138 lines
5.3 KiB
C++
138 lines
5.3 KiB
C++
///////////////////////////////////////////////////////////////////////////////
|
|
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
|
|
// all rights reserved.
|
|
//
|
|
// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
|
|
//
|
|
// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
|
|
// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
|
|
//
|
|
// License can be found in OpenFace-license.txt
|
|
//
|
|
// * Any publications arising from the use of this software, including but
|
|
// not limited to academic journal and conference publications, technical
|
|
// reports and manuals, must cite at least one of the following works:
|
|
//
|
|
// OpenFace: an open source facial behavior analysis toolkit
|
|
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
|
|
// in IEEE Winter Conference on Applications of Computer Vision, 2016
|
|
//
|
|
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
|
|
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
|
|
// in IEEE International. Conference on Computer Vision (ICCV), 2015
|
|
//
|
|
// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
|
|
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
|
|
// in Facial Expression Recognition and Analysis Challenge,
|
|
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
|
|
//
|
|
// Constrained Local Neural Fields for robust facial landmark detection in the wild.
|
|
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
|
|
// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
|
|
//
|
|
///////////////////////////////////////////////////////////////////////////////
|
|
|
|
#ifndef __PAW_h_
|
|
#define __PAW_h_
|
|
|
|
// OpenCV includes
|
|
#include <opencv2/core/core.hpp>
|
|
|
|
namespace FaceAnalysis
|
|
{
|
|
//===========================================================================
|
|
/**
|
|
A Piece-wise Affine Warp
|
|
The ideas for this piece-wise affine triangular warping are taken from the
|
|
Active appearance models revisited by Iain Matthews and Simon Baker in IJCV 2004
|
|
This is used for both validation of landmark detection, and for avatar animation
|
|
|
|
The code is based on the CLM tracker by Jason Saragih et al.
|
|
*/
|
|
|
|
class PAW{
|
|
public:
|
|
// Number of pixels after the warping to neutral shape
|
|
int number_of_pixels;
|
|
|
|
// Minimum x coordinate in destination
|
|
float min_x;
|
|
|
|
// minimum y coordinate in destination
|
|
float min_y;
|
|
|
|
// Destination points (landmarks to be warped to)
|
|
cv::Mat_<float> destination_landmarks;
|
|
|
|
// Destination points (landmarks to be warped from)
|
|
cv::Mat_<float> source_landmarks;
|
|
|
|
// Triangulation, each triangle is warped using an affine transform
|
|
cv::Mat_<int> triangulation;
|
|
|
|
// Triangle index, indicating which triangle each of destination pixels lies in
|
|
cv::Mat_<int> triangle_id;
|
|
|
|
// Indicating if the destination warped pixels is valid (lies within a face)
|
|
cv::Mat_<uchar> pixel_mask;
|
|
|
|
// A number of precomputed coefficients that are helpful for quick warping
|
|
|
|
// affine coefficients for all triangles (see Matthews and Baker 2004)
|
|
// 6 coefficients for each triangle (are computed from alpha and beta)
|
|
// This is computed during each warp based on source landmarks
|
|
cv::Mat_<float> coefficients;
|
|
|
|
// matrix of (c,x,y) coeffs for alpha
|
|
cv::Mat_<float> alpha;
|
|
|
|
// matrix of (c,x,y) coeffs for alpha
|
|
cv::Mat_<float> beta;
|
|
|
|
// x-source of warped points
|
|
cv::Mat_<float> map_x;
|
|
|
|
// y-source of warped points
|
|
cv::Mat_<float> map_y;
|
|
|
|
// Default constructor
|
|
PAW(){;}
|
|
|
|
// Construct a warp from a destination shape and triangulation
|
|
PAW(const cv::Mat_<float>& destination_shape, const cv::Mat_<int>& triangulation);
|
|
|
|
// The final optional argument allows for optimisation if the triangle indices from previous frame are known (for tracking in video)
|
|
PAW(const cv::Mat_<float>& destination_shape, const cv::Mat_<int>& triangulation, float in_min_x, float in_min_y, float in_max_x, float in_max_y);
|
|
|
|
// Copy constructor
|
|
PAW(const PAW& other);
|
|
|
|
void Read(std::ifstream &s);
|
|
|
|
// The actual warping
|
|
void Warp(const cv::Mat& image_to_warp, cv::Mat& destination_image, const cv::Mat_<float>& landmarks_to_warp);
|
|
|
|
// Compute coefficients needed for warping
|
|
void CalcCoeff();
|
|
|
|
// Perform the actual warping
|
|
void WarpRegion(cv::Mat_<float>& map_x, cv::Mat_<float>& map_y);
|
|
|
|
inline int NumberOfLandmarks() const {return destination_landmarks.rows/2;} ;
|
|
inline int NumberOfTriangles() const {return triangulation.rows;} ;
|
|
|
|
// The width and height of the warped image
|
|
inline int constWidth() const {return pixel_mask.cols;}
|
|
inline int Height() const {return pixel_mask.rows;}
|
|
|
|
private:
|
|
|
|
// Helper functions for dealing with triangles
|
|
static bool sameSide(float x0, float y0, float x1, float y1, float x2, float y2, float x3, float y3);
|
|
static bool pointInTriangle(float x0, float y0, float x1, float y1, float x2, float y2, float x3, float y3);
|
|
static int findTriangle(const cv::Point_<float>& point, const std::vector<std::vector<float>>& control_points, int guess = -1);
|
|
|
|
};
|
|
//===========================================================================
|
|
}
|
|
#endif
|