pycpd
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siavashk
Pure Numpy Implementation of the Coherent Point Drift Algorithm
############# Python-CPD ############# .. image:: https://travis-ci.com/siavashk/pycpd.svg?branch=master :target: https://travis-ci.com/siavashk/pycpd
Documentation <https://siavashk.github.io/pycpd/>_
Pure Numpy Implementation of the Coherent Point Drift Algorithm.
MIT License.
Introduction
This is a pure numpy implementation of the coherent point drift CPD <https://arxiv.org/abs/0905.2635/>_ algorithm by Myronenko and Song. It provides three registration methods for point clouds: 1) Scale and rigid registration; 2) Affine registration; and 3) Gaussian regularized non-rigid registration.
The CPD algorithm is a registration method for aligning two point clouds. In this method, the moving point cloud is modelled as a Gaussian Mixture Model (GMM) and the fixed point cloud are treated as observations from the GMM. The optimal transformation parameters maximze the Maximum A Posteriori (MAP) estimation that the observed point cloud is drawn from the GMM.
The registration methods work for 2D and 3D point clouds. For more information, please refer to my blog <http://siavashk.github.io/2017/05/14/coherent-point-drift/>_.
Pip Install
.. code-block:: bash
pip install pycpd
Installation From Source
Clone the repository to a location, referred to as the root folder. For example:
.. code-block:: bash
git clone https://github.com/siavashk/pycpd.git $HOME/pycpd
Install the package:
.. code-block:: bash
pip install .
or
.. code-block:: bash
make requirements make build
For running sample registration examples under examples, you will need matplotlib to visualize the registration. This can be downloaded by running:
.. code-block:: bash
pip install matplotlib
or
.. code-block:: bash
make visualize
Usage
Each registration method is contained within a single class inside the pycpd subfolder. To try out the registration, you can simply run:
.. code-block:: bash
python examples/fish_{Transform}_{Dimension}.py
where Transform is either rigid, affine or deformable and Dimension is either 2D or 3D. Note that examples are meant to be run from the root folder.
Testing
Tests can be run using pytest:
.. code-block:: bash
pip install pytest pytest
or
.. code-block:: bash
make dev make test
Documentation
The documentation can be built using pydoc3
.. code-block:: bash
make dev make doc
siavashk