Function register

deepface/modules/datastore.py:34–136

Register identities to database for face recognition Args: img (str or np.ndarray or IO[bytes] or list): The exact path to the image, a numpy array in BGR format, a file object that supports at least `.read` and is opened in binary mode, or a base64 encoded i

(
    img: Union[str, NDArray[Any], IO[bytes], List[str], List[NDArray[Any]], List[IO[bytes]]],
    img_name: Optional[str] = None,
    model_name: str = "VGG-Face",
    detector_backend: str = "opencv",
    enforce_detection: bool = True,
    align: bool = True,
    l2_normalize: bool = False,
    expand_percentage: int = 0,
    normalization: str = "base",
    anti_spoofing: bool = False,
    database_type: str = "postgres",
    connection_details: Optional[Union[Dict[str, Any], str]] = None,
    connection: Any = None,
)

# pylint: disable=too-many-positional-arguments, no-else-return
def register(
    img: Union[str, NDArray[Any], IO[bytes], List[str], List[NDArray[Any]], List[IO[bytes]]],
    img_name: Optional[str] = None,
    model_name: str = "VGG-Face",
    detector_backend: str = "opencv",
    enforce_detection: bool = True,
    align: bool = True,
    l2_normalize: bool = False,
    expand_percentage: int = 0,
    normalization: str = "base",
    anti_spoofing: bool = False,
    database_type: str = "postgres",
    connection_details: Optional[Union[Dict[str, Any], str]] = None,
    connection: Any = None,
) -> Dict[str, Any]:
    """
    Register identities to database for face recognition
    Args:
        img (str or np.ndarray or IO[bytes] or list): The exact path to the image, a numpy array
            in BGR format, a file object that supports at least `.read` and is opened in binary
            mode, or a base64 encoded image. If a list is provided, each element should be a string
            or numpy array representing an image, and the function will process images in batch.
        img_name (optional str): image name to store in db, if not provided then we will try to
            extract it from given img.
        model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,
            OpenFace, DeepFace, DeepID, Dlib, ArcFace, SFace and GhostFaceNet (default is VGG-Face).
        detector_backend (string): face detector backend. Options: 'opencv', 'retinaface',
            'mtcnn', 'ssd', 'dlib', 'mediapipe', 'yolov8n', 'yolov8m', 'yolov8l', 'yolov11n',
            'yolov11s', 'yolov11m', 'yolov11l', 'yolov12n', 'yolov12s', 'yolov12m', 'yolov12l',
            'centerface' or 'skip' (default is opencv).
        enforce_detection (boolean): If no face is detected in an image, raise an exception.
            Set to False to avoid the exception for low-resolution images (default is True).
        align (bool): Flag to enable face alignment (default is True).
        l2_normalize (bool): Flag to enable L2 normalization (unit vector normalization)
        expand_percentage (int): expand detected facial area with a percentage (default is 0).
        normalization (string): Normalize the input image before feeding it to the model.
            Options: base, raw, Facenet, Facenet2018, VGGFace, VGGFace2, ArcFace (default is base).
        anti_spoofing (boolean): Flag to enable anti spoofing (default is False).
        database_type (str): Type of database to register identities. Options: 'postgres', 'mongo',
            'weaviate', 'neo4j', 'pgvector', 'pinecone' (default is 'postgres').
        connection_details (dict or str): Connection details for the database.
        connection (Any): Existing database connection object. If provided, this connection
            will be used instead of creating a new one.

        Note:
            Instead of providing `connection` or `connection_details`, database connection
            information can be supplied via environment variables:
            - DEEPFACE_POSTGRES_URI
            - DEEPFACE_MONGO_URI
            - DEEPFACE_WEAVIATE_URI
            - DEEPFACE_NEO4J_URI
            - DEEPFACE_PINECONE_API_KEY
    Returns:
        result (dict): A dictionary containing registration results with following keys.
            - inserted (int): Number of embeddings successfully registered to the database.
    """
    db_client = __connect_database(
        database_type=database_type,