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318 lines
9.1 KiB
C++
318 lines
9.1 KiB
C++
/*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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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 Intel Corporation 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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#include "precomp.hpp"
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#include <float.h>
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#include <limits.h>
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/* Valery Mosyagin */
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//#define TRACKLEVMAR
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typedef void (*pointer_LMJac)( const CvMat* src, CvMat* dst );
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typedef void (*pointer_LMFunc)( const CvMat* src, CvMat* dst );
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#if 0
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/* Optimization using Levenberg-Marquardt */
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void cvLevenbergMarquardtOptimization(pointer_LMJac JacobianFunction,
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pointer_LMFunc function,
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/*pointer_Err error_function,*/
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CvMat *X0,CvMat *observRes,CvMat *resultX,
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int maxIter,double epsilon)
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{
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/* This is not sparse method */
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/* Make optimization using */
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/* func - function to compute */
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/* uses function to compute jacobian */
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/* Allocate memory */
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CvMat *vectX = 0;
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CvMat *vectNewX = 0;
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CvMat *resFunc = 0;
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CvMat *resNewFunc = 0;
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CvMat *error = 0;
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CvMat *errorNew = 0;
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CvMat *Jac = 0;
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CvMat *delta = 0;
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CvMat *matrJtJ = 0;
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CvMat *matrJtJN = 0;
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CvMat *matrJt = 0;
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CvMat *vectB = 0;
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CV_FUNCNAME( "cvLevenbegrMarquardtOptimization" );
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__BEGIN__;
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if( JacobianFunction == 0 || function == 0 || X0 == 0 || observRes == 0 || resultX == 0 )
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{
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CV_ERROR( CV_StsNullPtr, "Some of parameters is a NULL pointer" );
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}
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if( !CV_IS_MAT(X0) || !CV_IS_MAT(observRes) || !CV_IS_MAT(resultX) )
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{
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CV_ERROR( CV_StsUnsupportedFormat, "Some of input parameters must be a matrices" );
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}
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int numVal;
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int numFunc;
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double valError;
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double valNewError;
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numVal = X0->rows;
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numFunc = observRes->rows;
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/* test input data */
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if( X0->cols != 1 )
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{
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CV_ERROR( CV_StsUnmatchedSizes, "Number of colomn of vector X0 must be 1" );
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}
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if( observRes->cols != 1 )
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{
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CV_ERROR( CV_StsUnmatchedSizes, "Number of colomn of vector observed rusult must be 1" );
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}
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if( resultX->cols != 1 || resultX->rows != numVal )
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{
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CV_ERROR( CV_StsUnmatchedSizes, "Size of result vector X must be equals to X0" );
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}
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if( maxIter <= 0 )
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{
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CV_ERROR( CV_StsUnmatchedSizes, "Number of maximum iteration must be > 0" );
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}
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if( epsilon < 0 )
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{
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CV_ERROR( CV_StsUnmatchedSizes, "Epsilon must be >= 0" );
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}
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/* copy x0 to current value of x */
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CV_CALL( vectX = cvCreateMat(numVal, 1, CV_64F) );
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CV_CALL( vectNewX = cvCreateMat(numVal, 1, CV_64F) );
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CV_CALL( resFunc = cvCreateMat(numFunc,1, CV_64F) );
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CV_CALL( resNewFunc = cvCreateMat(numFunc,1, CV_64F) );
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CV_CALL( error = cvCreateMat(numFunc,1, CV_64F) );
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CV_CALL( errorNew = cvCreateMat(numFunc,1, CV_64F) );
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CV_CALL( Jac = cvCreateMat(numFunc,numVal, CV_64F) );
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CV_CALL( delta = cvCreateMat(numVal, 1, CV_64F) );
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CV_CALL( matrJtJ = cvCreateMat(numVal, numVal, CV_64F) );
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CV_CALL( matrJtJN = cvCreateMat(numVal, numVal, CV_64F) );
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CV_CALL( matrJt = cvCreateMat(numVal, numFunc,CV_64F) );
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CV_CALL( vectB = cvCreateMat(numVal, 1, CV_64F) );
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cvCopy(X0,vectX);
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/* ========== Main optimization loop ============ */
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double change;
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int currIter;
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double alpha;
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change = 1;
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currIter = 0;
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alpha = 0.001;
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do {
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/* Compute value of function */
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function(vectX,resFunc);
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/* Print result of function to file */
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/* Compute error */
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cvSub(observRes,resFunc,error);
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//valError = error_function(observRes,resFunc);
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/* Need to use new version of computing error (norm) */
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valError = cvNorm(observRes,resFunc);
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/* Compute Jacobian for given point vectX */
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JacobianFunction(vectX,Jac);
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/* Define optimal delta for J'*J*delta=J'*error */
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/* compute J'J */
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cvMulTransposed(Jac,matrJtJ,1);
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cvCopy(matrJtJ,matrJtJN);
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/* compute J'*error */
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cvTranspose(Jac,matrJt);
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cvmMul(matrJt,error,vectB);
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/* Solve normal equation for given alpha and Jacobian */
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do
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{
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/* Increase diagonal elements by alpha */
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for( int i = 0; i < numVal; i++ )
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{
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double val;
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val = cvmGet(matrJtJ,i,i);
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cvmSet(matrJtJN,i,i,(1+alpha)*val);
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}
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/* Solve system to define delta */
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cvSolve(matrJtJN,vectB,delta,CV_SVD);
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/* We know delta and we can define new value of vector X */
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cvAdd(vectX,delta,vectNewX);
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/* Compute result of function for new vector X */
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function(vectNewX,resNewFunc);
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cvSub(observRes,resNewFunc,errorNew);
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valNewError = cvNorm(observRes,resNewFunc);
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currIter++;
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if( valNewError < valError )
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{/* accept new value */
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valError = valNewError;
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/* Compute relative change of required parameter vectorX. change = norm(curr-prev) / norm(curr) ) */
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change = cvNorm(vectX, vectNewX, CV_RELATIVE_L2);
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alpha /= 10;
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cvCopy(vectNewX,vectX);
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break;
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}
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else
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{
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alpha *= 10;
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}
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} while ( currIter < maxIter );
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/* new value of X and alpha were accepted */
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} while ( change > epsilon && currIter < maxIter );
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/* result was computed */
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cvCopy(vectX,resultX);
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__END__;
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cvReleaseMat(&vectX);
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cvReleaseMat(&vectNewX);
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cvReleaseMat(&resFunc);
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cvReleaseMat(&resNewFunc);
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cvReleaseMat(&error);
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cvReleaseMat(&errorNew);
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cvReleaseMat(&Jac);
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cvReleaseMat(&delta);
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cvReleaseMat(&matrJtJ);
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cvReleaseMat(&matrJtJN);
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cvReleaseMat(&matrJt);
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cvReleaseMat(&vectB);
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return;
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}
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#endif
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/*------------------------------------------------------------------------------*/
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#if 0
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//tests
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void Jac_Func2(CvMat *vectX,CvMat *Jac)
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{
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double x = cvmGet(vectX,0,0);
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double y = cvmGet(vectX,1,0);
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cvmSet(Jac,0,0,2*(x-2));
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cvmSet(Jac,0,1,2*(y+3));
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cvmSet(Jac,1,0,1);
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cvmSet(Jac,1,1,1);
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return;
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}
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void Res_Func2(CvMat *vectX,CvMat *res)
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{
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double x = cvmGet(vectX,0,0);
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double y = cvmGet(vectX,1,0);
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cvmSet(res,0,0,(x-2)*(x-2)+(y+3)*(y+3));
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cvmSet(res,1,0,x+y);
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return;
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}
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double Err_Func2(CvMat *obs,CvMat *res)
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{
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CvMat *tmp;
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tmp = cvCreateMat(obs->rows,1,CV_64F);
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cvSub(obs,res,tmp);
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double e;
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e = cvNorm(tmp);
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return e;
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}
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void TestOptimX2Y2()
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{
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CvMat vectX0;
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double vectX0_dat[2];
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vectX0 = cvMat(2,1,CV_64F,vectX0_dat);
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vectX0_dat[0] = 5;
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vectX0_dat[1] = -7;
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CvMat observRes;
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double observRes_dat[2];
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observRes = cvMat(2,1,CV_64F,observRes_dat);
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observRes_dat[0] = 0;
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observRes_dat[1] = -1;
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observRes_dat[0] = 0;
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observRes_dat[1] = -1.2;
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CvMat optimX;
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double optimX_dat[2];
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optimX = cvMat(2,1,CV_64F,optimX_dat);
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LevenbegrMarquardtOptimization( Jac_Func2, Res_Func2, Err_Func2,
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&vectX0,&observRes,&optimX,100,0.000001);
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return;
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}
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#endif
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