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import os
from basicsr.utils import imwrite
from gfpgan import GFPGANer
from tqdm import tqdm
def enhancer(images, method='gfpgan'):
# ------------------------ set up GFPGAN restorer ------------------------
if method == 'gfpgan':
arch = 'clean'
channel_multiplier = 2
model_name = 'GFPGANv1.4'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth'
elif method == 'RestoreFormer':
arch = 'RestoreFormer'
channel_multiplier = 2
model_name = 'RestoreFormer'
url = 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth'
elif method == 'codeformer':
arch = 'CodeFormer'
channel_multiplier = 2
model_name = 'CodeFormer'
url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
else:
raise ValueError(f'Wrong model version {method}.')
# determine model paths
model_path = os.path.join('experiments/pretrained_models', model_name + '.pth')
if not os.path.isfile(model_path):
model_path = os.path.join('checkpoints', model_name + '.pth')
if not os.path.isfile(model_path):
# download pre-trained models from url
model_path = url
restorer = GFPGANer(
model_path=model_path,
upscale=2,
arch=arch,
channel_multiplier=channel_multiplier,
bg_upsampler=None)
# ------------------------ restore ------------------------
restored_img = []
for idx in tqdm(range(len(images)), 'Face Enhancer:'):
# restore faces and background if necessary
cropped_faces, restored_faces, _ = restorer.enhance(
images[idx],
has_aligned=True,
only_center_face=False,
paste_back=True,
weight=0.5)
restored_img += restored_faces
return restored_img |