# This is a combination of 6 commits.

# The first commit's message is:
test image preprocessing

# The 2nd commit message will be skipped:

#	add PIL to enable testing of preprocessing code

# The 3rd commit message will be skipped:

#	try a different way to install PIL on travis

# The 4th commit message will be skipped:

#	include PIL only in python 2.7

# The 5th commit message will be skipped:

#	test image preprocessing

# The 6th commit message will be skipped:

#	fall back to Pillow for python 3 image processing

.travis.yml

@@ -32,6 +32,14 @@ install:
   - source activate test-environment
   - pip install pytest-cov python-coveralls pytest-xdist coverage==3.7.1 #we need this version of coverage for coveralls.io to work
   - pip install git+git://github.com/Theano/Theano.git
+
+  # install PIL for preprocessing tests
+  - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
+      conda install pil;
+    elif [[ "$TRAVIS_PYTHON_VERSION" == "3.4" ]]; then
+      conda install Pillow;
+    fi
+
   - python setup.py install
 
   # install TensorFlow

tests/keras/preprocessing/test_image.py

@@ -0,0 +1,62 @@
+import pytest
+from keras.preprocessing.image import *
+from PIL import Image
+import numpy as np
+import os
+import shutil
+
+def setup_function(func):
+    np.random.seed(1337)
+
+    os.mkdir('test_images')
+    os.mkdir('test_images/rgb')
+    os.mkdir('test_images/gsc')
+
+    img_w = img_h = 20
+    for n in range(8):
+        bias = np.random.rand(img_w,img_h,1)*64
+        variance = np.random.rand(img_w,img_h,1)*(255-64)
+        imarray = np.random.rand(img_w,img_h,3) * variance + bias
+        im = Image.fromarray(imarray.astype('uint8')).convert('RGBA')
+        im.save('test_images/rgb/rgb_test_image_'+str(n)+'.png')
+
+        imarray = np.random.rand(img_w,img_h,1) * variance + bias
+        im = Image.fromarray(imarray.astype('uint8').squeeze()).convert('L')
+        im.save('test_images/gsc/gsc_test_image_'+str(n)+'.png')
+
+def teardown_function(func):
+    shutil.rmtree('test_images')
+
+def test_image_data_generator():
+    np.random.seed(1337)
+
+    for color_mode in ['gsc','rgb']:
+        file_list = list_pictures('test_images/'+color_mode)
+        img_list = []
+        for f in file_list:
+            img_list.append(img_to_array(load_img(f))[None,...])
+
+        images = np.vstack(img_list)
+        generator = ImageDataGenerator(
+            featurewise_center=True,
+            samplewise_center=True,
+            featurewise_std_normalization=True,
+            samplewise_std_normalization=True,
+
+            zca_whitening=True,
+            rotation_range=90.,
+            width_shift_range=10.,
+            height_shift_range=10.,
+            horizontal_flip=True,
+            vertical_flip=True
+        )
+
+        generator.fit(images,augment=True)
+
+        for x,y in generator.flow(images,np.arange(images.shape[0]), shuffle=True, save_to_dir='test_images'):
+            assert x.shape[1:] == images.shape[1:]
+            #TODO: make sure the normalization is working as inteded
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])

tests/test_loss_masking.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pytest
-np.random.seed(1337)
 
 from keras.models import Sequential, weighted_objective
 from keras.layers.core import TimeDistributedDense, Masking
@@ -10,6 +9,7 @@
 
 @pytest.mark.skipif(K._BACKEND=='tensorflow', reason="currently not working with TensorFlow")
 def test_masking():
+    np.random.seed(1337)
     X = np.array(
         [[[1, 1], [2, 1], [3, 1], [5, 5]],
          [[1, 5], [5, 0], [0, 0], [0, 0]]], dtype=np.int32)