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import os |
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import gradio as gr |
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from PIL import Image |
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import torch |
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os.system('wget https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth -P experiments/pretrained_models') |
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def inference(img): |
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os.system('mkdir test') |
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basewidth = 256 |
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wpercent = (basewidth/float(img.size[0])) |
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hsize = int((float(img.size[1])*float(wpercent))) |
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img = img.resize((basewidth,hsize), Image.ANTIALIAS) |
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img.save("test/1.jpg", "JPEG") |
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os.system('python main_test_swinir.py --task real_sr --model_path experiments/pretrained_models/003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth --folder_lq test --scale 4') |
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return 'results/swinir_real_sr_x4/1_SwinIR.jpg' |
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title = "Real-ESRGAN" |
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description = "Gradio demo for Real-ESRGAN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please click submit only once" |
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.10833'>Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data</a> | <a href='https://github.com/xinntao/Real-ESRGAN'>Github Repo</a></p>" |
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gr.Interface( |
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inference, |
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[gr.inputs.Image(type="pil", label="Input")], |
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gr.outputs.Image(type="file", label="Output"), |
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title=title, |
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description=description, |
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article=article, |
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enable_queue=True |
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).launch(debug=True) |