from collections import namedtuple from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, TypedDict import numpy from numpy.typing import NDArray from onnxruntime import InferenceSession Scale = float Score = float Angle = int Detection = NDArray[Any] Prediction = NDArray[Any] BoundingBox = NDArray[Any] FaceLandmark5 = NDArray[Any] FaceLandmark68 = NDArray[Any] FaceLandmarkSet = TypedDict('FaceLandmarkSet', { '5' : FaceLandmark5, #type:ignore[valid-type] '5/68' : FaceLandmark5, #type:ignore[valid-type] '68' : FaceLandmark68, #type:ignore[valid-type] '68/5' : FaceLandmark68 #type:ignore[valid-type] }) FaceScoreSet = TypedDict('FaceScoreSet', { 'detector' : Score, 'landmarker' : Score }) Embedding = NDArray[numpy.float64] Gender = Literal['female', 'male'] Age = range Race = Literal['white', 'black', 'latino', 'asian', 'indian', 'arabic'] Face = namedtuple('Face', [ 'bounding_box', 'score_set', 'landmark_set', 'angle', 'embedding', 'normed_embedding', 'gender', 'age', 'race' ]) FaceSet = Dict[str, List[Face]] FaceStore = TypedDict('FaceStore', { 'static_faces' : FaceSet, 'reference_faces' : FaceSet }) VisionFrame = NDArray[Any] Mask = NDArray[Any] Points = NDArray[Any] Distance = NDArray[Any] Matrix = NDArray[Any] Anchors = NDArray[Any] Translation = NDArray[Any] AudioBuffer = bytes Audio = NDArray[Any] AudioChunk = NDArray[Any] AudioFrame = NDArray[Any] Spectrogram = NDArray[Any] Mel = NDArray[Any] MelFilterBank = NDArray[Any] Fps = float Duration = float Padding = Tuple[int, int, int, int] Orientation = Literal['landscape', 'portrait'] Resolution = Tuple[int, int] ProcessState = Literal['checking', 'processing', 'stopping', 'pending'] QueuePayload = TypedDict('QueuePayload', { 'frame_number' : int, 'frame_path' : str }) Args = Dict[str, Any] UpdateProgress = Callable[[int], None] ProcessFrames = Callable[[List[str], List[QueuePayload], UpdateProgress], None] ProcessStep = Callable[[str, int, Args], bool] Content = Dict[str, Any] WarpTemplate = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'dfl_whole_face', 'ffhq_512', 'mtcnn_512', 'styleganex_384'] WarpTemplateSet = Dict[WarpTemplate, NDArray[Any]] ProcessMode = Literal['output', 'preview', 'stream'] ErrorCode = Literal[0, 1, 2, 3, 4] LogLevel = Literal['error', 'warn', 'info', 'debug'] LogLevelSet = Dict[LogLevel, int] TableHeaders = List[str] TableContents = List[List[Any]] FaceDetectorModel = Literal['many', 'retinaface', 'scrfd', 'yoloface'] FaceLandmarkerModel = Literal['many', '2dfan4', 'peppa_wutz'] FaceDetectorSet = Dict[FaceDetectorModel, List[str]] FaceSelectorMode = Literal['many', 'one', 'reference'] FaceSelectorOrder = Literal['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best'] FaceOccluderModel = Literal['xseg_1', 'xseg_2'] FaceParserModel = Literal['bisenet_resnet_18', 'bisenet_resnet_34'] FaceMaskType = Literal['box', 'occlusion', 'region'] FaceMaskRegion = Literal['skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip'] FaceMaskRegionSet = Dict[FaceMaskRegion, int] TempFrameFormat = Literal['bmp', 'jpg', 'png'] OutputAudioEncoder = Literal['aac', 'libmp3lame', 'libopus', 'libvorbis'] OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf','h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox'] OutputVideoPreset = Literal['ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow'] ModelOptions = Dict[str, Any] ModelSet = Dict[str, ModelOptions] ModelInitializer = NDArray[Any] ExecutionProvider = Literal['cpu', 'coreml', 'cuda', 'directml', 'openvino', 'rocm', 'tensorrt'] ExecutionProviderValue = Literal['CPUExecutionProvider', 'CoreMLExecutionProvider', 'CUDAExecutionProvider', 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'ROCMExecutionProvider', 'TensorrtExecutionProvider'] ExecutionProviderSet = Dict[ExecutionProvider, ExecutionProviderValue] ValueAndUnit = TypedDict('ValueAndUnit', { 'value' : int, 'unit' : str }) ExecutionDeviceFramework = TypedDict('ExecutionDeviceFramework', { 'name' : str, 'version' : str }) ExecutionDeviceProduct = TypedDict('ExecutionDeviceProduct', { 'vendor' : str, 'name' : str }) ExecutionDeviceVideoMemory = TypedDict('ExecutionDeviceVideoMemory', { 'total' : Optional[ValueAndUnit], 'free' : Optional[ValueAndUnit] }) ExecutionDeviceTemperature = TypedDict('ExecutionDeviceTemperature', { 'gpu' : Optional[ValueAndUnit], 'memory' : Optional[ValueAndUnit] }) ExecutionDeviceUtilization = TypedDict('ExecutionDeviceUtilization', { 'gpu' : Optional[ValueAndUnit], 'memory' : Optional[ValueAndUnit] }) ExecutionDevice = TypedDict('ExecutionDevice', { 'driver_version' : str, 'framework' : ExecutionDeviceFramework, 'product' : ExecutionDeviceProduct, 'video_memory' : ExecutionDeviceVideoMemory, 'temperature': ExecutionDeviceTemperature, 'utilization' : ExecutionDeviceUtilization }) DownloadProvider = Literal['github', 'huggingface'] DownloadProviderValue = TypedDict('DownloadProviderValue', { 'url' : str, 'path' : str }) DownloadProviderSet = Dict[DownloadProvider, DownloadProviderValue] DownloadScope = Literal['lite', 'full'] Download = TypedDict('Download', { 'url' : str, 'path' : str }) DownloadSet = Dict[str, Download] VideoMemoryStrategy = Literal['strict', 'moderate', 'tolerant'] File = TypedDict('File', { 'name' : str, 'extension' : str, 'path': str }) AppContext = Literal['cli', 'ui'] InferencePool = Dict[str, InferenceSession] InferencePoolSet = Dict[AppContext, Dict[str, InferencePool]] UiWorkflow = Literal['instant_runner', 'job_runner', 'job_manager'] JobStore = TypedDict('JobStore', { 'job_keys' : List[str], 'step_keys' : List[str] }) JobOutputSet = Dict[str, List[str]] JobStatus = Literal['drafted', 'queued', 'completed', 'failed'] JobStepStatus = Literal['drafted', 'queued', 'started', 'completed', 'failed'] JobStep = TypedDict('JobStep', { 'args' : Args, 'status' : JobStepStatus }) Job = TypedDict('Job', { 'version' : str, 'date_created' : str, 'date_updated' : Optional[str], 'steps' : List[JobStep] }) JobSet = Dict[str, Job] ApplyStateItem = Callable[[Any, Any], None] StateKey = Literal\ [ 'command', 'config_path', 'temp_path', 'jobs_path', 'source_paths', 'target_path', 'output_path', 'source_pattern', 'target_pattern', 'output_pattern', 'face_detector_model', 'face_detector_size', 'face_detector_angles', 'face_detector_score', 'face_landmarker_model', 'face_landmarker_score', 'face_selector_mode', 'face_selector_order', 'face_selector_gender', 'face_selector_race', 'face_selector_age_start', 'face_selector_age_end', 'reference_face_position', 'reference_face_distance', 'reference_frame_number', 'face_occluder_model', 'face_parser_model', 'face_mask_types', 'face_mask_blur', 'face_mask_padding', 'face_mask_regions', 'trim_frame_start', 'trim_frame_end', 'temp_frame_format', 'keep_temp', 'output_image_quality', 'output_image_resolution', 'output_audio_encoder', 'output_video_encoder', 'output_video_preset', 'output_video_quality', 'output_video_resolution', 'output_video_fps', 'skip_audio', 'processors', 'open_browser', 'ui_layouts', 'ui_workflow', 'execution_device_id', 'execution_providers', 'execution_thread_count', 'execution_queue_count', 'download_providers', 'download_scope', 'video_memory_strategy', 'system_memory_limit', 'log_level', 'job_id', 'job_status', 'step_index' ] State = TypedDict('State', { 'command' : str, 'config_path' : str, 'temp_path' : str, 'jobs_path' : str, 'source_paths' : List[str], 'target_path' : str, 'output_path' : str, 'source_pattern' : str, 'target_pattern' : str, 'output_pattern' : str, 'face_detector_model' : FaceDetectorModel, 'face_detector_size' : str, 'face_detector_angles' : List[Angle], 'face_detector_score' : Score, 'face_landmarker_model' : FaceLandmarkerModel, 'face_landmarker_score' : Score, 'face_selector_mode' : FaceSelectorMode, 'face_selector_order' : FaceSelectorOrder, 'face_selector_race' : Race, 'face_selector_gender' : Gender, 'face_selector_age_start' : int, 'face_selector_age_end' : int, 'reference_face_position' : int, 'reference_face_distance' : float, 'reference_frame_number' : int, 'face_occluder_model' : FaceOccluderModel, 'face_parser_model' : FaceParserModel, 'face_mask_types' : List[FaceMaskType], 'face_mask_blur' : float, 'face_mask_padding' : Padding, 'face_mask_regions' : List[FaceMaskRegion], 'trim_frame_start' : int, 'trim_frame_end' : int, 'temp_frame_format' : TempFrameFormat, 'keep_temp' : bool, 'output_image_quality' : int, 'output_image_resolution' : str, 'output_audio_encoder' : OutputAudioEncoder, 'output_video_encoder' : OutputVideoEncoder, 'output_video_preset' : OutputVideoPreset, 'output_video_quality' : int, 'output_video_resolution' : str, 'output_video_fps' : float, 'skip_audio' : bool, 'processors' : List[str], 'open_browser' : bool, 'ui_layouts' : List[str], 'ui_workflow' : UiWorkflow, 'execution_device_id' : str, 'execution_providers' : List[ExecutionProvider], 'execution_thread_count' : int, 'execution_queue_count' : int, 'download_providers' : List[DownloadProvider], 'download_scope' : DownloadScope, 'video_memory_strategy' : VideoMemoryStrategy, 'system_memory_limit' : int, 'log_level' : LogLevel, 'job_id' : str, 'job_status' : JobStatus, 'step_index' : int }) StateSet = Dict[AppContext, State]