284 lines
11 KiB
Python
Executable File
284 lines
11 KiB
Python
Executable File
from typing import Any, List, Dict, Literal, Optional
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from argparse import ArgumentParser
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import threading
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import numpy
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import onnx
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import onnxruntime
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from onnx import numpy_helper
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import facefusion.globals
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import facefusion.processors.frame.core as frame_processors
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from facefusion import wording
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from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_faces, clear_face_analyser
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from facefusion.face_helper import warp_face, paste_back
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from facefusion.face_reference import get_face_reference
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from facefusion.content_analyser import clear_content_analyser
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from facefusion.typing import Face, Frame, Update_Process, ProcessMode, ModelValue, OptionsWithModel, Embedding
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from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video, is_file, is_download_done, update_status
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from facefusion.vision import read_image, read_static_image, write_image
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from facefusion.processors.frame import globals as frame_processors_globals
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from facefusion.processors.frame import choices as frame_processors_choices
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FRAME_PROCESSOR = None
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MODEL_MATRIX = None
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THREAD_LOCK : threading.Lock = threading.Lock()
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NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_SWAPPER'
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MODELS : Dict[str, ModelValue] =\
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{
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'blendswap_256':
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{
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'type': 'blendswap',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/blendswap_256.onnx',
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'path': resolve_relative_path('../.assets/models/blendswap_256.onnx'),
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'template': 'ffhq',
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'size': (512, 256),
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'mean': [ 0.0, 0.0, 0.0 ],
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'standard_deviation': [ 1.0, 1.0, 1.0 ]
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},
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'inswapper_128':
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{
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'type': 'inswapper',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx',
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'path': resolve_relative_path('../.assets/models/inswapper_128.onnx'),
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'template': 'arcface_v2',
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'size': (128, 128),
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'mean': [ 0.0, 0.0, 0.0 ],
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'standard_deviation': [ 1.0, 1.0, 1.0 ]
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},
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'inswapper_128_fp16':
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{
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'type': 'inswapper',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128_fp16.onnx',
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'path': resolve_relative_path('../.assets/models/inswapper_128_fp16.onnx'),
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'template': 'arcface_v2',
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'size': (128, 128),
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'mean': [ 0.0, 0.0, 0.0 ],
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'standard_deviation': [ 1.0, 1.0, 1.0 ]
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},
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'simswap_256':
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{
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'type': 'simswap',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_256.onnx',
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'path': resolve_relative_path('../.assets/models/simswap_256.onnx'),
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'template': 'arcface_v1',
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'size': (112, 256),
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'mean': [ 0.485, 0.456, 0.406 ],
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'standard_deviation': [ 0.229, 0.224, 0.225 ]
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},
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'simswap_512_unofficial':
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{
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'type': 'simswap',
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'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models/simswap_512_unofficial.onnx',
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'path': resolve_relative_path('../.assets/models/simswap_512_unofficial.onnx'),
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'template': 'arcface_v1',
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'size': (112, 512),
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'mean': [ 0.0, 0.0, 0.0 ],
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'standard_deviation': [ 1.0, 1.0, 1.0 ]
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}
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}
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OPTIONS : Optional[OptionsWithModel] = None
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def get_frame_processor() -> Any:
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global FRAME_PROCESSOR
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with THREAD_LOCK:
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if FRAME_PROCESSOR is None:
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model_path = get_options('model').get('path')
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FRAME_PROCESSOR = onnxruntime.InferenceSession(model_path, providers = facefusion.globals.execution_providers)
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return FRAME_PROCESSOR
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def clear_frame_processor() -> None:
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global FRAME_PROCESSOR
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FRAME_PROCESSOR = None
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def get_model_matrix() -> Any:
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global MODEL_MATRIX
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with THREAD_LOCK:
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if MODEL_MATRIX is None:
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model_path = get_options('model').get('path')
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model = onnx.load(model_path)
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MODEL_MATRIX = numpy_helper.to_array(model.graph.initializer[-1])
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return MODEL_MATRIX
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def clear_model_matrix() -> None:
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global MODEL_MATRIX
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MODEL_MATRIX = None
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def get_options(key : Literal['model']) -> Any:
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global OPTIONS
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if OPTIONS is None:
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OPTIONS =\
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{
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'model': MODELS[frame_processors_globals.face_swapper_model]
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}
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return OPTIONS.get(key)
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def set_options(key : Literal['model'], value : Any) -> None:
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global OPTIONS
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OPTIONS[key] = value
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def register_args(program : ArgumentParser) -> None:
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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)
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def apply_args(program : ArgumentParser) -> None:
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args = program.parse_args()
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frame_processors_globals.face_swapper_model = args.face_swapper_model
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if args.face_swapper_model == 'blendswap_256':
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facefusion.globals.face_recognizer_model = 'arcface_blendswap'
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if args.face_swapper_model == 'inswapper_128' or args.face_swapper_model == 'inswapper_128_fp16':
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facefusion.globals.face_recognizer_model = 'arcface_inswapper'
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if args.face_swapper_model == 'simswap_256' or args.face_swapper_model == 'simswap_512_unofficial':
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facefusion.globals.face_recognizer_model = 'arcface_simswap'
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def pre_check() -> bool:
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if not facefusion.globals.skip_download:
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download_directory_path = resolve_relative_path('../.assets/models')
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model_url = get_options('model').get('url')
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conditional_download(download_directory_path, [ model_url ])
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return True
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def pre_process(mode : ProcessMode) -> bool:
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model_url = get_options('model').get('url')
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model_path = get_options('model').get('path')
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if not facefusion.globals.skip_download and not is_download_done(model_url, model_path):
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update_status(wording.get('model_download_not_done') + wording.get('exclamation_mark'), NAME)
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return False
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elif not is_file(model_path):
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update_status(wording.get('model_file_not_present') + wording.get('exclamation_mark'), NAME)
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return False
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if not is_image(facefusion.globals.source_path):
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update_status(wording.get('select_image_source') + wording.get('exclamation_mark'), NAME)
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return False
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elif not get_one_face(read_static_image(facefusion.globals.source_path)):
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update_status(wording.get('no_source_face_detected') + wording.get('exclamation_mark'), NAME)
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return False
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if mode in [ 'output', 'preview' ] and not is_image(facefusion.globals.target_path) and not is_video(facefusion.globals.target_path):
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update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME)
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return False
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if mode == 'output' and not facefusion.globals.output_path:
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update_status(wording.get('select_file_or_directory_output') + wording.get('exclamation_mark'), NAME)
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return False
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return True
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def post_process() -> None:
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clear_frame_processor()
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clear_model_matrix()
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clear_face_analyser()
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clear_content_analyser()
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read_static_image.cache_clear()
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def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
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frame_processor = get_frame_processor()
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model_template = get_options('model').get('template')
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model_size = get_options('model').get('size')
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model_type = get_options('model').get('type')
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crop_frame, affine_matrix = warp_face(temp_frame, target_face.kps, model_template, model_size)
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crop_frame = prepare_crop_frame(crop_frame)
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frame_processor_inputs = {}
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for frame_processor_input in frame_processor.get_inputs():
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if frame_processor_input.name == 'source':
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if model_type == 'blendswap':
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frame_processor_inputs[frame_processor_input.name] = prepare_source_frame(source_face)
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else:
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frame_processor_inputs[frame_processor_input.name] = prepare_source_embedding(source_face)
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if frame_processor_input.name == 'target':
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frame_processor_inputs[frame_processor_input.name] = crop_frame
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crop_frame = frame_processor.run(None, frame_processor_inputs)[0][0]
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crop_frame = normalize_crop_frame(crop_frame)
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temp_frame = paste_back(temp_frame, crop_frame, affine_matrix, facefusion.globals.face_mask_blur, facefusion.globals.face_mask_padding)
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return temp_frame
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def prepare_source_frame(source_face : Face) -> numpy.ndarray[Any, Any]:
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source_frame = read_static_image(facefusion.globals.source_path)
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source_frame, _ = warp_face(source_frame, source_face.kps, 'arcface_v2', (112, 112))
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source_frame = source_frame[:, :, ::-1] / 255.0
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source_frame = source_frame.transpose(2, 0, 1)
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source_frame = numpy.expand_dims(source_frame, axis = 0).astype(numpy.float32)
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return source_frame
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def prepare_source_embedding(source_face : Face) -> Embedding:
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model_type = get_options('model').get('type')
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if model_type == 'inswapper':
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model_matrix = get_model_matrix()
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source_embedding = source_face.embedding.reshape((1, -1))
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source_embedding = numpy.dot(source_embedding, model_matrix) / numpy.linalg.norm(source_embedding)
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else:
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source_embedding = source_face.normed_embedding.reshape(1, -1)
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return source_embedding
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def prepare_crop_frame(crop_frame : Frame) -> Frame:
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model_mean = get_options('model').get('mean')
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model_standard_deviation = get_options('model').get('standard_deviation')
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crop_frame = crop_frame[:, :, ::-1] / 255.0
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crop_frame = (crop_frame - model_mean) / model_standard_deviation
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crop_frame = crop_frame.transpose(2, 0, 1)
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crop_frame = numpy.expand_dims(crop_frame, axis = 0).astype(numpy.float32)
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return crop_frame
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def normalize_crop_frame(crop_frame : Frame) -> Frame:
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crop_frame = crop_frame.transpose(1, 2, 0)
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crop_frame = (crop_frame * 255.0).round()
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crop_frame = crop_frame[:, :, ::-1].astype(numpy.uint8)
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return crop_frame
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def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame:
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if 'reference' in facefusion.globals.face_selector_mode:
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similar_faces = find_similar_faces(temp_frame, reference_face, facefusion.globals.reference_face_distance)
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if similar_faces:
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for similar_face in similar_faces:
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temp_frame = swap_face(source_face, similar_face, temp_frame)
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if 'one' in facefusion.globals.face_selector_mode:
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target_face = get_one_face(temp_frame)
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if target_face:
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temp_frame = swap_face(source_face, target_face, temp_frame)
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if 'many' in facefusion.globals.face_selector_mode:
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many_faces = get_many_faces(temp_frame)
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if many_faces:
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for target_face in many_faces:
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temp_frame = swap_face(source_face, target_face, temp_frame)
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return temp_frame
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def process_frames(source_path : str, temp_frame_paths : List[str], update_progress : Update_Process) -> None:
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source_face = get_one_face(read_static_image(source_path))
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reference_face = get_face_reference() if 'reference' in facefusion.globals.face_selector_mode else None
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for temp_frame_path in temp_frame_paths:
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temp_frame = read_image(temp_frame_path)
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result_frame = process_frame(source_face, reference_face, temp_frame)
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write_image(temp_frame_path, result_frame)
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update_progress()
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def process_image(source_path : str, target_path : str, output_path : str) -> None:
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source_face = get_one_face(read_static_image(source_path))
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target_frame = read_static_image(target_path)
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reference_face = get_one_face(target_frame, facefusion.globals.reference_face_position) if 'reference' in facefusion.globals.face_selector_mode else None
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result_frame = process_frame(source_face, reference_face, target_frame)
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write_image(output_path, result_frame)
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def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
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frame_processors.multi_process_frames(source_path, temp_frame_paths, process_frames)
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