Spaces:
Running
Running
File size: 6,057 Bytes
8fb085a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
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)
|