sustaining_gazes/lib/local/FaceAnalyser/src/SVM_dynamic_lin.cpp

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///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
// all rights reserved.
//
// THIS SOFTWARE IS PROVIDED <20>AS IS<49> FOR ACADEMIC USE ONLY AND ANY EXPRESS
// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Notwithstanding the license granted herein, Licensee acknowledges that certain components
// of the Software may be covered by so-called <20>open source<63> software licenses (<28>Open Source
// Components<74>), 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<65>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 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.
//
///////////////////////////////////////////////////////////////////////////////
#include "SVM_dynamic_lin.h"
#include "LandmarkCoreIncludes.h"
using namespace FaceAnalysis;
void SVM_dynamic_lin::Read(std::ifstream& stream, const std::vector<std::string>& au_names)
{
if(this->means.empty())
{
LandmarkDetector::ReadMatBin(stream, this->means);
}
else
{
cv::Mat_<double> m_tmp;
LandmarkDetector::ReadMatBin(stream, m_tmp);
if(cv::norm(m_tmp - this->means > 0.00001))
{
cout << "Something went wrong with the SVM dynamic classifiers" << endl;
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}
}
cv::Mat_<double> support_vectors_curr;
LandmarkDetector::ReadMatBin(stream, support_vectors_curr);
double bias;
stream.read((char *)&bias, 8);
// Read in positive or negative class
double pos_class;
stream.read((char *)&pos_class, 8);
double neg_class;
stream.read((char *)&neg_class, 8);
// Add a column vector to the matrix of support vectors (each column is a support vector)
if(!this->support_vectors.empty())
{
cv::transpose(this->support_vectors, this->support_vectors);
cv::transpose(support_vectors_curr, support_vectors_curr);
this->support_vectors.push_back(support_vectors_curr);
cv::transpose(this->support_vectors, this->support_vectors);
cv::transpose(this->biases, this->biases);
this->biases.push_back(cv::Mat_<double>(1, 1, bias));
cv::transpose(this->biases, this->biases);
}
else
{
this->support_vectors.push_back(support_vectors_curr);
this->biases.push_back(cv::Mat_<double>(1, 1, bias));
}
this->pos_classes.push_back(pos_class);
this->neg_classes.push_back(neg_class);
for(size_t i=0; i < au_names.size(); ++i)
{
this->AU_names.push_back(au_names[i]);
}
}
// Prediction using the HOG descriptor
void SVM_dynamic_lin::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)
{
if(AU_names.size() > 0)
{
cv::Mat_<double> preds;
if(fhog_descriptor.cols == this->means.cols)
{
preds = (fhog_descriptor - this->means - running_median) * this->support_vectors + this->biases;
}
else
{
cv::Mat_<double> input;
cv::hconcat(fhog_descriptor, geom_params, input);
cv::Mat_<double> run_med;
cv::hconcat(running_median, running_median_geom, run_med);
preds = (input - this->means - run_med) * this->support_vectors + this->biases;
}
for(int i = 0; i < preds.cols; ++i)
{
if(preds.at<double>(i) > 0)
{
predictions.push_back(pos_classes[i]);
}
else
{
predictions.push_back(neg_classes[i]);
}
}
names = this->AU_names;
}
}