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some improvements

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- pickle file is out dated, then user informed with meaningful message.
- response df of find, we were just showing source image's region,
we just added the target image's region
This commit is contained in:
Sefik Ilkin Serengil 2023-12-08 22:15:34 +00:00
parent adfb29dfc7
commit 76a6bce824
1 changed files with 34 additions and 10 deletions
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44
deepface/DeepFace.py
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@ -30,6 +30,8 @@ from deepface.extendedmodels import Age, Gender, Race, Emotion
from deepface.commons import functions, realtime, distance as dst
from deepface.commons.logger import Logger
# pylint: disable=no-else-raise
logger = Logger(module="DeepFace")
# -----------------------------------
@ -465,8 +467,16 @@ def find(
file_name = f"representations_{model_name}.pkl"
file_name = file_name.replace("-", "_").lower()
if path.exists(db_path + "/" + file_name):
df_cols = [
"identity",
f"{model_name}_representation",
"target_x",
"target_y",
"target_w",
"target_h",
]
if path.exists(db_path + "/" + file_name):
if not silent:
logger.warn(
f"Representations for images in {db_path} folder were previously stored"
@ -477,6 +487,12 @@ def find(
with open(f"{db_path}/{file_name}", "rb") as f:
representations = pickle.load(f)
if len(representations) > 0 and len(representations[0]) != len(df_cols):
raise ValueError(
f"Seems existing {db_path}/{file_name} is out-of-the-date."
"Delete it and re-run."
)
if not silent:
logger.info(f"There are {len(representations)} representations found in {file_name}")
@ -523,7 +539,7 @@ def find(
align=align,
)
for img_content, _, _ in img_objs:
for img_content, img_region, _ in img_objs:
embedding_obj = represent(
img_path=img_content,
model_name=model_name,
@ -538,6 +554,10 @@ def find(
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)
# -------------------------------
@ -553,10 +573,13 @@ def find(
# ----------------------------
# now, we got representations for facial database
df = pd.DataFrame(representations, columns=["identity", f"{model_name}_representation"])
df = pd.DataFrame(
representations,
columns=df_cols,
)
# img path might have more than once face
target_objs = functions.extract_faces(
source_objs = functions.extract_faces(
img=img_path,
target_size=target_size,
detector_backend=detector_backend,
@ -567,9 +590,9 @@ def find(
resp_obj = []
for target_img, target_region, _ in target_objs:
for source_img, source_region, _ in source_objs:
target_embedding_obj = represent(
img_path=target_img,
img_path=source_img,
model_name=model_name,
enforce_detection=enforce_detection,
detector_backend="skip",
@ -580,10 +603,10 @@ def find(
target_representation = target_embedding_obj[0]["embedding"]
result_df = df.copy() # df will be filtered in each img
result_df["source_x"] = target_region["x"]
result_df["source_y"] = target_region["y"]
result_df["source_w"] = target_region["w"]
result_df["source_h"] = target_region["h"]
result_df["source_x"] = source_region["x"]
result_df["source_y"] = source_region["y"]
result_df["source_w"] = source_region["w"]
result_df["source_h"] = source_region["h"]
distances = []
for index, instance in df.iterrows():
@ -815,6 +838,7 @@ def extract_faces(
"""
resp_objs = []
img_objs = functions.extract_faces(
img=img_path,
target_size=target_size,