diff --git a/bob/learn/tensorflow/dataset/tfrecords.py b/bob/learn/tensorflow/dataset/tfrecords.py
index 84c21ab9f822bfe5311a7f1c2a44e7924daee048..faa0938a23db82957339149dcf914ce270a1208e 100644
--- a/bob/learn/tensorflow/dataset/tfrecords.py
+++ b/bob/learn/tensorflow/dataset/tfrecords.py
@@ -291,4 +291,55 @@ def batch_data_and_labels(tfrecord_filenames, data_shape, data_type,
features['key'] = key
return features, labels
+
+
+def batch_data_and_labels_image_augmentation(tfrecord_filenames, data_shape, data_type,
+ batch_size, epochs=1,
+ gray_scale=False,
+ output_shape=None,
+ random_flip=False,
+ random_brightness=False,
+ random_contrast=False,
+ random_saturation=False,
+ per_image_normalization=True):
+ """
+ Dump in order batches from a list of tf-record files
+
+ **Parameters**
+
+ tfrecord_filenames:
+ List containing the tf-record paths
+
+ data_shape:
+ Samples shape saved in the tf-record
+
+ data_type:
+ tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
+
+ batch_size:
+ Size of the batch
+
+ epochs:
+ Number of epochs to be batched
+
+ """
+
+ dataset = create_dataset_from_records_with_augmentation(tfrecord_filenames, data_shape,
+ data_type,
+ gray_scale=gray_scale,
+ output_shape=output_shape,
+ random_flip=random_flip,
+ random_brightness=random_brightness,
+ random_contrast=random_contrast,
+ random_saturation=random_saturation,
+ per_image_normalization=per_image_normalization)
+
+ dataset = dataset.batch(batch_size).repeat(epochs)
+
+ data, labels, key = dataset.make_one_shot_iterator().get_next()
+ features = dict()
+ features['data'] = data
+ features['key'] = key
+
+ return features, labels
diff --git a/bob/learn/tensorflow/script/lfw_db_to_tfrecords.py b/bob/learn/tensorflow/script/lfw_db_to_tfrecords.py
deleted file mode 100755
index 7999b635731baffc9800ead015581221667a31e3..0000000000000000000000000000000000000000
--- a/bob/learn/tensorflow/script/lfw_db_to_tfrecords.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-
-"""Script that converts bob.db.lfw database to TF records
-
-Usage:
- %(prog)s <data-path> <output-file> [--extension=<arg> --protocol=<arg> --data-type=<arg> --verbose]
- %(prog)s --help
- %(prog)s --version
-
-Options:
- -h --help show this help message and exit
- <data-path> Path that contains the features
- --extension=<arg> Default feature extension [default: .hdf5]
- --protocol=<arg> One of the LFW protocols [default: view1]
- --data-type=<arg> TFRecord data type [default: uint8]
-
-
-The possible protocol options are the following:
- 'view1', 'fold1', 'fold2', 'fold3', 'fold4', 'fold5', 'fold6', 'fold7', 'fold8', 'fold9', 'fold10'
-
-More details about our interface to LFW database can be found in
-https://www.idiap.ch/software/bob/docs/bob/bob.db.lfw/master/index.html.
-
-
-"""
-
-import tensorflow as tf
-from bob.io.base import create_directories_safe
-from bob.bio.base.utils import load, read_config_file
-from bob.core.log import setup, set_verbosity_level
-import bob.db.lfw
-import os
-import bob.io.image
-import bob.io.base
-import numpy
-
-logger = setup(__name__)
-
-
-def _bytes_feature(value):
- return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
-
-
-def _int64_feature(value):
- return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
-
-def file_to_label(client_ids, f):
- return client_ids[str(f.client_id)]
-
-def get_pairs(all_pairs, match=True):
-
- enroll = []
- probe = []
- for p in all_pairs:
- if p.is_match == match:
- enroll.append(p.enroll_file)
- probe.append(p.probe_file)
-
- return enroll, probe
-
-
-def main(argv=None):
- from docopt import docopt
- args = docopt(__doc__, version='')
-
- data_path = args['<data-path>']
- output_file = args['<output-file>']
- extension = args['--extension']
- protocol = args['--protocol']
- data_type = args['--data-type']
-
- # Sets-up logging
- if args['--verbose']:
- verbosity = 2
- set_verbosity_level(logger, verbosity)
-
- # Loading LFW models
- database = bob.db.lfw.Database()
- enroll, probe = get_pairs(database.pairs(protocol=protocol), match=True)
- #client_ids = list(set([f.client_id for f in all_pairs]))
-
- client_ids = list(set([f.client_id for f in enroll] + [f.client_id for f in probe]))
- client_ids = dict(zip(client_ids, range(len(client_ids))))
-
- create_directories_safe(os.path.dirname(output_file))
-
- n_files = len(enroll)
- with tf.python_io.TFRecordWriter(output_file) as writer:
- for e, p, i in zip(enroll, probe, range(len(enroll)) ):
- logger.info('Processing pair %d out of %d', i + 1, n_files)
-
- if os.path.exists(e.make_path(data_path, extension)) and os.path.exists(p.make_path(data_path, extension)):
- for f in [e, p]:
- path = f.make_path(data_path, extension)
- data = bob.io.image.to_matplotlib(bob.io.base.load(path)).astype(data_type)
- data = data.tostring()
-
- feature = {'train/data': _bytes_feature(data),
- 'train/label': _int64_feature(file_to_label(client_ids, f))}
-
- example = tf.train.Example(features=tf.train.Features(feature=feature))
- writer.write(example.SerializeToString())
- else:
- logger.debug("... Processing original data file '{0}' was not successful".format(path))
-
-if __name__ == '__main__':
- main()
diff --git a/setup.py b/setup.py
index 58a11fcdfaacee564021490a98d6ae43e6ec88b9..1ee1774bd46d277d363f980ad309bf6bb8b1b0a6 100755
--- a/setup.py
+++ b/setup.py
@@ -50,7 +50,6 @@ setup(
'bob_tf_compute_statistics.py = bob.learn.tensorflow.script.compute_statistics:main',
'bob_tf_db_to_tfrecords = bob.learn.tensorflow.script.db_to_tfrecords:main',
'bob_tf_load_and_debug.py = bob.learn.tensorflow.script.load_and_debug:main',
- 'bob_tf_lfw_db_to_tfrecords.py = bob.learn.tensorflow.script.lfw_db_to_tfrecords:main',
'bob_tf_train_generic = bob.learn.tensorflow.script.train_generic:main',
'bob_tf_eval_generic = bob.learn.tensorflow.script.eval_generic:main',
'bob_tf_predict_generic = bob.learn.tensorflow.script.predict_generic:main',