| import numpy as np |
| from PIL import Image |
| from tqdm import tqdm |
| import torch |
| import glob |
| import pdb |
| import os |
| from skimage.io import imsave |
| import argparse |
|
|
| parser = argparse.ArgumentParser(description="resize image using nearest-neighbor upsampling") |
| parser.add_argument("--img_dir", default='/mnt/session_space/home/lingzzha/EgoHOS/data/train/label', type=str) |
| parser.add_argument("--mask_tf", default=True, type=bool) |
| parser.add_argument("--max_scale", default=512, type=int) |
| args = parser.parse_args() |
|
|
| save_dir = args.img_dir+'_'+str(args.max_scale) |
| os.makedirs(save_dir, exist_ok = True) |
|
|
| for file in tqdm(glob.glob(args.img_dir + '/*')): |
| fname = file.split('/')[-1] |
| img = Image.open(file); w, h = img.size |
| |
| if w > h: |
| aspect_ratio = h/w |
| if args.mask_tf: |
| img_resize = Image.open(file).resize((args.max_scale, int(args.max_scale * aspect_ratio)), resample = Image.NEAREST) |
| else: |
| img_resize = Image.open(file).resize((args.max_scale, int(args.max_scale * aspect_ratio)), resample = Image.BICUBIC) |
| else: |
| aspect_ratio = w/h |
| if args.mask_tf: |
| img_resize = Image.open(file).resize((int(args.max_scale * aspect_ratio), args.max_scale), resample = Image.NEAREST) |
| else: |
| img_resize = Image.open(file).resize((int(args.max_scale * aspect_ratio), args.max_scale), resample = Image.BICUBIC) |
| |
|
|
| |
| |
|
|
| img_resize.save(os.path.join(save_dir, fname)) |
