Merge pull request #3 from IndicoDataSolutions/imagefeatures

Fix error handling for python api wrapper

CHANGES.txt

@@ -8,3 +8,4 @@ v0.3.3, Fri Aug 1 -- Added language detection api
 v0.4.0, Fri Aug 1 -- Changed api import to lowercase, added language example
 v0.4.1, Fri Aug 1 -- Updated __version__ variable to be accurate
 v0.4.2, Wed Aug 6 -- Updated README to accurately reflect political analysis results
+v0.4.3, Thu Sep 11 -- Added image features api and sphinx compliant documentation

README

@@ -1,12 +1,16 @@
 indicoio-python
 ===============
 
-A wrapper for a series of APIs made by Indico Data Solutions.
+A wrapper for a series of APIs made by indico.
 
 Check out the main site on:
 
 http://indico.io
 
+Check out our documentation on:
+
+http://indicoiopython.s3-website-us-west-2.amazonaws.com/indicoio.html
+
 Our APIs are totally free to use, and ready to be used in your application. No data or training required.
 
 Current APIs
@@ -14,8 +18,9 @@ Current APIs
 
 Right now this wrapper supports the following apps:
 
-- Political Sentiment Analysis
 - Positive/Negative Sentiment Analysis
+- Political Sentiment Analysis
+- Image Feature Extraction
 - Facial Emotion Recognition
 - Facial Feature Extraction
 - Language Detection

README.md

@@ -1,12 +1,16 @@
 indicoio-python
 ===============
 
-A wrapper for a series of APIs made by Indico Data Solutions.
+A wrapper for a series of APIs made by indico.
 
 Check out the main site on:
 
 http://indico.io
 
+Check out our documentation on:
+
+http://indicoiopython.s3-website-us-west-2.amazonaws.com/indicoio.html
+
 Our APIs are totally free to use, and ready to be used in your application. No data or training required.
 
 Current APIs
@@ -14,8 +18,9 @@ Current APIs
 
 Right now this wrapper supports the following apps:
 
-- Political Sentiment Analysis
 - Positive/Negative Sentiment Analysis
+- Political Sentiment Analysis
+- Image Feature Extraction
 - Facial Emotion Recognition
 - Facial Feature Extraction
 - Language Detection

indicoio/__init__.py

@@ -1,9 +1,10 @@
 JSON_HEADERS = {'Content-type': 'application/json', 'Accept': 'text/plain'}
 
-Version, version, __version__, VERSION = ('0.4.2',) * 4
+Version, version, __version__, VERSION = ('0.4.3',) * 4
 
 from text.sentiment import political, posneg
 from text.sentiment import posneg as sentiment
 from text.lang import language
 from images.fer import fer
 from images.features import facial_features
+from images.features import image_features

indicoio/images/features.py

@@ -4,6 +4,7 @@ import requests
 import numpy as np
 
 from indicoio import JSON_HEADERS
+from indicoio.utils import image_preprocess
 
 def facial_features(image):
     """
@@ -30,5 +31,47 @@ def facial_features(image):
     
     data_dict = json.dumps({"face": image})
     response = requests.post("http://api.indico.io/facialfeatures", data=data_dict, headers=JSON_HEADERS)
-    response_dict = json.loads(response.content)
+    response_dict = response.json()
-    return response_dict['response']
+    if 'response' not in response_dict:
+      raise ValueError(response_dict.values()[0])
+    else:
+      return response_dict['response']
+
+def image_features(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
+
+       >>> from indicoio import image_features
+       >>> import numpy as np
+       >>> image = np.zeros((64,64,3))
+       >>> features = image_features(image)
+       >>> len(features),np.min(features),np.max(features),np.sum(np.asarray(f)!=0)
+       (2048, 0.0, 6.97088623046875, 571)
+
+    Since the image features returned are a semantic description of the contents of an image they can be used
+    to implement many other common image related tasks such as object recognition or image similarity and retrieval.
+
+    For image similarity, simple distance metrics applied to collections of image feature vectors can work very well.
+
+    :param image: The image to be analyzed.
+    :type image: numpy.ndarray
+    :rtype: List containing features
+    """
+    image = image_preprocess(image)
+    data_dict = json.dumps({"image": image})
+    response = requests.post("http://api.indico.io/imagefeatures", data=data_dict, headers=JSON_HEADERS)
+    response_dict = response.json()
+    if 'Features' not in response_dict:
+      raise ValueError(response_dict.values()[0])
+    else:
+      return response_dict['Features']

indicoio/images/fer.py

@@ -30,4 +30,8 @@ def fer(image):
     
     data_dict = json.dumps({"face": image})
     response = requests.post("http://api.indico.io/fer", data=data_dict, headers=JSON_HEADERS)
-    return json.loads(response.content)
+    response_dict = response.json()
+    if len(response_dict) < 2:
+      raise ValueError(response_dict.values()[0])
+    else:
+      return response_dict

indicoio/text/lang.py

@@ -28,4 +28,8 @@ def language(text):
     
     data_dict = json.dumps({'text': text})
     response = requests.post("http://api.indico.io/language", data=data_dict, headers=JSON_HEADERS)
-    return json.loads(response.content)
+    response_dict = response.json()
+    if len(response_dict) < 2:
+      raise ValueError(response_dict.values()[0])
+    else:
+      return response_dict

indicoio/text/sentiment.py

@@ -32,13 +32,17 @@ def political(text):
 
     data_dict = json.dumps({'text': text})
     response = requests.post("http://api.indico.io/political", data=data_dict, headers=JSON_HEADERS)
-    return json.loads(response.content)
+    response_dict = response.json()
+    if len(response_dict) < 2:
+      raise ValueError(response_dict.values()[0])
+    else:
+      return response_dict
 
 def posneg(text):
     """
     Given input text, returns a scalar estimate of the sentiment of that text.
     Values are roughly in the range 0 to 1 with 0.5 indicating neutral sentiment.
-    Likewise, 0 would suggest very negative sentiment and 1 would suggest very positive sentiment.
+    For reference, 0 suggests very negative sentiment and 1 suggests very positive sentiment.
 
     Example usage:
 
@@ -48,13 +52,17 @@ def posneg(text):
        >>> text = 'Thanks everyone for the birthday wishes!! It was a crazy few days ><'
        >>> sentiment = sentiment(text)
        >>> sentiment
-       {u'Sentiment': 0.6946439339979863}
+       0.6946439339979863
 
     :param text: The text to be analyzed.
     :type text: str or unicode
-    :rtype: Dictionary containing Sentiment key with a float value
+    :rtype: Float
     """
     
     data_dict = json.dumps({'text': text})
     response = requests.post("http://api.indico.io/sentiment", data=data_dict, headers=JSON_HEADERS)
-    return json.loads(response.content)
+    response_dict = response.json()
+    if 'Sentiment' not in response_dict:
+      raise ValueError(response_dict.values()[0])
+    else:
+      return response_dict['Sentiment']

indicoio/utils/__init__.py

@@ -1,5 +1,6 @@
 import inspect
 import numpy as np
+from skimage.transform import resize
 
 class TypeCheck(object):
     """
@@ -84,3 +85,22 @@ def normalize(array, distribution=1, norm_range=(0, 1), **kwargs):
     if dict_array:
         return dict(zip(keys, norm_array))
     return norm_array
+
+def image_preprocess(image):
+    """
+    Takes an image and prepares it for sending to the api including 
+    resizing and image data/structure standardizing.
+    """
+    if isinstance(image,list):
+        image = np.asarray(image)
+    if type(image).__module__ != np.__name__:
+        raise ValueError('Image was not of type numpy.ndarray or list.')
+    if str(image.dtype) in ['int64','uint8']:
+        image = image/255.
+    if len(image.shape) == 2:
+        image = np.dstack((image,image,image))
+    if len(image.shape) == 4:
+        image = image[:,:,:3]
+    image = resize(image,(64,64))
+    image = image.tolist()
+    return image

setup.py

@@ -8,7 +8,7 @@ except ImportError:
 
 setup(
     name="IndicoIo",
-    version='0.4.2',
+    version='0.4.3',
     packages=[
         "indicoio",
         "indicoio.text",