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:tada: First Commit
Browse files- Examples/gta_0.jpg +0 -0
- Examples/gta_1.jpg +0 -0
- Examples/gta_2.jpg +0 -0
- Examples/gta_3.jpg +0 -0
- Examples/gta_4.jpg +0 -0
- Examples/real_0.jpg +0 -0
- Examples/real_1.jpg +0 -0
- Examples/real_2.jpg +0 -0
- Examples/real_3.jpg +0 -0
- Examples/real_4.jpg +0 -0
- app.py +31 -0
- requirements.txt +1 -0
Examples/gta_0.jpg
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Examples/gta_1.jpg
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Examples/gta_2.jpg
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Examples/gta_3.jpg
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Examples/gta_4.jpg
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Examples/real_0.jpg
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Examples/real_1.jpg
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Examples/real_2.jpg
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Examples/real_3.jpg
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Examples/real_4.jpg
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app.py
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from random import choices
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import numpy as np
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import gradio as gr
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from glob import glob
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from huggingface_hub import from_pretrained_keras
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model = from_pretrained_keras('Jorgvt/CycleGAN_GTA_REAL', compile=False)
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def transform(img, direction):
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img = (img / 127.5) - 1
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if direction==0:
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pred = model.generator_g.predict(img[None,:,:,:])[0]
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else:
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pred = model.generator_f.predict(img[None,:,:,:])[0]
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pred = (pred-pred.min())/(pred.max()-pred.min())
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pred = (pred * 255).astype(np.uint8)
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return pred
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examples_gta = [[path, 'GTA->REAL'] for path in glob('Examples/gta*')]
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examples_real = [[path, 'REAL->GTA'] for path in glob('Examples/real*')]
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examples = [*examples_gta, *examples_real]
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demo = gr.Interface(fn=transform,
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inputs=[gr.inputs.Image(shape=(256, 256), type='numpy'),
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gr.inputs.Radio(choices=['GTA->REAL', 'REAL->GTA'],
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type='index')],
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outputs=gr.outputs.Image(type='numpy'),
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examples=examples)
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if __name__ == '__main__':
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demo.launch()
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requirements.txt
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tensorflow>2.6
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