def test_auto_file_format():
"""Test saving and loading pointclouds via the pcl loader"""
# make and save a pointcloud
pc = pcl.PointCloud(10)
pc_arr = np.asarray(pc)
pc_arr[:] = np.random.randn(*pc_arr.shape)
with NamedTemporaryFile(suffix='.ply') as f:
utils.save(pc, f.name)
pc2 = utils.load(f.name)
_compare(pc, pc2)
with NamedTemporaryFile(suffix='.pcd') as f:
utils.save(pc, f.name)
pc2 = utils.load(f.name)
_compare(pc, pc2)
with NamedTemporaryFile(suffix='.las') as f:
utils.save(pc, f.name, format="PLY")
pc2 = utils.load(f.name, format="PLY")
_compare(pc, pc2)
with NamedTemporaryFile(suffix='.las') as f:
utils.save(pc, f.name, format="PCD")
pc2 = utils.load(f.name, format="PCD")
_compare(pc, pc2)
def test_auto_file_format():
"""Test saving and loading pointclouds via the pcl loader"""
# make and save a pointcloud
pc = pcl.PointCloud(10)
pc_arr = np.asarray(pc)
pc_arr[:] = np.random.randn(*pc_arr.shape)
with NamedTemporaryFile(suffix=".ply") as f:
utils.save(pc, f.name)
pc2 = utils.load(f.name)
_compare(pc, pc2)
with NamedTemporaryFile(suffix=".pcd") as f:
utils.save(pc, f.name)
pc2 = utils.load(f.name)
_compare(pc, pc2)
with NamedTemporaryFile(suffix=".las") as f:
utils.save(pc, f.name, format="PLY")
pc2 = utils.load(f.name, format="PLY")
_compare(pc, pc2)
with NamedTemporaryFile(suffix=".las") as f:
utils.save(pc, f.name, format="PCD")
pc2 = utils.load(f.name, format="PCD")
_compare(pc, pc2)
def test_read_write():
""" Test read and write LAS files functionality"""
filename = "./testIO.las"
# make and save a pointcloud
pc1 = pcl.PointCloud(10)
pc1_arr = np.asarray(pc1)
pc1_arr[:] = np.random.randn(*pc1_arr.shape)
utils.save(pc1, filename)
# reload it
pc2 = utils.load(filename)
_compare(pc1, pc2)
os.remove(filename)
def test_read_write():
''' Test read and write LAS files functionality'''
filename = './testIO.las'
# make and save a pointcloud
pc1 = pcl.PointCloud(10)
pc1_arr = np.asarray(pc1)
pc1_arr[:] = np.random.randn(*pc1_arr.shape)
utils.save(pc1, filename)
# reload it
pc2 = utils.load(filename)
_compare(pc1, pc2)
os.remove(filename)
Initial_scale = float(args['-s'])
except:
Initial_scale = None
assert os.path.exists(sourcefile), sourcefile + ' does not exist'
assert os.path.exists(drivemapfile), drivemapfile + ' does not exist'
assert os.path.exists(footprintcsv), footprintcsv + ' does not exist'
#####
# Setup * the low-res drivemap
# * footprint
# * pointcloud
# * up-vector
log("Reading drivemap", drivemapfile)
drivemap = load(drivemapfile)
force_srs(drivemap, srs="EPSG:32633")
log("Reading footprint", footprintcsv)
footprint = load(footprintcsv)
force_srs(footprint, srs="EPSG:32633")
set_srs(footprint, same_as=drivemap)
log("Reading object", sourcefile)
pointcloud = load(sourcefile)
Up = None
try:
with open(up_file) as f:
dic = json.load(f)
Up = np.array(dic['estimatedUpDirection'])
#!/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>'])
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 __future__ import print_function
from docopt import docopt
import numpy as np
from patty.utils import load, save
def csv_read(path):
return np.genfromtxt(path, dtype=float, delimiter=',')
if __name__ == '__main__':
args = docopt(__doc__)
pc = load(args['<source>'])
try:
offset = csv_read(args['-o'])
except:
offset = None
try:
matrix = csv_read(args['-r'])
pc.rotate(matrix, origin=offset)
except Exception as e:
print('Problem with rotate: ', e)
try:
factor = csv_read(args['-s'])
pc.scale(factor, origin=offset)
Initial_scale = float(args['-s'])
except:
Initial_scale = None
assert os.path.exists(sourcefile), sourcefile + ' does not exist'
assert os.path.exists(drivemapfile), drivemapfile + ' does not exist'
assert os.path.exists(footprintcsv), footprintcsv + ' does not exist'
#####
# Setup * the low-res drivemap
# * footprint
# * pointcloud
# * up-vector
log("Reading drivemap", drivemapfile)
drivemap = load(drivemapfile)
force_srs(drivemap, srs="EPSG:32633")
log("Reading footprint", footprintcsv)
footprint = load(footprintcsv)
force_srs(footprint, srs="EPSG:32633")
set_srs(footprint, same_as=drivemap)
log("Reading object", sourcefile)
pointcloud = load(sourcefile)
Up = None
try:
with open(up_file) as f:
dic = json.load(f)
Up = np.array(dic['estimatedUpDirection'])
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)
