File size: 1,765 Bytes
5f3481c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | from PIL import Image
from tqdm import tqdm
import numpy as np
import argparse
import glob
import pdb
import os
parser = argparse.ArgumentParser(description="resize image using bicubic upsampling")
parser.add_argument("--in_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/label_foreground_512', type=str)
parser.add_argument("--out_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/label_foreground_512_pad', type=str)
parser.add_argument("--pad_info_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/label_foreground_512_pad_info', type=str)
parser.add_argument("--size", default=512, type=int)
args = parser.parse_args()
os.makedirs(args.out_dir, exist_ok=True)
os.makedirs(args.pad_info_dir, exist_ok=True)
i = 0
for img_file in tqdm(glob.glob(args.in_dir+'/*')):
fname = img_file.split('/')[-1]
img = np.array(Image.open(img_file))
w, h = img.shape[0], img.shape[1]
if len(img.shape) == 3:
img_pad = np.zeros((args.size, args.size, img.shape[2]))
else:
img_pad = np.zeros((args.size, args.size))
pad_info = np.zeros((1,2))
if w < h:
if len(img.shape) == 3:
img_pad[args.size - w:, :, :] = img
else:
img_pad[args.size - w:, :] = img
pad_info[0, 0] = 0
pad_info[0, 1] = w
else:
if len(img.shape) == 3:
img_pad[:, :h, :] = img
else:
img_pad[:, :h] = img
pad_info[0, 0] = 1
pad_info[0, 1] = h
img_pad = Image.fromarray(img_pad.astype('uint8'))
img_pad.save(os.path.join(args.out_dir, fname))
np.save(os.path.join(args.pad_info_dir, str(fname.split('.')[0])+'.npy'), pad_info)
i += 1
# pdb.set_trace()
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