sustaining_gazes/lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h

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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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// 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.
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//
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// 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.
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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 __LANDMARK_DETECTION_VALIDATOR_h_
#define __LANDMARK_DETECTION_VALIDATOR_h_
// OpenCV includes
#include <opencv2/core/core.hpp>
// System includes
#include <vector>
// Local includes
#include "PAW.h"
using namespace std;
namespace LandmarkDetector
{
//===========================================================================
//
// Checking if landmark detection was successful using an SVR regressor
// Using multiple validators trained add different views
// The regressor outputs -1 for ideal alignment and 1 for worst alignment
//===========================================================================
class DetectionValidator
{
public:
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// What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net, 3 - new version of convolutional neural net
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int validator_type;
// The orientations of each of the landmark detection validator
vector<cv::Vec3d> orientations;
// Piecewise affine warps to the reference shape (per orientation)
vector<PAW> paws;
//==========================================
// Linear SVR
// SVR biases
vector<double> bs;
// SVR weights
vector<cv::Mat_<double> > ws;
//==========================================
// Neural Network
// Neural net weights
vector<vector<cv::Mat_<double> > > ws_nn;
// What type of activation or output functions are used
// 0 - sigmoid, 1 - tanh_opt, 2 - ReLU
vector<int> activation_fun;
vector<int> output_fun;
//==========================================
// Convolutional Neural Network
// CNN layers for each view
// view -> layer -> input maps -> kernels
vector<vector<vector<vector<cv::Mat_<float> > > > > cnn_convolutional_layers;
// Bit ugly with so much nesting, but oh well
vector<vector<vector<vector<pair<int, cv::Mat_<double> > > > > > cnn_convolutional_layers_dft;
vector<vector<vector<float > > > cnn_convolutional_layers_bias;
vector< vector<int> > cnn_subsampling_layers;
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vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers_weights;
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vector< vector<float > > cnn_fully_connected_layers_bias;
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// OLD CNN: 0 - convolutional, 1 - subsampling, 2 - fully connected
// NEW CNN: 0 - convolutional, 1 - max pooling (2x2 stride 2), 2 - fully connected, 3 - relu, 4 - sigmoid
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vector<vector<int> > cnn_layer_types;
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// Extra params for the new CNN
vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers_biases;
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//==========================================
// Normalisation for face validation
vector<cv::Mat_<double> > mean_images;
vector<cv::Mat_<double> > standard_deviations;
// Default constructor
DetectionValidator(){;}
// Copy constructor
DetectionValidator(const DetectionValidator& other);
// Given an image, orientation and detected landmarks output the result of the appropriate regressor
double Check(const cv::Vec3d& orientation, const cv::Mat_<uchar>& intensity_img, cv::Mat_<double>& detected_landmarks);
// Reading in the model
void Read(string location);
// Getting the closest view center based on orientation
int GetViewId(const cv::Vec3d& orientation) const;
private:
// The actual regressor application on the image
// Support Vector Regression (linear kernel)
double CheckSVR(const cv::Mat_<double>& warped_img, int view_id);
// Feed-forward Neural Network
double CheckNN(const cv::Mat_<double>& warped_img, int view_id);
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// Convolutional Neural Network
double CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id);
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// Convolutional Neural Network
double CheckCNN(const cv::Mat_<double>& warped_img, int view_id);
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// Convolutional Neural Network
double CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id);
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// A normalisation helper
void NormaliseWarpedToVector(const cv::Mat_<double>& warped_img, cv::Mat_<double>& feature_vec, int view_id);
};
}
#endif