sustaining_gazes/lib/local/LandmarkDetector/include/SVR_patch_expert.h

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///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// 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
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//
// * 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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);
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// The actual response computation from intensity
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void Response(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);
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// actual response computation from intensity
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void Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
};
}
#endif