Python-PCL Tutorial

About Python-PCL

Python-PCL is a python interface for Point Cloud Library (PCL), a popular point cloud processing library written in C++. It can be used to segment, subsample, smooth, and perform other complex operations on point cloud data.

Installation

*Note that more detailed installation instructions can be found on Python-PCL's github page; however, for completeness, I have included some installation instructions.

Linux

If you're running Linux, the installation process is quite involved. You will need to run following commands:

> sudo apt-get install build-essential devscripts

> dget -u https://launchpad.net/ubuntu/+archive/primary/+files/pcl_1.7.2-14ubuntu1.16.04.1.dsc

> cd pcl-1.7.2

> sudo dpkg-buildpackage -r -uc -b

> sudo dpkg -i pcl_*.deb

Now, in this step, you will need to install several packages manually using dpkg, namely:

libpcl1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl1.7-dbg_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-apps1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-common1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-dev_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-doc_1.7.2-14ubuntu1.16.04.1_all.deb
libpcl-features1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-filters1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-io1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-kdtree1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-keypoints1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-octree1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-outofcore1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-people1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-recognition1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-registration1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-sample-consensus1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-search1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-segmentation1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-surface1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-tracking1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
libpcl-visualization1.7_1.7.2-14ubuntu1.16.04.1_amd64.deb
pcl-tools_1.7.2-14ubuntu1.16.04.1_amd64.deb

Each of these packages should be in your pcl-1.7.2 directory and form a complex tree of dependencies; simply put, installing of these packages is a non-trivial process.
Next, you need to clone the Python-PCL repository and run the following commands:

> python setup.py build_ext -i

> python setup.py install

Finally, to verify that Python-PCL installed correctly. type import pcl in your python interpreter.

Mac OS

On Mac OS, the installation process is simple.
Install Homebrew and run the following commands:

> brew tap homebrew-science

> brew install pcl

Next, you need to clone the Python-PCL repository and run the following commands:

> python setup.py build_ext -i

> python setup.py install

Finally, to verify that Python-PCL installed correctly. type import pcl in your python interpreter.

Windows

See Python-PCL's Github page!

Processing Point Cloud Data

Subsampling Point Cloud Data

When processing point cloud data, raw data files typically have hundreds of millions of points, which are often repetitive. Subsampling provides a way to prune your point cloud data within minimal loss in fidelity. In this tutorial, we will explore a technique called voxel filtering; to learn more about voxel filtering, look here.
First, import pcl and load your point cloud data:

Notice that we need to bind the point cloud data to a PointCloud object.
Then, we will create a voxel filter from the point cloud, set a filter size, and apply the filter:

Now, we're finished! The size of the new point cloud data will depend on the size and density of the original point cloud; you should print the size of the new point cloud to verify that the filter was applied correctly.

Reducing

Python-PCL can also be used to find the k-th points closest to a single point in your point cloud.
This approach uses a KdTree, which you can read more about here.
First, import pcl and load your point cloud data:

Next, we will create a KdTree from our point cloud data , pick a point to start from, and run the search:

nearest_k_search_for_cloud() will return the indices of the points closest to the points we selected!

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