text: import os.path
  import torchvision.transforms as transforms
  from data.base_dataset import BaseDataset, get_transform , get_sparse_transform , get_mask_transform
  from data.image_folder import make_dataset
  from PIL import Image
  import PIL
  import random
  import os
  import numpy as np
  class LabeledDataset(BaseDataset):
      def initialize(self, opt):
          self.opt = opt
          self.root = opt.dataroot
          self.dir_scribbles = os.path.join(opt.dataroot, 'scribbles')  #'pix2pix') #'scribbles' )  #'masks')
          self.dir_images = os.path.join(opt.dataroot, 'images') #os.path.join(opt.dataroot, 'images')
  
          self.classes = sorted(os.listdir(self.dir_images)) # sorted so that the same order in all cases; check if you've to change this with other models
          self.num_classes = len(self.classes)
  
          self.scribble_paths = []
          self.images_paths = []
          for cl in self.classes:
              self.scribble_paths.append(sorted( make_dataset( os.path.join( self.dir_scribbles , cl  )  )  ) )
              self.images_paths.append( sorted(  make_dataset( os.path.join( self.dir_images , cl  )  )  ) )
  
          self.cum_sizes = []
          self.sizes = []
          size =0
          for i in range(self.num_classes):
              size += len(self.scribble_paths[i])
              self.cum_sizes.append(size)
              self.sizes.append(size)
  
          self.transform = get_transform(opt)
          self.sparse_transform = get_sparse_transform(opt)
          self.mask_transform =  get_mask_transform(opt)
      def find_label(self,index):
          sub=0
          for i in range(self.num_classes):
              if index < self.cum_sizes[i]:
                  return i,(index-sub)
              sub= self.cum_sizes[i]
  
      def __getitem__(self, index):
          index = index % self.cum_sizes[ self.num_classes -1  ]
          label , relative_index = self.find_label(index)
          if self.opt.sketchy_dataset:
              A_path = self.scribble_paths[label][ relative_index  ]
              B_path = A_path.replace('scribbles','images').split('-')[0]+'.jpg'
  
          elif self.opt.autocomplete_dataset_outline:
              A_path = self.scribble_paths[label][ relative_index  ]
              B_path = A_path.replace('scribbles','images').split('_')[0]+'.png'
  
          elif self.opt.autocomplete_dataset_edges:
              A_path = self.scribble_paths[label][ relative_index  ]
              B_path = A_path.replace('scribbles','images').split('_')[0]+'_AB.jpg'
          elif self.opt.edges_outlines_dataset:
              A_path = self.scribble_paths[label][ relative_index  ]
              B_path = A_path.replace('scribbles','images')
              if np.random.multinomial(1, [1.0 / 3, 2.0 / 3])[0]==1:
                  A_path=A_path.replace('scribbles','edges_outlines')
          else :
              A_path = self.scribble_paths[label][ relative_index  ]
              B_path = self.images_paths[label][relative_index]
  
          A_img = Image.open(A_path).convert('RGB')
          B_img = Image.open(B_path).convert('RGB')
  
  
  
          A = self.transform(A_img)
          B = self.transform(B_img)
          A_mask = self.mask_transform(A_img)
          A_sparse = self.sparse_transform(A_img)
          if self.opt.which_direction == 'BtoA':
              input_nc = self.opt.output_nc
              output_nc = self.opt.input_nc
          else:
              input_nc = self.opt.input_nc
              output_nc = self.opt.output_nc
  
          if input_nc == 1:  # RGB to gray
              tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114
              A = tmp.unsqueeze(0)
  
          if input_nc == 1:  # RGB to gray
              tmp = A_sparse[0, ...] * 0.299 + A_sparse[1, ...] * 0.587 + A_sparse[2, ...] * 0.114
              A_sparse = tmp.unsqueeze(0)
  
  
  
          if output_nc == 1:  # RGB to gray
              tmp = B[0, ...] * 0.299 + B[1, ...] * 0.587 + B[2, ...] * 0.114
              B = tmp.unsqueeze(0)
  
          return {'A': A,'A_sparse':A_sparse, 'A_mask':A_mask, 'B': B,
                  'A_paths': A_path, 'B_paths': B_path, 'label': label }
  
      def __len__(self):
          return self.cum_sizes[ self.num_classes - 1  ]
  
      def get_transform(self):
          return self.transform
  
      def get_root(self):
          return self.root
  
      def get_classes(self):
          return self.classes
  
      def get_num_classes(self):
          return len(self.classes)
  
      def name(self):
          return 'LabeledDataset'
  