Spaces:
Running
Running
| from collections import namedtuple | |
| from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, TypeAlias, TypedDict | |
| import cv2 | |
| import numpy | |
| from numpy.typing import NDArray | |
| from onnxruntime import InferenceSession | |
| Scale : TypeAlias = float | |
| Score : TypeAlias = float | |
| Angle : TypeAlias = int | |
| Detection : TypeAlias = NDArray[Any] | |
| Prediction : TypeAlias = NDArray[Any] | |
| BoundingBox : TypeAlias = NDArray[Any] | |
| FaceLandmark5 : TypeAlias = NDArray[Any] | |
| FaceLandmark68 : TypeAlias = 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 : TypeAlias = NDArray[numpy.float64] | |
| Gender = Literal['female', 'male'] | |
| Age : TypeAlias = 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 : TypeAlias = Dict[str, List[Face]] | |
| FaceStore = TypedDict('FaceStore', | |
| { | |
| 'static_faces' : FaceSet, | |
| 'reference_faces' : FaceSet | |
| }) | |
| VideoPoolSet : TypeAlias = Dict[str, cv2.VideoCapture] | |
| VisionFrame : TypeAlias = NDArray[Any] | |
| Mask : TypeAlias = NDArray[Any] | |
| Points : TypeAlias = NDArray[Any] | |
| Distance : TypeAlias = NDArray[Any] | |
| Matrix : TypeAlias = NDArray[Any] | |
| Anchors : TypeAlias = NDArray[Any] | |
| Translation : TypeAlias = NDArray[Any] | |
| AudioBuffer : TypeAlias = bytes | |
| Audio : TypeAlias = NDArray[Any] | |
| AudioChunk : TypeAlias = NDArray[Any] | |
| AudioFrame : TypeAlias = NDArray[Any] | |
| Spectrogram : TypeAlias = NDArray[Any] | |
| Mel : TypeAlias = NDArray[Any] | |
| MelFilterBank : TypeAlias = NDArray[Any] | |
| Fps : TypeAlias = float | |
| Duration : TypeAlias = float | |
| Padding : TypeAlias = Tuple[int, int, int, int] | |
| Orientation = Literal['landscape', 'portrait'] | |
| Resolution : TypeAlias = Tuple[int, int] | |
| ProcessState = Literal['checking', 'processing', 'stopping', 'pending'] | |
| QueuePayload = TypedDict('QueuePayload', | |
| { | |
| 'frame_number' : int, | |
| 'frame_path' : str | |
| }) | |
| Args : TypeAlias = Dict[str, Any] | |
| UpdateProgress : TypeAlias = Callable[[int], None] | |
| ProcessFrames : TypeAlias = Callable[[List[str], List[QueuePayload], UpdateProgress], None] | |
| ProcessStep : TypeAlias = Callable[[str, int, Args], bool] | |
| Content : TypeAlias = Dict[str, Any] | |
| Commands : TypeAlias = List[str] | |
| WarpTemplate = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128', 'dfl_whole_face', 'ffhq_512', 'mtcnn_512', 'styleganex_384'] | |
| WarpTemplateSet : TypeAlias = Dict[WarpTemplate, NDArray[Any]] | |
| ProcessMode = Literal['output', 'preview', 'stream'] | |
| ErrorCode = Literal[0, 1, 2, 3, 4] | |
| LogLevel = Literal['error', 'warn', 'info', 'debug'] | |
| LogLevelSet : TypeAlias = Dict[LogLevel, int] | |
| TableHeaders = List[str] | |
| TableContents = List[List[Any]] | |
| FaceDetectorModel = Literal['many', 'retinaface', 'scrfd', 'yolo_face'] | |
| FaceLandmarkerModel = Literal['many', '2dfan4', 'peppa_wutz'] | |
| FaceDetectorSet : TypeAlias = 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', 'xseg_3'] | |
| FaceParserModel = Literal['bisenet_resnet_18', 'bisenet_resnet_34'] | |
| FaceMaskType = Literal['box', 'occlusion', 'area', 'region'] | |
| FaceMaskArea = Literal['upper-face', 'lower-face', 'mouth'] | |
| FaceMaskRegion = Literal['skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip'] | |
| FaceMaskRegionSet : TypeAlias = Dict[FaceMaskRegion, int] | |
| FaceMaskAreaSet : TypeAlias = Dict[FaceMaskArea, List[int]] | |
| AudioFormat = Literal['flac', 'm4a', 'mp3', 'ogg', 'opus', 'wav'] | |
| ImageFormat = Literal['bmp', 'jpeg', 'png', 'tiff', 'webp'] | |
| VideoFormat = Literal['avi', 'm4v', 'mkv', 'mov', 'mp4', 'webm'] | |
| TempFrameFormat = Literal['bmp', 'jpeg', 'png', 'tiff'] | |
| AudioTypeSet : TypeAlias = Dict[AudioFormat, str] | |
| ImageTypeSet : TypeAlias = Dict[ImageFormat, str] | |
| VideoTypeSet : TypeAlias = Dict[VideoFormat, str] | |
| AudioEncoder = Literal['flac', 'aac', 'libmp3lame', 'libopus', 'libvorbis', 'pcm_s16le', 'pcm_s32le'] | |
| VideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc', 'h264_amf', 'hevc_amf', 'h264_qsv', 'hevc_qsv', 'h264_videotoolbox', 'hevc_videotoolbox', 'rawvideo'] | |
| EncoderSet = TypedDict('EncoderSet', | |
| { | |
| 'audio' : List[AudioEncoder], | |
| 'video' : List[VideoEncoder] | |
| }) | |
| VideoPreset = Literal['ultrafast', 'superfast', 'veryfast', 'faster', 'fast', 'medium', 'slow', 'slower', 'veryslow'] | |
| BenchmarkResolution = Literal['240p', '360p', '540p', '720p', '1080p', '1440p', '2160p'] | |
| BenchmarkSet : TypeAlias = Dict[BenchmarkResolution, str] | |
| BenchmarkCycleSet = TypedDict('BenchmarkCycleSet', | |
| { | |
| 'target_path' : str, | |
| 'cycle_count' : int, | |
| 'average_run' : float, | |
| 'fastest_run' : float, | |
| 'slowest_run' : float, | |
| 'relative_fps' : float | |
| }) | |
| WebcamMode = Literal['inline', 'udp', 'v4l2'] | |
| StreamMode = Literal['udp', 'v4l2'] | |
| ModelOptions : TypeAlias = Dict[str, Any] | |
| ModelSet : TypeAlias = Dict[str, ModelOptions] | |
| ModelInitializer : TypeAlias = NDArray[Any] | |
| ExecutionProvider = Literal['cpu', 'coreml', 'cuda', 'directml', 'openvino', 'rocm', 'tensorrt'] | |
| ExecutionProviderValue = Literal['CPUExecutionProvider', 'CoreMLExecutionProvider', 'CUDAExecutionProvider', 'DmlExecutionProvider', 'OpenVINOExecutionProvider', 'ROCMExecutionProvider', 'TensorrtExecutionProvider'] | |
| ExecutionProviderSet : TypeAlias = Dict[ExecutionProvider, ExecutionProviderValue] | |
| InferenceSessionProvider : TypeAlias = Any | |
| 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', | |
| { | |
| 'urls' : List[str], | |
| 'path' : str | |
| }) | |
| DownloadProviderSet : TypeAlias = Dict[DownloadProvider, DownloadProviderValue] | |
| DownloadScope = Literal['lite', 'full'] | |
| Download = TypedDict('Download', | |
| { | |
| 'url' : str, | |
| 'path' : str | |
| }) | |
| DownloadSet : TypeAlias = Dict[str, Download] | |
| VideoMemoryStrategy = Literal['strict', 'moderate', 'tolerant'] | |
| AppContext = Literal['cli', 'ui'] | |
| InferencePool : TypeAlias = Dict[str, InferenceSession] | |
| InferencePoolSet : TypeAlias = 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 : TypeAlias = 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 : TypeAlias = Dict[str, Job] | |
| StateKey = Literal\ | |
| [ | |
| 'command', | |
| 'config_path', | |
| 'temp_path', | |
| 'jobs_path', | |
| 'source_paths', | |
| 'target_path', | |
| 'output_path', | |
| 'source_pattern', | |
| 'target_pattern', | |
| 'output_pattern', | |
| 'download_providers', | |
| 'download_scope', | |
| 'benchmark_resolutions', | |
| 'benchmark_cycle_count', | |
| '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_areas', | |
| 'face_mask_regions', | |
| 'face_mask_blur', | |
| 'face_mask_padding', | |
| 'trim_frame_start', | |
| 'trim_frame_end', | |
| 'temp_frame_format', | |
| 'keep_temp', | |
| 'output_image_quality', | |
| 'output_image_resolution', | |
| 'output_audio_encoder', | |
| 'output_audio_quality', | |
| 'output_audio_volume', | |
| 'output_video_encoder', | |
| 'output_video_preset', | |
| 'output_video_quality', | |
| 'output_video_resolution', | |
| 'output_video_fps', | |
| 'processors', | |
| 'open_browser', | |
| 'ui_layouts', | |
| 'ui_workflow', | |
| 'execution_device_id', | |
| 'execution_providers', | |
| 'execution_thread_count', | |
| 'execution_queue_count', | |
| 'video_memory_strategy', | |
| 'system_memory_limit', | |
| 'log_level', | |
| 'halt_on_error', | |
| '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, | |
| 'download_providers': List[DownloadProvider], | |
| 'download_scope': DownloadScope, | |
| 'benchmark_resolutions': List[BenchmarkResolution], | |
| 'benchmark_cycle_count': int, | |
| '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_areas' : List[FaceMaskArea], | |
| 'face_mask_regions' : List[FaceMaskRegion], | |
| 'face_mask_blur' : float, | |
| 'face_mask_padding' : Padding, | |
| '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' : AudioEncoder, | |
| 'output_audio_quality' : int, | |
| 'output_audio_volume' : int, | |
| 'output_video_encoder' : VideoEncoder, | |
| 'output_video_preset' : VideoPreset, | |
| 'output_video_quality' : int, | |
| 'output_video_resolution' : str, | |
| 'output_video_fps' : float, | |
| '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, | |
| 'video_memory_strategy' : VideoMemoryStrategy, | |
| 'system_memory_limit' : int, | |
| 'log_level' : LogLevel, | |
| 'halt_on_error' : bool, | |
| 'job_id' : str, | |
| 'job_status' : JobStatus, | |
| 'step_index' : int | |
| }) | |
| ApplyStateItem : TypeAlias = Callable[[Any, Any], None] | |
| StateSet : TypeAlias = Dict[AppContext, State] | |