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// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
// all rights reserved.
//
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// POSSIBILITY OF SUCH DAMAGE.
//
// Notwithstanding the license granted herein, Licensee acknowledges that certain components
// of the Software may be covered by so-called “open source” software licenses (“Open Source
// Components”), which means any software licenses approved as open source licenses by the
// Open Source Initiative or any substantially similar licenses, including without limitation any
// license that, as a condition of distribution of the software licensed under such license,
// requires that the distributor make the software available in source code format. Licensor shall
// provide a list of Open Source Components for a particular version of the Software upon
// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
// the extent required by the licenses covering Open Source Components, the terms of such
// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
// licenses applicable to Open Source Components prohibit any of the restrictions in this
// License Agreement with respect to such Open Source Component, such restrictions will not
// apply to such Open Source Component. To the extent the terms of the licenses applicable to
// Open Source Components require Licensor to make an offer to provide source code or
// related information in connection with the Software, such offer is hereby made. Any request
// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
// Licensee acknowledges receipt of notices for the Open Source Components for the initial
// delivery of the Software.

//     * Any publications arising from the use of this software, including but
//       not limited to academic journal and conference publications, technical
//       reports and manuals, must cite at least one of the following works:
//
//       OpenFace: an open source facial behavior analysis toolkit
//       Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
//       in IEEE Winter Conference on Applications of Computer Vision, 2016  
//
//       Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
//       Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling 
//       in IEEE International. Conference on Computer Vision (ICCV),  2015 
//
//       Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
//       Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson 
//       in Facial Expression Recognition and Analysis Challenge, 
//       IEEE International Conference on Automatic Face and Gesture Recognition, 2015 
//
//       Constrained Local Neural Fields for robust facial landmark detection in the wild.
//       Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. 
//       in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.    
//
///////////////////////////////////////////////////////////////////////////////

#ifndef __SVMDYNAMICLIN_h_
#define __SVMDYNAMICLIN_h_

#include <vector>
#include <string>

#include <stdio.h>
#include <iostream>

#include <opencv2/core/core.hpp>

namespace FaceAnalysis
{

// Collection of linear SVR regressors for AU prediction
class SVM_dynamic_lin{

public:

	SVM_dynamic_lin()
	{}

	// Predict the AU from HOG appearance of the face
	void Predict(std::vector<double>& predictions, std::vector<std::string>& names, const cv::Mat_<double>& fhog_descriptor, const cv::Mat_<double>& geom_params, const cv::Mat_<double>& running_median, const cv::Mat_<double>& running_median_geom);

	// Reading in the model (or adding to it)
	void Read(std::ifstream& stream, const std::vector<std::string>& au_names);

	std::vector<std::string> GetAUNames() const
	{
		return AU_names;
	}

private:

	// The names of Action Units this model is responsible for
	std::vector<std::string> AU_names;

	// For normalisation
	cv::Mat_<double> means;
	
	// For actual prediction
	cv::Mat_<double> support_vectors;	
	cv::Mat_<double> biases;

	std::vector<double> pos_classes;
	std::vector<double> neg_classes;

};
  //===========================================================================
}
#endif