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simplify getting pickled images

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Sefik Ilkin Serengil 2024-03-08 19:52:38 +00:00
parent b3c98e3b1f
commit 259add4a13
1 changed files with 16 additions and 9 deletions
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25
deepface/modules/recognition.py
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@ -132,23 +132,20 @@ def find(
f"Consider to delete {datastore_path}"
)
# Check if the representations are out-of-date
if len(representations) > 0:
pickled_images = [representation["identity"] for representation in representations]
else:
pickled_images = []
# embedded images
pickled_images = [representation["identity"] for representation in representations]
# Get the list of images on storage
storage_images = __list_images(path=db_path)
if len(storage_images) == 0:
raise ValueError(f"No item found in {db_path}")
# Enforce data consistency amongst on disk images and pickle file
must_save_pickle = False
new_images = list(set(storage_images) - set(pickled_images)) # images added to storage
old_images = list(set(pickled_images) - set(storage_images)) # images removed from storage
if not silent and (len(new_images) > 0 or len(old_images) > 0):
logger.info(f"Found {len(new_images)} new images and {len(old_images)} removed images")
# detect replaced images
replaced_images = []
for current_representation in representations:
@ -158,9 +155,16 @@ def find(
alpha_hash = current_representation["hash"]
beta_hash = package_utils.find_hash_of_file(identity)
if alpha_hash != beta_hash:
logger.warn(f"Even though {identity} represented before, it's replaced later.")
logger.debug(f"Even though {identity} represented before, it's replaced later.")
replaced_images.append(identity)
if not silent and (len(new_images) > 0 or len(old_images) > 0 or len(replaced_images) > 0):
logger.info(
f"Found {len(new_images)} newly added image(s)"
f", {len(old_images)} removed image(s)"
f", {len(replaced_images)} replaced image(s)."
)
# append replaced images into both old and new images. these will be dropped and re-added.
new_images = new_images + replaced_images
old_images = old_images + replaced_images
@ -201,6 +205,9 @@ def find(
# now, we got representations for facial database
df = pd.DataFrame(representations)
if silent is False:
logger.info(f"Searching {img_path} in {df.shape[0]} length datastore")
# img path might have more than once face
source_objs = detection.extract_faces(
img_path=img_path,