使用适当的正则表达式来处理

re.sub（r‘re’，‘’，input）可以用指定字符替换输入文本中的字符

用\t分割的文本和标签 可以处理为两个小列表之后使用 zip转化为元组

  1.   def load_dataset(self):
  2.         train_path = os.path.join(self.dataset_path, 'images_background')
  3.         for alphabet in os.listdir(train_path):
  4.             alphabet_path = os.path.join(train_path, alphabet)
  5.             for character in os.listdir(alphabet_path):
  6.                 character_path = os.path.join(alphabet_path, character)
  7.                 for image in os.listdir(character_path):
  8.                     self.train_lines.append(os.path.join(character_path, image))
  9.                     self.train_labels.append(self.types)
  10.                 self.types += 1
  1.  c               = random.randint(0, self.types - 1)
  2.                 selected_path   = lines[labels[:] == c]

这里根据随机选择的标签，选取对应标签里面所有的图片路径

这里面

  1. image_indexes = random.sample(range(0, len(selected_path)), 3)
  2.                 # 取出两张类似的图片
  3.                 batch_images_path.append(selected_path[image_indexes[0]])
  4.                 batch_images_path.append(selected_path[image_indexes[1]])
  6.                 # 取出两张不类似的图片
  7.                 batch_images_path.append(selected_path[image_indexes[2]])
  8.                 # 取出与当前的小类别不同的类
  9.                 different_c         = list(range(self.types))
  10.                 different_c.pop(c)
  11.                 different_c_index   = np.random.choice(range(0, self.types - 1), 1)
  12.                 current_c           = different_c[different_c_index[0]]
  13.                 selected_path       = lines[labels == current_c]
  14.                 while len(selected_path)<1:
  15.                     different_c_index   = np.random.choice(range(0, self.types - 1), 1)
  16.                     current_c           = different_c[different_c_index[0]]
  17.                     selected_path       = lines[labels == current_c]
  19.                 image_indexes = random.sample(range(0, len(selected_path)), 1)
  20.                 batch_images_path.append(selected_path[image_indexes[0]])