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import gradio as gr
import argparse
from realesrgan import RealESRGANer
from realesrgan.archs.srvgg_arch import SRVGGNetCompact
import os
from basicsr.archs.rrdbnet_arch import RRDBNet
from basicsr.utils.download_util import load_file_from_url
def Generate(img, model_name):
global output
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, default='inputs', help='Input image or folder')
parser.add_argument('-o', '--output', type=str, default='results', help='Output folder')
parser.add_argument(
'-dn',
'--denoise_strength',
type=float,
default=0.5,
help=('Denoise strength. 0 for weak denoise (keep noise), 1 for strong denoise ability. '
'Only used for the realesr-general-x4v3 model'))
parser.add_argument('-s', '--outscale', type=float, default=4, help='The final upsampling scale of the image')
parser.add_argument(
'--model_path', type=str, default=None, help='[Option] Model path. Usually, you do not need to specify it')
parser.add_argument('--suffix', type=str, default='out', help='Suffix of the restored image')
parser.add_argument('-t', '--tile', type=int, default=0, help='Tile size, 0 for no tile during testing')
parser.add_argument('--tile_pad', type=int, default=10, help='Tile padding')
parser.add_argument('--pre_pad', type=int, default=0, help='Pre padding size at each border')
parser.add_argument('--face_enhance', action='store_true',help='Use GFPGAN to enhance face')
parser.add_argument(
'--fp32', action='store_true',default=True,help='Use fp32 precision during inference. Default: fp16 (half precision).')
parser.add_argument(
'--alpha_upsampler',
type=str,
default='realesrgan',
help='The upsampler for the alpha channels. Options: realesrgan | bicubic')
parser.add_argument(
'--ext',
type=str,
default='auto',
help='Image extension. Options: auto | jpg | png, auto means using the same extension as inputs')
parser.add_argument(
'-g', '--gpu-id', type=int, default=None, help='gpu device to use (default=None) can be 0,1,2 for multi-gpu')
args = parser.parse_args()
if model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
elif model_name == 'RealESRNet_x4plus': # x4 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
elif model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
elif model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model
model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
netscale = 2
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
elif model_name == 'realesr-animevideov3': # x4 VGG-style model (XS size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
netscale = 4
file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth']
elif model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
netscale = 4
file_url = [
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
]
model_path = os.path.join('weights', model_name + '.pth')
print(model_path)
if not os.path.isfile(model_path):
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
for url in file_url:
# model_path will be updated
model_path = load_file_from_url(
url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
dni_weight = None
if model_name == 'realesr-general-x4v3' and args.denoise_strength != 1:
wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
model_path = [model_path, wdn_model_path]
dni_weight = [args.denoise_strength, 1 - args.denoise_strength]
# restorer
upsampler = RealESRGANer(
scale=netscale,
model_path=model_path,
dni_weight=dni_weight,
model=model,
tile=args.tile,
tile_pad=args.tile_pad,
pre_pad=args.pre_pad,
half=not args.fp32,
gpu_id=args.gpu_id)
if args.face_enhance: # Use GFPGAN for face enhancement
from gfpgan import GFPGANer
face_enhancer = GFPGANer(
model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
upscale=args.outscale,
arch='clean',
channel_multiplier=2,
bg_upsampler=upsampler)
os.makedirs(args.output, exist_ok=True)
try:
if args.face_enhance:
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
else:
output, _ = upsampler.enhance(img, outscale=args.outscale)
print("生成成功")
except RuntimeError as error:
print('Error', error)
print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
output = None
return output
with gr.Blocks() as demo:
gr.Markdown(
"""
# <center> Real-ESRGAN 在线体验程序
""")
gr.Markdown("""
1. **项目模型运行在CPU上,等待时间略长**
2. **原工程项目旨在对图片就行修复**
3. **项目源地址为:[Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN)**
""")
with gr.Row():
with gr.Column():
img = gr.Image(type="numpy",label = "输入图片")
model_name = gr.Dropdown(["RealESRGAN_x4plus","RealESRGAN_x4plus_anime_6B","RealESRGAN_x2plus",
"realesr-animevideov3","realesr-general-x4v3"],info="选择模型")
with gr.Column():
img_out = gr.Image(type="numpy",label = "输出图片")
btn = gr.Button("Generate")
btn.click(Generate, inputs=[img,model_name], outputs=[img_out])
if __name__ == "__main__":
demo.launch() |