sustaining_gazes/lib/local/LandmarkDetector/include/LandmarkDetectorParameters.h

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///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// 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
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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.
//
///////////////////////////////////////////////////////////////////////////////
// Parameters of the CLNF, CLM-Z and CLM trackers
#ifndef __LANDMARK_DETECTOR_PARAM_H
#define __LANDMARK_DETECTOR_PARAM_H
#include <vector>
using namespace std;
namespace LandmarkDetector
{
struct FaceModelParameters
{
// A number of RLMS or NU-RLMS iterations
int num_optimisation_iteration;
// Should pose be limited to 180 degrees frontal
bool limit_pose;
// Should face validation be done
bool validate_detections;
// Landmark detection validator boundary for correct detection, the regressor output 1 (perfect alignment) 0 (bad alignment),
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double validation_boundary;
// Used when tracking is going well
vector<int> window_sizes_small;
// Used when initialising or tracking fails
vector<int> window_sizes_init;
// Used for the current frame
vector<int> window_sizes_current;
// How big is the tracking template that helps with large motions
double face_template_scale;
bool use_face_template;
// Where to load the model from
string model_location;
// this is used for the smooting of response maps (KDE sigma)
double sigma;
double reg_factor; // weight put to regularisation
double weight_factor; // factor for weighted least squares
// should multiple views be considered during reinit
bool multi_view;
// How often should face detection be used to attempt reinitialisation, every n frames (set to negative not to reinit)
int reinit_video_every;
// Determining which face detector to use for (re)initialisation, HAAR is quicker but provides more false positives and is not goot for in-the-wild conditions
// Also HAAR detector can detect smaller faces while HOG SVM is only capable of detecting faces at least 70px across
enum FaceDetector{HAAR_DETECTOR, HOG_SVM_DETECTOR};
string face_detector_location;
FaceDetector curr_face_detector;
// Should the results be visualised and reported to console
bool quiet_mode;
// Should the model be refined hierarchically (if available)
bool refine_hierarchical;
// Should the parameters be refined for different scales
bool refine_parameters;
FaceModelParameters();
FaceModelParameters(vector<string> &arguments);
private:
void init();
void check_model_path(const std::string& root="/");
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};
}
#endif // __LANDMARK_DETECTOR_PARAM_H