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Open Source Computer Vision Library
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108 lines
4.9 KiB
108 lines
4.9 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the OpenCV Foundation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#ifndef __OPENCV_OPTIM_HPP__ |
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#define __OPENCV_OPTIM_HPP__ |
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#include "opencv2/core.hpp" |
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namespace cv{namespace optim |
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{ |
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class CV_EXPORTS Solver : public Algorithm |
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{ |
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public: |
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class CV_EXPORTS Function |
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{ |
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public: |
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virtual ~Function() {} |
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virtual double calc(const double* x) const = 0; |
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virtual void getGradient(const double* /*x*/,double* /*grad*/) {} |
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}; |
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virtual Ptr<Function> getFunction() const = 0; |
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virtual void setFunction(const Ptr<Function>& f) = 0; |
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virtual TermCriteria getTermCriteria() const = 0; |
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virtual void setTermCriteria(const TermCriteria& termcrit) = 0; |
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// x contain the initial point before the call and the minima position (if algorithm converged) after. x is assumed to be (something that |
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// after getMat() will return) row-vector or column-vector. *It's size and should |
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// be consisted with previous dimensionality data given, if any (otherwise, it determines dimensionality)* |
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virtual double minimize(InputOutputArray x) = 0; |
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}; |
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//! downhill simplex class |
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class CV_EXPORTS DownhillSolver : public Solver |
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{ |
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public: |
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//! returns row-vector, even if the column-vector was given |
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virtual void getInitStep(OutputArray step) const=0; |
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//!This should be called at least once before the first call to minimize() and step is assumed to be (something that |
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//! after getMat() will return) row-vector or column-vector. *It's dimensionality determines the dimensionality of a problem.* |
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virtual void setInitStep(InputArray step)=0; |
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}; |
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// both minRange & minError are specified by termcrit.epsilon; In addition, user may specify the number of iterations that the algorithm does. |
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CV_EXPORTS_W Ptr<DownhillSolver> createDownhillSolver(const Ptr<Solver::Function>& f=Ptr<Solver::Function>(), |
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InputArray initStep=Mat_<double>(1,1,0.0), |
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TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); |
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//! conjugate gradient method |
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class CV_EXPORTS ConjGradSolver : public Solver{ |
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}; |
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CV_EXPORTS_W Ptr<ConjGradSolver> createConjGradSolver(const Ptr<Solver::Function>& f=Ptr<ConjGradSolver::Function>(), |
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TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)); |
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//!the return codes for solveLP() function |
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enum |
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{ |
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SOLVELP_UNBOUNDED = -2, //problem is unbounded (target function can achieve arbitrary high values) |
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SOLVELP_UNFEASIBLE = -1, //problem is unfeasible (there are no points that satisfy all the constraints imposed) |
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SOLVELP_SINGLE = 0, //there is only one maximum for target function |
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SOLVELP_MULTI = 1 //there are multiple maxima for target function - the arbitrary one is returned |
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}; |
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CV_EXPORTS_W int solveLP(const Mat& Func, const Mat& Constr, Mat& z); |
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CV_EXPORTS_W void denoise_TVL1(const std::vector<Mat>& observations,Mat& result, double lambda=1.0, int niters=30); |
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}}// cv |
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#endif
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