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import os |
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import gradio as gr |
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from PIL import Image, ImageOps |
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import numpy as np |
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os.system("pip install opencv-python") |
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os.system("pip install torch") |
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os.system("pip install jinja2") |
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if not os.path.exists("data"): |
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os.mkdir("data") |
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if not os.path.exists("results"): |
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os.mkdir("results") |
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def infer(img): |
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width, height = img.size |
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res=np.ones_like((width, height,3)) |
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print(res.shape) |
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print(width) |
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img.save("./data/data.png") |
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img.save("./results/data.png") |
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os.system('python main_test_swinir.py') |
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res=Image.open("./results/data.png") |
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return "./results/data.png","./results/data.png" |
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inputs = [gr.inputs.Image(type='pil', label="Original Image")] |
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outputs = [gr.outputs.Image(type="file", label="output"), gr.outputs.File(label="download")] |
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title = "SwinIR: Image Restoration Using Swin Transformer,Super-Resolution part " |
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description = "Gradio demo for SwinIR: Super-Resolution part. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below." |
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article = "<p style='text-align: center'><a href='https://arxiv.org/pdf/2108.10257.pdf' target='_blank'>SwinIR: Image Restoration Using Swin Transformer</a> | <a href='https://github.com/JingyunLiang/SwinIR' target='_blank'>Github Repo</a></p>" |
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examples = [ |
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['butterfly.png'] |
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] |
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gr.Interface(infer, inputs, outputs, title=title, description=description, article=article, examples=examples).launch( |
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enable_queue=True, cache_examples=True) |
