ixc_utils.py

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