把各个类别的原始数据打乱之后分成训练集和测试集
但是我找不到 打乱 的代码了 这版应该是按顺序分成训练集和测试集，并没有把原始数据打乱
# -*- coding:utf-8 -*-# In[1]:import osimport shutilimport numpy as npbase_dir = r'C:\Users\10133\Desktop\bishe\matlab\traintest'if os.path.exists(base_dir):shutil.rmtree(base_dir)os.mkdir(base_dir) # 在该路径下创建目录train_dir = os.path.join(base_dir, 'train') # 训练文件夹os.mkdir(train_dir)test_dir = os.path.join(base_dir, 'test') # 测试文件夹os.mkdir(test_dir)print('主目录已经建立好了！')# In[2]:train_normal_dir = os.path.join(train_dir, '0')os.mkdir(train_normal_dir)train_fault1_dir = os.path.join(train_dir, '1')os.mkdir(train_fault1_dir)train_fault2_dir = os.path.join(train_dir, '2')os.mkdir(train_fault2_dir)train_fault3_dir = os.path.join(train_dir, '3')os.mkdir(train_fault3_dir)train_fault4_dir = os.path.join(train_dir, '4')os.mkdir(train_fault4_dir)train_fault5_dir = os.path.join(train_dir, '5')os.mkdir(train_fault5_dir)test_normal_dir = os.path.join(test_dir, '0')os.mkdir(test_normal_dir)test_fault1_dir = os.path.join(test_dir, '1')os.mkdir(test_fault1_dir)test_fault2_dir = os.path.join(test_dir, '2')os.mkdir(test_fault2_dir)test_fault3_dir = os.path.join(test_dir, '3')os.mkdir(test_fault3_dir)test_fault4_dir = os.path.join(test_dir, '4')os.mkdir(test_fault4_dir)test_fault5_dir = os.path.join(test_dir, '5')os.mkdir(test_fault5_dir)print('类别目录已经建立好了！')train_normal = r'C:\Users\10133\Desktop\bishe\matlab\classification\0'train_fault1 = r'C:\Users\10133\Desktop\bishe\matlab\classification\1'train_fault2 = r'C:\Users\10133\Desktop\bishe\matlab\classification\2'train_fault3 = r'C:\Users\10133\Desktop\bishe\matlab\classification\3'train_fault4 = r'C:\Users\10133\Desktop\bishe\matlab\classification\4'train_fault5 = r'C:\Users\10133\Desktop\bishe\matlab\classification\5'original_dataset = r'C:\Users\10133\Desktop\bishe\matlab\SC4001jpg'#datapath=r'C:\Users\10133\Desktop\bishe\matlab\operation\sc4002e0_data.txt'labelpath=r'C:\Users\10133\PycharmProjects\practice\结果.txt'#x=np.loadtxt(fname=datapath,delimiter='\n')y=np.loadtxt(fname=labelpath,delimiter='\n')#y=[int(s) for s in y]#y=np.delete(y, -1, axis=0)#y=np.delete(y, 936, axis=0)print(y[0])print(y.shape)print('finished!')for i in range(len(y)):if y[i]==0:src = os.path.join(original_dataset, i, '.jpg')dst = os.path.join(train_normal, i, '.jpg')shutil.copyfile(src, dst)elif y[i]==1:src = os.path.join(original_dataset, i, '.jpg')dst = os.path.join(train_fault1, i, '.jpg')shutil.copyfile(src, dst)elif y[i]==2:src = os.path.join(original_dataset, i, '.jpg')dst = os.path.join(train_fault2, i, '.jpg')shutil.copyfile(src, dst)elif y[i]==3:src = os.path.join(original_dataset, i, '.jpg')dst = os.path.join(train_fault3, i, '.jpg')shutil.copyfile(src, dst)elif y[i]==4:src = os.path.join(original_dataset, i, '.jpg')dst = os.path.join(train_fault4, i, '.jpg')shutil.copyfile(src, dst)else:src = os.path.join(original_dataset, i, '.jpg')dst = os.path.join(train_fault5, i, '.jpg')shutil.copyfile(src, dst)
