def create_tracks_graph(features, colors, matches, config):
logging.debug('Merging features onto tracks')
uf = UnionFind()
for im1, im2 in matches:
for f1, f2 in matches[im1, im2]:
uf.union((im1, f1), (im2, f2))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
tracks = [t for t in sets.values() if good_track(t, config.get('min_track_length', 2))]
logging.debug('Good tracks: {}'.format(len(tracks)))
tracks_graph = nx.Graph()
for track_id, track in enumerate(tracks):
for image_feature in track:
image = image_feature[0]
featureid = image_feature[1]
x, y = features[image][featureid]
r, g, b = colors[image][featureid]
tracks_graph.add_node(image, bipartite=0)
tracks_graph.add_node(str(track_id), bipartite=1)
tracks_graph.add_edge(image, str(track_id), feature=(x,y), feature_id=featureid, feature_color=(float(r),float(g),float(b)))
return tracks_graph
def create_tracks_graph(features, colors, matches, config):
logger.debug('Merging features onto tracks')
uf = UnionFind()
for im1, im2 in matches:
for f1, f2 in matches[im1, im2]:
uf.union((im1, f1), (im2, f2))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
tracks = [t for t in sets.values() if good_track(t, config.get('min_track_length', 2))]
logger.debug('Good tracks: {}'.format(len(tracks)))
tracks_graph = nx.Graph()
for track_id, track in enumerate(tracks):
for image_feature in track:
image = image_feature[0]
featureid = image_feature[1]
x, y = features[image][featureid]
r, g, b = colors[image][featureid]
tracks_graph.add_node(image, bipartite=0)
tracks_graph.add_node(str(track_id), bipartite=1)
tracks_graph.add_edge(image,
str(track_id),
feature=(x, y),
feature_id=featureid,
feature_color=(float(r), float(g), float(b)))
return tracks_graph
def create_tracks_manager(features, colors, matches, config):
"""Link matches into tracks."""
logger.debug('Merging features onto tracks')
uf = UnionFind()
for im1, im2 in matches:
for f1, f2 in matches[im1, im2]:
uf.union((im1, f1), (im2, f2))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
min_length = config['min_track_length']
tracks = [t for t in sets.values() if _good_track(t, min_length)]
logger.debug('Good tracks: {}'.format(len(tracks)))
tracks_manager = pysfm.TracksManager()
for track_id, track in enumerate(tracks):
for image, featureid in track:
if image not in features:
continue
x, y, s = features[image][featureid]
r, g, b = colors[image][featureid]
obs = pysfm.Observation(x, y, s, int(r), int(g), int(b), featureid)
tracks_manager.add_observation(image, str(track_id), obs)
return tracks_manager
def create_tags_graph(tag_matches, config):
# tag track setup
logger.debug('Merging tags into tracks')
uf = UnionFind()
for im1, im2 in tag_matches:
for f1, f2, tagid in tag_matches[im1, im2]:
uf.union((im1, tagid), (im2, tagid))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
tag_tracks = [
t for t in sets.values()
if good_track(t, config.get('min_track_length', 2))
]
logger.debug('Good tag tracks: {}'.format(len(tag_tracks)))
# create feature tracks graph
tags_graph = nx.Graph()
for track_id, track in enumerate(tag_tracks):
for image, tagid in track:
if image not in tags_graph:
tags_graph.add_node(image, bipartite=0)
if tagid not in tags_graph:
tags_graph.add_node(tagid, bipartite=1)
tags_graph.add_edge(image, tagid)
# return
return tags_graph
def create_tracks_manager(
features: t.Dict[str, np.ndarray],
colors: t.Dict[str, np.ndarray],
segmentations: t.Dict[str, np.ndarray],
instances: t.Dict[str, np.ndarray],
matches: t.Dict[t.Tuple[str, str], t.List[t.Tuple[int, int]]],
min_length: int,
):
"""Link matches into tracks."""
logger.debug("Merging features onto tracks")
uf = UnionFind()
for im1, im2 in matches:
for f1, f2 in matches[im1, im2]:
uf.union((im1, f1), (im2, f2))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
tracks = [t for t in sets.values() if _good_track(t, min_length)]
logger.debug("Good tracks: {}".format(len(tracks)))
NO_VALUE = pymap.Observation.NO_SEMANTIC_VALUE
tracks_manager = pymap.TracksManager()
for track_id, track in enumerate(tracks):
for image, featureid in track:
if image not in features:
continue
x, y, s = features[image][featureid]
r, g, b = colors[image][featureid]
segmentation, instance = (
segmentations[image][featureid] if image in segmentations else NO_VALUE,
instances[image][featureid] if image in instances else NO_VALUE,
)
obs = pymap.Observation(
x, y, s, int(r), int(g), int(b), featureid, segmentation, instance
)
tracks_manager.add_observation(image, str(track_id), obs)
return tracks_manager
def create_tracks_graph(features, colors, matches, config):
"""Link matches into tracks."""
logger.debug('Merging features onto tracks')
uf = UnionFind()
for im1, im2 in matches:
for f1, f2 in matches[im1, im2]:
uf.union((im1, f1), (im2, f2))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
min_length = config['min_track_length']
tracks = [t for t in sets.values() if _good_track(t, min_length)]
logger.debug('Good tracks: {}'.format(len(tracks)))
tracks_graph = nx.Graph()
for track_id, track in enumerate(tracks):
logger.debug('track: track_id: {} '.format(track_id))
for image, featureid in track:
if image not in features:
continue
logger.debug(
'track: track_id: {}, image: {}, feature_id: {}'.format(
track_id, image, featureid))
x, y, s = features[image][featureid]
r, g, b = colors[image][featureid]
tracks_graph.add_node(str(image), bipartite=0)
tracks_graph.add_node(str(track_id), bipartite=1)
tracks_graph.add_edge(str(image),
str(track_id),
feature=(float(x), float(y)),
feature_scale=float(s),
feature_id=int(featureid),
feature_color=(float(r), float(g), float(b)))
return tracks_graph
def create_tracks_manager(features, colors, segmentations, instances, matches,
config):
"""Link matches into tracks."""
logger.debug("Merging features onto tracks")
uf = UnionFind()
for im1, im2 in matches:
for f1, f2 in matches[im1, im2]:
uf.union((im1, f1), (im2, f2))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
min_length = config["min_track_length"]
tracks = [t for t in sets.values() if _good_track(t, min_length)]
logger.debug("Good tracks: {}".format(len(tracks)))
NO_VALUE = pysfm.Observation.NO_SEMANTIC_VALUE
tracks_manager = pysfm.TracksManager()
for track_id, track in enumerate(tracks):
for image, featureid in track:
if image not in features:
continue
x, y, s = features[image][featureid]
r, g, b = colors[image][featureid]
segmentation, instance = (
segmentations[image][featureid]
if segmentations[image] is not None else NO_VALUE,
instances[image][featureid]
if instances[image] is not None else NO_VALUE,
)
obs = pysfm.Observation(x, y, s, int(r), int(g), int(b), featureid,
segmentation, instance)
tracks_manager.add_observation(image, str(track_id), obs)
return tracks_manager
def create_tracks_graph(features, colors, matches, config):
"""Link matches into tracks."""
logger.debug('Merging features onto tracks')
uf = UnionFind()
for im1, im2 in matches:
for f1, f2 in matches[im1, im2]:
uf.union((im1, f1), (im2, f2))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
min_length = config['min_track_length']
tracks = [t for t in sets.values() if _good_track(t, min_length)]
logger.debug('Good tracks: {}'.format(len(tracks)))
tracks_graph = nx.Graph()
for track_id, track in enumerate(tracks):
for image, featureid in track:
if image not in features:
continue
x, y = features[image][featureid]
r, g, b = colors[image][featureid]
tracks_graph.add_node(str(image), bipartite=0)
tracks_graph.add_node(str(track_id), bipartite=1)
tracks_graph.add_edge(str(image),
str(track_id),
feature=(float(x), float(y)),
feature_id=int(featureid),
feature_color=(float(r), float(g), float(b)))
return tracks_graph
def create_tracks_graph(features, colors, matches, config):
"""Link matches into tracks."""
logger.debug('Merging features onto tracks')
uf = UnionFind()
for im1, im2 in matches:
if len(matches[im1, im2]) > 0 and matches[im1, im2].shape[1] == 5:
for f1, f2, _, _, _ in matches[im1, im2]:
uf.union((im1, int(f1)), (im2, int(f2)))
elif len(matches[im1, im2]) > 0 and matches[im1, im2].shape[1] == 4:
for f1, f2, _, _ in matches[im1, im2]:
uf.union((im1, int(f1)), (im2, int(f2)))
else:
for f1, f2 in matches[im1, im2]:
uf.union((im1, int(f1)), (im2, int(f2)))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
tracks = [
t for t in sets.values() if _good_track(t, config['min_track_length'])
]
logger.debug('Good tracks: {}'.format(len(tracks)))
tracks_graph = nx.Graph()
for track_id, track in enumerate(tracks):
for image, featureid in track:
if image not in features:
continue
x, y = features[image][featureid]
r, g, b = colors[image][featureid]
tracks_graph.add_node(image, bipartite=0)
tracks_graph.add_node(str(track_id), bipartite=1)
tracks_graph.add_edge(image,
str(track_id),
feature=(x, y),
feature_id=featureid,
feature_color=(float(r), float(g), float(b)))
return tracks_graph
def common_ratio(n1, n2, m, matches):
"""
calculates the ratio of # of common features of the triplet (n1, n2, m) to
# of common features of the pair (n1, n2). the larger the ratio the more
likely m is correctly related to n1, n2.
:param n1:
:param n2:
:param m:
:param matches:
:return:
"""
uf = UnionFind()
if (n1, n2) in matches:
base_cnt = len(matches[n1, n2])
for f1, f2 in matches[n1, n2]:
uf.union((n1, f1), (n2, f2))
else:
base_cnt = len(matches[n2, n1])
for f1, f2 in matches[n2, n1]:
uf.union((n2, f1), (n1, f2))
if (n1, m) in matches:
for f1, f2 in matches[n1, m]:
uf.union((n1, f1), (m, f2))
else:
for f1, f2 in matches[m, n1]:
uf.union((m, f1), (n1, f2))
if (n2, m) in matches:
for f1, f2 in matches[n2, m]:
uf.union((n2, f1), (m, f2))
else:
for f1, f2 in matches[m, n2]:
uf.union((m, f1), (n2, f2))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
tracks = [t for t in sets.values() if _good_track(t, 3)]
cnt = 0
fids = []
if (n1, n2) in matches:
for f1, f2 in matches[n1, n2]:
for track in tracks:
if (n1, f1) in track and (n2, f2) in track:
fids.append(f1)
cnt += 1
break
else:
for f1, f2 in matches[n2, n1]:
for track in tracks:
if (n2, f1) in track and (n1, f2) in track:
fids.append(f2)
cnt += 1
break
return cnt / base_cnt, fids
def create_tracks_graph(features, colors, matches, tag_features, tag_idx,
tag_colors, tag_matches, tag_ids, config):
# feature track setup
logger.debug('Merging features onto tracks')
uf = UnionFind()
for im1, im2 in matches:
for f1, f2 in matches[im1, im2]:
uf.union((im1, f1), (im2, f2))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
tracks = [
t for t in sets.values()
if good_track(t, config.get('min_track_length', 2))
]
logger.debug('Good tracks: {}'.format(len(tracks)))
#print '===== tracks ====='
#print tracks
# tag track setup
if config.get('tag_tracks', False):
logger.debug('Merging tag features into tracks')
uf = UnionFind()
for im1, im2 in tag_matches:
for f1, f2, tag_id in tag_matches[im1, im2]:
uf.union((im1, f1, tag_id), (im2, f2, tag_id))
sets = {}
for i in uf:
p = uf[i]
if p in sets:
sets[p].append(i)
else:
sets[p] = [i]
tag_tracks = [t for t in sets.values()]
logger.debug('Good tag feature tracks: {}'.format(len(tag_tracks)))
#print '===== tag tracks ====='
#print tag_tracks
# create feature tracks graph
print_one_track = 10
tracks_graph = nx.Graph()
for track_id, track in enumerate(tracks):
for image, featureid in track:
if image not in features:
continue
x, y = features[image][featureid]
#if print_one_track > 0:
# print image,': x = ',str(x), '. y = ',str(y)
r, g, b = colors[image][featureid]
tracks_graph.add_node(image, bipartite=0)
tracks_graph.add_node(str(track_id), bipartite=1)
tracks_graph.add_edge(image,
str(track_id),
feature=(x, y),
feature_id=featureid,
feature_color=(float(r), float(g), float(b)),
tag_feature=0,
tag_id=0,
corner_id=0)
# if print_one_track >= 0:
# print_one_track -= 1
#print '|'
# add tag tracks to graph
if config.get('tag_tracks', False):
addt = len(tracks)
print_one_track = True
for track_id, track in enumerate(tag_tracks):
for image, featureid, tagid in track:
if image not in tag_features:
continue
tag_track_id = addt + track_id
x, y = tag_features[image][featureid]
#if print_one_track:
# print image,': x = ',str(x), '. y = ',str(y)
r, g, b = tag_colors[image][featureid]
cid = tag_idx[image][featureid]
if image not in tracks_graph:
tracks_graph.add_node(image, bipartite=0)
tracks_graph.add_node(str(tag_track_id), bipartite=1)
tracks_graph.add_edge(image,
str(tag_track_id),
feature=(x, y),
feature_id=featureid,
feature_color=(float(r), float(g),
float(b)),
tag_feature=1,
tag_id=tagid,
corner_id=cid)
# print_one_track = False
# return
return tracks_graph
