mirror of
https://github.com/yakhyo/uniface.git
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0c93598007
* feat: Update linting and type annotations, return types in detect * feat: add face analyzer and face classes * chore: Update the format and clean up some docstrings * docs: Update usage documentation * feat: Change AgeGender model output to 0, 1 instead of string (Female, Male) * test: Update testing code * feat: Add Apple silicon backend for torchscript inference * feat: Add face analyzer example and add run emotion for testing
104 lines
4.0 KiB
Python
104 lines
4.0 KiB
Python
# Copyright 2025 Yakhyokhuja Valikhujaev
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# Author: Yakhyokhuja Valikhujaev
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# GitHub: https://github.com/yakhyo
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from typing import Optional
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from uniface.constants import ArcFaceWeights, MobileFaceWeights, SphereFaceWeights
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from uniface.model_store import verify_model_weights
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from .base import BaseRecognizer, PreprocessConfig
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__all__ = ['ArcFace', 'MobileFace', 'SphereFace']
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class ArcFace(BaseRecognizer):
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"""ArcFace model for robust face recognition.
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This class provides a concrete implementation of the BaseRecognizer,
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pre-configured for ArcFace models. It handles the loading of specific
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ArcFace weights and sets up the appropriate default preprocessing.
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Args:
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model_name (ArcFaceWeights): The specific ArcFace model variant to use.
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Defaults to `ArcFaceWeights.MNET`.
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preprocessing (Optional[PreprocessConfig]): An optional custom preprocessing
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configuration. If None, a default config for ArcFace is used.
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Example:
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>>> from uniface.recognition import ArcFace
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>>> recognizer = ArcFace()
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>>> # embedding = recognizer.get_normalized_embedding(image, landmarks)
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"""
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def __init__(
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self,
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model_name: ArcFaceWeights = ArcFaceWeights.MNET,
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preprocessing: Optional[PreprocessConfig] = None,
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) -> None:
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if preprocessing is None:
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preprocessing = PreprocessConfig(input_mean=127.5, input_std=127.5, input_size=(112, 112))
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model_path = verify_model_weights(model_name)
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super().__init__(model_path=model_path, preprocessing=preprocessing)
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class MobileFace(BaseRecognizer):
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"""Lightweight MobileFaceNet model for fast face recognition.
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This class provides a concrete implementation of the BaseRecognizer,
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pre-configured for MobileFaceNet models. It is optimized for speed,
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making it suitable for edge devices.
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Args:
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model_name (MobileFaceWeights): The specific MobileFaceNet model variant to use.
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Defaults to `MobileFaceWeights.MNET_V2`.
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preprocessing (Optional[PreprocessConfig]): An optional custom preprocessing
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configuration. If None, a default config for MobileFaceNet is used.
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Example:
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>>> from uniface.recognition import MobileFace
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>>> recognizer = MobileFace()
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>>> # embedding = recognizer.get_normalized_embedding(image, landmarks)
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"""
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def __init__(
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self,
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model_name: MobileFaceWeights = MobileFaceWeights.MNET_V2,
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preprocessing: Optional[PreprocessConfig] = None,
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) -> None:
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if preprocessing is None:
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preprocessing = PreprocessConfig(input_mean=127.5, input_std=127.5, input_size=(112, 112))
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model_path = verify_model_weights(model_name)
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super().__init__(model_path=model_path, preprocessing=preprocessing)
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class SphereFace(BaseRecognizer):
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"""SphereFace model using angular margin for face recognition.
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This class provides a concrete implementation of the BaseRecognizer,
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pre-configured for SphereFace models, which were among the first to
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introduce angular margin loss functions.
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Args:
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model_name (SphereFaceWeights): The specific SphereFace model variant to use.
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Defaults to `SphereFaceWeights.SPHERE20`.
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preprocessing (Optional[PreprocessConfig]): An optional custom preprocessing
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configuration. If None, a default config for SphereFace is used.
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Example:
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>>> from uniface.recognition import SphereFace
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>>> recognizer = SphereFace()
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>>> # embedding = recognizer.get_normalized_embedding(image, landmarks)
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"""
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def __init__(
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self,
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model_name: SphereFaceWeights = SphereFaceWeights.SPHERE20,
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preprocessing: Optional[PreprocessConfig] = None,
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) -> None:
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if preprocessing is None:
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preprocessing = PreprocessConfig(input_mean=127.5, input_std=127.5, input_size=(112, 112))
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model_path = verify_model_weights(model_name)
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super().__init__(model_path=model_path, preprocessing=preprocessing)
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