156 lines
6.3 KiB
C++
156 lines
6.3 KiB
C++
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///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// THIS SOFTWARE IS PROVIDED <20>AS IS<49> FOR ACADEMIC USE ONLY AND ANY EXPRESS
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// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
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// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
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// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Notwithstanding the license granted herein, Licensee acknowledges that certain components
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// of the Software may be covered by so-called <20>open source<63> software licenses (<28>Open Source
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// Components<74>), which means any software licenses approved as open source licenses by the
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// Open Source Initiative or any substantially similar licenses, including without limitation any
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// license that, as a condition of distribution of the software licensed under such license,
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// requires that the distributor make the software available in source code format. Licensor shall
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// provide a list of Open Source Components for a particular version of the Software upon
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// Licensee<65>s request. Licensee will comply with the applicable terms of such licenses and to
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// the extent required by the licenses covering Open Source Components, the terms of such
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// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
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// licenses applicable to Open Source Components prohibit any of the restrictions in this
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// License Agreement with respect to such Open Source Component, such restrictions will not
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// apply to such Open Source Component. To the extent the terms of the licenses applicable to
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// Open Source Components require Licensor to make an offer to provide source code or
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// related information in connection with the Software, such offer is hereby made. Any request
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// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
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// Licensee acknowledges receipt of notices for the Open Source Components for the initial
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// delivery of the Software.
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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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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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#include "SVM_static_lin.h"
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#include "LandmarkCoreIncludes.h"
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using namespace FaceAnalysis;
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void SVM_static_lin::Read(std::ifstream& stream, const std::vector<std::string>& au_names)
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{
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if(this->means.empty())
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{
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LandmarkDetector::ReadMatBin(stream, this->means);
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}
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else
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{
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cv::Mat_<double> m_tmp;
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LandmarkDetector::ReadMatBin(stream, m_tmp);
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if(cv::norm(m_tmp - this->means > 0.00001))
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{
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cout << "Something went wrong with the SVR dynamic regressors" << endl;
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}
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}
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cv::Mat_<double> support_vectors_curr;
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LandmarkDetector::ReadMatBin(stream, support_vectors_curr);
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double bias;
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stream.read((char *)&bias, 8);
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// Read in positive or negative class
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double pos_class;
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stream.read((char *)&pos_class, 8);
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double neg_class;
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stream.read((char *)&neg_class, 8);
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// Add a column vector to the matrix of support vectors (each column is a support vector)
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if(!this->support_vectors.empty())
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{
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cv::transpose(this->support_vectors, this->support_vectors);
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cv::transpose(support_vectors_curr, support_vectors_curr);
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this->support_vectors.push_back(support_vectors_curr);
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cv::transpose(this->support_vectors, this->support_vectors);
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cv::transpose(this->biases, this->biases);
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this->biases.push_back(cv::Mat_<double>(1, 1, bias));
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cv::transpose(this->biases, this->biases);
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}
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else
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{
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this->support_vectors.push_back(support_vectors_curr);
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this->biases.push_back(cv::Mat_<double>(1, 1, bias));
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}
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this->pos_classes.push_back(pos_class);
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this->neg_classes.push_back(neg_class);
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for(size_t i=0; i < au_names.size(); ++i)
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{
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this->AU_names.push_back(au_names[i]);
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}
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}
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// Prediction using the HOG descriptor
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void SVM_static_lin::Predict(std::vector<double>& predictions, std::vector<std::string>& names, const cv::Mat_<double>& fhog_descriptor, const cv::Mat_<double>& geom_params)
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{
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if(AU_names.size() > 0)
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{
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cv::Mat_<double> preds;
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if(fhog_descriptor.cols == this->means.cols)
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{
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preds = (fhog_descriptor - this->means) * this->support_vectors + this->biases;
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}
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else
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{
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cv::Mat_<double> input;
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cv::hconcat(fhog_descriptor, geom_params, input);
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preds = (input - this->means) * this->support_vectors + this->biases;
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}
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for(int i = 0; i < preds.cols; ++i)
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{
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if(preds.at<double>(i) > 0)
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{
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predictions.push_back(pos_classes[i]);
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}
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else
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{
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predictions.push_back(neg_classes[i]);
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}
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}
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names = this->AU_names;
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}
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}
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