def run_dataset(data):
""" Link matches pair-wise matches into tracks. """
start = timer()
features, colors, segmentations, instances = tracking.load_features(
data, data.images())
features_end = timer()
matches = tracking.load_matches(data, data.images())
matches_end = timer()
tracks_manager = tracking.create_tracks_manager(
features,
colors,
segmentations,
instances,
matches,
data.config,
)
tracks_end = timer()
data.save_tracks_manager(tracks_manager)
write_report(
data,
tracks_manager,
features_end - start,
matches_end - features_end,
tracks_end - matches_end,
)
def run(self, args):
data = dataset.DataSet(args.dataset)
queryImages = None
if (args.localize):
localizeDir = os.path.join(args.dataset, "localize")
print("Localization - creating tracks: ")
queryImages = [x for x in os.listdir(localizeDir)]
[print(os.path.join(localizeDir, str(x))) for x in queryImages]
else:
queryImages = data.images()
start = timer()
features, colors = tracking.load_features(data, data.images(
)) #TODO: Just get those images for which matches have been computed.
features_end = timer()
matches = tracking.load_matches(data, data.images(
)) #TODO: Just get those images for which matches have been computed.
matches_end = timer()
tracks_manager = tracking.create_tracks_manager(
features, colors, matches, data.config)
tracks_end = timer()
data.save_tracks_manager(tracks_manager)
end = timer()
with open(data.profile_log(), 'a') as fout:
fout.write('create_tracks: {0}\n'.format(end - start))
self.write_report(data, tracks_manager, features_end - start,
matches_end - features_end, tracks_end - matches_end)
def run(self, args):
data = dataset.DataSet(args.dataset)
start = timer()
features, colors = tracking.load_features(data, data.images())
if data.config['feature_use_superpoint']:
for im in features:
p_s, f_s, c_s = superpoint.load_features(im)
if p_s is not None:
features[im] = np.concatenate((features[im], p_s), axis=0)
colors[im] = np.concatenate((colors[im], c_s), axis=0)
features_end = timer()
matches = tracking.load_matches(data, data.images())
matches_end = timer()
pairs = filtering.load_pairwise_transforms(data, data.images())
matches = filtering.triplet_filter(data, data.images(), matches, pairs)
matches = filtering.loop_filter(data, data.images(), features, matches, pairs)
filter_end = timer()
'''
# debugging
edges = defaultdict(list)
for i in data.images():
for (im1, im2) in matches:
if i == im1:
edges[i].append(im2)
elif i == im2:
edges[i].append(im1)
for i in sorted(edges.keys()):
logger.debug("{} has edges with {}".format(i, sorted(edges[i])))
'''
graph = tracking.create_tracks_graph(features, colors, matches,
data.config)
tracks_end = timer()
data.save_tracks_graph(graph)
end = timer()
with open(data.profile_log(), 'a') as fout:
fout.write('create_tracks: {0}\n'.format(end - start))
self.write_report(data,
graph,
features_end - start,
matches_end - features_end,
filter_end - matches_end,
tracks_end - filter_end)
def run(self, args):
data = dataset.DataSet(args.dataset)
start = timer()
features, colors = tracking.load_features(data, data.images())
features_end = timer()
matches = tracking.load_matches(data, data.images())
matches_end = timer()
graph = tracking.create_tracks_graph(features, colors, matches,
data.config)
tracks_end = timer()
data.save_tracks_graph(graph)
end = timer()
with open(data.profile_log(), 'a') as fout:
fout.write('create_tracks: {0}\n'.format(end - start))
self.write_report(data, graph, features_end - start,
matches_end - features_end, tracks_end - matches_end)