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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 basicsr.archs.rrdbnet_arch import RRDBNet |
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from realesrgan import RealESRGANer |
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import DeepFakeAI.processors.frame.core as frame_processors |
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from DeepFakeAI.typing import Frame, Face |
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from DeepFakeAI.utilities import conditional_download, resolve_relative_path |
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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.FRAME_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/RealESRGAN_x4plus.pth') |
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FRAME_PROCESSOR = RealESRGANer( |
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model_path = model_path, |
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model = RRDBNet( |
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num_in_ch = 3, |
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num_out_ch = 3, |
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num_feat = 64, |
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num_block = 23, |
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num_grow_ch = 32, |
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scale = 4 |
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), |
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device = frame_processors.get_device(), |
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tile = 512, |
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tile_pad = 32, |
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pre_pad = 0, |
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scale = 4 |
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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/RealESRGAN_x4plus.pth']) |
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return True |
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def pre_process() -> bool: |
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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_frame(temp_frame : Frame) -> Frame: |
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with THREAD_SEMAPHORE: |
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temp_frame, _ = get_frame_processor().enhance(temp_frame, outscale = 1) |
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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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return enhance_frame(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 = process_frame(None, None, target_frame) |
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cv2.imwrite(output_path, result) |
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def process_video(source_path : str, temp_frame_paths : List[str]) -> None: |
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frame_processors.process_video(None, temp_frame_paths, process_frames) |
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