///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
// all rights reserved.
//
// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
//
// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.  
// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
//
// License can be found in OpenFace-license.txt
//
//     * 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 __SVR_PATCH_EXPERT_h_
#define __SVR_PATCH_EXPERT_h_

// system includes
#include <map>

// OpenCV includes
#include <opencv2/core/core.hpp>

namespace LandmarkDetector
{
  //===========================================================================
  /** 
      The classes describing the SVR patch experts
  */

class SVR_patch_expert{
	public:

		// Type of data the patch expert operated on (0=raw, 1=grad)
		int     type;					

		// Logistic regression slope
		double  scaling;
		
		// Logistic regression bias
		double  bias;

		// Support vector regression weights
		cv::Mat_<float> weights;

		// Discrete Fourier Transform of SVR weights, precalculated for speed (at different window sizes)
		std::map<int, cv::Mat_<double> > weights_dfts;

		// Confidence of the current patch expert (used for NU_RLMS optimisation)
		double  confidence;

		SVR_patch_expert(){;}
		
		// A copy constructor
		SVR_patch_expert(const SVR_patch_expert& other);

		// Reading in the patch expert
		void Read(std::ifstream &stream);

		// The actual response computation from intensity or depth (for CLM-Z)
		void Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
		void ResponseDepth(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);

};
//===========================================================================
/**
    A Multi-patch Expert that can include different patch types. Raw pixel values or image gradients
*/
class Multi_SVR_patch_expert{
	public:
		
		// Width and height of the patch expert support area
		int width;
		int height;						

		// Vector of all of the patch experts (different modalities) for this particular Multi patch expert
		std::vector<SVR_patch_expert> svr_patch_experts;	

		// Default constructor
		Multi_SVR_patch_expert(){;}
	
		// Copy constructor				
		Multi_SVR_patch_expert(const Multi_SVR_patch_expert& other);

		void Read(std::ifstream &stream);

		// actual response computation from intensity of depth (for CLM-Z)
		void Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
		void ResponseDepth(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);

};
}
#endif