115 lines
4.0 KiB
C++
115 lines
4.0 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
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//
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// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
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// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
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//
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// License can be found in OpenFace-license.txt
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//
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// * Any publications arising from the use of this software, including but
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// not limited to academic journal and conference publications, technical
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// reports and manuals, must cite at least one of the following works:
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//
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// OpenFace: an open source facial behavior analysis toolkit
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
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// in IEEE Winter Conference on Applications of Computer Vision, 2016
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//
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// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
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// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
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// in IEEE International. Conference on Computer Vision (ICCV), 2015
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//
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// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
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// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
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// in Facial Expression Recognition and Analysis Challenge,
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// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
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//
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// Constrained Local Neural Fields for robust facial landmark detection in the wild.
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
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// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
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//
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///////////////////////////////////////////////////////////////////////////////
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#ifndef __SVR_PATCH_EXPERT_h_
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#define __SVR_PATCH_EXPERT_h_
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// system includes
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#include <map>
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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namespace LandmarkDetector
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{
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//===========================================================================
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/**
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The classes describing the SVR patch experts
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*/
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class SVR_patch_expert{
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public:
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// Type of data the patch expert operated on (0=raw, 1=grad)
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int type;
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// Logistic regression slope
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double scaling;
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// Logistic regression bias
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double bias;
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// Support vector regression weights
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cv::Mat_<float> weights;
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// Discrete Fourier Transform of SVR weights, precalculated for speed (at different window sizes)
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std::map<int, cv::Mat_<double> > weights_dfts;
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// Confidence of the current patch expert (used for NU_RLMS optimisation)
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double confidence;
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SVR_patch_expert(){;}
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// A copy constructor
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SVR_patch_expert(const SVR_patch_expert& other);
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// Reading in the patch expert
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void Read(std::ifstream &stream);
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// The actual response computation from intensity or depth (for CLM-Z)
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void Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
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void ResponseDepth(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
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};
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//===========================================================================
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/**
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A Multi-patch Expert that can include different patch types. Raw pixel values or image gradients
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*/
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class Multi_SVR_patch_expert{
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public:
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// Width and height of the patch expert support area
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int width;
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int height;
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// Vector of all of the patch experts (different modalities) for this particular Multi patch expert
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std::vector<SVR_patch_expert> svr_patch_experts;
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// Default constructor
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Multi_SVR_patch_expert(){;}
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// Copy constructor
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Multi_SVR_patch_expert(const Multi_SVR_patch_expert& other);
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void Read(std::ifstream &stream);
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// actual response computation from intensity of depth (for CLM-Z)
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void Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
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void ResponseDepth(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
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};
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}
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#endif
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