Metadata-Version: 1.1
Name: pycpd
Version: 0.1
Summary: Pure Numpy Implementation of the Coherent Point Drift Algorithm
Home-page: https://github.com/siavashk/pycpd
Author: Siavash Khallaghi
Author-email: siavashk@ece.ubc.ca
License: MIT
Description: #############
        Python-CPD
        #############
        
        Pure Numpy Implementation of the Coherent Point Drift Algorithm.
        
        MIT License.
        
        *************
        Introduction
        *************
        
        This is a pure numpy implmenetation 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.
        
        *************
        Pip Install
        *************
        .. code-block:: bash
        
          $ pip install pycpd
        
        ************************
        Installation From Source
        ************************
        
        Clone the repository to a location in your home directory. For example:
        
        .. code-block:: bash
        
          $ git clone https://github.com/siavashk/pycpd.git $HOME/pycpd
        
        Install the package:
        
        .. code-block:: bash
        
          $ pip install .
        
        For running sample registration examples under `/tests`, you will need two additional packages.
        
        Scipy (for loading `.mat` files) and matplotlib (for visualizing the reigstration). These can be downloaded by running:
        
        .. code-block:: bash
        
         $ pip install -r requirements.txt
        
        *****
        Usage
        *****
        
        Each registration method is contained within a single class inside the pycpd subfolder. To try out the registration, you can simply call:
        
        .. code-block:: bash
        
         $ python tests/fish{Transform}{Dimension}.py
        
        where Transform is either Rigid, Affine or Deformable and Dimension is either 2D or 3D.
        
Keywords: image processing,point cloud,registration,mesh,surface
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering
