105 lines
4.9 KiB
C++
105 lines
4.9 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 __PDMA_h_
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#define __PDMA_h_
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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#include <string>
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#include <iostream>
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#include <fstream>
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namespace FaceAnalysis
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{
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//===========================================================================
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// A linear 3D Point Distribution Model (constructed using Non-Rigid structure from motion or PCA)
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// Only describes the model but does not contain an instance of it (no local or global parameters are stored here)
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// Contains the utility functions to help manipulate the model
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class PDM{
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public:
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// The 3D mean shape vector of the PDM [x1,..,xn,y1,...yn,z1,...,zn]
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cv::Mat_<float> mean_shape;
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// Principal components or variation bases of the model,
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cv::Mat_<float> princ_comp;
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// Eigenvalues (variances) corresponding to the bases
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cv::Mat_<float> eigen_values;
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PDM(){;}
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// A copy constructor
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PDM(const PDM& other);
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void Read(std::string location);
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// Number of vertices
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inline int NumberOfPoints() const {return mean_shape.rows/3;}
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// Listing the number of modes of variation
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inline int NumberOfModes() const {return princ_comp.cols;}
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// Compute shape in object space (3D)
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void CalcShape3D(cv::Mat_<float>& out_shape, const cv::Mat_<float>& params_local) const;
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// Compute shape in image space (2D)
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void CalcShape2D(cv::Mat_<float>& out_shape, const cv::Mat_<float>& params_local, const cv::Vec6f& params_global) const;
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// provided the bounding box of a face and the local parameters (with optional rotation), generates the global parameters that can generate the face with the provided bounding box
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void CalcParams(cv::Vec6f& out_params_global, const cv::Rect_<float>& bounding_box, const cv::Mat_<float>& params_local, const cv::Vec3f rotation = cv::Vec3f(0.0f)) const;
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// Provided the landmark location compute global and local parameters best fitting it (can provide optional rotation for potentially better results)
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void CalcParams(cv::Vec6f& out_params_global, cv::Mat_<float>& out_params_local, const cv::Mat_<float>& landmark_locations, const cv::Vec3f rotation = cv::Vec3f(0.0f)) const;
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// provided the model parameters, compute the bounding box of a face
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void CalcBoundingBox(cv::Rect_<float>& out_bounding_box, const cv::Vec6f& params_global, const cv::Mat_<float>& params_local) const;
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// Helpers for computing Jacobians, and Jacobians with the weight matrix
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void ComputeRigidJacobian(const cv::Mat_<float>& params_local, const cv::Vec6f& params_global, cv::Mat_<float> &Jacob, const cv::Mat_<float> W, cv::Mat_<float> &Jacob_t_w) const;
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void ComputeJacobian(const cv::Mat_<float>& params_local, const cv::Vec6f& params_global, cv::Mat_<float> &Jacobian, const cv::Mat_<float> W, cv::Mat_<float> &Jacob_t_w) const;
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// Given the current parameters, and the computed delta_p compute the updated parameters
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void UpdateModelParameters(const cv::Mat_<float>& delta_p, cv::Mat_<float>& params_local, cv::Vec6f& params_global) const;
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// Helper utilities
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private:
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static void Orthonormalise(cv::Matx33f &R);
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};
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//===========================================================================
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}
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#endif
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