text: import itertools
  import random
  import os
  import xml.etree.ElementTree as ET
  from fvcore.common.file_io import PathManager
  
  from detectron2.utils.store_non_list import Store
  
  VOC_CLASS_NAMES_COCOFIED = [
      "airplane",  "dining table", "motorcycle",
      "potted plant", "couch", "tv"
  ]
  
  BASE_VOC_CLASS_NAMES = [
      "aeroplane", "diningtable", "motorbike",
      "pottedplant",  "sofa", "tvmonitor"
  ]
  
  VOC_CLASS_NAMES = [
      "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat",
      "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person",
      "pottedplant", "sheep", "sofa", "train", "tvmonitor"
  ]
  
  T2_CLASS_NAMES = [
      "truck", "traffic light", "fire hydrant", "stop sign", "parking meter",
      "bench", "elephant", "bear", "zebra", "giraffe",
      "backpack", "umbrella", "handbag", "tie", "suitcase",
      "microwave", "oven", "toaster", "sink", "refrigerator"
  ]
  
  T3_CLASS_NAMES = [
      "frisbee", "skis", "snowboard", "sports ball", "kite",
      "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket",
      "banana", "apple", "sandwich", "orange", "broccoli",
      "carrot", "hot dog", "pizza", "donut", "cake"
  ]
  
  T4_CLASS_NAMES = [
      "bed", "toilet", "laptop", "mouse",
      "remote", "keyboard", "cell phone", "book", "clock",
      "vase", "scissors", "teddy bear", "hair drier", "toothbrush",
      "wine glass", "cup", "fork", "knife", "spoon", "bowl"
  ]
  
  UNK_CLASS = ["unknown"]
  
  # Change this accodingly for each task t*
  known_classes = list(itertools.chain(VOC_CLASS_NAMES, T2_CLASS_NAMES))
  train_files = ['../VOC2007/ImageSets/Main/t2_train.txt','../VOC2007/ImageSets/Main/t1_train.txt']
  
  # known_classes = list(itertools.chain(VOC_CLASS_NAMES))
  # train_files = ['/home/fk1/workspace/OWOD/datasets/VOC2007/ImageSets/Main/train.txt']
  annotation_location = '../VOC2007/Annotations'
  
  items_per_class = 1
  dest_file = './ImageSets/Main/t2_ft_' + str(items_per_class) + '.txt'
  
  file_names = []
  for tf in train_files:
      with open(tf, mode="r") as myFile:
          file_names.extend(myFile.readlines())
  
  random.shuffle(file_names)
  
  image_store = Store(len(known_classes), items_per_class)
  
  current_min_item_count = 0
  
  for fileid in file_names:
      fileid = fileid.strip()
      anno_file = os.path.join(annotation_location, fileid + ".xml")
  
      with PathManager.open(anno_file) as f:
          tree = ET.parse(f)
  
      for obj in tree.findall("object"):
          cls = obj.find("name").text
          if cls in VOC_CLASS_NAMES_COCOFIED:
              cls = BASE_VOC_CLASS_NAMES[VOC_CLASS_NAMES_COCOFIED.index(cls)]
          if cls in known_classes:
              image_store.add((fileid,), (known_classes.index(cls),))
  
      current_min_item_count = min([len(items) for items in image_store.retrieve(-1)])
      print(current_min_item_count)
      if current_min_item_count == items_per_class:
          break
  
  filtered_file_names = []
  for items in image_store.retrieve(-1):
      filtered_file_names.extend(items)
  
  print(image_store)
  print(len(filtered_file_names))
  print(len(set(filtered_file_names)))
  
  filtered_file_names = set(filtered_file_names)
  filtered_file_names = map(lambda x: x + '\n', filtered_file_names)
  
  with open(dest_file, mode="w") as myFile:
      myFile.writelines(filtered_file_names)
  
  print('Saved to file: ' + dest_file)
  