Visual Servoing Platform version 3.6.0
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vpTemplateTrackerMI.h
1/****************************************************************************
2 *
3 * ViSP, open source Visual Servoing Platform software.
4 * Copyright (C) 2005 - 2023 by Inria. All rights reserved.
5 *
6 * This software is free software; you can redistribute it and/or modify
7 * it under the terms of the GNU General Public License as published by
8 * the Free Software Foundation; either version 2 of the License, or
9 * (at your option) any later version.
10 * See the file LICENSE.txt at the root directory of this source
11 * distribution for additional information about the GNU GPL.
12 *
13 * For using ViSP with software that can not be combined with the GNU
14 * GPL, please contact Inria about acquiring a ViSP Professional
15 * Edition License.
16 *
17 * See https://visp.inria.fr for more information.
18 *
19 * This software was developed at:
20 * Inria Rennes - Bretagne Atlantique
21 * Campus Universitaire de Beaulieu
22 * 35042 Rennes Cedex
23 * France
24 *
25 * If you have questions regarding the use of this file, please contact
26 * Inria at visp@inria.fr
27 *
28 * This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
29 * WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
30 *
31 * Description:
32 * Example of template tracking.
33 *
34 * Authors:
35 * Amaury Dame
36 * Aurelien Yol
37 *
38*****************************************************************************/
39#ifndef vpTemplateTrackerMI_hh
40#define vpTemplateTrackerMI_hh
41
42#include <visp3/core/vpConfig.h>
43
44#include <visp3/core/vpImageFilter.h>
45#include <visp3/tt/vpTemplateTracker.h>
46#include <visp3/tt/vpTemplateTrackerHeader.h>
47
52class VISP_EXPORT vpTemplateTrackerMI : public vpTemplateTracker
53{
54public:
56 typedef enum {
57 HESSIAN_NONSECOND = -1,
62 HESSIAN_NEW
63 } vpHessienApproximationType;
64
66 typedef enum { USE_HESSIEN_NORMAL, USE_HESSIEN_DESIRE, USE_HESSIEN_BEST_COND } vpHessienType;
67
69 typedef enum { BSPLINE_THIRD_ORDER = 3, BSPLINE_FOURTH_ORDER = 4 } vpBsplineType;
70
71protected:
74 double lambda;
75
76 double *temp;
77 double *Prt;
78 double *dPrt;
79 double *Pt;
80 double *Pr;
81 double *d2Prt;
82 double *PrtTout;
83 double *dprtemp;
84
85 double *PrtD;
86 double *dPrtD;
88
90 // Nombre de couleur concidere dans l'histogramme
91 int Nc;
92 int Ncb;
93
97
100
103
106
107 // Internal vars for computeHessienNormalized()
108 std::vector<double> m_du;
109 std::vector<double> m_dv;
110 std::vector<double> m_A;
111 std::vector<double> m_dB;
112 std::vector<std::vector<double> > m_d2u;
113 std::vector<std::vector<double> > m_d2v;
114 std::vector<std::vector<double> > m_dA;
115
116protected:
117 void computeGradient();
118 void computeHessien(vpMatrix &H);
119 void computeHessienNormalized(vpMatrix &H);
120 void computeMI(double &MI);
121 void computeProba(int &nbpoint);
122
123 double getCost(const vpImage<unsigned char> &I, const vpColVector &tp);
124 double getCost(const vpImage<unsigned char> &I) { return getCost(I, p); }
125 double getNormalizedCost(const vpImage<unsigned char> &I, const vpColVector &tp);
127 virtual void initHessienDesired(const vpImage<unsigned char> &I) = 0;
128 virtual void trackNoPyr(const vpImage<unsigned char> &I) = 0;
129 void zeroProbabilities();
130
131 // private:
132 //#ifndef DOXYGEN_SHOULD_SKIP_THIS
133 // vpTemplateTrackerMI(const vpTemplateTrackerMI &)
134 // : vpTemplateTracker(), hessianComputation(USE_HESSIEN_NORMAL),
135 // ApproxHessian(HESSIAN_0), lambda(0),
136 // temp(NULL), Prt(NULL), dPrt(NULL), Pt(NULL), Pr(NULL), d2Prt(NULL),
137 // PrtTout(NULL), dprtemp(NULL), PrtD(NULL), dPrtD(NULL),
138 // influBspline(0), bspline(0), Nc(0), Ncb(0), d2Ix(), d2Iy(), d2Ixy(),
139 // MI_preEstimation(0), MI_postEstimation(0), NMI_preEstimation(0),
140 // NMI_postEstimation(0), covarianceMatrix(), computeCovariance(false)
141 // {
142 // throw vpException(vpException::functionNotImplementedError, "Not
143 // implemented!");
144 // }
145 // vpTemplateTrackerMI &operator=(const vpTemplateTrackerMI &){
146 // throw vpException(vpException::functionNotImplementedError, "Not
147 // implemented!"); return *this;
148 // }
149 //#endif
150
151public:
152 // constructeur
155 : vpTemplateTracker(), hessianComputation(USE_HESSIEN_NORMAL), ApproxHessian(HESSIAN_0), lambda(0), temp(NULL),
156 Prt(NULL), dPrt(NULL), Pt(NULL), Pr(NULL), d2Prt(NULL), PrtTout(NULL), dprtemp(NULL), PrtD(NULL), dPrtD(NULL),
157 influBspline(0), bspline(0), Nc(0), Ncb(0), d2Ix(), d2Iy(), d2Ixy(), MI_preEstimation(0), MI_postEstimation(0),
158 NMI_preEstimation(0), NMI_postEstimation(0), covarianceMatrix(), computeCovariance(false), m_du(), m_dv(), m_A(),
159 m_dB(), m_d2u(), m_d2v(), m_dA()
160 {
161 }
163 virtual ~vpTemplateTrackerMI();
164 vpMatrix getCovarianceMatrix() const { return covarianceMatrix; }
165 double getMI() const { return MI_postEstimation; }
166 double getMI(const vpImage<unsigned char> &I, int &nc, const int &bspline, vpColVector &tp);
167 double getMI256(const vpImage<unsigned char> &I, const vpColVector &tp);
168 double getNMI() const { return NMI_postEstimation; }
169 // initialisation du Hessien en position desiree
170 void setApprocHessian(vpHessienApproximationType approx) { ApproxHessian = approx; }
171 void setCovarianceComputation(const bool &flag) { computeCovariance = flag; }
172 void setHessianComputation(vpHessienType type) { hessianComputation = type; }
173 void setBspline(const vpBsplineType &newbs);
174 void setLambda(double _l) { lambda = _l; }
175 void setNc(int newNc);
176};
177
178#endif
Implementation of column vector and the associated operations.
Definition of the vpImage class member functions.
Definition vpImage.h:135
Implementation of a matrix and operations on matrices.
Definition vpMatrix.h:152
vpHessienApproximationType ApproxHessian
vpImage< double > d2Ix
std::vector< std::vector< double > > m_d2v
vpHessienType hessianComputation
void setHessianComputation(vpHessienType type)
std::vector< double > m_dB
std::vector< double > m_du
vpTemplateTrackerMI()
Default constructor.
void setApprocHessian(vpHessienApproximationType approx)
virtual void initHessienDesired(const vpImage< unsigned char > &I)=0
double getCost(const vpImage< unsigned char > &I)
std::vector< double > m_dv
std::vector< std::vector< double > > m_d2u
double getNormalizedCost(const vpImage< unsigned char > &I)
vpImage< double > d2Ixy
vpMatrix getCovarianceMatrix() const
std::vector< std::vector< double > > m_dA
vpImage< double > d2Iy
void setCovarianceComputation(const bool &flag)
virtual void trackNoPyr(const vpImage< unsigned char > &I)=0
std::vector< double > m_A
virtual double getCost(const vpImage< unsigned char > &I, const vpColVector &tp)=0