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cbcd89b167
* chore: Rename scripts to tools folder and unify argument parser * refactor: Centralize dataclasses in types.py and add __call__ to all models - Move Face and result dataclasses to uniface/types.py - Add GazeResult, SpoofingResult, EmotionResult (frozen=True) - Add __call__ to BaseDetector, BaseRecognizer, BaseLandmarker - Add __repr__ to all dataclasses - Replace print() with Logger in onnx_utils.py - Update tools and docs to use new dataclass return types - Add test_types.py with comprehensive dataclass testschore: Rename files under tools folder and unitify argument parser for them
48 lines
1.5 KiB
Python
48 lines
1.5 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 __future__ import annotations
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from uniface.constants import MiniFASNetWeights
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from uniface.types import SpoofingResult
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from .base import BaseSpoofer
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from .minifasnet import MiniFASNet
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__all__ = [
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'BaseSpoofer',
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'MiniFASNet',
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'MiniFASNetWeights',
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'SpoofingResult',
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'create_spoofer',
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]
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def create_spoofer(
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model_name: MiniFASNetWeights = MiniFASNetWeights.V2,
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scale: float | None = None,
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) -> MiniFASNet:
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"""Factory function to create a face anti-spoofing model.
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This is a convenience function that creates a MiniFASNet instance
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with the specified model variant and optional custom scale.
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Args:
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model_name: The model variant to use. Options:
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- MiniFASNetWeights.V2: Improved version (default), uses scale=2.7
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- MiniFASNetWeights.V1SE: Squeeze-and-excitation version, uses scale=4.0
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scale: Custom crop scale factor for face region. If None, uses the
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default scale for the selected model variant.
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Returns:
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An initialized face anti-spoofing model.
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Example:
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>>> from uniface.spoofing import create_spoofer, MiniFASNetWeights
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>>> spoofer = create_spoofer()
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>>> result = spoofer.predict(image, face.bbox)
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>>> print(f'Is real: {result.is_real}, Confidence: {result.confidence:.2%}')
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"""
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return MiniFASNet(model_name=model_name, scale=scale)
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