def _construct_matches(self): matches = {} for im1 in self.images(): for im2 in self.images(): if im1 == im2: continue image_matches = matches.setdefault(im1, {}) tracks = tracking.common_tracks(self.graph, im1, im2)[0] if len(tracks) > 10: pair_matches = np.array( [np.array([self.graph[t][im1]['feature_id'], self.graph[t][im2]['feature_id']]) for t in tracks]) image_matches[im2] = pair_matches return matches
def _construct_matches(self): matches = {} for im1 in self.images(): for im2 in self.images(): if im1 == im2: continue image_matches = matches.setdefault(im1, {}) tracks = tracking.common_tracks(self.graph, im1, im2)[0] if len(tracks) > 10: pair_matches = np.array( [np.array([self.graph[t][im1]['feature_id'], self.graph[t][im2]['feature_id']]) for t in tracks]) image_matches[im2] = pair_matches return matches
def _construct_matches(self): matches = {} for im1 in self.images(): for im2 in self.images(): if im1 == im2: continue image_matches = matches.setdefault(im1, {}) tracks = tracking.common_tracks(self.tracks_manager, im1, im2)[0] if len(tracks) > 10: pair_matches = [] for t in tracks: observations = self.tracks_manager.get_track_observations(t) pair_matches.append( np.array([observations[im1].id, observations[im2].id]) ) image_matches[im2] = np.array(pair_matches) return matches