text: import os.path
  from data.base_dataset import BaseDataset, get_transform
  from data.image_folder import make_dataset
  from PIL import Image
  import random
  
  
  class UnalignedDataset(BaseDataset):
      """
      This dataset class can load unaligned/unpaired datasets.
  
      It requires two directories to host training images from domain A '/path/to/data/trainA'
      and from domain B '/path/to/data/trainB' respectively.
      You can train the model with the dataset flag '--dataroot /path/to/data'.
      Similarly, you need to prepare two directories:
      '/path/to/data/testA' and '/path/to/data/testB' during test time.
      """
  
      def __init__(self, opt):
          """Initialize this dataset class.
  
          Parameters:
              opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
          """
          BaseDataset.__init__(self, opt)
          self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')  # create a path '/path/to/data/trainA'
          self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')  # create a path '/path/to/data/trainB'
  
          self.A_img_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size))   # load images from '/path/to/data/trainA'
          self.B_img_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size))    # load images from '/path/to/data/trainB'
          self.A_size = len(self.A_img_paths)  # get the size of dataset A
          self.B_size = len(self.B_img_paths)  # get the size of dataset B
          btoA = self.opt.direction == 'BtoA'
          input_nc = self.opt.output_nc if btoA else self.opt.input_nc       # get the number of channels of input image
          output_nc = self.opt.input_nc if btoA else self.opt.output_nc      # get the number of channels of output image
          self.transform_A = get_transform(self.opt, grayscale=(input_nc == 1))
          self.transform_B = get_transform(self.opt, grayscale=(output_nc == 1))
  
      def get_img(self,A_img_path,A_label_path,B_img_path=None,B_label_path=None,index=None):
          A_img = Image.open(A_img_path).convert('RGB')
          B_img = Image.open(B_img_path).convert('RGB')
          # apply image transformation
          A = self.transform_A(A_img)
          B = self.transform_B(B_img)
  
          return {'A': A, 'B': B, 'A_img_paths': A_img_path, 'B_img_paths': B_img_path}
          
      def __len__(self):
          """Return the total number of images in the dataset.
  
          As we have two datasets with potentially different number of images,
          we take a maximum of
          """
          return max(self.A_size, self.B_size)
  