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
