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/DeepFakeAI/DeepFakeAI-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)