ADD: basic min axis content resizing

tests/test_remote.py

@@ -336,6 +336,11 @@ class FullAPIRun(unittest.TestCase):
         response = content_filtering(test_face)
         self.assertTrue(response < 0.5)
 
+    def test_resize_content_filtering(self):
+        test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
+        response = content_filtering(test_face)
+        self.assertTrue(isinstance(response, float))
+
     def test_good_facial_features(self):
         test_face = os.path.normpath(os.path.join(DIR, "data/48by48.png"))
         response = facial_features(test_face)
@@ -551,6 +556,11 @@ class NumpyImagesRun(FullAPIRun):
         test_image = np.random.randint(255, 300, size=(48,48, 5))
         self.assertRaises(IndicoError, image_features, test_image)
 
+    def test_resize_content_filtering_numpy_arrays(self):
+        test_image = np.random.randint(0, 255, size=(480,248, 3))
+        response = content_filtering(test_image)
+        self.assertTrue(isinstance(response, float))
+
 def flatten(container):
     for i in container:
         if isinstance(i, list) or isinstance(i, tuple):

tests/unit_test.py

@@ -0,0 +1,16 @@
+from indicoio.utils.image import image_preprocess
+from PIL import Image
+import os, unittest, base64, StringIO
+
+DIR = os.path.dirname(os.path.realpath(__file__))
+
+class ResizeTests(unittest.TestCase):
+    """
+    test image resizing
+    """
+    def test_min_axis_resize(self):
+        test_image = os.path.normpath(os.path.join(DIR, "data/fear.png"))
+        resized_image = image_preprocess(test_image, min_axis=360)
+        image_string = StringIO.StringIO(base64.b64decode(resized_image))
+        image = Image.open(image_string)
+        self.assertEqual(image.size, (360.0, 360.0))