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import gradio as gr
import os
from model.model import TextureSynthesisCNN
from model.utils import convert_tensor_to_PIL_image
def synth_image(image, epochs=10):
synthesizer = TextureSynthesisCNN(tex_exemplar_image=image)
output_tensor = synthesizer.synthesize_texture(num_epochs=int(epochs))
output_image = convert_tensor_to_PIL_image(output_tensor)
return output_image
demo = gr.Interface(
fn=synth_image,
inputs=[gr.Image(type="numpy"),
gr.Slider(label="Num epochs to optimize for", value=1, minimum=1, maximum=400, step=1, info="Optimizing for higher epochs gives better results. However, note that since this is a CNN based texture synthesiszer, it can take upto 5-10mins per synthesis.")],
outputs=[gr.Image(type="pil")],
flagging_options=["blurry", "incorrect"],
examples=[
[os.path.join(os.path.dirname(__file__), "images/blotchy_0025.png"), 10],
[os.path.join(os.path.dirname(__file__), "images/blotchy_0027.png"), 10],
[os.path.join(os.path.dirname(__file__), "images/cracked_0080.png"), 10],
[os.path.join(os.path.dirname(__file__), "images/scenery.png"), 10],
],
)
if __name__ == "__main__":
demo.launch()
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