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Introduction to python-pcl

We have been implementing python binding to the pointcloud <http://pointclouds.org/>_ library (PCL). This is a work in progress and currently the following modules/functionalities of the API are wrapped:

  • I/O and integration; saving and loading PCD (Point Cloud Data) files.
  • segmentation
  • SAC (Simple Alignment Consensus)
  • smoothing
  • filtering
  • registration

We aim to wrap the following modules/functionalities:

  • feature extraction
  • ....

Note that all methods operate in Cartesian coordinate system i.e. (x, y, z). Point cloud data comes in two formats (x,y,z) and (x,y,z, R, G, B). These methods work on both.

some useful links to PCL:

Disclaimer

This release has been tested on:

Ubuntu 13.10 with,

  • Python 2.7.5
  • pcl 1.7.1
  • Cython 0.20.2

CentOS 6.5 with,

  • Python 2.6.6
  • pcl 1.6.0
  • Cython 0.20.2

Fedora 20 with,

  • Python 2.7.5
  • pcl 1.7.0
  • Cython 0.21.1

The instruction below holds for Ubuntu and Fedora.

preparatory steps to use python-pcl

dependencies

You need to have the following packages installed on your system:

In Ubuntu

sudo apt-get install cmake libgdal-dev pcl
sudo apt-get install cmake libgdal-dev libgeotiff-dev

In Fedora

System-wide:

  • sudo yum install gdal-devel libgeotiff-devel
  • sudo yum install boost-devel
  • sudo yum install gcc-c++

Python libraries:

  • sudo yum install python-pip python-devel
  • sudo pip install numpy
  • sudo pip install scipy

LibLAS:

git clone git://github.com/libLAS/libLAS.git liblas
cd liblas/
mkdir makefiles
cd makefiles/
cmake -G "Unix Makefiles" ../ --GEOTIFF_INCLUDE_DIR=/usr/include/libgeotiff/
make
sudo make install

NOTE: You might need to export LD_LIBRARY_PATH i.e in your .bashrc file, add this line:

export LD_LIBRARY_PATH=/usr/local/lib

PCL:

sudo yum install pcl pcl-devel pcl-tools pcl-doc

python-pcl

Clone repository, compile and test that it works:

git clone 'http://github.com/larsmans/python-pcl'
cd python-pcl/
make
python -m unittest discover tests

Now you should be able to use python-pcl modules and functionalities as explained below.

Quick Tutorial: how to use python-pcl

PointCloud

It instantiates a point could object. It also provides functionality for manipulating point cloud data in a numpy array.

pcl.PointCloud()
pcl.PointCloudXYZRGB()

example

import numpy as np
import pcl
arr = np.zeros((100, 3)).astype(np.float32)
pc = pcl.PointCloud()
pc.from_array(arr)
pc.to_array()


import numpy as np
import pcl
arr = np.zeros((100, 6)).astype(np.float32)
pc = pcl.PointCloudXYZRGB()
pc.from_array(arr)
pc.to_array()

I/O

It helps loading and saving point cloud files.

pcl.load(path, format=None, loadRGB=False)
pcl.save(cloud, path, format=None, binary=False)

example

Load a point cloud from PCD file:

import pcl
pc = pcl.load('tests/bun0.pcd')

Load a point cloud from PLY file:

import pcl
pc = pcl.load('tests/rock2.ply')

Load a point cloud from PLY file

import pcl
pc = pcl.load('tests/rock2.ply', loadRGB=True)

Save point cloud to PLY file (save to PCD is also supported).

pcl.save(pc, 'tests/tmp.ply', format='PLY')

segmentation

pcl.Segmentation()
...

example

import pcl
pc = pcl.load('some_cloud.pcd')
seg = pc.make_segmenter()
seg.set_optimize_coefficients (True)
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
seg.set_distance_threshold (0.01)
indices, model = seg.segment()
pc2 = pcl.PointCloud()
pc2.from_array(pc.to_array()[indices,:])
pcl.save(pc2, 'other_cloud.ply', format='PLY')


import pcl
pc = pcl.load("some_cloud.pcd")
seg = pc.make_segmenter_normals(50)
seg.set_optimize_coefficients (True);
seg.set_model_type (pcl.SACMODEL_CYLINDER)
seg.set_method_type (pcl.SAC_RANSAC)
seg.set_normal_distance_weight (0.1)
seg.set_max_iterations (10000)
seg.set_distance_threshold (0.05)
seg.set_radius_limits (0, 0.1)
indices, model = seg.segment()
pc2 = pcl.PointCloud() # to get a second point cloud of the segmented point cloud
pc2.from_array(pc.to_array()[indices,:])
pcl.save(pc2, 'other_cloud.ply', format='PLY')

SAC

example

smoothing

smoothing does ... and it is implemented as ....

example

    import pcl
    p = pcl.load("C/table_scene_lms400.pcd")
    fil = p.make_statistical_outlier_filter()
    fil.set_mean_k (50)
    fil.set_std_dev_mul_thresh (1.0)
    fil.filter().to_file("inliers.pcd")

filtering

...

example

registration

In registration module, we have wrapped four registration method ICP (Iterative Closest Point), generalized ICP, generalized non-linear ICP, initial alignment RANSAC ():

pcl.registration.icp(BasePointCloud source, BasePointCloud target, max_iter = None)
pcl.registartion.gicp(BasePointCloud source, BasePointCloud target, max_iter = None)
pcl.registartion.icp_nl(BasePointCloud source, BasePointCloud target, max_iter = None)
pcl.registartion.ia_ransac(BasePointCloud source, BasePointCloud target, max_iter = None, radius=0.05, minSampleDistance=0.05, maxCorrespondenceDistance=0.2)

example

import pcl
import pcl.registration

sourceFile = 'tests/color.ply'
targetFile = 'tests/color.ply'

source = pcl.load(sourceFile)
target = pcl.load(targetFile)

converged, transf, estimate, fitness = pcl.registration.icp(source, target)

import pcl
import pcl.registration

sourceFile = 'tests/color.ply'
targetFile = 'tests/color.ply'

source = pcl.load(sourceFile)
target = pcl.load(targetFile)
converged, transf, estimate, fitness = pcl.registration.gicp(source, target)

import pcl
import pcl.registration

sourceFile = 'tests/color.ply'
targetFile = 'tests/color.ply'

source = pcl.load(sourceFile)
target = pcl.load(targetFile)

converged, transf, estimate, fitness = pcl.registration.icp_nl(source, target)

import pcl
import pcl.registration

sourceFile = 'tests/color.ply'
targetFile = 'tests/color.ply'

source = pcl.load(sourceFile)
target = pcl.load(targetFile)

converged, transf, estimate, fitness = pcl.registration.ia_ransac(source, target)

more with numpy helper

numpy helper does ...

example

import pcl
import numpy as np
p = pcl.PointCloud(np.array([[1, 2, 3], [3, 4, 5]], dtype=np.float32))
seg = p.make_segmenter()
seg.set_model_type(pcl.SACMODEL_PLANE)
seg.set_method_type(pcl.SAC_RANSAC)
indices, model = seg.segment()

API Documentation

.. autosummary:: pcl.PointCloud pcl.Segmentation pcl.SegmentationNormal pcl.StatisticalOutlierRemovalFilter pcl.MovingLeastSquares pcl.PassThroughFilter pcl.VoxelGridFilter