text: import tensorflow as tf
  import glob
  import os
  import cv2
  
  tf.enable_eager_execution()
  
  """
  
  small helper function that reads the data from the training tfrecords
  to ensure they are formatted correctly...
  
  """
  
  input_folder = '../data'
  
  def get_filenames(folder):
      filenames = []
      for filename in glob.glob(os.path.join(folder, '*.tfrecords')):
          filenames.append(filename)
      return filenames
  
  def get_image(record):
      feature_dict = {
          'image/height': tf.FixedLenFeature([], tf.int64),
          'image/width': tf.FixedLenFeature([], tf.int64),
          'image/filename': tf.VarLenFeature(tf.string),
          'image/source_id': tf.VarLenFeature(tf.string),
          'image/encoded': tf.FixedLenFeature([], tf.string),
          'image/format': tf.VarLenFeature(tf.string),
          'image/object/bbox/xmin': tf.VarLenFeature(tf.float32),
          'image/object/bbox/xmax': tf.VarLenFeature(tf.float32),
          'image/object/bbox/ymin': tf.VarLenFeature(tf.float32),
          'image/object/bbox/ymax': tf.VarLenFeature(tf.float32),
          'image/object/class/text': tf.VarLenFeature(tf.string),
          'image/object/class/label': tf.VarLenFeature(tf.int64)
      }
  
      features = tf.parse_single_example(record, features=feature_dict)
      img = tf.image.decode_jpeg(features['image/encoded']).numpy()
      xmin = tf.sparse_tensor_to_dense(features['image/object/bbox/xmin']).numpy()
      xmax = tf.sparse_tensor_to_dense(features['image/object/bbox/xmax']).numpy()
      ymin = tf.sparse_tensor_to_dense(features['image/object/bbox/ymin']).numpy()
      ymax = tf.sparse_tensor_to_dense(features['image/object/bbox/ymax']).numpy()
  
      width = tf.cast(features['image/width'], tf.int32)
      height = tf.cast(features['image/height'], tf.int32)
      
      for i in range(len(xmin)):
          x1 = int(round(xmin[i] * width.numpy()))
          y1 = int(round(ymin[i] * height.numpy()))
          x2 = int(round(xmax[i] * width.numpy()))
          y2 = int(round(ymax[i] * height.numpy()))
          
          img = cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
          print((x1, y1, x2, y2))
  
      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
      cv2.imshow('image', img)
      cv2.waitKey(1000)
  
  
  if __name__ == "__main__":
      F = get_filenames(input_folder)
      D = tf.data.TFRecordDataset(F)
      D = D.shuffle(10000)
      it = D.make_one_shot_iterator()
      while True:
          get_image(it.get_next())
  