/usr/include/x86_64-linux-gnu/visp3/core/vpRansac.h is in libvisp-core-dev 3.0.0-4.
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*
* This file is part of the ViSP software.
* Copyright (C) 2005 - 2015 by Inria. All rights reserved.
*
* This software is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public License
* ("GPL") version 2 as published by the Free Software Foundation.
* See the file LICENSE.txt at the root directory of this source
* distribution for additional information about the GNU GPL.
*
* For using ViSP with software that can not be combined with the GNU
* GPL, please contact Inria about acquiring a ViSP Professional
* Edition License.
*
* See http://visp.inria.fr for more information.
*
* This software was developed at:
* Inria Rennes - Bretagne Atlantique
* Campus Universitaire de Beaulieu
* 35042 Rennes Cedex
* France
*
* If you have questions regarding the use of this file, please contact
* Inria at visp@inria.fr
*
* This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE
* WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
*
* Description:
* Ransac robust algorithm.
*
* Authors:
* Eric Marchand
*
*****************************************************************************/
/*!
\file vpRansac.h
template class for
*/
#ifndef vpRANSAC_HH
#define vpRANSAC_HH
#include <visp3/core/vpUniRand.h> // random number generation
#include <visp3/core/vpDebug.h> // debug and trace
#include <visp3/core/vpColVector.h>
#include <visp3/core/vpMath.h>
#include <ctime>
/*!
\class vpRansac
\ingroup group_core_robust
\brief This class is a generic implementation of the Ransac algorithm. It
cannot be used alone.
RANSAC is described in \cite Fischler81 and \cite Hartley01a.
The code of this class is inspired by :
Peter Kovesi
School of Computer Science & Software Engineering
The University of Western Australia
pk at csse uwa edu au
http://www.csse.uwa.edu.au/~pk
\sa vpHomography
*/
template <class vpTransformation>
class vpRansac
{
public:
static bool ransac(unsigned int npts,
vpColVector &x,
unsigned int s, double t,
vpColVector &model,
vpColVector &inliers,
int consensus = 1000,
double areaThreshold = 0.0,
const int maxNbumbersOfTrials = 10000);
};
/*!
\brief
RANSAC - Robustly fits a model to data with the RANSAC algorithm
\param npts : The number of data points.
\param x : Data sets to which we are seeking to fit a model M It is assumed
that x is of size [d x Npts] where d is the dimensionality of the data and
npts is the number of data points.
\param s : The minimum number of samples from x required by fitting fn to
fit a model. Value should be greater or equal to 4.
\param t : The distance threshold between data point and the model used to
decide whether a point is an inlier or not.
\param M : The model having the greatest number of inliers.
\param inliers : An array of indices of the elements of x that were the
inliers for the best model.
\param consensus : Consensus
\param areaThreshold : Not used.
\param maxNbumbersOfTrials : Maximum number of trials. Even if a solution is
not found, the method is stopped.
*/
template <class vpTransformation>
bool
vpRansac<vpTransformation>::ransac(unsigned int npts, vpColVector &x,
unsigned int s, double t,
vpColVector &M,
vpColVector &inliers,
int consensus,
double /* areaThreshold */,
const int maxNbumbersOfTrials)
{
/* bool isplanar; */
/* if (s == 4) isplanar = true; */
/* else isplanar = false; */
double eps = 1e-6 ;
double p = 0.99; // Desired probability of choosing at least one sample
// free from outliers
int maxTrials = maxNbumbersOfTrials; // Maximum number of trials before we give up.
int maxDataTrials = 1000; // Max number of attempts to select a non-degenerate
// data set.
if (s<4)
s = 4;
// Sentinel value allowing detection of solution failure.
bool solutionFind = false ;
vpColVector bestM ;
int trialcount = 0;
int bestscore = -1;
double N = 1; // Dummy initialisation for number of trials.
vpUniRand random((const long)time(NULL)) ;
vpColVector bestinliers ;
unsigned int *ind = new unsigned int [s] ;
int numiter = 0;
int ninliers = 0;
double residual = 0.0;
while(( N > trialcount) && (consensus > bestscore))
{
// Select at random s datapoints to form a trial model, M.
// In selecting these points we have to check that they are not in
// a degenerate configuration.
bool degenerate = true;
int count = 1;
while ( degenerate == true)
{
// Generate s random indicies in the range 1..npts
for (unsigned int i=0 ; i < s ; i++)
ind[i] = (unsigned int)ceil(random()*npts) -1;
// Test that these points are not a degenerate configuration.
degenerate = vpTransformation::degenerateConfiguration(x,ind) ;
// degenerate = feval(degenfn, x(:,ind));
// Safeguard against being stuck in this loop forever
count = count + 1;
if (count > maxDataTrials) {
delete [] ind;
vpERROR_TRACE("Unable to select a nondegenerate data set");
throw(vpException(vpException::fatalError, "Unable to select a nondegenerate data set"));
//return false; //Useless after a throw() function
}
}
// Fit model to this random selection of data points.
vpTransformation::computeTransformation(x,ind, M);
vpColVector d ;
// Evaluate distances between points and model.
vpTransformation::computeResidual(x, M, d) ;
// Find the indices of points that are inliers to this model.
residual = 0.0;
ninliers =0 ;
for (unsigned int i=0 ; i < npts ; i++)
{
double resid = fabs(d[i]);
if (resid < t)
{
inliers[i] = 1 ;
ninliers++ ;
residual += fabs(d[i]);
}
else inliers[i] = 0;
}
if (ninliers > bestscore) // Largest set of inliers so far...
{
bestscore = ninliers; // Record data for this model
bestinliers = inliers;
bestM = M;
solutionFind = true ;
// Update estimate of N, the number of trials to ensure we pick,
// with probability p, a data set with no outliers.
double fracinliers = (double)ninliers / (double)npts;
double pNoOutliers = 1 - pow(fracinliers,static_cast<int>(s));
pNoOutliers = vpMath::maximum(eps, pNoOutliers); // Avoid division by -Inf
pNoOutliers = vpMath::minimum(1-eps, pNoOutliers);// Avoid division by 0.
N = (log(1-p)/log(pNoOutliers));
}
trialcount = trialcount+1;
// Safeguard against being stuck in this loop forever
if (trialcount > maxTrials)
{
vpTRACE("ransac reached the maximum number of %d trials", maxTrials);
break ;
}
numiter++;
}
if (solutionFind==true) // We got a solution
{
M = bestM;
inliers = bestinliers;
}
else
{
vpTRACE("ransac was unable to find a useful solution");
M = 0;
}
if(ninliers > 0)
residual /= ninliers;
delete [] ind;
return true;
}
#endif
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