forked from bartvdbraak/blender
27d42c63d9
===========================
Commiting camera tracking integration gsoc project into trunk.
This commit includes:
- Bundled version of libmv library (with some changes against official repo,
re-sync with libmv repo a bit later)
- New datatype ID called MovieClip which is optimized to work with movie
clips (both of movie files and image sequences) and doing camera/motion
tracking operations.
- New editor called Clip Editor which is currently used for motion/tracking
stuff only, but which can be easily extended to work with masks too.
This editor supports:
* Loading movie files/image sequences
* Build proxies with different size for loaded movie clip, also supports
building undistorted proxies to increase speed of playback in
undistorted mode.
* Manual lens distortion mode calibration using grid and grease pencil
* Supervised 2D tracking using two different algorithms KLT and SAD.
* Basic algorithm for feature detection
* Camera motion solving. scene orientation
- New constraints to "link" scene objects with solved motions from clip:
* Follow Track (make object follow 2D motion of track with given name
or parent object to reconstructed 3D position of track)
* Camera Solver to make camera moving in the same way as reconstructed camera
This commit NOT includes changes from tomato branch:
- New nodes (they'll be commited as separated patch)
- Automatic image offset guessing for image input node and image editor
(need to do more tests and gather more feedback)
- Code cleanup in libmv-capi. It's not so critical cleanup, just increasing
readability and understanadability of code. Better to make this chaneg when
Keir will finish his current patch.
More details about this project can be found on this page:
http://wiki.blender.org/index.php/User:Nazg-gul/GSoC-2011
Further development of small features would be done in trunk, bigger/experimental
features would first be implemented in tomato branch.
274 lines
10 KiB
C++
274 lines
10 KiB
C++
// -*- C++ -*-
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/*
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Copyright (c) 2008 University of North Carolina at Chapel Hill
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This file is part of SSBA (Simple Sparse Bundle Adjustment).
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SSBA is free software: you can redistribute it and/or modify it under the
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terms of the GNU Lesser General Public License as published by the Free
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Software Foundation, either version 3 of the License, or (at your option) any
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later version.
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SSBA is distributed in the hope that it will be useful, but WITHOUT ANY
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WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
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A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
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details.
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You should have received a copy of the GNU Lesser General Public License along
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with SSBA. If not, see <http://www.gnu.org/licenses/>.
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*/
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#ifndef V3D_OPTIMIZATION_H
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#define V3D_OPTIMIZATION_H
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#include "Math/v3d_linear.h"
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#include "Math/v3d_mathutilities.h"
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#include <vector>
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#include <iostream>
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namespace V3D
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{
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enum
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{
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LEVENBERG_OPTIMIZER_TIMEOUT = 0,
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LEVENBERG_OPTIMIZER_SMALL_UPDATE = 1,
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LEVENBERG_OPTIMIZER_CONVERGED = 2
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};
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extern int optimizerVerbosenessLevel;
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struct LevenbergOptimizerCommon
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{
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LevenbergOptimizerCommon()
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: status(LEVENBERG_OPTIMIZER_TIMEOUT), currentIteration(0), maxIterations(50),
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tau(1e-3), lambda(1e-3),
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gradientThreshold(1e-10), updateThreshold(1e-10),
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_nu(2.0)
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{ }
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virtual ~LevenbergOptimizerCommon() {}
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// See Madsen et al., "Methods for non-linear least squares problems."
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virtual void increaseLambda()
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{
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lambda *= _nu; _nu *= 2.0;
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}
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virtual void decreaseLambda(double const rho)
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{
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double const r = 2*rho - 1.0;
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lambda *= std::max(1.0/3.0, 1 - r*r*r);
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if (lambda < 1e-10) lambda = 1e-10;
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_nu = 2;
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}
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bool applyGradientStoppingCriteria(double maxGradient) const
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{
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return maxGradient < gradientThreshold;
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}
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bool applyUpdateStoppingCriteria(double paramLength, double updateLength) const
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{
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return updateLength < updateThreshold * (paramLength + updateThreshold);
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}
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int status;
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int currentIteration, maxIterations;
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double tau, lambda;
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double gradientThreshold, updateThreshold;
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protected:
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double _nu;
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}; // end struct LevenbergOptimizerCommon
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# if defined(V3DLIB_ENABLE_SUITESPARSE)
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struct SparseLevenbergOptimizer : public LevenbergOptimizerCommon
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{
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SparseLevenbergOptimizer(int measurementDimension,
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int nParametersA, int paramDimensionA,
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int nParametersB, int paramDimensionB,
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int paramDimensionC,
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std::vector<int> const& correspondingParamA,
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std::vector<int> const& correspondingParamB)
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: LevenbergOptimizerCommon(),
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_nMeasurements(correspondingParamA.size()),
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_measurementDimension(measurementDimension),
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_nParametersA(nParametersA), _paramDimensionA(paramDimensionA),
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_nParametersB(nParametersB), _paramDimensionB(paramDimensionB),
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_paramDimensionC(paramDimensionC),
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_nNonvaryingA(0), _nNonvaryingB(0), _nNonvaryingC(0),
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_correspondingParamA(correspondingParamA),
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_correspondingParamB(correspondingParamB)
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{
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assert(correspondingParamA.size() == correspondingParamB.size());
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}
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~SparseLevenbergOptimizer() { }
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void setNonvaryingCounts(int nNonvaryingA, int nNonvaryingB, int nNonvaryingC)
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{
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_nNonvaryingA = nNonvaryingA;
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_nNonvaryingB = nNonvaryingB;
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_nNonvaryingC = nNonvaryingC;
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}
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void getNonvaryingCounts(int& nNonvaryingA, int& nNonvaryingB, int& nNonvaryingC) const
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{
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nNonvaryingA = _nNonvaryingA;
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nNonvaryingB = _nNonvaryingB;
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nNonvaryingC = _nNonvaryingC;
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}
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void minimize();
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virtual void evalResidual(VectorArray<double>& residuals) = 0;
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virtual void fillWeights(VectorArray<double> const& residuals, Vector<double>& w)
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{
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(void)residuals;
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std::fill(w.begin(), w.end(), 1.0);
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}
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void fillAllJacobians(Vector<double> const& w,
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MatrixArray<double>& Ak,
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MatrixArray<double>& Bk,
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MatrixArray<double>& Ck)
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{
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int const nVaryingA = _nParametersA - _nNonvaryingA;
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int const nVaryingB = _nParametersB - _nNonvaryingB;
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int const nVaryingC = _paramDimensionC - _nNonvaryingC;
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for (unsigned k = 0; k < _nMeasurements; ++k)
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{
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int const i = _correspondingParamA[k];
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int const j = _correspondingParamB[k];
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if (i < _nNonvaryingA && j < _nNonvaryingB) continue;
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fillJacobians(Ak[k], Bk[k], Ck[k], i, j, k);
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} // end for (k)
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if (nVaryingA > 0)
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{
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for (unsigned k = 0; k < _nMeasurements; ++k)
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scaleMatrixIP(w[k], Ak[k]);
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}
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if (nVaryingB > 0)
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{
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for (unsigned k = 0; k < _nMeasurements; ++k)
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scaleMatrixIP(w[k], Bk[k]);
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}
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if (nVaryingC > 0)
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{
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for (unsigned k = 0; k < _nMeasurements; ++k)
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scaleMatrixIP(w[k], Ck[k]);
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}
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} // end fillAllJacobians()
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virtual void setupJacobianGathering() { }
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virtual void fillJacobians(Matrix<double>& Ak, Matrix<double>& Bk, Matrix<double>& Ck,
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int i, int j, int k) = 0;
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virtual double getParameterLength() const = 0;
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virtual void updateParametersA(VectorArray<double> const& deltaAi) = 0;
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virtual void updateParametersB(VectorArray<double> const& deltaBj) = 0;
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virtual void updateParametersC(Vector<double> const& deltaC) = 0;
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virtual void saveAllParameters() = 0;
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virtual void restoreAllParameters() = 0;
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int currentIteration, maxIterations;
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protected:
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void serializeNonZerosJtJ(std::vector<std::pair<int, int> >& dst) const;
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void setupSparseJtJ();
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void fillSparseJtJ(MatrixArray<double> const& Ui, MatrixArray<double> const& Vj, MatrixArray<double> const& Wk,
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Matrix<double> const& Z, Matrix<double> const& X, Matrix<double> const& Y);
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int const _nMeasurements, _measurementDimension;
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int const _nParametersA, _paramDimensionA;
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int const _nParametersB, _paramDimensionB;
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int const _paramDimensionC;
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int _nNonvaryingA, _nNonvaryingB, _nNonvaryingC;
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std::vector<int> const& _correspondingParamA;
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std::vector<int> const& _correspondingParamB;
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std::vector<pair<int, int> > _jointNonzerosW;
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std::vector<int> _jointIndexW;
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std::vector<int> _JtJ_Lp, _JtJ_Parent, _JtJ_Lnz;
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std::vector<int> _perm_JtJ, _invPerm_JtJ;
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CCS_Matrix<double> _JtJ;
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}; // end struct SparseLevenbergOptimizer
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struct StdSparseLevenbergOptimizer : public SparseLevenbergOptimizer
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{
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StdSparseLevenbergOptimizer(int measurementDimension,
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int nParametersA, int paramDimensionA,
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int nParametersB, int paramDimensionB,
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int paramDimensionC,
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std::vector<int> const& correspondingParamA,
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std::vector<int> const& correspondingParamB)
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: SparseLevenbergOptimizer(measurementDimension, nParametersA, paramDimensionA,
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nParametersB, paramDimensionB, paramDimensionC,
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correspondingParamA, correspondingParamB),
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curParametersA(nParametersA, paramDimensionA), savedParametersA(nParametersA, paramDimensionA),
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curParametersB(nParametersB, paramDimensionB), savedParametersB(nParametersB, paramDimensionB),
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curParametersC(paramDimensionC), savedParametersC(paramDimensionC)
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{ }
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virtual double getParameterLength() const
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{
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double res = 0.0;
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for (int i = 0; i < _nParametersA; ++i) res += sqrNorm_L2(curParametersA[i]);
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for (int j = 0; j < _nParametersB; ++j) res += sqrNorm_L2(curParametersB[j]);
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res += sqrNorm_L2(curParametersC);
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return sqrt(res);
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}
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virtual void updateParametersA(VectorArray<double> const& deltaAi)
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{
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for (int i = 0; i < _nParametersA; ++i) addVectors(deltaAi[i], curParametersA[i], curParametersA[i]);
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}
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virtual void updateParametersB(VectorArray<double> const& deltaBj)
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{
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for (int j = 0; j < _nParametersB; ++j) addVectors(deltaBj[j], curParametersB[j], curParametersB[j]);
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}
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virtual void updateParametersC(Vector<double> const& deltaC)
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{
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addVectors(deltaC, curParametersC, curParametersC);
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}
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virtual void saveAllParameters()
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{
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for (int i = 0; i < _nParametersA; ++i) savedParametersA[i] = curParametersA[i];
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for (int j = 0; j < _nParametersB; ++j) savedParametersB[j] = curParametersB[j];
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savedParametersC = curParametersC;
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}
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virtual void restoreAllParameters()
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{
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for (int i = 0; i < _nParametersA; ++i) curParametersA[i] = savedParametersA[i];
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for (int j = 0; j < _nParametersB; ++j) curParametersB[j] = savedParametersB[j];
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curParametersC = savedParametersC;
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}
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VectorArray<double> curParametersA, savedParametersA;
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VectorArray<double> curParametersB, savedParametersB;
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Vector<double> curParametersC, savedParametersC;
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}; // end struct StdSparseLevenbergOptimizer
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# endif
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} // end namespace V3D
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#endif
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