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from typing import Any, List, Dict, Literal, Optional
from argparse import ArgumentParser
import threading
import cv2
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
import facefusion.globals
import facefusion.processors.frame.core as frame_processors
from facefusion import wording
from facefusion.face_analyser import clear_face_analyser
from facefusion.content_analyser import clear_content_analyser
from facefusion.typing import Frame, Face, Update_Process, ProcessMode, ModelValue, OptionsWithModel
from facefusion.utilities import conditional_download, resolve_relative_path, is_file, is_download_done, map_device, create_metavar, update_status
from facefusion.vision import read_image, read_static_image, write_image
from facefusion.processors.frame import globals as frame_processors_globals
from facefusion.processors.frame import choices as frame_processors_choices
FRAME_PROCESSOR = None
THREAD_SEMAPHORE : threading.Semaphore = threading.Semaphore()
THREAD_LOCK : threading.Lock = threading.Lock()
NAME = 'FACEFUSION.FRAME_PROCESSOR.FRAME_ENHANCER'
MODELS: Dict[str, ModelValue] =\
{
'real_esrgan_x2plus':
{
'url': 'https://github.com/facefusion/facefusion-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/facefusion/facefusion-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/facefusion/facefusion-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(facefusion.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'), dest = 'frame_enhancer_model', default = 'real_esrgan_x2plus', choices = frame_processors_choices.frame_enhancer_models)
program.add_argument('--frame-enhancer-blend', help = wording.get('frame_processor_blend_help'), dest = 'frame_enhancer_blend', 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 facefusion.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 facefusion.globals.skip_download and not is_download_done(model_url, model_path):
update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
return False
elif not is_file(model_path):
update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
return False
if mode == 'output' and not facefusion.globals.output_path:
update_status(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 process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
return enhance_frame(temp_frame)
def process_frames(source_path : 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_path : 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_path : str, temp_frame_paths : List[str]) -> None:
frame_processors.multi_process_frames(None, temp_frame_paths, process_frames)
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