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data_prep.py
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data_prep.py
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import os
import shutil
import random
from PIL import Image
import matplotlib.pyplot as plt
# data_root = './data'
# source_dir = os.path.join(data_root, 'CelebA500_cropped')
# dest_dir = os.path.join(data_root, 'CelebA_face_new_cropped')
# if not os.path.exists(dest_dir):
# os.makedirs(dest_dir)
# file_list = []
# for root, _, filenames in os.walk(source_dir):
# for filename in filenames:
# file_list.append(os.path.join(root, filename))
# for source in file_list:
# shutil.copy(source, dest_dir)
data_root = './data/CASIA_WebFace_20000/train'
file_list= []
for root, _, filenames in os.walk(data_root):
for filename in filenames:
file_list.append(os.path.join(root, filename))
# plt.figure(1, figsize=(8, 2))
# plt.axis('off')
# n = 0
# for i in range(16):
# n += 1
# random_img = random.choice(file_list)
# imgs = Image.open(random_img)
# plt.subplot(2, 8, n)
# plt.axis('off')
# plt.imshow(imgs)
# plt.subplots_adjust(wspace=0, hspace=0)
# plt.savefig('/home/sda1/Jinge/Attention_analysis/result/dataset_sample.jpg')
dest = './data/exampler'
if not os.path.exists(dest):
os.makedirs(dest)
for i in range(16):
random_img = random.choice(file_list)
shutil.copy(random_img, dest)