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from typing import Any, List, Dict, Literal, Optional
from argparse import ArgumentParser
import cv2
import threading
import numpy
import onnxruntime
import facefusion.globals
import facefusion.processors.frame.core as frame_processors
from facefusion import wording
from facefusion.face_analyser import get_many_faces, clear_face_analyser
from facefusion.face_helper import warp_face, paste_back
from facefusion.content_analyser import clear_content_analyser
from facefusion.typing import Face, Frame, Update_Process, ProcessMode, ModelValue, OptionsWithModel
from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video, is_file, is_download_done, 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.FACE_ENHANCER'
MODELS : Dict[str, ModelValue] =\
{
'codeformer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/codeformer.onnx',
'path': resolve_relative_path('../.assets/models/codeformer.onnx'),
'template': 'ffhq',
'size': (512, 512)
},
'gfpgan_1.2':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.2.onnx',
'path': resolve_relative_path('../.assets/models/gfpgan_1.2.onnx'),
'template': 'ffhq',
'size': (512, 512)
},
'gfpgan_1.3':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.3.onnx',
'path': resolve_relative_path('../.assets/models/gfpgan_1.3.onnx'),
'template': 'ffhq',
'size': (512, 512)
},
'gfpgan_1.4':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gfpgan_1.4.onnx',
'path': resolve_relative_path('../.assets/models/gfpgan_1.4.onnx'),
'template': 'ffhq',
'size': (512, 512)
},
'gpen_bfr_256':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_256.onnx',
'path': resolve_relative_path('../.assets/models/gpen_bfr_256.onnx'),
'template': 'arcface_v2',
'size': (128, 256)
},
'gpen_bfr_512':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/gpen_bfr_512.onnx',
'path': resolve_relative_path('../.assets/models/gpen_bfr_512.onnx'),
'template': 'ffhq',
'size': (512, 512)
},
'restoreformer':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/restoreformer.onnx',
'path': resolve_relative_path('../.assets/models/restoreformer.onnx'),
'template': 'ffhq',
'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 = [ 'CUDAExecutionProvider' ])
# FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers)
model_path = get_options('model').get('path')
FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = [ 'CPUExecutionProvider' ])
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'), dest = 'face_enhancer_model', default = 'gfpgan_1.4', choices = frame_processors_choices.face_enhancer_models)
program.add_argument('--face-enhancer-blend', help = wording.get('frame_processor_blend_help'), dest = 'face_enhancer_blend', 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 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 in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
update_status(wording.get('select_image_or_video_target') + 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_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_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)
paste_frame = paste_back(temp_frame, crop_frame, affine_matrix, facefusion.globals.face_mask_blur, (0, 0, 0, 0))
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 process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
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 : 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_frame = process_frame(None, None, target_frame)
write_image(output_path, result_frame)
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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