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- 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.")
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