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from typing import Any, List, Callable
import cv2
import threading
import gfpgan
import os
from huggingface_hub import hf_hub_download # Thêm import này
import torch
import roop.globals
import roop.processors.frame.core
from roop.core import update_status
from roop.face_analyser import get_one_face
from roop.typing import Frame, Face
from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
FACE_ENHANCER = None
THREAD_SEMAPHORE = threading.Semaphore()
THREAD_LOCK = threading.Lock()
NAME = 'ROOP.FACE-ENHANCER'
frame_name = 'face_enhancer'
# Lấy token từ biến môi trường
token = os.getenv('HF_TOKEN')
if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'
def get_face_enhancer() -> Any:
global FACE_ENHANCER
with THREAD_LOCK:
if FACE_ENHANCER is None:
# Tải model từ Hugging Face Hub
model_path = hf_hub_download(repo_id="Arrcttacsrks/TencentARC_GFPGAN", filename="GFPGANv1.4.pth", token=token)
FACE_ENHANCER = gfpgan.GFPGANer(model_path=model_path, upscale=1, device=device) # type: ignore[attr-defined]
return FACE_ENHANCER
def pre_check() -> bool:
# Không cần điều kiện download nữa vì đã tải mô hình trực tiếp từ Hugging Face Hub
return True
def pre_start() -> bool:
if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path):
update_status('Select an image or video for target path.', NAME)
return False
return True
def post_process() -> None:
global FACE_ENHANCER
FACE_ENHANCER = None
def enhance_face(temp_frame: Frame) -> Frame:
with THREAD_SEMAPHORE:
_, _, temp_frame = get_face_enhancer().enhance(
temp_frame,
paste_back=True
)
return temp_frame
def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
target_face = get_one_face(temp_frame)
if target_face:
temp_frame = enhance_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 = process_frame(None, temp_frame)
cv2.imwrite(temp_frame_path, result)
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, target_frame)
cv2.imwrite(output_path, result)
def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
roop.processors.frame.core.process_video(None, temp_frame_paths, process_frames)
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