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import numpy as np
import gradio as gr
from Segmentation.segmentation import get_mask,replace_sofa
from StyleTransfer.styleTransfer import resize_sofa,resize_style,create_styledSofa
from PIL import Image
def style_sofa(input_img: np.ndarray, style_img: np.ndarray):
"""
Styles (all) the sofas in the image to the given style.
This function uses a transformer to combine the image with the desired style according
to a generated mask of the sofas in the image.
Input:
input_img = image containing a sofa
style_img = image containing a style
Return:
new_sofa = image containing the styled sofa
"""
# preprocess input images to be (640,640) squares to fit requirements of the segmentation model
resized_img = resize_sofa(input_img)
resized_style = resize_style(style_img)
# generate mask for image
mask = get_mask(resized_img)
styled_sofa = create_styledSofa(resized_img,resized_style)
new_sofa = replace_sofa(resized_img,mask,styled_sofa)
return new_sofa
image = gr.inputs.Image()
style = gr.inputs.Image()
demo = gr.Interface(
style_sofa,
[image,style],
'image',
examples=[
['input/sofa_example1.jpg','input/style_example1.jpg'],
['input/sofa_example1.jpg','input/style_example2.jpg'],
['input/sofa_example1.jpg','input/style_example3.jpg'],
['input/sofa_example1.jpg','input/style_example4.jpg'],
['input/sofa_example1.jpg','input/style_example5.jpg'],
],
title="Style your sofa",
description="🛋 Customize your sofa to your wildest dreams! 🛋",
)
if __name__ == "__main__":
demo.launch(share=True)
#https://github.com/dhawan98/Post-Processing-of-Image-Segmentation-using-CRF |