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app.py
CHANGED
@@ -42,21 +42,23 @@ def infer(image):
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"RGB"
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)
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out = {}
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out.update( {'input image size':
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out.update( {'output image size':
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return (pd.DataFrame(data=out.values(), index=out.keys()).transpose(), img,out_img)
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1609.05158' target='_blank'>Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network</a></p><center> <a href='https://keras.io/examples/vision/super_resolution_sub_pixel/' target='_blank'>Image Super-Resolution using an Efficient Sub-Pixel CNN</a></p>"
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# examples = [['examples/2000-04-28-18-21-24_L5_rgb.jpg'],['examples/2000-08-02-18-23-18_L5_rgb.jpg'],
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# ['examples/2000-08-18-18-23-46_L5_rgb.jpg'],['examples/2000-09-19-18-24-18_L5_rgb.jpg'],['examples/2000-10-21-18-24-43_L5_rgb.jpg']]
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examples= [[l] for l in glob('examples/tiles/*.jpg')]
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out1 = gr.outputs.Dataframe(label='Summary', headers=["Input (px)", "Output (px)"], type='pandas')
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out2 = gr.outputs.Image(label="Cropped input image", type='pil')
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out3 = gr.outputs.Image(label="Super-resolution x3 image", type='pil')
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iface = gr.Interface(
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fn=infer,
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@@ -64,6 +66,6 @@ iface = gr.Interface(
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description = "This space is a demo of Satellite image Super-Resolution using a Sub-Pixel Convolutional Neural Network",
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article = article,
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inputs=gr.inputs.Image(label="Input Image"),
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outputs=[out1,out2,out3],
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examples=examples,
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).launch()
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"RGB"
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)
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out_img.save('output.png')
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out = {}
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out.update( {'input image size x': nx } )
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out.update( {'output image size x': nxo } )
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out.update( {'input image size y': ny } )
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out.update( {'output image size y': nyo } )
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return (pd.DataFrame(data=out.values(), index=out.keys()).transpose(), img,out_img, 'output.png')
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1609.05158' target='_blank'>Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network</a></p><center> <a href='https://keras.io/examples/vision/super_resolution_sub_pixel/' target='_blank'>Image Super-Resolution using an Efficient Sub-Pixel CNN</a></p>"
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examples= [[l] for l in glob('examples/tiles/*.jpg')]
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out1 = gr.outputs.Dataframe(label='Summary', headers=["Input X (px)", "Output X (px)", "Input Y (px)", "Output Y (px)"], type='pandas')
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out2 = gr.outputs.Image(label="Cropped input image", type='pil')
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out3 = gr.outputs.Image(label="Super-resolution x3 image", type='pil')
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out4 = gr.outputs.File(label='Click to download super-resolved image')
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iface = gr.Interface(
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fn=infer,
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description = "This space is a demo of Satellite image Super-Resolution using a Sub-Pixel Convolutional Neural Network",
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article = article,
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inputs=gr.inputs.Image(label="Input Image"),
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outputs=[out1,out2,out3,out4],
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examples=examples,
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).launch()
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