Computed statistics

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
+
+Usage:
+  compute_statistics.py <base_path> --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
+import os
+import numpy
+import bob.io.image
+
+def process_images(base_path, extension, shape):
+
+    files = os.listdir(base_path)
+    sum_data = numpy.zeros(shape=shape)
+    count = 0
+    for f in files:
+        path = os.path.join(base_path, f)
+        if os.path.isdir(path):
+            c, s = process_images(path, extension, shape)
+            count += c
+            sum_data += s
+
+        if os.path.splitext(path)[1] == extension:
+            data = bob.io.base.load(path)
+            count += 1
+            sum_data += data
+
+    return count, sum_data
+
+
+def main():
+    args = docopt(__doc__, version='Mnist training with TensorFlow')
+
+    BASE_PATH = args['<base_path>']
+    EXTENSION = args['--extension']
+    SHAPE = [3, 250, 250]
+
+    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, "means.hdf5")