106 lines
4.2 KiB
Python
106 lines
4.2 KiB
Python
from typing import Any, List, Callable
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import cv2
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import insightface
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import threading
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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.core import update_status
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from facefusion.face_analyser import get_one_face, get_many_faces, find_similar_faces
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from facefusion.face_reference import get_face_reference, set_face_reference
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from facefusion.typing import Face, Frame
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from facefusion.utilities import conditional_download, resolve_relative_path, is_image, is_video
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FRAME_PROCESSOR = None
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THREAD_LOCK = threading.Lock()
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NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_SWAPPER'
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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 = resolve_relative_path('../.assets/models/inswapper_128.onnx')
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FRAME_PROCESSOR = insightface.model_zoo.get_model(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 pre_check() -> bool:
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download_directory_path = resolve_relative_path('../.assets/models')
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conditional_download(download_directory_path, ['https://github.com/facefusion/facefusion-assets/releases/download/models/inswapper_128.onnx'])
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return True
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def pre_process() -> bool:
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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(cv2.imread(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 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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return True
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def post_process() -> None:
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clear_frame_processor()
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def swap_face(source_face : Face, target_face : Face, temp_frame : Frame) -> Frame:
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return get_frame_processor().get(temp_frame, target_face, source_face, paste_back = True)
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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_recognition:
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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 'many' in facefusion.globals.face_recognition:
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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: Callable[[], None]) -> None:
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source_face = get_one_face(cv2.imread(source_path))
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reference_face = get_face_reference() if 'reference' in facefusion.globals.face_recognition else None
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for temp_frame_path in temp_frame_paths:
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temp_frame = cv2.imread(temp_frame_path)
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result_frame = process_frame(source_face, reference_face, temp_frame)
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cv2.imwrite(temp_frame_path, result_frame)
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if update:
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update()
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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(cv2.imread(source_path))
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target_frame = cv2.imread(target_path)
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reference_face = get_one_face(target_frame, facefusion.globals.reference_face_position) if 'reference' in facefusion.globals.face_recognition else None
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result_frame = process_frame(source_face, reference_face, target_frame)
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cv2.imwrite(output_path, result_frame)
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def process_video(source_path : str, temp_frame_paths : List[str]) -> None:
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conditional_set_face_reference(temp_frame_paths)
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frame_processors.process_video(source_path, temp_frame_paths, process_frames)
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def conditional_set_face_reference(temp_frame_paths : List[str]) -> None:
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if 'reference' in facefusion.globals.face_recognition and not get_face_reference():
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reference_frame = cv2.imread(temp_frame_paths[facefusion.globals.reference_frame_number])
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reference_face = get_one_face(reference_frame, facefusion.globals.reference_face_position)
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set_face_reference(reference_face)
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