110 lines
3.2 KiB
Python
110 lines
3.2 KiB
Python
from typing import Any, List
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import cv2
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import threading
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import gfpgan
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import os
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import modules.globals
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import modules.processors.frame.core
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from modules.core import update_status
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from modules.face_analyser import get_one_face
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from modules.typing import Frame, Face
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import platform
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import torch
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from modules.utilities import (
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conditional_download,
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is_image,
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is_video,
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)
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FACE_ENHANCER = None
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THREAD_SEMAPHORE = threading.Semaphore()
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THREAD_LOCK = threading.Lock()
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NAME = "DLC.FACE-ENHANCER"
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abs_dir = os.path.dirname(os.path.abspath(__file__))
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models_dir = os.path.join(
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os.path.dirname(os.path.dirname(os.path.dirname(abs_dir))), "models"
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)
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def pre_check() -> bool:
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download_directory_path = models_dir
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conditional_download(
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download_directory_path,
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[
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"https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth"
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],
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)
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return True
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def pre_start() -> bool:
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if not is_image(modules.globals.target_path) and not is_video(
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modules.globals.target_path
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):
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update_status("Select an image or video for target path.", NAME)
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return False
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return True
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def get_face_enhancer() -> Any:
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global FACE_ENHANCER
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with THREAD_LOCK:
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if FACE_ENHANCER is None:
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model_path = os.path.join(models_dir, "GFPGANv1.4.pth")
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match platform.system():
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case "Darwin": # Mac OS
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if torch.backends.mps.is_available():
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mps_device = torch.device("mps")
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FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=mps_device) # type: ignore[attr-defined]
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else:
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FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1) # type: ignore[attr-defined]
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case _: # Other OS
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FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1) # type: ignore[attr-defined]
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return FACE_ENHANCER
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def enhance_face(temp_frame: Frame) -> Frame:
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with THREAD_SEMAPHORE:
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_, _, temp_frame = get_face_enhancer().enhance(temp_frame, paste_back=True)
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return temp_frame
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def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
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target_face = get_one_face(temp_frame)
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if target_face:
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temp_frame = enhance_face(temp_frame)
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return temp_frame
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def process_frames(
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source_path: str, temp_frame_paths: List[str], progress: Any = None
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) -> 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 = process_frame(None, temp_frame)
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cv2.imwrite(temp_frame_path, result)
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if progress:
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progress.update(1)
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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, 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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modules.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
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def process_frame_v2(temp_frame: Frame) -> Frame:
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target_face = get_one_face(temp_frame)
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if target_face:
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temp_frame = enhance_face(temp_frame)
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return temp_frame
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