ADD: ImageRecognition

indicoio/__init__.py

@@ -18,9 +18,9 @@ from indicoio.text.tagging import text_tags
 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.features import facial_features, image_features
 from indicoio.images.faciallocalization import facial_localization
-from indicoio.images.features import image_features
+from indicoio.images.recognition import image_recognition
 from indicoio.images.filtering import content_filtering
 from indicoio.utils.multi import analyze_image, analyze_text, intersections
 

indicoio/config.py

@@ -60,11 +60,12 @@ IMAGE_APIS = [
     'fer',
     'facial_features',
     'image_features',
+    'image_recognition',
     'content_filtering'
 ]
 
 OTHER_APIS = [
-    "analyze_text", 
+    "analyze_text",
     "analyze_image",
     "intersections"
 ]

indicoio/images/recognition.py

@@ -0,0 +1,28 @@
+import requests
+
+from indicoio.utils.image import image_preprocess
+from indicoio.utils.api import api_handler
+
+def image_recognition(image, cloud=None, batch=False, api_key=None, version=None, **kwargs):
+    """
+    Given an input image, returns a dictionary of image classifications with associated scores
+
+    * 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, minaxis resizing will be done internally to 144 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_recognition
+       >>> features = image_recognition(<filename>)
+
+    :param image: The image to be analyzed.
+    :type image: str
+    :rtype: dict containing classifications
+    """
+    image = image_preprocess(image, size=144, min_axis=True, batch=batch)
+    url_params = {"batch": batch, "api_key": api_key, "version": version}
+    return api_handler(image, cloud=cloud, api="imagerecognition", url_params=url_params, **kwargs)