149 lines
6.4 KiB
C
149 lines
6.4 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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#ifndef __CCNF_PATCH_EXPERT_h_
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#define __CCNF_PATCH_EXPERT_h_
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#include <opencv2/core/core.hpp>
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#include <map>
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#include <vector>
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namespace LandmarkDetector
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{
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//===========================================================================
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/**
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A single Neuron response
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*/
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class CCNF_neuron{
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public:
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// Type of patch (0=raw,1=grad,3=depth, other types besides raw are not actually used now)
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int neuron_type;
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// scaling of weights (needed as the energy of neuron might not be 1)
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double norm_weights;
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// Weight bias
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double bias;
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// Neural weights
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cv::Mat_<float> weights;
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// can have neural weight dfts that are calculated on the go as needed, this allows us not to recompute
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// the dft of the template each time, improving the speed of tracking
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std::map<int, cv::Mat_<double> > weights_dfts;
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// the alpha associated with the neuron
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double alpha;
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// Default constructor
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CCNF_neuron(){;}
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// Copy constructor
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CCNF_neuron(const CCNF_neuron& other);
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void Read(std::ifstream &stream);
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// The im_dft, integral_img, and integral_img_sq are precomputed images for convolution speedups (they get set if passed in empty values)
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void Response(cv::Mat_<float> &im, cv::Mat_<double> &im_dft, cv::Mat &integral_img, cv::Mat &integral_img_sq, cv::Mat_<float> &resp);
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};
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//===========================================================================
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/**
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A CCNF patch expert
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*/
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class CCNF_patch_expert{
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public:
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// Width and height of the patch expert support region
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int width;
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int height;
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// Collection of neurons for this patch expert
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std::vector<CCNF_neuron> neurons;
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// Information about the vertex features (association potentials)
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std::vector<int> window_sizes;
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std::vector<cv::Mat_<float> > Sigmas;
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std::vector<double> betas;
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// How confident we are in the patch
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double patch_confidence;
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// Default constructor
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CCNF_patch_expert(){;}
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// Copy constructor
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CCNF_patch_expert(const CCNF_patch_expert& other);
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void Read(std::ifstream &stream, std::vector<int> window_sizes, std::vector<std::vector<cv::Mat_<float> > > sigma_components);
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// actual work (can pass in an image and a potential depth image, if the CCNF is trained with depth)
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void Response(cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
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// Helper function to compute relevant sigmas
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void ComputeSigmas(std::vector<cv::Mat_<float> > sigma_components, int window_size);
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};
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//===========================================================================
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}
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#endif
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