Commit 0a2eb894 authored by Evin Dunn's avatar Evin Dunn
Browse files

Updated matcher.match() to be static; Takes "edge" arg

parent f2242f97

autocnet/__init__.py

+2 −2
Original line number Diff line number Diff line
@@ -38,7 +38,7 @@ def cuda(enable=False, gpu=0): 
            Node._extract_features = staticmethod(extract_features)



            from autocnet.matcher.cuda_matcher import match

            Edge.match = match

            Edge._match = staticmethod(match)



            from autocnet.matcher.cuda_decompose import decompose_and_match

            Edge.decompose_and_match = decompose_and_match
@@ -52,7 +52,7 @@ def cuda(enable=False, gpu=0): 
    Node._extract_features = staticmethod(extract_features)



    from autocnet.matcher.cpu_matcher import match

    Edge.match = match

    Edge._match = staticmethod(match)



    from autocnet.matcher.cpu_decompose import decompose_and_match

    Edge.decompose_and_match = decompose_and_match

autocnet/graph/edge.py

+15 −4
Original line number Diff line number Diff line
@@ -107,11 +107,22 @@ class Edge(dict, MutableMapping): 
        ----------

        k : int

            The number of neighbors to find

        """

        Edge._match(self, k, **kwargs)



    @staticmethod

    def _match(edge, k=2, **kwargs):

        """

        Patches the static cpu_matcher.match(edge) or cuda_match.match(edge)

        into the member method Edge.match()



        overlap : boolean

                  Apply the matcher only to the overlapping area defined by

                  the source_mbr and destin_mbr attributes (stored in the

                  edge dict).

        Parameters

        ----------

        edge : Edge

               The edge object to compute matches for; Edge.match() calls this

               with self

        k : int

            The number of neighbors to find

        """

        pass

autocnet/matcher/cpu_matcher.py

+8 −8
Original line number Diff line number Diff line
@@ -8,7 +8,7 @@ FLANN_INDEX_KDTREE = 1 # Algorithm to set centers, 
DEFAULT_FLANN_PARAMETERS = dict(algorithm=FLANN_INDEX_KDTREE, trees=3)





def match(self, k=2, **kwargs):

def match(edge, k=2, **kwargs):

    """

    Given two sets of descriptors, utilize a FLANN (Approximate Nearest

    Neighbor KDTree) matcher to find the k nearest matches.  Nearness is
@@ -32,11 +32,11 @@ def match(self, k=2, **kwargs): 
        matches : dataframe

                  A dataframe of matches

        """

        if self.matches is None:

            self.matches = matches

        if edge.matches is None:

            edge.matches = matches

        else:

            df = self.matches

            self.matches = df.append(matches,

            df = edge.matches

            edge.matches = df.append(matches,

                                     ignore_index=True,

                                     verify_integrity=True)
@@ -92,11 +92,11 @@ def match(self, k=2, **kwargs): 
    else:

        bidx = None



    mono_matches(self.source, self.destination, aidx=aidx, bidx=bidx, **kwargs)

    mono_matches(edge.source, edge.destination, aidx=aidx, bidx=bidx, **kwargs)

    # Swap the indices since mono_matches is generic and source/destin are

    # swapped

    mono_matches(self.destination, self.source, aidx=bidx, bidx=aidx, **kwargs)

    self.matches.sort_values(by=['distance'])

    mono_matches(edge.destination, edge.source, aidx=bidx, bidx=aidx, **kwargs)

    edge.matches.sort_values(by=['distance'])





class FlannMatcher(object):

autocnet/matcher/cuda_matcher.py

+9 −9
Original line number Diff line number Diff line
@@ -4,7 +4,7 @@ import cudasift as cs 
import numpy as np

import pandas as pd



def match(self, ratio=0.8, **kwargs):

def match(edge, ratio=0.8, **kwargs):



    """

    Apply a composite CUDA matcher and ratio check.  If this method is used,
@@ -14,11 +14,11 @@ def match(self, ratio=0.8, **kwargs): 
    without significant gain in accuracy when using this implementation.

    """



    source_kps = self.source.get_keypoints()

    source_des = self.source.descriptors

    source_kps = edge.source.get_keypoints()

    source_des = edge.source.descriptors



    destin_kps = self.destination.get_keypoints()

    destin_des = self.destination.descriptors

    destin_kps = edge.destination.get_keypoints()

    destin_des = edge.destination.descriptors



    s_siftdata = cs.PySiftData.from_data_frame(source_kps, source_des)

    d_siftdata = cs.PySiftData.from_data_frame(destin_kps, destin_des)
@@ -26,9 +26,9 @@ def match(self, ratio=0.8, **kwargs): 
    cs.PyMatchSiftData(s_siftdata, d_siftdata)

    matches, _ = s_siftdata.to_data_frame()

    source = np.empty(len(matches))

    source[:] = self.source['node_id']

    source[:] = edge.source['node_id']

    destination = np.empty(len(matches))

    destination[:] = self.destination['node_id']

    destination[:] = edge.destination['node_id']



    df = pd.concat([pd.Series(source), pd.Series(matches.index),

            pd.Series(destination), matches.match,
@@ -37,5 +37,5 @@ def match(self, ratio=0.8, **kwargs): 
            'destination_idx', 'score', 'ambiguity']



    # Set the matches and set the 'ratio' (ambiguity) mask

    self.matches = df

    self.masks['ratio'] =  df['ambiguity'] <= ratio
 
    edge.matches = df

    edge.masks['ratio'] = df['ambiguity'] <= ratio