from __future__ import annotations
import paravision.recognition.sdk
import typing
import numpy
import paravision.recognition.types
_Shape = typing.Tuple[int, ...]

__all__ = [
    "Metadata",
    "SDK"
]


class Metadata():
    def __init__(self) -> None: ...

    @property
    def embedding_size(self) -> int:
        """
        The embedding size of the Recognition models being used.

        :type: int
        """
    @embedding_size.setter
    def embedding_size(self, arg0: int) -> None:
        """
        The embedding size of the Recognition models being used.
        """
    @property
    def engine(self) -> str:
        """
        The engine or accelerator of the Recognition SDK instance being used.

        :type: str
        """
    @engine.setter
    def engine(self, arg0: str) -> None:
        """
        The engine or accelerator of the Recognition SDK instance being used.
        """
    @property
    def engine_version(self) -> str:
        """
        The version of the engine or accelerator being used.

        :type: str
        """
    @engine_version.setter
    def engine_version(self, arg0: str) -> None:
        """
        The version of the engine or accelerator being used.
        """
    @property
    def generation(self) -> int:
        """
        The generation of the Recognition SDK.

        :type: int
        """
    @generation.setter
    def generation(self, arg0: int) -> None:
        """
        The generation of the Recognition SDK.
        """
    @property
    def model(self) -> str:
        """
        The name of the Recognition models.

        :type: str
        """
    @model.setter
    def model(self, arg0: str) -> None:
        """
        The name of the Recognition models.
        """
    @property
    def model_version(self) -> str:
        """
        The version of the Recognition models.

        :type: str
        """
    @model_version.setter
    def model_version(self, arg0: str) -> None:
        """
        The version of the Recognition models.
        """
    @property
    def sdk_version(self) -> str:
        """
        The version of the Recognition SDK.

        :type: str
        """
    @sdk_version.setter
    def sdk_version(self, arg0: str) -> None:
        """
        The version of the Recognition SDK.
        """
    pass


class SDK():
    """
    SDK()

    A sdk object contains an instance of the Paravision model and its
    associated resources.

    SDK objects are long-living and do not need to be re-instantiated between
    method calls.
    """
    def __init__(self, models_dir: typing.Optional[str] = None, settings: typing.Optional[paravision.recognition.types.Settings] = None) -> None: 
        """
        Create a new SDK instance with settings as a struct
        """

    @typing.overload
    def get_bounding_boxes(self, imgs: list[numpy.ndarray], image_source: paravision.recognition.types.ImageSource = ImageSource.UNKNOWN) -> paravision.recognition.types.InferenceResult: 
        """
        Detect bounding boxes of faces in the image, returning a list of Faces.
        """

    @typing.overload
    def get_bounding_boxes(self, imgs: list[paravision.recognition.types.Image], detection_model: paravision.recognition.types.ImageSource = '') -> paravision.recognition.types.InferenceResult: 
        """
        Accepts a list of NumPy arrays (images).
        """

    @typing.overload
    def get_embedding_from_prepared_image(self, prepared_image: numpy.ndarray) -> paravision.recognition.types.Embedding: 
        """
        Get the embedding for a prepared image.
        """

    @typing.overload
    def get_embedding_from_prepared_image(self, prepared_image: paravision.recognition.types.Image) -> paravision.recognition.types.Embedding: 
        """
        Accepts one prepared image (numpy array).
        """

    def get_embeddings(self, faces: list[paravision.recognition.types.Face]) -> None: 
        """
        Get the embeddings for faces.
        """

    @typing.overload
    def get_embeddings_from_landmarks(self, image: numpy.ndarray, landmarks: list[paravision.recognition.types.Landmarks]) -> list[paravision.recognition.types.Embedding]: 
        """
        Get the embeddings for faces.
        """

    @typing.overload
    def get_embeddings_from_landmarks(self, image: paravision.recognition.types.Image, landmarks: list[paravision.recognition.types.Landmarks]) -> list[paravision.recognition.types.Embedding]: 
        """
        Accepts a NumPy array (image) and a list of landmarks.
        """

    @typing.overload
    def get_faces(self, imgs: list[numpy.ndarray], qualities: bool = False, landmarks: bool = False, embeddings: bool = False, image_source: paravision.recognition.types.ImageSource = ImageSource.UNKNOWN) -> paravision.recognition.types.InferenceResult: 
        """
        Detect faces in the image.
        """

    @typing.overload
    def get_faces(self, imgs: list[paravision.recognition.types.Image], qualities: bool = False, landmarks: bool = False, embeddings: bool = False, image_source: paravision.recognition.types.ImageSource = ImageSource.UNKNOWN) -> paravision.recognition.types.InferenceResult: 
        """
        Includes bounding boxes, landmarks, and [optionally] image quality
        details.
        """

    def get_landmarks(self, faces: list[paravision.recognition.types.Face]) -> None: 
        """
        Get the landmarks for faces.
        """

    @typing.overload
    def get_landmarks_from_bounding_boxes(self, img: numpy.ndarray, bboxes: list[paravision.recognition.types.BoundingBox]) -> paravision.recognition.types.InferenceResult: 
        """
        Get the landmarks from a bounding box.
        """

    @typing.overload
    def get_landmarks_from_bounding_boxes(self, img: paravision.recognition.types.Image, bboxes: list[paravision.recognition.types.BoundingBox]) -> paravision.recognition.types.InferenceResult: 
        """
        Accepts a NumPy array (image) and a list of bounding boxes.
        """

    @staticmethod
    def get_match_score(emb1: paravision.recognition.types.Embedding, emb2: paravision.recognition.types.Embedding, scoring_mode: paravision.recognition.types.ScoringMode = ScoringMode.EnhancedEmbedding) -> int: 
        """
        Compute the difference score of two faces embeddings. A larger number indicates a
        greater similarity between the two embeddings; a lower number indicates a
        greater difference between the two embeddings.
        """

    @staticmethod
    def get_metadata(models_dir: typing.Optional[str] = None) -> Metadata: 
        """
        Returns metadata for SDK and model info.
        """

    def get_qualities(self, faces: list[paravision.recognition.types.Face]) -> None: 
        """
        Get the quality of the faces in the image.
        """

    @staticmethod
    def get_similarity(emb1: paravision.recognition.types.Embedding, emb2: paravision.recognition.types.Embedding, scoring_mode: paravision.recognition.types.ScoringMode = ScoringMode.EnhancedEmbedding) -> float: 
        """
        Compute the difference score of two faces embeddings. A larger number indicates a
        greater similarity between the two embeddings; a lower number indicates a
        greater difference between the two embeddings.
        """

    pass