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Merge pull request #1521 from nailbiter:optimCG

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Roman Donchenko 2013-10-29 16:14:29 +04:00 committed by OpenCV Buildbot
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13
modules/optim/doc/downhill_simplex_method.rst
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@ -8,14 +8,15 @@ optim::DownhillSolver
.. ocv:class:: optim::DownhillSolver
This class is used to perform the non-linear non-constrained *minimization* of a function, given on an *n*-dimensional Euclidean space,
This class is used to perform the non-linear non-constrained *minimization* of a function, defined on an *n*-dimensional Euclidean space,
using the **Nelder-Mead method**, also known as **downhill simplex method**. The basic idea about the method can be obtained from
(`http://en.wikipedia.org/wiki/Nelder-Mead\_method <http://en.wikipedia.org/wiki/Nelder-Mead_method>`_). It should be noted, that
this method, although deterministic, is rather a heuristic and therefore may converge to a local minima, not necessary a global one.
It is iterative optimization technique, which at each step uses an information about the values of a function evaluated only at
*n+1* points, arranged as a *simplex* in *n*-dimensional space (hence the second name of the method). At each step new point is
chosen to evaluate function at, obtained value is compared with previous ones and based on this information simplex changes it's shape
, slowly moving to the local minimum.
, slowly moving to the local minimum. Thus this method is using *only* function values to make decision, on contrary to, say, Nonlinear
Conjugate Gradient method (which is also implemented in ``optim``).
Algorithm stops when the number of function evaluations done exceeds ``termcrit.maxCount``, when the function values at the
vertices of simplex are within ``termcrit.epsilon`` range or simplex becomes so small that it
@ -30,9 +31,9 @@ positive integer ``termcrit.maxCount`` and positive non-integer ``termcrit.epsil
class CV_EXPORTS Function
{
public:
virtual ~Function() {}
//! ndim - dimensionality
virtual double calc(const double* x) const = 0;
virtual ~Function() {}
virtual double calc(const double* x) const = 0;
virtual void getGradient(const double* /*x*/,double* /*grad*/) {}
};
virtual Ptr<Function> getFunction() const = 0;
@ -150,7 +151,7 @@ optim::createDownhillSolver
This function returns the reference to the ready-to-use ``DownhillSolver`` object. All the parameters are optional, so this procedure can be called
even without parameters at all. In this case, the default values will be used. As default value for terminal criteria are the only sensible ones,
``DownhillSolver::setFunction()`` and ``DownhillSolver::setInitStep()`` should be called upon the obtained object, if the respective parameters
were not given to ``createDownhillSolver()``. Otherwise, the two ways (give parameters to ``createDownhillSolver()`` or miss the out and call the
were not given to ``createDownhillSolver()``. Otherwise, the two ways (give parameters to ``createDownhillSolver()`` or miss them out and call the
``DownhillSolver::setFunction()`` and ``DownhillSolver::setInitStep()``) are absolutely equivalent (and will drop the same errors in the same way,
should invalid input be detected).

136
modules/optim/doc/nonlinear_conjugate_gradient.rst Normal file
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@ -0,0 +1,136 @@
Nonlinear Conjugate Gradient
===============================
.. highlight:: cpp
optim::ConjGradSolver
---------------------------------
.. ocv:class:: optim::ConjGradSolver
This class is used to perform the non-linear non-constrained *minimization* of a function with *known gradient*
, defined on an *n*-dimensional Euclidean space,
using the **Nonlinear Conjugate Gradient method**. The implementation was done based on the beautifully clear explanatory article `An Introduction to the Conjugate Gradient Method Without the Agonizing Pain <http://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf>`_
by Jonathan Richard Shewchuk. The method can be seen as an adaptation of a standard Conjugate Gradient method (see, for example
`http://en.wikipedia.org/wiki/Conjugate_gradient_method <http://en.wikipedia.org/wiki/Conjugate_gradient_method>`_) for numerically solving the
systems of linear equations.
It should be noted, that
this method, although deterministic, is rather a heuristic method and therefore may converge to a local minima, not necessary a global one. What
is even more disastrous, most of its behaviour is ruled by gradient, therefore it essentially cannot distinguish between local minima and maxima.
Therefore, if it starts sufficiently near to the local maximum, it may converge to it. Another obvious restriction is that it should be possible
to compute the gradient of a function at any point, thus it is preferable to have analytic expression for gradient and computational burden
should be born by the user.
The latter responsibility is accompilished via the ``getGradient(const double* x,double* grad)`` method of a
``Solver::Function`` interface (which represents function that is being optimized). This method takes point a point in *n*-dimensional space
(first argument represents the array of coordinates of that point) and comput its gradient (it should be stored in the second argument as an array).
::
class CV_EXPORTS Solver : public Algorithm
{
public:
class CV_EXPORTS Function
{
public:
virtual ~Function() {}
virtual double calc(const double* x) const = 0;
virtual void getGradient(const double* /*x*/,double* /*grad*/) {}
};
virtual Ptr<Function> getFunction() const = 0;
virtual void setFunction(const Ptr<Function>& f) = 0;
virtual TermCriteria getTermCriteria() const = 0;
virtual void setTermCriteria(const TermCriteria& termcrit) = 0;
// x contain the initial point before the call and the minima position (if algorithm converged) after. x is assumed to be (something that
// after getMat() will return) row-vector or column-vector. *It's size and should
// be consisted with previous dimensionality data given, if any (otherwise, it determines dimensionality)*
virtual double minimize(InputOutputArray x) = 0;
};
class CV_EXPORTS ConjGradSolver : public Solver{
};
Note, that class ``ConjGradSolver`` thus does not add any new methods to the basic ``Solver`` interface.
optim::ConjGradSolver::getFunction
--------------------------------------------
Getter for the optimized function. The optimized function is represented by ``Solver::Function`` interface, which requires
derivatives to implement the method ``calc(double*)`` to evaluate the function. It should be emphasized once more, that since Nonlinear
Conjugate Gradient method requires gradient to be computable in addition to the function values,
``getGradient(const double* x,double* grad)`` method of a ``Solver::Function`` interface should be also implemented meaningfully.
.. ocv:function:: Ptr<Solver::Function> optim::ConjGradSolver::getFunction()
:return: Smart-pointer to an object that implements ``Solver::Function`` interface - it represents the function that is being optimized. It can be empty, if no function was given so far.
optim::ConjGradSolver::setFunction
-----------------------------------------------
Setter for the optimized function. *It should be called at least once before the call to* ``ConjGradSolver::minimize()``, as
default value is not usable.
.. ocv:function:: void optim::ConjGradSolver::setFunction(const Ptr<Solver::Function>& f)
:param f: The new function to optimize.
optim::ConjGradSolver::getTermCriteria
----------------------------------------------------
Getter for the previously set terminal criteria for this algorithm.
.. ocv:function:: TermCriteria optim::ConjGradSolver::getTermCriteria()
:return: Deep copy of the terminal criteria used at the moment.
optim::ConjGradSolver::setTermCriteria
------------------------------------------
Set terminal criteria for downhill simplex method. Two things should be noted. First, this method *is not necessary* to be called
before the first call to ``ConjGradSolver::minimize()``, as the default value is sensible. Second, the method will raise an error
if ``termcrit.type!=(TermCriteria::MAX_ITER+TermCriteria::EPS)`` and ``termcrit.type!=TermCriteria::MAX_ITER``. This means that termination criteria
has to restrict maximum number of iterations to be done and may optionally allow algorithm to stop earlier if certain tolerance
is achieved (what we mean by "tolerance is achieved" will be clarified below). If ``termcrit`` restricts both tolerance and maximum iteration
number, both ``termcrit.epsilon`` and ``termcrit.maxCount`` should be positive. In case, if ``termcrit.type==TermCriteria::MAX_ITER``,
only member ``termcrit.maxCount`` is required to be positive and in this case algorithm will just work for required number of iterations.
In current implementation, "tolerance is achieved" means that we have arrived at the point where the :math:`L_2`-norm of the gradient is less
than the tolerance value.
.. ocv:function:: void optim::ConjGradSolver::setTermCriteria(const TermCriteria& termcrit)
:param termcrit: Terminal criteria to be used, represented as ``TermCriteria`` structure (defined elsewhere in openCV). Mind you, that it should meet ``termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && termcrit.epsilon>0 && termcrit.maxCount>0`` or ``termcrit.type==TermCriteria::MAX_ITER) && termcrit.maxCount>0``, otherwise the error will be raised.
optim::ConjGradSolver::minimize
-----------------------------------
The main method of the ``ConjGradSolver``. It actually runs the algorithm and performs the minimization. The sole input parameter determines the
centroid of the starting simplex (roughly, it tells where to start), all the others (terminal criteria and function to be minimized)
are supposed to be set via the setters before the call to this method or the default values (not always sensible) will be used. Sometimes it may
throw an error, if these default values cannot be used (say, you forgot to set the function to minimize and default value, that is, empty function,
cannot be used).
.. ocv:function:: double optim::ConjGradSolver::minimize(InputOutputArray x)
:param x: The initial point. It is hard to overemphasize how important the choise of initial point is when you are using the heuristic algorithm like this one. Badly chosen initial point can make algorithm converge to (local) maximum instead of minimum, do not converge at all, converge to local minimum instead of global one.
:return: The value of a function at the point found.
optim::createConjGradSolver
------------------------------------
This function returns the reference to the ready-to-use ``ConjGradSolver`` object. All the parameters are optional, so this procedure can be called
even without parameters at all. In this case, the default values will be used. As default value for terminal criteria are the only sensible ones,
``ConjGradSolver::setFunction()`` should be called upon the obtained object, if the function
was not given to ``createConjGradSolver()``. Otherwise, the two ways (submit it to ``createConjGradSolver()`` or miss it out and call the
``ConjGradSolver::setFunction()``) are absolutely equivalent (and will drop the same errors in the same way,
should invalid input be detected).
.. ocv:function:: Ptr<optim::ConjGradSolver> optim::createConjGradSolver(const Ptr<Solver::Function>& f, TermCriteria termcrit)
:param f: Pointer to the function that will be minimized, similarly to the one you submit via ``ConjGradSolver::setFunction``.
:param termcrit: Terminal criteria to the algorithm, similarly to the one you submit via ``ConjGradSolver::setTermCriteria``.

1
modules/optim/doc/optim.rst
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@ -10,3 +10,4 @@ optim. Generic numerical optimization
linear_programming
downhill_simplex_method
primal_dual_algorithm
nonlinear_conjugate_gradient

14
modules/optim/include/opencv2/optim.hpp
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@ -10,8 +10,7 @@
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
@ -30,7 +29,7 @@
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// In no event shall the OpenCV Foundation 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
@ -54,8 +53,8 @@ public:
{
public:
virtual ~Function() {}
//! ndim - dimensionality
virtual double calc(const double* x) const = 0;
virtual void getGradient(const double* /*x*/,double* /*grad*/) {}
};
virtual Ptr<Function> getFunction() const = 0;
@ -86,6 +85,13 @@ CV_EXPORTS_W Ptr<DownhillSolver> createDownhillSolver(const Ptr<Solver::Function
InputArray initStep=Mat_<double>(1,1,0.0),
TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
//! conjugate gradient method
class CV_EXPORTS ConjGradSolver : public Solver{
};
CV_EXPORTS_W Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f=Ptr<ConjGradSolver::Function>(),
TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001));
//!the return codes for solveLP() function
enum
{

6
modules/optim/include/opencv2/optim/optim.hpp
View File

@ -7,11 +7,9 @@
// copy or use the software.
//
//
// License Agreement
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
@ -31,7 +29,7 @@
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// In no event shall the OpenCV Foundation 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

184
modules/optim/src/conjugate_gradient.cpp Normal file
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@ -0,0 +1,184 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation 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.
//
//M*/
#include "precomp.hpp"
#undef ALEX_DEBUG
#include "debug.hpp"
namespace cv{namespace optim{
#define SEC_METHOD_ITERATIONS 4
#define INITIAL_SEC_METHOD_SIGMA 0.1
class ConjGradSolverImpl : public ConjGradSolver
{
public:
Ptr<Function> getFunction() const;
void setFunction(const Ptr<Function>& f);
TermCriteria getTermCriteria() const;
ConjGradSolverImpl();
void setTermCriteria(const TermCriteria& termcrit);
double minimize(InputOutputArray x);
protected:
Ptr<Solver::Function> _Function;
TermCriteria _termcrit;
Mat_<double> d,r,buf_x,r_old;
Mat_<double> minimizeOnTheLine_buf1,minimizeOnTheLine_buf2;
private:
static void minimizeOnTheLine(Ptr<Solver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,Mat_<double>& buf2);
};
void ConjGradSolverImpl::minimizeOnTheLine(Ptr<Solver::Function> _f,Mat_<double>& x,const Mat_<double>& d,Mat_<double>& buf1,
Mat_<double>& buf2){
double sigma=INITIAL_SEC_METHOD_SIGMA;
buf1=0.0;
buf2=0.0;
dprintf(("before minimizeOnTheLine\n"));
dprintf(("x:\n"));
print_matrix(x);
dprintf(("d:\n"));
print_matrix(d);
for(int i=0;i<SEC_METHOD_ITERATIONS;i++){
_f->getGradient((double*)x.data,(double*)buf1.data);
dprintf(("buf1:\n"));
print_matrix(buf1);
x=x+sigma*d;
_f->getGradient((double*)x.data,(double*)buf2.data);
dprintf(("buf2:\n"));
print_matrix(buf2);
double d1=buf1.dot(d), d2=buf2.dot(d);
if((d1-d2)==0){
break;
}
double alpha=-sigma*d1/(d2-d1);
dprintf(("(buf2.dot(d)-buf1.dot(d))=%f\nalpha=%f\n",(buf2.dot(d)-buf1.dot(d)),alpha));
x=x+(alpha-sigma)*d;
sigma=-alpha;
}
dprintf(("after minimizeOnTheLine\n"));
print_matrix(x);
}
double ConjGradSolverImpl::minimize(InputOutputArray x){
CV_Assert(_Function.empty()==false);
dprintf(("termcrit:\n\ttype: %d\n\tmaxCount: %d\n\tEPS: %g\n",_termcrit.type,_termcrit.maxCount,_termcrit.epsilon));
Mat x_mat=x.getMat();
CV_Assert(MIN(x_mat.rows,x_mat.cols)==1);
int ndim=MAX(x_mat.rows,x_mat.cols);
CV_Assert(x_mat.type()==CV_64FC1);
if(d.cols!=ndim){
d.create(1,ndim);
r.create(1,ndim);
r_old.create(1,ndim);
minimizeOnTheLine_buf1.create(1,ndim);
minimizeOnTheLine_buf2.create(1,ndim);
}
Mat_<double> proxy_x;
if(x_mat.rows>1){
buf_x.create(1,ndim);
Mat_<double> proxy(ndim,1,(double*)buf_x.data);
x_mat.copyTo(proxy);
proxy_x=buf_x;
}else{
proxy_x=x_mat;
}
_Function->getGradient((double*)proxy_x.data,(double*)d.data);
d*=-1.0;
d.copyTo(r);
//here everything goes. check that everything is setted properly
dprintf(("proxy_x\n"));print_matrix(proxy_x);
dprintf(("d first time\n"));print_matrix(d);
dprintf(("r\n"));print_matrix(r);
double beta=0;
for(int count=0;count<_termcrit.maxCount;count++){
minimizeOnTheLine(_Function,proxy_x,d,minimizeOnTheLine_buf1,minimizeOnTheLine_buf2);
r.copyTo(r_old);
_Function->getGradient((double*)proxy_x.data,(double*)r.data);
r*=-1.0;
double r_norm_sq=norm(r);
if(_termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && r_norm_sq<_termcrit.epsilon){
break;
}
r_norm_sq=r_norm_sq*r_norm_sq;
beta=MAX(0.0,(r_norm_sq-r.dot(r_old))/r_norm_sq);
d=r+beta*d;
}
if(x_mat.rows>1){
Mat(ndim, 1, CV_64F, (double*)proxy_x.data).copyTo(x);
}
return _Function->calc((double*)proxy_x.data);
}
ConjGradSolverImpl::ConjGradSolverImpl(){
_Function=Ptr<Function>();
}
Ptr<Solver::Function> ConjGradSolverImpl::getFunction()const{
return _Function;
}
void ConjGradSolverImpl::setFunction(const Ptr<Function>& f){
_Function=f;
}
TermCriteria ConjGradSolverImpl::getTermCriteria()const{
return _termcrit;
}
void ConjGradSolverImpl::setTermCriteria(const TermCriteria& termcrit){
CV_Assert((termcrit.type==(TermCriteria::MAX_ITER+TermCriteria::EPS) && termcrit.epsilon>0 && termcrit.maxCount>0) ||
((termcrit.type==TermCriteria::MAX_ITER) && termcrit.maxCount>0));
_termcrit=termcrit;
}
// both minRange & minError are specified by termcrit.epsilon; In addition, user may specify the number of iterations that the algorithm does.
Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f, TermCriteria termcrit){
ConjGradSolver *CG=new ConjGradSolverImpl();
CG->setFunction(f);
CG->setTermCriteria(termcrit);
return Ptr<ConjGradSolver>(CG);
}
}}

40
modules/optim/src/debug.hpp
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@ -1,3 +1,43 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation 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.
//
//M*/
namespace cv{namespace optim{
#ifdef ALEX_DEBUG
#define dprintf(x) printf x

40
modules/optim/src/denoise_tvl1.cpp
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@ -1,3 +1,43 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation 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.
//
//M*/
#include "precomp.hpp"
#undef ALEX_DEBUG
#include "debug.hpp"

40
modules/optim/src/lpsolver.cpp
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@ -1,3 +1,43 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation 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.
//
//M*/
#include "precomp.hpp"
#include <climits>
#include <algorithm>

7
modules/optim/src/precomp.hpp
View File

@ -7,11 +7,10 @@
// copy or use the software.
//
//
// License Agreement
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
@ -30,7 +29,7 @@
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// In no event shall the OpenCV Foundation 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

47
modules/optim/src/simplex.cpp
View File

@ -1,3 +1,43 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation 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.
//
//M*/
#include "precomp.hpp"
#include "debug.hpp"
#include "opencv2/core/core_c.h"
@ -19,6 +59,8 @@ namespace cv{namespace optim{
Ptr<Solver::Function> _Function;
TermCriteria _termcrit;
Mat _step;
Mat_<double> buf_x;
private:
inline void createInitialSimplex(Mat_<double>& simplex,Mat& step);
inline double innerDownhillSimplex(cv::Mat_<double>& p,double MinRange,double MinError,int& nfunk,
@ -209,7 +251,10 @@ namespace cv{namespace optim{
Mat_<double> proxy_x;
if(x_mat.rows>1){
proxy_x=x_mat.t();
buf_x.create(1,_step.cols);
Mat_<double> proxy(_step.cols,1,(double*)buf_x.data);
x_mat.copyTo(proxy);
proxy_x=buf_x;
}else{
proxy_x=x_mat;
}

102
modules/optim/test/test_conjugate_gradient.cpp Normal file
View File

@ -0,0 +1,102 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation 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.
//
//M*/
#include "test_precomp.hpp"
#include <cstdlib>
static void mytest(cv::Ptr<cv::optim::ConjGradSolver> solver,cv::Ptr<cv::optim::Solver::Function> ptr_F,cv::Mat& x,
cv::Mat& etalon_x,double etalon_res){
solver->setFunction(ptr_F);
//int ndim=MAX(step.cols,step.rows);
double res=solver->minimize(x);
std::cout<<"res:\n\t"<<res<<std::endl;
std::cout<<"x:\n\t"<<x<<std::endl;
std::cout<<"etalon_res:\n\t"<<etalon_res<<std::endl;
std::cout<<"etalon_x:\n\t"<<etalon_x<<std::endl;
double tol=solver->getTermCriteria().epsilon;
ASSERT_TRUE(std::abs(res-etalon_res)<tol);
/*for(cv::Mat_<double>::iterator it1=x.begin<double>(),it2=etalon_x.begin<double>();it1!=x.end<double>();it1++,it2++){
ASSERT_TRUE(std::abs((*it1)-(*it2))<tol);
}*/
std::cout<<"--------------------------\n";
}
class SphereF:public cv::optim::Solver::Function{
public:
double calc(const double* x)const{
return x[0]*x[0]+x[1]*x[1]+x[2]*x[2]+x[3]*x[3];
}
void getGradient(const double* x,double* grad){
for(int i=0;i<4;i++){
grad[i]=2*x[i];
}
}
};
class RosenbrockF:public cv::optim::Solver::Function{
double calc(const double* x)const{
return 100*(x[1]-x[0]*x[0])*(x[1]-x[0]*x[0])+(1-x[0])*(1-x[0]);
}
void getGradient(const double* x,double* grad){
grad[0]=-2*(1-x[0])-400*(x[1]-x[0]*x[0])*x[0];
grad[1]=200*(x[1]-x[0]*x[0]);
}
};
TEST(Optim_ConjGrad, regression_basic){
cv::Ptr<cv::optim::ConjGradSolver> solver=cv::optim::createConjGradSolver();
#if 1
{
cv::Ptr<cv::optim::Solver::Function> ptr_F(new SphereF());
cv::Mat x=(cv::Mat_<double>(4,1)<<50.0,10.0,1.0,-10.0),
etalon_x=(cv::Mat_<double>(1,4)<<0.0,0.0,0.0,0.0);
double etalon_res=0.0;
mytest(solver,ptr_F,x,etalon_x,etalon_res);
}
#endif
#if 1
{
cv::Ptr<cv::optim::Solver::Function> ptr_F(new RosenbrockF());
cv::Mat x=(cv::Mat_<double>(2,1)<<0.0,0.0),
etalon_x=(cv::Mat_<double>(2,1)<<1.0,1.0);
double etalon_res=0.0;
mytest(solver,ptr_F,x,etalon_x,etalon_res);
}
#endif
}

40
modules/optim/test/test_denoise_tvl1.cpp
View File

@ -1,3 +1,43 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation 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.
//
//M*/
#include "test_precomp.hpp"
#include "opencv2/highgui.hpp"

40
modules/optim/test/test_downhill_simplex.cpp
View File

@ -1,3 +1,43 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation 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.
//
//M*/
#include "test_precomp.hpp"
#include <cstdlib>
#include <cmath>

40
modules/optim/test/test_lpsolver.cpp
View File

@ -1,3 +1,43 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the OpenCV Foundation 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.
//
//M*/
#include "test_precomp.hpp"
#include <iostream>