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']) conditional_download(download_directory_path, ['https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/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)