pointcloudset

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

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.. 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
• Directly read ROS files and many pointcloud file formats.
• Generate a dataset from multiple pointclouds. For example from thousands of .las files.
• 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, scipy, scikit-learn
• Docker image is available
• Optimised - but not limited to - automotive lidar
• 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

Optional extras

For faster clustering on large point clouds, install the optional ``numba`` extra to enable JIT-accelerated union-find in ``PointCloud.get_cluster()``:

.. code-block:: console

   pip install pointcloudset[numba]

Without this extra, a pure-Python fallback is used automatically with no change in behaviour.

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

Reading ROS1 or ROS2 files:

.. code-block:: python

   import pointcloudset as pcs
   from pathlib import Path
   import urllib.request

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

   dataset = pcs.Dataset.from_file(Path("test.bag"), topic="/os1_cloud_node/points", keep_zeros=False)
   pointcloud = dataset[1]
   pointcloud.plot("x", hover_data=True)

You can also generate a dataset from multiple pointclouds from formats like las, pcd, csv, and xyz.

``PointCloud.to_file(...)`` currently writes ``csv``, ``xyz``, ``las``, and ``pcd``.
For text formats, ``csv`` defaults to writing a header and also supports ``header=False``;
``xyz`` defaults to headerless output and also supports ``header=True``.
When reading files, ``PointCloud.from_file(...)`` supports ``normalize_xyz`` (default ``False``).
If a file uses uppercase coordinate headers ``X``, ``Y``, ``Z``, reading fails unless you pass ``normalize_xyz=True``.
This makes the conversion explicit while keeping internal processing consistent with lowercase ``x``, ``y``, ``z``.

.. code-block:: python

   import pointcloudset as pcs
   from pathlib import Path
   import urllib.request

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

   las_pc = pcs.PointCloud.from_file(Path("test_tree.las"), normalize_xyz=True)
   pcd_pc = pcs.PointCloud.from_file(Path("test_tree.pcd"))
   dataset = pcs.Dataset.from_instance("pointclouds", [las_pc, pcd_pc])
   pointcloud = dataset[1]

   pointcloud.plot("z", hover_data=True)

* 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 CLI to convert pointclouds in ROS1 and ROS2 files into ``pointcloudset`` directories or native file formats.
It currently writes ``csv``, ``xyz``, ``las``, and ``pcd`` files and handles both mcap and db3 ROS2 inputs.

.. code-block:: console

   pointcloudset convert test.bag --output-format las --output-dir converted_las

.. 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

Tipp: If you have uv installed you can simply run:

.. code-block:: console

   uvx pointcloudset --help

Comparison to related packages
################################################

#. `ROS <http://wiki.ros.org/rosbag/Code%20API>`_ -  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 <https://github.com/daavoo/pyntcloud>`_ - Only for single point clouds. This package is used as the basis for the
   PointCloud object. Last update in 2022, and the project seems to be inactive. Pointcloudset has removed the dependency on pyntcloud.
#. `open3d <https://github.com/intel-isl/Open3D>`_ - Only for single point clouds.
#. `pdal <https://github.com/PDAL/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 <https://orcid.org/0000-0002-3925-6260>`_
email: thomas.goelles@v2c2.at

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}
   }