Passing in a root_url variable and pulling config from environment variables

indicoio/images/features.py

@@ -4,12 +4,13 @@ import requests
 import numpy as np
 
 from indicoio.utils import image_preprocess, api_handler
+import indicoio.config as config
 
-def facial_features(api_root, image, batch=False, auth=None, **kwargs):
+def facial_features(image, url_root=config.api_root, batch=False, auth=None, **kwargs):
     """
     Given an grayscale input image of a face, returns a 48 dimensional feature vector explaining that face.
     Useful as a form of feature engineering for face oriented tasks.
-    Input should be in a list of list format, resizing will be attempted internally but for best 
+    Input should be in a list of list format, resizing will be attempted internally but for best
     performance, images should be already sized at 48x48 pixels.
 
     Example usage:
@@ -27,18 +28,18 @@ def facial_features(api_root, image, batch=False, auth=None, **kwargs):
     :type image: list of lists
     :rtype: List containing feature responses
     """
-    return api_handler(image, api_root + "facialfeatures", batch=batch, auth=auth, **kwargs)
+    return api_handler(image, url_root + "facialfeatures", batch=batch, auth=auth, **kwargs)
 
-def image_features(api_root, image, batch=False, auth=None, **kwargs):
+def image_features(image, url_root=config.api_root, batch=False, auth=None, **kwargs):
     """
-    Given an input image, returns a 2048 dimensional sparse feature vector explaining that image. 
+    Given an input image, returns a 2048 dimensional sparse feature vector explaining that image.
     Useful as a form of feature engineering for image oriented tasks.
 
     * Input can be either grayscale or rgb color and should either be a numpy array or nested list format.
     * Input data should be either uint8 0-255 range values or floating point between 0 and 1.
     * Large images (i.e. 1024x768+) are much bigger than needed, resizing will be done internally to 64x64 if needed.
     * For ideal performance, images should be square aspect ratio but non-square aspect ratios are supported as well.
-    
+
     Example usage:
 
     .. code-block:: python
@@ -60,4 +61,4 @@ def image_features(api_root, image, batch=False, auth=None, **kwargs):
     :rtype: List containing features
     """
     image = image_preprocess(image, batch=batch)
-    return api_handler(image, api_root + "imagefeatures", batch=batch, auth=auth, **kwargs)
+    return api_handler(image, url_root + "imagefeatures", batch=batch, auth=auth, **kwargs)

indicoio/images/fer.py

@@ -2,12 +2,14 @@ import json
 
 import requests
 import numpy as np
-from indicoio.utils import api_handler
 
-def fer(api_root, image, batch=False, auth=None, **kwargs):
+from indicoio.utils import api_handler
+import indicoio.config as config
+
+def fer(image, url_root=config.api_root, batch=False, auth=None, **kwargs):
     """
     Given a grayscale input image of a face, returns a probability distribution over emotional state.
-    Input should be in a list of list format, resizing will be attempted internally but for best 
+    Input should be in a list of list format, resizing will be attempted internally but for best
     performance, images should be already sized at 48x48 pixels..
 
     Example usage:
@@ -19,13 +21,13 @@ def fer(api_root, image, batch=False, auth=None, **kwargs):
        >>> face = np.zeros((48,48)).tolist()
        >>> emotions = fer(face)
        >>> emotions
-	   {u'Angry': 0.6340586827229989, u'Sad': 0.1764309536057839, 
-	   u'Neutral': 0.05582989039191157, u'Surprise': 0.0072685938275375344, 
+	   {u'Angry': 0.6340586827229989, u'Sad': 0.1764309536057839,
+	   u'Neutral': 0.05582989039191157, u'Surprise': 0.0072685938275375344,
 	   u'Fear': 0.08523385724298838, u'Happy': 0.04117802220878012}
 
     :param image: The image to be analyzed.
     :type image: list of lists
     :rtype: Dictionary containing emotion probability pairs
     """
-    
-    return api_handler(image, api_root + "fer", batch=batch, auth=auth, **kwargs)
+
+    return api_handler(image, url_root + "fer", batch=batch, auth=auth, **kwargs)