File size: 1,747 Bytes
2fe55e2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
import cv2
from loguru import logger
from lama_cleaner.helper import download_model
from lama_cleaner.plugins.base_plugin import BasePlugin
class RestoreFormerPlugin(BasePlugin):
name = "RestoreFormer"
def __init__(self, device, upscaler=None):
super().__init__()
from .gfpganer import MyGFPGANer
url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth"
model_md5 = "eaeeff6c4a1caa1673977cb374e6f699"
model_path = download_model(url, model_md5)
logger.info(f"RestoreFormer model path: {model_path}")
import facexlib
if hasattr(facexlib.detection.retinaface, "device"):
facexlib.detection.retinaface.device = device
self.face_enhancer = MyGFPGANer(
model_path=model_path,
upscale=1,
arch="RestoreFormer",
channel_multiplier=2,
device=device,
bg_upsampler=upscaler.model if upscaler is not None else None,
)
def __call__(self, rgb_np_img, files, form):
weight = 0.5
bgr_np_img = cv2.cvtColor(rgb_np_img, cv2.COLOR_RGB2BGR)
logger.info(f"RestoreFormer input shape: {bgr_np_img.shape}")
_, _, bgr_output = self.face_enhancer.enhance(
bgr_np_img,
has_aligned=False,
only_center_face=False,
paste_back=True,
weight=weight,
)
logger.info(f"RestoreFormer output shape: {bgr_output.shape}")
return bgr_output
def check_dep(self):
try:
import gfpgan
except ImportError:
return (
"gfpgan is not installed, please install it first. pip install gfpgan"
)
|