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