Metadata-Version: 2.1
Name: pyntcloud
Version: 0.1.5
Summary: Python library for working with 3D point clouds.
Home-page: https://github.com/daavoo/pyntcloud
Author: David de la Iglesia Castro
Author-email: daviddelaiglesiacastro@gmail.com
License: The MIT License
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >3.6
Description-Content-Type: text/x-rst
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: pandas
Provides-Extra: las
Requires-Dist: pylas ; extra == 'las'
Requires-Dist: lazrs ; extra == 'las'
Provides-Extra: numba
Requires-Dist: numba ; extra == 'numba'
Provides-Extra: plot
Requires-Dist: ipython ; extra == 'plot'
Requires-Dist: matplotlib ; extra == 'plot'
Requires-Dist: pyvista ; extra == 'plot'

=============================
Making point clouds fun again
=============================

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.. image:: https://readthedocs.org/projects/pyntcloud/badge/?version=latest
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.. image:: https://anaconda.org/conda-forge/pyntcloud/badges/version.svg   
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.. image:: https://raw.githubusercontent.com/daavoo/pyntcloud/master/docs/images/pyntcloud_logo.png
    :alt: pyntcloud logo

**pyntcloud** is a Python **3** library for working with 3D point clouds leveraging the power of the Python scientific stack.

- Examples_ (We encourage you to try out the examples by launching `Binder <https://mybinder.org/v2/gh/daavoo/pyntcloud/master>`_.)
- Documentation_

.. _Examples: https://github.com/daavoo/pyntcloud/tree/master/examples
.. _Documentation: http://pyntcloud.readthedocs.io/en/latest/

Installation
============

.. code-block:: bash

    conda install pyntcloud -c conda-forge

Or:

.. code-block:: bash

    pip install pyntcloud

Quick Overview
==============

You can access most of pyntcloud's functionality from its core class: PyntCloud.

With PyntCloud you can perform complex 3D processing operations with minimum lines of
code. For example you can:

- Load a PLY point cloud from disk.
- Add 3 new scalar fields by converting RGB to HSV.
- Build a grid of voxels from the point cloud.
- Build a new point cloud keeping only the nearest point to each occupied voxel center.
- Save the new point cloud in numpy's NPZ format.

With the following concise code:

.. code-block:: python

    from pyntcloud import PyntCloud

    cloud = PyntCloud.from_file("some_file.ply")

    cloud.add_scalar_field("hsv")

    voxelgrid_id = cloud.add_structure("voxelgrid", n_x=32, n_y=32, n_z=32)

    new_cloud = cloud.get_sample("voxelgrid_nearest", voxelgrid_id=voxelgrid_id, as_PyntCloud=True)

    new_cloud.to_file("out_file.npz")

Integration with other libraries
================================

pyntcloud offers seamless integration with other 3D processing libraries.

You can create / convert PyntCloud instances from / to many 3D processing libraries using the `from_instance` / `to_instance` methods:

- `Open3D <https://www.open3d.org>`_

.. code-block:: python

    import open3d as o3d
    from pyntcloud import PyntCloud

    # FROM Open3D
    original_triangle_mesh = o3d.io.read_triangle_mesh("diamond.ply")
    cloud = PyntCloud.from_instance("open3d", original_triangle_mesh)

    # TO Open3D
    cloud = PyntCloud.from_file("diamond.ply")
    converted_triangle_mesh = cloud.to_instance("open3d", mesh=True)  # mesh=True by default

- `PyVista <https://docs.pyvista.org>`_

.. code-block:: python

    import pyvista as pv
    from pyntcloud import PyntCloud

    # FROM PyVista
    original_point_cloud = pv.read("diamond.ply")
    cloud = PyntCloud.from_instance("pyvista", original_point_cloud)

    # TO PyVista
    cloud = PyntCloud.from_file("diamond.ply")
    converted_triangle_mesh = cloud.to_instance("pyvista", mesh=True)


