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/*
* 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
*
* 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.
*/
//! \example tutorial-pf-curve-fitting-lms.cpp
#include <visp3/core/vpConfig.h>
#include <visp3/core/vpException.h>
#include <visp3/core/vpMouseButton.h>
#include <visp3/core/vpTime.h>
#ifdef VISP_HAVE_DISPLAY
#include <visp3/gui/vpPlot.h>
#endif
#include "vpTutoCommonData.h"
#include "vpTutoMeanSquareFitting.h"
#include "vpTutoParabolaModel.h"
#include "vpTutoSegmentation.h"
#ifdef ENABLE_VISP_NAMESPACE
using namespace VISP_NAMESPACE_NAME;
#endif
#if (VISP_CXX_STANDARD >= VISP_CXX_STANDARD_11) && defined(VISP_HAVE_DISPLAY)
int main(const int argc, const char *argv[])
{
tutorial::vpTutoCommonData data;
int returnCode = data.init(argc, argv);
if (returnCode != tutorial::vpTutoCommonData::SOFTWARE_CONTINUE) {
return returnCode;
}
tutorial::vpTutoMeanSquareFitting lmsFitter(data.m_degree, data.m_I_orig.getHeight(), data.m_I_orig.getWidth());
const unsigned int vertOffset = static_cast<unsigned int>(data.m_legendOffset.get_i());
const unsigned int horOffset = static_cast<unsigned int>(data.m_ipLegend.get_j());
const unsigned int legendLmsVert = data.m_I_orig.getHeight() - 2 * vertOffset;
const unsigned int legendLmsHor = horOffset;
//! [Init_plot]
#ifdef VISP_HAVE_DISPLAY
unsigned int plotHeight = 350, plotWidth = 350;
int plotXpos = static_cast<int>(data.m_legendOffset.get_u());
int plotYpos = static_cast<int>(data.m_I_orig.getHeight() + 4. * data.m_legendOffset.get_v());
vpPlot plot(1, plotHeight, plotWidth, plotXpos, plotYpos, "Root mean-square error");
plot.initGraph(0, 1);
plot.setLegend(0, 0, "LMS estimator");
plot.setColor(0, 0, vpColor::gray);
#endif
//! [Init_plot]
bool run = true;
unsigned int nbIter = 0;
double meanDtLMS = 0.;
double meanRootMeanSquareErrorLMS = 0.;
while (!data.m_grabber.end() && run) {
//! [Measurements_extraction]
std::cout << "Iter " << nbIter << std::endl;
data.m_grabber.acquire(data.m_I_orig);
// Perform color segmentation
tutorial::performSegmentationHSV(data);
/// Extracting the skeleton of the mask
std::vector<vpImagePoint> edgePoints = tutorial::extractSkeleton(data);
/// Simulate sensor noise
std::vector<vpImagePoint> noisyEdgePoints = tutorial::addSaltAndPepperNoise(edgePoints, data);
//! [Measurements_extraction]
#ifdef VISP_HAVE_DISPLAY
/// Initial display of the images
vpDisplay::display(data.m_I_orig);
vpDisplay::display(data.m_I_segmented);
vpDisplay::display(data.m_IskeletonNoisy);
#endif
/// Fit using least-square
double tLms = vpTime::measureTimeMs();
//! [LMS_interpolation]
lmsFitter.fit(noisyEdgePoints);
//! [LMS_interpolation]
double dtLms = vpTime::measureTimeMs() - tLms;
double lmsRootMeanSquareError = lmsFitter.evaluate(edgePoints);
std::cout << " [Least-Mean Square method] " << std::endl;
std::cout << " Coeffs = [" << lmsFitter.getCoeffs().transpose() << " ]" << std::endl;
std::cout << " Root Mean Square Error = " << lmsRootMeanSquareError << " pixels" << std::endl;
std::cout << " Fitting duration = " << dtLms << " ms" << std::endl;
meanDtLMS += dtLms;
meanRootMeanSquareErrorLMS += lmsRootMeanSquareError;
#ifdef VISP_HAVE_DISPLAY
// Update image overlay
lmsFitter.display<unsigned char>(data.m_IskeletonNoisy, vpColor::gray, legendLmsVert, legendLmsHor);
// Update plot
plot.plot(0, 0, nbIter, lmsRootMeanSquareError);
// Display the images with overlayed info
data.displayLegend(data.m_I_orig);
vpDisplay::flush(data.m_I_orig);
vpDisplay::flush(data.m_I_segmented);
vpDisplay::flush(data.m_IskeletonNoisy);
run = data.manageClicks(data.m_I_orig, data.m_stepbystep);
#endif
++nbIter;
}
double iterAsDouble = static_cast<double>(nbIter);
std::cout << std::endl << std::endl << "-----[Statistics summary]-----" << std::endl;
std::cout << " [LMS method] " << std::endl;
std::cout << " Average Root Mean Square Error = " << meanRootMeanSquareErrorLMS / iterAsDouble << " pixels" << std::endl;
std::cout << " Average fitting duration = " << meanDtLMS / iterAsDouble << " ms" << std::endl;
#ifdef VISP_HAVE_DISPLAY
if (data.m_grabber.end() && (!data.m_stepbystep)) {
/// Initial display of the images
vpDisplay::display(data.m_I_orig);
vpDisplay::displayText(data.m_I_orig, data.m_ipLegend, "End of sequence reached. Click to exit.", data.m_colorLegend);
/// Update the display
vpDisplay::flush(data.m_I_orig);
/// Get the user input
vpDisplay::getClick(data.m_I_orig, true);
}
#endif
return 0;
}
#else
int main()
{
std::cerr << "ViSP must be compiled with C++ standard >= C++11 to use this tutorial." << std::endl;
std::cerr << "ViSP must also have a 3rd party enabling display features, such as X11 or OpenCV." << std::endl;
return EXIT_FAILURE;
}
#endif
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