Commit 305aba0b authored by kberry's avatar kberry
Browse files

Merge remote-tracking branch 'upstream/master' into jwbacker_visualization

parents 27e669bc 4a851f2c

autocnet/examples/Apollo15/three_image_adjacency.json

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+6 −0
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{"autocnet/examples/Apollo15/AS15-M-0297_SML.png": ["autocnet/examples/Apollo15/AS15-M-0298_SML.png",

                                                    "autocnet/examples/Apollo15/AS15-M-0299_SML.png"],

"autocnet/examples/Apollo15/AS15-M-0298_SML.png": ["autocnet/examples/Apollo15/AS15-M-0297_SML.png",

                                                   "autocnet/examples/Apollo15/AS15-M-0299_SML.png"],

"autocnet/examples/Apollo15/AS15-M-0299_SML.png": ["autocnet/examples/Apollo15/AS15-M-0297_SML.png",

                                                   "autocnet/examples/Apollo15/AS15-M-0298_SML.png"]}

autocnet/fileio/io_controlnetwork.py

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@@ -80,6 +80,7 @@ def to_isis(path, C, mode='w', version=VERSION, 


            store.write(header)





class IsisStore(object):

    """

    Class to manage IO of an ISIS control network (version 2).

autocnet/graph/network.py

+66 −5
Original line number Diff line number Diff line 
from functools import reduce

import operator as op

import os



import networkx as nx
@@ -14,6 +12,8 @@ from autocnet.fileio import io_json 
from autocnet.fileio.io_gdal import GeoDataset

from autocnet.matcher import feature_extractor as fe # extract features from image

from autocnet.matcher import outlier_detector as od

from autocnet.matcher import subpixel as sp





class CandidateGraph(nx.Graph):

    """
@@ -269,18 +269,76 @@ class CandidateGraph(nx.Graph): 
            attributes['homography'] = transformation_matrix

            attributes['ransac'] = mask



    def compute_subpixel_offsets(self):

        """

        For the entire graph, compute the subpixel offsets using pattern-matching and add the result

        as an attribute to each edge of the graph.



        Returns

        -------

        subpixel_offsets : ndarray

                           A numpy array containing all the subpixel offsets for the entire graph.

        """

        subpixel_offsets = []

        for source, destination, attributes in self.edges_iter(data=True): #for each edge

            matches = attributes['matches'] #grab the matches

            src_image = self.node[source]['image']

            dest_image = self.node[destination]['image']

            edge_offsets = []

            for i, (idx, row) in enumerate(matches.iterrows()): #for each edge, calculate this for each keypoint pair

                s_idx = int(row['source_idx'])

                d_idx = int(row['destination_idx'])

                src_keypoint = self.node[source]['keypoints'][s_idx]

                dest_keypoint = self.node[destination]['keypoints'][d_idx]

                edge_offsets.append(sp.subpixel_offset(src_keypoint, dest_keypoint, src_image, dest_image))

            attributes['subpixel_offsets'] = np.array(edge_offsets)

            subpixel_offsets.append(np.array(edge_offsets))

        return subpixel_offsets



    def to_cnet(self, clean_keys=[]):

        """

        Generate a control network (C) object from a graph



        Parameters

        ----------

        clean_keys : list

             of strings identifying the masking arrays to use, e.g. ratio, symmetry



        Returns

        -------

        merged_cnet : C

                      A control network object

        """



        clean_keys : list

                     of strings identifying the masking arrays to use, e.g. ratio, symmetry

        def _validate_cnet(cnet):

            """

            Once the control network is aggregated from graph edges,

            ensure that a given correspondence in a given image does

            not match multiple correspondences in a different image.



            Parameters

            ----------

            cnet : C

                   control network object



            Returns

            -------

             : C

               the cleaned control network

            """



            mask = np.zeros(len(cnet), dtype=bool)

            counter = 0

            for i, group in cnet.groupby('pid'):

                group_size = len(group)

                if len(group) != len(group['nid'].unique()):

                    mask[counter: counter + group_size] = False

                else:

                    mask[counter: counter + group_size] = True

                counter += group_size



            return cnet[mask]



        merged_cnet = None



        for source, destination, attributes in self.edges_iter(data=True):
@@ -338,6 +396,9 @@ class CandidateGraph(nx.Graph): 
                merged_cnet = pd.concat([merged_cnet, cnet])

                merged_cnet.drop_duplicates(['idx', 'pid'], keep='first', inplace=True)



        # Final validation to remove any correspondence with multiple correspondences in the same image

        merged_cnet = _validate_cnet(merged_cnet)



        return merged_cnet



    def to_json_file(self, outputfile):

autocnet/graph/tests/test_network.py

+0 −1
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@@ -41,7 +41,6 @@ class TestCandidateGraph(unittest.TestCase): 
        self.assertIsInstance(node['descriptors'][0], np.ndarray)

        self.assertEquals(self.graph.get_keypoints(node_number), node['keypoints'])





    def tearDown(self):

        try:

            os.remove('test_graph_to_json.json')

autocnet/matcher/matcher.py

+0 −3
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@@ -41,9 +41,6 @@ def pattern_match(template, image, upsampling=10, 


    strength : float

               The strength of the correlation in the range [-1, 1].







    """

    if upsampling < 1:

        raise ValueError