pclpy: PCL for python

Python bindings for the Point Cloud Library (PCL). Generated from headers using CppHeaderParser and pybind11.

Install using conda: conda install -c conda-forge -c davidcaron pclpy (see Installation below)

Contributions, issues, comments are welcome!

Github repository: https://www.github.com/davidcaron/pclpy

Motivation

Many other python libraries tried to bind PCL. The most popular one being python-pcl, which uses Cython. While Cython is really powerful, binding C++ templates isn't one of its strenghts (and PCL uses templates heavily). The result for python-pcl is a lot of code repetition, which is hard to maintain and to add features to, and incomplete bindings of PCL's classes and point types.

Using pybind11, we use C++ directly. Templates, boost::smart_ptr and the buffer protocol are examples of things that are simpler to implement.

The results so far are very promising. A large percentage of PCL is covered.

Installation

We use conda to release pclpy. To install, use this command:

conda install -c conda-forge -c davidcaron pclpy

Don't forget to add both channels, or else conda won't be able to find all dependencies.

Windows: python 3.6 and 3.7 are supported

Linux: python 3.6, 3.7 and 3.8 are supported

Features

Most point types are implemented (those specified by PCL_ONLY_CORE_POINT_TYPES in PCL)
You can get a numpy view of point cloud data using python properties (e.g. cloud.x or cloud.xyz)
boost::shared_ptr is handled by pybind11 so it's completely abstracted at the python level
laspy integration for reading/writing las files

Example

You can use either a high level, more pythonic api, or the wrapper over the PCL api. The wrapper is meant to be as close as possible to the original PCL C++ api.

Here is how you would use the library to process Moving Least Squares. See the PCL documentation: http://pointclouds.org/documentation/tutorials/resampling.php

Using the higher level api:

import pclpy

# read a las file
point_cloud = pclpy.read("street.las", "PointXYZRGBA")
# compute mls
output = point_cloud.moving_least_squares(search_radius=0.05, compute_normals=True, num_threads=8)

Or the wrapper over the PCL api:

import pclpy
from pclpy import pcl

point_cloud = pclpy.read("street.las", "PointXYZRGBA")
mls = pcl.surface.MovingLeastSquaresOMP.PointXYZRGBA_PointNormal()
tree = pcl.search.KdTree.PointXYZRGBA()
mls.setSearchRadius(0.05)
mls.setPolynomialFit(False)
mls.setNumberOfThreads(12)
mls.setInputCloud(point_cloud)
mls.setSearchMethod(tree)
mls.setComputeNormals(True)
output = pcl.PointCloud.PointNormal()
mls.process(output)

You can see the wrapper is very close to the C++ version:

// C++ version

pcl::PointCloud<pcl::PointXYZ>::Ptr point_cloud (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::io::loadPCDFile ("bunny.pcd", *point_cloud);
pcl::MovingLeastSquaresOMP<pcl::PointXYZ, pcl::PointNormal> mls;
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);
mls.setSearchRadius (0.05);
mls.setPolynomialFit (false);
mls.setNumberOfThreads (12);
mls.setInputCloud (point_cloud);
mls.setSearchMethod (tree);
mls.setComputeNormals (true);
pcl::PointCloud<pcl::PointNormal> output;
mls.process (output);

Modules

2d
common
geometry
features
filters
io (meshes are not implemented)
kdtree
keypoints
octree
recognition
sample_consensus
search
segmentation
stereo
surface
tracking

These modules are skipped for now

ml
people
outofcore
registration
visualization
every module not in the PCL Windows release (gpu, cuda, etc.)

Not Implemented

(see github issues and the what to skip section in generators/config.py)

Building

Build scripts are in the scripts folder.

Create your conda environment: conda env create -n pclpy -f environment.yml
Activate your environment: conda activate pclpy
Install development dependencies: pip install -r requirements-dev.txt
Download a copy of PCL Windows: powershell scripts\download_pcl.ps1 Linux: scripts\download_pcl.sh
Generate pybind11 bindings Windows: powershell scripts\generate_points_and_bindings.ps1 Linux: scripts\generate_points_and_bindings.sh
For development, build inplace using python python setup.py build_ext -i

For a release, use the scripts/conda_build.bat (or conda_build.sh) script

On Windows, these setup.py arguments can cpeed up the build: - --msvc-mp-build enables a multiprocessed build - --msvc-no-code-link makes the linking step faster (not meant for releases) - --use-clcache to cache msvc builds (clcache must be installed)

Roadmap

Wrap as much of PCL as reasonably possible
More tests

turtlesoupy/pclpy