<reponame>andrewnc/geometric-neural-processes
from skimage.transform import resize
import matplotlib.pyplot as plt
import numpy as np
import os
from tqdm import tqdm

#for f in tqdm(os.listdir("./train2014")):
#    image = plt.imread("./train2014/{}".format(f))
#    resized = resize(image, (32,32), anti_aliasing=True)
#    plt.imsave("./resized_small_train2014/{}".format(f), resized)
for f in tqdm(os.listdir("../data/img_align_celeba")):
    image = plt.imread("../data/img_align_celeba/{}".format(f))
    resized = resize(image, (32,32), anti_aliasing=True)
    plt.imsave("../data/resized_small_img_align_celeb_a/{}".format(f), resized)

from skimage.transform import resize
import matplotlib.pyplot as plt
import numpy as np
import os
from tqdm import tqdm

#for f in tqdm(os.listdir("./train2014")):
#    image = plt.imread("./train2014/{}".format(f))
#    resized = resize(image, (32,32), anti_aliasing=True)
#    plt.imsave("./resized_small_train2014/{}".format(f), resized)
for f in tqdm(os.listdir("../data/img_align_celeba")):
    image = plt.imread("../data/img_align_celeba/{}".format(f))
    resized = resize(image, (32,32), anti_aliasing=True)
    plt.imsave("../data/resized_small_img_align_celeb_a/{}".format(f), resized)
#for f in tqdm(os.listdir("./train2014")): #    image = plt.imread("./train2014/{}".format(f)) #    resized = resize(image, (32,32), anti_aliasing=True) #    plt.imsave("./resized_small_train2014/{}".format(f), resized)