https://github.com/menpo/menpofit

Menpo's 2D deformable modelling toolkit (AAMs/CLMs/SDMs)
https://github.com/menpo/menpofit

aam clm deformable-model landmark-detection menpo python sdm

Last synced: 15 days ago
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Menpo's 2D deformable modelling toolkit (AAMs/CLMs/SDMs)

• Host: GitHub
• URL: https://github.com/menpo/menpofit
• Owner: menpo
• License: other
• Created: 2014-10-27T20:36:39.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2024-05-07T11:00:31.000Z (7 months ago)
• Last Synced: 2024-08-01T15:34:49.996Z (4 months ago)
• Topics: aam, clm, deformable-model, landmark-detection, menpo, python, sdm
• Language: Python
• Homepage: http://www.menpo.org
• Size: 3.46 MB
• Stars: 127
• Watchers: 20
• Forks: 63
• Open Issues: 26
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt
  • Authors: AUTHORS.txt

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README

        


  

  



  

  

  

  



menpofit - A deformable modelling toolkit

=========================================

The [Menpo Project](http://www.menpo.org/) package for state-of-the-art 2D deformable modelling techniques.

Currently, the techniques that have been implemented include:

### Affine Image Alignment

  - **Lucas-Kanade Image Alignment**

    - _Optimization algorithms:_ Forward Additive, Forward/Inverse Compositional

    - _Residuals:_ SSD, Fourier SSD, ECC, Gradient Correlation, Gradient Images

### Deformable Image Alignment

  - **Active Template Model**

    - _Model variants:_ Holistic, Patch-based, Masked, Linear, Linear Masked

    - _Optimization algorithm:_ Lucas-Kanade Gradient Descent (Forward/Inverse Compositional)

### Landmark Localization

  - **Active Appearance Model**

    - _Model variants:_ Holistic, Patch-based, Masked, Linear, Linear Masked

    - _Optimization algorithms:_ Lucas-Kanade Gradient Descent (Alternating, Modified Alternating, Project Out, Simultaneous, Wiberg), Casaded-Regression

  - **Active Pictorial Structures**

    - _Model variant:_ Generative

    - _Optimization algorithm:_ Weighted Gauss-Newton Optimisation with fixed Jacobian and Hessian

  - **Constrained Local Model**

    - Active Shape Models

    - Regularized Landmark Mean-Shift

  - **Unified Active Appearance Model and Constrained Local Model**

    - Alternating/Project Out Regularized Landmark Mean-Shift

  - **Ensemble of Regression Trees**

    - \[provided by [DLib](http://dlib.net/)\]

  - **Supervised Descent Method**

    - _Model variants:_ Non Parametric, Parametric Shape, Parametric Appearance, Fully Parametric

Installation

------------

Here in the Menpo team, we are firm believers in making installation as simple

as possible. Unfortunately, we are a complex project that relies on satisfying

a number of complex 3rd party library dependencies. The default Python packing

environment does not make this an easy task. Therefore, we evangelise the use

of the conda ecosystem, provided by

[Anaconda](https://store.continuum.io/cshop/anaconda/). In order to make things

as simple as possible, we suggest that you use conda too! To try and persuade

you, go to the [Menpo website](http://www.menpo.io/installation/) to find

installation instructions for all major platforms.

Documentation

-------------

See our documentation on [ReadTheDocs](http://menpofit.readthedocs.org)

Pretrained Models

-----------------

Any pretrained models are provided under the assumption that they are used only for **academic** purposes and may not be used for commercial applications. Please see the license of the [300W](https://ibug.doc.ic.ac.uk/resources/300-W/) project - upon which our pretrained models are trained.

Specifically, the pretrained models in `menpofit.aam.pretrained` may only be used for academic purposes.