A Python package for point cloud, voxel and raster processing.
Project description
Pyoints
Pyoints is a python package to conveniently process and analyze point cloud data, voxels and raster images. It is intended to be used to support the development of advanced algorithms for geo-data processing.
General concept
The key idea of Pyoints is to provide unified data structures to handle
points, voxels and rasters in the same manner. It is assumed that the
data can be interpreted as a two or three dimensional point cloud. Thus we have
a collection of geo-objects, here called GeoRecords, which are characterized
by two or three dimensional coordinates coords, a spatial reference proj
and a transformation matrix t. The spatial reference and transformation
matrix are required to define the global location of the geo-objects. Next to
the origin, the transformation matrix also stores the scale and rotation of the
local coordinate system.
The unified data structures simplify the joint use of point clouds, voxels and rasters significantly, while keeping their natural characteristics. To ease the processing and analysis, each point, voxel or raster cell are stored in the commonly used numpy record array according to its natural structure. For example, a three dimensional point cloud represents a set of points, each characterized by a coordinate and none to many additional attributes. So the points are stored in a one dimensional record array. Conversely raster images are stored in a two dimensional record array, which allows for the commonly used index access of raster cells. Still, each cell is characterized by a coordinate and none to many additional attributes. Three dimensional voxels are accordingly represented by three dimensional record arrays.
Since all objects of a voxel or raster are also interpreted as a point cloud,
spatial neighborhood queries, like nearest neighbors or distance search, can
be performed with unified functions. Thus, each extension of GeoRecords
provides a spatial index IndexKD to conveniently perform efficient
neighborhood queries. The class IndexKD is a wrapper of different spatial
indices, to handle k-dimensional spatial queries of different kinds. The
spatial indices are always only initialized on demand for performance reasons,
but cached for future queries. For example, an instance geoRecords of
GeoRecords representing three dimensional points allows to create a three
dimensional spatial index by calling geoRecords.indexKD(). If you are not
interested in the third dimension, you can call geoRecords.indexKD(2) to
perform two dimensional spatial queries.
If you just want to handle coordinates without additional attributes, the class
Coords might interest you. This class also provides the IndexKD feature,
but waives to use of a projection and transformation matrix.
When is Pyoints suitable for me?
Based on the general concept of Pyoints presented above, a bunch of algorithms, functions and filters have been implemented, to process geo-data with low programming efforts.
Of course, one might wonder whether Python is adequate to analyze point cloud data since other languages languages are much more efficient. This is obviously true, but in the experience of the author, Python is very useful to implement and test new algorithms very quickly. Algorithms for point cloud analysis and processing often rely on spatial neighborhood queries. Since Pyoints takes advantage of very efficient Python libraries, which are basically wrappers for binaries written in more efficient languages, the performance loss is limited. Additionally, Pyoints takes care of selecting and caching appropiate spatial indices for you, so you can focus on the core of your algorithm idea.
So, if you have an algorithm idea and you want to implement it quickly and play around with different settings, Pyoints is made for you. After finding an approiate algorithm it can be implemented in a more efficient language if you like. Thus Pyoints is particulary designed for scientists and developers of new geo-spatial algorithms.
Installation
Conda Installation
Conda installation is recommended. To install Pyoints with [Conda] (https://conda.io/docs/user-guide/getting-started.html) run:
conda install -c leampy pyoints
Currently Linux (64 bit) and Windows (64 bit) are supported.
Conda Build
If your system is not supported yet, you might build and install it using the conda reciepe.
Build
conda build /path/to/pyoints/conda
If wished, you can specify the variants, like:
conda build /path/to/pyoints/conda --variants={python: [3.6], target_platform: ['osx-64']}
Installation
Create a virtual environment myenv and add the newly build Pyoints package.
conda create -n myenv /path/to/builded/package/pyoints-*-py*_*.tar.bz2 --use-local
The correct package path can be found with conda build . --output.
Finally install the package:
conda activate myenv
conda install pyoints
Tests
Test the package by running the provided doctests:
python /path/to/pyoints/tests/test_pyoints.py
Documentation
You can find the documentation at docs. Here you can find docstrings with examples for each module and some comprehensive examples. You can also take a look at the source files directly.
License
Copyright (c) 2018, Sebastian Lamprecht, Trier University, lamprecht@uni-trier.de
Pyoints is free software made available under the GNU General Public License v3 or later (GPLv3+). See LICENSE for details.
Citing
Please cite Pyoints as:
Sebastian Lamprecht (2018): "Pyoints: A Python package for point cloud, voxel and raster processing.", URL https://github.com/laempy/pyoints, [Online; accessed date].
@Misc{,
author = {Sebastian Lamprecht},
organization = {Trier University}
title = {{Pyoints}: A Python package for point cloud, voxel and raster processing.},
year = {2018--},
url = "https://github.com/laempy/pyoints",
note = {[Online; accessed <today>]}
}
Contributing
Any conribution to the pyoints project is welcome. See CONTRIBUTING for details.
Acknowledgements
Funding
This work was supported by the PANTHEON project which is funded by the European Community Horizon 2020 programme under grant agreement 774571.
Python Depencencies
This software dynamically binds the following Python packages (distributed via pip). Many thanks to the authors for providing such great open source software.
cylinder_fitting
Xingjie Pan
dill
Mike McKerns
pygdal
Frank Warmerdam
laspy
Grant Brown and Howard Butler
networkx
NetworkX Developers
numpy
Travis E. Oliphant et al.
opencv-python
Olli-Pekka Heinisuo
pandas
The PyData Development Team
plyfile
Darsh Ranjan
pyproj
Jeff Whitaker
scikit-learn
Andreas Mueller
scipy
SciPy Developers
rtree
Howard Butler
External Depencencies
``Pyoints` binds following external libraries as required by some of the pip packages.
GDAL
Frank Warmerdam
Liblas
Howard Butler, Mateusz Loskot et. al.
libspatialindex
Marios Hadjieleftheriou
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