Created a function that batches a tf-record in order and apply data augmentation

Updated the LFW to tfrecord script to the new feature format.

Mind to review that (just few lines of code)?

Thanks

bob/learn/tensorflow/script/lfw_db_to_tfrecords.py

-#!/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 = {'data': _bytes_feature(data),
-                           'label': _int64_feature(file_to_label(client_ids, f)),
-                           'key': _bytes_feature(str(f.path)),
-                           }
-
-                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()