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https://github.com/virtual-vehicle/pointcloudset

Efficient analysis of large datasets of point clouds recorded over time
https://github.com/virtual-vehicle/pointcloudset

3d 4d 4d-point-cloud convert lidar lidar-point-cloud livox open3d ouster point-cloud pointcloud python riegl ros ros2 rosbag rostopic time-series time-series-analysis velodyne-sensor

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Efficient analysis of large datasets of point clouds recorded over time

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README 

        
pointcloudset

=========================================



*Analyze large datasets of point clouds recorded over time in an efficient way.*



.. image:: https://github.com/virtual-vehicle/pointcloudset/actions/workflows/tests_docker.yml/badge.svg

   :target: https://github.com/virtual-vehicle/pointcloudset/actions/workflows/tests_docker.yml

   :alt: test status



.. image:: images/coverage.svg

   :target: https://github.com/virtual-vehicle/pointcloudset/actions/workflows/tests.yml

   :alt: test coverage



.. image:: https://github.com/virtual-vehicle/pointcloudset/actions/workflows/doc.yml/badge.svg

   :target: https://virtual-vehicle.github.io/pointcloudset/

    :alt: Documentation Status



.. image:: https://github.com/virtual-vehicle/pointcloudset/actions/workflows/docker.yml/badge.svg

   :target: https://hub.docker.com/repository/docker/tgoelles/pointcloudset

   :alt: Docker



.. image:: https://badge.fury.io/py/pointcloudset.svg

    :target: https://badge.fury.io/py/pointcloudset

    :alt: PyPi badge



.. image:: https://pepy.tech/badge/pointcloudset/month

    :target: https://pepy.tech/project/pointcloudset

    :alt: PyPi badge



.. image:: https://joss.theoj.org/papers/10.21105/joss.03471/status.svg

   :target: https://joss.theoj.org/papers/10.21105/joss.03471#

   :alt: JOSS badge



.. image:: https://img.shields.io/badge/code%20style-ruff-000000.svg

   :target: https://github.com/astral-sh/ruff

   :alt: code style ruff



.. inclusion-marker-do-not-remove



`Code`_ | `Documentation`_



.. _Code: https://github.com/virtual-vehicle/pointcloudset

.. _Documentation: https://virtual-vehicle.github.io/pointcloudset/



Features

################################################

* Handles point clouds over time

* Building complex pipelines with a clean and maintainable code



.. code-block:: python



   newpointcloud = pointcloud.limit("x",-5,5).filter("quantile","reflectivity", ">",0.5)



* Apply arbitrary functions to datasets of point clouds



.. code-block:: python



   def isolate_target(frame: PointCloud) -> PointCloud:

      return frame.limit("x",0,1).limit("y",0,1)



   def diff_to_pointcloud(pointcloud: PointCloud, to_compare: PointCloud) -> PointCloud:

      return pointcloud.diff("pointcloud", to_compare)



   result = dataset.apply(isolate_target).apply(diff_to_pointcloud, to_compare=dataset[0])



* Includes powerful aggregation method *agg* similar to pandas



.. code-block:: python



  dataset.agg(["min","max","mean","std"])



* Support for large files with lazy evaluation and parallel processing



.. image:: https://raw.githubusercontent.com/virtual-vehicle/pointcloudset/master/images/dask.gif

   :width: 600



* Support for numerical data per point (intensity, range, noise …)

* Interactive 3D visualisation



.. image:: https://raw.githubusercontent.com/virtual-vehicle/pointcloudset/master/images/tree.gif

   :width: 600



* High level processing based on dask, pandas, open3D and pyntcloud

* Docker image is available

* Optimised - but not limited to - automotive lidar

* Directly read ROS files and many pointcloud file formats

* A command line tool to convert ROS 1 & 2 files



Use case examples

################################################



- Post processing and analytics of a lidar dataset recorded by ROS

- A collection of multiple lidar scans from a terrestrial laser scanner

- Comparison of multiple point clouds to a ground truth

- Analytics of point clouds over time

- Developing algorithms on a single frame and then applying them to huge datasets



Installation with pip

################################################



Install python package with pip:



.. code-block:: console



   pip install pointcloudset



Installation with Docker

################################################



The easiest way to get started is to use the pre-build docker `tgoelles/pointcloudset`_.



.. _tgoelles/pointcloudset: https://hub.docker.com/repository/docker/tgoelles/pointcloudset



Quickstart

################################################



.. code-block:: python



   from pointcloudset import Dataset, PointCloud

   from pathlib import Path

   import urllib.request



   urllib.request.urlretrieve("https://github.com/virtual-vehicle/pointcloudset/raw/master/tests/testdata/test.bag", "test.bag")

   urllib.request.urlretrieve("https://github.com/virtual-vehicle/pointcloudset/raw/master/tests/testdata/las_files/test_tree.las", "test_tree.las")



   dataset = Dataset.from_file(Path("test.bag"), topic="/os1_cloud_node/points", keep_zeros=False)

   pointcloud = dataset[1]

   tree = PointCloud.from_file(Path("test_tree.las"))



   tree.plot("x", hover_data=True)



This produces the plot from the animation above.



* Read the `html documentation`_.

* Have a look at the `tutorial notebooks`_ in the documentation folder

* For even more usage examples you can have a look at the tests



.. _html documentation: https://virtual-vehicle.github.io/pointcloudset/

.. _tutorial notebooks: https://github.com/virtual-vehicle/pointcloudset/tree/master/doc/sphinx/source/tutorial_notebooks



CLI to convert ROS1 and ROS2 files: pointcloudset convert

##########################################################



The package includes a powerful CLI to convert pointclouds in ROS1 & 2 files into many formats like pointcloudset, csv, las and many more.

It is capable of handling both mcap and db3 ROS files.



.. code-block:: console



   pointcloudset convert --output-format csv --output-dir converted_csv test.bag



.. image:: https://raw.githubusercontent.com/virtual-vehicle/pointcloudset/master/images/cli_demo.gif

   :width: 600



You can view PointCloud2 messages with



.. code-block:: console



   pointcloudset topics test.bag



Comparison to related packages

################################################



#. `ROS `_ -  bagfiles can contain many point clouds from different sensors.

   The downside of the format is that it is only suitable for serial access and not well suited for data analytics and post processing.

#. `pyntcloud `_ - Only for single point clouds. This package is used as the basis for the

   PointCloud object.

#. `open3d `_ - Only for single point clouds. Excellent package, which is used for some

   methods on the PointCloud.

#. `pdal `_ - Works also with pipelines on point clouds but is mostly focused on single point cloud processing.

   Pointcloudset is purely in python and based on pandas DataFrames. In addition pointcloudset works in parallel to process large datasets.



Citation and contact

################################################



.. |orcid| image:: https://orcid.org/sites/default/files/images/orcid_16x16.png

   :target: https://orcid.org/0000-0002-3925-6260>



|orcid| `Thomas Gölles `_

email: [email protected]



Please cite our `JOSS paper`_ if you use pointcloudset.



.. _JOSS paper: https://joss.theoj.org/papers/10.21105/joss.03471#



.. code-block:: bib



   @article{Goelles2021,

     doi = {10.21105/joss.03471},

     url = {https://doi.org/10.21105/joss.03471},

     year = {2021},

     publisher = {The Open Journal},

     volume = {6},

     number = {65},

     pages = {3471},

     author = {Thomas Goelles and Birgit Schlager and Stefan Muckenhuber and Sarah Haas and Tobias Hammer},

     title = {`pointcloudset`: Efficient Analysis of Large Datasets of Point Clouds Recorded Over Time},

     journal = {Journal of Open Source Software}

   }