Awesome
<p align="center"> <img src="menpofit-logo.png" alt="menpo" width="30%"></center> <br><br> <a href="https://github.com/menpo/menpofit/blob/master/LICENSE.txt"><img src="http://img.shields.io/badge/License-BSD-green.svg" alt="BSD License"/></a> <img src="https://img.shields.io/badge/Python-2.7-green.svg" alt="Python 2.7 Support"/> <img src="https://img.shields.io/badge/Python-3.4-green.svg" alt="Python 3.4 Support"/> <img src="https://img.shields.io/badge/Python-3.5-green.svg" alt="Python 3.5 Support"/> </p>menpofit - A deformable modelling toolkit
The Menpo Project 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]
- 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. 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 to find installation instructions for all major platforms.
Documentation
See our documentation on ReadTheDocs
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 project - upon which our pretrained models are trained.
Specifically, the pretrained models in menpofit.aam.pretrained may only be used for academic purposes.