Changed input parameter for feature extractor to take a dictionary of keywords.

import cv2

from scipy import misc

def extract_features(image_array, num_nodes=500):

def extract_features(image_array, extractor_parameters):

    """

    Extracts keypoints and corresponding descriptors from a numpy array

    that represents an image. The extracted information is returned as a tuple whose first element is

    a list of KeyPoints and whose second element is a numpy array of geometric vector descriptors.

    This method finds and extracts features from an image using the given dictionary of keyword arguments. 

    The input image is represented as NumPy array and the output features are represented as keypoint IDs 

    with corresponding descriptors.

    Parameters

    ----------

    image_array : ndarray

                  a numpy array that represents an image

    num_nodes : int

                the number of best features to retain

                  a NumPy array that represents an image

    extractor_parameters : dict

                           A dictionary containing OpenCV SIFT parameters names and values. 

    Returns

    -------

    : tuple

      This tuple is in the form (list of KeyPoints, array of descriptors)

      in the form ([list of OpenCV KeyPoints], [NumPy array of descriptors as geometric vectors])

    """

    sift = cv2.xfeatures2d.SIFT_create(num_nodes)

    sift = cv2.xfeatures2d.SIFT_create(**extractor_parameters)

    converted_array = misc.bytescale(image_array)

    return sift.detectAndCompute(converted_array, None)

    def setUp(self):

        self.dataset = io_gdal.GeoDataset(get_path('Mars_MGS_MOLA_ClrShade_MAP2_0.0N0.0_MERC.tif'))

        self.data_array = self.dataset.read_array()

        self.parameters = {"nfeatures" : 10,

                           "nOctaveLayers" : 3,

                           "contrastThreshold" : 0.02,

                           "edgeThreshold" : 10,

                           "sigma" : 1.6}

    def test_extract_features(self):

        features = feature_extractor.extract_features(self.data_array, 10)

        features = feature_extractor.extract_features(self.data_array, self.parameters)

        self.assertEquals(len(features), 2)

        self.assertIsInstance(features[0][0], type(cv2.KeyPoint()))

        self.assertIsInstance(features[1][0], np.ndarray)