vpTutoMeanSquareFitting.cpp

/*
 * ViSP, open source Visual Servoing Platform software.
 * Copyright (C) 2005 - 2024 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 as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 * 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 https://visp.inria.fr for more information.
 *
 * This software was developed at:
 * Inria Rennes - Bretagne Atlantique
 * Campus Universitaire de Beaulieu
 * 35042 Rennes Cedex
 * France
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 */
 
#include "vpTutoMeanSquareFitting.h"
 
#if (VISP_CXX_STANDARD >= VISP_CXX_STANDARD_11)
#ifndef DOXYGEN_SHOULD_SKIP_THIS
namespace tutorial
{
#ifdef ENABLE_VISP_NAMESPACE
using namespace VISP_NAMESPACE_NAME;
#endif
 
vpTutoMeanSquareFitting::vpTutoMeanSquareFitting(const unsigned int &degree, const unsigned int &height, const unsigned int &width)
  : m_degree(degree)
  , m_height(static_cast<unsigned int>(height))
  , m_width(static_cast<unsigned int>(width))
  , m_model(degree, height, width)
  , m_isFitted(false)
{ }
 
void vpTutoMeanSquareFitting::fit(const std::vector<vpImagePoint> &pts)
{
  vpMatrix A; // The matrix that contains the u^i
  vpMatrix X; // The matrix we want to estimate, that contains the polynomial coefficients.
  vpMatrix b; // The matrix that contains the v values
 
  // Fill the matrices that form the system we want to solve
  vpTutoParabolaModel::fillSystem(m_degree, m_height, m_width, pts, A, b);
 
  // Compute the parabola coefficients using the least-mean-square method.
  X = A.pseudoInverse() * b;
  m_model = vpTutoParabolaModel(X, m_height, m_width);
  m_isFitted = true;
}
 
double vpTutoMeanSquareFitting::evaluate(const std::vector<vpImagePoint> &pts)
{
  if (!m_isFitted) {
    throw(vpException(vpException::notInitialized, "fit() has not been called."));
  }
  unsigned int nbPts = static_cast<unsigned int>(pts.size());
 
  // Compute the mean absolute error
  double meanSquareError = 0.f;
  for (unsigned int i = 0; i < nbPts; ++i) {
    double squareError = evaluate(pts[i]);
    meanSquareError += squareError;
  }
  meanSquareError /= static_cast<double>(nbPts);
  return std::sqrt(meanSquareError);
}
 
double vpTutoMeanSquareFitting::evaluate(const vpImagePoint &pt)
{
  if (!m_isFitted) {
    throw(vpException(vpException::notInitialized, "fit() has not been called."));
  }
  double u = pt.get_u();
  double v = pt.get_v();
  double v_model = model(u);
  double error = v - v_model;
  double squareError = error * error;
  return squareError;
}
 
double vpTutoMeanSquareFitting::model(const double &u)
{
  if (!m_isFitted) {
    throw(vpException(vpException::notInitialized, "fit() has not been called."));
  }
  double v = m_model.eval(u);
  return v;
}
}
#endif
#else
void dummy_vpTutoMeanSquareFitting() { }
#endif