mirror of
https://github.com/facefusion/facefusion.git
synced 2026-06-09 22:13:55 +02:00
Henry Ruhs
64 lines
2.2 KiB
Python
Executable File
64 lines
2.2 KiB
Python
Executable File
from typing import Any, Literal, Callable, List, Tuple, Dict, TypedDict
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from collections import namedtuple
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import numpy
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Bbox = numpy.ndarray[Any, Any]
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Kps = numpy.ndarray[Any, Any]
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Score = float
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Embedding = numpy.ndarray[Any, Any]
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Face = namedtuple('Face',
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[
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'bbox',
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'kps',
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'score',
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'embedding',
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'normed_embedding',
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'gender',
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'age'
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])
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FaceSet = Dict[str, List[Face]]
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FaceStore = TypedDict('FaceStore',
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{
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'static_faces' : FaceSet,
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'reference_faces': FaceSet
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})
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VisionFrame = numpy.ndarray[Any, Any]
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Mask = numpy.ndarray[Any, Any]
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Matrix = numpy.ndarray[Any, Any]
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AudioBuffer = bytes
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Audio = numpy.ndarray[Any, Any]
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AudioFrame = numpy.ndarray[Any, Any]
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Spectrogram = numpy.ndarray[Any, Any]
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Fps = float
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Padding = Tuple[int, int, int, int]
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Resolution = Tuple[int, int]
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Update_Process = Callable[[], None]
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Process_Frames = Callable[[List[str], List[str], Update_Process], None]
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Template = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'ffhq_512']
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ProcessMode = Literal['output', 'preview', 'stream']
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LogLevel = Literal['error', 'warn', 'info', 'debug']
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VideoMemoryStrategy = Literal['strict', 'moderate', 'tolerant']
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FaceSelectorMode = Literal['reference', 'one', 'many']
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FaceAnalyserOrder = Literal['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best']
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FaceAnalyserAge = Literal['child', 'teen', 'adult', 'senior']
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FaceAnalyserGender = Literal['male', 'female']
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FaceDetectorModel = Literal['retinaface', 'yoloface', 'yunet']
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FaceRecognizerModel = Literal['arcface_blendswap', 'arcface_inswapper', 'arcface_simswap']
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FaceMaskType = Literal['box', 'occlusion', 'region']
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FaceMaskRegion = Literal['skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'eye-glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip']
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TempFrameFormat = Literal['jpg', 'png', 'bmp']
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OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc']
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OutputVideoPreset = Literal['ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow']
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ModelValue = Dict[str, Any]
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ModelSet = Dict[str, ModelValue]
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OptionsWithModel = TypedDict('OptionsWithModel',
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{
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'model' : ModelValue
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})
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