vpRansac.h

/****************************************************************************
 *
 * This file is part of the ViSP software.
 * Copyright (C) 2005 - 2017 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
 *
 *****************************************************************************/


#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>
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 not_used = 0.0,
          const int maxNbumbersOfTrials = 10000);
};

template <class vpTransformation>
bool
vpRansac<vpTransformation>::ransac(unsigned int npts, vpColVector &x,
                   unsigned int s, double t,
                   vpColVector &M,
                   vpColVector &inliers,
                   int consensus,
           double not_used,
           const int maxNbumbersOfTrials)
{
  /*   bool isplanar; */
  /*   if (s == 4) isplanar = true; */
  /*   else isplanar = false; */
  (void)not_used;
  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