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import numpy as np
import gradio as gr
from segmentation import get_mask,replace_sofa
from styleTransfer import create_styledSofa
from PIL import Image
def resize_sofa(img):
"""
This function adds padding to make the orignal image square and 640by640.
It also returns the orignal ratio of the image, such that it can be reverted later.
Parameters:
img = original image
Return:
im1 = squared image
box = parameters to later crop the image to it original ratio
"""
width, height = img.size
idx = np.argmin([width,height])
newsize = (640, 640) # parameters from test script
if idx==0:
img1 = Image.new(img.mode, (height, height), (255, 255, 255))
img1.paste(img, ((height-width)//2, 0))
box = ( newsize[0]*(1-width/height)//2,
0,
newsize[0]-newsize[0]*(1-width/height)//2,
newsize[1])
else:
img1 = Image.new(img.mode, (width, width), (255, 255, 255))
img1.paste(img, (0, (width-height)//2))
box = (0,
newsize[1]*(1-height/width)//2,
newsize[0],
newsize[1]-newsize[1]*(1-height/width)//2)
im1 = img1.resize(newsize)
return im1,box
def resize_style(img):
"""
This function generates a zoomed out version of the style image and resizes it to a 640by640 square.
Parameters:
img = image containing the style/pattern
Return:
dst = a zoomed-out and resized version of the pattern
"""
width, height = img.size
idx = np.argmin([width,height])
# Makes the image square by cropping
if idx==0:
top= (height-width)//2
bottom= height-(height-width)//2
left = 0
right= width
else:
left = (width-height)//2
right = width - (width-height)//2
top = 0
bottom = height
newsize = (640, 640) # parameters from test script
im1 = img.crop((left, top, right, bottom))
# Constructs a zoomed-out version
copies = 8
resize = (newsize[0]//copies,newsize[1]//copies)
dst = Image.new('RGB', (resize[0]*copies,resize[1]*copies))
im2 = im1.resize((resize))
for row in range(copies):
im2 = im2.transpose(Image.FLIP_LEFT_RIGHT)
for column in range(copies):
im2 = im2.transpose(Image.FLIP_TOP_BOTTOM)
dst.paste(im2, (resize[0]*row, resize[1]*column))
dst = dst.resize((newsize))
return dst
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
input_img,style_img = Image.fromarray(input_img),Image.fromarray(style_img)
resized_img,box = 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)
print(type(styled_sofa))
print(styled_sofa.shape)
# postprocess the final image
new_sofa = replace_sofa(resized_img,mask,styled_sofa)
new_sofa = new_sofa.crop(box)
return new_sofa
image = gr.inputs.Image()
style = gr.inputs.Image()
# Examples
example1 = ['sofa_example1.jpg','style_example1.jpg'],
example2 = ['sofa_example1.jpg','style_example2.jpg'],
example3 = ['sofa_example1.jpg','style_example3.jpg'],
example4 = ['sofa_example1.jpg','style_example4.jpg'],
example5 = ['sofa_example1.jpg','style_example5.jpg'],
demo = gr.Interface(
style_sofa,
inputs = [image,style],
outputs = 'image',
examples=[example1,example2,example3,example4,example5],
title="🛋 Style your sofa 🛋 ",
description="Customize your sofa to your wildest dreams!\
\nProvide a picture of your sofa and a desired pattern\
or choose one of the examples below",
# article="**References**\n\n"
# "<a href='https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization' target='_blank'>1. Tutorial to implement Fast Neural Style Transfer using the pretrained model from TensorFlow Hub</a> \n"
# "<a href='https://huggingface.co/spaces/luca-martial/neural-style-transfer' target='_blank'>2. The idea to build a neural style transfer application was inspired from this Hugging Face Space </a>"
)
if __name__ == "__main__":
demo.launch()
#https://github.com/dhawan98/Post-Processing-of-Image-Segmentation-using-CRF |