180 lines
7.7 KiB
C++
180 lines
7.7 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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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 “AS IS” 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 “open source” software licenses (“Open Source
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// Components”), 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’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 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 __LANDMARK_DETECTION_VALIDATOR_h_
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#define __LANDMARK_DETECTION_VALIDATOR_h_
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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// System includes
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#include <vector>
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// Local includes
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#include "PAW.h"
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using namespace std;
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namespace LandmarkDetector
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{
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//===========================================================================
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//
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// Checking if landmark detection was successful using an SVR regressor
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// Using multiple validators trained add different views
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// The regressor outputs -1 for ideal alignment and 1 for worst alignment
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//===========================================================================
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class DetectionValidator
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{
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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;
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// The orientations of each of the landmark detection validator
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vector<cv::Vec3d> orientations;
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// Piecewise affine warps to the reference shape (per orientation)
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vector<PAW> paws;
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//==========================================
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// Linear SVR
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// SVR biases
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vector<double> bs;
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// SVR weights
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vector<cv::Mat_<double> > ws;
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//==========================================
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// Neural Network
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// Neural net weights
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vector<vector<cv::Mat_<double> > > ws_nn;
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// What type of activation or output functions are used
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// 0 - sigmoid, 1 - tanh_opt, 2 - ReLU
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vector<int> activation_fun;
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vector<int> output_fun;
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//==========================================
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// Convolutional Neural Network
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// CNN layers for each view
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// view -> layer -> input maps -> kernels
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vector<vector<vector<vector<cv::Mat_<float> > > > > cnn_convolutional_layers;
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// Bit ugly with so much nesting, but oh well
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vector<vector<vector<vector<pair<int, cv::Mat_<double> > > > > > cnn_convolutional_layers_dft;
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vector<vector<vector<float > > > cnn_convolutional_layers_bias;
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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
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// 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
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vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers_biases;
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//==========================================
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// Normalisation for face validation
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vector<cv::Mat_<double> > mean_images;
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vector<cv::Mat_<double> > standard_deviations;
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// Default constructor
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DetectionValidator(){;}
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// Copy constructor
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DetectionValidator(const DetectionValidator& other);
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// Given an image, orientation and detected landmarks output the result of the appropriate regressor
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double Check(const cv::Vec3d& orientation, const cv::Mat_<uchar>& intensity_img, cv::Mat_<double>& detected_landmarks);
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// Reading in the model
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void Read(string location);
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// Getting the closest view center based on orientation
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int GetViewId(const cv::Vec3d& orientation) const;
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private:
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// The actual regressor application on the image
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// Support Vector Regression (linear kernel)
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double CheckSVR(const cv::Mat_<double>& warped_img, int view_id);
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// Feed-forward Neural Network
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double CheckNN(const cv::Mat_<double>& warped_img, int view_id);
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// Convolutional Neural Network
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double CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id);
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// Convolutional Neural Network
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double CheckCNN(const cv::Mat_<double>& warped_img, int view_id);
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// Convolutional Neural Network
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double CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id);
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// A normalisation helper
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void NormaliseWarpedToVector(const cv::Mat_<double>& warped_img, cv::Mat_<double>& feature_vec, int view_id);
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};
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}
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#endif
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