236 lines
11 KiB
C++
236 lines
11 KiB
C++
///////////////////////////////////////////////////////////////////////////////
|
||
// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
|
||
// all rights reserved.
|
||
//
|
||
// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
|
||
// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
|
||
// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
|
||
// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
|
||
// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
|
||
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
|
||
// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
|
||
// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
|
||
// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||
// POSSIBILITY OF SUCH DAMAGE.
|
||
//
|
||
// Notwithstanding the license granted herein, Licensee acknowledges that certain components
|
||
// of the Software may be covered by so-called “open source” software licenses (“Open Source
|
||
// Components”), which means any software licenses approved as open source licenses by the
|
||
// Open Source Initiative or any substantially similar licenses, including without limitation any
|
||
// license that, as a condition of distribution of the software licensed under such license,
|
||
// requires that the distributor make the software available in source code format. Licensor shall
|
||
// provide a list of Open Source Components for a particular version of the Software upon
|
||
// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
|
||
// the extent required by the licenses covering Open Source Components, the terms of such
|
||
// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
|
||
// licenses applicable to Open Source Components prohibit any of the restrictions in this
|
||
// License Agreement with respect to such Open Source Component, such restrictions will not
|
||
// apply to such Open Source Component. To the extent the terms of the licenses applicable to
|
||
// Open Source Components require Licensor to make an offer to provide source code or
|
||
// related information in connection with the Software, such offer is hereby made. Any request
|
||
// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
|
||
// Licensee acknowledges receipt of notices for the Open Source Components for the initial
|
||
// delivery of the Software.
|
||
|
||
// * 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 __LANDMARK_DETECTOR_MODEL_h_
|
||
#define __LANDMARK_DETECTOR_MODEL_h_
|
||
|
||
// OpenCV dependencies
|
||
#include <opencv2/core/core.hpp>
|
||
#include <opencv2/objdetect.hpp>
|
||
|
||
// dlib dependencies for face detection
|
||
#include <dlib/image_processing/frontal_face_detector.h>
|
||
#include <dlib/opencv.h>
|
||
|
||
#include "PDM.h"
|
||
#include "Patch_experts.h"
|
||
#include "LandmarkDetectionValidator.h"
|
||
#include "LandmarkDetectorParameters.h"
|
||
|
||
using namespace std;
|
||
|
||
namespace LandmarkDetector
|
||
{
|
||
|
||
// A main class containing all the modules required for landmark detection
|
||
// Face shape model
|
||
// Patch experts
|
||
// Optimization techniques
|
||
class CLNF{
|
||
|
||
public:
|
||
|
||
//===========================================================================
|
||
// Member variables that contain the model description
|
||
|
||
// The linear 3D Point Distribution Model
|
||
PDM pdm;
|
||
// The set of patch experts
|
||
Patch_experts patch_experts;
|
||
|
||
// The local and global parameters describing the current model instance (current landmark detections)
|
||
|
||
// Local parameters describing the non-rigid shape
|
||
cv::Mat_<double> params_local;
|
||
|
||
// Global parameters describing the rigid shape [scale, euler_x, euler_y, euler_z, tx, ty]
|
||
cv::Vec6d params_global;
|
||
|
||
// A collection of hierarchical CLNF models that can be used for refinement
|
||
vector<CLNF> hierarchical_models;
|
||
vector<string> hierarchical_model_names;
|
||
vector<vector<pair<int,int>>> hierarchical_mapping;
|
||
vector<FaceModelParameters> hierarchical_params;
|
||
|
||
//==================== Helpers for face detection and landmark detection validation =========================================
|
||
|
||
// Haar cascade classifier for face detection
|
||
cv::CascadeClassifier face_detector_HAAR;
|
||
string face_detector_location;
|
||
|
||
// A HOG SVM-struct based face detector
|
||
dlib::frontal_face_detector face_detector_HOG;
|
||
|
||
|
||
// Validate if the detected landmarks are correct using an SVR regressor
|
||
DetectionValidator landmark_validator;
|
||
|
||
// Indicating if landmark detection succeeded (based on SVR validator)
|
||
bool detection_success;
|
||
|
||
// Indicating if the tracking has been initialised (for video based tracking)
|
||
bool tracking_initialised;
|
||
|
||
// The actual output of the regressor (-1 is perfect detection 1 is worst detection)
|
||
double detection_certainty;
|
||
|
||
// Indicator if eye model is there for eye detection
|
||
bool eye_model = false;
|
||
|
||
// the triangulation per each view (for drawing purposes only)
|
||
vector<cv::Mat_<int> > triangulations;
|
||
|
||
//===========================================================================
|
||
// Member variables that retain the state of the tracking (reflecting the state of the lastly tracked (detected) image
|
||
|
||
// Lastly detect 2D model shape [x1,x2,...xn,y1,...yn]
|
||
cv::Mat_<double> detected_landmarks;
|
||
|
||
// The landmark detection likelihoods (combined and per patch expert)
|
||
double model_likelihood;
|
||
cv::Mat_<double> landmark_likelihoods;
|
||
|
||
// Keeping track of how many frames the tracker has failed in so far when tracking in videos
|
||
// This is useful for knowing when to initialise and reinitialise tracking
|
||
int failures_in_a_row;
|
||
|
||
// A template of a face that last succeeded with tracking (useful for large motions in video)
|
||
cv::Mat_<uchar> face_template;
|
||
|
||
// Useful when resetting or initialising the model closer to a specific location (when multiple faces are present)
|
||
cv::Point_<double> preference_det;
|
||
|
||
// A default constructor
|
||
CLNF();
|
||
|
||
// Constructor from a model file
|
||
CLNF(string fname);
|
||
|
||
// Copy constructor (makes a deep copy of the detector)
|
||
CLNF(const CLNF& other);
|
||
|
||
// Assignment operator for lvalues (makes a deep copy of the detector)
|
||
CLNF & operator= (const CLNF& other);
|
||
|
||
// Empty Destructor as the memory of every object will be managed by the corresponding libraries (no pointers)
|
||
~CLNF(){}
|
||
|
||
// Move constructor
|
||
CLNF(const CLNF&& other);
|
||
|
||
// Assignment operator for rvalues
|
||
CLNF & operator= (const CLNF&& other);
|
||
|
||
// Does the actual work - landmark detection
|
||
bool DetectLandmarks(const cv::Mat_<uchar> &image, const cv::Mat_<float> &depth, FaceModelParameters& params);
|
||
|
||
// Gets the shape of the current detected landmarks in camera space (given camera calibration)
|
||
// Can only be called after a call to DetectLandmarksInVideo or DetectLandmarksInImage
|
||
cv::Mat_<double> GetShape(double fx, double fy, double cx, double cy) const;
|
||
|
||
// A utility bounding box function
|
||
cv::Rect_<double> GetBoundingBox() const;
|
||
|
||
// Reset the model (useful if we want to completelly reinitialise, or we want to track another video)
|
||
void Reset();
|
||
|
||
// Reset the model, choosing the face nearest (x,y) where x and y are between 0 and 1.
|
||
void Reset(double x, double y);
|
||
|
||
// Reading the model in
|
||
void Read(string name);
|
||
|
||
// Helper reading function
|
||
void Read_CLNF(string clnf_location);
|
||
|
||
private:
|
||
|
||
// the speedup of RLMS using precalculated KDE responses (described in Saragih 2011 RLMS paper)
|
||
map<int, cv::Mat_<float> > kde_resp_precalc;
|
||
|
||
// The model fitting: patch response computation and optimisation steps
|
||
bool Fit(const cv::Mat_<uchar>& intensity_image, const cv::Mat_<float>& depth_image, const std::vector<int>& window_sizes, const FaceModelParameters& parameters);
|
||
|
||
// Mean shift computation that uses precalculated kernel density estimators (the one actually used)
|
||
void NonVectorisedMeanShift_precalc_kde(cv::Mat_<float>& out_mean_shifts, const vector<cv::Mat_<float> >& patch_expert_responses, const cv::Mat_<float> &dxs, const cv::Mat_<float> &dys, int resp_size, float a, int scale, int view_id, map<int, cv::Mat_<float> >& mean_shifts);
|
||
|
||
// The actual model optimisation (update step), returns the model likelihood
|
||
double NU_RLMS(cv::Vec6d& final_global, cv::Mat_<double>& final_local, const vector<cv::Mat_<float> >& patch_expert_responses, const cv::Vec6d& initial_global, const cv::Mat_<double>& initial_local,
|
||
const cv::Mat_<double>& base_shape, const cv::Matx22d& sim_img_to_ref, const cv::Matx22f& sim_ref_to_img, int resp_size, int view_idx, bool rigid, int scale, cv::Mat_<double>& landmark_lhoods, const FaceModelParameters& parameters);
|
||
|
||
// Removing background image from the depth
|
||
bool RemoveBackground(cv::Mat_<float>& out_depth_image, const cv::Mat_<float>& depth_image);
|
||
|
||
// Generating the weight matrix for the Weighted least squares
|
||
void GetWeightMatrix(cv::Mat_<float>& WeightMatrix, int scale, int view_id, const FaceModelParameters& parameters);
|
||
|
||
//=======================================================
|
||
// Legacy functions that are not used at the moment
|
||
//=======================================================
|
||
|
||
// Mean shift computation
|
||
void NonVectorisedMeanShift(cv::Mat_<double>& out_mean_shifts, const vector<cv::Mat_<float> >& patch_expert_responses, const cv::Mat_<double> &dxs, const cv::Mat_<double> &dys, int resp_size, double a, int scale, int view_id);
|
||
|
||
// A vectorised version of mean shift (Not actually used)
|
||
void VectorisedMeanShift(cv::Mat_<double>& meanShifts, const vector<cv::Mat_<float> >& patch_expert_responses, const cv::Mat_<double> &iis, const cv::Mat_<double> &jjs, const cv::Mat_<double> &dxs, const cv::Mat_<double> &dys, const cv::Size patchSize, double sigma, int scale, int view_id);
|
||
|
||
};
|
||
//===========================================================================
|
||
}
|
||
#endif
|