Commit 0a288fa0 authored by Jay's avatar Jay
Browse files

Fixes functional testing issues - tested using pytest

parent df094f2b

.travis.yml

+1 −1
Changes for .travis.yml: 1 added line, 1 removed line.
Original line number Diff line number Diff line
@@ -49,7 +49,7 @@ install: 
  - conda install -c conda-forge vlfeat

  - conda install -c menpo cyvlfeat

  - pip install pillow pysal

  - conda install scipy networkx numexpr dill cython pyyaml matplotlib runipy

  - conda install scipy networkx numexpr dill cython pyyaml matplotlib runipy pytest



  # Development installation

  - conda install nose coverage sh anaconda-client

autocnet/graph/network.py

+1 −1
Changes for autocnet/graph/network.py: 1 added line, 1 removed line.
Original line number Diff line number Diff line
@@ -487,7 +487,7 @@ class CandidateGraph(nx.Graph): 
        """

        filelist = []

        for i, node in self.nodes_iter(data=True):

            filelist.append(node.image_path)

            filelist.append(node['image_path'])

        return filelist



    def generate_cnet(self, *args, deepen=False, **kwargs):

autocnet/matcher/add_depth.py

+1 −1
Changes for autocnet/matcher/add_depth.py: 1 added line, 1 removed line.
Original line number Diff line number Diff line
@@ -44,7 +44,7 @@ def deepen_correspondences(ab_kp, bc, source_idx, 
    bc_x[:, :2] = bc.destination.get_keypoint_coordinates().values



    # Grab F for reprojection

    f_matrix = bc.fundamental_matrix

    f_matrix = bc['fundamental_matrix']



    # Compute the epipolar line projecting point ab into bc

    epipolar_line = normalize_vector(ab_kp.dot(f_matrix.T))

autocnet/matcher/cpu_decompose.py

+5 −5
Changes for autocnet/matcher/cpu_decompose.py: 5 added lines, 5 removed lines.
Original line number Diff line number Diff line
@@ -79,9 +79,9 @@ def decompose_and_match(self, k=2, maxiteration=3, size=18, buf_dist=3,**kwargs) 
            bd = b.descriptors



        # Load, train, and match

        fl.add(ad, a.node_id, index=aidx)

        fl.add(ad, a['node_id'], index=aidx)

        fl.train()

        matches = fl.query(bd, b.node_id, k, index=bidx)

        matches = fl.query(bd, b['node_id'], k, index=bidx)

        if self.matches is None:

            self.matches = matches

        else:
@@ -144,7 +144,7 @@ def decompose_and_match(self, k=2, maxiteration=3, size=18, buf_dist=3,**kwargs) 
            bsub = ddata[mindy:maxdy, mindx:maxdx]



            # Utilize the FLANN matcher to find a match to approximate a center

            fl.add(self.destination.descriptors, self.destination.node_id)

            fl.add(self.destination.descriptors, self.destination['node_id'])

            fl.train()



            scounter = 0
@@ -158,7 +158,7 @@ def decompose_and_match(self, k=2, maxiteration=3, size=18, buf_dist=3,**kwargs) 
                    size = len(sub_skp)

                candidate_idx = np.random.choice(sub_skp.index, size=size, replace=False)

                candidates = self.source.descriptors[candidate_idx]

                matches = fl.query(candidates, self.source.node_id, k=3, index=candidate_idx)

                matches = fl.query(candidates, self.source['node_id'], k=3, index=candidate_idx)



                # Apply Lowe's ratio test to try to find a 'good' starting point

                mask = matches.groupby('source_idx')['distance'].transform(func).astype('bool')
@@ -182,7 +182,7 @@ def decompose_and_match(self, k=2, maxiteration=3, size=18, buf_dist=3,**kwargs) 


                # Grab the corresponding point in the destination

                q = candidate_matches.query('source_idx == {}'.format(closest.name))

                dest_idx = q['destination_idx'].iat[0]

                dest_idx = int(q['destination_idx'].iat[0])

                doriginx = dkp.at[dest_idx, 'x']

                doriginy = dkp.at[dest_idx, 'y']

functional_tests/test_three_image.py

+1 −1
Changes for functional_tests/test_three_image.py: 1 added line, 1 removed line.
Original line number Diff line number Diff line
@@ -46,7 +46,7 @@ class TestThreeImageMatching(unittest.TestCase): 
        for i, node, in cg.nodes_iter(data=True):

            self.assertIn(node.nkeypoints, range(490, 511))



        cg.match_features(k=5)

        cg.match(k=5)

        cg.symmetry_checks()

        cg.ratio_checks()