from typing import Any, List, Callable import cv2 import threading from basicsr.archs.rrdbnet_arch import RRDBNet from realesrgan import RealESRGANer import DeepFakeAI.processors.frame.core as frame_processors from DeepFakeAI.typing import Frame, Face from DeepFakeAI.utilities import conditional_download, resolve_relative_path FRAME_PROCESSOR = None THREAD_SEMAPHORE = threading.Semaphore() THREAD_LOCK = threading.Lock() NAME = 'FACEFUSION.FRAME_PROCESSOR.FRAME_ENHANCER' def get_frame_processor() -> Any: global FRAME_PROCESSOR with THREAD_LOCK: if FRAME_PROCESSOR is None: model_path = resolve_relative_path('../.assets/models/RealESRGAN_x4plus.pth') FRAME_PROCESSOR = RealESRGANer( model_path = model_path, model = RRDBNet( num_in_ch = 3, num_out_ch = 3, num_feat = 64, num_block = 23, num_grow_ch = 32, scale = 4 ), device = frame_processors.get_device(), tile = 512, tile_pad = 32, pre_pad = 0, scale = 4 ) 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/DeepFakeAI/DeepFakeAI-assets/releases/download/models/RealESRGAN_x4plus.pth']) return True def pre_process() -> bool: return True def post_process() -> None: clear_frame_processor() def enhance_frame(temp_frame : Frame) -> Frame: with THREAD_SEMAPHORE: temp_frame, _ = get_frame_processor().enhance(temp_frame, outscale = 1) return temp_frame def process_frame(source_face : Face, reference_face : Face, temp_frame : Frame) -> Frame: return enhance_frame(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 = process_frame(None, None, target_frame) cv2.imwrite(output_path, result) def process_video(source_path : str, temp_frame_paths : List[str]) -> None: frame_processors.process_video(None, temp_frame_paths, process_frames)