Commit f469aa45 authored by jay's avatar jay
Browse files

Pushing up code coverage, testing edge more via functional testing

parent 4f526638

autocnet/graph/edge.py

+8 −5
Original line number Diff line number Diff line
@@ -118,6 +118,7 @@ class Edge(dict, MutableMapping): 
    def decompose_and_match(*args, **kwargs):

        pass



    """

    def extract_subset(self, *args, **kwargs):

        self.compute_overlap()
@@ -136,7 +137,7 @@ class Edge(dict, MutableMapping): 
        node = self.destination

        arr = node.geodata.read_array(pixels=pixels)

        node.extract_features(arr, xystart=xystart, *args, **kwargs)



    """

    def symmetry_check(self):

        self.masks['symmetry'] = od.mirroring_test(self.matches)
@@ -167,10 +168,8 @@ class Edge(dict, MutableMapping): 
        matches, mask = self.clean(clean_keys)



        # TODO: Homogeneous is horribly inefficient here, use Numpy array notation

        s_keypoints = self.source.get_keypoint_coordinates(index=matches['source_idx'],

                                                                 homogeneous=True)

        d_keypoints = self.destination.get_keypoint_coordinates(index=matches['destination_idx'],

                                                                homogeneous=True)

        s_keypoints = self.get_keypoints('source', index=matches['source_idx'])

        d_keypoints = self.get_keypoints('destination', index=matches['destination_idx'])





        # Replace the index with the matches index.
@@ -186,6 +185,10 @@ class Edge(dict, MutableMapping): 
            # Set the initial state of the fundamental mask in the masks

            self.masks[maskname] = mask



    def get_keypoints(self, node, index=None, homogeneous=True):

        node = getattr(self, node)

        return node.get_keypoint_coordinates(index=index, homogeneous=homogeneous)



    def compute_fundamental_error(self, clean_keys=[]):

        """

        Given a fundamental matrix, compute the reprojective error between

autocnet/graph/tests/test_edge.py

+0 −21
Original line number Diff line number Diff line
@@ -22,30 +22,9 @@ class TestEdge(unittest.TestCase): 
        destination = Mock(node.Node)

        self.edge = edge.Edge(source=source, destination=destination)



        '''

        # Define a matches dataframe

        source_image = np.zeros(20)

        destination_image = np.ones(20)

        source_idx = np.repeat(np.arange(10), 2)

        destination_idx = np.array([336,  78, 267, 467, 214, 212, 463, 241,  27, 154, 320, 108, 196,

                                    460,  67, 135,  80, 122, 106, 343])

        distance = np.array([263.43121338,  287.05050659,  231.03895569,  242.14459229,

                             140.07498169,  299.86331177,  332.05722046,  337.71438599,

                             94.9052124,  208.04806519,  102.21056366,  173.48774719,

                             102.19099426,  237.63206482,  240.93359375,  277.74627686,

                             217.82791138,  224.22979736,  260.3939209,  287.91143799])

        data = np.stack((source_image, source_idx, destination_image, destination_idx, distance), axis=-1)

        self.edge.matches = pd.DataFrame(data, columns=['source_image', 'source_idx',

                                                 'destination_image', 'destination_idx',

                                                 'distance'])

        '''



    def test_masks(self):

        self.assertIsInstance(self.edge.masks, pd.DataFrame)



    def test_compute_fundamental_matrix(self):

        pass



    def test_edge_overlap(self):

        e = edge.Edge()

        e.weight = {}

functional_tests/test_two_image.py

+10 −0
Original line number Diff line number Diff line
@@ -63,13 +63,23 @@ class TestTwoImageMatching(unittest.TestCase): 
        # Create fundamental matrix

        cg.compute_fundamental_matrices()



        for s, d, e in cg.edges_iter(data=True):

            assert isinstance(e['fundamental_matrix'], np.ndarray)

            err = e.compute_fundamental_error(clean_keys=['fundamental'])

            assert isinstance(err, pd.Series)

            matches, _ = e.clean(clean_keys=['fundamental'])

            assert matches.index.all() == err.index.all()

                    



        # Apply AMNS

        cg.suppress(k=30, suppression_func=error)



        # Step: Compute subpixel offsets for candidate points

        cg.subpixel_register(clean_keys=['suppression'], tiled=True)

        cg.subpixel_register(clean_keys=['suppression'])

        





        # Step: And create a C object

        cg.generate_cnet(clean_keys=['subpixel'])