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Merge remote-tracking branch 'origin/master' into patch/adjustment-0103-1

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NatLee 2025-01-07 03:57:29 +08:00
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19
README.md
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@ -16,6 +16,9 @@
[![GitHub Sponsors](https://img.shields.io/github/sponsors/serengil?logo=GitHub&color=lightgray)](https://github.com/sponsors/serengil)
[![Buy Me a Coffee](https://img.shields.io/badge/-buy_me_a%C2%A0coffee-gray?logo=buy-me-a-coffee)](https://buymeacoffee.com/serengil)
[![Hacker News](https://img.shields.io/badge/dynamic/json?color=orange&label=Hacker%20News&query=score&url=https%3A%2F%2Fhacker-news.firebaseio.com%2Fv0%2Fitem%2F42584896.json&logo=y-combinator)](https://news.ycombinator.com/item?id=42584896)
[![Product Hunt](https://img.shields.io/badge/Product%20Hunt-%E2%96%B2-orange?logo=producthunt)](https://www.producthunt.com/posts/deepface?embed=true&utm_source=badge-featured&utm_medium=badge&utm_souce=badge-deepface)
<!-- [![DOI](http://img.shields.io/:DOI-10.1109/ICEET53442.2021.9659697-blue.svg?style=flat)](https://doi.org/10.1109/ICEET53442.2021.9659697) -->
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@ -392,7 +395,7 @@ Before creating a PR, you should run the unit tests and linting locally by runni
There are many ways to support a project - starring⭐️ the GitHub repo is just one 🙏
If you do like this work, then you can support it financially on [Patreon](https://www.patreon.com/serengil?repo=deepface), [GitHub Sponsors](https://github.com/sponsors/serengil) or [Buy Me a Coffee](https://buymeacoffee.com/serengil).
If you do like this work, then you can support it financially on [Patreon](https://www.patreon.com/serengil?repo=deepface), [GitHub Sponsors](https://github.com/sponsors/serengil) or [Buy Me a Coffee](https://buymeacoffee.com/serengil). Also, your company's logo will be shown on README on GitHub and PyPI if you become a sponsor in gold, silver or bronze tiers.
<a href="https://www.patreon.com/serengil?repo=deepface">
<img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/patreon.png" width="30%" height="30%">
@ -402,7 +405,19 @@ If you do like this work, then you can support it financially on [Patreon](https
<img src="https://raw.githubusercontent.com/serengil/deepface/master/icon/bmc-button.png" width="25%" height="25%">
</a>
Also, your company's logo will be shown on README on GitHub and PyPI if you become a sponsor in gold, silver or bronze tiers.
Additionally, you can help us reach a wider audience by upvoting our posts on Hacker News and Product Hunt.
<div style="display: flex; align-items: center; gap: 10px;">
<!-- Hacker News Badge -->
<a href="https://news.ycombinator.com/item?id=42584896">
<img src="https://hackerbadge.vercel.app/api?id=42584896&type=orange" style="width: 250px; height: 54px;" width="250" alt="Featured on Hacker News">
</a>
<!-- Product Hunt Badge -->
<a href="https://www.producthunt.com/posts/deepface?embed=true&utm_source=badge-featured&utm_medium=badge&utm_souce=badge-deepface" target="_blank">
<img src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=753599&theme=light" alt="DeepFace - A Lightweight Deep Face Recognition Library for Python | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" />
</a>
</div>
## Citation

138
deepface/DeepFace.py
View File

@ -68,18 +68,18 @@ def build_model(model_name: str, task: str = "facial_recognition") -> Any:
def verify(
img1_path: Union[str, np.ndarray, List[float]],
img2_path: Union[str, np.ndarray, List[float]],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
silent: bool = False,
threshold: Optional[float] = None,
anti_spoofing: bool = False,
img1_path: Union[str, np.ndarray, List[float]],
img2_path: Union[str, np.ndarray, List[float]],
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
silent: bool = False,
threshold: Optional[float] = None,
anti_spoofing: bool = False,
) -> Dict[str, Any]:
"""
Verify if an image pair represents the same person or different persons.
@ -164,14 +164,14 @@ def verify(
def analyze(
img_path: Union[str, np.ndarray],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent: bool = False,
anti_spoofing: bool = False,
img_path: Union[str, np.ndarray],
actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
silent: bool = False,
anti_spoofing: bool = False,
) -> List[Dict[str, Any]]:
"""
Analyze facial attributes such as age, gender, emotion, and race in the provided image.
@ -263,20 +263,20 @@ def analyze(
def find(
img_path: Union[str, np.ndarray],
db_path: str,
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
threshold: Optional[float] = None,
normalization: str = "base",
silent: bool = False,
refresh_database: bool = True,
anti_spoofing: bool = False,
batched: bool = False,
img_path: Union[str, np.ndarray],
db_path: str,
model_name: str = "VGG-Face",
distance_metric: str = "cosine",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
threshold: Optional[float] = None,
normalization: str = "base",
silent: bool = False,
refresh_database: bool = True,
anti_spoofing: bool = False,
batched: bool = False,
) -> Union[List[pd.DataFrame], List[List[Dict[str, Any]]]]:
"""
Identify individuals in a database
@ -369,15 +369,15 @@ def find(
def represent(
img_path: Union[str, np.ndarray],
model_name: str = "VGG-Face",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
anti_spoofing: bool = False,
max_faces: Optional[int] = None,
img_path: Union[str, np.ndarray],
model_name: str = "VGG-Face",
enforce_detection: bool = True,
detector_backend: str = "opencv",
align: bool = True,
expand_percentage: int = 0,
normalization: str = "base",
anti_spoofing: bool = False,
max_faces: Optional[int] = None,
) -> List[Dict[str, Any]]:
"""
Represent facial images as multi-dimensional vector embeddings.
@ -441,15 +441,16 @@ def represent(
def stream(
db_path: str = "",
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enable_face_analysis: bool = True,
source: Any = 0,
time_threshold: int = 5,
frame_threshold: int = 5,
anti_spoofing: bool = False,
db_path: str = "",
model_name: str = "VGG-Face",
detector_backend: str = "opencv",
distance_metric: str = "cosine",
enable_face_analysis: bool = True,
source: Any = 0,
time_threshold: int = 5,
frame_threshold: int = 5,
anti_spoofing: bool = False,
output_path: Optional[str] = None,
) -> None:
"""
Run real time face recognition and facial attribute analysis
@ -478,6 +479,10 @@ def stream(
frame_threshold (int): The frame threshold for face recognition (default is 5).
anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
output_path (str): Path to save the output video. (default is None
If None, no video is saved).
Returns:
None
"""
@ -495,19 +500,20 @@ def stream(
time_threshold=time_threshold,
frame_threshold=frame_threshold,
anti_spoofing=anti_spoofing,
output_path=output_path,
)
def extract_faces(
img_path: Union[str, np.ndarray],
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
grayscale: bool = False,
color_face: str = "rgb",
normalize_face: bool = True,
anti_spoofing: bool = False,
img_path: Union[str, np.ndarray],
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
expand_percentage: int = 0,
grayscale: bool = False,
color_face: str = "rgb",
normalize_face: bool = True,
anti_spoofing: bool = False,
) -> List[Dict[str, Any]]:
"""
Extract faces from a given image
@ -584,11 +590,11 @@ def cli() -> None:
def detectFace(
img_path: Union[str, np.ndarray],
target_size: tuple = (224, 224),
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
img_path: Union[str, np.ndarray],
target_size: tuple = (224, 224),
detector_backend: str = "opencv",
enforce_detection: bool = True,
align: bool = True,
) -> Union[np.ndarray, None]:
"""
Deprecated face detection function. Use extract_faces for same functionality.

4
deepface/modules/recognition.py
View File

@ -149,7 +149,7 @@ def find(
# Ensure the proper pickle file exists
if not os.path.exists(datastore_path):
with open(datastore_path, "wb") as f:
pickle.dump([], f)
pickle.dump([], f, pickle.HIGHEST_PROTOCOL)
# Load the representations from the pickle file
with open(datastore_path, "rb") as f:
@ -232,7 +232,7 @@ def find(
if must_save_pickle:
with open(datastore_path, "wb") as f:
pickle.dump(representations, f)
pickle.dump(representations, f, pickle.HIGHEST_PROTOCOL)
if not silent:
logger.info(f"There are now {len(representations)} representations in {file_name}")

48
deepface/modules/streaming.py
View File

@ -22,6 +22,7 @@ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
IDENTIFIED_IMG_SIZE = 112
TEXT_COLOR = (255, 255, 255)
# pylint: disable=unused-variable
def analysis(
db_path: str,
@ -33,6 +34,7 @@ def analysis(
time_threshold=5,
frame_threshold=5,
anti_spoofing: bool = False,
output_path: Optional[str] = None,
):
"""
Run real time face recognition and facial attribute analysis
@ -62,6 +64,8 @@ def analysis(
anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
output_path (str): Path to save the output video. (default is None
If None, no video is saved).
Returns:
None
"""
@ -77,12 +81,31 @@ def analysis(
model_name=model_name,
)
cap = cv2.VideoCapture(source if isinstance(source, str) else int(source))
if not cap.isOpened():
logger.error(f"Cannot open video source: {source}")
return
# Get video properties
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)
fourcc = cv2.VideoWriter_fourcc(*"mp4v") # Codec for output file
# Ensure the output directory exists if output_path is provided
if output_path:
os.makedirs(os.path.dirname(output_path), exist_ok=True)
# Initialize video writer if output_path is provided
video_writer = (
cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
if output_path
else None
)
freezed_img = None
freeze = False
num_frames_with_faces = 0
tic = time.time()
cap = cv2.VideoCapture(source) # webcam
while True:
has_frame, img = cap.read()
if not has_frame:
@ -91,9 +114,9 @@ def analysis(
# we are adding some figures into img such as identified facial image, age, gender
# that is why, we need raw image itself to make analysis
raw_img = img.copy()
faces_coordinates = []
if freeze is False:
if not freeze:
faces_coordinates = grab_facial_areas(
img=img, detector_backend=detector_backend, anti_spoofing=anti_spoofing
)
@ -101,7 +124,6 @@ def analysis(
# we will pass img to analyze modules (identity, demography) and add some illustrations
# that is why, we will not be able to extract detected face from img clearly
detected_faces = extract_facial_areas(img=img, faces_coordinates=faces_coordinates)
img = highlight_facial_areas(img=img, faces_coordinates=faces_coordinates)
img = countdown_to_freeze(
img=img,
@ -111,8 +133,8 @@ def analysis(
)
num_frames_with_faces = num_frames_with_faces + 1 if len(faces_coordinates) else 0
freeze = num_frames_with_faces > 0 and num_frames_with_faces % frame_threshold == 0
if freeze:
# add analyze results into img - derive from raw_img
img = highlight_facial_areas(
@ -144,22 +166,28 @@ def analysis(
tic = time.time()
logger.info("freezed")
elif freeze is True and time.time() - tic > time_threshold:
elif freeze and time.time() - tic > time_threshold:
freeze = False
freezed_img = None
# reset counter for freezing
tic = time.time()
logger.info("freeze released")
logger.info("Freeze released")
freezed_img = countdown_to_release(img=freezed_img, tic=tic, time_threshold=time_threshold)
display_img = img if freezed_img is None else freezed_img
cv2.imshow("img", img if freezed_img is None else freezed_img)
# Save the frame to output video if writer is initialized
if video_writer:
video_writer.write(display_img)
if cv2.waitKey(1) & 0xFF == ord("q"): # press q to quit
cv2.imshow("img", display_img)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# kill open cv things
# Release resources
cap.release()
if video_writer:
video_writer.release()
cv2.destroyAllWindows()