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https://github.com/serengil/deepface.git
synced 2025-06-06 11:35:21 +00:00
saving video output in stream is enabled
This commit is contained in:
Mehrab Shahbazi
parent
33a502f609
commit
2f9ef19e09
@ -68,18 +68,18 @@ def build_model(model_name: str, task: str = "facial_recognition") -> Any:
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def verify(
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img1_path: Union[str, np.ndarray, List[float]],
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img2_path: Union[str, np.ndarray, List[float]],
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model_name: str = "VGG-Face",
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detector_backend: str = "opencv",
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distance_metric: str = "cosine",
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enforce_detection: bool = True,
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align: bool = True,
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expand_percentage: int = 0,
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normalization: str = "base",
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silent: bool = False,
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threshold: Optional[float] = None,
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anti_spoofing: bool = False,
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img1_path: Union[str, np.ndarray, List[float]],
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img2_path: Union[str, np.ndarray, List[float]],
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model_name: str = "VGG-Face",
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detector_backend: str = "opencv",
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distance_metric: str = "cosine",
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enforce_detection: bool = True,
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align: bool = True,
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expand_percentage: int = 0,
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normalization: str = "base",
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silent: bool = False,
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threshold: Optional[float] = None,
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anti_spoofing: bool = False,
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) -> Dict[str, Any]:
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"""
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Verify if an image pair represents the same person or different persons.
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@ -164,14 +164,14 @@ def verify(
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def analyze(
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img_path: Union[str, np.ndarray],
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actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
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enforce_detection: bool = True,
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detector_backend: str = "opencv",
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align: bool = True,
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expand_percentage: int = 0,
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silent: bool = False,
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anti_spoofing: bool = False,
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img_path: Union[str, np.ndarray],
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actions: Union[tuple, list] = ("emotion", "age", "gender", "race"),
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enforce_detection: bool = True,
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detector_backend: str = "opencv",
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align: bool = True,
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expand_percentage: int = 0,
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silent: bool = False,
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anti_spoofing: bool = False,
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) -> List[Dict[str, Any]]:
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"""
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Analyze facial attributes such as age, gender, emotion, and race in the provided image.
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@ -263,20 +263,20 @@ def analyze(
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def find(
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img_path: Union[str, np.ndarray],
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db_path: str,
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model_name: str = "VGG-Face",
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distance_metric: str = "cosine",
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enforce_detection: bool = True,
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detector_backend: str = "opencv",
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align: bool = True,
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expand_percentage: int = 0,
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threshold: Optional[float] = None,
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normalization: str = "base",
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silent: bool = False,
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refresh_database: bool = True,
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anti_spoofing: bool = False,
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batched: bool = False,
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img_path: Union[str, np.ndarray],
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db_path: str,
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model_name: str = "VGG-Face",
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distance_metric: str = "cosine",
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enforce_detection: bool = True,
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detector_backend: str = "opencv",
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align: bool = True,
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expand_percentage: int = 0,
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threshold: Optional[float] = None,
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normalization: str = "base",
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silent: bool = False,
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refresh_database: bool = True,
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anti_spoofing: bool = False,
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batched: bool = False,
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) -> Union[List[pd.DataFrame], List[List[Dict[str, Any]]]]:
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"""
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Identify individuals in a database
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@ -369,15 +369,15 @@ def find(
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def represent(
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img_path: Union[str, np.ndarray],
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model_name: str = "VGG-Face",
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enforce_detection: bool = True,
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detector_backend: str = "opencv",
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align: bool = True,
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expand_percentage: int = 0,
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normalization: str = "base",
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anti_spoofing: bool = False,
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max_faces: Optional[int] = None,
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img_path: Union[str, np.ndarray],
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model_name: str = "VGG-Face",
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enforce_detection: bool = True,
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detector_backend: str = "opencv",
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align: bool = True,
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expand_percentage: int = 0,
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normalization: str = "base",
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anti_spoofing: bool = False,
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max_faces: Optional[int] = None,
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) -> List[Dict[str, Any]]:
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"""
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Represent facial images as multi-dimensional vector embeddings.
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@ -441,15 +441,16 @@ def represent(
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def stream(
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db_path: str = "",
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model_name: str = "VGG-Face",
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detector_backend: str = "opencv",
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distance_metric: str = "cosine",
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enable_face_analysis: bool = True,
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source: Any = 0,
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time_threshold: int = 5,
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frame_threshold: int = 5,
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anti_spoofing: bool = False,
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db_path: str = "",
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model_name: str = "VGG-Face",
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detector_backend: str = "opencv",
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distance_metric: str = "cosine",
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enable_face_analysis: bool = True,
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source: Any = 0,
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time_threshold: int = 5,
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frame_threshold: int = 5,
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anti_spoofing: bool = False,
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output_path: Optional[str] = None, # New parameter
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) -> None:
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"""
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Run real time face recognition and facial attribute analysis
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@ -478,6 +479,9 @@ def stream(
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frame_threshold (int): The frame threshold for face recognition (default is 5).
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anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
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output_path (str): Path to save the output video. If None, no video is saved.
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Returns:
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None
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"""
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@ -495,19 +499,20 @@ def stream(
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time_threshold=time_threshold,
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frame_threshold=frame_threshold,
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anti_spoofing=anti_spoofing,
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output_path=output_path, # Pass the output_path to analysis
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)
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def extract_faces(
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img_path: Union[str, np.ndarray],
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detector_backend: str = "opencv",
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enforce_detection: bool = True,
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align: bool = True,
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expand_percentage: int = 0,
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grayscale: bool = False,
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color_face: str = "rgb",
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normalize_face: bool = True,
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anti_spoofing: bool = False,
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img_path: Union[str, np.ndarray],
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detector_backend: str = "opencv",
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enforce_detection: bool = True,
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align: bool = True,
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expand_percentage: int = 0,
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grayscale: bool = False,
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color_face: str = "rgb",
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normalize_face: bool = True,
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anti_spoofing: bool = False,
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) -> List[Dict[str, Any]]:
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"""
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Extract faces from a given image
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@ -584,11 +589,11 @@ def cli() -> None:
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def detectFace(
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img_path: Union[str, np.ndarray],
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target_size: tuple = (224, 224),
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detector_backend: str = "opencv",
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enforce_detection: bool = True,
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align: bool = True,
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img_path: Union[str, np.ndarray],
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target_size: tuple = (224, 224),
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detector_backend: str = "opencv",
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enforce_detection: bool = True,
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align: bool = True,
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) -> Union[np.ndarray, None]:
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"""
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Deprecated face detection function. Use extract_faces for same functionality.
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@ -34,42 +34,29 @@ def analysis(
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time_threshold=5,
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frame_threshold=5,
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anti_spoofing: bool = False,
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output_path: Optional[str] = None, # New parameter
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):
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"""
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Run real time face recognition and facial attribute analysis
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Run real-time face recognition and facial attribute analysis, with optional video output.
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Args:
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db_path (string): Path to the folder containing image files. All detected faces
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in the database will be considered in the decision-making process.
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model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
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OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face)
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detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
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'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8', 'yolov11n', 'yolov11s', 'yolov11m',
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'centerface' or 'skip' (default is opencv).
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distance_metric (string): Metric for measuring similarity. Options: 'cosine',
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'euclidean', 'euclidean_l2' (default is cosine).
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enable_face_analysis (bool): Flag to enable face analysis (default is True).
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source (Any): The source for the video stream (default is 0, which represents the
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default camera).
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time_threshold (int): The time threshold (in seconds) for face recognition (default is 5).
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frame_threshold (int): The frame threshold for face recognition (default is 5).
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anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
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db_path (str): Path to the folder containing image files.
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model_name (str): Model for face recognition.
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detector_backend (str): Face detector backend.
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distance_metric (str): Metric for measuring similarity.
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enable_face_analysis (bool): Flag to enable face analysis.
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source (Any): The source for the video stream (camera index or video file path).
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time_threshold (int): Time threshold (in seconds) for face recognition.
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frame_threshold (int): Frame threshold for face recognition.
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anti_spoofing (bool): Flag to enable anti-spoofing.
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output_path (str): Path to save the output video. If None, no video is saved.
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Returns:
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None
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"""
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# initialize models
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# Initialize models
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build_demography_models(enable_face_analysis=enable_face_analysis)
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build_facial_recognition_model(model_name=model_name)
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# call a dummy find function for db_path once to create embeddings before starting webcam
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_ = search_identity(
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detected_face=np.zeros([224, 224, 3]),
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db_path=db_path,
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@ -78,35 +65,40 @@ def analysis(
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model_name=model_name,
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)
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cap = cv2.VideoCapture(source if isinstance(source, str) else int(source))
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if not cap.isOpened():
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logger.error(f"Cannot open video source: {source}")
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return
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# Get video properties
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = cap.get(cv2.CAP_PROP_FPS)
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fourcc = cv2.VideoWriter_fourcc(*"mp4v") # Codec for output file
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# Initialize video writer if output_path is provided
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video_writer = None
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if output_path:
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video_writer = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
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freezed_img = None
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freeze = False
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num_frames_with_faces = 0
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tic = time.time()
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# If source is an integer, use it as a webcam index. Otherwise, treat it as a video file path.
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if isinstance(source, int):
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cap = cv2.VideoCapture(source) # webcam
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else:
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cap = cv2.VideoCapture(str(source)) # video file
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while True:
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has_frame, img = cap.read()
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if not has_frame:
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break
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# we are adding some figures into img such as identified facial image, age, gender
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# that is why, we need raw image itself to make analysis
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raw_img = img.copy()
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faces_coordinates = []
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if freeze is False:
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if not freeze:
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faces_coordinates = grab_facial_areas(
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img=img, detector_backend=detector_backend, anti_spoofing=anti_spoofing
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)
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# we will pass img to analyze modules (identity, demography) and add some illustrations
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# that is why, we will not be able to extract detected face from img clearly
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detected_faces = extract_facial_areas(img=img, faces_coordinates=faces_coordinates)
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img = highlight_facial_areas(img=img, faces_coordinates=faces_coordinates)
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img = countdown_to_freeze(
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img=img,
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@ -116,22 +108,18 @@ def analysis(
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)
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num_frames_with_faces = num_frames_with_faces + 1 if len(faces_coordinates) else 0
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freeze = num_frames_with_faces > 0 and num_frames_with_faces % frame_threshold == 0
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if freeze:
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# add analyze results into img - derive from raw_img
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img = highlight_facial_areas(
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img=raw_img, faces_coordinates=faces_coordinates, anti_spoofing=anti_spoofing
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)
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# age, gender and emotion analysis
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img = perform_demography_analysis(
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enable_face_analysis=enable_face_analysis,
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img=raw_img,
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faces_coordinates=faces_coordinates,
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detected_faces=detected_faces,
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)
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# facial recogntion analysis
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img = perform_facial_recognition(
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img=img,
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faces_coordinates=faces_coordinates,
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@ -141,30 +129,31 @@ def analysis(
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distance_metric=distance_metric,
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model_name=model_name,
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)
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# freeze the img after analysis
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freezed_img = img.copy()
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# start counter for freezing
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tic = time.time()
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logger.info("freezed")
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logger.info("Image frozen for analysis")
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elif freeze is True and time.time() - tic > time_threshold:
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elif freeze and time.time() - tic > time_threshold:
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freeze = False
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freezed_img = None
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# reset counter for freezing
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tic = time.time()
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logger.info("freeze released")
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logger.info("Freeze released")
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freezed_img = countdown_to_release(img=freezed_img, tic=tic, time_threshold=time_threshold)
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display_img = img if freezed_img is None else freezed_img
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cv2.imshow("img", img if freezed_img is None else freezed_img)
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# Save the frame to output video if writer is initialized
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if video_writer:
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video_writer.write(display_img)
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if cv2.waitKey(1) & 0xFF == ord("q"): # press q to quit
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cv2.imshow("img", display_img)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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# kill open cv things
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# Release resources
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cap.release()
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if video_writer:
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video_writer.release()
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cv2.destroyAllWindows()
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