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///////////////////////////////////////////////////////////////////////////////
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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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// 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
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//
// * 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 Baltru<72> aitis, 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 Baltru<72> aitis, 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 Baltru<72> aitis, 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 Baltru<72> aitis, Peter Robinson, and Louis-Philippe Morency.
// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
//
///////////////////////////////////////////////////////////////////////////////
// Header for all external CLNF/CLM-Z/CLM methods of interest to the user
# ifndef __LANDMARK_DETECTOR_UTILS_h_
# define __LANDMARK_DETECTOR_UTILS_h_
// OpenCV includes
# include <opencv2/core/core.hpp>
# include "LandmarkDetectorModel.h"
using namespace std ;
namespace LandmarkDetector
{
//===========================================================================
// Defining a set of useful utility functions to be used within CLNF
//=============================================================================================
// Helper functions for parsing the inputs
//=============================================================================================
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void get_video_input_output_params ( vector < string > & input_video_file , vector < string > & output_files ,
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vector < string > & output_video_files , string & output_codec , vector < string > & arguments ) ;
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void get_camera_params ( int & device , float & fx , float & fy , float & cx , float & cy , vector < string > & arguments ) ;
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void get_image_input_output_params ( vector < string > & input_image_files , vector < string > & output_feature_files , vector < string > & output_pose_files , vector < string > & output_image_files ,
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vector < cv : : Rect_ < double > > & input_bounding_boxes , vector < string > & arguments ) ;
//===========================================================================
// Fast patch expert response computation (linear model across a ROI) using normalised cross-correlation
//===========================================================================
// This is a modified version of openCV code that allows for precomputed dfts of templates and for precomputed dfts of an image
// _img is the input img, _img_dft it's dft (optional), _integral_img the images integral image (optional), squared integral image (optional),
// templ is the template we are convolving with, templ_dfts it's dfts at varying windows sizes (optional), _result - the output, method the type of convolution
void matchTemplate_m ( const cv : : Mat_ < float > & input_img , cv : : Mat_ < double > & img_dft , cv : : Mat & _integral_img , cv : : Mat & _integral_img_sq , const cv : : Mat_ < float > & templ , map < int , cv : : Mat_ < double > > & templ_dfts , cv : : Mat_ < float > & result , int method ) ;
//===========================================================================
// Point set and landmark manipulation functions
//===========================================================================
// Using Kabsch's algorithm for aligning shapes
//This assumes that align_from and align_to are already mean normalised
cv : : Matx22d AlignShapesKabsch2D ( const cv : : Mat_ < double > & align_from , const cv : : Mat_ < double > & align_to ) ;
//=============================================================================
// Basically Kabsch's algorithm but also allows the collection of points to be different in scale from each other
cv : : Matx22d AlignShapesWithScale ( cv : : Mat_ < double > & src , cv : : Mat_ < double > dst ) ;
//===========================================================================
// Visualisation functions
//===========================================================================
void Project ( cv : : Mat_ < double > & dest , const cv : : Mat_ < double > & mesh , double fx , double fy , double cx , double cy ) ;
void DrawBox ( cv : : Mat image , cv : : Vec6d pose , cv : : Scalar color , int thickness , float fx , float fy , float cx , float cy ) ;
// Drawing face bounding box
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vector < std : : pair < cv : : Point2d , cv : : Point2d > > CalculateBox ( cv : : Vec6d pose , float fx , float fy , float cx , float cy ) ;
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void DrawBox ( vector < pair < cv : : Point , cv : : Point > > lines , cv : : Mat image , cv : : Scalar color , int thickness ) ;
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vector < cv : : Point2d > CalculateVisibleLandmarks ( const cv : : Mat_ < double > & shape2D , const cv : : Mat_ < int > & visibilities ) ;
vector < cv : : Point2d > CalculateVisibleLandmarks ( const CLNF & clnf_model ) ;
vector < cv : : Point2d > CalculateVisibleEyeLandmarks ( const CLNF & clnf_model ) ;
vector < cv : : Point2d > CalculateAllLandmarks ( const cv : : Mat_ < double > & shape2D ) ;
vector < cv : : Point2d > CalculateAllLandmarks ( const CLNF & clnf_model ) ;
vector < cv : : Point2d > CalculateAllEyeLandmarks ( const CLNF & clnf_model ) ;
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void DrawLandmarks ( cv : : Mat img , vector < cv : : Point > landmarks ) ;
void Draw ( cv : : Mat img , const cv : : Mat_ < double > & shape2D , const cv : : Mat_ < int > & visibilities ) ;
void Draw ( cv : : Mat img , const cv : : Mat_ < double > & shape2D ) ;
void Draw ( cv : : Mat img , const CLNF & clnf_model ) ;
//===========================================================================
// Angle representation conversion helpers
//===========================================================================
cv : : Matx33d Euler2RotationMatrix ( const cv : : Vec3d & eulerAngles ) ;
// Using the XYZ convention R = Rx * Ry * Rz, left-handed positive sign
cv : : Vec3d RotationMatrix2Euler ( const cv : : Matx33d & rotation_matrix ) ;
cv : : Vec3d Euler2AxisAngle ( const cv : : Vec3d & euler ) ;
cv : : Vec3d AxisAngle2Euler ( const cv : : Vec3d & axis_angle ) ;
cv : : Matx33d AxisAngle2RotationMatrix ( const cv : : Vec3d & axis_angle ) ;
cv : : Vec3d RotationMatrix2AxisAngle ( const cv : : Matx33d & rotation_matrix ) ;
//============================================================================
// Face detection helpers
//============================================================================
// Face detection using Haar cascade classifier
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bool DetectFaces ( vector < cv : : Rect_ < double > > & o_regions , const cv : : Mat_ < uchar > & intensity , double min_width = - 1 , cv : : Rect_ < double > roi = cv : : Rect_ < double > ( 0.0 , 0.0 , 1.0 , 1.0 ) ) ;
bool DetectFaces ( vector < cv : : Rect_ < double > > & o_regions , const cv : : Mat_ < uchar > & intensity , cv : : CascadeClassifier & classifier , double min_width = - 1 , cv : : Rect_ < double > roi = cv : : Rect_ < double > ( 0.0 , 0.0 , 1.0 , 1.0 ) ) ;
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// The preference point allows for disambiguation if multiple faces are present (pick the closest one), if it is not set the biggest face is chosen
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bool DetectSingleFace ( cv : : Rect_ < double > & o_region , const cv : : Mat_ < uchar > & intensity , cv : : CascadeClassifier & classifier , const cv : : Point preference = cv : : Point ( - 1 , - 1 ) , double min_width = - 1 , cv : : Rect_ < double > roi = cv : : Rect_ < double > ( 0.0 , 0.0 , 1.0 , 1.0 ) ) ;
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// Face detection using HOG-SVM classifier
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bool DetectFacesHOG ( vector < cv : : Rect_ < double > > & o_regions , const cv : : Mat_ < uchar > & intensity , std : : vector < double > & confidences , double min_width = - 1 , cv : : Rect_ < double > roi = cv : : Rect_ < double > ( 0.0 , 0.0 , 1.0 , 1.0 ) ) ;
bool DetectFacesHOG ( vector < cv : : Rect_ < double > > & o_regions , const cv : : Mat_ < uchar > & intensity , dlib : : frontal_face_detector & classifier , std : : vector < double > & confidences , double min_width = - 1 , cv : : Rect_ < double > roi = cv : : Rect_ < double > ( 0.0 , 0.0 , 1.0 , 1.0 ) ) ;
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// The preference point allows for disambiguation if multiple faces are present (pick the closest one), if it is not set the biggest face is chosen
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bool DetectSingleFaceHOG ( cv : : Rect_ < double > & o_region , const cv : : Mat_ < uchar > & intensity , dlib : : frontal_face_detector & classifier , double & confidence , const cv : : Point preference = cv : : Point ( - 1 , - 1 ) , double min_width = - 1 , cv : : Rect_ < double > roi = cv : : Rect_ < double > ( 0.0 , 0.0 , 1.0 , 1.0 ) ) ;
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//============================================================================
// Matrix reading functionality
//============================================================================
// Reading a matrix written in a binary format
void ReadMatBin ( std : : ifstream & stream , cv : : Mat & output_mat ) ;
// Reading in a matrix from a stream
void ReadMat ( std : : ifstream & stream , cv : : Mat & output_matrix ) ;
// Skipping comments (lines starting with # symbol)
void SkipComments ( std : : ifstream & stream ) ;
}
# endif