111 lines
5 KiB
C++
111 lines
5 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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#ifndef __Patch_experts_h_
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#define __Patch_experts_h_
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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#include "SVR_patch_expert.h"
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#include "CCNF_patch_expert.h"
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#include "PDM.h"
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namespace LandmarkDetector
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{
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//===========================================================================
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/**
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Combined class for all of the patch experts
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*/
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class Patch_experts
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{
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public:
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// The collection of SVR patch experts (for intensity/grayscale images), the experts are laid out scale->view->landmark
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vector<vector<vector<Multi_SVR_patch_expert> > > svr_expert_intensity;
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// The collection of SVR patch experts (for depth/range images), the experts are laid out scale->view->landmark
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vector<vector<vector<Multi_SVR_patch_expert> > > svr_expert_depth;
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// The collection of LNF (CCNF) patch experts (for intensity images), the experts are laid out scale->view->landmark
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vector<vector<vector<CCNF_patch_expert> > > ccnf_expert_intensity;
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// The node connectivity for CCNF experts, at different window sizes and corresponding to separate edge features
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vector<vector<cv::Mat_<float> > > sigma_components;
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// The available scales for intensity patch experts
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vector<double> patch_scaling;
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// The available views for the patch experts at every scale (in radians)
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vector<vector<cv::Vec3d> > centers;
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// Landmark visibilities for each scale and view
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vector<vector<cv::Mat_<int> > > visibilities;
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// A default constructor
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Patch_experts(){;}
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// A copy constructor
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Patch_experts(const Patch_experts& other);
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// Returns the patch expert responses given a grayscale and an optional depth image.
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// Additionally returns the transform from the image coordinates to the response coordinates (and vice versa).
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// The computation also requires the current landmark locations to compute response around, the PDM corresponding to the desired model, and the parameters describing its instance
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// Also need to provide the size of the area of interest and the desired scale of analysis
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void Response(vector<cv::Mat_<float> >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_<uchar>& grayscale_image, const cv::Mat_<float>& depth_image,
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const PDM& pdm, const cv::Vec6d& params_global, const cv::Mat_<double>& params_local, int window_size, int scale);
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// Getting the best view associated with the current orientation
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int GetViewIdx(const cv::Vec6d& params_global, int scale) const;
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// The number of views at a particular scale
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inline int nViews(size_t scale = 0) const { return (int)centers[scale].size(); };
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// Reading in all of the patch experts
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void Read(vector<string> intensity_svr_expert_locations, vector<string> depth_svr_expert_locations, vector<string> intensity_ccnf_expert_locations);
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private:
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void Read_SVR_patch_experts(string expert_location, std::vector<cv::Vec3d>& centers, std::vector<cv::Mat_<int> >& visibility, std::vector<std::vector<Multi_SVR_patch_expert> >& patches, double& scale);
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void Read_CCNF_patch_experts(string patchesFileLocation, std::vector<cv::Vec3d>& centers, std::vector<cv::Mat_<int> >& visibility, std::vector<std::vector<CCNF_patch_expert> >& patches, double& patchScaling);
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};
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}
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#endif
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