%global _empty_manifest_terminate_build 0 Name: python-pycpd Version: 2.0.0 Release: 1 Summary: Pure Numpy Implementation of the Coherent Point Drift Algorithm License: MIT URL: https://github.com/siavashk/pycpd Source0: https://mirrors.aliyun.com/pypi/web/packages/c7/e3/e867299fbc745cbec071c13d45b74e255e86752da9ebd8962bf55fc54aa4/pycpd-2.0.0.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-future %description ############# Python-CPD ############# .. image:: https://travis-ci.com/siavashk/pycpd.svg?branch=master :target: https://travis-ci.com/siavashk/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 `_ 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 `_. ************* 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 . 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 ***** 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. %package -n python3-pycpd Summary: Pure Numpy Implementation of the Coherent Point Drift Algorithm Provides: python-pycpd BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-pycpd ############# Python-CPD ############# .. image:: https://travis-ci.com/siavashk/pycpd.svg?branch=master :target: https://travis-ci.com/siavashk/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 `_ 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 `_. ************* 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 . 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 ***** 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. %package help Summary: Development documents and examples for pycpd Provides: python3-pycpd-doc %description help ############# Python-CPD ############# .. image:: https://travis-ci.com/siavashk/pycpd.svg?branch=master :target: https://travis-ci.com/siavashk/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 `_ 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 `_. ************* 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 . 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 ***** 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. %prep %autosetup -n pycpd-2.0.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-pycpd -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue Jun 20 2023 Python_Bot - 2.0.0-1 - Package Spec generated