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| from typing import Any, List, Callable | |
| import cv2 | |
| import threading | |
| import gfpgan | |
| import roop.globals | |
| import roop.processors.frame.core | |
| from roop.core import update_status | |
| from roop.face_analyser import get_one_face | |
| from roop.typing import Frame, Face | |
| from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video | |
| import torch | |
| FACE_ENHANCER = None | |
| THREAD_SEMAPHORE = threading.Semaphore() | |
| THREAD_LOCK = threading.Lock() | |
| NAME = 'ROOP.FACE-ENHANCER' | |
| frame_name = 'face_enhancer' | |
| if torch.cuda.is_available(): | |
| device='cuda' | |
| else: | |
| device='cpu' | |
| def get_face_enhancer() -> Any: | |
| global FACE_ENHANCER | |
| with THREAD_LOCK: | |
| if FACE_ENHANCER is None: | |
| model_path = resolve_relative_path('../models/GFPGANv1.4.pth') | |
| # todo: set models path https://github.com/TencentARC/GFPGAN/issues/399 | |
| FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1,device=device) # type: ignore[attr-defined] | |
| return FACE_ENHANCER | |
| def pre_check() -> bool: | |
| download_directory_path = resolve_relative_path('../models') | |
| conditional_download(download_directory_path, ['https://huggingface.co/henryruhs/roop/resolve/main/GFPGANv1.4.pth']) | |
| return True | |
| def pre_start() -> bool: | |
| if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path): | |
| update_status('Select an image or video for target path.', NAME) | |
| return False | |
| return True | |
| def post_process() -> None: | |
| global FACE_ENHANCER | |
| FACE_ENHANCER = None | |
| def enhance_face(temp_frame: Frame) -> Frame: | |
| with THREAD_SEMAPHORE: | |
| _, _, temp_frame = get_face_enhancer().enhance( | |
| temp_frame, | |
| paste_back=True | |
| ) | |
| return temp_frame | |
| def process_frame(source_face: Face, temp_frame: Frame) -> Frame: | |
| target_face = get_one_face(temp_frame) | |
| if target_face: | |
| temp_frame = enhance_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 = process_frame(None, temp_frame) | |
| cv2.imwrite(temp_frame_path, result) | |
| if update: | |
| update() | |
| def process_image(source_path: str, target_path: str, output_path: str) -> None: | |
| target_frame = cv2.imread(target_path) | |
| result = process_frame(None, target_frame) | |
| cv2.imwrite(output_path, result) | |
| def process_video(source_path: str, temp_frame_paths: List[str]) -> None: | |
| roop.processors.frame.core.process_video(None, temp_frame_paths, process_frames) | |