REFACTOR: indicio.utils.__init__.py to multiple utils modules

indicoio/utils/image.py

@@ -0,0 +1,110 @@
+"""
+Image Utils
+Handles preprocessing images before they are sent to the server
+"""
+import os.path, base64, StringIO, re, warnings
+
+from PIL import Image
+
+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), batch=False):
+    """
+    Takes an image and prepares it for sending to the api including
+    resizing and image data/structure standardizing.
+    """
+    if batch:
+        return [image_preprocess(img, batch=False) for img in image]
+
+    if isinstance(image, basestring):
+        b64_str = re.sub('^data:image/.+;base64,', '', image)
+        if os.path.isfile(image):
+            # check type of element
+            outImage = Image.open(image)
+        elif B64_PATTERN.match(b64_str) is not None:
+            return b64_str
+        else:
+            raise IndicoError("Snose tring provided must be a valid filepath or base64 encoded string")
+
+    elif isinstance(image, list): # image passed in is a list and not np.array
+        warnings.warn(
+            "Input as lists of pixels will be deprecated in the next major update",
+            DeprecationWarning
+        )
+        outImage = process_list_image(image)
+    elif isinstance(image, Image.Image):
+        outImage = image
+    elif type(image).__name__ == "ndarray": # image is from numpy/scipy
+        out_image = Image.fromarray(image)
+    else:
+        raise IndicoError("Image must be a filepath, base64 encoded string, or a numpy array")
+
+    # image resizing
+    outImage = outImage.resize(size)
+
+    # convert to base64
+    temp_output = StringIO.StringIO()
+    outImage.save(temp_output, format='PNG')
+    temp_output.seek(0)
+    output_s = temp_output.read()
+
+    return base64.b64encode(output_s)
+
+
+def get_list_dimensions(_list):
+    """
+    Takes a nested list and returns the size of each dimension followed
+    by the element type in the list
+    """
+    if isinstance(_list, list) or isinstance(_list, tuple):
+        return [len(_list)] + get_list_dimensions(_list[0])
+    return []
+
+
+def get_element_type(_list, dimens):
+    """
+    Given the dimensions of a nested list and the list, returns the type of the
+    elements in the inner list.
+    """
+    elem = _list
+    for _ in xrange(len(dimens)):
+        elem = elem[0]
+    return type(elem)
+
+
+def process_list_image(_list):
+    """
+    Processes list to be [[(int, int, int), ...]]
+    """
+    # Check if list is empty
+    if not _list:
+        return _list
+
+    dimens = get_list_dimensions(_list)
+    data_type = get_element_type(_list, dimens)
+
+    seq_obj = []
+
+    outImage = Image.new("RGB", (dimens[0], dimens[1]))
+    for i in xrange(dimens[0]):
+        for j in xrange(dimens[1]):
+            elem = _list[i][j]
+            if len(dimens) >= 3:
+                #RGB(A)
+                if data_type == float:
+                    seq_obj.append((int(elem[0] * 255), int(elem[1] * 255), int(elem[2] * 255)))
+                else:
+                    seq_obj.append(elem[0:3])
+            elif data_type == float:
+                #Grayscale 0 - 1.0f
+                seq_obj.append((int(elem * 255), ) * 3)
+            else:
+                #Grayscale 0 - 255
+                seq_obj.append((elem, ) * 3)
+
+    #Needs to be 0 - 255 in flattened list of (R, G, B)
+    outImage.putdata(data = seq_obj)
+
+    return outImage