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import os
import sys
from torchvision.transforms import functional
sys.modules["torchvision.transforms.functional_tensor"] = functional
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from gfpgan.utils import GFPGANer
from realesrgan.utils import RealESRGANer
import torch
import cv2
import gradio as gr
#Download Required Models
if not os.path.exists('realesr-general-x4v3.pth'):
os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
if not os.path.exists('GFPGANv1.2.pth'):
os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
if not os.path.exists('GFPGANv1.3.pth'):
os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
if not os.path.exists('GFPGANv1.4.pth'):
os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
if not os.path.exists('RestoreFormer.pth'):
os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
model_path = 'realesr-general-x4v3.pth'
half = True if torch.cuda.is_available() else False
upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
# Save Image to the Directory
# os.makedirs('output', exist_ok=True)
def upscaler(img, version, scale):
try:
img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
if len(img.shape) == 3 and img.shape[2] == 4:
img_mode = 'RGBA'
elif len(img.shape) == 2:
img_mode = None
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
else:
img_mode = None
h, w = img.shape[0:2]
if h < 300:
img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
face_enhancer = GFPGANer(
model_path=f'{version}.pth',
upscale=2,
arch='RestoreFormer' if version=='RestoreFormer' else 'clean',
channel_multiplier=2,
bg_upsampler=upsampler
)
try:
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
except RuntimeError as error:
print('Error', error)
try:
if scale != 2:
interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
h, w = img.shape[0:2]
output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
except Exception as error:
print('wrong scale input.', error)
# Save Image to the Directory
# ext = os.path.splitext(os.path.basename(str(img)))[1]
# if img_mode == 'RGBA':
# ext = 'png'
# else:
# ext = 'jpg'
#
# save_path = f'output/out.{ext}'
# cv2.imwrite(save_path, output)
# return output, save_path
output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
return output
except Exception as error:
print('global exception', error)
return None, None
if __name__ == "__main__":
title = "Image Upscaler & Restoring [GFPGAN Algorithm]"
demo = gr.Interface(
upscaler, [
gr.Image(type="filepath", label="Input"),
gr.Radio(['GFPGANv1.2', 'GFPGANv1.3', 'GFPGANv1.4', 'RestoreFormer'], type="value", label='version'),
gr.Number(label="Rescaling factor"),
], [
gr.Image(type="numpy", label="Output"),
],
title=title,
allow_flagging="never"
)
demo.queue()
demo.launch() |