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import torch
import numpy as np
import torchvision
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
import torchvision.transforms as transforms
def padding_336(b):
width, height = b.size
tar = int(np.ceil(height / 336) * 336)
top_padding = int((tar - height)/2)
bottom_padding = tar - height - top_padding
left_padding = 0
right_padding = 0
b = transforms.functional.pad(b, [left_padding, top_padding, right_padding, bottom_padding], fill=[255,255,255])
return b
def HD_transform(img, hd_num=16):
width, height = img.size
trans = False
if width < height:
img = img.transpose(Image.TRANSPOSE)
trans = True
width, height = img.size
ratio = (width/ height)
scale = 1
while scale*np.ceil(scale/ratio) <= hd_num:
scale += 1
scale -= 1
new_w = int(scale * 336)
new_h = int(new_w / ratio)
img = transforms.functional.resize(img, [new_h, new_w],)
img = padding_336(img)
width, height = img.size
if trans:
img = img.transpose(Image.TRANSPOSE)
return img
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