Skip to main content

Pure Numpy Implementation of the Coherent Point Drift Algorithm

Project description

https://travis-ci.com/siavashk/pycpd.svg?branch=master

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

pip install pycpd

Installation From Source

Clone the repository to a location, referred to as the root folder. For example:

git clone https://github.com/siavashk/pycpd.git $HOME/pycpd

Install the package:

pip install .

For running sample registration examples under examples, you will need two additional packages.

Scipy (for loading .mat files) and matplotlib (for visualizing the registration). These can be downloaded by running:

pip install scipy matplotlib

Usage

Each registration method is contained within a single class inside the pycpd subfolder. To try out the registration, you can simply run:

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pycpd-1.0.3.tar.gz (5.7 kB view details)

Uploaded Source

Built Distribution

pycpd-1.0.3-py2-none-any.whl (9.1 kB view details)

Uploaded Python 2

File details

Details for the file pycpd-1.0.3.tar.gz.

File metadata

  • Download URL: pycpd-1.0.3.tar.gz
  • Upload date:
  • Size: 5.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.8.1 pkginfo/1.4.1 requests/2.13.0 setuptools/39.2.0 requests-toolbelt/0.7.1 clint/0.5.1 CPython/2.7.10 Darwin/17.6.0

File hashes

Hashes for pycpd-1.0.3.tar.gz
Algorithm Hash digest
SHA256 4d7253f9ce790bf3b10c23a411afd7e27de523c2e5e022ce55cc3ce116c92c37
MD5 c79167bd7a63aa71f4cfd429b845efb1
BLAKE2b-256 6342bf447fddc700eccf44b19f75c054282dc62924387fa521f1e28ed9d9bc42

See more details on using hashes here.

File details

Details for the file pycpd-1.0.3-py2-none-any.whl.

File metadata

  • Download URL: pycpd-1.0.3-py2-none-any.whl
  • Upload date:
  • Size: 9.1 kB
  • Tags: Python 2
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.8.1 pkginfo/1.4.1 requests/2.13.0 setuptools/39.2.0 requests-toolbelt/0.7.1 clint/0.5.1 CPython/2.7.10 Darwin/17.6.0

File hashes

Hashes for pycpd-1.0.3-py2-none-any.whl
Algorithm Hash digest
SHA256 e750f7c09246f62232b882e3410206cf5d96214d26497d9f3b3a5513ae414b9e
MD5 ed2bc802236f97ebb2c016ec93614e52
BLAKE2b-256 6ea78af2ebb6ecaa4b379d07b374e25b3785d388fe339612d3845730caa4652e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page