mexca.video.mefarg
==================

.. py:module:: mexca.video.mefarg

.. autoapi-nested-parse::

   Multi-dimensional edge feature-based AU relation graph (MEFARG) learning.

   Implementation of the MEFARG model from the paper:

       Luo, C., Song, S., Xie, W., Shen, L., Gunes, H. (2022). Learning multi-dimentionsal edge
       feature-based AU relation graph for facial action unit recognition. *arXiv*.
       `<https://arxiv.org/pdf/2205.01782.pdf>`_

   Code adapted from the `OpenGraphAU <https://github.com/lingjivoo/OpenGraphAU/tree/main>`_ code base
   (licensed under Apache 2.0).



Classes
-------

.. autoapisummary::

   mexca.video.mefarg.MEFARG


Module Contents
---------------

.. py:class:: MEFARG(config: Dict)



   Apply a multi-dimensional edge feature-based action unit (AU) relation graph (MEFARG) model.

   Predict activations of 27 main and 14 sub AUs from representations of a face image.

   :param config: Configuration dict of the model with two keys:
                  `n_main_aus` is the number of main and `n_sub_aus` is the number of sub AUs to be predicted.
                  If pretrained model weights are loaded, these must match the configuration.
   :type config: dict

   .. rubric:: Notes

   First generates face representations using a ResNet50 backbone (default weights),
   then transforms them into AU activations using a multi-dimensional edge feature learning (MEFL) head (see :func:`mexca.video.mefl.mefl`).