github/deepface
1
1
Fork 0
mirror of https://github.com/serengil/deepface.git synced 2025-06-11 05:47:08 +00:00
Code Issues Packages Projects Releases Wiki Activity

Use path join and amend undetected faces addition repetition

Browse Source
This commit is contained in:
Andrea Lanfranchi 2024-02-21 19:18:01 +01:00
parent c4b48ce46f
commit 72e5669302
No known key found for this signature in database
GPG Key ID: 2D623A60BB279915
1 changed files with 36 additions and 29 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

65
deepface/modules/recognition.py
View File

@ -99,7 +99,7 @@ def find(
file_name = f"representations_{model_name}.pkl" file_name = f"representations_{model_name}.pkl"
file_name = file_name.replace("-", "_").lower() file_name = file_name.replace("-", "_").lower()
datastore_path = f"{db_path}/{file_name}" datastore_path = os.path.join(db_path, file_name)
df_cols = [ df_cols = [
"identity", "identity",
@ -162,7 +162,7 @@ def find(
logger.info( logger.info(
f"{len(newbies)} new representations are just added" f"{len(newbies)} new representations are just added"
f" whereas {len(oldies)} represented one(s) are just dropped" f" whereas {len(oldies)} represented one(s) are just dropped"
f" in {db_path}/{file_name} file." f" in {os.path.join(db_path,file_name)} file."
) )
if not silent: if not silent:
@ -173,8 +173,8 @@ def find(
if len(employees) == 0: if len(employees) == 0:
raise ValueError( raise ValueError(
f"There is no image in {db_path} folder!" f"Could not find any valid image in {db_path} folder!"
"Validate .jpg, .jpeg or .png files exist in this path.", "Valid images are .jpg, .jpeg or .png files.",
) )
# ------------------------ # ------------------------
@ -196,7 +196,7 @@ def find(
pickle.dump(representations, f) pickle.dump(representations, f)
if not silent: if not silent:
logger.info(f"Representations stored in {db_path}/{file_name} file.") logger.info(f"Representations stored in {datastore_path} file.")
# ---------------------------- # ----------------------------
# now, we got representations for facial database # now, we got representations for facial database
@ -241,6 +241,9 @@ def find(
distances = [] distances = []
for _, instance in df.iterrows(): for _, instance in df.iterrows():
source_representation = instance[f"{model_name}_representation"] source_representation = instance[f"{model_name}_representation"]
if source_representation is None:
distances.append(float("inf")) # no representation for this image
continue
target_dims = len(list(target_representation)) target_dims = len(list(target_representation))
source_dims = len(list(source_representation)) source_dims = len(list(source_representation))
@ -292,7 +295,7 @@ def find(
return resp_obj return resp_obj
def __list_images(path: str) -> list: def __list_images(path: str) -> list[str]:
""" """
List images in a given path List images in a given path
Args: Args:
@ -304,7 +307,7 @@ def __list_images(path: str) -> list:
for r, _, f in os.walk(path): for r, _, f in os.walk(path):
for file in f: for file in f:
if file.lower().endswith((".jpg", ".jpeg", ".png")): if file.lower().endswith((".jpg", ".jpeg", ".png")):
exact_path = f"{r}/{file}" exact_path = os.path.join(r, file)
images.append(exact_path) images.append(exact_path)
return images return images
@ -365,31 +368,35 @@ def __find_bulk_embeddings(
expand_percentage=expand_percentage, expand_percentage=expand_percentage,
) )
except ValueError as err: except ValueError as err:
logger.warn( logger.error(
f"Exception while extracting faces from {employee}: {str(err)}. Skipping it." f"Exception while extracting faces from {employee}: {str(err)}"
) )
img_objs = [] img_objs = []
for img_obj in img_objs: if len(img_objs) == 0:
img_content = img_obj["face"] logger.warn(f"No face detected in {employee}. It will be skipped in detection.")
img_region = img_obj["facial_area"] representations.append((employee, None, 0, 0, 0, 0))
embedding_obj = representation.represent( else:
img_path=img_content, for img_obj in img_objs:
model_name=model_name, img_content = img_obj["face"]
enforce_detection=enforce_detection, img_region = img_obj["facial_area"]
detector_backend="skip", embedding_obj = representation.represent(
align=align, img_path=img_content,
normalization=normalization, model_name=model_name,
) enforce_detection=enforce_detection,
detector_backend="skip",
align=align,
normalization=normalization,
)
img_representation = embedding_obj[0]["embedding"] img_representation = embedding_obj[0]["embedding"]
representations.append((
employee,
img_representation,
img_region["x"],
img_region["y"],
img_region["w"],
img_region["h"]
))
instance = []
instance.append(employee)
instance.append(img_representation)
instance.append(img_region["x"])
instance.append(img_region["y"])
instance.append(img_region["w"])
instance.append(img_region["h"])
representations.append(instance)
return representations return representations