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face_swapper.py
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face_swapper.py
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from typing import Any, List, Dict, Literal, Optional
from argparse import ArgumentParser
import insightface
import threading
import facefusion.globals
import facefusion.processors.frame.core as frame_processors
from facefusion import wording
from facefusion.core import update_status
from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser
from facefusion.face_reference import get_face_reference, set_face_reference
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
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_LOCK : threading.Lock = threading.Lock()
NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_SWAPPER'
MODELS : Dict[str, ModelValue] =\
{
'inswapper_128':
{
'url': 'https://ghproxy.com/https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx',
'path': resolve_relative_path('../.assets/models/inswapper_128.onnx')
},
'inswapper_128_fp16':
{
'url': 'https://ghproxy.com/https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx',
'path': resolve_relative_path('../.assets/models/inswapper_128_fp16.onnx')
}
}
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 = insightface.model_zoo.get_model(model_path, providers = facefusion.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_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'), dest = 'face_swapper_model', 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
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 not is_image(facefusion.globals.source_path):
update_status(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
return False
elif not get_one_face(read_static_image(facefusion.globals.source_path)):
update_status(wording.get('no_source_face_detected') + 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()
read_static_image.cache_clear()
def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
return get_frame_processor().get(temp_frame, target_face, source_face, paste_back = True)
def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
if 'reference' in facefusion.globals.face_recognition:
similar_faces = find_similar_faces(temp_frame, reference_face, facefusion.globals.reference_face_distance)
if similar_faces:
for similar_face in similar_faces:
temp_frame = swap_face(source_face, similar_face, temp_frame)
if 'many' in facefusion.globals.face_recognition:
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_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None:
source_face = get_one_face(read_static_image(source_path))
reference_face = get_face_reference() if 'reference' in facefusion.globals.face_recognition else None
for temp_frame_path in temp_frame_paths:
temp_frame = read_image(temp_frame_path)
result_frame = process_frame(source_face, reference_face, temp_frame)
write_image(temp_frame_path, result_frame)
update_progress()
def process_image(source_path : str, target_path : str, output_path : str) -> None:
source_face = get_one_face(read_static_image(source_path))
target_frame = read_static_image(target_path)
reference_face = get_one_face(target_frame, facefusion.globals.reference_face_position) if 'reference' in facefusion.globals.face_recognition else None
result_frame = process_frame(source_face, reference_face, target_frame)
write_image(output_path, result_frame)
def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
conditional_set_face_reference(temp_frame_paths)
frame_processors.multi_process_frames(source_path, temp_frame_paths, process_frames)
def conditional_set_face_reference(temp_frame_paths : List[str]) -> None:
if 'reference' in facefusion.globals.face_recognition and not get_face_reference():
reference_frame = read_static_image(temp_frame_paths[facefusion.globals.reference_frame_number])
reference_face = get_one_face(reference_frame, facefusion.globals.reference_face_position)
set_face_reference(reference_face)