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import torch
from PIL import Image
import numpy as np
import os
import cv2
def resize_sofa(img):
img = Image.fromarray(img)
width, height = img.size
idx = np.argmin([width,height])
if idx==0:
img1 = Image.new(img.mode, (height, height), (255, 255, 255))
img1.paste(img, ((height-width)//2, 0))
else:
img1 = Image.new(img.mode, (width, width), (255, 255, 255))
img1.paste(img, (0, (width-height)//2))
newsize = (640, 640) # parameters from test script
im1 = img1.resize(newsize)
return im1
def resize_style(img):
#img = Image.open(path)#"../style5.jpg")
img = Image.fromarray(img)
width, height = img.size
idx = np.argmin([width,height])
#print(width,height)
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))
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 create_styledSofa(sofa,style):
path_sofa,path_style = 'sofa.jpg','style.jpg'
sofa.save(path_sofa)
style.save(path_style)
#newpath_sofa = resize_sofa(path_sofa)
#newpath_style = resize_style(path_style)
os.system("time python3 test.py --content "+path_sofa+" \
--style "+path_style+" \
--output . \
--vgg vgg_normalised.pth \
--decoder_path decoder_iter_160000.pth \
--Trans_path transformer_iter_160000.pth \
--embedding_path embedding_iter_160000.pth")
styled_sofa = cv2.imread('sofa_stylized_style.jpg')
return styled_sofa
# image = Image.open('input/sofa.jpg')
# image = np.array(image)[:,:600]
# image = resize_sofa(image) |