Many changes

• Removing legacy code
• Port to the new ci
• Estimate the size of the tfrecords file that will be created
• bug-fixes
• Add a new architecture for patch-based CNN
• Refactored the scripts to use the new click tools
• Changed the compute_statistics script completely
• Removed the unused predict_generic script
• Removed the unused train script and renamed train_generic to train

Fixes #50 (closed) #52 (closed) #48 (closed)

bob/learn/tensorflow/script/compute_statistics.py

 #!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# @date: Wed 11 May 2016 09:39:36 CEST
+"""Script that computes statistics for image.
 """
-Script that computes statistics for image
-
-Usage:
-  compute_statistics.py <base_path> <output_file> --extension=<arg>
-  compute_statistics.py -h | --help
-Options:
-  -h --help     Show this screen.
-  --extension=<arg>    [default: .hdf5]
-"""
-
-from docopt import docopt
-import bob.io.base
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+# import pkg_resources so that bob imports work properly:
+import pkg_resources
 import os
+import logging
+import click
 import numpy
-import bob.io.image
+import bob.io.image  # to be able to load images
+from bob.io.base import save, load
+from bob.extension.scripts.click_helper import verbosity_option
+
+logger = logging.getLogger(__name__)
 
 
 def process_images(base_path, extension, shape):
 
     files = os.listdir(base_path)
     sum_data = numpy.zeros(shape=shape)
-    print("Processing {0}".format(base_path))
+    logging.info("Processing {0}".format(base_path))
     count = 0
     for f in files:
         path = os.path.join(base_path, f)
@@ -34,27 +31,34 @@ def process_images(base_path, extension, shape):
             sum_data += s
 
         if os.path.splitext(path)[1] == extension:
-            data = bob.io.base.load(path)
+            data = load(path)
             count += 1
             sum_data += data
 
     return count, sum_data
 
 
-def main():
-    args = docopt(__doc__, version='Mnist training with TensorFlow')
+@click.command()
+@click.argument('base_path')
+@click.argument('output_file')
+@click.option('--extension', default='.hdf5', show_default=True)
+@verbosity_option()
+def compute_statistics(base_path, output_file, extension, **kwargs):
+    """Script that computes statistics for image.
+    """
+    logger.debug('base_path: %s', base_path)
+    logger.debug('output_file: %s', output_file)
+    logger.debug('extension: %s', extension)
+    logger.debug('kwargs: %s', kwargs)
 
-    BASE_PATH = args['<base_path>']
-    EXTENSION = args['--extension']
-    OUTPUT_FILE = args['<output_file>']
-    #SHAPE = [3, 224, 224]
+    # SHAPE = [3, 224, 224]
     SHAPE = [1, 64, 64]
 
-    count, sum_data = process_images(BASE_PATH, EXTENSION, SHAPE)
+    count, sum_data = process_images(base_path, extension, SHAPE)
 
     means = numpy.zeros(shape=SHAPE)
     for s in range(SHAPE[0]):
         means[s, ...] = sum_data[s, ...] / float(count)
 
-    bob.io.base.save(means, OUTPUT_FILE)
-    bob.io.base.save(means[0, :, :].astype("uint8"), "xuxa.png")
+    save(means, output_file)
+    save(means[0, :, :].astype("uint8"), "xuxa.png")