Delete DeepFakeAI

DeepFakeAI/choices.py

@@ -1,26 +0,0 @@
-from typing import List
-
-from DeepFakeAI.typing import FaceSelectorMode, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, FaceMaskType, FaceMaskRegion, TempFrameFormat, OutputVideoEncoder
-from DeepFakeAI.common_helper import create_range
-
-face_analyser_orders : List[FaceAnalyserOrder] = [ 'left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best' ]
-face_analyser_ages : List[FaceAnalyserAge] = [ 'child', 'teen', 'adult', 'senior' ]
-face_analyser_genders : List[FaceAnalyserGender] = [ 'male', 'female' ]
-face_detector_models : List[str] = [ 'retinaface', 'yunet' ]
-face_detector_sizes : List[str] = [ '160x160', '320x320', '480x480', '512x512', '640x640', '768x768', '960x960', '1024x1024' ]
-face_selector_modes : List[FaceSelectorMode] = [ 'reference', 'one', 'many' ]
-face_mask_types : List[FaceMaskType] = [ 'box', 'occlusion', 'region' ]
-face_mask_regions : List[FaceMaskRegion] = [ 'skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'eye-glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip' ]
-temp_frame_formats : List[TempFrameFormat] = [ 'jpg', 'png' ]
-output_video_encoders : List[OutputVideoEncoder] = [ 'libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc' ]
-
-execution_thread_count_range : List[float] = create_range(1, 128, 1)
-execution_queue_count_range : List[float] = create_range(1, 32, 1)
-max_memory_range : List[float] = create_range(0, 128, 1)
-face_detector_score_range : List[float] = create_range(0.0, 1.0, 0.05)
-face_mask_blur_range : List[float] = create_range(0.0, 1.0, 0.05)
-face_mask_padding_range : List[float] = create_range(0, 100, 1)
-reference_face_distance_range : List[float] = create_range(0.0, 1.5, 0.05)
-temp_frame_quality_range : List[float] = create_range(0, 100, 1)
-output_image_quality_range : List[float] = create_range(0, 100, 1)
-output_video_quality_range : List[float] = create_range(0, 100, 1)

DeepFakeAI/common_helper.py

@@ -1,10 +0,0 @@
-from typing import List, Any
-import numpy
-
-
-def create_metavar(ranges : List[Any]) -> str:
-	return '[' + str(ranges[0]) + '-' + str(ranges[-1]) + ']'
-
-
-def create_range(start : float, stop : float, step : float) -> List[float]:
-	return (numpy.around(numpy.arange(start, stop + step, step), decimals = 2)).tolist()

DeepFakeAI/content_analyser.py

@@ -1,103 +0,0 @@
-from typing import Any, Dict
-from functools import lru_cache
-import threading
-import cv2
-import numpy
-import onnxruntime
-from tqdm import tqdm
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.typing import Frame, ModelValue
-from DeepFakeAI.vision import get_video_frame, count_video_frame_total, read_image, detect_fps
-from DeepFakeAI.filesystem import resolve_relative_path
-from DeepFakeAI.download import conditional_download
-
-CONTENT_ANALYSER = None
-THREAD_LOCK : threading.Lock = threading.Lock()
-MODELS : Dict[str, ModelValue] =\
-{
-	'open_nsfw':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/open_nsfw.onnx',
-		'path': resolve_relative_path('../.assets/models/open_nsfw.onnx')
-	}
-}
-MAX_PROBABILITY = 0.80
-MAX_RATE = 5
-STREAM_COUNTER = 0
-
-
-def get_content_analyser() -> Any:
-	global CONTENT_ANALYSER
-
-	with THREAD_LOCK:
-		if CONTENT_ANALYSER is None:
-			model_path = MODELS.get('open_nsfw').get('path')
-			CONTENT_ANALYSER = onnxruntime.InferenceSession(model_path, providers = DeepFakeAI.globals.execution_providers)
-	return CONTENT_ANALYSER
-
-
-def clear_content_analyser() -> None:
-	global CONTENT_ANALYSER
-
-	CONTENT_ANALYSER = None
-
-
-def pre_check() -> bool:
-	if not DeepFakeAI.globals.skip_download:
-		download_directory_path = resolve_relative_path('../.assets/models')
-		model_url = MODELS.get('open_nsfw').get('url')
-		conditional_download(download_directory_path, [ model_url ])
-	return True
-
-
-def analyse_stream(frame : Frame, fps : float) -> bool:
-	global STREAM_COUNTER
-
-	STREAM_COUNTER = STREAM_COUNTER + 1
-	if STREAM_COUNTER % int(fps) == 0:
-		return analyse_frame(frame)
-	return False
-
-
-def prepare_frame(frame : Frame) -> Frame:
-	frame = cv2.resize(frame, (224, 224)).astype(numpy.float32)
-	frame -= numpy.array([ 104, 117, 123 ]).astype(numpy.float32)
-	frame = numpy.expand_dims(frame, axis = 0)
-	return frame
-
-
-def analyse_frame(frame : Frame) -> bool:
-	content_analyser = get_content_analyser()
-	frame = prepare_frame(frame)
-	probability = content_analyser.run(None,
-	{
-		'input:0': frame
-	})[0][0][1]
-	return probability > MAX_PROBABILITY
-
-
-@lru_cache(maxsize = None)
-def analyse_image(image_path : str) -> bool:
-	frame = read_image(image_path)
-	return analyse_frame(frame)
-
-
-@lru_cache(maxsize = None)
-def analyse_video(video_path : str, start_frame : int, end_frame : int) -> bool:
-	video_frame_total = count_video_frame_total(video_path)
-	fps = detect_fps(video_path)
-	frame_range = range(start_frame or 0, end_frame or video_frame_total)
-	rate = 0.0
-	counter = 0
-	with tqdm(total = len(frame_range), desc = wording.get('analysing'), unit = 'frame', ascii = ' =', disable = DeepFakeAI.globals.log_level in [ 'warn', 'error' ]) as progress:
-		for frame_number in frame_range:
-			if frame_number % int(fps) == 0:
-				frame = get_video_frame(video_path, frame_number)
-				if analyse_frame(frame):
-					counter += 1
-			rate = counter * int(fps) / len(frame_range) * 100
-			progress.update()
-			progress.set_postfix(rate = rate)
-	return rate > MAX_RATE

DeepFakeAI/core.py

@@ -1,299 +0,0 @@
-import os
-
-os.environ['OMP_NUM_THREADS'] = '1'
-
-import signal
-import ssl
-import sys
-import warnings
-import platform
-import shutil
-import onnxruntime
-from argparse import ArgumentParser, HelpFormatter
-
-import DeepFakeAI.choices
-import DeepFakeAI.globals
-from DeepFakeAI.face_analyser import get_one_face, get_average_face
-from DeepFakeAI.face_store import get_reference_faces, append_reference_face
-from DeepFakeAI.vision import get_video_frame, detect_fps, read_image, read_static_images
-from DeepFakeAI import face_analyser, face_masker, content_analyser, metadata, logger, wording
-from DeepFakeAI.content_analyser import analyse_image, analyse_video
-from DeepFakeAI.processors.frame.core import get_frame_processors_modules, load_frame_processor_module
-from DeepFakeAI.common_helper import create_metavar
-from DeepFakeAI.execution_helper import encode_execution_providers, decode_execution_providers
-from DeepFakeAI.normalizer import normalize_output_path, normalize_padding
-from DeepFakeAI.filesystem import is_image, is_video, list_module_names, get_temp_frame_paths, create_temp, move_temp, clear_temp
-from DeepFakeAI.ffmpeg import extract_frames, compress_image, merge_video, restore_audio
-
-onnxruntime.set_default_logger_severity(3)
-warnings.filterwarnings('ignore', category = UserWarning, module = 'gradio')
-warnings.filterwarnings('ignore', category = UserWarning, module = 'torchvision')
-
-if platform.system().lower() == 'darwin':
-	ssl._create_default_https_context = ssl._create_unverified_context
-
-
-def cli() -> None:
-	signal.signal(signal.SIGINT, lambda signal_number, frame: destroy())
-	program = ArgumentParser(formatter_class = lambda prog: HelpFormatter(prog, max_help_position = 120), add_help = False)
-	# general
-	program.add_argument('-s', '--source', action = 'append', help = wording.get('source_help'), dest = 'source_paths')
-	program.add_argument('-t', '--target', help = wording.get('target_help'), dest = 'target_path')
-	program.add_argument('-o', '--output', help = wording.get('output_help'), dest = 'output_path')
-	program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
-	# misc
-	group_misc = program.add_argument_group('misc')
-	group_misc.add_argument('--skip-download', help = wording.get('skip_download_help'), action = 'store_true')
-	group_misc.add_argument('--headless', help = wording.get('headless_help'), action = 'store_true')
-	group_misc.add_argument('--log-level', help = wording.get('log_level_help'), default = 'info', choices = logger.get_log_levels())
-	# execution
-	execution_providers = encode_execution_providers(onnxruntime.get_available_providers())
-	group_execution = program.add_argument_group('execution')
-	group_execution.add_argument('--execution-providers', help = wording.get('execution_providers_help').format(choices = ', '.join(execution_providers)), default = [ 'cpu' ], choices = execution_providers, nargs = '+', metavar = 'EXECUTION_PROVIDERS')
-	group_execution.add_argument('--execution-thread-count', help = wording.get('execution_thread_count_help'), type = int, default = 4, choices = DeepFakeAI.choices.execution_thread_count_range, metavar = create_metavar(DeepFakeAI.choices.execution_thread_count_range))
-	group_execution.add_argument('--execution-queue-count', help = wording.get('execution_queue_count_help'), type = int, default = 1, choices = DeepFakeAI.choices.execution_queue_count_range, metavar = create_metavar(DeepFakeAI.choices.execution_queue_count_range))
-	group_execution.add_argument('--max-memory', help = wording.get('max_memory_help'), type = int, choices = DeepFakeAI.choices.max_memory_range, metavar = create_metavar(DeepFakeAI.choices.max_memory_range))
-	# face analyser
-	group_face_analyser = program.add_argument_group('face analyser')
-	group_face_analyser.add_argument('--face-analyser-order', help = wording.get('face_analyser_order_help'), default = 'left-right', choices = DeepFakeAI.choices.face_analyser_orders)
-	group_face_analyser.add_argument('--face-analyser-age', help = wording.get('face_analyser_age_help'), choices = DeepFakeAI.choices.face_analyser_ages)
-	group_face_analyser.add_argument('--face-analyser-gender', help = wording.get('face_analyser_gender_help'), choices = DeepFakeAI.choices.face_analyser_genders)
-	group_face_analyser.add_argument('--face-detector-model', help = wording.get('face_detector_model_help'), default = 'retinaface', choices = DeepFakeAI.choices.face_detector_models)
-	group_face_analyser.add_argument('--face-detector-size', help = wording.get('face_detector_size_help'), default = '640x640', choices = DeepFakeAI.choices.face_detector_sizes)
-	group_face_analyser.add_argument('--face-detector-score', help = wording.get('face_detector_score_help'), type = float, default = 0.5, choices = DeepFakeAI.choices.face_detector_score_range, metavar = create_metavar(DeepFakeAI.choices.face_detector_score_range))
-	# face selector
-	group_face_selector = program.add_argument_group('face selector')
-	group_face_selector.add_argument('--face-selector-mode', help = wording.get('face_selector_mode_help'), default = 'reference', choices = DeepFakeAI.choices.face_selector_modes)
-	group_face_selector.add_argument('--reference-face-position', help = wording.get('reference_face_position_help'), type = int, default = 0)
-	group_face_selector.add_argument('--reference-face-distance', help = wording.get('reference_face_distance_help'), type = float, default = 0.6, choices = DeepFakeAI.choices.reference_face_distance_range, metavar = create_metavar(DeepFakeAI.choices.reference_face_distance_range))
-	group_face_selector.add_argument('--reference-frame-number', help = wording.get('reference_frame_number_help'), type = int, default = 0)
-	# face mask
-	group_face_mask = program.add_argument_group('face mask')
-	group_face_mask.add_argument('--face-mask-types', help = wording.get('face_mask_types_help').format(choices = ', '.join(DeepFakeAI.choices.face_mask_types)), default = [ 'box' ], choices = DeepFakeAI.choices.face_mask_types, nargs = '+', metavar = 'FACE_MASK_TYPES')
-	group_face_mask.add_argument('--face-mask-blur', help = wording.get('face_mask_blur_help'), type = float, default = 0.3, choices = DeepFakeAI.choices.face_mask_blur_range, metavar = create_metavar(DeepFakeAI.choices.face_mask_blur_range))
-	group_face_mask.add_argument('--face-mask-padding', help = wording.get('face_mask_padding_help'), type = int, default = [ 0, 0, 0, 0 ], nargs = '+')
-	group_face_mask.add_argument('--face-mask-regions', help = wording.get('face_mask_regions_help').format(choices = ', '.join(DeepFakeAI.choices.face_mask_regions)), default = DeepFakeAI.choices.face_mask_regions, choices = DeepFakeAI.choices.face_mask_regions,  nargs = '+', metavar = 'FACE_MASK_REGIONS')
-	# frame extraction
-	group_frame_extraction = program.add_argument_group('frame extraction')
-	group_frame_extraction.add_argument('--trim-frame-start', help = wording.get('trim_frame_start_help'), type = int)
-	group_frame_extraction.add_argument('--trim-frame-end', help = wording.get('trim_frame_end_help'), type = int)
-	group_frame_extraction.add_argument('--temp-frame-format', help = wording.get('temp_frame_format_help'), default = 'jpg', choices = DeepFakeAI.choices.temp_frame_formats)
-	group_frame_extraction.add_argument('--temp-frame-quality', help = wording.get('temp_frame_quality_help'), type = int, default = 100, choices = DeepFakeAI.choices.temp_frame_quality_range, metavar = create_metavar(DeepFakeAI.choices.temp_frame_quality_range))
-	group_frame_extraction.add_argument('--keep-temp', help = wording.get('keep_temp_help'), action = 'store_true')
-	# output creation
-	group_output_creation = program.add_argument_group('output creation')
-	group_output_creation.add_argument('--output-image-quality', help = wording.get('output_image_quality_help'), type = int, default = 80, choices = DeepFakeAI.choices.output_image_quality_range, metavar = create_metavar(DeepFakeAI.choices.output_image_quality_range))
-	group_output_creation.add_argument('--output-video-encoder', help = wording.get('output_video_encoder_help'), default = 'libx264', choices = DeepFakeAI.choices.output_video_encoders)
-	group_output_creation.add_argument('--output-video-quality', help = wording.get('output_video_quality_help'), type = int, default = 80, choices = DeepFakeAI.choices.output_video_quality_range, metavar = create_metavar(DeepFakeAI.choices.output_video_quality_range))
-	group_output_creation.add_argument('--keep-fps', help = wording.get('keep_fps_help'), action = 'store_true')
-	group_output_creation.add_argument('--skip-audio', help = wording.get('skip_audio_help'), action = 'store_true')
-	# frame processors
-	available_frame_processors = list_module_names('DeepFakeAI/processors/frame/modules')
-	program = ArgumentParser(parents = [ program ], formatter_class = program.formatter_class, add_help = True)
-	group_frame_processors = program.add_argument_group('frame processors')
-	group_frame_processors.add_argument('--frame-processors', help = wording.get('frame_processors_help').format(choices = ', '.join(available_frame_processors)), default = [ 'face_swapper' ], nargs = '+')
-	for frame_processor in available_frame_processors:
-		frame_processor_module = load_frame_processor_module(frame_processor)
-		frame_processor_module.register_args(group_frame_processors)
-	# uis
-	group_uis = program.add_argument_group('uis')
-	group_uis.add_argument('--ui-layouts', help = wording.get('ui_layouts_help').format(choices = ', '.join(list_module_names('DeepFakeAI/uis/layouts'))), default = [ 'default' ], nargs = '+')
-	run(program)
-
-
-def apply_args(program : ArgumentParser) -> None:
-	args = program.parse_args()
-	# general
-	DeepFakeAI.globals.source_paths = args.source_paths
-	DeepFakeAI.globals.target_path = args.target_path
-	DeepFakeAI.globals.output_path = normalize_output_path(DeepFakeAI.globals.source_paths, DeepFakeAI.globals.target_path, args.output_path)
-	# misc
-	DeepFakeAI.globals.skip_download = args.skip_download
-	DeepFakeAI.globals.headless = args.headless
-	DeepFakeAI.globals.log_level = args.log_level
-	# execution
-	DeepFakeAI.globals.execution_providers = decode_execution_providers(args.execution_providers)
-	DeepFakeAI.globals.execution_thread_count = args.execution_thread_count
-	DeepFakeAI.globals.execution_queue_count = args.execution_queue_count
-	DeepFakeAI.globals.max_memory = args.max_memory
-	# face analyser
-	DeepFakeAI.globals.face_analyser_order = args.face_analyser_order
-	DeepFakeAI.globals.face_analyser_age = args.face_analyser_age
-	DeepFakeAI.globals.face_analyser_gender = args.face_analyser_gender
-	DeepFakeAI.globals.face_detector_model = args.face_detector_model
-	DeepFakeAI.globals.face_detector_size = args.face_detector_size
-	DeepFakeAI.globals.face_detector_score = args.face_detector_score
-	# face selector
-	DeepFakeAI.globals.face_selector_mode = args.face_selector_mode
-	DeepFakeAI.globals.reference_face_position = args.reference_face_position
-	DeepFakeAI.globals.reference_face_distance = args.reference_face_distance
-	DeepFakeAI.globals.reference_frame_number = args.reference_frame_number
-	# face mask
-	DeepFakeAI.globals.face_mask_types = args.face_mask_types
-	DeepFakeAI.globals.face_mask_blur = args.face_mask_blur
-	DeepFakeAI.globals.face_mask_padding = normalize_padding(args.face_mask_padding)
-	DeepFakeAI.globals.face_mask_regions = args.face_mask_regions
-	# frame extraction
-	DeepFakeAI.globals.trim_frame_start = args.trim_frame_start
-	DeepFakeAI.globals.trim_frame_end = args.trim_frame_end
-	DeepFakeAI.globals.temp_frame_format = args.temp_frame_format
-	DeepFakeAI.globals.temp_frame_quality = args.temp_frame_quality
-	DeepFakeAI.globals.keep_temp = args.keep_temp
-	# output creation
-	DeepFakeAI.globals.output_image_quality = args.output_image_quality
-	DeepFakeAI.globals.output_video_encoder = args.output_video_encoder
-	DeepFakeAI.globals.output_video_quality = args.output_video_quality
-	DeepFakeAI.globals.keep_fps = args.keep_fps
-	DeepFakeAI.globals.skip_audio = args.skip_audio
-	# frame processors
-	available_frame_processors = list_module_names('DeepFakeAI/processors/frame/modules')
-	DeepFakeAI.globals.frame_processors = args.frame_processors
-	for frame_processor in available_frame_processors:
-		frame_processor_module = load_frame_processor_module(frame_processor)
-		frame_processor_module.apply_args(program)
-	# uis
-	DeepFakeAI.globals.ui_layouts = args.ui_layouts
-
-
-def run(program : ArgumentParser) -> None:
-	apply_args(program)
-	logger.init(DeepFakeAI.globals.log_level)
-	limit_resources()
-	if not pre_check() or not content_analyser.pre_check() or not face_analyser.pre_check() or not face_masker.pre_check():
-		return
-	for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
-		if not frame_processor_module.pre_check():
-			return
-	if DeepFakeAI.globals.headless:
-		conditional_process()
-	else:
-		import DeepFakeAI.uis.core as ui
-
-		for ui_layout in ui.get_ui_layouts_modules(DeepFakeAI.globals.ui_layouts):
-			if not ui_layout.pre_check():
-				return
-		ui.launch()
-
-
-def destroy() -> None:
-	if DeepFakeAI.globals.target_path:
-		clear_temp(DeepFakeAI.globals.target_path)
-	sys.exit()
-
-
-def limit_resources() -> None:
-	if DeepFakeAI.globals.max_memory:
-		memory = DeepFakeAI.globals.max_memory * 1024 ** 3
-		if platform.system().lower() == 'darwin':
-			memory = DeepFakeAI.globals.max_memory * 1024 ** 6
-		if platform.system().lower() == 'windows':
-			import ctypes
-
-			kernel32 = ctypes.windll.kernel32 # type: ignore[attr-defined]
-			kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory))
-		else:
-			import resource
-
-			resource.setrlimit(resource.RLIMIT_DATA, (memory, memory))
-
-
-def pre_check() -> bool:
-	if sys.version_info < (3, 9):
-		logger.error(wording.get('python_not_supported').format(version = '3.9'), __name__.upper())
-		return False
-	if not shutil.which('ffmpeg'):
-		logger.error(wording.get('ffmpeg_not_installed'), __name__.upper())
-		return False
-	return True
-
-
-def conditional_process() -> None:
-	conditional_append_reference_faces()
-	for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
-		if not frame_processor_module.pre_process('output'):
-			return
-	if is_image(DeepFakeAI.globals.target_path):
-		process_image()
-	if is_video(DeepFakeAI.globals.target_path):
-		process_video()
-
-
-def conditional_append_reference_faces() -> None:
-	if 'reference' in DeepFakeAI.globals.face_selector_mode and not get_reference_faces():
-		source_frames = read_static_images(DeepFakeAI.globals.source_paths)
-		source_face = get_average_face(source_frames)
-		if is_video(DeepFakeAI.globals.target_path):
-			reference_frame = get_video_frame(DeepFakeAI.globals.target_path, DeepFakeAI.globals.reference_frame_number)
-		else:
-			reference_frame = read_image(DeepFakeAI.globals.target_path)
-		reference_face = get_one_face(reference_frame, DeepFakeAI.globals.reference_face_position)
-		append_reference_face('origin', reference_face)
-		if source_face and reference_face:
-			for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
-				reference_frame = frame_processor_module.get_reference_frame(source_face, reference_face, reference_frame)
-				reference_face = get_one_face(reference_frame, DeepFakeAI.globals.reference_face_position)
-				append_reference_face(frame_processor_module.__name__, reference_face)
-
-
-def process_image() -> None:
-	if analyse_image(DeepFakeAI.globals.target_path):
-		return
-	shutil.copy2(DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path)
-	# process frame
-	for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
-		logger.info(wording.get('processing'), frame_processor_module.NAME)
-		frame_processor_module.process_image(DeepFakeAI.globals.source_paths, DeepFakeAI.globals.output_path, DeepFakeAI.globals.output_path)
-		frame_processor_module.post_process()
-	# compress image
-	logger.info(wording.get('compressing_image'), __name__.upper())
-	if not compress_image(DeepFakeAI.globals.output_path):
-		logger.error(wording.get('compressing_image_failed'), __name__.upper())
-	# validate image
-	if is_image(DeepFakeAI.globals.output_path):
-		logger.info(wording.get('processing_image_succeed'), __name__.upper())
-	else:
-		logger.error(wording.get('processing_image_failed'), __name__.upper())
-
-
-def process_video() -> None:
-	if analyse_video(DeepFakeAI.globals.target_path, DeepFakeAI.globals.trim_frame_start, DeepFakeAI.globals.trim_frame_end):
-		return
-	fps = detect_fps(DeepFakeAI.globals.target_path) if DeepFakeAI.globals.keep_fps else 25.0
-	# create temp
-	logger.info(wording.get('creating_temp'), __name__.upper())
-	create_temp(DeepFakeAI.globals.target_path)
-	# extract frames
-	logger.info(wording.get('extracting_frames_fps').format(fps = fps), __name__.upper())
-	extract_frames(DeepFakeAI.globals.target_path, fps)
-	# process frame
-	temp_frame_paths = get_temp_frame_paths(DeepFakeAI.globals.target_path)
-	if temp_frame_paths:
-		for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
-			logger.info(wording.get('processing'), frame_processor_module.NAME)
-			frame_processor_module.process_video(DeepFakeAI.globals.source_paths, temp_frame_paths)
-			frame_processor_module.post_process()
-	else:
-		logger.error(wording.get('temp_frames_not_found'), __name__.upper())
-		return
-	# merge video
-	logger.info(wording.get('merging_video_fps').format(fps = fps), __name__.upper())
-	if not merge_video(DeepFakeAI.globals.target_path, fps):
-		logger.error(wording.get('merging_video_failed'), __name__.upper())
-		return
-	# handle audio
-	if DeepFakeAI.globals.skip_audio:
-		logger.info(wording.get('skipping_audio'), __name__.upper())
-		move_temp(DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path)
-	else:
-		logger.info(wording.get('restoring_audio'), __name__.upper())
-		if not restore_audio(DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path):
-			logger.warn(wording.get('restoring_audio_skipped'), __name__.upper())
-			move_temp(DeepFakeAI.globals.target_path, DeepFakeAI.globals.output_path)
-	# clear temp
-	logger.info(wording.get('clearing_temp'), __name__.upper())
-	clear_temp(DeepFakeAI.globals.target_path)
-	# validate video
-	if is_video(DeepFakeAI.globals.output_path):
-		logger.info(wording.get('processing_video_succeed'), __name__.upper())
-	else:
-		logger.error(wording.get('processing_video_failed'), __name__.upper())

DeepFakeAI/download.py

@@ -1,44 +0,0 @@
-import os
-import subprocess
-import urllib.request
-from typing import List
-from concurrent.futures import ThreadPoolExecutor
-from functools import lru_cache
-from tqdm import tqdm
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.filesystem import is_file
-
-
-def conditional_download(download_directory_path : str, urls : List[str]) -> None:
-	with ThreadPoolExecutor() as executor:
-		for url in urls:
-			executor.submit(get_download_size, url)
-	for url in urls:
-		download_file_path = os.path.join(download_directory_path, os.path.basename(url))
-		initial = os.path.getsize(download_file_path) if is_file(download_file_path) else 0
-		total = get_download_size(url)
-		if initial < total:
-			with tqdm(total = total, initial = initial, desc = wording.get('downloading'), unit = 'B', unit_scale = True, unit_divisor = 1024, ascii = ' =', disable = DeepFakeAI.globals.log_level in [ 'warn', 'error' ]) as progress:
-				subprocess.Popen([ 'curl', '--create-dirs', '--silent', '--insecure', '--location', '--continue-at', '-', '--output', download_file_path, url ])
-				current = initial
-				while current < total:
-					if is_file(download_file_path):
-						current = os.path.getsize(download_file_path)
-						progress.update(current - progress.n)
-
-
-@lru_cache(maxsize = None)
-def get_download_size(url : str) -> int:
-	try:
-		response = urllib.request.urlopen(url, timeout = 10)
-		return int(response.getheader('Content-Length'))
-	except (OSError, ValueError):
-		return 0
-
-
-def is_download_done(url : str, file_path : str) -> bool:
-	if is_file(file_path):
-		return get_download_size(url) == os.path.getsize(file_path)
-	return False

DeepFakeAI/execution_helper.py

@@ -1,22 +0,0 @@
-from typing import List
-import onnxruntime
-
-
-def encode_execution_providers(execution_providers : List[str]) -> List[str]:
-	return [ execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers ]
-
-
-def decode_execution_providers(execution_providers: List[str]) -> List[str]:
-	available_execution_providers = onnxruntime.get_available_providers()
-	encoded_execution_providers = encode_execution_providers(available_execution_providers)
-	return [ execution_provider for execution_provider, encoded_execution_provider in zip(available_execution_providers, encoded_execution_providers) if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers) ]
-
-
-def map_device(execution_providers : List[str]) -> str:
-	if 'CoreMLExecutionProvider' in execution_providers:
-		return 'mps'
-	if 'CUDAExecutionProvider' in execution_providers or 'ROCMExecutionProvider' in execution_providers :
-		return 'cuda'
-	if 'OpenVINOExecutionProvider' in execution_providers:
-		return 'mkl'
-	return 'cpu'

DeepFakeAI/face_analyser.py

@@ -1,347 +0,0 @@
-from typing import Any, Optional, List, Tuple
-import threading
-import cv2
-import numpy
-import onnxruntime
-
-import DeepFakeAI.globals
-from DeepFakeAI.download import conditional_download
-from DeepFakeAI.face_store import get_static_faces, set_static_faces
-from DeepFakeAI.face_helper import warp_face, create_static_anchors, distance_to_kps, distance_to_bbox, apply_nms
-from DeepFakeAI.filesystem import resolve_relative_path
-from DeepFakeAI.typing import Frame, Face, FaceSet, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, ModelSet, Bbox, Kps, Score, Embedding
-from DeepFakeAI.vision import resize_frame_dimension
-
-FACE_ANALYSER = None
-THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
-THREAD_LOCK : threading.Lock = threading.Lock()
-MODELS : ModelSet =\
-{
-	'face_detector_retinaface':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/retinaface_10g.onnx',
-		'path': resolve_relative_path('../.assets/models/retinaface_10g.onnx')
-	},
-	'face_detector_yunet':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/yunet_2023mar.onnx',
-		'path': resolve_relative_path('../.assets/models/yunet_2023mar.onnx')
-	},
-	'face_recognizer_arcface_blendswap':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/arcface_w600k_r50.onnx',
-		'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.onnx')
-	},
-	'face_recognizer_arcface_inswapper':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/arcface_w600k_r50.onnx',
-		'path': resolve_relative_path('../.assets/models/arcface_w600k_r50.onnx')
-	},
-	'face_recognizer_arcface_simswap':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/arcface_simswap.onnx',
-		'path': resolve_relative_path('../.assets/models/arcface_simswap.onnx')
-	},
-	'gender_age':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/gender_age.onnx',
-		'path': resolve_relative_path('../.assets/models/gender_age.onnx')
-	}
-}
-
-
-def get_face_analyser() -> Any:
-	global FACE_ANALYSER
-
-	with THREAD_LOCK:
-		if FACE_ANALYSER is None:
-			if DeepFakeAI.globals.face_detector_model == 'retinaface':
-				face_detector = onnxruntime.InferenceSession(MODELS.get('face_detector_retinaface').get('path'), providers = DeepFakeAI.globals.execution_providers)
-			if DeepFakeAI.globals.face_detector_model == 'yunet':
-				face_detector = cv2.FaceDetectorYN.create(MODELS.get('face_detector_yunet').get('path'), '', (0, 0))
-			if DeepFakeAI.globals.face_recognizer_model == 'arcface_blendswap':
-				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_blendswap').get('path'), providers = DeepFakeAI.globals.execution_providers)
-			if DeepFakeAI.globals.face_recognizer_model == 'arcface_inswapper':
-				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_inswapper').get('path'), providers = DeepFakeAI.globals.execution_providers)
-			if DeepFakeAI.globals.face_recognizer_model == 'arcface_simswap':
-				face_recognizer = onnxruntime.InferenceSession(MODELS.get('face_recognizer_arcface_simswap').get('path'), providers = DeepFakeAI.globals.execution_providers)
-			gender_age = onnxruntime.InferenceSession(MODELS.get('gender_age').get('path'), providers = DeepFakeAI.globals.execution_providers)
-			FACE_ANALYSER =\
-			{
-				'face_detector': face_detector,
-				'face_recognizer': face_recognizer,
-				'gender_age': gender_age
-			}
-	return FACE_ANALYSER
-
-
-def clear_face_analyser() -> Any:
-	global FACE_ANALYSER
-
-	FACE_ANALYSER = None
-
-
-def pre_check() -> bool:
-	if not DeepFakeAI.globals.skip_download:
-		download_directory_path = resolve_relative_path('../.assets/models')
-		model_urls =\
-		[
-			MODELS.get('face_detector_retinaface').get('url'),
-			MODELS.get('face_detector_yunet').get('url'),
-			MODELS.get('face_recognizer_arcface_inswapper').get('url'),
-			MODELS.get('face_recognizer_arcface_simswap').get('url'),
-			MODELS.get('gender_age').get('url')
-		]
-		conditional_download(download_directory_path, model_urls)
-	return True
-
-
-def extract_faces(frame: Frame) -> List[Face]:
-	face_detector_width, face_detector_height = map(int, DeepFakeAI.globals.face_detector_size.split('x'))
-	frame_height, frame_width, _ = frame.shape
-	temp_frame = resize_frame_dimension(frame, face_detector_width, face_detector_height)
-	temp_frame_height, temp_frame_width, _ = temp_frame.shape
-	ratio_height = frame_height / temp_frame_height
-	ratio_width = frame_width / temp_frame_width
-	if DeepFakeAI.globals.face_detector_model == 'retinaface':
-		bbox_list, kps_list, score_list = detect_with_retinaface(temp_frame, temp_frame_height, temp_frame_width, face_detector_height, face_detector_width, ratio_height, ratio_width)
-		return create_faces(frame, bbox_list, kps_list, score_list)
-	elif DeepFakeAI.globals.face_detector_model == 'yunet':
-		bbox_list, kps_list, score_list = detect_with_yunet(temp_frame, temp_frame_height, temp_frame_width, ratio_height, ratio_width)
-		return create_faces(frame, bbox_list, kps_list, score_list)
-	return []
-
-
-def detect_with_retinaface(temp_frame : Frame, temp_frame_height : int, temp_frame_width : int, face_detector_height : int, face_detector_width : int, ratio_height : float, ratio_width : float) -> Tuple[List[Bbox], List[Kps], List[Score]]:
-	face_detector = get_face_analyser().get('face_detector')
-	bbox_list = []
-	kps_list = []
-	score_list = []
-	feature_strides = [ 8, 16, 32 ]
-	feature_map_channel = 3
-	anchor_total = 2
-	prepare_frame = numpy.zeros((face_detector_height, face_detector_width, 3))
-	prepare_frame[:temp_frame_height, :temp_frame_width, :] = temp_frame
-	temp_frame = (prepare_frame - 127.5) / 128.0
-	temp_frame = numpy.expand_dims(temp_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
-	with THREAD_SEMAPHORE:
-		detections = face_detector.run(None,
-		{
-			face_detector.get_inputs()[0].name: temp_frame
-		})
-	for index, feature_stride in enumerate(feature_strides):
-		keep_indices = numpy.where(detections[index] >= DeepFakeAI.globals.face_detector_score)[0]
-		if keep_indices.any():
-			stride_height = face_detector_height // feature_stride
-			stride_width = face_detector_width // feature_stride
-			anchors = create_static_anchors(feature_stride, anchor_total, stride_height, stride_width)
-			bbox_raw = (detections[index + feature_map_channel] * feature_stride)
-			kps_raw = detections[index + feature_map_channel * 2] * feature_stride
-			for bbox in distance_to_bbox(anchors, bbox_raw)[keep_indices]:
-				bbox_list.append(numpy.array(
-				[
-					bbox[0] * ratio_width,
-					bbox[1] * ratio_height,
-					bbox[2] * ratio_width,
-					bbox[3] * ratio_height
-				]))
-			for kps in distance_to_kps(anchors, kps_raw)[keep_indices]:
-				kps_list.append(kps * [ ratio_width, ratio_height ])
-			for score in detections[index][keep_indices]:
-				score_list.append(score[0])
-	return bbox_list, kps_list, score_list
-
-
-def detect_with_yunet(temp_frame : Frame, temp_frame_height : int, temp_frame_width : int, ratio_height : float, ratio_width : float) -> Tuple[List[Bbox], List[Kps], List[Score]]:
-	face_detector = get_face_analyser().get('face_detector')
-	face_detector.setInputSize((temp_frame_width, temp_frame_height))
-	face_detector.setScoreThreshold(DeepFakeAI.globals.face_detector_score)
-	bbox_list = []
-	kps_list = []
-	score_list = []
-	with THREAD_SEMAPHORE:
-		_, detections = face_detector.detect(temp_frame)
-	if detections.any():
-		for detection in detections:
-			bbox_list.append(numpy.array(
-			[
-				detection[0] * ratio_width,
-				detection[1] * ratio_height,
-				(detection[0] + detection[2]) * ratio_width,
-				(detection[1] + detection[3]) * ratio_height
-			]))
-			kps_list.append(detection[4:14].reshape((5, 2)) * [ ratio_width, ratio_height])
-			score_list.append(detection[14])
-	return bbox_list, kps_list, score_list
-
-
-def create_faces(frame : Frame, bbox_list : List[Bbox], kps_list : List[Kps], score_list : List[Score]) -> List[Face]:
-	faces = []
-	if DeepFakeAI.globals.face_detector_score > 0:
-		sort_indices = numpy.argsort(-numpy.array(score_list))
-		bbox_list = [ bbox_list[index] for index in sort_indices ]
-		kps_list = [ kps_list[index] for index in sort_indices ]
-		score_list = [ score_list[index] for index in sort_indices ]
-		keep_indices = apply_nms(bbox_list, 0.4)
-		for index in keep_indices:
-			bbox = bbox_list[index]
-			kps = kps_list[index]
-			score = score_list[index]
-			embedding, normed_embedding = calc_embedding(frame, kps)
-			gender, age = detect_gender_age(frame, kps)
-			faces.append(Face(
-				bbox = bbox,
-				kps = kps,
-				score = score,
-				embedding = embedding,
-				normed_embedding = normed_embedding,
-				gender = gender,
-				age = age
-			))
-	return faces
-
-
-def calc_embedding(temp_frame : Frame, kps : Kps) -> Tuple[Embedding, Embedding]:
-	face_recognizer = get_face_analyser().get('face_recognizer')
-	crop_frame, matrix = warp_face(temp_frame, kps, 'arcface_112_v2', (112, 112))
-	crop_frame = crop_frame.astype(numpy.float32) / 127.5 - 1
-	crop_frame = crop_frame[:, :, ::-1].transpose(2, 0, 1)
-	crop_frame = numpy.expand_dims(crop_frame, axis = 0)
-	embedding = face_recognizer.run(None,
-	{
-		face_recognizer.get_inputs()[0].name: crop_frame
-	})[0]
-	embedding = embedding.ravel()
-	normed_embedding = embedding / numpy.linalg.norm(embedding)
-	return embedding, normed_embedding
-
-
-def detect_gender_age(frame : Frame, kps : Kps) -> Tuple[int, int]:
-	gender_age = get_face_analyser().get('gender_age')
-	crop_frame, affine_matrix = warp_face(frame, kps, 'arcface_112_v2', (96, 96))
-	crop_frame = numpy.expand_dims(crop_frame, axis = 0).transpose(0, 3, 1, 2).astype(numpy.float32)
-	prediction = gender_age.run(None,
-	{
-		gender_age.get_inputs()[0].name: crop_frame
-	})[0][0]
-	gender = int(numpy.argmax(prediction[:2]))
-	age = int(numpy.round(prediction[2] * 100))
-	return gender, age
-
-
-def get_one_face(frame : Frame, position : int = 0) -> Optional[Face]:
-	many_faces = get_many_faces(frame)
-	if many_faces:
-		try:
-			return many_faces[position]
-		except IndexError:
-			return many_faces[-1]
-	return None
-
-
-def get_average_face(frames : List[Frame], position : int = 0) -> Optional[Face]:
-	average_face = None
-	faces = []
-	embedding_list = []
-	normed_embedding_list = []
-	for frame in frames:
-		face = get_one_face(frame, position)
-		if face:
-			faces.append(face)
-			embedding_list.append(face.embedding)
-			normed_embedding_list.append(face.normed_embedding)
-	if faces:
-		average_face = Face(
-			bbox = faces[0].bbox,
-			kps = faces[0].kps,
-			score = faces[0].score,
-			embedding = numpy.mean(embedding_list, axis = 0),
-			normed_embedding = numpy.mean(normed_embedding_list, axis = 0),
-			gender = faces[0].gender,
-			age = faces[0].age
-		)
-	return average_face
-
-
-def get_many_faces(frame : Frame) -> List[Face]:
-	try:
-		faces_cache = get_static_faces(frame)
-		if faces_cache:
-			faces = faces_cache
-		else:
-			faces = extract_faces(frame)
-			set_static_faces(frame, faces)
-		if DeepFakeAI.globals.face_analyser_order:
-			faces = sort_by_order(faces, DeepFakeAI.globals.face_analyser_order)
-		if DeepFakeAI.globals.face_analyser_age:
-			faces = filter_by_age(faces, DeepFakeAI.globals.face_analyser_age)
-		if DeepFakeAI.globals.face_analyser_gender:
-			faces = filter_by_gender(faces, DeepFakeAI.globals.face_analyser_gender)
-		return faces
-	except (AttributeError, ValueError):
-		return []
-
-
-def find_similar_faces(frame : Frame, reference_faces : FaceSet, face_distance : float) -> List[Face]:
-	similar_faces : List[Face] = []
-	many_faces = get_many_faces(frame)
-
-	if reference_faces:
-		for reference_set in reference_faces:
-			if not similar_faces:
-				for reference_face in reference_faces[reference_set]:
-					for face in many_faces:
-						if compare_faces(face, reference_face, face_distance):
-							similar_faces.append(face)
-	return similar_faces
-
-
-def compare_faces(face : Face, reference_face : Face, face_distance : float) -> bool:
-	if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'):
-		current_face_distance = 1 - numpy.dot(face.normed_embedding, reference_face.normed_embedding)
-		return current_face_distance < face_distance
-	return False
-
-
-def sort_by_order(faces : List[Face], order : FaceAnalyserOrder) -> List[Face]:
-	if order == 'left-right':
-		return sorted(faces, key = lambda face: face.bbox[0])
-	if order == 'right-left':
-		return sorted(faces, key = lambda face: face.bbox[0], reverse = True)
-	if order == 'top-bottom':
-		return sorted(faces, key = lambda face: face.bbox[1])
-	if order == 'bottom-top':
-		return sorted(faces, key = lambda face: face.bbox[1], reverse = True)
-	if order == 'small-large':
-		return sorted(faces, key = lambda face: (face.bbox[2] - face.bbox[0]) * (face.bbox[3] - face.bbox[1]))
-	if order == 'large-small':
-		return sorted(faces, key = lambda face: (face.bbox[2] - face.bbox[0]) * (face.bbox[3] - face.bbox[1]), reverse = True)
-	if order == 'best-worst':
-		return sorted(faces, key = lambda face: face.score, reverse = True)
-	if order == 'worst-best':
-		return sorted(faces, key = lambda face: face.score)
-	return faces
-
-
-def filter_by_age(faces : List[Face], age : FaceAnalyserAge) -> List[Face]:
-	filter_faces = []
-	for face in faces:
-		if face.age < 13 and age == 'child':
-			filter_faces.append(face)
-		elif face.age < 19 and age == 'teen':
-			filter_faces.append(face)
-		elif face.age < 60 and age == 'adult':
-			filter_faces.append(face)
-		elif face.age > 59 and age == 'senior':
-			filter_faces.append(face)
-	return filter_faces
-
-
-def filter_by_gender(faces : List[Face], gender : FaceAnalyserGender) -> List[Face]:
-	filter_faces = []
-	for face in faces:
-		if face.gender == 0 and gender == 'female':
-			filter_faces.append(face)
-		if face.gender == 1 and gender == 'male':
-			filter_faces.append(face)
-	return filter_faces

DeepFakeAI/face_helper.py

@@ -1,111 +0,0 @@
-from typing import Any, Dict, Tuple, List
-from cv2.typing import Size
-from functools import lru_cache
-import cv2
-import numpy
-
-from DeepFakeAI.typing import Bbox, Kps, Frame, Mask, Matrix, Template
-
-TEMPLATES : Dict[Template, numpy.ndarray[Any, Any]] =\
-{
-	'arcface_112_v1': numpy.array(
-	[
-		[ 39.7300, 51.1380 ],
-		[ 72.2700, 51.1380 ],
-		[ 56.0000, 68.4930 ],
-		[ 42.4630, 87.0100 ],
-		[ 69.5370, 87.0100 ]
-	]),
-	'arcface_112_v2': numpy.array(
-	[
-		[ 38.2946, 51.6963 ],
-		[ 73.5318, 51.5014 ],
-		[ 56.0252, 71.7366 ],
-		[ 41.5493, 92.3655 ],
-		[ 70.7299, 92.2041 ]
-	]),
-	'arcface_128_v2': numpy.array(
-	[
-		[ 46.2946, 51.6963 ],
-		[ 81.5318, 51.5014 ],
-		[ 64.0252, 71.7366 ],
-		[ 49.5493, 92.3655 ],
-		[ 78.7299, 92.2041 ]
-	]),
-	'ffhq_512': numpy.array(
-	[
-		[ 192.98138, 239.94708 ],
-		[ 318.90277, 240.1936 ],
-		[ 256.63416, 314.01935 ],
-		[ 201.26117, 371.41043 ],
-		[ 313.08905, 371.15118 ]
-	])
-}
-
-
-def warp_face(temp_frame : Frame, kps : Kps, template : Template, size : Size) -> Tuple[Frame, Matrix]:
-	normed_template = TEMPLATES.get(template) * size[1] / size[0]
-	affine_matrix = cv2.estimateAffinePartial2D(kps, normed_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
-	crop_frame = cv2.warpAffine(temp_frame, affine_matrix, (size[1], size[1]), borderMode = cv2.BORDER_REPLICATE)
-	return crop_frame, affine_matrix
-
-
-def paste_back(temp_frame : Frame, crop_frame: Frame, crop_mask : Mask, affine_matrix : Matrix) -> Frame:
-	inverse_matrix = cv2.invertAffineTransform(affine_matrix)
-	temp_frame_size = temp_frame.shape[:2][::-1]
-	inverse_crop_mask = cv2.warpAffine(crop_mask, inverse_matrix, temp_frame_size).clip(0, 1)
-	inverse_crop_frame = cv2.warpAffine(crop_frame, inverse_matrix, temp_frame_size, borderMode = cv2.BORDER_REPLICATE)
-	paste_frame = temp_frame.copy()
-	paste_frame[:, :, 0] = inverse_crop_mask * inverse_crop_frame[:, :, 0] + (1 - inverse_crop_mask) * temp_frame[:, :, 0]
-	paste_frame[:, :, 1] = inverse_crop_mask * inverse_crop_frame[:, :, 1] + (1 - inverse_crop_mask) * temp_frame[:, :, 1]
-	paste_frame[:, :, 2] = inverse_crop_mask * inverse_crop_frame[:, :, 2] + (1 - inverse_crop_mask) * temp_frame[:, :, 2]
-	return paste_frame
-
-
-@lru_cache(maxsize = None)
-def create_static_anchors(feature_stride : int, anchor_total : int, stride_height : int, stride_width : int) -> numpy.ndarray[Any, Any]:
-	y, x = numpy.mgrid[:stride_height, :stride_width][::-1]
-	anchors = numpy.stack((y, x), axis = -1)
-	anchors = (anchors * feature_stride).reshape((-1, 2))
-	anchors = numpy.stack([ anchors ] * anchor_total, axis = 1).reshape((-1, 2))
-	return anchors
-
-
-def distance_to_bbox(points : numpy.ndarray[Any, Any], distance : numpy.ndarray[Any, Any]) -> Bbox:
-	x1 = points[:, 0] - distance[:, 0]
-	y1 = points[:, 1] - distance[:, 1]
-	x2 = points[:, 0] + distance[:, 2]
-	y2 = points[:, 1] + distance[:, 3]
-	bbox = numpy.column_stack([ x1, y1, x2, y2 ])
-	return bbox
-
-
-def distance_to_kps(points : numpy.ndarray[Any, Any], distance : numpy.ndarray[Any, Any]) -> Kps:
-	x = points[:, 0::2] + distance[:, 0::2]
-	y = points[:, 1::2] + distance[:, 1::2]
-	kps = numpy.stack((x, y), axis = -1)
-	return kps
-
-
-def apply_nms(bbox_list : List[Bbox], iou_threshold : float) -> List[int]:
-	keep_indices = []
-	dimension_list = numpy.reshape(bbox_list, (-1, 4))
-	x1 = dimension_list[:, 0]
-	y1 = dimension_list[:, 1]
-	x2 = dimension_list[:, 2]
-	y2 = dimension_list[:, 3]
-	areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-	indices = numpy.arange(len(bbox_list))
-	while indices.size > 0:
-		index = indices[0]
-		remain_indices = indices[1:]
-		keep_indices.append(index)
-		xx1 = numpy.maximum(x1[index], x1[remain_indices])
-		yy1 = numpy.maximum(y1[index], y1[remain_indices])
-		xx2 = numpy.minimum(x2[index], x2[remain_indices])
-		yy2 = numpy.minimum(y2[index], y2[remain_indices])
-		width = numpy.maximum(0, xx2 - xx1 + 1)
-		height = numpy.maximum(0, yy2 - yy1 + 1)
-		iou = width * height / (areas[index] + areas[remain_indices] - width * height)
-		indices = indices[numpy.where(iou <= iou_threshold)[0] + 1]
-	return keep_indices

DeepFakeAI/face_masker.py

@@ -1,128 +0,0 @@
-from typing import Any, Dict, List
-from cv2.typing import Size
-from functools import lru_cache
-import threading
-import cv2
-import numpy
-import onnxruntime
-
-import DeepFakeAI.globals
-from DeepFakeAI.typing import Frame, Mask, Padding, FaceMaskRegion, ModelSet
-from DeepFakeAI.filesystem import resolve_relative_path
-from DeepFakeAI.download import conditional_download
-
-FACE_OCCLUDER = None
-FACE_PARSER = None
-THREAD_LOCK : threading.Lock = threading.Lock()
-MODELS : ModelSet =\
-{
-	'face_occluder':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/face_occluder.onnx',
-		'path': resolve_relative_path('../.assets/models/face_occluder.onnx')
-	},
-	'face_parser':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/face_parser.onnx',
-		'path': resolve_relative_path('../.assets/models/face_parser.onnx')
-	}
-}
-FACE_MASK_REGIONS : Dict[FaceMaskRegion, int] =\
-{
-	'skin': 1,
-	'left-eyebrow': 2,
-	'right-eyebrow': 3,
-	'left-eye': 4,
-	'right-eye': 5,
-	'eye-glasses': 6,
-	'nose': 10,
-	'mouth': 11,
-	'upper-lip': 12,
-	'lower-lip': 13
-}
-
-
-def get_face_occluder() -> Any:
-	global FACE_OCCLUDER
-
-	with THREAD_LOCK:
-		if FACE_OCCLUDER is None:
-			model_path = MODELS.get('face_occluder').get('path')
-			FACE_OCCLUDER = onnxruntime.InferenceSession(model_path, providers = DeepFakeAI.globals.execution_providers)
-	return FACE_OCCLUDER
-
-
-def get_face_parser() -> Any:
-	global FACE_PARSER
-
-	with THREAD_LOCK:
-		if FACE_PARSER is None:
-			model_path = MODELS.get('face_parser').get('path')
-			FACE_PARSER = onnxruntime.InferenceSession(model_path, providers = DeepFakeAI.globals.execution_providers)
-	return FACE_PARSER
-
-
-def clear_face_occluder() -> None:
-	global FACE_OCCLUDER
-
-	FACE_OCCLUDER = None
-
-
-def clear_face_parser() -> None:
-	global FACE_PARSER
-
-	FACE_PARSER = None
-
-
-def pre_check() -> bool:
-	if not DeepFakeAI.globals.skip_download:
-		download_directory_path = resolve_relative_path('../.assets/models')
-		model_urls =\
-		[
-			MODELS.get('face_occluder').get('url'),
-			MODELS.get('face_parser').get('url'),
-		]
-		conditional_download(download_directory_path, model_urls)
-	return True
-
-
-@lru_cache(maxsize = None)
-def create_static_box_mask(crop_size : Size, face_mask_blur : float, face_mask_padding : Padding) -> Mask:
-	blur_amount = int(crop_size[0] * 0.5 * face_mask_blur)
-	blur_area = max(blur_amount // 2, 1)
-	box_mask = numpy.ones(crop_size, numpy.float32)
-	box_mask[:max(blur_area, int(crop_size[1] * face_mask_padding[0] / 100)), :] = 0
-	box_mask[-max(blur_area, int(crop_size[1] * face_mask_padding[2] / 100)):, :] = 0
-	box_mask[:, :max(blur_area, int(crop_size[0] * face_mask_padding[3] / 100))] = 0
-	box_mask[:, -max(blur_area, int(crop_size[0] * face_mask_padding[1] / 100)):] = 0
-	if blur_amount > 0:
-		box_mask = cv2.GaussianBlur(box_mask, (0, 0), blur_amount * 0.25)
-	return box_mask
-
-
-def create_occlusion_mask(crop_frame : Frame) -> Mask:
-	face_occluder = get_face_occluder()
-	prepare_frame = cv2.resize(crop_frame, face_occluder.get_inputs()[0].shape[1:3][::-1])
-	prepare_frame = numpy.expand_dims(prepare_frame, axis = 0).astype(numpy.float32) / 255
-	prepare_frame = prepare_frame.transpose(0, 1, 2, 3)
-	occlusion_mask = face_occluder.run(None,
-	{
-		face_occluder.get_inputs()[0].name: prepare_frame
-	})[0][0]
-	occlusion_mask = occlusion_mask.transpose(0, 1, 2).clip(0, 1).astype(numpy.float32)
-	occlusion_mask = cv2.resize(occlusion_mask, crop_frame.shape[:2][::-1])
-	return occlusion_mask
-
-
-def create_region_mask(crop_frame : Frame, face_mask_regions : List[FaceMaskRegion]) -> Mask:
-	face_parser = get_face_parser()
-	prepare_frame = cv2.flip(cv2.resize(crop_frame, (512, 512)), 1)
-	prepare_frame = numpy.expand_dims(prepare_frame, axis = 0).astype(numpy.float32)[:, :, ::-1] / 127.5 - 1
-	prepare_frame = prepare_frame.transpose(0, 3, 1, 2)
-	region_mask = face_parser.run(None,
-	{
-		face_parser.get_inputs()[0].name: prepare_frame
-	})[0][0]
-	region_mask = numpy.isin(region_mask.argmax(0), [ FACE_MASK_REGIONS[region] for region in face_mask_regions ])
-	region_mask = cv2.resize(region_mask.astype(numpy.float32), crop_frame.shape[:2][::-1])
-	return region_mask

DeepFakeAI/face_store.py

@@ -1,47 +0,0 @@
-from typing import Optional, List
-import hashlib
-
-from DeepFakeAI.typing import Frame, Face, FaceStore, FaceSet
-
-FACE_STORE: FaceStore =\
-{
-	'static_faces': {},
-	'reference_faces': {}
-}
-
-
-def get_static_faces(frame : Frame) -> Optional[List[Face]]:
-	frame_hash = create_frame_hash(frame)
-	if frame_hash in FACE_STORE['static_faces']:
-		return FACE_STORE['static_faces'][frame_hash]
-	return None
-
-
-def set_static_faces(frame : Frame, faces : List[Face]) -> None:
-	frame_hash = create_frame_hash(frame)
-	if frame_hash:
-		FACE_STORE['static_faces'][frame_hash] = faces
-
-
-def clear_static_faces() -> None:
-	FACE_STORE['static_faces'] = {}
-
-
-def create_frame_hash(frame: Frame) -> Optional[str]:
-	return hashlib.sha1(frame.tobytes()).hexdigest() if frame.any() else None
-
-
-def get_reference_faces() -> Optional[FaceSet]:
-	if FACE_STORE['reference_faces']:
-		return FACE_STORE['reference_faces']
-	return None
-
-
-def append_reference_face(name : str, face : Face) -> None:
-	if name not in FACE_STORE['reference_faces']:
-		FACE_STORE['reference_faces'][name] = []
-	FACE_STORE['reference_faces'][name].append(face)
-
-
-def clear_reference_faces() -> None:
-	FACE_STORE['reference_faces'] = {}

DeepFakeAI/ffmpeg.py

@@ -1,81 +0,0 @@
-from typing import List
-import subprocess
-
-import DeepFakeAI.globals
-from DeepFakeAI import logger
-from DeepFakeAI.filesystem import get_temp_frames_pattern, get_temp_output_video_path
-from DeepFakeAI.vision import detect_fps
-
-
-def run_ffmpeg(args : List[str]) -> bool:
-	commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'error' ]
-	commands.extend(args)
-	try:
-		subprocess.run(commands, stderr = subprocess.PIPE, check = True)
-		return True
-	except subprocess.CalledProcessError as exception:
-		logger.debug(exception.stderr.decode().strip(), __name__.upper())
-		return False
-
-
-def open_ffmpeg(args : List[str]) -> subprocess.Popen[bytes]:
-	commands = [ 'ffmpeg', '-hide_banner', '-loglevel', 'error' ]
-	commands.extend(args)
-	return subprocess.Popen(commands, stdin = subprocess.PIPE)
-
-
-def extract_frames(target_path : str, fps : float) -> bool:
-	temp_frame_compression = round(31 - (DeepFakeAI.globals.temp_frame_quality * 0.31))
-	trim_frame_start = DeepFakeAI.globals.trim_frame_start
-	trim_frame_end = DeepFakeAI.globals.trim_frame_end
-	temp_frames_pattern = get_temp_frames_pattern(target_path, '%04d')
-	commands = [ '-hwaccel', 'auto', '-i', target_path, '-q:v', str(temp_frame_compression), '-pix_fmt', 'rgb24' ]
-	if trim_frame_start is not None and trim_frame_end is not None:
-		commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ':end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
-	elif trim_frame_start is not None:
-		commands.extend([ '-vf', 'trim=start_frame=' + str(trim_frame_start) + ',fps=' + str(fps) ])
-	elif trim_frame_end is not None:
-		commands.extend([ '-vf', 'trim=end_frame=' + str(trim_frame_end) + ',fps=' + str(fps) ])
-	else:
-		commands.extend([ '-vf', 'fps=' + str(fps) ])
-	commands.extend([ '-vsync', '0', temp_frames_pattern ])
-	return run_ffmpeg(commands)
-
-
-def compress_image(output_path : str) -> bool:
-	output_image_compression = round(31 - (DeepFakeAI.globals.output_image_quality * 0.31))
-	commands = [ '-hwaccel', 'auto', '-i', output_path, '-q:v', str(output_image_compression), '-y', output_path ]
-	return run_ffmpeg(commands)
-
-
-def merge_video(target_path : str, fps : float) -> bool:
-	temp_output_video_path = get_temp_output_video_path(target_path)
-	temp_frames_pattern = get_temp_frames_pattern(target_path, '%04d')
-	commands = [ '-hwaccel', 'auto', '-r', str(fps), '-i', temp_frames_pattern, '-c:v', DeepFakeAI.globals.output_video_encoder ]
-	if DeepFakeAI.globals.output_video_encoder in [ 'libx264', 'libx265' ]:
-		output_video_compression = round(51 - (DeepFakeAI.globals.output_video_quality * 0.51))
-		commands.extend([ '-crf', str(output_video_compression) ])
-	if DeepFakeAI.globals.output_video_encoder in [ 'libvpx-vp9' ]:
-		output_video_compression = round(63 - (DeepFakeAI.globals.output_video_quality * 0.63))
-		commands.extend([ '-crf', str(output_video_compression) ])
-	if DeepFakeAI.globals.output_video_encoder in [ 'h264_nvenc', 'hevc_nvenc' ]:
-		output_video_compression = round(51 - (DeepFakeAI.globals.output_video_quality * 0.51))
-		commands.extend([ '-cq', str(output_video_compression) ])
-	commands.extend([ '-pix_fmt', 'yuv420p', '-colorspace', 'bt709', '-y', temp_output_video_path ])
-	return run_ffmpeg(commands)
-
-
-def restore_audio(target_path : str, output_path : str) -> bool:
-	fps = detect_fps(target_path)
-	trim_frame_start = DeepFakeAI.globals.trim_frame_start
-	trim_frame_end = DeepFakeAI.globals.trim_frame_end
-	temp_output_video_path = get_temp_output_video_path(target_path)
-	commands = [ '-hwaccel', 'auto', '-i', temp_output_video_path ]
-	if trim_frame_start is not None:
-		start_time = trim_frame_start / fps
-		commands.extend([ '-ss', str(start_time) ])
-	if trim_frame_end is not None:
-		end_time = trim_frame_end / fps
-		commands.extend([ '-to', str(end_time) ])
-	commands.extend([ '-i', target_path, '-c', 'copy', '-map', '0:v:0', '-map', '1:a:0', '-shortest', '-y', output_path ])
-	return run_ffmpeg(commands)

DeepFakeAI/filesystem.py

@@ -1,91 +0,0 @@
-from typing import List, Optional
-import glob
-import os
-import shutil
-import tempfile
-import filetype
-from pathlib import Path
-
-import DeepFakeAI.globals
-
-TEMP_DIRECTORY_PATH = os.path.join(tempfile.gettempdir(), 'DeepFakeAI')
-TEMP_OUTPUT_VIDEO_NAME = 'temp.mp4'
-
-
-def get_temp_frame_paths(target_path : str) -> List[str]:
-	temp_frames_pattern = get_temp_frames_pattern(target_path, '*')
-	return sorted(glob.glob(temp_frames_pattern))
-
-
-def get_temp_frames_pattern(target_path : str, temp_frame_prefix : str) -> str:
-	temp_directory_path = get_temp_directory_path(target_path)
-	return os.path.join(temp_directory_path, temp_frame_prefix + '.' + DeepFakeAI.globals.temp_frame_format)
-
-
-def get_temp_directory_path(target_path : str) -> str:
-	target_name, _ = os.path.splitext(os.path.basename(target_path))
-	return os.path.join(TEMP_DIRECTORY_PATH, target_name)
-
-
-def get_temp_output_video_path(target_path : str) -> str:
-	temp_directory_path = get_temp_directory_path(target_path)
-	return os.path.join(temp_directory_path, TEMP_OUTPUT_VIDEO_NAME)
-
-
-def create_temp(target_path : str) -> None:
-	temp_directory_path = get_temp_directory_path(target_path)
-	Path(temp_directory_path).mkdir(parents = True, exist_ok = True)
-
-
-def move_temp(target_path : str, output_path : str) -> None:
-	temp_output_video_path = get_temp_output_video_path(target_path)
-	if is_file(temp_output_video_path):
-		if is_file(output_path):
-			os.remove(output_path)
-		shutil.move(temp_output_video_path, output_path)
-
-
-def clear_temp(target_path : str) -> None:
-	temp_directory_path = get_temp_directory_path(target_path)
-	parent_directory_path = os.path.dirname(temp_directory_path)
-	if not DeepFakeAI.globals.keep_temp and is_directory(temp_directory_path):
-		shutil.rmtree(temp_directory_path)
-	if os.path.exists(parent_directory_path) and not os.listdir(parent_directory_path):
-		os.rmdir(parent_directory_path)
-
-
-def is_file(file_path : str) -> bool:
-	return bool(file_path and os.path.isfile(file_path))
-
-
-def is_directory(directory_path : str) -> bool:
-	return bool(directory_path and os.path.isdir(directory_path))
-
-
-def is_image(image_path : str) -> bool:
-	if is_file(image_path):
-		return filetype.helpers.is_image(image_path)
-	return False
-
-
-def are_images(image_paths : List[str]) -> bool:
-	if image_paths:
-		return all(is_image(image_path) for image_path in image_paths)
-	return False
-
-
-def is_video(video_path : str) -> bool:
-	if is_file(video_path):
-		return filetype.helpers.is_video(video_path)
-	return False
-
-
-def resolve_relative_path(path : str) -> str:
-	return os.path.abspath(os.path.join(os.path.dirname(__file__), path))
-
-
-def list_module_names(path : str) -> Optional[List[str]]:
-	if os.path.exists(path):
-		files = os.listdir(path)
-		return [ Path(file).stem for file in files if not Path(file).stem.startswith(('.', '__')) ]
-	return None

DeepFakeAI/globals.py

@@ -1,51 +0,0 @@
-from typing import List, Optional
-
-from DeepFakeAI.typing import LogLevel, FaceSelectorMode, FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, FaceMaskType, FaceMaskRegion, OutputVideoEncoder, FaceDetectorModel, FaceRecognizerModel, TempFrameFormat, Padding
-
-# general
-source_paths : Optional[List[str]] = None
-target_path : Optional[str] = None
-output_path : Optional[str] = None
-# misc
-skip_download : Optional[bool] = None
-headless : Optional[bool] = None
-log_level : Optional[LogLevel] = None
-# execution
-execution_providers : List[str] = []
-execution_thread_count : Optional[int] = None
-execution_queue_count : Optional[int] = None
-max_memory : Optional[int] = None
-# face analyser
-face_analyser_order : Optional[FaceAnalyserOrder] = None
-face_analyser_age : Optional[FaceAnalyserAge] = None
-face_analyser_gender : Optional[FaceAnalyserGender] = None
-face_detector_model : Optional[FaceDetectorModel] = None
-face_detector_size : Optional[str] = None
-face_detector_score : Optional[float] = None
-face_recognizer_model : Optional[FaceRecognizerModel] = None
-# face selector
-face_selector_mode : Optional[FaceSelectorMode] = None
-reference_face_position : Optional[int] = None
-reference_face_distance : Optional[float] = None
-reference_frame_number : Optional[int] = None
-# face mask
-face_mask_types : Optional[List[FaceMaskType]] = None
-face_mask_blur : Optional[float] = None
-face_mask_padding : Optional[Padding] = None
-face_mask_regions : Optional[List[FaceMaskRegion]] = None
-# frame extraction
-trim_frame_start : Optional[int] = None
-trim_frame_end : Optional[int] = None
-temp_frame_format : Optional[TempFrameFormat] = None
-temp_frame_quality : Optional[int] = None
-keep_temp : Optional[bool] = None
-# output creation
-output_image_quality : Optional[int] = None
-output_video_encoder : Optional[OutputVideoEncoder] = None
-output_video_quality : Optional[int] = None
-keep_fps : Optional[bool] = None
-skip_audio : Optional[bool] = None
-# frame processors
-frame_processors : List[str] = []
-# uis
-ui_layouts : List[str] = []

DeepFakeAI/installer.py

@@ -1,92 +0,0 @@
-from typing import Dict, Tuple
-import sys
-import os
-import platform
-import tempfile
-import subprocess
-from argparse import ArgumentParser, HelpFormatter
-
-subprocess.call([ 'pip', 'install' , 'inquirer', '-q' ])
-
-import inquirer
-
-from DeepFakeAI import metadata, wording
-
-TORCH : Dict[str, str] =\
-{
-	'default': 'default',
-	'cpu': 'cpu'
-}
-ONNXRUNTIMES : Dict[str, Tuple[str, str]] =\
-{
-	'default': ('onnxruntime', '1.16.3')
-}
-if platform.system().lower() == 'linux' or platform.system().lower() == 'windows':
-	TORCH['cuda'] = 'cu118'
-	TORCH['cuda-nightly'] = 'cu121'
-	ONNXRUNTIMES['cuda'] = ('onnxruntime-gpu', '1.16.3')
-	ONNXRUNTIMES['cuda-nightly'] = ('ort-nightly-gpu', '1.17.0.dev20231205004')
-	ONNXRUNTIMES['openvino'] = ('onnxruntime-openvino', '1.16.0')
-if platform.system().lower() == 'linux':
-	TORCH['rocm'] = 'rocm5.6'
-	ONNXRUNTIMES['rocm'] = ('onnxruntime-rocm', '1.16.3')
-if platform.system().lower() == 'darwin':
-	ONNXRUNTIMES['coreml-legacy'] = ('onnxruntime-coreml', '1.13.1')
-	ONNXRUNTIMES['coreml-silicon'] = ('onnxruntime-silicon', '1.16.0')
-if platform.system().lower() == 'windows':
-	ONNXRUNTIMES['directml'] = ('onnxruntime-directml', '1.16.3')
-
-
-def cli() -> None:
-	program = ArgumentParser(formatter_class = lambda prog: HelpFormatter(prog, max_help_position = 120))
-	program.add_argument('--torch', help = wording.get('install_dependency_help').format(dependency = 'torch'), choices = TORCH.keys())
-	program.add_argument('--onnxruntime', help = wording.get('install_dependency_help').format(dependency = 'onnxruntime'), choices = ONNXRUNTIMES.keys())
-	program.add_argument('--skip-venv', help = wording.get('skip_venv_help'), action = 'store_true')
-	program.add_argument('-v', '--version', version = metadata.get('name') + ' ' + metadata.get('version'), action = 'version')
-	run(program)
-
-
-def run(program : ArgumentParser) -> None:
-	args = program.parse_args()
-	python_id = 'cp' + str(sys.version_info.major) + str(sys.version_info.minor)
-
-	if not args.skip_venv:
-		os.environ['PIP_REQUIRE_VIRTUALENV'] = '1'
-	if args.torch and args.onnxruntime:
-		answers =\
-		{
-			'torch': args.torch,
-			'onnxruntime': args.onnxruntime
-		}
-	else:
-		answers = inquirer.prompt(
-		[
-			inquirer.List('torch', message = wording.get('install_dependency_help').format(dependency = 'torch'), choices = list(TORCH.keys())),
-			inquirer.List('onnxruntime', message = wording.get('install_dependency_help').format(dependency = 'onnxruntime'), choices = list(ONNXRUNTIMES.keys()))
-		])
-	if answers:
-		torch = answers['torch']
-		torch_wheel = TORCH[torch]
-		onnxruntime = answers['onnxruntime']
-		onnxruntime_name, onnxruntime_version = ONNXRUNTIMES[onnxruntime]
-
-		subprocess.call([ 'pip', 'uninstall', 'torch', '-y', '-q' ])
-		if torch_wheel == 'default':
-			subprocess.call([ 'pip', 'install', '-r', 'requirements.txt' ])
-		else:
-			subprocess.call([ 'pip', 'install', '-r', 'requirements.txt', '--extra-index-url', 'https://download.pytorch.org/whl/' + torch_wheel ])
-		if onnxruntime == 'rocm':
-			if python_id in [ 'cp39', 'cp310', 'cp311' ]:
-				wheel_name = 'onnxruntime_training-' + onnxruntime_version + '+rocm56-' + python_id + '-' + python_id + '-manylinux_2_17_x86_64.manylinux2014_x86_64.whl'
-				wheel_path = os.path.join(tempfile.gettempdir(), wheel_name)
-				wheel_url = 'https://download.onnxruntime.ai/' + wheel_name
-				subprocess.call([ 'curl', '--silent', '--location', '--continue-at', '-', '--output', wheel_path, wheel_url ])
-				subprocess.call([ 'pip', 'uninstall', wheel_path, '-y', '-q' ])
-				subprocess.call([ 'pip', 'install', wheel_path ])
-				os.remove(wheel_path)
-		else:
-			subprocess.call([ 'pip', 'uninstall', 'onnxruntime', onnxruntime_name, '-y', '-q' ])
-			if onnxruntime == 'cuda-nightly':
-				subprocess.call([ 'pip', 'install', onnxruntime_name + '==' + onnxruntime_version, '--extra-index-url', 'https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple' ])
-			else:
-				subprocess.call([ 'pip', 'install', onnxruntime_name + '==' + onnxruntime_version ])

DeepFakeAI/logger.py

@@ -1,39 +0,0 @@
-from typing import Dict
-from logging import basicConfig, getLogger, Logger, DEBUG, INFO, WARNING, ERROR
-
-from DeepFakeAI.typing import LogLevel
-
-
-def init(log_level : LogLevel) -> None:
-	basicConfig(format = None)
-	get_package_logger().setLevel(get_log_levels()[log_level])
-
-
-def get_package_logger() -> Logger:
-	return getLogger('DeepFakeAI')
-
-
-def debug(message : str, scope : str) -> None:
-	get_package_logger().debug('[' + scope + '] ' + message)
-
-
-def info(message : str, scope : str) -> None:
-	get_package_logger().info('[' + scope + '] ' + message)
-
-
-def warn(message : str, scope : str) -> None:
-	get_package_logger().warning('[' + scope + '] ' + message)
-
-
-def error(message : str, scope : str) -> None:
-	get_package_logger().error('[' + scope + '] ' + message)
-
-
-def get_log_levels() -> Dict[LogLevel, int]:
-	return\
-	{
-		'error': ERROR,
-		'warn': WARNING,
-		'info': INFO,
-		'debug': DEBUG
-	}

DeepFakeAI/metadata.py

@@ -1,13 +0,0 @@
-METADATA =\
-{
-	'name': 'FaceFusion',
-	'description': 'Next generation face swapper and enhancer',
-	'version': '2.1.3',
-	'license': 'MIT',
-	'author': 'Henry Ruhs',
-	'url': 'https://DeepFakeAI.io'
-}
-
-
-def get(key : str) -> str:
-	return METADATA[key]

DeepFakeAI/normalizer.py

@@ -1,34 +0,0 @@
-from typing import List, Optional
-import os
-
-from DeepFakeAI.filesystem import is_file, is_directory
-from DeepFakeAI.typing import Padding
-
-
-def normalize_output_path(source_paths : List[str], target_path : str, output_path : str) -> Optional[str]:
-	if is_file(target_path) and is_directory(output_path):
-		target_name, target_extension = os.path.splitext(os.path.basename(target_path))
-		if source_paths and is_file(source_paths[0]):
-			source_name, _ = os.path.splitext(os.path.basename(source_paths[0]))
-			return os.path.join(output_path, source_name + '-' + target_name + target_extension)
-		return os.path.join(output_path, target_name + target_extension)
-	if is_file(target_path) and output_path:
-		_, target_extension = os.path.splitext(os.path.basename(target_path))
-		output_name, output_extension = os.path.splitext(os.path.basename(output_path))
-		output_directory_path = os.path.dirname(output_path)
-		if is_directory(output_directory_path) and output_extension:
-			return os.path.join(output_directory_path, output_name + target_extension)
-		return None
-	return output_path
-
-
-def normalize_padding(padding : Optional[List[int]]) -> Optional[Padding]:
-	if padding and len(padding) == 1:
-		return tuple([ padding[0], padding[0], padding[0], padding[0] ]) # type: ignore[return-value]
-	if padding and len(padding) == 2:
-		return tuple([ padding[0], padding[1], padding[0], padding[1] ]) # type: ignore[return-value]
-	if padding and len(padding) == 3:
-		return tuple([ padding[0], padding[1], padding[2], padding[1] ]) # type: ignore[return-value]
-	if padding and len(padding) == 4:
-		return tuple(padding) # type: ignore[return-value]
-	return None

DeepFakeAI/processors/frame/choices.py

@@ -1,13 +0,0 @@
-from typing import List
-import numpy
-
-from DeepFakeAI.processors.frame.typings import FaceSwapperModel, FaceEnhancerModel, FrameEnhancerModel, FaceDebuggerItem
-
-face_swapper_models : List[FaceSwapperModel] = [ 'blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial' ]
-face_enhancer_models : List[FaceEnhancerModel] = [ 'codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer' ]
-frame_enhancer_models : List[FrameEnhancerModel] = [ 'real_esrgan_x2plus', 'real_esrgan_x4plus', 'real_esrnet_x4plus' ]
-
-face_enhancer_blend_range : List[int] = numpy.arange(0, 101, 1).tolist()
-frame_enhancer_blend_range : List[int] = numpy.arange(0, 101, 1).tolist()
-
-face_debugger_items : List[FaceDebuggerItem] = [ 'bbox', 'kps', 'face-mask', 'score' ]

DeepFakeAI/processors/frame/core.py

@@ -1,98 +0,0 @@
-import sys
-import importlib
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from queue import Queue
-from types import ModuleType
-from typing import Any, List
-from tqdm import tqdm
-
-import DeepFakeAI.globals
-from DeepFakeAI.typing import Process_Frames
-from DeepFakeAI.execution_helper import encode_execution_providers
-from DeepFakeAI import logger, wording
-
-FRAME_PROCESSORS_MODULES : List[ModuleType] = []
-FRAME_PROCESSORS_METHODS =\
-[
-	'get_frame_processor',
-	'clear_frame_processor',
-	'get_options',
-	'set_options',
-	'register_args',
-	'apply_args',
-	'pre_check',
-	'pre_process',
-	'get_reference_frame',
-	'process_frame',
-	'process_frames',
-	'process_image',
-	'process_video',
-	'post_process'
-]
-
-
-def load_frame_processor_module(frame_processor : str) -> Any:
-	try:
-		frame_processor_module = importlib.import_module('DeepFakeAI.processors.frame.modules.' + frame_processor)
-		for method_name in FRAME_PROCESSORS_METHODS:
-			if not hasattr(frame_processor_module, method_name):
-				raise NotImplementedError
-	except ModuleNotFoundError as exception:
-		logger.debug(exception.msg, __name__.upper())
-		sys.exit(wording.get('frame_processor_not_loaded').format(frame_processor = frame_processor))
-	except NotImplementedError:
-		sys.exit(wording.get('frame_processor_not_implemented').format(frame_processor = frame_processor))
-	return frame_processor_module
-
-
-def get_frame_processors_modules(frame_processors : List[str]) -> List[ModuleType]:
-	global FRAME_PROCESSORS_MODULES
-
-	if not FRAME_PROCESSORS_MODULES:
-		for frame_processor in frame_processors:
-			frame_processor_module = load_frame_processor_module(frame_processor)
-			FRAME_PROCESSORS_MODULES.append(frame_processor_module)
-	return FRAME_PROCESSORS_MODULES
-
-
-def clear_frame_processors_modules() -> None:
-	global FRAME_PROCESSORS_MODULES
-
-	for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
-		frame_processor_module.clear_frame_processor()
-	FRAME_PROCESSORS_MODULES = []
-
-
-def multi_process_frames(source_paths : List[str], temp_frame_paths : List[str], process_frames : Process_Frames) -> None:
-	with tqdm(total = len(temp_frame_paths), desc = wording.get('processing'), unit = 'frame', ascii = ' =', disable = DeepFakeAI.globals.log_level in [ 'warn', 'error' ]) as progress:
-		progress.set_postfix(
-		{
-			'execution_providers': encode_execution_providers(DeepFakeAI.globals.execution_providers),
-			'execution_thread_count': DeepFakeAI.globals.execution_thread_count,
-			'execution_queue_count': DeepFakeAI.globals.execution_queue_count
-		})
-		with ThreadPoolExecutor(max_workers = DeepFakeAI.globals.execution_thread_count) as executor:
-			futures = []
-			queue_temp_frame_paths : Queue[str] = create_queue(temp_frame_paths)
-			queue_per_future = max(len(temp_frame_paths) // DeepFakeAI.globals.execution_thread_count * DeepFakeAI.globals.execution_queue_count, 1)
-			while not queue_temp_frame_paths.empty():
-				payload_temp_frame_paths = pick_queue(queue_temp_frame_paths, queue_per_future)
-				future = executor.submit(process_frames, source_paths, payload_temp_frame_paths, progress.update)
-				futures.append(future)
-			for future_done in as_completed(futures):
-				future_done.result()
-
-
-def create_queue(temp_frame_paths : List[str]) -> Queue[str]:
-	queue : Queue[str] = Queue()
-	for frame_path in temp_frame_paths:
-		queue.put(frame_path)
-	return queue
-
-
-def pick_queue(queue : Queue[str], queue_per_future : int) -> List[str]:
-	queues = []
-	for _ in range(queue_per_future):
-		if not queue.empty():
-			queues.append(queue.get())
-	return queues

DeepFakeAI/processors/frame/globals.py

@@ -1,10 +0,0 @@
-from typing import List, Optional
-
-from DeepFakeAI.processors.frame.typings import FaceSwapperModel, FaceEnhancerModel, FrameEnhancerModel, FaceDebuggerItem
-
-face_swapper_model : Optional[FaceSwapperModel] = None
-face_enhancer_model : Optional[FaceEnhancerModel] = None
-face_enhancer_blend : Optional[int] = None
-frame_enhancer_model : Optional[FrameEnhancerModel] = None
-frame_enhancer_blend : Optional[int] = None
-face_debugger_items : Optional[List[FaceDebuggerItem]] = None

DeepFakeAI/processors/frame/modules/face_debugger.py

@@ -1,142 +0,0 @@
-from typing import Any, List, Literal
-from argparse import ArgumentParser
-import cv2
-import numpy
-
-import DeepFakeAI.globals
-import DeepFakeAI.processors.frame.core as frame_processors
-from DeepFakeAI import wording
-from DeepFakeAI.face_analyser import get_one_face, get_average_face, get_many_faces, find_similar_faces, clear_face_analyser
-from DeepFakeAI.face_store import get_reference_faces
-from DeepFakeAI.content_analyser import clear_content_analyser
-from DeepFakeAI.typing import Face, FaceSet, Frame, Update_Process, ProcessMode
-from DeepFakeAI.vision import read_image, read_static_image, read_static_images, write_image
-from DeepFakeAI.face_helper import warp_face
-from DeepFakeAI.face_masker import create_static_box_mask, create_occlusion_mask, create_region_mask, clear_face_occluder, clear_face_parser
-from DeepFakeAI.processors.frame import globals as frame_processors_globals, choices as frame_processors_choices
-
-NAME = __name__.upper()
-
-
-def get_frame_processor() -> None:
-	pass
-
-
-def clear_frame_processor() -> None:
-	pass
-
-
-def get_options(key : Literal['model']) -> None:
-	pass
-
-
-def set_options(key : Literal['model'], value : Any) -> None:
-	pass
-
-
-def register_args(program : ArgumentParser) -> None:
-	program.add_argument('--face-debugger-items', help = wording.get('face_debugger_items_help').format(choices = ', '.join(frame_processors_choices.face_debugger_items)), default = [ 'kps', 'face-mask' ], choices = frame_processors_choices.face_debugger_items, nargs = '+', metavar = 'FACE_DEBUGGER_ITEMS')
-
-
-def apply_args(program : ArgumentParser) -> None:
-	args = program.parse_args()
-	frame_processors_globals.face_debugger_items = args.face_debugger_items
-
-
-def pre_check() -> bool:
-	return True
-
-
-def pre_process(mode : ProcessMode) -> bool:
-	return True
-
-
-def post_process() -> None:
-	clear_frame_processor()
-	clear_face_analyser()
-	clear_content_analyser()
-	clear_face_occluder()
-	clear_face_parser()
-	read_static_image.cache_clear()
-
-
-def debug_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
-	primary_color = (0, 0, 255)
-	secondary_color = (0, 255, 0)
-	bounding_box = target_face.bbox.astype(numpy.int32)
-	if 'bbox' in frame_processors_globals.face_debugger_items:
-		cv2.rectangle(temp_frame, (bounding_box[0], bounding_box[1]), (bounding_box[2], bounding_box[3]), secondary_color, 2)
-	if 'face-mask' in frame_processors_globals.face_debugger_items:
-		crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, 'arcface_128_v2', (128, 512))
-		inverse_matrix = cv2.invertAffineTransform(affine_matrix)
-		temp_frame_size = temp_frame.shape[:2][::-1]
-		crop_mask_list = []
-		if 'box' in DeepFakeAI.globals.face_mask_types:
-			crop_mask_list.append(create_static_box_mask(crop_frame.shape[:2][::-1], 0, DeepFakeAI.globals.face_mask_padding))
-		if 'occlusion' in DeepFakeAI.globals.face_mask_types:
-			crop_mask_list.append(create_occlusion_mask(crop_frame))
-		if 'region' in DeepFakeAI.globals.face_mask_types:
-			crop_mask_list.append(create_region_mask(crop_frame, DeepFakeAI.globals.face_mask_regions))
-		crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
-		crop_mask = (crop_mask * 255).astype(numpy.uint8)
-		inverse_mask_frame = cv2.warpAffine(crop_mask, inverse_matrix, temp_frame_size)
-		inverse_mask_frame_edges = cv2.threshold(inverse_mask_frame, 100, 255, cv2.THRESH_BINARY)[1]
-		inverse_mask_frame_edges[inverse_mask_frame_edges > 0] = 255
-		inverse_mask_contours = cv2.findContours(inverse_mask_frame_edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0]
-		cv2.drawContours(temp_frame, inverse_mask_contours, -1, primary_color, 2)
-	if bounding_box[3] - bounding_box[1] > 60 and bounding_box[2] - bounding_box[0] > 60:
-		if 'kps' in frame_processors_globals.face_debugger_items:
-			kps = target_face.kps.astype(numpy.int32)
-			for index in range(kps.shape[0]):
-				cv2.circle(temp_frame, (kps[index][0], kps[index][1]), 3, primary_color, -1)
-		if 'score' in frame_processors_globals.face_debugger_items:
-			score_text = str(round(target_face.score, 2))
-			score_position = (bounding_box[0] + 10, bounding_box[1] + 20)
-			cv2.putText(temp_frame, score_text, score_position, cv2.FONT_HERSHEY_SIMPLEX, 0.5, secondary_color, 2)
-	return temp_frame
-
-
-def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
-	pass
-
-
-def process_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
-	if 'reference' in DeepFakeAI.globals.face_selector_mode:
-		similar_faces = find_similar_faces(temp_frame, reference_faces, DeepFakeAI.globals.reference_face_distance)
-		if similar_faces:
-			for similar_face in similar_faces:
-				temp_frame = debug_face(source_face, similar_face, temp_frame)
-	if 'one' in DeepFakeAI.globals.face_selector_mode:
-		target_face = get_one_face(temp_frame)
-		if target_face:
-			temp_frame = debug_face(source_face, target_face, temp_frame)
-	if 'many' in DeepFakeAI.globals.face_selector_mode:
-		many_faces = get_many_faces(temp_frame)
-		if many_faces:
-			for target_face in many_faces:
-				temp_frame = debug_face(source_face, target_face, temp_frame)
-	return temp_frame
-
-
-def process_frames(source_paths : List[str], temp_frame_paths : List[str], update_progress : Update_Process) -> None:
-	source_frames = read_static_images(source_paths)
-	source_face = get_average_face(source_frames)
-	reference_faces = get_reference_faces() if 'reference' in DeepFakeAI.globals.face_selector_mode else None
-	for temp_frame_path in temp_frame_paths:
-		temp_frame = read_image(temp_frame_path)
-		result_frame = process_frame(source_face, reference_faces, temp_frame)
-		write_image(temp_frame_path, result_frame)
-		update_progress()
-
-
-def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
-	source_frames = read_static_images(source_paths)
-	source_face = get_average_face(source_frames)
-	target_frame = read_static_image(target_path)
-	reference_faces = get_reference_faces() if 'reference' in DeepFakeAI.globals.face_selector_mode else None
-	result_frame = process_frame(source_face, reference_faces, target_frame)
-	write_image(output_path, result_frame)
-
-
-def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
-	frame_processors.multi_process_frames(source_paths, temp_frame_paths, process_frames)

DeepFakeAI/processors/frame/modules/face_enhancer.py

@@ -1,249 +0,0 @@
-from typing import Any, List, Literal, Optional
-from argparse import ArgumentParser
-import cv2
-import threading
-import numpy
-import onnxruntime
-
-import DeepFakeAI.globals
-import DeepFakeAI.processors.frame.core as frame_processors
-from DeepFakeAI import logger, wording
-from DeepFakeAI.face_analyser import get_many_faces, clear_face_analyser, find_similar_faces, get_one_face
-from DeepFakeAI.face_helper import warp_face, paste_back
-from DeepFakeAI.content_analyser import clear_content_analyser
-from DeepFakeAI.face_store import get_reference_faces
-from DeepFakeAI.typing import Face, FaceSet, Frame, Update_Process, ProcessMode, ModelSet, OptionsWithModel
-from DeepFakeAI.common_helper import create_metavar
-from DeepFakeAI.filesystem import is_file, is_image, is_video, resolve_relative_path
-from DeepFakeAI.download import conditional_download, is_download_done
-from DeepFakeAI.vision import read_image, read_static_image, write_image
-from DeepFakeAI.processors.frame import globals as frame_processors_globals
-from DeepFakeAI.processors.frame import choices as frame_processors_choices
-from DeepFakeAI.face_masker import create_static_box_mask, create_occlusion_mask, clear_face_occluder
-
-FRAME_PROCESSOR = None
-THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
-THREAD_LOCK : threading.Lock = threading.Lock()
-NAME = __name__.upper()
-MODELS : ModelSet =\
-{
-	'codeformer':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/codeformer.onnx',
-		'path': resolve_relative_path('../.assets/models/codeformer.onnx'),
-		'template': 'ffhq_512',
-		'size': (512, 512)
-	},
-	'gfpgan_1.2':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/gfpgan_1.2.onnx',
-		'path': resolve_relative_path('../.assets/models/gfpgan_1.2.onnx'),
-		'template': 'ffhq_512',
-		'size': (512, 512)
-	},
-	'gfpgan_1.3':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/gfpgan_1.3.onnx',
-		'path': resolve_relative_path('../.assets/models/gfpgan_1.3.onnx'),
-		'template': 'ffhq_512',
-		'size': (512, 512)
-	},
-	'gfpgan_1.4':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/gfpgan_1.4.onnx',
-		'path': resolve_relative_path('../.assets/models/gfpgan_1.4.onnx'),
-		'template': 'ffhq_512',
-		'size': (512, 512)
-	},
-	'gpen_bfr_256':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/gpen_bfr_256.onnx',
-		'path': resolve_relative_path('../.assets/models/gpen_bfr_256.onnx'),
-		'template': 'arcface_128_v2',
-		'size': (128, 256)
-	},
-	'gpen_bfr_512':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/gpen_bfr_512.onnx',
-		'path': resolve_relative_path('../.assets/models/gpen_bfr_512.onnx'),
-		'template': 'ffhq_512',
-		'size': (512, 512)
-	},
-	'restoreformer':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/restoreformer.onnx',
-		'path': resolve_relative_path('../.assets/models/restoreformer.onnx'),
-		'template': 'ffhq_512',
-		'size': (512, 512)
-	}
-}
-OPTIONS : Optional[OptionsWithModel] = None
-
-
-def get_frame_processor() -> Any:
-	global FRAME_PROCESSOR
-
-	with THREAD_LOCK:
-		if FRAME_PROCESSOR is None:
-			model_path = get_options('model').get('path')
-			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = DeepFakeAI.globals.execution_providers)
-	return FRAME_PROCESSOR
-
-
-def clear_frame_processor() -> None:
-	global FRAME_PROCESSOR
-
-	FRAME_PROCESSOR = None
-
-
-def get_options(key : Literal['model']) -> Any:
-	global OPTIONS
-
-	if OPTIONS is None:
-		OPTIONS =\
-		{
-			'model': MODELS[frame_processors_globals.face_enhancer_model]
-		}
-	return OPTIONS.get(key)
-
-
-def set_options(key : Literal['model'], value : Any) -> None:
-	global OPTIONS
-
-	OPTIONS[key] = value
-
-
-def register_args(program : ArgumentParser) -> None:
-	program.add_argument('--face-enhancer-model', help = wording.get('frame_processor_model_help'), default = 'gfpgan_1.4', choices = frame_processors_choices.face_enhancer_models)
-	program.add_argument('--face-enhancer-blend', help = wording.get('frame_processor_blend_help'), type = int, default = 80, choices = frame_processors_choices.face_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.face_enhancer_blend_range))
-
-
-def apply_args(program : ArgumentParser) -> None:
-	args = program.parse_args()
-	frame_processors_globals.face_enhancer_model = args.face_enhancer_model
-	frame_processors_globals.face_enhancer_blend = args.face_enhancer_blend
-
-
-def pre_check() -> bool:
-	if not DeepFakeAI.globals.skip_download:
-		download_directory_path = resolve_relative_path('../.assets/models')
-		model_url = get_options('model').get('url')
-		conditional_download(download_directory_path, [ model_url ])
-	return True
-
-
-def pre_process(mode : ProcessMode) -> bool:
-	model_url = get_options('model').get('url')
-	model_path = get_options('model').get('path')
-	if not DeepFakeAI.globals.skip_download and not is_download_done(model_url, model_path):
-		logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
-		return False
-	elif not is_file(model_path):
-		logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
-		return False
-	if mode in [ 'output', 'preview' ] and not is_image(DeepFakeAI.globals.target_path) and not is_video(DeepFakeAI.globals.target_path):
-		logger.error(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
-		return False
-	if mode == 'output' and not DeepFakeAI.globals.output_path:
-		logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
-		return False
-	return True
-
-
-def post_process() -> None:
-	clear_frame_processor()
-	clear_face_analyser()
-	clear_content_analyser()
-	clear_face_occluder()
-	read_static_image.cache_clear()
-
-
-def enhance_face(target_face: Face, temp_frame: Frame) -> Frame:
-	frame_processor = get_frame_processor()
-	model_template = get_options('model').get('template')
-	model_size = get_options('model').get('size')
-	crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, model_template, model_size)
-	crop_mask_list =\
-	[
-		create_static_box_mask(crop_frame.shape[:2][::-1], DeepFakeAI.globals.face_mask_blur, (0, 0, 0, 0))
-	]
-	if 'occlusion' in DeepFakeAI.globals.face_mask_types:
-		crop_mask_list.append(create_occlusion_mask(crop_frame))
-	crop_frame = prepare_crop_frame(crop_frame)
-	frame_processor_inputs = {}
-	for frame_processor_input in frame_processor.get_inputs():
-		if frame_processor_input.name == 'input':
-			frame_processor_inputs[frame_processor_input.name] = crop_frame
-		if frame_processor_input.name == 'weight':
-			frame_processor_inputs[frame_processor_input.name] = numpy.array([ 1 ], dtype = numpy.double)
-	with THREAD_SEMAPHORE:
-		crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
-	crop_frame = normalize_crop_frame(crop_frame)
-	crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
-	paste_frame = paste_back(temp_frame, crop_frame, crop_mask, affine_matrix)
-	temp_frame = blend_frame(temp_frame, paste_frame)
-	return temp_frame
-
-
-def prepare_crop_frame(crop_frame : Frame) -> Frame:
-	crop_frame = crop_frame[:, :, ::-1] / 255.0
-	crop_frame = (crop_frame - 0.5) / 0.5
-	crop_frame = numpy.expand_dims(crop_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
-	return crop_frame
-
-
-def normalize_crop_frame(crop_frame : Frame) -> Frame:
-	crop_frame = numpy.clip(crop_frame, -1, 1)
-	crop_frame = (crop_frame + 1) / 2
-	crop_frame = crop_frame.transpose(1, 2, 0)
-	crop_frame = (crop_frame * 255.0).round()
-	crop_frame = crop_frame.astype(numpy.uint8)[:, :, ::-1]
-	return crop_frame
-
-
-def blend_frame(temp_frame : Frame, paste_frame : Frame) -> Frame:
-	face_enhancer_blend = 1 - (frame_processors_globals.face_enhancer_blend / 100)
-	temp_frame = cv2.addWeighted(temp_frame, face_enhancer_blend, paste_frame, 1 - face_enhancer_blend, 0)
-	return temp_frame
-
-
-def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Optional[Frame]:
-	return enhance_face(target_face, temp_frame)
-
-
-def process_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
-	if 'reference' in DeepFakeAI.globals.face_selector_mode:
-		similar_faces = find_similar_faces(temp_frame, reference_faces, DeepFakeAI.globals.reference_face_distance)
-		if similar_faces:
-			for similar_face in similar_faces:
-				temp_frame = enhance_face(similar_face, temp_frame)
-	if 'one' in DeepFakeAI.globals.face_selector_mode:
-		target_face = get_one_face(temp_frame)
-		if target_face:
-			temp_frame = enhance_face(target_face, temp_frame)
-	if 'many' in DeepFakeAI.globals.face_selector_mode:
-		many_faces = get_many_faces(temp_frame)
-		if many_faces:
-			for target_face in many_faces:
-				temp_frame = enhance_face(target_face, temp_frame)
-	return temp_frame
-
-
-def process_frames(source_path : List[str], temp_frame_paths : List[str], update_progress : Update_Process) -> None:
-	reference_faces = get_reference_faces() if 'reference' in DeepFakeAI.globals.face_selector_mode else None
-	for temp_frame_path in temp_frame_paths:
-		temp_frame = read_image(temp_frame_path)
-		result_frame = process_frame(None, reference_faces, temp_frame)
-		write_image(temp_frame_path, result_frame)
-		update_progress()
-
-
-def process_image(source_path : str, target_path : str, output_path : str) -> None:
-	reference_faces = get_reference_faces() if 'reference' in DeepFakeAI.globals.face_selector_mode else None
-	target_frame = read_static_image(target_path)
-	result_frame = process_frame(None, reference_faces, target_frame)
-	write_image(output_path, result_frame)
-
-
-def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
-	frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)

DeepFakeAI/processors/frame/modules/face_swapper.py

@@ -1,302 +0,0 @@
-from typing import Any, List, Literal, Optional
-from argparse import ArgumentParser
-import threading
-import numpy
-import onnx
-import onnxruntime
-from onnx import numpy_helper
-
-import DeepFakeAI.globals
-import DeepFakeAI.processors.frame.core as frame_processors
-from DeepFakeAI import logger, wording
-from DeepFakeAI.face_analyser import get_one_face, get_average_face, get_many_faces, find_similar_faces, clear_face_analyser
-from DeepFakeAI.face_helper import warp_face, paste_back
-from DeepFakeAI.face_store import get_reference_faces
-from DeepFakeAI.content_analyser import clear_content_analyser
-from DeepFakeAI.typing import Face, FaceSet, Frame, Update_Process, ProcessMode, ModelSet, OptionsWithModel, Embedding
-from DeepFakeAI.filesystem import is_file, is_image, are_images, is_video, resolve_relative_path
-from DeepFakeAI.download import conditional_download, is_download_done
-from DeepFakeAI.vision import read_image, read_static_image, read_static_images, write_image
-from DeepFakeAI.processors.frame import globals as frame_processors_globals
-from DeepFakeAI.processors.frame import choices as frame_processors_choices
-from DeepFakeAI.face_masker import create_static_box_mask, create_occlusion_mask, create_region_mask, clear_face_occluder, clear_face_parser
-
-FRAME_PROCESSOR = None
-MODEL_MATRIX = None
-THREAD_LOCK : threading.Lock = threading.Lock()
-NAME = __name__.upper()
-MODELS : ModelSet =\
-{
-	'blendswap_256':
-	{
-		'type': 'blendswap',
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/blendswap_256.onnx',
-		'path': resolve_relative_path('../.assets/models/blendswap_256.onnx'),
-		'template': 'ffhq_512',
-		'size': (512, 256),
-		'mean': [ 0.0, 0.0, 0.0 ],
-		'standard_deviation': [ 1.0, 1.0, 1.0 ]
-	},
-	'inswapper_128':
-	{
-		'type': 'inswapper',
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/inswapper_128.onnx',
-		'path': resolve_relative_path('../.assets/models/inswapper_128.onnx'),
-		'template': 'arcface_128_v2',
-		'size': (128, 128),
-		'mean': [ 0.0, 0.0, 0.0 ],
-		'standard_deviation': [ 1.0, 1.0, 1.0 ]
-	},
-	'inswapper_128_fp16':
-	{
-		'type': 'inswapper',
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/inswapper_128_fp16.onnx',
-		'path': resolve_relative_path('../.assets/models/inswapper_128_fp16.onnx'),
-		'template': 'arcface_128_v2',
-		'size': (128, 128),
-		'mean': [ 0.0, 0.0, 0.0 ],
-		'standard_deviation': [ 1.0, 1.0, 1.0 ]
-	},
-	'simswap_256':
-	{
-		'type': 'simswap',
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/simswap_256.onnx',
-		'path': resolve_relative_path('../.assets/models/simswap_256.onnx'),
-		'template': 'arcface_112_v1',
-		'size': (112, 256),
-		'mean': [ 0.485, 0.456, 0.406 ],
-		'standard_deviation': [ 0.229, 0.224, 0.225 ]
-	},
-	'simswap_512_unofficial':
-	{
-		'type': 'simswap',
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/simswap_512_unofficial.onnx',
-		'path': resolve_relative_path('../.assets/models/simswap_512_unofficial.onnx'),
-		'template': 'arcface_112_v1',
-		'size': (112, 512),
-		'mean': [ 0.0, 0.0, 0.0 ],
-		'standard_deviation': [ 1.0, 1.0, 1.0 ]
-	}
-}
-OPTIONS : Optional[OptionsWithModel] = None
-
-
-def get_frame_processor() -> Any:
-	global FRAME_PROCESSOR
-
-	with THREAD_LOCK:
-		if FRAME_PROCESSOR is None:
-			model_path = get_options('model').get('path')
-			FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = DeepFakeAI.globals.execution_providers)
-	return FRAME_PROCESSOR
-
-
-def clear_frame_processor() -> None:
-	global FRAME_PROCESSOR
-
-	FRAME_PROCESSOR = None
-
-
-def get_model_matrix() -> Any:
-	global MODEL_MATRIX
-
-	with THREAD_LOCK:
-		if MODEL_MATRIX is None:
-			model_path = get_options('model').get('path')
-			model = onnx.load(model_path)
-			MODEL_MATRIX = numpy_helper.to_array(model.graph.initializer[-1])
-	return MODEL_MATRIX
-
-
-def clear_model_matrix() -> None:
-	global MODEL_MATRIX
-
-	MODEL_MATRIX = None
-
-
-def get_options(key : Literal['model']) -> Any:
-	global OPTIONS
-
-	if OPTIONS is None:
-		OPTIONS =\
-		{
-			'model': MODELS[frame_processors_globals.face_swapper_model]
-		}
-	return OPTIONS.get(key)
-
-
-def set_options(key : Literal['model'], value : Any) -> None:
-	global OPTIONS
-
-	OPTIONS[key] = value
-
-
-def register_args(program : ArgumentParser) -> None:
-	program.add_argument('--face-swapper-model', help = wording.get('frame_processor_model_help'), default = 'inswapper_128', choices = frame_processors_choices.face_swapper_models)
-
-
-def apply_args(program : ArgumentParser) -> None:
-	args = program.parse_args()
-	frame_processors_globals.face_swapper_model = args.face_swapper_model
-	if args.face_swapper_model == 'blendswap_256':
-		DeepFakeAI.globals.face_recognizer_model = 'arcface_blendswap'
-	if args.face_swapper_model == 'inswapper_128' or args.face_swapper_model == 'inswapper_128_fp16':
-		DeepFakeAI.globals.face_recognizer_model = 'arcface_inswapper'
-	if args.face_swapper_model == 'simswap_256' or args.face_swapper_model == 'simswap_512_unofficial':
-		DeepFakeAI.globals.face_recognizer_model = 'arcface_simswap'
-
-
-def pre_check() -> bool:
-	if not DeepFakeAI.globals.skip_download:
-		download_directory_path = resolve_relative_path('../.assets/models')
-		model_url = get_options('model').get('url')
-		conditional_download(download_directory_path, [ model_url ])
-	return True
-
-
-def pre_process(mode : ProcessMode) -> bool:
-	model_url = get_options('model').get('url')
-	model_path = get_options('model').get('path')
-	if not DeepFakeAI.globals.skip_download and not is_download_done(model_url, model_path):
-		logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
-		return False
-	elif not is_file(model_path):
-		logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
-		return False
-	if not are_images(DeepFakeAI.globals.source_paths):
-		logger.error(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
-		return False
-	for source_frame in read_static_images(DeepFakeAI.globals.source_paths):
-		if not get_one_face(source_frame):
-			logger.error(wording.get('no_source_face_detected') + wording.get('exclamation_mark'), NAME)
-			return False
-	if mode in [ 'output', 'preview' ] and not is_image(DeepFakeAI.globals.target_path) and not is_video(DeepFakeAI.globals.target_path):
-		logger.error(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
-		return False
-	if mode == 'output' and not DeepFakeAI.globals.output_path:
-		logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
-		return False
-	return True
-
-
-def post_process() -> None:
-	clear_frame_processor()
-	clear_model_matrix()
-	clear_face_analyser()
-	clear_content_analyser()
-	clear_face_occluder()
-	clear_face_parser()
-	read_static_image.cache_clear()
-
-
-def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
-	frame_processor = get_frame_processor()
-	model_template = get_options('model').get('template')
-	model_size = get_options('model').get('size')
-	model_type = get_options('model').get('type')
-	crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, model_template, model_size)
-	crop_mask_list = []
-	if 'box' in DeepFakeAI.globals.face_mask_types:
-		crop_mask_list.append(create_static_box_mask(crop_frame.shape[:2][::-1], DeepFakeAI.globals.face_mask_blur, DeepFakeAI.globals.face_mask_padding))
-	if 'occlusion' in DeepFakeAI.globals.face_mask_types:
-		crop_mask_list.append(create_occlusion_mask(crop_frame))
-	crop_frame = prepare_crop_frame(crop_frame)
-	frame_processor_inputs = {}
-	for frame_processor_input in frame_processor.get_inputs():
-		if frame_processor_input.name == 'source':
-			if model_type == 'blendswap':
-				frame_processor_inputs[frame_processor_input.name] = prepare_source_frame(source_face)
-			else:
-				frame_processor_inputs[frame_processor_input.name] = prepare_source_embedding(source_face)
-		if frame_processor_input.name == 'target':
-			frame_processor_inputs[frame_processor_input.name] = crop_frame
-	crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
-	crop_frame = normalize_crop_frame(crop_frame)
-	if 'region' in DeepFakeAI.globals.face_mask_types:
-		crop_mask_list.append(create_region_mask(crop_frame, DeepFakeAI.globals.face_mask_regions))
-	crop_mask = numpy.minimum.reduce(crop_mask_list).clip(0, 1)
-	temp_frame = paste_back(temp_frame, crop_frame, crop_mask, affine_matrix)
-	return temp_frame
-
-
-def prepare_source_frame(source_face : Face) -> Frame:
-	source_frame = read_static_image(DeepFakeAI.globals.source_paths[0])
-	source_frame, _ = warp_face(source_frame, source_face.kps, 'arcface_112_v2', (112, 112))
-	source_frame = source_frame[:, :, ::-1] / 255.0
-	source_frame = source_frame.transpose(2, 0, 1)
-	source_frame = numpy.expand_dims(source_frame, axis = 0).astype(numpy.float32)
-	return source_frame
-
-
-def prepare_source_embedding(source_face : Face) -> Embedding:
-	model_type = get_options('model').get('type')
-	if model_type == 'inswapper':
-		model_matrix = get_model_matrix()
-		source_embedding = source_face.embedding.reshape((1, -1))
-		source_embedding = numpy.dot(source_embedding, model_matrix) / numpy.linalg.norm(source_embedding)
-	else:
-		source_embedding = source_face.normed_embedding.reshape(1, -1)
-	return source_embedding
-
-
-def prepare_crop_frame(crop_frame : Frame) -> Frame:
-	model_mean = get_options('model').get('mean')
-	model_standard_deviation = get_options('model').get('standard_deviation')
-	crop_frame = crop_frame[:, :, ::-1] / 255.0
-	crop_frame = (crop_frame - model_mean) / model_standard_deviation
-	crop_frame = crop_frame.transpose(2, 0, 1)
-	crop_frame = numpy.expand_dims(crop_frame, axis = 0).astype(numpy.float32)
-	return crop_frame
-
-
-def normalize_crop_frame(crop_frame : Frame) -> Frame:
-	crop_frame = crop_frame.transpose(1, 2, 0)
-	crop_frame = (crop_frame * 255.0).round()
-	crop_frame = crop_frame[:, :, ::-1].astype(numpy.uint8)
-	return crop_frame
-
-
-def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
-	return swap_face(source_face, target_face, temp_frame)
-
-
-def process_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
-	if 'reference' in DeepFakeAI.globals.face_selector_mode:
-		similar_faces = find_similar_faces(temp_frame, reference_faces, DeepFakeAI.globals.reference_face_distance)
-		if similar_faces:
-			for similar_face in similar_faces:
-				temp_frame = swap_face(source_face, similar_face, temp_frame)
-	if 'one' in DeepFakeAI.globals.face_selector_mode:
-		target_face = get_one_face(temp_frame)
-		if target_face:
-			temp_frame = swap_face(source_face, target_face, temp_frame)
-	if 'many' in DeepFakeAI.globals.face_selector_mode:
-		many_faces = get_many_faces(temp_frame)
-		if many_faces:
-			for target_face in many_faces:
-				temp_frame = swap_face(source_face, target_face, temp_frame)
-	return temp_frame
-
-
-def process_frames(source_paths : List[str], temp_frame_paths : List[str], update_progress : Update_Process) -> None:
-	source_frames = read_static_images(source_paths)
-	source_face = get_average_face(source_frames)
-	reference_faces = get_reference_faces() if 'reference' in DeepFakeAI.globals.face_selector_mode else None
-	for temp_frame_path in temp_frame_paths:
-		temp_frame = read_image(temp_frame_path)
-		result_frame = process_frame(source_face, reference_faces, temp_frame)
-		write_image(temp_frame_path, result_frame)
-		update_progress()
-
-
-def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
-	source_frames = read_static_images(source_paths)
-	source_face = get_average_face(source_frames)
-	reference_faces = get_reference_faces() if 'reference' in DeepFakeAI.globals.face_selector_mode else None
-	target_frame = read_static_image(target_path)
-	result_frame = process_frame(source_face, reference_faces, target_frame)
-	write_image(output_path, result_frame)
-
-
-def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
-	frame_processors.multi_process_frames(source_paths, temp_frame_paths, process_frames)

DeepFakeAI/processors/frame/modules/frame_enhancer.py

@@ -1,172 +0,0 @@
-from typing import Any, List, Literal, Optional
-from argparse import ArgumentParser
-import threading
-import cv2
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from realesrgan import RealESRGANer
-
-import DeepFakeAI.globals
-import DeepFakeAI.processors.frame.core as frame_processors
-from DeepFakeAI import logger, wording
-from DeepFakeAI.face_analyser import clear_face_analyser
-from DeepFakeAI.content_analyser import clear_content_analyser
-from DeepFakeAI.typing import Face, FaceSet, Frame, Update_Process, ProcessMode, ModelSet, OptionsWithModel
-from DeepFakeAI.common_helper import create_metavar
-from DeepFakeAI.execution_helper import map_device
-from DeepFakeAI.filesystem import is_file, resolve_relative_path
-from DeepFakeAI.download import conditional_download, is_download_done
-from DeepFakeAI.vision import read_image, read_static_image, write_image
-from DeepFakeAI.processors.frame import globals as frame_processors_globals
-from DeepFakeAI.processors.frame import choices as frame_processors_choices
-
-FRAME_PROCESSOR = None
-THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
-THREAD_LOCK : threading.Lock = threading.Lock()
-NAME = __name__.upper()
-MODELS : ModelSet =\
-{
-	'real_esrgan_x2plus':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/real_esrgan_x2plus.pth',
-		'path': resolve_relative_path('../.assets/models/real_esrgan_x2plus.pth'),
-		'scale': 2
-	},
-	'real_esrgan_x4plus':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/real_esrgan_x4plus.pth',
-		'path': resolve_relative_path('../.assets/models/real_esrgan_x4plus.pth'),
-		'scale': 4
-	},
-	'real_esrnet_x4plus':
-	{
-		'url': 'https://github.com/DeepFakeAI/DeepFakeAI-assets/releases/download/models/real_esrnet_x4plus.pth',
-		'path': resolve_relative_path('../.assets/models/real_esrnet_x4plus.pth'),
-		'scale': 4
-	}
-}
-OPTIONS : Optional[OptionsWithModel] = None
-
-
-def get_frame_processor() -> Any:
-	global FRAME_PROCESSOR
-
-	with THREAD_LOCK:
-		if FRAME_PROCESSOR is None:
-			model_path = get_options('model').get('path')
-			model_scale = get_options('model').get('scale')
-			FRAME_PROCESSOR = RealESRGANer(
-				model_path = model_path,
-				model = RRDBNet(
-					num_in_ch = 3,
-					num_out_ch = 3,
-					scale = model_scale
-				),
-				device = map_device(DeepFakeAI.globals.execution_providers),
-				scale = model_scale
-			)
-	return FRAME_PROCESSOR
-
-
-def clear_frame_processor() -> None:
-	global FRAME_PROCESSOR
-
-	FRAME_PROCESSOR = None
-
-
-def get_options(key : Literal['model']) -> Any:
-	global OPTIONS
-
-	if OPTIONS is None:
-		OPTIONS =\
-		{
-			'model': MODELS[frame_processors_globals.frame_enhancer_model]
-		}
-	return OPTIONS.get(key)
-
-
-def set_options(key : Literal['model'], value : Any) -> None:
-	global OPTIONS
-
-	OPTIONS[key] = value
-
-
-def register_args(program : ArgumentParser) -> None:
-	program.add_argument('--frame-enhancer-model', help = wording.get('frame_processor_model_help'), default = 'real_esrgan_x2plus', choices = frame_processors_choices.frame_enhancer_models)
-	program.add_argument('--frame-enhancer-blend', help = wording.get('frame_processor_blend_help'), type = int, default = 80, choices = frame_processors_choices.frame_enhancer_blend_range, metavar = create_metavar(frame_processors_choices.frame_enhancer_blend_range))
-
-
-def apply_args(program : ArgumentParser) -> None:
-	args = program.parse_args()
-	frame_processors_globals.frame_enhancer_model = args.frame_enhancer_model
-	frame_processors_globals.frame_enhancer_blend = args.frame_enhancer_blend
-
-
-def pre_check() -> bool:
-	if not DeepFakeAI.globals.skip_download:
-		download_directory_path = resolve_relative_path('../.assets/models')
-		model_url = get_options('model').get('url')
-		conditional_download(download_directory_path, [ model_url ])
-	return True
-
-
-def pre_process(mode : ProcessMode) -> bool:
-	model_url = get_options('model').get('url')
-	model_path = get_options('model').get('path')
-	if not DeepFakeAI.globals.skip_download and not is_download_done(model_url, model_path):
-		logger.error(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
-		return False
-	elif not is_file(model_path):
-		logger.error(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
-		return False
-	if mode == 'output' and not DeepFakeAI.globals.output_path:
-		logger.error(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
-		return False
-	return True
-
-
-def post_process() -> None:
-	clear_frame_processor()
-	clear_face_analyser()
-	clear_content_analyser()
-	read_static_image.cache_clear()
-
-
-def enhance_frame(temp_frame : Frame) -> Frame:
-	with THREAD_SEMAPHORE:
-		paste_frame, _ = get_frame_processor().enhance(temp_frame)
-		temp_frame = blend_frame(temp_frame, paste_frame)
-	return temp_frame
-
-
-def blend_frame(temp_frame : Frame, paste_frame : Frame) -> Frame:
-	frame_enhancer_blend = 1 - (frame_processors_globals.frame_enhancer_blend / 100)
-	paste_frame_height, paste_frame_width = paste_frame.shape[0:2]
-	temp_frame = cv2.resize(temp_frame, (paste_frame_width, paste_frame_height))
-	temp_frame = cv2.addWeighted(temp_frame, frame_enhancer_blend, paste_frame, 1 - frame_enhancer_blend, 0)
-	return temp_frame
-
-
-def get_reference_frame(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
-	pass
-
-
-def process_frame(source_face : Face, reference_faces : FaceSet, temp_frame : Frame) -> Frame:
-	return enhance_frame(temp_frame)
-
-
-def process_frames(source_paths : List[str], temp_frame_paths : List[str], update_progress : Update_Process) -> None:
-	for temp_frame_path in temp_frame_paths:
-		temp_frame = read_image(temp_frame_path)
-		result_frame = process_frame(None, None, temp_frame)
-		write_image(temp_frame_path, result_frame)
-		update_progress()
-
-
-def process_image(source_paths : List[str], target_path : str, output_path : str) -> None:
-	target_frame = read_static_image(target_path)
-	result = process_frame(None, None, target_frame)
-	write_image(output_path, result)
-
-
-def process_video(source_paths : List[str], temp_frame_paths : List[str]) -> None:
-	frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)

DeepFakeAI/processors/frame/typings.py

@@ -1,7 +0,0 @@
-from typing import Literal
-
-FaceSwapperModel = Literal['blendswap_256', 'inswapper_128', 'inswapper_128_fp16', 'simswap_256', 'simswap_512_unofficial']
-FaceEnhancerModel = Literal['codeformer', 'gfpgan_1.2', 'gfpgan_1.3', 'gfpgan_1.4', 'gpen_bfr_256', 'gpen_bfr_512', 'restoreformer']
-FrameEnhancerModel = Literal['real_esrgan_x2plus', 'real_esrgan_x4plus', 'real_esrnet_x4plus']
-
-FaceDebuggerItem = Literal['bbox', 'kps', 'face-mask', 'score']

DeepFakeAI/typing.py

@@ -1,51 +0,0 @@
-from typing import Any, Literal, Callable, List, Tuple, Dict, TypedDict
-from collections import namedtuple
-import numpy
-
-Bbox = numpy.ndarray[Any, Any]
-Kps = numpy.ndarray[Any, Any]
-Score = float
-Embedding = numpy.ndarray[Any, Any]
-Face = namedtuple('Face',
-[
-	'bbox',
-	'kps',
-	'score',
-	'embedding',
-	'normed_embedding',
-	'gender',
-	'age'
-])
-FaceSet = Dict[str, List[Face]]
-FaceStore = TypedDict('FaceStore',
-{
-	'static_faces' : FaceSet,
-	'reference_faces': FaceSet
-})
-Frame = numpy.ndarray[Any, Any]
-Mask = numpy.ndarray[Any, Any]
-Matrix = numpy.ndarray[Any, Any]
-Padding = Tuple[int, int, int, int]
-
-Update_Process = Callable[[], None]
-Process_Frames = Callable[[List[str], List[str], Update_Process], None]
-LogLevel = Literal['error',	'warn',	'info',	'debug']
-Template = Literal['arcface_112_v1', 'arcface_112_v2', 'arcface_128_v2', 'ffhq_512']
-ProcessMode = Literal['output', 'preview', 'stream']
-FaceSelectorMode = Literal['reference', 'one', 'many']
-FaceAnalyserOrder = Literal['left-right', 'right-left', 'top-bottom', 'bottom-top', 'small-large', 'large-small', 'best-worst', 'worst-best']
-FaceAnalyserAge = Literal['child', 'teen', 'adult', 'senior']
-FaceAnalyserGender = Literal['male', 'female']
-FaceDetectorModel = Literal['retinaface', 'yunet']
-FaceRecognizerModel = Literal['arcface_blendswap', 'arcface_inswapper', 'arcface_simswap']
-FaceMaskType = Literal['box', 'occlusion', 'region']
-FaceMaskRegion = Literal['skin', 'left-eyebrow', 'right-eyebrow', 'left-eye', 'right-eye', 'eye-glasses', 'nose', 'mouth', 'upper-lip', 'lower-lip']
-TempFrameFormat = Literal['jpg', 'png']
-OutputVideoEncoder = Literal['libx264', 'libx265', 'libvpx-vp9', 'h264_nvenc', 'hevc_nvenc']
-
-ModelValue = Dict[str, Any]
-ModelSet = Dict[str, ModelValue]
-OptionsWithModel = TypedDict('OptionsWithModel',
-{
-	'model' : ModelValue
-})

DeepFakeAI/uis/assets/fixes.css

@@ -1,7 +0,0 @@
-:root:root:root button:not([class])
-{
-	border-radius: 0.375rem;
-	float: left;
-	overflow: hidden;
-	width: 100%;
-}

DeepFakeAI/uis/assets/overrides.css

@@ -1,44 +0,0 @@
-:root:root:root input[type="number"]
-{
-	max-width: 6rem;
-}
-
-:root:root:root [type="checkbox"],
-:root:root:root [type="radio"]
-{
-	border-radius: 50%;
-	height: 1.125rem;
-	width: 1.125rem;
-}
-
-:root:root:root input[type="range"]
-{
-	height: 0.5rem;
-}
-
-:root:root:root input[type="range"]::-moz-range-thumb,
-:root:root:root input[type="range"]::-webkit-slider-thumb
-{
-	background: var(--neutral-300);
-	border: unset;
-	border-radius: 50%;
-	height: 1.125rem;
-	width: 1.125rem;
-}
-
-:root:root:root input[type="range"]::-webkit-slider-thumb
-{
-	margin-top: 0.375rem;
-}
-
-:root:root:root .grid-wrap.fixed-height
-{
-	min-height: unset;
-}
-
-:root:root:root .grid-container
-{
-	grid-auto-rows: minmax(5em, 1fr);
-	grid-template-columns: repeat(var(--grid-cols), minmax(5em, 1fr));
-	grid-template-rows: repeat(var(--grid-rows), minmax(5em, 1fr));
-}

DeepFakeAI/uis/choices.py

@@ -1,7 +0,0 @@
-from typing import List
-
-from DeepFakeAI.uis.typing import WebcamMode
-
-common_options : List[str] = [ 'keep-fps', 'keep-temp', 'skip-audio', 'skip-download' ]
-webcam_modes : List[WebcamMode] = [ 'inline', 'udp', 'v4l2' ]
-webcam_resolutions : List[str] = [ '320x240', '640x480', '800x600', '1024x768', '1280x720', '1280x960', '1920x1080', '2560x1440', '3840x2160' ]

DeepFakeAI/uis/components/about.py

@@ -1,23 +0,0 @@
-from typing import Optional
-import gradio
-
-from DeepFakeAI import metadata, wording
-
-ABOUT_BUTTON : Optional[gradio.HTML] = None
-DONATE_BUTTON : Optional[gradio.HTML] = None
-
-
-def render() -> None:
-	global ABOUT_BUTTON
-	global DONATE_BUTTON
-
-	ABOUT_BUTTON = gradio.Button(
-		value = metadata.get('name') + ' ' + metadata.get('version'),
-		variant = 'primary',
-		link = metadata.get('url')
-	)
-	DONATE_BUTTON = gradio.Button(
-		value = wording.get('donate_button_label'),
-		link = 'https://donate.DeepFakeAI.io',
-		size = 'sm'
-	)

DeepFakeAI/uis/components/benchmark.py

@@ -1,132 +0,0 @@
-from typing import Any, Optional, List, Dict, Generator
-import time
-import tempfile
-import statistics
-import gradio
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.face_analyser import get_face_analyser
-from DeepFakeAI.face_store import clear_static_faces
-from DeepFakeAI.processors.frame.core import get_frame_processors_modules
-from DeepFakeAI.vision import count_video_frame_total
-from DeepFakeAI.core import limit_resources, conditional_process
-from DeepFakeAI.normalizer import normalize_output_path
-from DeepFakeAI.filesystem import clear_temp
-from DeepFakeAI.uis.core import get_ui_component
-
-BENCHMARK_RESULTS_DATAFRAME : Optional[gradio.Dataframe] = None
-BENCHMARK_START_BUTTON : Optional[gradio.Button] = None
-BENCHMARK_CLEAR_BUTTON : Optional[gradio.Button] = None
-BENCHMARKS : Dict[str, str] =\
-{
-	'240p': '.assets/examples/target-240p.mp4',
-	'360p': '.assets/examples/target-360p.mp4',
-	'540p': '.assets/examples/target-540p.mp4',
-	'720p': '.assets/examples/target-720p.mp4',
-	'1080p': '.assets/examples/target-1080p.mp4',
-	'1440p': '.assets/examples/target-1440p.mp4',
-	'2160p': '.assets/examples/target-2160p.mp4'
-}
-
-
-def render() -> None:
-	global BENCHMARK_RESULTS_DATAFRAME
-	global BENCHMARK_START_BUTTON
-	global BENCHMARK_CLEAR_BUTTON
-
-	BENCHMARK_RESULTS_DATAFRAME = gradio.Dataframe(
-		label = wording.get('benchmark_results_dataframe_label'),
-		headers =
-		[
-			'target_path',
-			'benchmark_cycles',
-			'average_run',
-			'fastest_run',
-			'slowest_run',
-			'relative_fps'
-		],
-		datatype =
-		[
-			'str',
-			'number',
-			'number',
-			'number',
-			'number',
-			'number'
-		]
-	)
-	BENCHMARK_START_BUTTON = gradio.Button(
-		value = wording.get('start_button_label'),
-		variant = 'primary',
-		size = 'sm'
-	)
-	BENCHMARK_CLEAR_BUTTON = gradio.Button(
-		value = wording.get('clear_button_label'),
-		size = 'sm'
-	)
-
-
-def listen() -> None:
-	benchmark_runs_checkbox_group = get_ui_component('benchmark_runs_checkbox_group')
-	benchmark_cycles_slider = get_ui_component('benchmark_cycles_slider')
-	if benchmark_runs_checkbox_group and benchmark_cycles_slider:
-		BENCHMARK_START_BUTTON.click(start, inputs = [ benchmark_runs_checkbox_group, benchmark_cycles_slider ], outputs = BENCHMARK_RESULTS_DATAFRAME)
-	BENCHMARK_CLEAR_BUTTON.click(clear, outputs = BENCHMARK_RESULTS_DATAFRAME)
-
-
-def start(benchmark_runs : List[str], benchmark_cycles : int) -> Generator[List[Any], None, None]:
-	DeepFakeAI.globals.source_paths = [ '.assets/examples/source.jpg' ]
-	target_paths = [ BENCHMARKS[benchmark_run] for benchmark_run in benchmark_runs if benchmark_run in BENCHMARKS ]
-	benchmark_results = []
-	if target_paths:
-		pre_process()
-		for target_path in target_paths:
-			benchmark_results.append(benchmark(target_path, benchmark_cycles))
-			yield benchmark_results
-		post_process()
-
-
-def pre_process() -> None:
-	limit_resources()
-	get_face_analyser()
-	for frame_processor_module in get_frame_processors_modules(DeepFakeAI.globals.frame_processors):
-		frame_processor_module.get_frame_processor()
-
-
-def post_process() -> None:
-	clear_static_faces()
-
-
-def benchmark(target_path : str, benchmark_cycles : int) -> List[Any]:
-	process_times = []
-	total_fps = 0.0
-	for i in range(benchmark_cycles):
-		DeepFakeAI.globals.target_path = target_path
-		DeepFakeAI.globals.output_path = normalize_output_path(DeepFakeAI.globals.source_paths, DeepFakeAI.globals.target_path, tempfile.gettempdir())
-		video_frame_total = count_video_frame_total(DeepFakeAI.globals.target_path)
-		start_time = time.perf_counter()
-		conditional_process()
-		end_time = time.perf_counter()
-		process_time = end_time - start_time
-		total_fps += video_frame_total / process_time
-		process_times.append(process_time)
-	average_run = round(statistics.mean(process_times), 2)
-	fastest_run = round(min(process_times), 2)
-	slowest_run = round(max(process_times), 2)
-	relative_fps = round(total_fps / benchmark_cycles, 2)
-	return\
-	[
-		DeepFakeAI.globals.target_path,
-		benchmark_cycles,
-		average_run,
-		fastest_run,
-		slowest_run,
-		relative_fps
-	]
-
-
-def clear() -> gradio.Dataframe:
-	if DeepFakeAI.globals.target_path:
-		clear_temp(DeepFakeAI.globals.target_path)
-	return gradio.Dataframe(value = None)

DeepFakeAI/uis/components/benchmark_options.py

@@ -1,29 +0,0 @@
-from typing import Optional
-import gradio
-
-from DeepFakeAI import wording
-from DeepFakeAI.uis.core import register_ui_component
-from DeepFakeAI.uis.components.benchmark import BENCHMARKS
-
-BENCHMARK_RUNS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
-BENCHMARK_CYCLES_SLIDER : Optional[gradio.Button] = None
-
-
-def render() -> None:
-	global BENCHMARK_RUNS_CHECKBOX_GROUP
-	global BENCHMARK_CYCLES_SLIDER
-
-	BENCHMARK_RUNS_CHECKBOX_GROUP = gradio.CheckboxGroup(
-		label = wording.get('benchmark_runs_checkbox_group_label'),
-		value = list(BENCHMARKS.keys()),
-		choices = list(BENCHMARKS.keys())
-	)
-	BENCHMARK_CYCLES_SLIDER = gradio.Slider(
-		label = wording.get('benchmark_cycles_slider_label'),
-		value = 3,
-		step = 1,
-		minimum = 1,
-		maximum = 10
-	)
-	register_ui_component('benchmark_runs_checkbox_group', BENCHMARK_RUNS_CHECKBOX_GROUP)
-	register_ui_component('benchmark_cycles_slider', BENCHMARK_CYCLES_SLIDER)

DeepFakeAI/uis/components/common_options.py

@@ -1,38 +0,0 @@
-from typing import Optional, List
-import gradio
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.uis import choices as uis_choices
-
-COMMON_OPTIONS_CHECKBOX_GROUP : Optional[gradio.Checkboxgroup] = None
-
-
-def render() -> None:
-	global COMMON_OPTIONS_CHECKBOX_GROUP
-
-	value = []
-	if DeepFakeAI.globals.keep_fps:
-		value.append('keep-fps')
-	if DeepFakeAI.globals.keep_temp:
-		value.append('keep-temp')
-	if DeepFakeAI.globals.skip_audio:
-		value.append('skip-audio')
-	if DeepFakeAI.globals.skip_download:
-		value.append('skip-download')
-	COMMON_OPTIONS_CHECKBOX_GROUP = gradio.Checkboxgroup(
-		label = wording.get('common_options_checkbox_group_label'),
-		choices = uis_choices.common_options,
-		value = value
-	)
-
-
-def listen() -> None:
-	COMMON_OPTIONS_CHECKBOX_GROUP.change(update, inputs = COMMON_OPTIONS_CHECKBOX_GROUP)
-
-
-def update(common_options : List[str]) -> None:
-	DeepFakeAI.globals.keep_fps = 'keep-fps' in common_options
-	DeepFakeAI.globals.keep_temp = 'keep-temp' in common_options
-	DeepFakeAI.globals.skip_audio = 'skip-audio' in common_options
-	DeepFakeAI.globals.skip_download = 'skip-download' in common_options

DeepFakeAI/uis/components/execution.py

@@ -1,34 +0,0 @@
-from typing import List, Optional
-import gradio
-import onnxruntime
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.face_analyser import clear_face_analyser
-from DeepFakeAI.processors.frame.core import clear_frame_processors_modules
-from DeepFakeAI.execution_helper import encode_execution_providers, decode_execution_providers
-
-EXECUTION_PROVIDERS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
-
-
-def render() -> None:
-	global EXECUTION_PROVIDERS_CHECKBOX_GROUP
-
-	EXECUTION_PROVIDERS_CHECKBOX_GROUP = gradio.CheckboxGroup(
-		label = wording.get('execution_providers_checkbox_group_label'),
-		choices = encode_execution_providers(onnxruntime.get_available_providers()),
-		value = encode_execution_providers(DeepFakeAI.globals.execution_providers)
-	)
-
-
-def listen() -> None:
-	EXECUTION_PROVIDERS_CHECKBOX_GROUP.change(update_execution_providers, inputs = EXECUTION_PROVIDERS_CHECKBOX_GROUP, outputs = EXECUTION_PROVIDERS_CHECKBOX_GROUP)
-
-
-def update_execution_providers(execution_providers : List[str]) -> gradio.CheckboxGroup:
-	clear_face_analyser()
-	clear_frame_processors_modules()
-	if not execution_providers:
-		execution_providers = encode_execution_providers(onnxruntime.get_available_providers())
-	DeepFakeAI.globals.execution_providers = decode_execution_providers(execution_providers)
-	return gradio.CheckboxGroup(value = execution_providers)

DeepFakeAI/uis/components/execution_queue_count.py

@@ -1,28 +0,0 @@
-from typing import Optional
-import gradio
-
-import DeepFakeAI.globals
-import DeepFakeAI.choices
-from DeepFakeAI import wording
-
-EXECUTION_QUEUE_COUNT_SLIDER : Optional[gradio.Slider] = None
-
-
-def render() -> None:
-	global EXECUTION_QUEUE_COUNT_SLIDER
-
-	EXECUTION_QUEUE_COUNT_SLIDER = gradio.Slider(
-		label = wording.get('execution_queue_count_slider_label'),
-		value = DeepFakeAI.globals.execution_queue_count,
-		step = DeepFakeAI.choices.execution_queue_count_range[1] - DeepFakeAI.choices.execution_queue_count_range[0],
-		minimum = DeepFakeAI.choices.execution_queue_count_range[0],
-		maximum = DeepFakeAI.choices.execution_queue_count_range[-1]
-	)
-
-
-def listen() -> None:
-	EXECUTION_QUEUE_COUNT_SLIDER.change(update_execution_queue_count, inputs = EXECUTION_QUEUE_COUNT_SLIDER)
-
-
-def update_execution_queue_count(execution_queue_count : int = 1) -> None:
-	DeepFakeAI.globals.execution_queue_count = execution_queue_count

DeepFakeAI/uis/components/execution_thread_count.py

@@ -1,29 +0,0 @@
-from typing import Optional
-import gradio
-
-import DeepFakeAI.globals
-import DeepFakeAI.choices
-from DeepFakeAI import wording
-
-EXECUTION_THREAD_COUNT_SLIDER : Optional[gradio.Slider] = None
-
-
-def render() -> None:
-	global EXECUTION_THREAD_COUNT_SLIDER
-
-	EXECUTION_THREAD_COUNT_SLIDER = gradio.Slider(
-		label = wording.get('execution_thread_count_slider_label'),
-		value = DeepFakeAI.globals.execution_thread_count,
-		step = DeepFakeAI.choices.execution_thread_count_range[1] - DeepFakeAI.choices.execution_thread_count_range[0],
-		minimum = DeepFakeAI.choices.execution_thread_count_range[0],
-		maximum = DeepFakeAI.choices.execution_thread_count_range[-1]
-	)
-
-
-def listen() -> None:
-	EXECUTION_THREAD_COUNT_SLIDER.change(update_execution_thread_count, inputs = EXECUTION_THREAD_COUNT_SLIDER)
-
-
-def update_execution_thread_count(execution_thread_count : int = 1) -> None:
-	DeepFakeAI.globals.execution_thread_count = execution_thread_count
-

DeepFakeAI/uis/components/face_analyser.py

@@ -1,98 +0,0 @@
-from typing import Optional
-
-import gradio
-
-import DeepFakeAI.globals
-import DeepFakeAI.choices
-from DeepFakeAI import wording
-from DeepFakeAI.typing import FaceAnalyserOrder, FaceAnalyserAge, FaceAnalyserGender, FaceDetectorModel
-from DeepFakeAI.uis.core import register_ui_component
-
-FACE_ANALYSER_ORDER_DROPDOWN : Optional[gradio.Dropdown] = None
-FACE_ANALYSER_AGE_DROPDOWN : Optional[gradio.Dropdown] = None
-FACE_ANALYSER_GENDER_DROPDOWN : Optional[gradio.Dropdown] = None
-FACE_DETECTOR_SIZE_DROPDOWN : Optional[gradio.Dropdown] = None
-FACE_DETECTOR_SCORE_SLIDER : Optional[gradio.Slider] = None
-FACE_DETECTOR_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
-
-
-def render() -> None:
-	global FACE_ANALYSER_ORDER_DROPDOWN
-	global FACE_ANALYSER_AGE_DROPDOWN
-	global FACE_ANALYSER_GENDER_DROPDOWN
-	global FACE_DETECTOR_SIZE_DROPDOWN
-	global FACE_DETECTOR_SCORE_SLIDER
-	global FACE_DETECTOR_MODEL_DROPDOWN
-
-	with gradio.Row():
-		FACE_ANALYSER_ORDER_DROPDOWN = gradio.Dropdown(
-			label = wording.get('face_analyser_order_dropdown_label'),
-			choices = DeepFakeAI.choices.face_analyser_orders,
-			value = DeepFakeAI.globals.face_analyser_order
-		)
-		FACE_ANALYSER_AGE_DROPDOWN = gradio.Dropdown(
-			label = wording.get('face_analyser_age_dropdown_label'),
-			choices = [ 'none' ] + DeepFakeAI.choices.face_analyser_ages,
-			value = DeepFakeAI.globals.face_analyser_age or 'none'
-		)
-		FACE_ANALYSER_GENDER_DROPDOWN = gradio.Dropdown(
-			label = wording.get('face_analyser_gender_dropdown_label'),
-			choices = [ 'none' ] + DeepFakeAI.choices.face_analyser_genders,
-			value = DeepFakeAI.globals.face_analyser_gender or 'none'
-		)
-	FACE_DETECTOR_MODEL_DROPDOWN = gradio.Dropdown(
-		label = wording.get('face_detector_model_dropdown_label'),
-		choices = DeepFakeAI.choices.face_detector_models,
-		value = DeepFakeAI.globals.face_detector_model
-	)
-	FACE_DETECTOR_SIZE_DROPDOWN = gradio.Dropdown(
-		label = wording.get('face_detector_size_dropdown_label'),
-		choices = DeepFakeAI.choices.face_detector_sizes,
-		value = DeepFakeAI.globals.face_detector_size
-	)
-	FACE_DETECTOR_SCORE_SLIDER = gradio.Slider(
-		label = wording.get('face_detector_score_slider_label'),
-		value = DeepFakeAI.globals.face_detector_score,
-		step = DeepFakeAI.choices.face_detector_score_range[1] - DeepFakeAI.choices.face_detector_score_range[0],
-		minimum = DeepFakeAI.choices.face_detector_score_range[0],
-		maximum = DeepFakeAI.choices.face_detector_score_range[-1]
-	)
-	register_ui_component('face_analyser_order_dropdown', FACE_ANALYSER_ORDER_DROPDOWN)
-	register_ui_component('face_analyser_age_dropdown', FACE_ANALYSER_AGE_DROPDOWN)
-	register_ui_component('face_analyser_gender_dropdown', FACE_ANALYSER_GENDER_DROPDOWN)
-	register_ui_component('face_detector_model_dropdown', FACE_DETECTOR_MODEL_DROPDOWN)
-	register_ui_component('face_detector_size_dropdown', FACE_DETECTOR_SIZE_DROPDOWN)
-	register_ui_component('face_detector_score_slider', FACE_DETECTOR_SCORE_SLIDER)
-
-
-def listen() -> None:
-	FACE_ANALYSER_ORDER_DROPDOWN.select(update_face_analyser_order, inputs = FACE_ANALYSER_ORDER_DROPDOWN)
-	FACE_ANALYSER_AGE_DROPDOWN.select(update_face_analyser_age, inputs = FACE_ANALYSER_AGE_DROPDOWN)
-	FACE_ANALYSER_GENDER_DROPDOWN.select(update_face_analyser_gender, inputs = FACE_ANALYSER_GENDER_DROPDOWN)
-	FACE_DETECTOR_MODEL_DROPDOWN.change(update_face_detector_model, inputs = FACE_DETECTOR_MODEL_DROPDOWN)
-	FACE_DETECTOR_SIZE_DROPDOWN.select(update_face_detector_size, inputs = FACE_DETECTOR_SIZE_DROPDOWN)
-	FACE_DETECTOR_SCORE_SLIDER.change(update_face_detector_score, inputs = FACE_DETECTOR_SCORE_SLIDER)
-
-
-def update_face_analyser_order(face_analyser_order : FaceAnalyserOrder) -> None:
-	DeepFakeAI.globals.face_analyser_order = face_analyser_order if face_analyser_order != 'none' else None
-
-
-def update_face_analyser_age(face_analyser_age : FaceAnalyserAge) -> None:
-	DeepFakeAI.globals.face_analyser_age = face_analyser_age if face_analyser_age != 'none' else None
-
-
-def update_face_analyser_gender(face_analyser_gender : FaceAnalyserGender) -> None:
-	DeepFakeAI.globals.face_analyser_gender = face_analyser_gender if face_analyser_gender != 'none' else None
-
-
-def update_face_detector_model(face_detector_model : FaceDetectorModel) -> None:
-	DeepFakeAI.globals.face_detector_model = face_detector_model
-
-
-def update_face_detector_size(face_detector_size : str) -> None:
-	DeepFakeAI.globals.face_detector_size = face_detector_size
-
-
-def update_face_detector_score(face_detector_score : float) -> None:
-	DeepFakeAI.globals.face_detector_score = face_detector_score

DeepFakeAI/uis/components/face_masker.py

@@ -1,123 +0,0 @@
-from typing import Optional, Tuple, List
-import gradio
-
-import DeepFakeAI.globals
-import DeepFakeAI.choices
-from DeepFakeAI import wording
-from DeepFakeAI.typing import FaceMaskType, FaceMaskRegion
-from DeepFakeAI.uis.core import register_ui_component
-
-FACE_MASK_TYPES_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
-FACE_MASK_BLUR_SLIDER : Optional[gradio.Slider] = None
-FACE_MASK_BOX_GROUP : Optional[gradio.Group] = None
-FACE_MASK_REGION_GROUP : Optional[gradio.Group] = None
-FACE_MASK_PADDING_TOP_SLIDER : Optional[gradio.Slider] = None
-FACE_MASK_PADDING_RIGHT_SLIDER : Optional[gradio.Slider] = None
-FACE_MASK_PADDING_BOTTOM_SLIDER : Optional[gradio.Slider] = None
-FACE_MASK_PADDING_LEFT_SLIDER : Optional[gradio.Slider] = None
-FACE_MASK_REGION_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
-
-
-def render() -> None:
-	global FACE_MASK_TYPES_CHECKBOX_GROUP
-	global FACE_MASK_BLUR_SLIDER
-	global FACE_MASK_BOX_GROUP
-	global FACE_MASK_REGION_GROUP
-	global FACE_MASK_PADDING_TOP_SLIDER
-	global FACE_MASK_PADDING_RIGHT_SLIDER
-	global FACE_MASK_PADDING_BOTTOM_SLIDER
-	global FACE_MASK_PADDING_LEFT_SLIDER
-	global FACE_MASK_REGION_CHECKBOX_GROUP
-
-	has_box_mask = 'box' in DeepFakeAI.globals.face_mask_types
-	has_region_mask = 'region' in DeepFakeAI.globals.face_mask_types
-	FACE_MASK_TYPES_CHECKBOX_GROUP = gradio.CheckboxGroup(
-		label = wording.get('face_mask_types_checkbox_group_label'),
-		choices = DeepFakeAI.choices.face_mask_types,
-		value = DeepFakeAI.globals.face_mask_types
-	)
-	with gradio.Group(visible = has_box_mask) as FACE_MASK_BOX_GROUP:
-		FACE_MASK_BLUR_SLIDER = gradio.Slider(
-			label = wording.get('face_mask_blur_slider_label'),
-			step = DeepFakeAI.choices.face_mask_blur_range[1] - DeepFakeAI.choices.face_mask_blur_range[0],
-			minimum = DeepFakeAI.choices.face_mask_blur_range[0],
-			maximum = DeepFakeAI.choices.face_mask_blur_range[-1],
-			value = DeepFakeAI.globals.face_mask_blur
-		)
-		with gradio.Row():
-			FACE_MASK_PADDING_TOP_SLIDER = gradio.Slider(
-				label = wording.get('face_mask_padding_top_slider_label'),
-				step = DeepFakeAI.choices.face_mask_padding_range[1] - DeepFakeAI.choices.face_mask_padding_range[0],
-				minimum = DeepFakeAI.choices.face_mask_padding_range[0],
-				maximum = DeepFakeAI.choices.face_mask_padding_range[-1],
-				value = DeepFakeAI.globals.face_mask_padding[0]
-			)
-			FACE_MASK_PADDING_RIGHT_SLIDER = gradio.Slider(
-				label = wording.get('face_mask_padding_right_slider_label'),
-				step = DeepFakeAI.choices.face_mask_padding_range[1] - DeepFakeAI.choices.face_mask_padding_range[0],
-				minimum = DeepFakeAI.choices.face_mask_padding_range[0],
-				maximum = DeepFakeAI.choices.face_mask_padding_range[-1],
-				value = DeepFakeAI.globals.face_mask_padding[1]
-			)
-		with gradio.Row():
-			FACE_MASK_PADDING_BOTTOM_SLIDER = gradio.Slider(
-				label = wording.get('face_mask_padding_bottom_slider_label'),
-				step = DeepFakeAI.choices.face_mask_padding_range[1] - DeepFakeAI.choices.face_mask_padding_range[0],
-				minimum = DeepFakeAI.choices.face_mask_padding_range[0],
-				maximum = DeepFakeAI.choices.face_mask_padding_range[-1],
-				value = DeepFakeAI.globals.face_mask_padding[2]
-			)
-			FACE_MASK_PADDING_LEFT_SLIDER = gradio.Slider(
-				label = wording.get('face_mask_padding_left_slider_label'),
-				step = DeepFakeAI.choices.face_mask_padding_range[1] - DeepFakeAI.choices.face_mask_padding_range[0],
-				minimum = DeepFakeAI.choices.face_mask_padding_range[0],
-				maximum = DeepFakeAI.choices.face_mask_padding_range[-1],
-				value = DeepFakeAI.globals.face_mask_padding[3]
-			)
-	with gradio.Row():
-		FACE_MASK_REGION_CHECKBOX_GROUP = gradio.CheckboxGroup(
-			label = wording.get('face_mask_region_checkbox_group_label'),
-			choices = DeepFakeAI.choices.face_mask_regions,
-			value = DeepFakeAI.globals.face_mask_regions,
-			visible = has_region_mask
-		)
-	register_ui_component('face_mask_types_checkbox_group', FACE_MASK_TYPES_CHECKBOX_GROUP)
-	register_ui_component('face_mask_blur_slider', FACE_MASK_BLUR_SLIDER)
-	register_ui_component('face_mask_padding_top_slider', FACE_MASK_PADDING_TOP_SLIDER)
-	register_ui_component('face_mask_padding_right_slider', FACE_MASK_PADDING_RIGHT_SLIDER)
-	register_ui_component('face_mask_padding_bottom_slider', FACE_MASK_PADDING_BOTTOM_SLIDER)
-	register_ui_component('face_mask_padding_left_slider', FACE_MASK_PADDING_LEFT_SLIDER)
-	register_ui_component('face_mask_region_checkbox_group', FACE_MASK_REGION_CHECKBOX_GROUP)
-
-
-def listen() -> None:
-	FACE_MASK_TYPES_CHECKBOX_GROUP.change(update_face_mask_type, inputs = FACE_MASK_TYPES_CHECKBOX_GROUP, outputs = [ FACE_MASK_TYPES_CHECKBOX_GROUP, FACE_MASK_BOX_GROUP, FACE_MASK_REGION_CHECKBOX_GROUP ])
-	FACE_MASK_BLUR_SLIDER.change(update_face_mask_blur, inputs = FACE_MASK_BLUR_SLIDER)
-	FACE_MASK_REGION_CHECKBOX_GROUP.change(update_face_mask_regions, inputs = FACE_MASK_REGION_CHECKBOX_GROUP, outputs = FACE_MASK_REGION_CHECKBOX_GROUP)
-	face_mask_padding_sliders = [ FACE_MASK_PADDING_TOP_SLIDER, FACE_MASK_PADDING_RIGHT_SLIDER, FACE_MASK_PADDING_BOTTOM_SLIDER, FACE_MASK_PADDING_LEFT_SLIDER ]
-	for face_mask_padding_slider in face_mask_padding_sliders:
-		face_mask_padding_slider.change(update_face_mask_padding, inputs = face_mask_padding_sliders)
-
-
-def update_face_mask_type(face_mask_types : List[FaceMaskType]) -> Tuple[gradio.CheckboxGroup, gradio.Group, gradio.CheckboxGroup]:
-	if not face_mask_types:
-		face_mask_types = DeepFakeAI.choices.face_mask_types
-	DeepFakeAI.globals.face_mask_types = face_mask_types
-	has_box_mask = 'box' in face_mask_types
-	has_region_mask = 'region' in face_mask_types
-	return gradio.CheckboxGroup(value = face_mask_types), gradio.Group(visible = has_box_mask), gradio.CheckboxGroup(visible = has_region_mask)
-
-
-def update_face_mask_blur(face_mask_blur : float) -> None:
-	DeepFakeAI.globals.face_mask_blur = face_mask_blur
-
-
-def update_face_mask_padding(face_mask_padding_top : int, face_mask_padding_right : int, face_mask_padding_bottom : int, face_mask_padding_left : int) -> None:
-	DeepFakeAI.globals.face_mask_padding = (face_mask_padding_top, face_mask_padding_right, face_mask_padding_bottom, face_mask_padding_left)
-
-
-def update_face_mask_regions(face_mask_regions : List[FaceMaskRegion]) -> gradio.CheckboxGroup:
-	if not face_mask_regions:
-		face_mask_regions = DeepFakeAI.choices.face_mask_regions
-	DeepFakeAI.globals.face_mask_regions = face_mask_regions
-	return gradio.CheckboxGroup(value = face_mask_regions)

DeepFakeAI/uis/components/face_selector.py

@@ -1,164 +0,0 @@
-from typing import List, Optional, Tuple, Any, Dict
-
-import gradio
-
-import DeepFakeAI.globals
-import DeepFakeAI.choices
-from DeepFakeAI import wording
-from DeepFakeAI.face_store import clear_static_faces, clear_reference_faces
-from DeepFakeAI.vision import get_video_frame, read_static_image, normalize_frame_color
-from DeepFakeAI.face_analyser import get_many_faces
-from DeepFakeAI.typing import Frame, FaceSelectorMode
-from DeepFakeAI.filesystem import is_image, is_video
-from DeepFakeAI.uis.core import get_ui_component, register_ui_component
-from DeepFakeAI.uis.typing import ComponentName
-
-FACE_SELECTOR_MODE_DROPDOWN : Optional[gradio.Dropdown] = None
-REFERENCE_FACE_POSITION_GALLERY : Optional[gradio.Gallery] = None
-REFERENCE_FACE_DISTANCE_SLIDER : Optional[gradio.Slider] = None
-
-
-def render() -> None:
-	global FACE_SELECTOR_MODE_DROPDOWN
-	global REFERENCE_FACE_POSITION_GALLERY
-	global REFERENCE_FACE_DISTANCE_SLIDER
-
-	reference_face_gallery_args: Dict[str, Any] =\
-	{
-		'label': wording.get('reference_face_gallery_label'),
-		'object_fit': 'cover',
-		'columns': 8,
-		'allow_preview': False,
-		'visible': 'reference' in DeepFakeAI.globals.face_selector_mode
-	}
-	if is_image(DeepFakeAI.globals.target_path):
-		reference_frame = read_static_image(DeepFakeAI.globals.target_path)
-		reference_face_gallery_args['value'] = extract_gallery_frames(reference_frame)
-	if is_video(DeepFakeAI.globals.target_path):
-		reference_frame = get_video_frame(DeepFakeAI.globals.target_path, DeepFakeAI.globals.reference_frame_number)
-		reference_face_gallery_args['value'] = extract_gallery_frames(reference_frame)
-	FACE_SELECTOR_MODE_DROPDOWN = gradio.Dropdown(
-		label = wording.get('face_selector_mode_dropdown_label'),
-		choices = DeepFakeAI.choices.face_selector_modes,
-		value = DeepFakeAI.globals.face_selector_mode
-	)
-	REFERENCE_FACE_POSITION_GALLERY = gradio.Gallery(**reference_face_gallery_args)
-	REFERENCE_FACE_DISTANCE_SLIDER = gradio.Slider(
-		label = wording.get('reference_face_distance_slider_label'),
-		value = DeepFakeAI.globals.reference_face_distance,
-		step = DeepFakeAI.choices.reference_face_distance_range[1] - DeepFakeAI.choices.reference_face_distance_range[0],
-		minimum = DeepFakeAI.choices.reference_face_distance_range[0],
-		maximum = DeepFakeAI.choices.reference_face_distance_range[-1],
-		visible = 'reference' in DeepFakeAI.globals.face_selector_mode
-	)
-	register_ui_component('face_selector_mode_dropdown', FACE_SELECTOR_MODE_DROPDOWN)
-	register_ui_component('reference_face_position_gallery', REFERENCE_FACE_POSITION_GALLERY)
-	register_ui_component('reference_face_distance_slider', REFERENCE_FACE_DISTANCE_SLIDER)
-
-
-def listen() -> None:
-	FACE_SELECTOR_MODE_DROPDOWN.select(update_face_selector_mode, inputs = FACE_SELECTOR_MODE_DROPDOWN, outputs = [ REFERENCE_FACE_POSITION_GALLERY, REFERENCE_FACE_DISTANCE_SLIDER ])
-	REFERENCE_FACE_POSITION_GALLERY.select(clear_and_update_reference_face_position)
-	REFERENCE_FACE_DISTANCE_SLIDER.change(update_reference_face_distance, inputs = REFERENCE_FACE_DISTANCE_SLIDER)
-	multi_component_names : List[ComponentName] =\
-	[
-		'target_image',
-		'target_video'
-	]
-	for component_name in multi_component_names:
-		component = get_ui_component(component_name)
-		if component:
-			for method in [ 'upload', 'change', 'clear' ]:
-				getattr(component, method)(update_reference_face_position)
-				getattr(component, method)(update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
-	change_one_component_names : List[ComponentName] =\
-	[
-		'face_analyser_order_dropdown',
-		'face_analyser_age_dropdown',
-		'face_analyser_gender_dropdown'
-	]
-	for component_name in change_one_component_names:
-		component = get_ui_component(component_name)
-		if component:
-			component.change(update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
-	change_two_component_names : List[ComponentName] =\
-	[
-		'face_detector_model_dropdown',
-		'face_detector_size_dropdown',
-		'face_detector_score_slider'
-	]
-	for component_name in change_two_component_names:
-		component = get_ui_component(component_name)
-		if component:
-			component.change(clear_and_update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
-	preview_frame_slider = get_ui_component('preview_frame_slider')
-	if preview_frame_slider:
-		preview_frame_slider.change(update_reference_frame_number, inputs = preview_frame_slider)
-		preview_frame_slider.release(update_reference_position_gallery, outputs = REFERENCE_FACE_POSITION_GALLERY)
-
-
-def update_face_selector_mode(face_selector_mode : FaceSelectorMode) -> Tuple[gradio.Gallery, gradio.Slider]:
-	if face_selector_mode == 'reference':
-		DeepFakeAI.globals.face_selector_mode = face_selector_mode
-		return gradio.Gallery(visible = True), gradio.Slider(visible = True)
-	if face_selector_mode == 'one':
-		DeepFakeAI.globals.face_selector_mode = face_selector_mode
-		return gradio.Gallery(visible = False), gradio.Slider(visible = False)
-	if face_selector_mode == 'many':
-		DeepFakeAI.globals.face_selector_mode = face_selector_mode
-		return gradio.Gallery(visible = False), gradio.Slider(visible = False)
-
-
-def clear_and_update_reference_face_position(event : gradio.SelectData) -> gradio.Gallery:
-	clear_reference_faces()
-	clear_static_faces()
-	update_reference_face_position(event.index)
-	return update_reference_position_gallery()
-
-
-def update_reference_face_position(reference_face_position : int = 0) -> None:
-	DeepFakeAI.globals.reference_face_position = reference_face_position
-
-
-def update_reference_face_distance(reference_face_distance : float) -> None:
-	DeepFakeAI.globals.reference_face_distance = reference_face_distance
-
-
-def update_reference_frame_number(reference_frame_number : int) -> None:
-	DeepFakeAI.globals.reference_frame_number = reference_frame_number
-
-
-def clear_and_update_reference_position_gallery() -> gradio.Gallery:
-	clear_reference_faces()
-	clear_static_faces()
-	return update_reference_position_gallery()
-
-
-def update_reference_position_gallery() -> gradio.Gallery:
-	gallery_frames = []
-	if is_image(DeepFakeAI.globals.target_path):
-		reference_frame = read_static_image(DeepFakeAI.globals.target_path)
-		gallery_frames = extract_gallery_frames(reference_frame)
-	if is_video(DeepFakeAI.globals.target_path):
-		reference_frame = get_video_frame(DeepFakeAI.globals.target_path, DeepFakeAI.globals.reference_frame_number)
-		gallery_frames = extract_gallery_frames(reference_frame)
-	if gallery_frames:
-		return gradio.Gallery(value = gallery_frames)
-	return gradio.Gallery(value = None)
-
-
-def extract_gallery_frames(reference_frame : Frame) -> List[Frame]:
-	crop_frames = []
-	faces = get_many_faces(reference_frame)
-	for face in faces:
-		start_x, start_y, end_x, end_y = map(int, face.bbox)
-		padding_x = int((end_x - start_x) * 0.25)
-		padding_y = int((end_y - start_y) * 0.25)
-		start_x = max(0, start_x - padding_x)
-		start_y = max(0, start_y - padding_y)
-		end_x = max(0, end_x + padding_x)
-		end_y = max(0, end_y + padding_y)
-		crop_frame = reference_frame[start_y:end_y, start_x:end_x]
-		crop_frame = normalize_frame_color(crop_frame)
-		crop_frames.append(crop_frame)
-	return crop_frames

DeepFakeAI/uis/components/frame_processors.py

@@ -1,40 +0,0 @@
-from typing import List, Optional
-import gradio
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.processors.frame.core import load_frame_processor_module, clear_frame_processors_modules
-from DeepFakeAI.filesystem import list_module_names
-from DeepFakeAI.uis.core import register_ui_component
-
-FRAME_PROCESSORS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
-
-
-def render() -> None:
-	global FRAME_PROCESSORS_CHECKBOX_GROUP
-
-	FRAME_PROCESSORS_CHECKBOX_GROUP = gradio.CheckboxGroup(
-		label = wording.get('frame_processors_checkbox_group_label'),
-		choices = sort_frame_processors(DeepFakeAI.globals.frame_processors),
-		value = DeepFakeAI.globals.frame_processors
-	)
-	register_ui_component('frame_processors_checkbox_group', FRAME_PROCESSORS_CHECKBOX_GROUP)
-
-
-def listen() -> None:
-	FRAME_PROCESSORS_CHECKBOX_GROUP.change(update_frame_processors, inputs = FRAME_PROCESSORS_CHECKBOX_GROUP, outputs = FRAME_PROCESSORS_CHECKBOX_GROUP)
-
-
-def update_frame_processors(frame_processors : List[str]) -> gradio.CheckboxGroup:
-	DeepFakeAI.globals.frame_processors = frame_processors
-	clear_frame_processors_modules()
-	for frame_processor in frame_processors:
-		frame_processor_module = load_frame_processor_module(frame_processor)
-		if not frame_processor_module.pre_check():
-			return gradio.CheckboxGroup()
-	return gradio.CheckboxGroup(value = frame_processors, choices = sort_frame_processors(frame_processors))
-
-
-def sort_frame_processors(frame_processors : List[str]) -> list[str]:
-	available_frame_processors = list_module_names('DeepFakeAI/processors/frame/modules')
-	return sorted(available_frame_processors, key = lambda frame_processor : frame_processors.index(frame_processor) if frame_processor in frame_processors else len(frame_processors))

DeepFakeAI/uis/components/frame_processors_options.py

@@ -1,141 +0,0 @@
-from typing import List, Optional, Tuple
-import gradio
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.processors.frame.core import load_frame_processor_module
-from DeepFakeAI.processors.frame import globals as frame_processors_globals, choices as frame_processors_choices
-from DeepFakeAI.processors.frame.typings import FaceSwapperModel, FaceEnhancerModel, FrameEnhancerModel, FaceDebuggerItem
-from DeepFakeAI.uis.core import get_ui_component, register_ui_component
-
-FACE_SWAPPER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
-FACE_ENHANCER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
-FACE_ENHANCER_BLEND_SLIDER : Optional[gradio.Slider] = None
-FRAME_ENHANCER_MODEL_DROPDOWN : Optional[gradio.Dropdown] = None
-FRAME_ENHANCER_BLEND_SLIDER : Optional[gradio.Slider] = None
-FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP : Optional[gradio.CheckboxGroup] = None
-
-
-def render() -> None:
-	global FACE_SWAPPER_MODEL_DROPDOWN
-	global FACE_ENHANCER_MODEL_DROPDOWN
-	global FACE_ENHANCER_BLEND_SLIDER
-	global FRAME_ENHANCER_MODEL_DROPDOWN
-	global FRAME_ENHANCER_BLEND_SLIDER
-	global FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP
-
-	FACE_SWAPPER_MODEL_DROPDOWN = gradio.Dropdown(
-		label = wording.get('face_swapper_model_dropdown_label'),
-		choices = frame_processors_choices.face_swapper_models,
-		value = frame_processors_globals.face_swapper_model,
-		visible = 'face_swapper' in DeepFakeAI.globals.frame_processors
-	)
-	FACE_ENHANCER_MODEL_DROPDOWN = gradio.Dropdown(
-		label = wording.get('face_enhancer_model_dropdown_label'),
-		choices = frame_processors_choices.face_enhancer_models,
-		value = frame_processors_globals.face_enhancer_model,
-		visible = 'face_enhancer' in DeepFakeAI.globals.frame_processors
-	)
-	FACE_ENHANCER_BLEND_SLIDER = gradio.Slider(
-		label = wording.get('face_enhancer_blend_slider_label'),
-		value = frame_processors_globals.face_enhancer_blend,
-		step = frame_processors_choices.face_enhancer_blend_range[1] - frame_processors_choices.face_enhancer_blend_range[0],
-		minimum = frame_processors_choices.face_enhancer_blend_range[0],
-		maximum = frame_processors_choices.face_enhancer_blend_range[-1],
-		visible = 'face_enhancer' in DeepFakeAI.globals.frame_processors
-	)
-	FRAME_ENHANCER_MODEL_DROPDOWN = gradio.Dropdown(
-		label = wording.get('frame_enhancer_model_dropdown_label'),
-		choices = frame_processors_choices.frame_enhancer_models,
-		value = frame_processors_globals.frame_enhancer_model,
-		visible = 'frame_enhancer' in DeepFakeAI.globals.frame_processors
-	)
-	FRAME_ENHANCER_BLEND_SLIDER = gradio.Slider(
-		label = wording.get('frame_enhancer_blend_slider_label'),
-		value = frame_processors_globals.frame_enhancer_blend,
-		step = frame_processors_choices.frame_enhancer_blend_range[1] - frame_processors_choices.frame_enhancer_blend_range[0],
-		minimum = frame_processors_choices.frame_enhancer_blend_range[0],
-		maximum = frame_processors_choices.frame_enhancer_blend_range[-1],
-		visible = 'face_enhancer' in DeepFakeAI.globals.frame_processors
-	)
-	FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP = gradio.CheckboxGroup(
-		label = wording.get('face_debugger_items_checkbox_group_label'),
-		choices = frame_processors_choices.face_debugger_items,
-		value = frame_processors_globals.face_debugger_items,
-		visible = 'face_debugger' in DeepFakeAI.globals.frame_processors
-	)
-
-	register_ui_component('face_swapper_model_dropdown', FACE_SWAPPER_MODEL_DROPDOWN)
-	register_ui_component('face_enhancer_model_dropdown', FACE_ENHANCER_MODEL_DROPDOWN)
-	register_ui_component('face_enhancer_blend_slider', FACE_ENHANCER_BLEND_SLIDER)
-	register_ui_component('frame_enhancer_model_dropdown', FRAME_ENHANCER_MODEL_DROPDOWN)
-	register_ui_component('frame_enhancer_blend_slider', FRAME_ENHANCER_BLEND_SLIDER)
-	register_ui_component('face_debugger_items_checkbox_group', FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP)
-
-
-def listen() -> None:
-	FACE_SWAPPER_MODEL_DROPDOWN.change(update_face_swapper_model, inputs = FACE_SWAPPER_MODEL_DROPDOWN, outputs = FACE_SWAPPER_MODEL_DROPDOWN)
-	FACE_ENHANCER_MODEL_DROPDOWN.change(update_face_enhancer_model, inputs = FACE_ENHANCER_MODEL_DROPDOWN, outputs = FACE_ENHANCER_MODEL_DROPDOWN)
-	FACE_ENHANCER_BLEND_SLIDER.change(update_face_enhancer_blend, inputs = FACE_ENHANCER_BLEND_SLIDER)
-	FRAME_ENHANCER_MODEL_DROPDOWN.change(update_frame_enhancer_model, inputs = FRAME_ENHANCER_MODEL_DROPDOWN, outputs = FRAME_ENHANCER_MODEL_DROPDOWN)
-	FRAME_ENHANCER_BLEND_SLIDER.change(update_frame_enhancer_blend, inputs = FRAME_ENHANCER_BLEND_SLIDER)
-	FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP.change(update_face_debugger_items, inputs = FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP)
-	frame_processors_checkbox_group = get_ui_component('frame_processors_checkbox_group')
-	if frame_processors_checkbox_group:
-		frame_processors_checkbox_group.change(toggle_face_swapper_model, inputs = frame_processors_checkbox_group, outputs = [ FACE_SWAPPER_MODEL_DROPDOWN, FACE_ENHANCER_MODEL_DROPDOWN, FACE_ENHANCER_BLEND_SLIDER, FRAME_ENHANCER_MODEL_DROPDOWN, FRAME_ENHANCER_BLEND_SLIDER, FACE_DEBUGGER_ITEMS_CHECKBOX_GROUP ])
-
-
-def update_face_swapper_model(face_swapper_model : FaceSwapperModel) -> gradio.Dropdown:
-	frame_processors_globals.face_swapper_model = face_swapper_model
-	if face_swapper_model == 'blendswap_256':
-		DeepFakeAI.globals.face_recognizer_model = 'arcface_blendswap'
-	if face_swapper_model == 'inswapper_128' or face_swapper_model == 'inswapper_128_fp16':
-		DeepFakeAI.globals.face_recognizer_model = 'arcface_inswapper'
-	if face_swapper_model == 'simswap_256' or face_swapper_model == 'simswap_512_unofficial':
-		DeepFakeAI.globals.face_recognizer_model = 'arcface_simswap'
-	face_swapper_module = load_frame_processor_module('face_swapper')
-	face_swapper_module.clear_frame_processor()
-	face_swapper_module.set_options('model', face_swapper_module.MODELS[face_swapper_model])
-	if not face_swapper_module.pre_check():
-		return gradio.Dropdown()
-	return gradio.Dropdown(value = face_swapper_model)
-
-
-def update_face_enhancer_model(face_enhancer_model : FaceEnhancerModel) -> gradio.Dropdown:
-	frame_processors_globals.face_enhancer_model = face_enhancer_model
-	face_enhancer_module = load_frame_processor_module('face_enhancer')
-	face_enhancer_module.clear_frame_processor()
-	face_enhancer_module.set_options('model', face_enhancer_module.MODELS[face_enhancer_model])
-	if not face_enhancer_module.pre_check():
-		return gradio.Dropdown()
-	return gradio.Dropdown(value = face_enhancer_model)
-
-
-def update_face_enhancer_blend(face_enhancer_blend : int) -> None:
-	frame_processors_globals.face_enhancer_blend = face_enhancer_blend
-
-
-def update_frame_enhancer_model(frame_enhancer_model : FrameEnhancerModel) -> gradio.Dropdown:
-	frame_processors_globals.frame_enhancer_model = frame_enhancer_model
-	frame_enhancer_module = load_frame_processor_module('frame_enhancer')
-	frame_enhancer_module.clear_frame_processor()
-	frame_enhancer_module.set_options('model', frame_enhancer_module.MODELS[frame_enhancer_model])
-	if not frame_enhancer_module.pre_check():
-		return gradio.Dropdown()
-	return gradio.Dropdown(value = frame_enhancer_model)
-
-
-def update_frame_enhancer_blend(frame_enhancer_blend : int) -> None:
-	frame_processors_globals.frame_enhancer_blend = frame_enhancer_blend
-
-
-def update_face_debugger_items(face_debugger_items : List[FaceDebuggerItem]) -> None:
-	frame_processors_globals.face_debugger_items = face_debugger_items
-
-
-def toggle_face_swapper_model(frame_processors : List[str]) -> Tuple[gradio.Dropdown, gradio.Dropdown, gradio.Slider, gradio.Dropdown, gradio.Slider, gradio.CheckboxGroup]:
-	has_face_swapper = 'face_swapper' in frame_processors
-	has_face_enhancer = 'face_enhancer' in frame_processors
-	has_frame_enhancer = 'frame_enhancer' in frame_processors
-	has_face_debugger = 'face_debugger' in frame_processors
-	return gradio.Dropdown(visible = has_face_swapper), gradio.Dropdown(visible = has_face_enhancer), gradio.Slider(visible = has_face_enhancer), gradio.Dropdown(visible = has_frame_enhancer), gradio.Slider(visible = has_frame_enhancer), gradio.CheckboxGroup(visible = has_face_debugger)

DeepFakeAI/uis/components/limit_resources.py

@@ -1,27 +0,0 @@
-from typing import Optional
-import gradio
-
-import DeepFakeAI.globals
-import DeepFakeAI.choices
-from DeepFakeAI import wording
-
-MAX_MEMORY_SLIDER : Optional[gradio.Slider] = None
-
-
-def render() -> None:
-	global MAX_MEMORY_SLIDER
-
-	MAX_MEMORY_SLIDER = gradio.Slider(
-		label = wording.get('max_memory_slider_label'),
-		step = DeepFakeAI.choices.max_memory_range[1] - DeepFakeAI.choices.max_memory_range[0],
-		minimum = DeepFakeAI.choices.max_memory_range[0],
-		maximum = DeepFakeAI.choices.max_memory_range[-1]
-	)
-
-
-def listen() -> None:
-	MAX_MEMORY_SLIDER.change(update_max_memory, inputs = MAX_MEMORY_SLIDER)
-
-
-def update_max_memory(max_memory : int) -> None:
-	DeepFakeAI.globals.max_memory = max_memory if max_memory > 0 else None

DeepFakeAI/uis/components/output.py

@@ -1,62 +0,0 @@
-from typing import Tuple, Optional
-import gradio
-
-import DeepFakeAI.globals
-from DeepFakeAI import wording
-from DeepFakeAI.core import limit_resources, conditional_process
-from DeepFakeAI.uis.core import get_ui_component
-from DeepFakeAI.normalizer import normalize_output_path
-from DeepFakeAI.filesystem import is_image, is_video, clear_temp
-
-OUTPUT_IMAGE : Optional[gradio.Image] = None
-OUTPUT_VIDEO : Optional[gradio.Video] = None
-OUTPUT_START_BUTTON : Optional[gradio.Button] = None
-OUTPUT_CLEAR_BUTTON : Optional[gradio.Button] = None
-
-
-def render() -> None:
-	global OUTPUT_IMAGE
-	global OUTPUT_VIDEO
-	global OUTPUT_START_BUTTON
-	global OUTPUT_CLEAR_BUTTON
-
-	OUTPUT_IMAGE = gradio.Image(
-		label = wording.get('output_image_or_video_label'),
-		visible = False
-	)
-	OUTPUT_VIDEO = gradio.Video(
-		label = wording.get('output_image_or_video_label')
-	)
-	OUTPUT_START_BUTTON = gradio.Button(
-		value = wording.get('start_button_label'),
-		variant = 'primary',
-		size = 'sm'
-	)
-	OUTPUT_CLEAR_BUTTON = gradio.Button(
-		value = wording.get('clear_button_label'),
-		size = 'sm'
-	)
-
-
-def listen() -> None:
-	output_path_textbox = get_ui_component('output_path_textbox')
-	if output_path_textbox:
-		OUTPUT_START_BUTTON.click(start, inputs = output_path_textbox, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
-	OUTPUT_CLEAR_BUTTON.click(clear, outputs = [ OUTPUT_IMAGE, OUTPUT_VIDEO ])
-
-
-def start(output_path : str) -> Tuple[gradio.Image, gradio.Video]:
-	DeepFakeAI.globals.output_path = normalize_output_path(DeepFakeAI.globals.source_paths, DeepFakeAI.globals.target_path, output_path)
-	limit_resources()
-	conditional_process()
-	if is_image(DeepFakeAI.globals.output_path):
-		return gradio.Image(value = DeepFakeAI.globals.output_path, visible = True), gradio.Video(value = None, visible = False)
-	if is_video(DeepFakeAI.globals.output_path):
-		return gradio.Image(value = None, visible = False), gradio.Video(value = DeepFakeAI.globals.output_path, visible = True)
-	return gradio.Image(), gradio.Video()
-
-
-def clear() -> Tuple[gradio.Image, gradio.Video]:
-	if DeepFakeAI.globals.target_path:
-		clear_temp(DeepFakeAI.globals.target_path)
-	return gradio.Image(value = None), gradio.Video(value = None)

DeepFakeAI/uis/components/output_options.py

@@ -1,94 +0,0 @@
-from typing import Optional, Tuple, List
-import tempfile
-import gradio
-
-import DeepFakeAI.globals
-import DeepFakeAI.choices
-from DeepFakeAI import wording
-from DeepFakeAI.typing import OutputVideoEncoder
-from DeepFakeAI.filesystem import is_image, is_video
-from DeepFakeAI.uis.typing import ComponentName
-from DeepFakeAI.uis.core import get_ui_component, register_ui_component
-
-OUTPUT_PATH_TEXTBOX : Optional[gradio.Textbox] = None
-OUTPUT_IMAGE_QUALITY_SLIDER : Optional[gradio.Slider] = None
-OUTPUT_VIDEO_ENCODER_DROPDOWN : Optional[gradio.Dropdown] = None
-OUTPUT_VIDEO_QUALITY_SLIDER : Optional[gradio.Slider] = None
-
-
-def render() -> None:
-	global OUTPUT_PATH_TEXTBOX
-	global OUTPUT_IMAGE_QUALITY_SLIDER
-	global OUTPUT_VIDEO_ENCODER_DROPDOWN
-	global OUTPUT_VIDEO_QUALITY_SLIDER
-
-	OUTPUT_PATH_TEXTBOX = gradio.Textbox(
-		label = wording.get('output_path_textbox_label'),
-		value = DeepFakeAI.globals.output_path or tempfile.gettempdir(),
-		max_lines = 1
-	)
-	OUTPUT_IMAGE_QUALITY_SLIDER = gradio.Slider(
-		label = wording.get('output_image_quality_slider_label'),
-		value = DeepFakeAI.globals.output_image_quality,
-		step = DeepFakeAI.choices.output_image_quality_range[1] - DeepFakeAI.choices.output_image_quality_range[0],
-		minimum = DeepFakeAI.choices.output_image_quality_range[0],
-		maximum = DeepFakeAI.choices.output_image_quality_range[-1],
-		visible = is_image(DeepFakeAI.globals.target_path)
-	)
-	OUTPUT_VIDEO_ENCODER_DROPDOWN = gradio.Dropdown(
-		label = wording.get('output_video_encoder_dropdown_label'),
-		choices = DeepFakeAI.choices.output_video_encoders,
-		value = DeepFakeAI.globals.output_video_encoder,
-		visible = is_video(DeepFakeAI.globals.target_path)
-	)
-	OUTPUT_VIDEO_QUALITY_SLIDER = gradio.Slider(
-		label = wording.get('output_video_quality_slider_label'),
-		value = DeepFakeAI.globals.output_video_quality,
-		step = DeepFakeAI.choices.output_video_quality_range[1] - DeepFakeAI.choices.output_video_quality_range[0],
-		minimum = DeepFakeAI.choices.output_video_quality_range[0],
-		maximum = DeepFakeAI.choices.output_video_quality_range[-1],
-		visible = is_video(DeepFakeAI.globals.target_path)
-	)
-	register_ui_component('output_path_textbox', OUTPUT_PATH_TEXTBOX)
-
-
-def listen() -> None:
-	OUTPUT_PATH_TEXTBOX.change(update_output_path, inputs = OUTPUT_PATH_TEXTBOX)
-	OUTPUT_IMAGE_QUALITY_SLIDER.change(update_output_image_quality, inputs = OUTPUT_IMAGE_QUALITY_SLIDER)
-	OUTPUT_VIDEO_ENCODER_DROPDOWN.select(update_output_video_encoder, inputs = OUTPUT_VIDEO_ENCODER_DROPDOWN)
-	OUTPUT_VIDEO_QUALITY_SLIDER.change(update_output_video_quality, inputs = OUTPUT_VIDEO_QUALITY_SLIDER)
-	multi_component_names : List[ComponentName] =\
-	[
-		'source_image',
-		'target_image',
-		'target_video'
-	]
-	for component_name in multi_component_names:
-		component = get_ui_component(component_name)
-		if component:
-			for method in [ 'upload', 'change', 'clear' ]:
-				getattr(component, method)(remote_update, outputs = [ OUTPUT_IMAGE_QUALITY_SLIDER, OUTPUT_VIDEO_ENCODER_DROPDOWN, OUTPUT_VIDEO_QUALITY_SLIDER ])
-
-
-def remote_update() -> Tuple[gradio.Slider, gradio.Dropdown, gradio.Slider]:
-	if is_image(DeepFakeAI.globals.target_path):
-		return gradio.Slider(visible = True), gradio.Dropdown(visible = False), gradio.Slider(visible = False)
-	if is_video(DeepFakeAI.globals.target_path):
-		return gradio.Slider(visible = False), gradio.Dropdown(visible = True), gradio.Slider(visible = True)
-	return gradio.Slider(visible = False), gradio.Dropdown(visible = False), gradio.Slider(visible = False)
-
-
-def update_output_path(output_path : str) -> None:
-	DeepFakeAI.globals.output_path = output_path
-
-
-def update_output_image_quality(output_image_quality : int) -> None:
-	DeepFakeAI.globals.output_image_quality = output_image_quality
-
-
-def update_output_video_encoder(output_video_encoder: OutputVideoEncoder) -> None:
-	DeepFakeAI.globals.output_video_encoder = output_video_encoder
-
-
-def update_output_video_quality(output_video_quality : int) -> None:
-	DeepFakeAI.globals.output_video_quality = output_video_quality