create a tfrecord and add a sample

writer = tfrecord.ImageLablePairTfrecordCreator(
    save_path='classification_tfrecord',
    encode_type='jpg',
    data_name='img',
    compression_type=0)

writer.add(np.array(img), {"class": np.array(label)})

load a tfrecord and get a batch

TR = tfrecord.TfrecordData(
    tfrecord_path='classification_tfrecord',
    batch_size=5,
    shuffle=True,
    num_threads=4)

for i in range(iter):
    img_batch, class_batch = TR.batch(['img', 'class'])

or

for batch in TR:
    img_batch, class_batch = batch['img'], batch['class']

• preprocessing

  def img_preprecess_fn(img):
      img = tf.image.resize_images(img, [256, 128])
      img = img / 255
      return img
  
  TR = tfrecord.TfrecordData(
      tfrecord_path='classification_tfrecord',
      batch_size=5,
      shuffle=True,
      num_threads=4,
      preprocess_fns={
          'img': img_preprecess_fn
      })
  
  img_batch, class_batch = TR.batch(['img', 'class'])

More examples are in examples_create.py and examples_load.py, just run it!