删除文件 deep_leakage by xiaodouzi/DLG-FOR-Mindspore/extract_cifar100.py

deep_leakage by xiaodouzi/DLG-FOR-Mindspore/extract_cifar100.py

@@ -1,55 +0,0 @@
-import cv2
-import numpy as np
-import pickle
-import os
-
-
-# 解压缩，返回解压后的字典
-def unpickle(file):
-    fo = open(file, 'rb')
-    dict = pickle.load(fo, encoding='latin1')
-    fo.close()
-    return dict
-
-
-def cifar100_to_images():
-    tar_dir = '/root/DLG-FOR-Mindspore/data/cifar-100-python'  # 原始数据库目录
-    train_root_dir = '/root/DLG-FOR-Mindspore/data/train'  # 图片保存目录
-    test_root_dir = '/root/DLG-FOR-Mindspore/data/test'
-    if not os.path.exists(train_root_dir):
-        os.makedirs(train_root_dir)
-    if not os.path.exists(test_root_dir):
-        os.makedirs(test_root_dir)
-
-    # 获取label对应的class，分为20个coarse class，共100个 fine class
-    meta_Name = tar_dir + "meta"
-    Meta_dic = unpickle(meta_Name)
-    coarse_label_names = Meta_dic['coarse_label_names']
-    fine_label_names = Meta_dic['fine_label_names']
-    print(fine_label_names)
-
-    # 生成训练集图片
-    dataName = tar_dir + "train"
-    Xtr = unpickle(dataName)
-    print(dataName + " is loading...")
-    for i in range(0, Xtr['data'].shape[0]):
-        img = np.reshape(Xtr['data'][i], (3, 32, 32))  # Xtr['data']为图片二进制数据
-        img = img.transpose(1, 2, 0)  # 读取image
-        ###img_name:fine_label+coarse_label+fine_class+coarse_class+index
-        picName = train_root_dir + str(Xtr['fine_labels'][i]) + '_' + str(Xtr['coarse_labels'][i]) + '_&' + \
-                  fine_label_names[Xtr['fine_labels'][i]] + '&_' + coarse_label_names[
-                      Xtr['coarse_labels'][i]] + '_' + str(i) + '.jpg'
-        cv2.imwrite(picName, img)
-    print(dataName + " loaded.")
-
-    print("test_batch is loading...")
-    # 生成测试集图片
-    testXtr = unpickle(tar_dir + "test")
-    for i in range(0, testXtr['data'].shape[0]):
-        img = np.reshape(testXtr['data'][i], (3, 32, 32))
-        img = img.transpose(1, 2, 0)
-        picName = test_root_dir + str(testXtr['fine_labels'][i]) + '_' + str(testXtr['coarse_labels'][i]) + '_&' + \
-                  fine_label_names[testXtr['fine_labels'][i]] + '&_' + coarse_label_names[
-                      testXtr['coarse_labels'][i]] + '_' + str(i) + '.jpg'
-        cv2.imwrite(picName, img)
-    print("test_batch loaded.")