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| from typing import Any, List, Callable | |
| import cv2 | |
| import threading | |
| from gfpgan.utils import GFPGANer | |
| import DeepFakeAI.globals | |
| import DeepFakeAI.processors.frame.core as frame_processors | |
| from DeepFakeAI import wording | |
| from DeepFakeAI.core import update_status | |
| from DeepFakeAI.face_analyser import get_many_faces | |
| from DeepFakeAI.typing import Frame, Face | |
| from DeepFakeAI.utilities import conditional_download, resolve_relative_path, is_image, is_video | |
| FRAME_PROCESSOR = None | |
| THREAD_SEMAPHORE = threading.Semaphore() | |
| THREAD_LOCK = threading.Lock() | |
| NAME = 'FACEFUSION.FRAME_PROCESSOR.FACE_ENHANCER' | |
| def get_frame_processor() -> Any: | |
| global FRAME_PROCESSOR | |
| with THREAD_LOCK: | |
| if FRAME_PROCESSOR is None: | |
| model_path = resolve_relative_path('../.assets/models/GFPGANv1.4.pth') | |
| FRAME_PROCESSOR = GFPGANer( | |
| model_path = model_path, | |
| upscale = 1, | |
| device = frame_processors.get_device() | |
| ) | |
| return FRAME_PROCESSOR | |
| def clear_frame_processor() -> None: | |
| global FRAME_PROCESSOR | |
| FRAME_PROCESSOR = None | |
| def pre_check() -> bool: | |
| download_directory_path = resolve_relative_path('../.assets/models') | |
| conditional_download(download_directory_path, ['https://github.com/facefusion/facefusion-assets/releases/download/models/GFPGANv1.4.pth']) | |
| return True | |
| def pre_process() -> bool: | |
| if not is_image(DeepFakeAI.globals.target_path) and not is_video(DeepFakeAI.globals.target_path): | |
| update_status(wording.get('select_image_or_video_target') + wording.get('exclamation_mark'), NAME) | |
| return False | |
| return True | |
| def post_process() -> None: | |
| clear_frame_processor() | |
| def enhance_face(target_face : Face, temp_frame : Frame) -> Frame: | |
| start_x, start_y, end_x, end_y = map(int, target_face['bbox']) | |
| padding_x = int((end_x - start_x) * 0.5) | |
| padding_y = int((end_y - start_y) * 0.5) | |
| start_x = max(0, start_x - padding_x) | |
| start_y = max(0, start_y - padding_y) | |
| end_x = max(0, end_x + padding_x) | |
| end_y = max(0, end_y + padding_y) | |
| crop_frame = temp_frame[start_y:end_y, start_x:end_x] | |
| if crop_frame.size: | |
| with THREAD_SEMAPHORE: | |
| _, _, crop_frame = get_frame_processor().enhance( | |
| crop_frame, | |
| paste_back = True | |
| ) | |
| temp_frame[start_y:end_y, start_x:end_x] = crop_frame | |
| 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: Callable[[], None]) -> None: | |
| for temp_frame_path in temp_frame_paths: | |
| temp_frame = cv2.imread(temp_frame_path) | |
| result_frame = process_frame(None, None, temp_frame) | |
| cv2.imwrite(temp_frame_path, result_frame) | |
| if update: | |
| update() | |
| def process_image(source_path : str, target_path : str, output_path : str) -> None: | |
| target_frame = cv2.imread(target_path) | |
| result_frame = process_frame(None, None, target_frame) | |
| cv2.imwrite(output_path, result_frame) | |
| def process_video(source_path : str, temp_frame_paths : List[str]) -> None: | |
| DeepFakeAI.processors.frame.core.process_video(None, temp_frame_paths, process_frames) | |