134 lines
No EOL
4.4 KiB
C++
134 lines
No EOL
4.4 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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#include "Face_utils.h"
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#include "SVM_dynamic_lin.h"
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using namespace FaceAnalysis;
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void SVM_dynamic_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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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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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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std::cout << "Something went wrong with the SVM dynamic classifiers" << std::endl;
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}
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}
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cv::Mat_<double> support_vectors_curr;
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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_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)
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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 - running_median) * 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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cv::Mat_<double> run_med;
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cv::hconcat(running_median, running_median_geom, run_med);
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preds = (input - this->means - run_med) * 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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} |