Merge pull request #87 from IndicoDataSolutions/contentFiltering

added content filtering

indicoio/__init__.py

@@ -18,6 +18,7 @@ from indicoio.text.ner import named_entities
 from indicoio.images.fer import fer
 from indicoio.images.features import facial_features
 from indicoio.images.features import image_features
+from indicoio.images.filtering import content_filtering
 from indicoio.utils.multi import predict_image, predict_text
 
 from indicoio.config import API_NAMES

indicoio/config.py

@@ -58,7 +58,8 @@ TEXT_APIS = [
 IMAGE_APIS = [
     'fer',
     'facial_features',
-    'image_features'
+    'image_features',
+    'content_filtering'
 ]
 
 API_NAMES = IMAGE_APIS + TEXT_APIS + ["predict_text", "predict_image"]

indicoio/images/filtering.py

@@ -0,0 +1,29 @@
+import requests
+
+from indicoio.utils.api import api_handler
+from indicoio.utils.image import image_preprocess
+import indicoio.config as config
+
+def content_filtering(image, cloud=None, batch=False, api_key=None, **kwargs):
+    """
+    Given a grayscale input image, returns how obcene the image is.
+    Input should be in a list of list format.
+
+    Example usage:
+
+    .. code-block:: python
+
+       >>> from indicoio import content_filtering
+       >>> import numpy as np
+       >>> face = np.zeros((48,48)).tolist()
+       >>> res = content_filtering(face)
+       >>> res
+	   .056
+
+    :param image: The image to be analyzed.
+    :type image: list of lists
+    :rtype: float of nsfwness
+    """
+    image = image_preprocess(image, batch=batch, size=None)
+    url_params = {"batch": batch, "api_key": api_key}
+    return api_handler(image, cloud=cloud, api="contentfiltering", url_params=url_params, **kwargs)

indicoio/utils/image.py

@@ -48,7 +48,8 @@ def image_preprocess(image, size=(48,48), batch=False):
         raise IndicoError("Image must be a filepath, base64 encoded string, or a numpy array")
 
     # image resizing
-    out_image = out_image.resize(size)
+    if size:
+        out_image = out_image.resize(size)
 
     # convert to base64
     temp_output = StringIO.StringIO()

tests/test_remote.py

@@ -6,8 +6,8 @@ from requests import ConnectionError
 from nose.plugins.skip import Skip, SkipTest
 
 from indicoio import config
-from indicoio import political, sentiment, fer, facial_features, language, image_features, text_tags
-from indicoio import batch_political, batch_sentiment, batch_fer, batch_facial_features
+from indicoio import political, sentiment, fer, facial_features, content_filtering, language, image_features, text_tags
+from indicoio import batch_political, batch_sentiment, batch_fer, batch_content_filtering, batch_facial_features
 from indicoio import batch_language, batch_image_features, batch_text_tags
 from indicoio import keywords, batch_keywords
 from indicoio import sentiment_hq, batch_sentiment_hq
@@ -64,6 +64,12 @@ class BatchAPIRun(unittest.TestCase):
         self.assertTrue(isinstance(response, list))
         self.assertTrue(isinstance(response[0], dict))
 
+    def test_batch_content_filtering(self):
+        test_data = [generate_array((48,48))]
+        response = batch_content_filtering(test_data, api_key=self.api_key)
+        self.assertTrue(isinstance(response, list))
+        self.assertTrue(isinstance(response[0], float))
+
     def test_batch_fer_bad_b64(self):
         test_data = ["$bad#FI jeaf9(#0"]
         self.assertRaises(IndicoError, batch_fer, test_data, api_key=self.api_key)
@@ -324,6 +330,11 @@ class FullAPIRun(unittest.TestCase):
         self.assertTrue(isinstance(response, dict))
         self.assertEqual(fer_set, set(response.keys()))
 
+    def test_safe_content_filtering(self):
+        test_face = self.load_image("data/happy.png", as_grey=True)
+        response = content_filtering(test_face)
+        self.assertTrue(response < 0.5)
+
     def test_good_facial_features(self):
         test_face = generate_array((48,48))
         response = facial_features(test_face)