = self.get_closest_clusters(
cluster_dict, new_embedding)
# find cluster containing the closest embedding to new_embedding
shortest_emb_dist, closest_cluster = self.find_closest_cluster_to_embedding(
closest_clusters, new_embedding)
if shortest_emb_dist <= self.classification_threshold:
closest_cluster.add_embedding(new_embedding, embedding_id)
modified_clusters_ids.add(closest_cluster.cluster_id)
else:
new_cluster = Cluster(next_cluster_id, [new_embedding],
[embedding_id])
next_cluster_id += 1
cluster_dict.add_cluster(new_cluster)
modified_clusters_ids.add(new_cluster.cluster_id)
if final_clusters_only:
return cluster_dict
modified_clusters = cluster_dict.get_clusters_by_ids(
modified_clusters_ids)
removed_clusters = cluster_dict.get_clusters_by_ids(
removed_clusters_ids)
return cluster_dict, ClusterDict(modified_clusters), ClusterDict(
removed_clusters)
def split_cluster(cls,
cluster_to_split,
next_cluster_id=None,
ret_new_next_id=False):
"""
Split cluster into two new clusters as follows:
1. Find two embeddings e1, e2 in the cluster with the greatest distance between them.
2. Create a new cluster C1, C2 for each of the two.
3. For each embedding e of the remaining embeddings:
Add e to the cluster (C1 or C2) whose center is closer to it.
The given cluster must contain at least 2 embeddings.
:param ret_new_next_id:
:param next_cluster_id:
:param cluster_to_split: Cluster to be split
:return: Two new clusters containing embeddings of old one
"""
# TODO: Does this fail due to bad analogy to low-dim. space?!
embeddings_with_ids = cluster_to_split.get_embeddings(
with_embeddings_ids=True, as_dict=True)
(emb1_id, cluster_start_emb1), (
emb2_id, cluster_start_emb2
) = cls.find_most_distant_embeddings(embeddings_with_ids)
remove_multiple(embeddings_with_ids, [emb1_id, emb2_id])
label = cluster_to_split.label
if next_cluster_id is None:
next_cluster_id = DBManager.get_max_cluster_id() + 1
new_cluster1_id, new_cluster2_id = next_cluster_id, next_cluster_id + 1
new_cluster1, new_cluster2 = (Cluster(new_cluster1_id,
[cluster_start_emb1], [emb1_id],
label=label),
Cluster(new_cluster2_id,
[cluster_start_emb2], [emb2_id],
label=label))
@spread_args_decorator
@ignore_first_n_args_decorator(n=1)
def is_closer_to_cluster1(emb):
dist_to_cluster1 = new_cluster1.compute_dist_to_center(emb)
dist_to_cluster2 = new_cluster2.compute_dist_to_center(emb)
return dist_to_cluster1 < dist_to_cluster2
def try_split(cluster_embs_with_ids, new_cluster):
split_result = split_items(cluster_embs_with_ids)
try:
cluster_embs_ids, cluster_embs = split_result
except ValueError:
# not enough values to unpack
pass
else:
new_cluster.add_embeddings(cluster_embs, cluster_embs_ids)
cluster2_embs_with_ids, cluster1_embs_with_ids = partition(
is_closer_to_cluster1, embeddings_with_ids.items())
try_split(cluster1_embs_with_ids, new_cluster1)
try_split(cluster2_embs_with_ids, new_cluster2)
new_clusters = (new_cluster1, new_cluster2)
if ret_new_next_id:
return new_clusters, new_cluster2_id + 1
return new_clusters
