def test_centered_line_on_x_axis():
'''Test get_red_mask function from patty.segmentation.segRedStick'''
# Arrange
ar = np.asarray([[0, 0, 0, 210, 25, 30],
[0, 0, 0, 0, 0, 150],
[0, 0, 0, 0, 150, 70]], dtype=np.float32)
pc = pcl.PointCloudXYZRGB(ar)
expected = 1
# Act
reds = get_red_mask(pc)
# Assert
assert_almost_equal(sum(reds), expected)
def test_centered_line_on_x_axis():
'''Test get_red_mask function from patty.segmentation.segRedStick'''
# Arrange
ar = np.asarray(
[[0, 0, 0, 210, 25, 30], [0, 0, 0, 0, 0, 150], [0, 0, 0, 0, 150, 70]],
dtype=np.float32)
pc = pcl.PointCloudXYZRGB(ar)
expected = 1
# Act
reds = get_red_mask(pc)
# Assert
assert_almost_equal(sum(reds), expected)
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
#!/usr/bin/env python
"""Segment points by colour from a pointcloud file and saves all reddish points
target pointcloud file. Autodectects ply, pcd and las files.
Usage: redstickdetection.py [-h] <infile> <outfile>
"""
from docopt import docopt
from patty.segmentation.segRedStick import get_red_mask
from patty.utils import extract_mask, load, save
if __name__ == '__main__':
args = docopt(__doc__)
pc = load(args['<infile>'])
red_pc = extract_mask(pc, get_red_mask(pc))
save(red_pc, args['<outfile>'])