def get_stick_scale(pointcloud, eps=0.1, min_samples=20):
"""Takes a point cloud, as a numpy array, looks for red segments
of scale sticks and returns the scale estimation with most support.
Method:
pointcloud --dbscan--> clusters --lengthEstimation-->
lengths --ransac--> best length
Arguments:
pointcloud Point cloud containing only measuring stick segments
(only the red, or only the white parts)
eps DBSCAN parameter: Maximum distance between two samples
for them to be considered as in the same neighborhood.
min_samples DBSCAN parameter: The number of samples in a neighborhood
for a point to be considered as a core point.
Returns:
scale Estimate of the size of one actual meter in expressed
in units of the pointcloud's coordinates.
confidence A number expressing the reliability of the estimated
scale. Confidence is in [0, 1]. With a confidence greater
than .5, the estimate can be considered useable for
further calculations.
"""
# quickly return for trivial case
if pointcloud.size == 0:
return 1, 0
# find the red segments to measure
pc_reds = extract_mask(pointcloud, get_red_mask(pointcloud))
if len(pc_reds) == 0:
# unit scale, zero confidence (ie. any other estimation is better)
return 1.0, 0.0
cluster_generator = segment_dbscan(
pc_reds, eps, min_samples, algorithm='kd_tree')
sizes = [{'len': len(cluster),
'meter': measure_length(cluster) * SEGMENTS_PER_METER}
for cluster in cluster_generator]
if len(sizes) == 0:
return 1.0, 0.0
scale, votes, n_clusters = ransac(sizes)
confidence = get_confidence_level(votes, n_clusters)
return scale, confidence
import sys
from patty.segmentation import segment_dbscan
from patty.utils import load, save
if __name__ == '__main__':
args = docopt(__doc__, sys.argv[1:])
rgb_weight = float(args['--rgb_weight'])
eps = float(args['<epsilon>'])
minpoints = int(args['<minpoints>'])
# Kludge to get a proper exception for file not found
# (PCL will report "problem parsing header!").
with open(args['<file>']) as _:
pc = load(args['<file>'])
print("%d points" % len(pc))
clusters = segment_dbscan(pc, epsilon=eps, minpoints=minpoints,
rgb_weight=rgb_weight)
n_outliers = len(pc)
for i, cluster in enumerate(clusters):
print("%d points in cluster %d" % (len(cluster), i))
filename = '%s/cluster%d.%s' % (args['--output_dir'], i,
args['--format'])
save(cluster, filename)
n_outliers -= len(cluster)
print("%d outliers" % n_outliers)
from patty.segmentation import segment_dbscan
from patty.utils import load, save
if __name__ == '__main__':
args = docopt(__doc__, sys.argv[1:])
rgb_weight = float(args['--rgb_weight'])
eps = float(args['<epsilon>'])
minpoints = int(args['<minpoints>'])
# Kludge to get a proper exception for file not found
# (PCL will report "problem parsing header!").
with open(args['<file>']) as _:
pc = load(args['<file>'])
print("%d points" % len(pc))
clusters = segment_dbscan(pc,
epsilon=eps,
minpoints=minpoints,
rgb_weight=rgb_weight)
n_outliers = len(pc)
for i, cluster in enumerate(clusters):
print("%d points in cluster %d" % (len(cluster), i))
filename = '%s/cluster%d.%s' % (args['--output_dir'], i,
args['--format'])
save(cluster, filename)
n_outliers -= len(cluster)
print("%d outliers" % n_outliers)