sustaining_gazes/lib/local/LandmarkDetector/include/CCNF_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 __CCNF_PATCH_EXPERT_h_
#define __CCNF_PATCH_EXPERT_h_
#include <opencv2/core/core.hpp>
#include <map>
#include <vector>
namespace LandmarkDetector
{
//===========================================================================
/**
A single Neuron response
*/
class CCNF_neuron{
public:
// Type of patch (0=raw,1=grad,3=depth, other types besides raw are not actually used now)
int neuron_type;
// scaling of weights (needed as the energy of neuron might not be 1)
double norm_weights;
// Weight bias
double bias;
// Neural weights
cv::Mat_<float> weights;
// can have neural weight dfts that are calculated on the go as needed, this allows us not to recompute
// the dft of the template each time, improving the speed of tracking
std::map<int, cv::Mat_<double> > weights_dfts;
// the alpha associated with the neuron
double alpha;
// Default constructor
CCNF_neuron(){;}
// Copy constructor
CCNF_neuron(const CCNF_neuron& other);
void Read(std::ifstream &stream);
// The im_dft, integral_img, and integral_img_sq are precomputed images for convolution speedups (they get set if passed in empty values)
void Response(cv::Mat_<float> &im, cv::Mat_<double> &im_dft, cv::Mat &integral_img, cv::Mat &integral_img_sq, cv::Mat_<float> &resp);
};
//===========================================================================
/**
A CCNF patch expert
*/
class CCNF_patch_expert{
public:
// Width and height of the patch expert support region
int width;
int height;
// Collection of neurons for this patch expert
std::vector<CCNF_neuron> neurons;
// Information about the vertex features (association potentials)
std::vector<int> window_sizes;
std::vector<cv::Mat_<float> > Sigmas;
std::vector<double> betas;
// How confident we are in the patch
double patch_confidence;
// Default constructor
CCNF_patch_expert(){;}
// Copy constructor
CCNF_patch_expert(const CCNF_patch_expert& other);
void Read(std::ifstream &stream, std::vector<int> window_sizes, std::vector<std::vector<cv::Mat_<float> > > sigma_components);
// actual work (can pass in an image and a potential depth image, if the CCNF is trained with depth)
void Response(cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
// Helper function to compute relevant sigmas
void ComputeSigmas(std::vector<cv::Mat_<float> > sigma_components, int window_size);
};
//===========================================================================
}
#endif