Merge pull request #12 from kree/matching

  - conda info -a

  # Create a virtual env and install dependencies

  - conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION nose gdal numpy scipy pandas networkx

  - conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION nose gdal numpy pillow scipy pandas networkx

  # Activate the env

  - source activate test-env

import cv2

from scipy import misc

def extract_features(image_array, num_nodes=500):

    """

    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.

    Parameters

    ----------

    image_array : ndarray

                  a numpy array that represents an image

    num_nodes : int

                the number of best features to retain

    Returns

    -------

    : tuple

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

    """

    sift = cv2.xfeatures2d.SIFT_create(num_nodes)

    converted_array = misc.bytescale(image_array)

    return sift.detectAndCompute(converted_array, None)

import os

import numpy as np

import unittest

from autocnet.examples import get_path

import cv2

import sys

sys.path.insert(0, os.path.abspath('..'))

from .. import feature_extractor

from autocnet.fileio import io_gdal

class TestFeatureExtractor(unittest.TestCase):

    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()

    def test_extract_features(self):

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

        self.assertEquals(len(features), 2)

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

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

pvl>=0.2.0

protobuf==3.0.0b2

networkx