121 lines
5.0 KiB
C++
121 lines
5.0 KiB
C++
/******************************************************************************
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* Authors: Laurent Kneip & Paul Furgale *
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* Contact: kneip.laurent@gmail.com *
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* License: Copyright (c) 2013 Laurent Kneip, ANU. All rights reserved. *
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* *
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* Redistribution and use in source and binary forms, with or without *
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* modification, are permitted provided that the following conditions *
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* are met: *
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* * Redistributions of source code must retain the above copyright *
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* notice, this list of conditions and the following disclaimer. *
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* * Redistributions in binary form must reproduce the above copyright *
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* notice, this list of conditions and the following disclaimer in the *
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* documentation and/or other materials provided with the distribution. *
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* * Neither the name of ANU nor the names of its contributors may be *
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* used to endorse or promote products derived from this software without *
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* specific prior written permission. *
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* *
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"*
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE *
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE *
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* ARE DISCLAIMED. IN NO EVENT SHALL ANU OR THE CONTRIBUTORS BE LIABLE *
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL *
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR *
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER *
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT *
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY *
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* OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF *
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* SUCH DAMAGE. *
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******************************************************************************/
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//Note: has been derived from ROS
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/**
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* \file MultiSampleConsensus.hpp
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* \brief This is a base class for sample consensus methods such as Ransac.
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* Derivatives call the three basic functions of a sample-consensus
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* problem (sample drawing, computation of a hypothesis, and verification
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* of a hypothesis). This version is intended for use with the
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* RelativeMultiAdapterBase, and attempts to do sampling in multiple
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* camera-pairs in each hypothesis instantiation.
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*/
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#ifndef OPENGV_SAC_MULTISAMPLECONSENSUS_HPP_
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#define OPENGV_SAC_MULTISAMPLECONSENSUS_HPP_
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/**
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* \brief The namespace of this library.
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*/
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namespace opengv
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{
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/**
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* \brief The namespace for the sample consensus methods.
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*/
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namespace sac
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{
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/**
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* Super-class for sample consensus methods, such as Ransac. This one is using
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* multi-indices for homogeneous sampling over groups of samples.
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*/
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template<typename PROBLEM_T>
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class MultiSampleConsensus
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{
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public:
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/** A child of MultiSampleConsensusProblem */
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typedef PROBLEM_T problem_t;
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/** The model we trying to fit */
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typedef typename problem_t::model_t model_t;
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/**
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* \brief Constructor.
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* \param[in] maxIterations The maximum number of hypothesis generations
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* \param[in] threshold Some threshold value for classifying samples as
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* an inlier or an outlier.
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* \param[in] probability The probability of being able to draw at least one
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* sample that is free of outliers (see [15])
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*/
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MultiSampleConsensus(
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int maxIterations = 1000,
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double threshold = 1.0,
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double probability = 0.99 );
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/**
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* \brief Destructor
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*/
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virtual ~MultiSampleConsensus();
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/**
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* \brief Fit the model to the data.
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* \param[in] debug_verbosity_level Sets the verbosity level.
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* \return bool True if success.
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*/
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virtual bool computeModel( int debug_verbosity_level = 0 ) = 0;
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// \todo accessors
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//private:
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/** the maximum number of iterations */
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int max_iterations_;
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/** the current number of iterations */
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int iterations_;
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/** the threshold for classifying inliers */
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double threshold_;
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/** the current probability (defines remaining iterations) */
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double probability_;
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/** the currently best model coefficients */
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model_t model_coefficients_;
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/** the group-wise indices for the currently best hypothesis */
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std::vector< std::vector<int> > model_;
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/** the group-wise indices of the samples that have been clasified as inliers */
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std::vector< std::vector<int> > inliers_;
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/** the multi-sample-consensus problem we are trying to solve */
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std::shared_ptr<PROBLEM_T> sac_model_;
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};
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} // namespace sac
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} // namespace opengv
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#include "implementation/MultiSampleConsensus.hpp"
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#endif /* OPENGV_MULTISAMPLECONSENSUS_HPP_ */
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