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Merge pull request #1415 from Mehrab-Shahbazi/master

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video path is enabled in stream
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Sefik Ilkin Serengil 2025-01-06 09:26:35 +00:00 committed by GitHub
commit c465234788
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2 changed files with 110 additions and 76 deletions
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138
deepface/DeepFace.py
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@ -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.

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()