pikahhh / python-pcl

Python bindings to the pointcloud library (pcl)

Introduction

This is a small python binding to the [pointcloud](http://pointclouds.org/) library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZ) point types

• I/O and integration; saving and loading PCD files
• segmentation
• SAC
• smoothing
• filtering
• registration (ICP, GICP, ICP_NL)

The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy. For example (from tests/test.py)

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()

or, for smoothing

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")

Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:

import numpy as np
import pcl
p = pcl.PointCloud(10)  # "empty" point cloud
a = np.asarray(p)       # NumPy view on the cloud
a[:] = 0                # fill with zeros
print(p[3])             # prints (0.0, 0.0, 0.0)
a[:, 0] = 1             # set x coordinates to 1
print(p[3])             # prints (1.0, 0.0, 0.0)

More samples can be found in the examples directory, and in the unit tests.

This work was supported by Strawlab.

Requirements

This release has been tested on Linux Ubuntu 14.04 with

• Python 2.7.6, 3.4.0, 3.5.2
• pcl 1.7.0
• Cython <= 0.25.2

and MacOS with

• Python 2.7.6, 3.4.0, 3.5.2
• pcl 1.8.1(use homebrew)
• Cython <= 0.25.2

and Windows with

• (Miniconda/Anaconda) - Python 3.4
• pcl 1.6.0(VS2010)
• Cython <= 0.25.2
• Gtk+

and Windows with

• (Miniconda/Anaconda) - Python 3.5
• pcl 1.8.0(VS2015)
• Cython <= 0.25.2
• Gtk+

Installation

Linux(Ubuntu)

PCL 1.7.0(use apt-get)

1. Install PCL Module.

sudo add-apt-repository ppa:v-launchpad-jochen-sprickerhof-de/pcl -y

sudo apt-get update -y

sudo apt-get install libpcl-all -y

PCL 1.8.0 (build module)([CI Test Timeout])

1. Build Module

   Reference here.

MacOSX

use homebrew

1. Install PCL Module.

brew tap homebrew/science

brew install pcl

Warning:

Current Installer (2017/10/02) Not generated pcl-2d-1.8.pc file.(Issue #119)

Reference PointCloudLibrary Issue.

Pull qequests 1679.

Issue 1978.

circumvent:

copy travis/pcl-2d-1.8.pc file to /usr/local/lib/pkgconfig folder.

Windows

before Install module

Case1. use PCL 1.6.0

Windows SDK 7.1

            PCL All-In-One Installer

            32 bit

64 bit

OpenNI2[(PCL Install FolderPath)\3rdParty\OpenNI\OpenNI-(win32/x64)-1.3.2-Dev.msi]

Case2. use 1.8.1

            Visual Studio 2015 C++ Compiler Tools

            PCL All-In-One Installer

            32 bit

            64 bit

OpenNI2[(PCL Install FolderPath)\3rdParty\OpenNI2\OpenNI-Windows-(win32/x64)-2.2.msi]

        Common setting

Windows Gtk+ Download

Download file unzip. Copy bin Folder to pkg-config Folder

or execute powershell file [Install-GTKPlus.ps1].

Python Version use VisualStudio Compiler

set before Environment variable

1. PCL_ROOT

   set PCL_ROOT=$(PCL Install FolderPath)

2. PATH

   (pcl 1.6.0) set PATH=$(PCL_ROOT)/bin/;$(OPEN_NI_ROOT)/Tools;$(VTK_ROOT)/bin;%PATH%

   (pcl 1.8.1) set PATH=$(PCL_ROOT)/bin/;$(OPEN_NI2_ROOT)/Tools;$(VTK_ROOT)/bin;%PATH%

Common setting

1. pip module install.

pip install --upgrade pip

pip install cython==0.25.2

pip install numpy

2. instal python module

python setup.py build_ext -i

python setup.py install

Build & Test Status

windows(1.6.0/1.8.1)

Mac OSX(1.8.1)/Ubuntu14.04(1.7.0)

A note about types

Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.

It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.

API Documentation

For deficiencies in this documentation, please consult the PCL API docs, and the PCL tutorials.