def eval_process_image_dir(cluster_dict, images_path, max_num_proc_imgs=None, metric=2, threshold=0.73):
Models.altered_mtcnn.keep_all = False
try:
eval_process_faces(images_path, max_num_proc_imgs=max_num_proc_imgs)
except IncompleteDatabaseOperation:
return
cluster_dict_copy = cluster_dict.copy()
def eval_process_image_dir_worker(con):
embeddings_with_ids = list(DBManager.get_all_embeddings(with_ids=True))
eval_core_algorithm = EvalCoreAlgorithm(metric=metric, classification_threshold=threshold)
# passing result cluster dict already overwrites it
clustering_result = eval_core_algorithm.cluster_embeddings_no_split(embeddings_with_ids,
existing_clusters_dict=cluster_dict,
should_reset_cluster_ids=True,
final_clusters_only=False)
_, modified_clusters_dict, removed_clusters_dict = clustering_result
DBManager.overwrite_clusters_simplified(modified_clusters_dict, removed_clusters_dict, con=con,
close_connections=False)
try:
DBManager.connection_wrapper(eval_process_image_dir_worker)
except IncompleteDatabaseOperation:
overwrite_dict(cluster_dict, cluster_dict_copy)
def clear_data_worker(con):
if data_kind_to_clear in ('l', 'b'):
# TODO: How to use local connections here? Rollback on multiple?
# clear_local_tables()
drop_local_tables()
if data_kind_to_clear in ('g', 'b'):
# clear_central_tables(con=con, close_connections=False)
drop_central_tables(con=con, close_connections=False)
overwrite_dict(cluster_dict, dict())
if data_kind_to_clear == 'c':
clear_clustering(con=con, close_connections=False)
overwrite_dict(cluster_dict, dict())
def process_image_dir(cluster_dict, threshold=0.73, metric=2, **kwargs):
"""
Extract faces from user-chosen images and cluster them
:param threshold:
:param metric:
:param cluster_dict:
:param kwargs:
:return:
"""
# TODO: Store entered paths(?) --> Makes it easier if user wants to revisit them, but probs rarely?
images_path = user_choose_images_path()
try:
process_faces(images_path)
except IncompleteDatabaseOperation:
return
cluster_dict_copy = cluster_dict.copy()
def cluster_processed_faces(con):
embeddings_with_ids = list(DBManager.get_all_embeddings(with_ids=True))
# TODO: Call reclassify handler here?
# TODO: Clear existing clusters? Issues with ids etc.????
core_algorithm = CoreAlgorithm(metric=metric,
classification_threshold=threshold)
# passing result cluster dict already overwrites it
clustering_result = core_algorithm.cluster_embeddings(
embeddings_with_ids,
existing_clusters_dict=cluster_dict,
should_reset_cluster_ids=True,
final_clusters_only=False)
_, modified_clusters_dict, removed_clusters_dict = clustering_result
DBManager.overwrite_clusters_simplified(modified_clusters_dict,
removed_clusters_dict,
con=con,
close_connections=False)
reset_cluster_ids(con=con, close_connections=False)
new_cluster_dict = DBManager.load_cluster_dict(con=con,
close_connections=False)
overwrite_dict(cluster_dict, new_cluster_dict)
try:
DBManager.connection_wrapper(cluster_processed_faces)
except IncompleteDatabaseOperation:
overwrite_dict(cluster_dict, cluster_dict_copy)
def reclassify_worker(con):
# all operations in worker, so if any DB operation raises error, it is caught
if embeddings_with_ids is not None:
local_embeddings_with_ids = embeddings_with_ids
else:
local_embeddings_with_ids = list(DBManager.get_all_embeddings(with_ids=True))
if not local_embeddings_with_ids:
log_error('no embeddings found, nothing to edit')
return
new_cluster_dict = DBManager.get_certain_clusters()
core_algorithm = CoreAlgorithm()
clustering_result = core_algorithm.cluster_embeddings(embeddings=local_embeddings_with_ids,
existing_clusters_dict=new_cluster_dict,
should_reset_cluster_ids=True,
final_clusters_only=False)
_, modified_clusters_dict, removed_clusters_dict = clustering_result
DBManager.overwrite_clusters(new_cluster_dict, removed_clusters_dict, no_new_embs=True,
clear_clusters=True, con=con, close_connections=False)
overwrite_dict(cluster_dict, new_cluster_dict)
def cluster_processed_faces(con):
embeddings_with_ids = list(DBManager.get_all_embeddings(with_ids=True))
# TODO: Call reclassify handler here?
# TODO: Clear existing clusters? Issues with ids etc.????
core_algorithm = CoreAlgorithm(metric=metric,
classification_threshold=threshold)
# passing result cluster dict already overwrites it
clustering_result = core_algorithm.cluster_embeddings(
embeddings_with_ids,
existing_clusters_dict=cluster_dict,
should_reset_cluster_ids=True,
final_clusters_only=False)
_, modified_clusters_dict, removed_clusters_dict = clustering_result
DBManager.overwrite_clusters_simplified(modified_clusters_dict,
removed_clusters_dict,
con=con,
close_connections=False)
reset_cluster_ids(con=con, close_connections=False)
new_cluster_dict = DBManager.load_cluster_dict(con=con,
close_connections=False)
overwrite_dict(cluster_dict, new_cluster_dict)
def process_images_dir_measure(cluster_dict, n):
images_path = DATASET_PATH
try:
print('------ PROCESSING FACES')
process_faces_measure(images_path, n)
print('------ DONE PROCESSING')
except IncompleteDatabaseOperation as e:
print('process_images_dir_measure error')
log_error(e)
return
cluster_dict_copy = cluster_dict.copy()
def cluster_processed_faces(con):
embeddings_with_ids = list(
DBManager.get_all_embeddings(with_ids=True))
# TODO: Call reclassify handler here?
# TODO: Clear existing clusters? Issues with ids etc.????
core_algorithm = CoreAlgorithm()
# passing result cluster dict already overwrites it
clustering_result = core_algorithm.cluster_embeddings(
embeddings_with_ids,
existing_clusters_dict=cluster_dict,
should_reset_cluster_ids=True,
final_clusters_only=False)
_, modified_clusters_dict, removed_clusters_dict = clustering_result
DBManager.overwrite_clusters_simplified(modified_clusters_dict,
removed_clusters_dict,
con=con,
close_connections=False)
try:
DBManager.connection_wrapper(cluster_processed_faces)
except IncompleteDatabaseOperation:
overwrite_dict(cluster_dict, cluster_dict_copy)
def clear_data_worker(central_con, local_con):
DBManager.clear_local_tables(path_to_local_db,
con=local_con,
close_connections=False)
clear_central_tables(con=central_con, close_connections=False)
overwrite_dict(cluster_dict, dict())