Merge pull request #111 from IndicoDataSolutions/diana-fl

ADD: facial localization api

indicoio/__init__.py

@@ -19,6 +19,7 @@ from indicoio.text.keywords import keywords
 from indicoio.text.ner import named_entities
 from indicoio.images.fer import fer
 from indicoio.images.features import facial_features
+from indicoio.images.faciallocalization import facial_localization
 from indicoio.images.features import image_features
 from indicoio.images.filtering import content_filtering
 from indicoio.utils.multi import predict_image, predict_text

indicoio/images/faciallocalization.py

@@ -0,0 +1,31 @@
+import requests
+
+from indicoio.utils.image import image_preprocess
+from indicoio.utils.api import api_handler
+
+
+def facial_localization(image, cloud=None, batch=False, api_key=None, **kwargs):
+    """
+    Given an image, returns a list of faces found within the image.
+    For each face, we return a dictionary containing the upper left corner and lower right corner.
+    If crop is True, the cropped face is included in the dictionary.
+    Input should be in a numpy ndarray or a filename.
+
+    Example usage:
+
+    .. code-block:: python
+
+       >>> from indicoio import facial_localization
+       >>> import numpy as np
+       >>> img = np.zeros([image of a face])
+       >>> faces = facial_localization(img)
+       >>> len(faces)
+       1
+
+    :param image: The image to be analyzed.
+    :type image: filepath or ndarray
+    :rtype: List of faces (dict) found. 
+    """
+    image = image_preprocess(image, batch=batch)
+    url_params = {"batch": batch, "api_key": api_key}
+    return api_handler(image, cloud=cloud, api="faciallocalization", url_params=url_params, **kwargs)

indicoio/images/features.py

@@ -16,7 +16,7 @@ def facial_features(image, cloud=None, batch=False, api_key=None, **kwargs):
 
        >>> from indicoio import facial_features
        >>> import numpy as np
-       >>> face = np.zeros((48,48)).tolist()
+       >>> face = np.zeros((48,48))
        >>> features = facial_features(face)
        >>> len(features)
        48
@@ -25,7 +25,7 @@ def facial_features(image, cloud=None, batch=False, api_key=None, **kwargs):
     :type image: list of lists
     :rtype: List containing feature responses
     """
-    image = image_preprocess(image, batch=batch)
+    image = image_preprocess(image, batch=batch, size=(48,48))
     url_params = {"batch": batch, "api_key": api_key}
     return api_handler(image, cloud=cloud, api="facialfeatures", url_params=url_params, **kwargs)
 

indicoio/images/filtering.py

@@ -24,6 +24,6 @@ def content_filtering(image, cloud=None, batch=False, api_key=None, **kwargs):
     :type image: list of lists
     :rtype: float of nsfwness
     """
-    image = image_preprocess(image, batch=batch, size=None, min_axis=128)
+    image = image_preprocess(image, batch=batch, min_axis=128)
     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

@@ -10,7 +10,7 @@ from indicoio.utils.errors import IndicoError, DataStructureException
 
 B64_PATTERN = re.compile("^([A-Za-z0-9+/]{4})*([A-Za-z0-9+/]{4}|[A-Za-z0-9+/]{3}=|[A-Za-z0-9+/]{2}==)")
 
-def image_preprocess(image, size=(48,48), min_axis=None, batch=False):
+def image_preprocess(image, size=None, min_axis=None, batch=False):
     """
     Takes an image and prepares it for sending to the api including
     resizing and image data/structure standardizing.

tests/test_remote.py

@@ -6,7 +6,7 @@ from requests import ConnectionError
 from nose.plugins.skip import Skip, SkipTest
 
 from indicoio import config
-from indicoio import political, sentiment, fer, facial_features, content_filtering, language, image_features, text_tags
+from indicoio import political, sentiment, fer, facial_features, facial_localization, 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
@@ -355,6 +355,24 @@ class FullAPIRun(unittest.TestCase):
         self.assertTrue(isinstance(response, dict))
         self.assertEqual(fer_set, set(response.keys()))
 
+    def test_facial_localization(self):
+        test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
+        res = facial_localization(test_face)[0]
+        self.assertTrue(res["top_left_corner"][0] < res["bottom_right_corner"][0])
+        self.assertTrue(res["top_left_corner"][1] < res["bottom_right_corner"][1])
+
+    def test_facial_localization_sensitivity(self):
+        test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
+        low_sens = facial_localization(test_face, sensitivity=0.1)
+        high_sens = facial_localization(test_face, sensitivity=0.9)
+        self.assertEqual(len(low_sens), 1)
+        self.assertTrue(len(high_sens) > 1)
+
+    def test_facial_localization_crop(self):
+        test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
+        res = facial_localization(test_face, crop=True)[0]
+        self.assertTrue(res.get("image"))
+
     def test_safe_content_filtering(self):
         test_face = os.path.normpath(os.path.join(DIR, "data/happy.png"))
         response = content_filtering(test_face)