text: # -*- coding: utf-8 -*- 
  import os
  import cv2
  import imageio
  from scipy import misc
  from PIL import Image
  import numpy as np
  
  # --- alterado -------------------
  print(os.system('pwd'))
  import matplotlib.pyplot as plt
  import matplotlib.image as mpimg
  # --------------------------------
  
  dataset_dir = 'datasets' # datasets
  dataset = 'data_crop_512_jpg'
  train_dir = 'train'
  test_dir = 'val_test'
  val_dir = 'val'
  resize_to = 256
  scale = 4
  
  if not os.path.exists(os.path.join(dataset_dir, train_dir)):
      os.mkdir(os.path.join(dataset_dir, train_dir))
  if not os.path.exists(os.path.join(dataset_dir, test_dir)):
      os.mkdir(os.path.join(dataset_dir, test_dir))
  if not os.path.exists(os.path.join(dataset_dir, val_dir)):
      os.mkdir(os.path.join(dataset_dir, val_dir))
  
  i = 1
  fileList = os.listdir(os.path.join(dataset_dir, dataset))
  for index, file in enumerate(fileList):
      imgPath = os.path.join(dataset_dir, dataset, file)
      if os.path.isdir(imgPath):
          continue
      if(i > 1000):
          i = i + 1
          raise Exception('Forced finish')
      print("procesing " + file + " " + str(i+1) + '/' + str(len(fileList)))
      
      #img = cv2.imread(imgPath)
      #print("DBG - img sizes 1", img.shape)
      #imgplot = plt.imshow(img)
      #plt.show()
      
      img = cv2.cvtColor(cv2.imread(imgPath), cv2.COLOR_BGR2RGB)
      #print("DBG - img sizes 2", img.shape)
      #img = misc.imresize(img, (resize_to, resize_to), interp='bilinear')
      img = np.array(Image.fromarray(img).resize((resize_to, resize_to), Image.BILINEAR))
      #print("DBG - img sizes 3", img.shape)
      size = int(img.shape[0] / scale)
      #print("DBG - size", size)
      #resizeImg = misc.imresize(img, (size, size), interp='bilinear')
      resizeImg = np.array(Image.fromarray(img).resize((size, size), Image.BILINEAR))
      #print("DBG - img sizes 4", resizeImg.shape)
      #finalImg = misc.imresize(resizeImg, (img.shape[0], img.shape[0]), interp='bilinear')
      finalImg = np.array(Image.fromarray(resizeImg).resize((img.shape[0], img.shape[0]), Image.BILINEAR))
      #print("DBG - img sizes 5", finalImg.shape)
      combineImg = Image.new('RGB', (img.shape[0]*2, img.shape[0]))
      combineImg.paste(Image.fromarray(finalImg), (0,0))
      combineImg.paste(Image.fromarray(img), (img.shape[0]+1,0))
      savePath = ""
      # sample ratio of train:test:val is 6:2:2
      if index % 10 < 6:
          savePath = os.path.join(dataset_dir, train_dir, file)
      elif index % 10 < 8:
          savePath = os.path.join(dataset_dir, test_dir, file)
      else:
          savePath = os.path.join(dataset_dir, val_dir, file)
      misc.imsave(savePath, combineImg)
      imageio.imwrite(savePath, combineImg)
  