f98ae0747e
- Adding a new boost version (1.63) - Decoupling FaceAnalyser from LandmarkDetector - FaceAnalyser is much easier to load with a parameters class - Moving GazeAnalyser as a separate library - GUI now uses new FaceAnalyser, LandmarkDetector, and GazeAnalyser
138 lines
5.3 KiB
C++
138 lines
5.3 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 __PAW_h_
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#define __PAW_h_
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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namespace FaceAnalysis
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{
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//===========================================================================
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/**
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A Piece-wise Affine Warp
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The ideas for this piece-wise affine triangular warping are taken from the
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Active appearance models revisited by Iain Matthews and Simon Baker in IJCV 2004
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This is used for both validation of landmark detection, and for avatar animation
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The code is based on the CLM tracker by Jason Saragih et al.
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*/
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class PAW{
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public:
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// Number of pixels after the warping to neutral shape
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int number_of_pixels;
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// Minimum x coordinate in destination
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double min_x;
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// minimum y coordinate in destination
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double min_y;
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// Destination points (landmarks to be warped to)
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cv::Mat_<double> destination_landmarks;
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// Destination points (landmarks to be warped from)
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cv::Mat_<double> source_landmarks;
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// Triangulation, each triangle is warped using an affine transform
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cv::Mat_<int> triangulation;
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// Triangle index, indicating which triangle each of destination pixels lies in
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cv::Mat_<int> triangle_id;
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// Indicating if the destination warped pixels is valid (lies within a face)
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cv::Mat_<uchar> pixel_mask;
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// A number of precomputed coefficients that are helpful for quick warping
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// affine coefficients for all triangles (see Matthews and Baker 2004)
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// 6 coefficients for each triangle (are computed from alpha and beta)
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// This is computed during each warp based on source landmarks
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cv::Mat_<double> coefficients;
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// matrix of (c,x,y) coeffs for alpha
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cv::Mat_<double> alpha;
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// matrix of (c,x,y) coeffs for alpha
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cv::Mat_<double> beta;
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// x-source of warped points
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cv::Mat_<float> map_x;
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// y-source of warped points
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cv::Mat_<float> map_y;
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// Default constructor
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PAW(){;}
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// Construct a warp from a destination shape and triangulation
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PAW(const cv::Mat_<double>& destination_shape, const cv::Mat_<int>& triangulation);
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// The final optional argument allows for optimisation if the triangle indices from previous frame are known (for tracking in video)
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PAW(const cv::Mat_<double>& destination_shape, const cv::Mat_<int>& triangulation, double in_min_x, double in_min_y, double in_max_x, double in_max_y);
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// Copy constructor
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PAW(const PAW& other);
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void Read(std::ifstream &s);
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// The actual warping
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void Warp(const cv::Mat& image_to_warp, cv::Mat& destination_image, const cv::Mat_<double>& landmarks_to_warp);
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// Compute coefficients needed for warping
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void CalcCoeff();
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// Perform the actual warping
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void WarpRegion(cv::Mat_<float>& map_x, cv::Mat_<float>& map_y);
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inline int NumberOfLandmarks() const {return destination_landmarks.rows/2;} ;
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inline int NumberOfTriangles() const {return triangulation.rows;} ;
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// The width and height of the warped image
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inline int constWidth() const {return pixel_mask.cols;}
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inline int Height() const {return pixel_mask.rows;}
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private:
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// Helper functions for dealing with triangles
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static bool sameSide(double x0, double y0, double x1, double y1, double x2, double y2, double x3, double y3);
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static bool pointInTriangle(double x0, double y0, double x1, double y1, double x2, double y2, double x3, double y3);
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static int findTriangle(const cv::Point_<double>& point, const std::vector<std::vector<double>>& control_points, int guess = -1);
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};
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//===========================================================================
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}
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#endif
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