vpKeyPoint.cpp

/****************************************************************************
 *
 * ViSP, open source Visual Servoing Platform software.
 * Copyright (C) 2005 - 2019 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 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:
 * Key point functionalities.
 *
 * Authors:
 * Souriya Trinh
 *
 *****************************************************************************/

#include <iomanip>
#include <limits>

#include <visp3/core/vpIoTools.h>
#include <visp3/vision/vpKeyPoint.h>

#if (VISP_HAVE_OPENCV_VERSION >= 0x020101)

#if (VISP_HAVE_OPENCV_VERSION >= 0x030000)
#include <opencv2/calib3d/calib3d.hpp>
#endif

#include <pugixml.hpp>

namespace
{
// Specific Type transformation functions
inline cv::DMatch knnToDMatch(const std::vector<cv::DMatch> &knnMatches)
{
  if (knnMatches.size() > 0) {
    return knnMatches[0];
  }

  return cv::DMatch();
}

inline vpImagePoint matchRansacToVpImage(const std::pair<cv::KeyPoint, cv::Point3f> &pair)
{
  return vpImagePoint(pair.first.pt.y, pair.first.pt.x);
}

}

vpKeyPoint::vpKeyPoint(const vpFeatureDetectorType &detectorType, const vpFeatureDescriptorType &descriptorType,
                       const std::string &matcherName, const vpFilterMatchingType &filterType)
  : m_computeCovariance(false), m_covarianceMatrix(), m_currentImageId(0), m_detectionMethod(detectionScore),
    m_detectionScore(0.15), m_detectionThreshold(100.0), m_detectionTime(0.), m_detectorNames(), m_detectors(),
    m_extractionTime(0.), m_extractorNames(), m_extractors(), m_filteredMatches(), m_filterType(filterType),
    m_imageFormat(jpgImageFormat), m_knnMatches(), m_mapOfImageId(), m_mapOfImages(), m_matcher(),
    m_matcherName(matcherName), m_matches(), m_matchingFactorThreshold(2.0), m_matchingRatioThreshold(0.85),
    m_matchingTime(0.), m_matchRansacKeyPointsToPoints(), m_nbRansacIterations(200), m_nbRansacMinInlierCount(100),
    m_objectFilteredPoints(), m_poseTime(0.), m_queryDescriptors(), m_queryFilteredKeyPoints(), m_queryKeyPoints(),
    m_ransacConsensusPercentage(20.0), m_ransacFilterFlag(vpPose::NO_FILTER), m_ransacInliers(), m_ransacOutliers(),
    m_ransacParallel(false), m_ransacParallelNbThreads(0), m_ransacReprojectionError(6.0),
    m_ransacThreshold(0.01), m_trainDescriptors(), m_trainKeyPoints(), m_trainPoints(), m_trainVpPoints(),
    m_useAffineDetection(false),
#if (VISP_HAVE_OPENCV_VERSION >= 0x020400 && VISP_HAVE_OPENCV_VERSION < 0x030000)
    m_useBruteForceCrossCheck(true),
#endif
    m_useConsensusPercentage(false), m_useKnn(false), m_useMatchTrainToQuery(false), m_useRansacVVS(true),
    m_useSingleMatchFilter(true), m_I(), m_maxFeatures(-1)
{
  initFeatureNames();

  m_detectorNames.push_back(m_mapOfDetectorNames[detectorType]);
  m_extractorNames.push_back(m_mapOfDescriptorNames[descriptorType]);

  init();
}

vpKeyPoint::vpKeyPoint(const std::string &detectorName, const std::string &extractorName,
                       const std::string &matcherName, const vpFilterMatchingType &filterType)
  : m_computeCovariance(false), m_covarianceMatrix(), m_currentImageId(0), m_detectionMethod(detectionScore),
    m_detectionScore(0.15), m_detectionThreshold(100.0), m_detectionTime(0.), m_detectorNames(), m_detectors(),
    m_extractionTime(0.), m_extractorNames(), m_extractors(), m_filteredMatches(), m_filterType(filterType),
    m_imageFormat(jpgImageFormat), m_knnMatches(), m_mapOfImageId(), m_mapOfImages(), m_matcher(),
    m_matcherName(matcherName), m_matches(), m_matchingFactorThreshold(2.0), m_matchingRatioThreshold(0.85),
    m_matchingTime(0.), m_matchRansacKeyPointsToPoints(), m_nbRansacIterations(200), m_nbRansacMinInlierCount(100),
    m_objectFilteredPoints(), m_poseTime(0.), m_queryDescriptors(), m_queryFilteredKeyPoints(), m_queryKeyPoints(),
    m_ransacConsensusPercentage(20.0), m_ransacFilterFlag(vpPose::NO_FILTER), m_ransacInliers(), m_ransacOutliers(),
    m_ransacParallel(false), m_ransacParallelNbThreads(0), m_ransacReprojectionError(6.0),
    m_ransacThreshold(0.01), m_trainDescriptors(), m_trainKeyPoints(), m_trainPoints(), m_trainVpPoints(),
    m_useAffineDetection(false),
#if (VISP_HAVE_OPENCV_VERSION >= 0x020400 && VISP_HAVE_OPENCV_VERSION < 0x030000)
    m_useBruteForceCrossCheck(true),
#endif
    m_useConsensusPercentage(false), m_useKnn(false), m_useMatchTrainToQuery(false), m_useRansacVVS(true),
    m_useSingleMatchFilter(true), m_I(), m_maxFeatures(-1)
{
  initFeatureNames();

  m_detectorNames.push_back(detectorName);
  m_extractorNames.push_back(extractorName);

  init();
}

vpKeyPoint::vpKeyPoint(const std::vector<std::string> &detectorNames, const std::vector<std::string> &extractorNames,
                       const std::string &matcherName, const vpFilterMatchingType &filterType)
  : m_computeCovariance(false), m_covarianceMatrix(), m_currentImageId(0), m_detectionMethod(detectionScore),
    m_detectionScore(0.15), m_detectionThreshold(100.0), m_detectionTime(0.), m_detectorNames(detectorNames),
    m_detectors(), m_extractionTime(0.), m_extractorNames(extractorNames), m_extractors(), m_filteredMatches(),
    m_filterType(filterType), m_imageFormat(jpgImageFormat), m_knnMatches(), m_mapOfImageId(), m_mapOfImages(),
    m_matcher(), m_matcherName(matcherName), m_matches(), m_matchingFactorThreshold(2.0),
    m_matchingRatioThreshold(0.85), m_matchingTime(0.), m_matchRansacKeyPointsToPoints(), m_nbRansacIterations(200),
    m_nbRansacMinInlierCount(100), m_objectFilteredPoints(), m_poseTime(0.), m_queryDescriptors(),
    m_queryFilteredKeyPoints(), m_queryKeyPoints(), m_ransacConsensusPercentage(20.0), m_ransacFilterFlag(vpPose::NO_FILTER), m_ransacInliers(),
    m_ransacOutliers(), m_ransacParallel(false), m_ransacParallelNbThreads(0), m_ransacReprojectionError(6.0), m_ransacThreshold(0.01),
    m_trainDescriptors(), m_trainKeyPoints(), m_trainPoints(), m_trainVpPoints(), m_useAffineDetection(false),
#if (VISP_HAVE_OPENCV_VERSION >= 0x020400 && VISP_HAVE_OPENCV_VERSION < 0x030000)
    m_useBruteForceCrossCheck(true),
#endif
    m_useConsensusPercentage(false), m_useKnn(false), m_useMatchTrainToQuery(false), m_useRansacVVS(true),
    m_useSingleMatchFilter(true), m_I(), m_maxFeatures(-1)
{
  initFeatureNames();
  init();
}

void vpKeyPoint::affineSkew(double tilt, double phi, cv::Mat &img, cv::Mat &mask, cv::Mat &Ai)
{
  int h = img.rows;
  int w = img.cols;

  mask = cv::Mat(h, w, CV_8UC1, cv::Scalar(255));

  cv::Mat A = cv::Mat::eye(2, 3, CV_32F);

  // if (phi != 0.0) {
  if (std::fabs(phi) > std::numeric_limits<double>::epsilon()) {
    phi *= M_PI / 180.;
    double s = sin(phi);
    double c = cos(phi);

    A = (cv::Mat_<float>(2, 2) << c, -s, s, c);

    cv::Mat corners = (cv::Mat_<float>(4, 2) << 0, 0, w, 0, w, h, 0, h);
    cv::Mat tcorners = corners * A.t();
    cv::Mat tcorners_x, tcorners_y;
    tcorners.col(0).copyTo(tcorners_x);
    tcorners.col(1).copyTo(tcorners_y);
    std::vector<cv::Mat> channels;
    channels.push_back(tcorners_x);
    channels.push_back(tcorners_y);
    cv::merge(channels, tcorners);

    cv::Rect rect = cv::boundingRect(tcorners);
    A = (cv::Mat_<float>(2, 3) << c, -s, -rect.x, s, c, -rect.y);

    cv::warpAffine(img, img, A, cv::Size(rect.width, rect.height), cv::INTER_LINEAR, cv::BORDER_REPLICATE);
  }
  // if (tilt != 1.0) {
  if (std::fabs(tilt - 1.0) > std::numeric_limits<double>::epsilon()) {
    double s = 0.8 * sqrt(tilt * tilt - 1);
    cv::GaussianBlur(img, img, cv::Size(0, 0), s, 0.01);
    cv::resize(img, img, cv::Size(0, 0), 1.0 / tilt, 1.0, cv::INTER_NEAREST);
    A.row(0) = A.row(0) / tilt;
  }
  // if (tilt != 1.0 || phi != 0.0) {
  if (std::fabs(tilt - 1.0) > std::numeric_limits<double>::epsilon() ||
      std::fabs(phi) > std::numeric_limits<double>::epsilon()) {
    h = img.rows;
    w = img.cols;
    cv::warpAffine(mask, mask, A, cv::Size(w, h), cv::INTER_NEAREST);
  }
  cv::invertAffineTransform(A, Ai);
}

unsigned int vpKeyPoint::buildReference(const vpImage<unsigned char> &I) { return buildReference(I, vpRect()); }

unsigned int vpKeyPoint::buildReference(const vpImage<vpRGBa> &I_color) { return buildReference(I_color, vpRect()); }

unsigned int vpKeyPoint::buildReference(const vpImage<unsigned char> &I, const vpImagePoint &iP,
                                        unsigned int height, unsigned int width)
{
  return buildReference(I, vpRect(iP, width, height));
}

unsigned int vpKeyPoint::buildReference(const vpImage<vpRGBa> &I_color, const vpImagePoint &iP,
                                        unsigned int height, unsigned int width)
{
  return buildReference(I_color, vpRect(iP, width, height));
}

unsigned int vpKeyPoint::buildReference(const vpImage<unsigned char> &I, const vpRect &rectangle)
{
  // Reset variables used when dealing with 3D models
  // So as no 3D point list is passed, we dont need this variables
  m_trainPoints.clear();
  m_mapOfImageId.clear();
  m_mapOfImages.clear();
  m_currentImageId = 1;

  if (m_useAffineDetection) {
    std::vector<std::vector<cv::KeyPoint> > listOfTrainKeyPoints;
    std::vector<cv::Mat> listOfTrainDescriptors;

    // Detect keypoints and extract descriptors on multiple images
    detectExtractAffine(I, listOfTrainKeyPoints, listOfTrainDescriptors);

    // Flatten the different train lists
    m_trainKeyPoints.clear();
    for (std::vector<std::vector<cv::KeyPoint> >::const_iterator it = listOfTrainKeyPoints.begin();
         it != listOfTrainKeyPoints.end(); ++it) {
      m_trainKeyPoints.insert(m_trainKeyPoints.end(), it->begin(), it->end());
    }

    bool first = true;
    for (std::vector<cv::Mat>::const_iterator it = listOfTrainDescriptors.begin(); it != listOfTrainDescriptors.end(); ++it) {
      if (first) {
        first = false;
        it->copyTo(m_trainDescriptors);
      } else {
        m_trainDescriptors.push_back(*it);
      }
    }
  } else {
    detect(I, m_trainKeyPoints, m_detectionTime, rectangle);
    extract(I, m_trainKeyPoints, m_trainDescriptors, m_extractionTime);
  }

  // Save the correspondence keypoint class_id with the training image_id in a map.
  // Used to display the matching with all the training images.
  for (std::vector<cv::KeyPoint>::const_iterator it = m_trainKeyPoints.begin(); it != m_trainKeyPoints.end(); ++it) {
    m_mapOfImageId[it->class_id] = m_currentImageId;
  }

  // Save the image in a map at a specific image_id
  m_mapOfImages[m_currentImageId] = I;

  // Convert OpenCV type to ViSP type for compatibility
  vpConvert::convertFromOpenCV(m_trainKeyPoints, referenceImagePointsList);

  _reference_computed = true;

  // Add train descriptors in matcher object
  m_matcher->clear();
  m_matcher->add(std::vector<cv::Mat>(1, m_trainDescriptors));

  return static_cast<unsigned int>(m_trainKeyPoints.size());
}

unsigned int vpKeyPoint::buildReference(const vpImage<vpRGBa> &I_color, const vpRect &rectangle)
{
  vpImageConvert::convert(I_color, m_I);
  return (buildReference(m_I, rectangle));
}

unsigned int vpKeyPoint::buildReference(const vpImage<unsigned char> &I, std::vector<cv::KeyPoint> &trainKeyPoints,
                                        std::vector<cv::Point3f> &points3f, bool append, int class_id)
{
  cv::Mat trainDescriptors;
  // Copy the input list of keypoints
  std::vector<cv::KeyPoint> trainKeyPoints_tmp = trainKeyPoints;

  extract(I, trainKeyPoints, trainDescriptors, m_extractionTime, &points3f);

  if (trainKeyPoints.size() != trainKeyPoints_tmp.size()) {
    // Keypoints have been removed
    // Store the hash of a keypoint as the key and the index of the keypoint
    // as the value
    std::map<size_t, size_t> mapOfKeypointHashes;
    size_t cpt = 0;
    for (std::vector<cv::KeyPoint>::const_iterator it = trainKeyPoints_tmp.begin(); it != trainKeyPoints_tmp.end();
         ++it, cpt++) {
      mapOfKeypointHashes[myKeypointHash(*it)] = cpt;
    }

    std::vector<cv::Point3f> trainPoints_tmp;
    for (std::vector<cv::KeyPoint>::const_iterator it = trainKeyPoints.begin(); it != trainKeyPoints.end(); ++it) {
      if (mapOfKeypointHashes.find(myKeypointHash(*it)) != mapOfKeypointHashes.end()) {
        trainPoints_tmp.push_back(points3f[mapOfKeypointHashes[myKeypointHash(*it)]]);
      }
    }

    // Copy trainPoints_tmp to points3f
    points3f = trainPoints_tmp;
  }

  return (buildReference(I, trainKeyPoints, trainDescriptors, points3f, append, class_id));
}

unsigned int vpKeyPoint::buildReference(const vpImage<vpRGBa> &I_color, std::vector<cv::KeyPoint> &trainKeyPoints,
                                        std::vector<cv::Point3f> &points3f, bool append, int class_id)
{
  cv::Mat trainDescriptors;
  // Copy the input list of keypoints
  std::vector<cv::KeyPoint> trainKeyPoints_tmp = trainKeyPoints;

  extract(I_color, trainKeyPoints, trainDescriptors, m_extractionTime, &points3f);

  if (trainKeyPoints.size() != trainKeyPoints_tmp.size()) {
    // Keypoints have been removed
    // Store the hash of a keypoint as the key and the index of the keypoint
    // as the value
    std::map<size_t, size_t> mapOfKeypointHashes;
    size_t cpt = 0;
    for (std::vector<cv::KeyPoint>::const_iterator it = trainKeyPoints_tmp.begin(); it != trainKeyPoints_tmp.end();
         ++it, cpt++) {
      mapOfKeypointHashes[myKeypointHash(*it)] = cpt;
    }

    std::vector<cv::Point3f> trainPoints_tmp;
    for (std::vector<cv::KeyPoint>::const_iterator it = trainKeyPoints.begin(); it != trainKeyPoints.end(); ++it) {
      if (mapOfKeypointHashes.find(myKeypointHash(*it)) != mapOfKeypointHashes.end()) {
        trainPoints_tmp.push_back(points3f[mapOfKeypointHashes[myKeypointHash(*it)]]);
      }
    }

    // Copy trainPoints_tmp to points3f
    points3f = trainPoints_tmp;
  }

  return (buildReference(I_color, trainKeyPoints, trainDescriptors, points3f, append, class_id));
}

unsigned int vpKeyPoint::buildReference(const vpImage<unsigned char> &I, const std::vector<cv::KeyPoint> &trainKeyPoints,
                                        const cv::Mat &trainDescriptors, const std::vector<cv::Point3f> &points3f,
                                        bool append, int class_id)
{
  if (!append) {
    m_trainPoints.clear();
    m_mapOfImageId.clear();
    m_mapOfImages.clear();
    m_currentImageId = 0;
    m_trainKeyPoints.clear();
  }

  m_currentImageId++;

  std::vector<cv::KeyPoint> trainKeyPoints_tmp = trainKeyPoints;
  // Set class_id if != -1
  if (class_id != -1) {
    for (std::vector<cv::KeyPoint>::iterator it = trainKeyPoints_tmp.begin(); it != trainKeyPoints_tmp.end(); ++it) {
      it->class_id = class_id;
    }
  }

  // Save the correspondence keypoint class_id with the training image_id in a map.
  // Used to display the matching with all the training images.
  for (std::vector<cv::KeyPoint>::const_iterator it = trainKeyPoints_tmp.begin(); it != trainKeyPoints_tmp.end(); ++it) {
    m_mapOfImageId[it->class_id] = m_currentImageId;
  }

  // Save the image in a map at a specific image_id
  m_mapOfImages[m_currentImageId] = I;

  // Append reference lists
  m_trainKeyPoints.insert(m_trainKeyPoints.end(), trainKeyPoints_tmp.begin(), trainKeyPoints_tmp.end());
  if (!append) {
    trainDescriptors.copyTo(m_trainDescriptors);
  } else {
    m_trainDescriptors.push_back(trainDescriptors);
  }
  this->m_trainPoints.insert(m_trainPoints.end(), points3f.begin(), points3f.end());

  // Convert OpenCV type to ViSP type for compatibility
  vpConvert::convertFromOpenCV(m_trainKeyPoints, referenceImagePointsList);
  vpConvert::convertFromOpenCV(m_trainPoints, m_trainVpPoints);

  // Add train descriptors in matcher object
  m_matcher->clear();
  m_matcher->add(std::vector<cv::Mat>(1, m_trainDescriptors));

  _reference_computed = true;

  return static_cast<unsigned int>(m_trainKeyPoints.size());
}

unsigned int vpKeyPoint::buildReference(const vpImage<vpRGBa> &I_color, const std::vector<cv::KeyPoint> &trainKeyPoints,
                                        const cv::Mat &trainDescriptors, const std::vector<cv::Point3f> &points3f,
                                        bool append, int class_id)
{
  vpImageConvert::convert(I_color, m_I);
  return (buildReference(m_I, trainKeyPoints, trainDescriptors, points3f, append, class_id));
}

void vpKeyPoint::compute3D(const cv::KeyPoint &candidate, const std::vector<vpPoint> &roi,
                           const vpCameraParameters &cam, const vpHomogeneousMatrix &cMo, cv::Point3f &point)
{
  /* compute plane equation */
  std::vector<vpPoint>::const_iterator it_roi = roi.begin();
  vpPoint pts[3];
  pts[0] = *it_roi;
  ++it_roi;
  pts[1] = *it_roi;
  ++it_roi;
  pts[2] = *it_roi;
  vpPlane Po(pts[0], pts[1], pts[2]);
  double xc = 0.0, yc = 0.0;
  vpPixelMeterConversion::convertPoint(cam, candidate.pt.x, candidate.pt.y, xc, yc);
  double Z = -Po.getD() / (Po.getA() * xc + Po.getB() * yc + Po.getC());
  double X = xc * Z;
  double Y = yc * Z;
  vpColVector point_cam(4);
  point_cam[0] = X;
  point_cam[1] = Y;
  point_cam[2] = Z;
  point_cam[3] = 1;
  vpColVector point_obj(4);
  point_obj = cMo.inverse() * point_cam;
  point = cv::Point3f((float)point_obj[0], (float)point_obj[1], (float)point_obj[2]);
}

void vpKeyPoint::compute3D(const vpImagePoint &candidate, const std::vector<vpPoint> &roi,
                           const vpCameraParameters &cam, const vpHomogeneousMatrix &cMo, vpPoint &point)
{
  /* compute plane equation */
  std::vector<vpPoint>::const_iterator it_roi = roi.begin();
  vpPoint pts[3];
  pts[0] = *it_roi;
  ++it_roi;
  pts[1] = *it_roi;
  ++it_roi;
  pts[2] = *it_roi;
  vpPlane Po(pts[0], pts[1], pts[2]);
  double xc = 0.0, yc = 0.0;
  vpPixelMeterConversion::convertPoint(cam, candidate, xc, yc);
  double Z = -Po.getD() / (Po.getA() * xc + Po.getB() * yc + Po.getC());
  double X = xc * Z;
  double Y = yc * Z;
  vpColVector point_cam(4);
  point_cam[0] = X;
  point_cam[1] = Y;
  point_cam[2] = Z;
  point_cam[3] = 1;
  vpColVector point_obj(4);
  point_obj = cMo.inverse() * point_cam;
  point.setWorldCoordinates(point_obj);
}

void vpKeyPoint::compute3DForPointsInPolygons(const vpHomogeneousMatrix &cMo, const vpCameraParameters &cam,
                                              std::vector<cv::KeyPoint> &candidates,
                                              const std::vector<vpPolygon> &polygons,
                                              const std::vector<std::vector<vpPoint> > &roisPt,
                                              std::vector<cv::Point3f> &points, cv::Mat *descriptors)
{
  std::vector<cv::KeyPoint> candidatesToCheck = candidates;
  candidates.clear();
  points.clear();
  vpImagePoint imPt;
  cv::Point3f pt;
  cv::Mat desc;

  std::vector<std::pair<cv::KeyPoint, size_t> > pairOfCandidatesToCheck(candidatesToCheck.size());
  for (size_t i = 0; i < candidatesToCheck.size(); i++) {
    pairOfCandidatesToCheck[i] = std::pair<cv::KeyPoint, size_t>(candidatesToCheck[i], i);
  }

  size_t cpt1 = 0;
  std::vector<vpPolygon> polygons_tmp = polygons;
  for (std::vector<vpPolygon>::iterator it1 = polygons_tmp.begin(); it1 != polygons_tmp.end(); ++it1, cpt1++) {
    std::vector<std::pair<cv::KeyPoint, size_t> >::iterator it2 = pairOfCandidatesToCheck.begin();

    while (it2 != pairOfCandidatesToCheck.end()) {
      imPt.set_ij(it2->first.pt.y, it2->first.pt.x);
      if (it1->isInside(imPt)) {
        candidates.push_back(it2->first);
        vpKeyPoint::compute3D(it2->first, roisPt[cpt1], cam, cMo, pt);
        points.push_back(pt);

        if (descriptors != NULL) {
          desc.push_back(descriptors->row((int)it2->second));
        }

        // Remove candidate keypoint which is located on the current polygon
        it2 = pairOfCandidatesToCheck.erase(it2);
      } else {
        ++it2;
      }
    }
  }

  if (descriptors != NULL) {
    desc.copyTo(*descriptors);
  }
}

void vpKeyPoint::compute3DForPointsInPolygons(const vpHomogeneousMatrix &cMo, const vpCameraParameters &cam,
                                              std::vector<vpImagePoint> &candidates,
                                              const std::vector<vpPolygon> &polygons,
                                              const std::vector<std::vector<vpPoint> > &roisPt,
                                              std::vector<vpPoint> &points, cv::Mat *descriptors)
{
  std::vector<vpImagePoint> candidatesToCheck = candidates;
  candidates.clear();
  points.clear();
  vpPoint pt;
  cv::Mat desc;

  std::vector<std::pair<vpImagePoint, size_t> > pairOfCandidatesToCheck(candidatesToCheck.size());
  for (size_t i = 0; i < candidatesToCheck.size(); i++) {
    pairOfCandidatesToCheck[i] = std::pair<vpImagePoint, size_t>(candidatesToCheck[i], i);
  }

  size_t cpt1 = 0;
  std::vector<vpPolygon> polygons_tmp = polygons;
  for (std::vector<vpPolygon>::iterator it1 = polygons_tmp.begin(); it1 != polygons_tmp.end(); ++it1, cpt1++) {
    std::vector<std::pair<vpImagePoint, size_t> >::iterator it2 = pairOfCandidatesToCheck.begin();

    while (it2 != pairOfCandidatesToCheck.end()) {
      if (it1->isInside(it2->first)) {
        candidates.push_back(it2->first);
        vpKeyPoint::compute3D(it2->first, roisPt[cpt1], cam, cMo, pt);
        points.push_back(pt);

        if (descriptors != NULL) {
          desc.push_back(descriptors->row((int)it2->second));
        }

        // Remove candidate keypoint which is located on the current polygon
        it2 = pairOfCandidatesToCheck.erase(it2);
      } else {
        ++it2;
      }
    }
  }
}

void vpKeyPoint::compute3DForPointsOnCylinders(
    const vpHomogeneousMatrix &cMo, const vpCameraParameters &cam, std::vector<cv::KeyPoint> &candidates,
    const std::vector<vpCylinder> &cylinders,
    const std::vector<std::vector<std::vector<vpImagePoint> > > &vectorOfCylinderRois, std::vector<cv::Point3f> &points,
    cv::Mat *descriptors)
{
  std::vector<cv::KeyPoint> candidatesToCheck = candidates;
  candidates.clear();
  points.clear();
  cv::Mat desc;

  // Keep only keypoints on cylinders
  size_t cpt_keypoint = 0;
  for (std::vector<cv::KeyPoint>::const_iterator it1 = candidatesToCheck.begin(); it1 != candidatesToCheck.end();
       ++it1, cpt_keypoint++) {
    size_t cpt_cylinder = 0;

    // Iterate through the list of vpCylinders
    for (std::vector<std::vector<std::vector<vpImagePoint> > >::const_iterator it2 = vectorOfCylinderRois.begin();
         it2 != vectorOfCylinderRois.end(); ++it2, cpt_cylinder++) {
      // Iterate through the list of the bounding boxes of the current
      // vpCylinder
      for (std::vector<std::vector<vpImagePoint> >::const_iterator it3 = it2->begin(); it3 != it2->end(); ++it3) {
        if (vpPolygon::isInside(*it3, it1->pt.y, it1->pt.x)) {
          candidates.push_back(*it1);

          // Calculate the 3D coordinates for each keypoint located on
          // cylinders
          double xm = 0.0, ym = 0.0;
          vpPixelMeterConversion::convertPoint(cam, it1->pt.x, it1->pt.y, xm, ym);
          double Z = cylinders[cpt_cylinder].computeZ(xm, ym);

          if (!vpMath::isNaN(Z) && Z > std::numeric_limits<double>::epsilon()) {
            vpColVector point_cam(4);
            point_cam[0] = xm * Z;
            point_cam[1] = ym * Z;
            point_cam[2] = Z;
            point_cam[3] = 1;
            vpColVector point_obj(4);
            point_obj = cMo.inverse() * point_cam;
            vpPoint pt;
            pt.setWorldCoordinates(point_obj);
            points.push_back(cv::Point3f((float)pt.get_oX(), (float)pt.get_oY(), (float)pt.get_oZ()));

            if (descriptors != NULL) {
              desc.push_back(descriptors->row((int)cpt_keypoint));
            }

            break;
          }
        }
      }
    }
  }

  if (descriptors != NULL) {
    desc.copyTo(*descriptors);
  }
}

void vpKeyPoint::compute3DForPointsOnCylinders(
    const vpHomogeneousMatrix &cMo, const vpCameraParameters &cam, std::vector<vpImagePoint> &candidates,
    const std::vector<vpCylinder> &cylinders,
    const std::vector<std::vector<std::vector<vpImagePoint> > > &vectorOfCylinderRois, std::vector<vpPoint> &points,
    cv::Mat *descriptors)
{
  std::vector<vpImagePoint> candidatesToCheck = candidates;
  candidates.clear();
  points.clear();
  cv::Mat desc;

  // Keep only keypoints on cylinders
  size_t cpt_keypoint = 0;
  for (std::vector<vpImagePoint>::const_iterator it1 = candidatesToCheck.begin(); it1 != candidatesToCheck.end();
       ++it1, cpt_keypoint++) {
    size_t cpt_cylinder = 0;

    // Iterate through the list of vpCylinders
    for (std::vector<std::vector<std::vector<vpImagePoint> > >::const_iterator it2 = vectorOfCylinderRois.begin();
         it2 != vectorOfCylinderRois.end(); ++it2, cpt_cylinder++) {
      // Iterate through the list of the bounding boxes of the current
      // vpCylinder
      for (std::vector<std::vector<vpImagePoint> >::const_iterator it3 = it2->begin(); it3 != it2->end(); ++it3) {
        if (vpPolygon::isInside(*it3, it1->get_i(), it1->get_j())) {
          candidates.push_back(*it1);

          // Calculate the 3D coordinates for each keypoint located on
          // cylinders
          double xm = 0.0, ym = 0.0;
          vpPixelMeterConversion::convertPoint(cam, it1->get_u(), it1->get_v(), xm, ym);
          double Z = cylinders[cpt_cylinder].computeZ(xm, ym);

          if (!vpMath::isNaN(Z) && Z > std::numeric_limits<double>::epsilon()) {
            vpColVector point_cam(4);
            point_cam[0] = xm * Z;
            point_cam[1] = ym * Z;
            point_cam[2] = Z;
            point_cam[3] = 1;
            vpColVector point_obj(4);
            point_obj = cMo.inverse() * point_cam;
            vpPoint pt;
            pt.setWorldCoordinates(point_obj);
            points.push_back(pt);

            if (descriptors != NULL) {
              desc.push_back(descriptors->row((int)cpt_keypoint));
            }

            break;
          }
        }
      }
    }
  }

  if (descriptors != NULL) {
    desc.copyTo(*descriptors);
  }
}

bool vpKeyPoint::computePose(const std::vector<cv::Point2f> &imagePoints, const std::vector<cv::Point3f> &objectPoints,
                             const vpCameraParameters &cam, vpHomogeneousMatrix &cMo, std::vector<int> &inlierIndex,
                             double &elapsedTime, bool (*func)(const vpHomogeneousMatrix &))
{
  double t = vpTime::measureTimeMs();

  if (imagePoints.size() < 4 || objectPoints.size() < 4 || imagePoints.size() != objectPoints.size()) {
    elapsedTime = (vpTime::measureTimeMs() - t);
    std::cerr << "Not enough points to compute the pose (at least 4 points "
                 "are needed)."
              << std::endl;

    return false;
  }

  cv::Mat cameraMatrix =
      (cv::Mat_<double>(3, 3) << cam.get_px(), 0, cam.get_u0(), 0, cam.get_py(), cam.get_v0(), 0, 0, 1);
  cv::Mat rvec, tvec;

  // Bug with OpenCV < 2.4.0 when zero distorsion is provided by an empty
  // array.  http://code.opencv.org/issues/1700 ;
  // http://code.opencv.org/issues/1718  what(): Distortion coefficients must
  // be 1x4, 4x1, 1x5, 5x1, 1x8 or 8x1 floating-point vector in function
  // cvProjectPoints2  Fixed in OpenCV 2.4.0 (r7558)
  //  cv::Mat distCoeffs;
  cv::Mat distCoeffs = cv::Mat::zeros(1, 5, CV_64F);

  try {
#if (VISP_HAVE_OPENCV_VERSION >= 0x030000)
    // OpenCV 3.0.0 (2014/12/12)
    cv::solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec, false, m_nbRansacIterations,
                       (float)m_ransacReprojectionError,
                       0.99, // confidence=0.99 (default) – The probability
                             // that the algorithm produces a useful result.
                       inlierIndex, cv::SOLVEPNP_ITERATIVE);
// SOLVEPNP_ITERATIVE (default): Iterative method is based on
// Levenberg-Marquardt optimization.  In this case the function finds such a
// pose that minimizes reprojection error, that is the sum of squared
// distances between the observed projections imagePoints and the projected
// (using projectPoints() ) objectPoints .  SOLVEPNP_P3P: Method is based on
// the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang “Complete Solution
// Classification  for the Perspective-Three-Point Problem”. In this case the
// function requires exactly four object and image points.  SOLVEPNP_EPNP:
// Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the
// paper “EPnP: Efficient  Perspective-n-Point Camera Pose Estimation”.
// SOLVEPNP_DLS: Method is based on the paper of Joel A. Hesch and Stergios I.
// Roumeliotis. “A Direct Least-Squares (DLS)  Method for PnP”.  SOLVEPNP_UPNP
// Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto,
// F.Moreno-Noguer. “Exhaustive Linearization for Robust Camera Pose and Focal
// Length Estimation”. In this case the function also  estimates the
// parameters
// f_x and f_y assuming that both have the same value. Then the cameraMatrix
// is updated with the  estimated focal length.
#else
    int nbInlierToReachConsensus = m_nbRansacMinInlierCount;
    if (m_useConsensusPercentage) {
      nbInlierToReachConsensus = (int)(m_ransacConsensusPercentage / 100.0 * (double)m_queryFilteredKeyPoints.size());
    }

    cv::solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec, false, m_nbRansacIterations,
                       (float)m_ransacReprojectionError, nbInlierToReachConsensus, inlierIndex);
#endif
  } catch (cv::Exception &e) {
    std::cerr << e.what() << std::endl;
    elapsedTime = (vpTime::measureTimeMs() - t);
    return false;
  }
  vpTranslationVector translationVec(tvec.at<double>(0), tvec.at<double>(1), tvec.at<double>(2));
  vpThetaUVector thetaUVector(rvec.at<double>(0), rvec.at<double>(1), rvec.at<double>(2));
  cMo = vpHomogeneousMatrix(translationVec, thetaUVector);

  if (func != NULL) {
    // Check the final pose returned by solvePnPRansac to discard
    // solutions which do not respect the pose criterion.
    if (!func(cMo)) {
      elapsedTime = (vpTime::measureTimeMs() - t);
      return false;
    }
  }

  elapsedTime = (vpTime::measureTimeMs() - t);
  return true;
}

bool vpKeyPoint::computePose(const std::vector<vpPoint> &objectVpPoints, vpHomogeneousMatrix &cMo,
                             std::vector<vpPoint> &inliers, double &elapsedTime, bool (*func)(const vpHomogeneousMatrix &))
{
  std::vector<unsigned int> inlierIndex;
  return computePose(objectVpPoints, cMo, inliers, inlierIndex, elapsedTime, func);
}

bool vpKeyPoint::computePose(const std::vector<vpPoint> &objectVpPoints, vpHomogeneousMatrix &cMo,
                             std::vector<vpPoint> &inliers, std::vector<unsigned int> &inlierIndex, double &elapsedTime,
                             bool (*func)(const vpHomogeneousMatrix &))
{
  double t = vpTime::measureTimeMs();

  if (objectVpPoints.size() < 4) {
    elapsedTime = (vpTime::measureTimeMs() - t);
    //    std::cerr << "Not enough points to compute the pose (at least 4
    //    points are needed)." << std::endl;

    return false;
  }

  vpPose pose;

  for (std::vector<vpPoint>::const_iterator it = objectVpPoints.begin(); it != objectVpPoints.end(); ++it) {
    pose.addPoint(*it);
  }

  unsigned int nbInlierToReachConsensus = (unsigned int)m_nbRansacMinInlierCount;
  if (m_useConsensusPercentage) {
    nbInlierToReachConsensus =
        (unsigned int)(m_ransacConsensusPercentage / 100.0 * (double)m_queryFilteredKeyPoints.size());
  }

  pose.setRansacFilterFlag(m_ransacFilterFlag);
  pose.setUseParallelRansac(m_ransacParallel);
  pose.setNbParallelRansacThreads(m_ransacParallelNbThreads);
  pose.setRansacNbInliersToReachConsensus(nbInlierToReachConsensus);
  pose.setRansacThreshold(m_ransacThreshold);
  pose.setRansacMaxTrials(m_nbRansacIterations);

  bool isRansacPoseEstimationOk = false;
  try {
    pose.setCovarianceComputation(m_computeCovariance);
    isRansacPoseEstimationOk = pose.computePose(vpPose::RANSAC, cMo, func);
    inliers = pose.getRansacInliers();
    inlierIndex = pose.getRansacInlierIndex();

    if (m_computeCovariance) {
      m_covarianceMatrix = pose.getCovarianceMatrix();
    }
  } catch (const vpException &e) {
    std::cerr << "e=" << e.what() << std::endl;
    elapsedTime = (vpTime::measureTimeMs() - t);
    return false;
  }

  //  if(func != NULL && isRansacPoseEstimationOk) {
  //    //Check the final pose returned by the Ransac VVS pose estimation as
  //    in rare some cases
  //    //we can converge toward a final cMo that does not respect the pose
  //    criterion even
  //    //if the 4 minimal points picked to respect the pose criterion.
  //    if(!func(&cMo)) {
  //      elapsedTime = (vpTime::measureTimeMs() - t);
  //      return false;
  //    }
  //  }

  elapsedTime = (vpTime::measureTimeMs() - t);
  return isRansacPoseEstimationOk;
}

double vpKeyPoint::computePoseEstimationError(const std::vector<std::pair<cv::KeyPoint, cv::Point3f> > &matchKeyPoints,
                                              const vpCameraParameters &cam, const vpHomogeneousMatrix &cMo_est)
{
  if (matchKeyPoints.size() == 0) {
    // return std::numeric_limits<double>::max(); // create an error under
    // Windows. To fix it we have to add #undef max
    return DBL_MAX;
  }

  std::vector<double> errors(matchKeyPoints.size());
  size_t cpt = 0;
  vpPoint pt;
  for (std::vector<std::pair<cv::KeyPoint, cv::Point3f> >::const_iterator it = matchKeyPoints.begin();
       it != matchKeyPoints.end(); ++it, cpt++) {
    pt.set_oX(it->second.x);
    pt.set_oY(it->second.y);
    pt.set_oZ(it->second.z);
    pt.project(cMo_est);
    double u = 0.0, v = 0.0;
    vpMeterPixelConversion::convertPoint(cam, pt.get_x(), pt.get_y(), u, v);
    errors[cpt] = std::sqrt((u - it->first.pt.x) * (u - it->first.pt.x) + (v - it->first.pt.y) * (v - it->first.pt.y));
  }

  return std::accumulate(errors.begin(), errors.end(), 0.0) / errors.size();
}

void vpKeyPoint::createImageMatching(vpImage<unsigned char> &IRef, vpImage<unsigned char> &ICurrent,
                                     vpImage<unsigned char> &IMatching)
{
  // Image matching side by side
  unsigned int width = IRef.getWidth() + ICurrent.getWidth();
  unsigned int height = ((std::max))(IRef.getHeight(), ICurrent.getHeight());

  IMatching = vpImage<unsigned char>(height, width);
}

void vpKeyPoint::createImageMatching(vpImage<unsigned char> &IRef, vpImage<vpRGBa> &ICurrent,
                                     vpImage<vpRGBa> &IMatching)
{
  // Image matching side by side
  unsigned int width = IRef.getWidth() + ICurrent.getWidth();
  unsigned int height = ((std::max))(IRef.getHeight(), ICurrent.getHeight());

  IMatching = vpImage<vpRGBa>(height, width);
}

void vpKeyPoint::createImageMatching(vpImage<unsigned char> &ICurrent, vpImage<unsigned char> &IMatching)
{
  // Nb images in the training database + the current image we want to detect
  // the object
  unsigned int nbImg = (unsigned int)(m_mapOfImages.size() + 1);

  if (m_mapOfImages.empty()) {
    std::cerr << "There is no training image loaded !" << std::endl;
    return;
  }

  if (nbImg == 2) {
    // Only one training image, so we display them side by side
    createImageMatching(m_mapOfImages.begin()->second, ICurrent, IMatching);
  } else {
    // Multiple training images, display them as a mosaic image
    //(unsigned int) std::floor(std::sqrt((double) nbImg) + 0.5);
    unsigned int nbImgSqrt = (unsigned int)vpMath::round(std::sqrt((double)nbImg));

    // Number of columns in the mosaic grid
    unsigned int nbWidth = nbImgSqrt;
    // Number of rows in the mosaic grid
    unsigned int nbHeight = nbImgSqrt;

    // Deals with non square mosaic grid and the total number of images
    if (nbImgSqrt * nbImgSqrt < nbImg) {
      nbWidth++;
    }

    unsigned int maxW = ICurrent.getWidth();
    unsigned int maxH = ICurrent.getHeight();
    for (std::map<int, vpImage<unsigned char> >::const_iterator it = m_mapOfImages.begin(); it != m_mapOfImages.end();
         ++it) {
      if (maxW < it->second.getWidth()) {
        maxW = it->second.getWidth();
      }

      if (maxH < it->second.getHeight()) {
        maxH = it->second.getHeight();
      }
    }

    IMatching = vpImage<unsigned char>(maxH * nbHeight, maxW * nbWidth);
  }
}

void vpKeyPoint::createImageMatching(vpImage<vpRGBa> &ICurrent, vpImage<vpRGBa> &IMatching)
{
  // Nb images in the training database + the current image we want to detect
  // the object
  unsigned int nbImg = (unsigned int)(m_mapOfImages.size() + 1);

  if (m_mapOfImages.empty()) {
    std::cerr << "There is no training image loaded !" << std::endl;
    return;
  }

  if (nbImg == 2) {
    // Only one training image, so we display them side by side
    createImageMatching(m_mapOfImages.begin()->second, ICurrent, IMatching);
  } else {
    // Multiple training images, display them as a mosaic image
    //(unsigned int) std::floor(std::sqrt((double) nbImg) + 0.5);
    unsigned int nbImgSqrt = (unsigned int)vpMath::round(std::sqrt((double)nbImg));

    // Number of columns in the mosaic grid
    unsigned int nbWidth = nbImgSqrt;
    // Number of rows in the mosaic grid
    unsigned int nbHeight = nbImgSqrt;

    // Deals with non square mosaic grid and the total number of images
    if (nbImgSqrt * nbImgSqrt < nbImg) {
      nbWidth++;
    }

    unsigned int maxW = ICurrent.getWidth();
    unsigned int maxH = ICurrent.getHeight();
    for (std::map<int, vpImage<unsigned char> >::const_iterator it = m_mapOfImages.begin(); it != m_mapOfImages.end();
         ++it) {
      if (maxW < it->second.getWidth()) {
        maxW = it->second.getWidth();
      }

      if (maxH < it->second.getHeight()) {
        maxH = it->second.getHeight();
      }
    }

    IMatching = vpImage<vpRGBa>(maxH * nbHeight, maxW * nbWidth);
  }
}

void vpKeyPoint::detect(const vpImage<unsigned char> &I, std::vector<cv::KeyPoint> &keyPoints, const vpRect &rectangle)
{
  double elapsedTime;
  detect(I, keyPoints, elapsedTime, rectangle);
}

void vpKeyPoint::detect(const vpImage<vpRGBa> &I_color, std::vector<cv::KeyPoint> &keyPoints, const vpRect &rectangle)
{
  double elapsedTime;
  detect(I_color, keyPoints, elapsedTime, rectangle);
}

void vpKeyPoint::detect(const cv::Mat &matImg, std::vector<cv::KeyPoint> &keyPoints, const cv::Mat &mask)
{
  double elapsedTime;
  detect(matImg, keyPoints, elapsedTime, mask);
}

void vpKeyPoint::detect(const vpImage<unsigned char> &I, std::vector<cv::KeyPoint> &keyPoints, double &elapsedTime,
                        const vpRect &rectangle)
{
  cv::Mat matImg;
  vpImageConvert::convert(I, matImg, false);
  cv::Mat mask = cv::Mat::zeros(matImg.rows, matImg.cols, CV_8U);

  if (rectangle.getWidth() > 0 && rectangle.getHeight() > 0) {
    cv::Point leftTop((int)rectangle.getLeft(), (int)rectangle.getTop()),
        rightBottom((int)rectangle.getRight(), (int)rectangle.getBottom());
    cv::rectangle(mask, leftTop, rightBottom, cv::Scalar(255), CV_FILLED);
  } else {
    mask = cv::Mat::ones(matImg.rows, matImg.cols, CV_8U) * 255;
  }

  detect(matImg, keyPoints, elapsedTime, mask);
}

void vpKeyPoint::detect(const vpImage<vpRGBa> &I_color, std::vector<cv::KeyPoint> &keyPoints, double &elapsedTime,
                        const vpRect &rectangle)
{
  cv::Mat matImg;
  vpImageConvert::convert(I_color, matImg);
  cv::Mat mask = cv::Mat::zeros(matImg.rows, matImg.cols, CV_8U);

  if (rectangle.getWidth() > 0 && rectangle.getHeight() > 0) {
    cv::Point leftTop((int)rectangle.getLeft(), (int)rectangle.getTop()),
        rightBottom((int)rectangle.getRight(), (int)rectangle.getBottom());
    cv::rectangle(mask, leftTop, rightBottom, cv::Scalar(255), CV_FILLED);
  } else {
    mask = cv::Mat::ones(matImg.rows, matImg.cols, CV_8U) * 255;
  }

  detect(matImg, keyPoints, elapsedTime, mask);
}

void vpKeyPoint::detect(const cv::Mat &matImg, std::vector<cv::KeyPoint> &keyPoints, double &elapsedTime,
                        const cv::Mat &mask)
{
  double t = vpTime::measureTimeMs();
  keyPoints.clear();

  for (std::map<std::string, cv::Ptr<cv::FeatureDetector> >::const_iterator it = m_detectors.begin();
       it != m_detectors.end(); ++it) {
    std::vector<cv::KeyPoint> kp;

    it->second->detect(matImg, kp, mask);
    keyPoints.insert(keyPoints.end(), kp.begin(), kp.end());
  }

  elapsedTime = vpTime::measureTimeMs() - t;
}

void vpKeyPoint::display(const vpImage<unsigned char> &IRef, const vpImage<unsigned char> &ICurrent, unsigned int size)
{
  std::vector<vpImagePoint> vpQueryImageKeyPoints;
  getQueryKeyPoints(vpQueryImageKeyPoints);
  std::vector<vpImagePoint> vpTrainImageKeyPoints;
  getTrainKeyPoints(vpTrainImageKeyPoints);

  for (std::vector<cv::DMatch>::const_iterator it = m_filteredMatches.begin(); it != m_filteredMatches.end(); ++it) {
    vpDisplay::displayCross(IRef, vpTrainImageKeyPoints[(size_t)(it->trainIdx)], size, vpColor::red);
    vpDisplay::displayCross(ICurrent, vpQueryImageKeyPoints[(size_t)(it->queryIdx)], size, vpColor::green);
  }
}

void vpKeyPoint::display(const vpImage<vpRGBa> &IRef, const vpImage<vpRGBa> &ICurrent, unsigned int size)
{
  std::vector<vpImagePoint> vpQueryImageKeyPoints;
  getQueryKeyPoints(vpQueryImageKeyPoints);
  std::vector<vpImagePoint> vpTrainImageKeyPoints;
  getTrainKeyPoints(vpTrainImageKeyPoints);

  for (std::vector<cv::DMatch>::const_iterator it = m_filteredMatches.begin(); it != m_filteredMatches.end(); ++it) {
    vpDisplay::displayCross(IRef, vpTrainImageKeyPoints[(size_t)(it->trainIdx)], size, vpColor::red);
    vpDisplay::displayCross(ICurrent, vpQueryImageKeyPoints[(size_t)(it->queryIdx)], size, vpColor::green);
  }
}

void vpKeyPoint::display(const vpImage<unsigned char> &ICurrent, unsigned int size, const vpColor &color)
{
  std::vector<vpImagePoint> vpQueryImageKeyPoints;
  getQueryKeyPoints(vpQueryImageKeyPoints);

  for (std::vector<cv::DMatch>::const_iterator it = m_filteredMatches.begin(); it != m_filteredMatches.end(); ++it) {
    vpDisplay::displayCross(ICurrent, vpQueryImageKeyPoints[(size_t)(it->queryIdx)], size, color);
  }
}

void vpKeyPoint::display(const vpImage<vpRGBa> &ICurrent, unsigned int size, const vpColor &color)
{
  std::vector<vpImagePoint> vpQueryImageKeyPoints;
  getQueryKeyPoints(vpQueryImageKeyPoints);

  for (std::vector<cv::DMatch>::const_iterator it = m_filteredMatches.begin(); it != m_filteredMatches.end(); ++it) {
    vpDisplay::displayCross(ICurrent, vpQueryImageKeyPoints[(size_t)(it->queryIdx)], size, color);
  }
}

void vpKeyPoint::displayMatching(const vpImage<unsigned char> &IRef, vpImage<unsigned char> &IMatching,
                                 unsigned int crossSize, unsigned int lineThickness, const vpColor &color)
{
  bool randomColor = (color == vpColor::none);
  srand((unsigned int)time(NULL));
  vpColor currentColor = color;

  std::vector<vpImagePoint> queryImageKeyPoints;
  getQueryKeyPoints(queryImageKeyPoints);
  std::vector<vpImagePoint> trainImageKeyPoints;
  getTrainKeyPoints(trainImageKeyPoints);

  vpImagePoint leftPt, rightPt;
  for (std::vector<cv::DMatch>::const_iterator it = m_filteredMatches.begin(); it != m_filteredMatches.end(); ++it) {
    if (randomColor) {
      currentColor = vpColor((rand() % 256), (rand() % 256), (rand() % 256));
    }

    leftPt = trainImageKeyPoints[(size_t)(it->trainIdx)];
    rightPt = vpImagePoint(queryImageKeyPoints[(size_t)(it->queryIdx)].get_i(),
                           queryImageKeyPoints[(size_t)it->queryIdx].get_j() + IRef.getWidth());
    vpDisplay::displayCross(IMatching, leftPt, crossSize, currentColor);
    vpDisplay::displayCross(IMatching, rightPt, crossSize, currentColor);
    vpDisplay::displayLine(IMatching, leftPt, rightPt, currentColor, lineThickness);
  }
}

void vpKeyPoint::displayMatching(const vpImage<unsigned char> &IRef, vpImage<vpRGBa> &IMatching,
                                 unsigned int crossSize, unsigned int lineThickness, const vpColor &color)
{
  bool randomColor = (color == vpColor::none);
  srand((unsigned int)time(NULL));
  vpColor currentColor = color;

  std::vector<vpImagePoint> queryImageKeyPoints;
  getQueryKeyPoints(queryImageKeyPoints);
  std::vector<vpImagePoint> trainImageKeyPoints;
  getTrainKeyPoints(trainImageKeyPoints);

  vpImagePoint leftPt, rightPt;
  for (std::vector<cv::DMatch>::const_iterator it = m_filteredMatches.begin(); it != m_filteredMatches.end(); ++it) {
    if (randomColor) {
      currentColor = vpColor((rand() % 256), (rand() % 256), (rand() % 256));
    }

    leftPt = trainImageKeyPoints[(size_t)(it->trainIdx)];
    rightPt = vpImagePoint(queryImageKeyPoints[(size_t)(it->queryIdx)].get_i(),
                           queryImageKeyPoints[(size_t)it->queryIdx].get_j() + IRef.getWidth());
    vpDisplay::displayCross(IMatching, leftPt, crossSize, currentColor);
    vpDisplay::displayCross(IMatching, rightPt, crossSize, currentColor);
    vpDisplay::displayLine(IMatching, leftPt, rightPt, currentColor, lineThickness);
  }
}

void vpKeyPoint::displayMatching(const vpImage<vpRGBa> &IRef, vpImage<vpRGBa> &IMatching,
                                 unsigned int crossSize, unsigned int lineThickness, const vpColor &color)
{
  bool randomColor = (color == vpColor::none);
  srand((unsigned int)time(NULL));
  vpColor currentColor = color;

  std::vector<vpImagePoint> queryImageKeyPoints;
  getQueryKeyPoints(queryImageKeyPoints);
  std::vector<vpImagePoint> trainImageKeyPoints;
  getTrainKeyPoints(trainImageKeyPoints);

  vpImagePoint leftPt, rightPt;
  for (std::vector<cv::DMatch>::const_iterator it = m_filteredMatches.begin(); it != m_filteredMatches.end(); ++it) {
    if (randomColor) {
      currentColor = vpColor((rand() % 256), (rand() % 256), (rand() % 256));
    }

    leftPt = trainImageKeyPoints[(size_t)(it->trainIdx)];
    rightPt = vpImagePoint(queryImageKeyPoints[(size_t)(it->queryIdx)].get_i(),
                           queryImageKeyPoints[(size_t)it->queryIdx].get_j() + IRef.getWidth());
    vpDisplay::displayCross(IMatching, leftPt, crossSize, currentColor);
    vpDisplay::displayCross(IMatching, rightPt, crossSize, currentColor);
    vpDisplay::displayLine(IMatching, leftPt, rightPt, currentColor, lineThickness);
  }
}

void vpKeyPoint::displayMatching(const vpImage<unsigned char> &ICurrent, vpImage<unsigned char> &IMatching,
                                 const std::vector<vpImagePoint> &ransacInliers, unsigned int crossSize,
                                 unsigned int lineThickness)
{
  if (m_mapOfImages.empty() || m_mapOfImageId.empty()) {
    // No training images so return
    std::cerr << "There is no training image loaded !" << std::endl;
    return;
  }

  // Nb images in the training database + the current image we want to detect
  // the object
  int nbImg = (int)(m_mapOfImages.size() + 1);

  if (nbImg == 2) {
    // Only one training image, so we display the matching result side-by-side
    displayMatching(m_mapOfImages.begin()->second, IMatching, crossSize);
  } else {
    // Multiple training images, display them as a mosaic image
    int nbImgSqrt = vpMath::round(std::sqrt((double)nbImg)); //(int) std::floor(std::sqrt((double) nbImg) + 0.5);
    int nbWidth = nbImgSqrt;
    int nbHeight = nbImgSqrt;

    if (nbImgSqrt * nbImgSqrt < nbImg) {
      nbWidth++;
    }

    std::map<int, int> mapOfImageIdIndex;
    int cpt = 0;
    unsigned int maxW = ICurrent.getWidth(), maxH = ICurrent.getHeight();
    for (std::map<int, vpImage<unsigned char> >::const_iterator it = m_mapOfImages.begin(); it != m_mapOfImages.end();
         ++it, cpt++) {
      mapOfImageIdIndex[it->first] = cpt;

      if (maxW < it->second.getWidth()) {
        maxW = it->second.getWidth();
      }

      if (maxH < it->second.getHeight()) {
        maxH = it->second.getHeight();
      }
    }

    // Indexes of the current image in the grid computed to put preferably the
    // image in the center of the mosaic grid
    int medianI = nbHeight / 2;
    int medianJ = nbWidth / 2;
    int medianIndex = medianI * nbWidth + medianJ;
    for (std::vector<cv::KeyPoint>::const_iterator it = m_trainKeyPoints.begin(); it != m_trainKeyPoints.end(); ++it) {
      vpImagePoint topLeftCorner;
      int current_class_id_index = 0;
      if (mapOfImageIdIndex[m_mapOfImageId[it->class_id]] < medianIndex) {
        current_class_id_index = mapOfImageIdIndex[m_mapOfImageId[it->class_id]];
      } else {
        // Shift of one unity the index of the training images which are after
        // the current image
        current_class_id_index = mapOfImageIdIndex[m_mapOfImageId[it->class_id]] + 1;
      }

      int indexI = current_class_id_index / nbWidth;
      int indexJ = current_class_id_index - (indexI * nbWidth);
      topLeftCorner.set_ij((int)maxH * indexI, (int)maxW * indexJ);

      // Display cross for keypoints in the learning database
      vpDisplay::displayCross(IMatching, (int)(it->pt.y + topLeftCorner.get_i()),
                              (int)(it->pt.x + topLeftCorner.get_j()), crossSize, vpColor::red);
    }

    vpImagePoint topLeftCorner((int)maxH * medianI, (int)maxW * medianJ);
    for (std::vector<cv::KeyPoint>::const_iterator it = m_queryKeyPoints.begin(); it != m_queryKeyPoints.end(); ++it) {
      // Display cross for keypoints detected in the current image
      vpDisplay::displayCross(IMatching, (int)(it->pt.y + topLeftCorner.get_i()),
                              (int)(it->pt.x + topLeftCorner.get_j()), crossSize, vpColor::red);
    }
    for (std::vector<vpImagePoint>::const_iterator it = ransacInliers.begin(); it != ransacInliers.end(); ++it) {
      // Display green circle for RANSAC inliers
      vpDisplay::displayCircle(IMatching, (int)(it->get_v() + topLeftCorner.get_i()),
                               (int)(it->get_u() + topLeftCorner.get_j()), 4, vpColor::green);
    }
    for (std::vector<vpImagePoint>::const_iterator it = m_ransacOutliers.begin(); it != m_ransacOutliers.end(); ++it) {
      // Display red circle for RANSAC outliers
      vpDisplay::displayCircle(IMatching, (int)(it->get_i() + topLeftCorner.get_i()),
                               (int)(it->get_j() + topLeftCorner.get_j()), 4, vpColor::red);
    }

    for (std::vector<cv::DMatch>::const_iterator it = m_filteredMatches.begin(); it != m_filteredMatches.end(); ++it) {
      int current_class_id = 0;
      if (mapOfImageIdIndex[m_mapOfImageId[m_trainKeyPoints[(size_t)it->trainIdx].class_id]] < medianIndex) {
        current_class_id = mapOfImageIdIndex[m_mapOfImageId[m_trainKeyPoints[(size_t)it->trainIdx].class_id]];
      } else {
        // Shift of one unity the index of the training images which are after
        // the current image
        current_class_id = mapOfImageIdIndex[m_mapOfImageId[m_trainKeyPoints[(size_t)it->trainIdx].class_id]] + 1;
      }

      int indexI = current_class_id / nbWidth;
      int indexJ = current_class_id - (indexI * nbWidth);

      vpImagePoint end((int)maxH * indexI + m_trainKeyPoints[(size_t)it->trainIdx].pt.y,
                       (int)maxW * indexJ + m_trainKeyPoints[(size_t)it->trainIdx].pt.x);
      vpImagePoint start((int)maxH * medianI + m_queryFilteredKeyPoints[(size_t)it->queryIdx].pt.y,
                         (int)maxW * medianJ + m_queryFilteredKeyPoints[(size_t)it->queryIdx].pt.x);

      // Draw line for matching keypoints detected in the current image and
      // those detected  in the training images
      vpDisplay::displayLine(IMatching, start, end, vpColor::green, lineThickness);
    }
  }
}

void vpKeyPoint::displayMatching(const vpImage<vpRGBa> &ICurrent, vpImage<vpRGBa> &IMatching,
                                 const std::vector<vpImagePoint> &ransacInliers, unsigned int crossSize,
                                 unsigned int lineThickness)
{
  if (m_mapOfImages.empty() || m_mapOfImageId.empty()) {
    // No training images so return
    std::cerr << "There is no training image loaded !" << std::endl;
    return;
  }

  // Nb images in the training database + the current image we want to detect
  // the object
  int nbImg = (int)(m_mapOfImages.size() + 1);

  if (nbImg == 2) {
    // Only one training image, so we display the matching result side-by-side
    displayMatching(m_mapOfImages.begin()->second, IMatching, crossSize);
  } else {
    // Multiple training images, display them as a mosaic image
    int nbImgSqrt = vpMath::round(std::sqrt((double)nbImg)); //(int) std::floor(std::sqrt((double) nbImg) + 0.5);
    int nbWidth = nbImgSqrt;
    int nbHeight = nbImgSqrt;

    if (nbImgSqrt * nbImgSqrt < nbImg) {
      nbWidth++;
    }

    std::map<int, int> mapOfImageIdIndex;
    int cpt = 0;
    unsigned int maxW = ICurrent.getWidth(), maxH = ICurrent.getHeight();
    for (std::map<int, vpImage<unsigned char> >::const_iterator it = m_mapOfImages.begin(); it != m_mapOfImages.end();
         ++it, cpt++) {
      mapOfImageIdIndex[it->first] = cpt;

      if (maxW < it->second.getWidth()) {
        maxW = it->second.getWidth();
      }

      if (maxH < it->second.getHeight()) {
        maxH = it->second.getHeight();
      }
    }

    // Indexes of the current image in the grid computed to put preferably the
    // image in the center of the mosaic grid
    int medianI = nbHeight / 2;
    int medianJ = nbWidth / 2;
    int medianIndex = medianI * nbWidth + medianJ;
    for (std::vector<cv::KeyPoint>::const_iterator it = m_trainKeyPoints.begin(); it != m_trainKeyPoints.end(); ++it) {
      vpImagePoint topLeftCorner;
      int current_class_id_index = 0;
      if (mapOfImageIdIndex[m_mapOfImageId[it->class_id]] < medianIndex) {
        current_class_id_index = mapOfImageIdIndex[m_mapOfImageId[it->class_id]];
      } else {
        // Shift of one unity the index of the training images which are after
        // the current image
        current_class_id_index = mapOfImageIdIndex[m_mapOfImageId[it->class_id]] + 1;
      }

      int indexI = current_class_id_index / nbWidth;
      int indexJ = current_class_id_index - (indexI * nbWidth);
      topLeftCorner.set_ij((int)maxH * indexI, (int)maxW * indexJ);

      // Display cross for keypoints in the learning database
      vpDisplay::displayCross(IMatching, (int)(it->pt.y + topLeftCorner.get_i()),
                              (int)(it->pt.x + topLeftCorner.get_j()), crossSize, vpColor::red);
    }

    vpImagePoint topLeftCorner((int)maxH * medianI, (int)maxW * medianJ);
    for (std::vector<cv::KeyPoint>::const_iterator it = m_queryKeyPoints.begin(); it != m_queryKeyPoints.end(); ++it) {
      // Display cross for keypoints detected in the current image
      vpDisplay::displayCross(IMatching, (int)(it->pt.y + topLeftCorner.get_i()),
                              (int)(it->pt.x + topLeftCorner.get_j()), crossSize, vpColor::red);
    }
    for (std::vector<vpImagePoint>::const_iterator it = ransacInliers.begin(); it != ransacInliers.end(); ++it) {
      // Display green circle for RANSAC inliers
      vpDisplay::displayCircle(IMatching, (int)(it->get_v() + topLeftCorner.get_i()),
                               (int)(it->get_u() + topLeftCorner.get_j()), 4, vpColor::green);
    }
    for (std::vector<vpImagePoint>::const_iterator it = m_ransacOutliers.begin(); it != m_ransacOutliers.end(); ++it) {
      // Display red circle for RANSAC outliers
      vpDisplay::displayCircle(IMatching, (int)(it->get_i() + topLeftCorner.get_i()),
                               (int)(it->get_j() + topLeftCorner.get_j()), 4, vpColor::red);
    }

    for (std::vector<cv::DMatch>::const_iterator it = m_filteredMatches.begin(); it != m_filteredMatches.end(); ++it) {
      int current_class_id = 0;
      if (mapOfImageIdIndex[m_mapOfImageId[m_trainKeyPoints[(size_t)it->trainIdx].class_id]] < medianIndex) {
        current_class_id = mapOfImageIdIndex[m_mapOfImageId[m_trainKeyPoints[(size_t)it->trainIdx].class_id]];
      } else {
        // Shift of one unity the index of the training images which are after
        // the current image
        current_class_id = mapOfImageIdIndex[m_mapOfImageId[m_trainKeyPoints[(size_t)it->trainIdx].class_id]] + 1;
      }

      int indexI = current_class_id / nbWidth;
      int indexJ = current_class_id - (indexI * nbWidth);

      vpImagePoint end((int)maxH * indexI + m_trainKeyPoints[(size_t)it->trainIdx].pt.y,
                       (int)maxW * indexJ + m_trainKeyPoints[(size_t)it->trainIdx].pt.x);
      vpImagePoint start((int)maxH * medianI + m_queryFilteredKeyPoints[(size_t)it->queryIdx].pt.y,
                         (int)maxW * medianJ + m_queryFilteredKeyPoints[(size_t)it->queryIdx].pt.x);

      // Draw line for matching keypoints detected in the current image and
      // those detected  in the training images
      vpDisplay::displayLine(IMatching, start, end, vpColor::green, lineThickness);
    }
  }
}

void vpKeyPoint::extract(const vpImage<unsigned char> &I, std::vector<cv::KeyPoint> &keyPoints, cv::Mat &descriptors,
                         std::vector<cv::Point3f> *trainPoints)
{
  double elapsedTime;
  extract(I, keyPoints, descriptors, elapsedTime, trainPoints);
}

void vpKeyPoint::extract(const vpImage<vpRGBa> &I_color, std::vector<cv::KeyPoint> &keyPoints, cv::Mat &descriptors,
                         std::vector<cv::Point3f> *trainPoints)
{
  double elapsedTime;
  extract(I_color, keyPoints, descriptors, elapsedTime, trainPoints);
}

void vpKeyPoint::extract(const cv::Mat &matImg, std::vector<cv::KeyPoint> &keyPoints, cv::Mat &descriptors,
                         std::vector<cv::Point3f> *trainPoints)
{
  double elapsedTime;
  extract(matImg, keyPoints, descriptors, elapsedTime, trainPoints);
}

void vpKeyPoint::extract(const vpImage<unsigned char> &I, std::vector<cv::KeyPoint> &keyPoints, cv::Mat &descriptors,
                         double &elapsedTime, std::vector<cv::Point3f> *trainPoints)
{
  cv::Mat matImg;
  vpImageConvert::convert(I, matImg, false);
  extract(matImg, keyPoints, descriptors, elapsedTime, trainPoints);
}

void vpKeyPoint::extract(const vpImage<vpRGBa> &I_color, std::vector<cv::KeyPoint> &keyPoints, cv::Mat &descriptors,
                         double &elapsedTime, std::vector<cv::Point3f> *trainPoints)
{
  cv::Mat matImg;
  vpImageConvert::convert(I_color, matImg);
  extract(matImg, keyPoints, descriptors, elapsedTime, trainPoints);
}

void vpKeyPoint::extract(const cv::Mat &matImg, std::vector<cv::KeyPoint> &keyPoints, cv::Mat &descriptors,
                         double &elapsedTime, std::vector<cv::Point3f> *trainPoints)
{
  double t = vpTime::measureTimeMs();
  bool first = true;

  for (std::map<std::string, cv::Ptr<cv::DescriptorExtractor> >::const_iterator itd = m_extractors.begin();
       itd != m_extractors.end(); ++itd) {
    if (first) {
      first = false;
      // Check if we have 3D object points information
      if (trainPoints != NULL && !trainPoints->empty()) {
        // Copy the input list of keypoints, keypoints that cannot be computed
        // are removed in the function compute
        std::vector<cv::KeyPoint> keyPoints_tmp = keyPoints;

        // Extract descriptors for the given list of keypoints
        itd->second->compute(matImg, keyPoints, descriptors);

        if (keyPoints.size() != keyPoints_tmp.size()) {
          // Keypoints have been removed
          // Store the hash of a keypoint as the key and the index of the
          // keypoint as the value
          std::map<size_t, size_t> mapOfKeypointHashes;
          size_t cpt = 0;
          for (std::vector<cv::KeyPoint>::const_iterator it = keyPoints_tmp.begin(); it != keyPoints_tmp.end();
               ++it, cpt++) {
            mapOfKeypointHashes[myKeypointHash(*it)] = cpt;
          }

          std::vector<cv::Point3f> trainPoints_tmp;
          for (std::vector<cv::KeyPoint>::const_iterator it = keyPoints.begin(); it != keyPoints.end(); ++it) {
            if (mapOfKeypointHashes.find(myKeypointHash(*it)) != mapOfKeypointHashes.end()) {
              trainPoints_tmp.push_back((*trainPoints)[mapOfKeypointHashes[myKeypointHash(*it)]]);
            }
          }

          // Copy trainPoints_tmp to m_trainPoints
          *trainPoints = trainPoints_tmp;
        }
      } else {
        // Extract descriptors for the given list of keypoints
        itd->second->compute(matImg, keyPoints, descriptors);
      }
    } else {
      // Copy the input list of keypoints, keypoints that cannot be computed
      // are removed in the function compute
      std::vector<cv::KeyPoint> keyPoints_tmp = keyPoints;

      cv::Mat desc;
      // Extract descriptors for the given list of keypoints
      itd->second->compute(matImg, keyPoints, desc);

      if (keyPoints.size() != keyPoints_tmp.size()) {
        // Keypoints have been removed
        // Store the hash of a keypoint as the key and the index of the
        // keypoint as the value
        std::map<size_t, size_t> mapOfKeypointHashes;
        size_t cpt = 0;
        for (std::vector<cv::KeyPoint>::const_iterator it = keyPoints_tmp.begin(); it != keyPoints_tmp.end();
             ++it, cpt++) {
          mapOfKeypointHashes[myKeypointHash(*it)] = cpt;
        }

        std::vector<cv::Point3f> trainPoints_tmp;
        cv::Mat descriptors_tmp;
        for (std::vector<cv::KeyPoint>::const_iterator it = keyPoints.begin(); it != keyPoints.end(); ++it) {
          if (mapOfKeypointHashes.find(myKeypointHash(*it)) != mapOfKeypointHashes.end()) {
            if (trainPoints != NULL && !trainPoints->empty()) {
              trainPoints_tmp.push_back((*trainPoints)[mapOfKeypointHashes[myKeypointHash(*it)]]);
            }

            if (!descriptors.empty()) {
              descriptors_tmp.push_back(descriptors.row((int)mapOfKeypointHashes[myKeypointHash(*it)]));
            }
          }
        }

        if (trainPoints != NULL) {
          // Copy trainPoints_tmp to m_trainPoints
          *trainPoints = trainPoints_tmp;
        }
        // Copy descriptors_tmp to descriptors
        descriptors_tmp.copyTo(descriptors);
      }

      // Merge descriptors horizontally
      if (descriptors.empty()) {
        desc.copyTo(descriptors);
      } else {
        cv::hconcat(descriptors, desc, descriptors);
      }
    }
  }

  if (keyPoints.size() != (size_t)descriptors.rows) {
    std::cerr << "keyPoints.size() != (size_t) descriptors.rows" << std::endl;
  }
  elapsedTime = vpTime::measureTimeMs() - t;
}

void vpKeyPoint::filterMatches()
{
  std::vector<cv::KeyPoint> queryKpts;
  std::vector<cv::Point3f> trainPts;
  std::vector<cv::DMatch> m;

  if (m_useKnn) {
    // double max_dist = 0;
    // double min_dist = std::numeric_limits<double>::max(); // create an
    // error under Windows. To fix it we have to add #undef max
    double min_dist = DBL_MAX;
    double mean = 0.0;
    std::vector<double> distance_vec(m_knnMatches.size());

    if (m_filterType == stdAndRatioDistanceThreshold) {
      for (size_t i = 0; i < m_knnMatches.size(); i++) {
        double dist = m_knnMatches[i][0].distance;
        mean += dist;
        distance_vec[i] = dist;

        if (dist < min_dist) {
          min_dist = dist;
        }
        // if (dist > max_dist) {
        //  max_dist = dist;
        //}
      }
      mean /= m_queryDescriptors.rows;
    }

    double sq_sum = std::inner_product(distance_vec.begin(), distance_vec.end(), distance_vec.begin(), 0.0);
    double stdev = std::sqrt(sq_sum / distance_vec.size() - mean * mean);
    double threshold = min_dist + stdev;

    for (size_t i = 0; i < m_knnMatches.size(); i++) {
      if (m_knnMatches[i].size() >= 2) {
        // Calculate ratio of the descriptor distance between the two nearest
        // neighbors of the keypoint
        float ratio = m_knnMatches[i][0].distance / m_knnMatches[i][1].distance;
        //        float ratio = std::sqrt((vecMatches[i][0].distance *
        //        vecMatches[i][0].distance)
        //            / (vecMatches[i][1].distance *
        //            vecMatches[i][1].distance));
        double dist = m_knnMatches[i][0].distance;

        if (ratio < m_matchingRatioThreshold || (m_filterType == stdAndRatioDistanceThreshold && dist < threshold)) {
          m.push_back(cv::DMatch((int)queryKpts.size(), m_knnMatches[i][0].trainIdx, m_knnMatches[i][0].distance));

          if (!m_trainPoints.empty()) {
            trainPts.push_back(m_trainPoints[(size_t)m_knnMatches[i][0].trainIdx]);
          }
          queryKpts.push_back(m_queryKeyPoints[(size_t)m_knnMatches[i][0].queryIdx]);
        }
      }
    }
  } else {
    // double max_dist = 0;
    // create an error under Windows. To fix it we have to add #undef max
    // double min_dist = std::numeric_limits<double>::max();
    double min_dist = DBL_MAX;
    double mean = 0.0;
    std::vector<double> distance_vec(m_matches.size());
    for (size_t i = 0; i < m_matches.size(); i++) {
      double dist = m_matches[i].distance;
      mean += dist;
      distance_vec[i] = dist;

      if (dist < min_dist) {
        min_dist = dist;
      }
      // if (dist > max_dist) {
      //  max_dist = dist;
      // }
    }
    mean /= m_queryDescriptors.rows;

    double sq_sum = std::inner_product(distance_vec.begin(), distance_vec.end(), distance_vec.begin(), 0.0);
    double stdev = std::sqrt(sq_sum / distance_vec.size() - mean * mean);

    // Define a threshold where we keep all keypoints whose the descriptor
    // distance falls below a factor of the  minimum descriptor distance (for
    // all the query keypoints)  or below the minimum descriptor distance +
    // the standard deviation (calculated on all the query descriptor
    // distances)
    double threshold =
        m_filterType == constantFactorDistanceThreshold ? m_matchingFactorThreshold * min_dist : min_dist + stdev;

    for (size_t i = 0; i < m_matches.size(); i++) {
      if (m_matches[i].distance <= threshold) {
        m.push_back(cv::DMatch((int)queryKpts.size(), m_matches[i].trainIdx, m_matches[i].distance));

        if (!m_trainPoints.empty()) {
          trainPts.push_back(m_trainPoints[(size_t)m_matches[i].trainIdx]);
        }
        queryKpts.push_back(m_queryKeyPoints[(size_t)m_matches[i].queryIdx]);
      }
    }
  }

  if (m_useSingleMatchFilter) {
    // Eliminate matches where multiple query keypoints are matched to the
    // same train keypoint
    std::vector<cv::DMatch> mTmp;
    std::vector<cv::Point3f> trainPtsTmp;
    std::vector<cv::KeyPoint> queryKptsTmp;

    std::map<int, int> mapOfTrainIdx;
    // Count the number of query points matched to the same train point
    for (std::vector<cv::DMatch>::const_iterator it = m.begin(); it != m.end(); ++it) {
      mapOfTrainIdx[it->trainIdx]++;
    }

    // Keep matches with only one correspondence
    for (std::vector<cv::DMatch>::const_iterator it = m.begin(); it != m.end(); ++it) {
      if (mapOfTrainIdx[it->trainIdx] == 1) {
        mTmp.push_back(cv::DMatch((int)queryKptsTmp.size(), it->trainIdx, it->distance));

        if (!m_trainPoints.empty()) {
          trainPtsTmp.push_back(m_trainPoints[(size_t)it->trainIdx]);
        }
        queryKptsTmp.push_back(queryKpts[(size_t)it->queryIdx]);
      }
    }

    m_filteredMatches = mTmp;
    m_objectFilteredPoints = trainPtsTmp;
    m_queryFilteredKeyPoints = queryKptsTmp;
  } else {
    m_filteredMatches = m;
    m_objectFilteredPoints = trainPts;
    m_queryFilteredKeyPoints = queryKpts;
  }
}

void vpKeyPoint::getObjectPoints(std::vector<cv::Point3f> &objectPoints) const
{
  objectPoints = m_objectFilteredPoints;
}

void vpKeyPoint::getObjectPoints(std::vector<vpPoint> &objectPoints) const
{
  vpConvert::convertFromOpenCV(m_objectFilteredPoints, objectPoints);
}

void vpKeyPoint::getQueryKeyPoints(std::vector<cv::KeyPoint> &keyPoints, bool matches) const
{
  if (matches) {
    keyPoints = m_queryFilteredKeyPoints;
  }
  else {
    keyPoints = m_queryKeyPoints;
  }
}

void vpKeyPoint::getQueryKeyPoints(std::vector<vpImagePoint> &keyPoints, bool matches) const
{
  if (matches) {
    keyPoints = currentImagePointsList;
  }
  else {
    vpConvert::convertFromOpenCV(m_queryKeyPoints, keyPoints);
  }
}

void vpKeyPoint::getTrainKeyPoints(std::vector<cv::KeyPoint> &keyPoints) const { keyPoints = m_trainKeyPoints; }

void vpKeyPoint::getTrainKeyPoints(std::vector<vpImagePoint> &keyPoints) const { keyPoints = referenceImagePointsList; }

void vpKeyPoint::getTrainPoints(std::vector<cv::Point3f> &points) const { points = m_trainPoints; }

void vpKeyPoint::getTrainPoints(std::vector<vpPoint> &points) const { points = m_trainVpPoints; }

void vpKeyPoint::init()
{
// Require 2.4.0 <= opencv < 3.0.0
#if defined(VISP_HAVE_OPENCV_NONFREE) && (VISP_HAVE_OPENCV_VERSION >= 0x020400) && (VISP_HAVE_OPENCV_VERSION < 0x030000)
  // The following line must be called in order to use SIFT or SURF
  if (!cv::initModule_nonfree()) {
    std::cerr << "Cannot init module non free, SIFT or SURF cannot be used." << std::endl;
  }
#endif

  // Use k-nearest neighbors (knn) to retrieve the two best matches for a
  // keypoint  So this is useful only for ratioDistanceThreshold method
  if (m_filterType == ratioDistanceThreshold || m_filterType == stdAndRatioDistanceThreshold) {
    m_useKnn = true;
  }

  initDetectors(m_detectorNames);
  initExtractors(m_extractorNames);
  initMatcher(m_matcherName);
}

void vpKeyPoint::initDetector(const std::string &detectorName)
{
#if (VISP_HAVE_OPENCV_VERSION < 0x030000)
  m_detectors[detectorName] = cv::FeatureDetector::create(detectorName);

  if (m_detectors[detectorName] == NULL) {
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the detector: " << detectorName
           << " or it is not available in OpenCV version: " << std::hex << VISP_HAVE_OPENCV_VERSION << ".";
    throw vpException(vpException::fatalError, ss_msg.str());
  }
#else
  std::string detectorNameTmp = detectorName;
  std::string pyramid = "Pyramid";
  std::size_t pos = detectorName.find(pyramid);
  bool usePyramid = false;
  if (pos != std::string::npos) {
    detectorNameTmp = detectorName.substr(pos + pyramid.size());
    usePyramid = true;
  }

  if (detectorNameTmp == "SIFT") {
#if (VISP_HAVE_OPENCV_VERSION >= 0x040500) // OpenCV >= 4.5.0
    cv::Ptr<cv::FeatureDetector> siftDetector = cv::SiftFeatureDetector::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = siftDetector;
    } else {
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(siftDetector);
    }
#else
#if defined(VISP_HAVE_OPENCV_XFEATURES2D) || \
    (VISP_HAVE_OPENCV_VERSION >= 0x030411 && CV_MAJOR_VERSION < 4) || (VISP_HAVE_OPENCV_VERSION >= 0x040400)
    // SIFT is no more patented since 09/03/2020
    cv::Ptr<cv::FeatureDetector> siftDetector;
    if (m_maxFeatures > 0) {
#if (VISP_HAVE_OPENCV_VERSION >= 0x030411 && CV_MAJOR_VERSION < 4) || (VISP_HAVE_OPENCV_VERSION >= 0x040400)
      siftDetector = cv::SIFT::create(m_maxFeatures);
#else
      siftDetector = cv::xfeatures2d::SIFT::create(m_maxFeatures);
#endif
    } else {
#if (VISP_HAVE_OPENCV_VERSION >= 0x030411 && CV_MAJOR_VERSION < 4) || (VISP_HAVE_OPENCV_VERSION >= 0x040400)
      siftDetector = cv::SIFT::create();
#else
      siftDetector = cv::xfeatures2d::SIFT::create();
#endif
    }
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = siftDetector;
    } else {
      std::cerr << "You should not use SIFT with Pyramid feature detection!" << std::endl;
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(siftDetector);
    }
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the detector: SIFT. OpenCV version " << std::hex << VISP_HAVE_OPENCV_VERSION
           << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
#endif
  } else if (detectorNameTmp == "SURF") {
#ifdef VISP_HAVE_OPENCV_XFEATURES2D
    cv::Ptr<cv::FeatureDetector> surfDetector = cv::xfeatures2d::SURF::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = surfDetector;
    } else {
      std::cerr << "You should not use SURF with Pyramid feature detection!" << std::endl;
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(surfDetector);
    }
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the detector: SURF. OpenCV version  " << std::hex << VISP_HAVE_OPENCV_VERSION
           << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
  } else if (detectorNameTmp == "FAST") {
    cv::Ptr<cv::FeatureDetector> fastDetector = cv::FastFeatureDetector::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = fastDetector;
    } else {
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(fastDetector);
    }
  } else if (detectorNameTmp == "MSER") {
    cv::Ptr<cv::FeatureDetector> fastDetector = cv::MSER::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = fastDetector;
    } else {
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(fastDetector);
    }
  } else if (detectorNameTmp == "ORB") {
    cv::Ptr<cv::FeatureDetector> orbDetector;
    if (m_maxFeatures > 0) {
      orbDetector = cv::ORB::create(m_maxFeatures);
    }
    else {
      orbDetector = cv::ORB::create();
    }
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = orbDetector;
    } else {
      std::cerr << "You should not use ORB with Pyramid feature detection!" << std::endl;
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(orbDetector);
    }
  } else if (detectorNameTmp == "BRISK") {
    cv::Ptr<cv::FeatureDetector> briskDetector = cv::BRISK::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = briskDetector;
    } else {
      std::cerr << "You should not use BRISK with Pyramid feature detection!" << std::endl;
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(briskDetector);
    }
  } else if (detectorNameTmp == "KAZE") {
    cv::Ptr<cv::FeatureDetector> kazeDetector = cv::KAZE::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = kazeDetector;
    } else {
      std::cerr << "You should not use KAZE with Pyramid feature detection!" << std::endl;
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(kazeDetector);
    }
  } else if (detectorNameTmp == "AKAZE") {
    cv::Ptr<cv::FeatureDetector> akazeDetector = cv::AKAZE::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = akazeDetector;
    } else {
      std::cerr << "You should not use AKAZE with Pyramid feature detection!" << std::endl;
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(akazeDetector);
    }
  } else if (detectorNameTmp == "GFTT") {
    cv::Ptr<cv::FeatureDetector> gfttDetector = cv::GFTTDetector::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = gfttDetector;
    } else {
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(gfttDetector);
    }
  } else if (detectorNameTmp == "SimpleBlob") {
    cv::Ptr<cv::FeatureDetector> simpleBlobDetector = cv::SimpleBlobDetector::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = simpleBlobDetector;
    } else {
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(simpleBlobDetector);
    }
  } else if (detectorNameTmp == "STAR") {
#ifdef VISP_HAVE_OPENCV_XFEATURES2D
    cv::Ptr<cv::FeatureDetector> starDetector = cv::xfeatures2d::StarDetector::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = starDetector;
    } else {
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(starDetector);
    }
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the detector: STAR. OpenCV version  " << std::hex << VISP_HAVE_OPENCV_VERSION
           << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
  } else if (detectorNameTmp == "AGAST") {
    cv::Ptr<cv::FeatureDetector> agastDetector = cv::AgastFeatureDetector::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = agastDetector;
    } else {
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(agastDetector);
    }
  } else if (detectorNameTmp == "MSD") {
#if (VISP_HAVE_OPENCV_VERSION >= 0x030100)
#if defined(VISP_HAVE_OPENCV_XFEATURES2D)
    cv::Ptr<cv::FeatureDetector> msdDetector = cv::xfeatures2d::MSDDetector::create();
    if (!usePyramid) {
      m_detectors[detectorNameTmp] = msdDetector;
    } else {
      std::cerr << "You should not use MSD with Pyramid feature detection!" << std::endl;
      m_detectors[detectorName] = cv::makePtr<PyramidAdaptedFeatureDetector>(msdDetector);
    }
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the detector: MSD. OpenCV version " << std::hex << VISP_HAVE_OPENCV_VERSION
           << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
#else
    std::stringstream ss_msg;
    ss_msg << "Feature " << detectorName << " is not available in OpenCV version: " << std::hex
           << VISP_HAVE_OPENCV_VERSION << " (require >= OpenCV 3.1).";
#endif
  } else {
    std::cerr << "The detector:" << detectorNameTmp << " is not available." << std::endl;
  }

  bool detectorInitialized = false;
  if (!usePyramid) {
    //if not null and to avoid warning C4800: forcing value to bool 'true' or 'false' (performance warning)
    detectorInitialized = !m_detectors[detectorNameTmp].empty();
  } else {
    //if not null and to avoid warning C4800: forcing value to bool 'true' or 'false' (performance warning)
    detectorInitialized = !m_detectors[detectorName].empty();
  }

  if (!detectorInitialized) {
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the detector: " << detectorNameTmp
           << " or it is not available in OpenCV version: " << std::hex << VISP_HAVE_OPENCV_VERSION << ".";
    throw vpException(vpException::fatalError, ss_msg.str());
  }
#endif
}

void vpKeyPoint::initDetectors(const std::vector<std::string> &detectorNames)
{
  for (std::vector<std::string>::const_iterator it = detectorNames.begin(); it != detectorNames.end(); ++it) {
    initDetector(*it);
  }
}

void vpKeyPoint::initExtractor(const std::string &extractorName)
{
#if (VISP_HAVE_OPENCV_VERSION < 0x030000)
  m_extractors[extractorName] = cv::DescriptorExtractor::create(extractorName);
#else
  if (extractorName == "SIFT") {
#if (VISP_HAVE_OPENCV_VERSION >= 0x040500) // OpenCV >= 4.5.0
    m_extractors[extractorName] = cv::SIFT::create();
#else
#if defined(VISP_HAVE_OPENCV_XFEATURES2D) || \
    (VISP_HAVE_OPENCV_VERSION >= 0x030411 && CV_MAJOR_VERSION < 4) || (VISP_HAVE_OPENCV_VERSION >= 0x040400)
    // SIFT is no more patented since 09/03/2020
#if (VISP_HAVE_OPENCV_VERSION >= 0x030411 && CV_MAJOR_VERSION < 4) || (VISP_HAVE_OPENCV_VERSION >= 0x040400)
    m_extractors[extractorName] = cv::SIFT::create();
#else
    m_extractors[extractorName] = cv::xfeatures2d::SIFT::create();
#endif
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: SIFT. OpenCV version  " << std::hex << VISP_HAVE_OPENCV_VERSION
           << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
#endif
  } else if (extractorName == "SURF") {
#ifdef VISP_HAVE_OPENCV_XFEATURES2D
    // Use extended set of SURF descriptors (128 instead of 64)
    m_extractors[extractorName] = cv::xfeatures2d::SURF::create(100, 4, 3, true);
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: SURF. OpenCV version  " << std::hex << VISP_HAVE_OPENCV_VERSION
           << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
  } else if (extractorName == "ORB") {
    m_extractors[extractorName] = cv::ORB::create();
  } else if (extractorName == "BRISK") {
    m_extractors[extractorName] = cv::BRISK::create();
  } else if (extractorName == "FREAK") {
#ifdef VISP_HAVE_OPENCV_XFEATURES2D
    m_extractors[extractorName] = cv::xfeatures2d::FREAK::create();
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: " << extractorName << ". OpenCV version " << std::hex
           << VISP_HAVE_OPENCV_VERSION << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
  } else if (extractorName == "BRIEF") {
#ifdef VISP_HAVE_OPENCV_XFEATURES2D
    m_extractors[extractorName] = cv::xfeatures2d::BriefDescriptorExtractor::create();
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: " << extractorName << ". OpenCV version " << std::hex
           << VISP_HAVE_OPENCV_VERSION << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
  } else if (extractorName == "KAZE") {
    m_extractors[extractorName] = cv::KAZE::create();
  } else if (extractorName == "AKAZE") {
    m_extractors[extractorName] = cv::AKAZE::create();
  } else if (extractorName == "DAISY") {
#ifdef VISP_HAVE_OPENCV_XFEATURES2D
    m_extractors[extractorName] = cv::xfeatures2d::DAISY::create();
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: " << extractorName << ". OpenCV version " << std::hex
           << VISP_HAVE_OPENCV_VERSION << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
  } else if (extractorName == "LATCH") {
#ifdef VISP_HAVE_OPENCV_XFEATURES2D
    m_extractors[extractorName] = cv::xfeatures2d::LATCH::create();
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: " << extractorName << ". OpenCV version " << std::hex
           << VISP_HAVE_OPENCV_VERSION << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
  } else if (extractorName == "LUCID") {
#ifdef VISP_HAVE_OPENCV_XFEATURES2D
    //    m_extractors[extractorName] = cv::xfeatures2d::LUCID::create(1, 2);
    // Not possible currently, need a color image
    throw vpException(vpException::badValue, "Not possible currently as it needs a color image.");
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: " << extractorName << ". OpenCV version " << std::hex
           << VISP_HAVE_OPENCV_VERSION << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
  } else if (extractorName == "VGG") {
#if (VISP_HAVE_OPENCV_VERSION >= 0x030200)
#if defined(VISP_HAVE_OPENCV_XFEATURES2D)
    m_extractors[extractorName] = cv::xfeatures2d::VGG::create();
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: " << extractorName << ". OpenCV version " << std::hex
           << VISP_HAVE_OPENCV_VERSION << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: " << extractorName << ". OpenCV version " << std::hex
           << VISP_HAVE_OPENCV_VERSION << " but requires at least OpenCV 3.2.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
  } else if (extractorName == "BoostDesc") {
#if (VISP_HAVE_OPENCV_VERSION >= 0x030200)
#if defined(VISP_HAVE_OPENCV_XFEATURES2D)
    m_extractors[extractorName] = cv::xfeatures2d::BoostDesc::create();
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: " << extractorName << ". OpenCV version " << std::hex
           << VISP_HAVE_OPENCV_VERSION << " was not build with xFeatures2d module.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
#else
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: " << extractorName << ". OpenCV version " << std::hex
           << VISP_HAVE_OPENCV_VERSION << " but requires at least OpenCV 3.2.";
    throw vpException(vpException::fatalError, ss_msg.str());
#endif
  } else {
    std::cerr << "The extractor:" << extractorName << " is not available." << std::endl;
  }
#endif

  if (!m_extractors[extractorName]) { //if null
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the extractor: " << extractorName
           << " or it is not available in OpenCV version: " << std::hex << VISP_HAVE_OPENCV_VERSION << ".";
    throw vpException(vpException::fatalError, ss_msg.str());
  }

#if (VISP_HAVE_OPENCV_VERSION >= 0x020400 && VISP_HAVE_OPENCV_VERSION < 0x030000)
  if (extractorName == "SURF") {
    // Use extended set of SURF descriptors (128 instead of 64)
    m_extractors[extractorName]->set("extended", 1);
  }
#endif
}

void vpKeyPoint::initExtractors(const std::vector<std::string> &extractorNames)
{
  for (std::vector<std::string>::const_iterator it = extractorNames.begin(); it != extractorNames.end(); ++it) {
    initExtractor(*it);
  }

  int descriptorType = CV_32F;
  bool firstIteration = true;
  for (std::map<std::string, cv::Ptr<cv::DescriptorExtractor> >::const_iterator it = m_extractors.begin();
       it != m_extractors.end(); ++it) {
    if (firstIteration) {
      firstIteration = false;
      descriptorType = it->second->descriptorType();
    } else {
      if (descriptorType != it->second->descriptorType()) {
        throw vpException(vpException::fatalError, "All the descriptors must have the same type !");
      }
    }
  }
}

void vpKeyPoint::initFeatureNames()
{
// Create map enum to string
#if (VISP_HAVE_OPENCV_VERSION >= 0x020403)
  m_mapOfDetectorNames[DETECTOR_FAST] = "FAST";
  m_mapOfDetectorNames[DETECTOR_MSER] = "MSER";
  m_mapOfDetectorNames[DETECTOR_ORB] = "ORB";
  m_mapOfDetectorNames[DETECTOR_BRISK] = "BRISK";
  m_mapOfDetectorNames[DETECTOR_GFTT] = "GFTT";
  m_mapOfDetectorNames[DETECTOR_SimpleBlob] = "SimpleBlob";
#if (VISP_HAVE_OPENCV_VERSION < 0x030000) || (defined(VISP_HAVE_OPENCV_XFEATURES2D))
  m_mapOfDetectorNames[DETECTOR_STAR] = "STAR";
#endif
#if defined(VISP_HAVE_OPENCV_NONFREE) || defined(VISP_HAVE_OPENCV_XFEATURES2D) || \
    (VISP_HAVE_OPENCV_VERSION >= 0x030411 && CV_MAJOR_VERSION < 4) || (VISP_HAVE_OPENCV_VERSION >= 0x040400)
  m_mapOfDetectorNames[DETECTOR_SIFT] = "SIFT";
#endif
#if defined(VISP_HAVE_OPENCV_NONFREE) || defined(VISP_HAVE_OPENCV_XFEATURES2D)
  m_mapOfDetectorNames[DETECTOR_SURF] = "SURF";
#endif
#if (VISP_HAVE_OPENCV_VERSION >= 0x030000)
  m_mapOfDetectorNames[DETECTOR_KAZE] = "KAZE";
  m_mapOfDetectorNames[DETECTOR_AKAZE] = "AKAZE";
  m_mapOfDetectorNames[DETECTOR_AGAST] = "AGAST";
#endif
#if (VISP_HAVE_OPENCV_VERSION >= 0x030100) && defined(VISP_HAVE_OPENCV_XFEATURES2D)
  m_mapOfDetectorNames[DETECTOR_MSD] = "MSD";
#endif
#endif

#if (VISP_HAVE_OPENCV_VERSION >= 0x020403)
  m_mapOfDescriptorNames[DESCRIPTOR_ORB] = "ORB";
  m_mapOfDescriptorNames[DESCRIPTOR_BRISK] = "BRISK";
#if (VISP_HAVE_OPENCV_VERSION < 0x030000) || (defined(VISP_HAVE_OPENCV_XFEATURES2D))
  m_mapOfDescriptorNames[DESCRIPTOR_FREAK] = "FREAK";
  m_mapOfDescriptorNames[DESCRIPTOR_BRIEF] = "BRIEF";
#endif
#if defined(VISP_HAVE_OPENCV_NONFREE) || defined(VISP_HAVE_OPENCV_XFEATURES2D) || \
    (VISP_HAVE_OPENCV_VERSION >= 0x030411 && CV_MAJOR_VERSION < 4) || (VISP_HAVE_OPENCV_VERSION >= 0x040400)
  m_mapOfDescriptorNames[DESCRIPTOR_SIFT] = "SIFT";
#endif
#if defined(VISP_HAVE_OPENCV_NONFREE) || defined(VISP_HAVE_OPENCV_XFEATURES2D)
  m_mapOfDescriptorNames[DESCRIPTOR_SURF] = "SURF";
#endif
#if (VISP_HAVE_OPENCV_VERSION >= 0x030000)
  m_mapOfDescriptorNames[DESCRIPTOR_KAZE] = "KAZE";
  m_mapOfDescriptorNames[DESCRIPTOR_AKAZE] = "AKAZE";
#if defined(VISP_HAVE_OPENCV_XFEATURES2D)
  m_mapOfDescriptorNames[DESCRIPTOR_DAISY] = "DAISY";
  m_mapOfDescriptorNames[DESCRIPTOR_LATCH] = "LATCH";
#endif
#endif
#if (VISP_HAVE_OPENCV_VERSION >= 0x030200) && defined(VISP_HAVE_OPENCV_XFEATURES2D)
  m_mapOfDescriptorNames[DESCRIPTOR_VGG] = "VGG";
  m_mapOfDescriptorNames[DESCRIPTOR_BoostDesc] = "BoostDesc";
#endif
#endif
}

void vpKeyPoint::initMatcher(const std::string &matcherName)
{
  int descriptorType = CV_32F;
  bool firstIteration = true;
  for (std::map<std::string, cv::Ptr<cv::DescriptorExtractor> >::const_iterator it = m_extractors.begin();
       it != m_extractors.end(); ++it) {
    if (firstIteration) {
      firstIteration = false;
      descriptorType = it->second->descriptorType();
    } else {
      if (descriptorType != it->second->descriptorType()) {
        throw vpException(vpException::fatalError, "All the descriptors must have the same type !");
      }
    }
  }

  if (matcherName == "FlannBased") {
    if (m_extractors.empty()) {
      std::cout << "Warning: No extractor initialized, by default use "
                   "floating values (CV_32F) "
                   "for descriptor type !"
                << std::endl;
    }

    if (descriptorType == CV_8U) {
#if (VISP_HAVE_OPENCV_VERSION >= 0x030000)
      m_matcher = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(12, 20, 2));
#else
      m_matcher = new cv::FlannBasedMatcher(new cv::flann::LshIndexParams(12, 20, 2));
#endif
    } else {
#if (VISP_HAVE_OPENCV_VERSION >= 0x030000)
      m_matcher = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::KDTreeIndexParams>());
#else
      m_matcher = new cv::FlannBasedMatcher(new cv::flann::KDTreeIndexParams());
#endif
    }
  } else {
    m_matcher = cv::DescriptorMatcher::create(matcherName);
  }

#if (VISP_HAVE_OPENCV_VERSION >= 0x020400 && VISP_HAVE_OPENCV_VERSION < 0x030000)
  if (m_matcher != NULL && !m_useKnn && matcherName == "BruteForce") {
    m_matcher->set("crossCheck", m_useBruteForceCrossCheck);
  }
#endif

  if (!m_matcher) { //if null
    std::stringstream ss_msg;
    ss_msg << "Fail to initialize the matcher: " << matcherName
           << " or it is not available in OpenCV version: " << std::hex << VISP_HAVE_OPENCV_VERSION << ".";
    throw vpException(vpException::fatalError, ss_msg.str());
  }
}

void vpKeyPoint::insertImageMatching(const vpImage<unsigned char> &IRef, const vpImage<unsigned char> &ICurrent,
                                     vpImage<unsigned char> &IMatching)
{
  vpImagePoint topLeftCorner(0, 0);
  IMatching.insert(IRef, topLeftCorner);
  topLeftCorner = vpImagePoint(0, IRef.getWidth());
  IMatching.insert(ICurrent, topLeftCorner);
}

void vpKeyPoint::insertImageMatching(const vpImage<vpRGBa> &IRef, const vpImage<vpRGBa> &ICurrent,
                                     vpImage<vpRGBa> &IMatching)
{
  vpImagePoint topLeftCorner(0, 0);
  IMatching.insert(IRef, topLeftCorner);
  topLeftCorner = vpImagePoint(0, IRef.getWidth());
  IMatching.insert(ICurrent, topLeftCorner);
}

void vpKeyPoint::insertImageMatching(const vpImage<unsigned char> &ICurrent, vpImage<unsigned char> &IMatching)
{
  // Nb images in the training database + the current image we want to detect
  // the object
  int nbImg = (int)(m_mapOfImages.size() + 1);

  if (m_mapOfImages.empty()) {
    std::cerr << "There is no training image loaded !" << std::endl;
    return;
  }

  if (nbImg == 2) {
    // Only one training image, so we display them side by side
    insertImageMatching(m_mapOfImages.begin()->second, ICurrent, IMatching);
  } else {
    // Multiple training images, display them as a mosaic image
    int nbImgSqrt = vpMath::round(std::sqrt((double)nbImg)); //(int) std::floor(std::sqrt((double) nbImg) + 0.5);
    int nbWidth = nbImgSqrt;
    int nbHeight = nbImgSqrt;

    if (nbImgSqrt * nbImgSqrt < nbImg) {
      nbWidth++;
    }

    unsigned int maxW = ICurrent.getWidth(), maxH = ICurrent.getHeight();
    for (std::map<int, vpImage<unsigned char> >::const_iterator it = m_mapOfImages.begin(); it != m_mapOfImages.end();
         ++it) {
      if (maxW < it->second.getWidth()) {
        maxW = it->second.getWidth();
      }

      if (maxH < it->second.getHeight()) {
        maxH = it->second.getHeight();
      }
    }

    // Indexes of the current image in the grid made in order to the image is
    // in the center square in the mosaic grid
    int medianI = nbHeight / 2;
    int medianJ = nbWidth / 2;
    int medianIndex = medianI * nbWidth + medianJ;

    int cpt = 0;
    for (std::map<int, vpImage<unsigned char> >::const_iterator it = m_mapOfImages.begin(); it != m_mapOfImages.end();
         ++it, cpt++) {
      int local_cpt = cpt;
      if (cpt >= medianIndex) {
        // Shift of one unity the index of the training images which are after
        // the current image
        local_cpt++;
      }
      int indexI = local_cpt / nbWidth;
      int indexJ = local_cpt - (indexI * nbWidth);
      vpImagePoint topLeftCorner((int)maxH * indexI, (int)maxW * indexJ);

      IMatching.insert(it->second, topLeftCorner);
    }

    vpImagePoint topLeftCorner((int)maxH * medianI, (int)maxW * medianJ);
    IMatching.insert(ICurrent, topLeftCorner);
  }
}

void vpKeyPoint::insertImageMatching(const vpImage<vpRGBa> &ICurrent, vpImage<vpRGBa> &IMatching)
{
  // Nb images in the training database + the current image we want to detect
  // the object
  int nbImg = (int)(m_mapOfImages.size() + 1);

  if (m_mapOfImages.empty()) {
    std::cerr << "There is no training image loaded !" << std::endl;
    return;
  }

  if (nbImg == 2) {
    // Only one training image, so we display them side by side
    vpImage<vpRGBa> IRef;
    vpImageConvert::convert(m_mapOfImages.begin()->second, IRef);
    insertImageMatching(IRef, ICurrent, IMatching);
  } else {
    // Multiple training images, display them as a mosaic image
    int nbImgSqrt = vpMath::round(std::sqrt((double)nbImg)); //(int) std::floor(std::sqrt((double) nbImg) + 0.5);
    int nbWidth = nbImgSqrt;
    int nbHeight = nbImgSqrt;

    if (nbImgSqrt * nbImgSqrt < nbImg) {
      nbWidth++;
    }

    unsigned int maxW = ICurrent.getWidth(), maxH = ICurrent.getHeight();
    for (std::map<int, vpImage<unsigned char> >::const_iterator it = m_mapOfImages.begin(); it != m_mapOfImages.end();
         ++it) {
      if (maxW < it->second.getWidth()) {
        maxW = it->second.getWidth();
      }

      if (maxH < it->second.getHeight()) {
        maxH = it->second.getHeight();
      }
    }

    // Indexes of the current image in the grid made in order to the image is
    // in the center square in the mosaic grid
    int medianI = nbHeight / 2;
    int medianJ = nbWidth / 2;
    int medianIndex = medianI * nbWidth + medianJ;

    int cpt = 0;
    for (std::map<int, vpImage<unsigned char> >::const_iterator it = m_mapOfImages.begin(); it != m_mapOfImages.end();
         ++it, cpt++) {
      int local_cpt = cpt;
      if (cpt >= medianIndex) {
        // Shift of one unity the index of the training images which are after
        // the current image
        local_cpt++;
      }
      int indexI = local_cpt / nbWidth;
      int indexJ = local_cpt - (indexI * nbWidth);
      vpImagePoint topLeftCorner((int)maxH * indexI, (int)maxW * indexJ);

      vpImage<vpRGBa> IRef;
      vpImageConvert::convert(it->second, IRef);
      IMatching.insert(IRef, topLeftCorner);
    }

    vpImagePoint topLeftCorner((int)maxH * medianI, (int)maxW * medianJ);
    IMatching.insert(ICurrent, topLeftCorner);
  }
}

void vpKeyPoint::loadConfigFile(const std::string &configFile)
{
  vpXmlConfigParserKeyPoint xmlp;

  try {
    // Reset detector and extractor
    m_detectorNames.clear();
    m_extractorNames.clear();
    m_detectors.clear();
    m_extractors.clear();

    std::cout << " *********** Parsing XML for configuration for vpKeyPoint "
                 "************ "
              << std::endl;
    xmlp.parse(configFile);

    m_detectorNames.push_back(xmlp.getDetectorName());
    m_extractorNames.push_back(xmlp.getExtractorName());
    m_matcherName = xmlp.getMatcherName();

    switch (xmlp.getMatchingMethod()) {
    case vpXmlConfigParserKeyPoint::constantFactorDistanceThreshold:
      m_filterType = constantFactorDistanceThreshold;
      break;

    case vpXmlConfigParserKeyPoint::stdDistanceThreshold:
      m_filterType = stdDistanceThreshold;
      break;

    case vpXmlConfigParserKeyPoint::ratioDistanceThreshold:
      m_filterType = ratioDistanceThreshold;
      break;

    case vpXmlConfigParserKeyPoint::stdAndRatioDistanceThreshold:
      m_filterType = stdAndRatioDistanceThreshold;
      break;

    case vpXmlConfigParserKeyPoint::noFilterMatching:
      m_filterType = noFilterMatching;
      break;

    default:
      break;
    }

    m_matchingFactorThreshold = xmlp.getMatchingFactorThreshold();
    m_matchingRatioThreshold = xmlp.getMatchingRatioThreshold();

    m_useRansacVVS = xmlp.getUseRansacVVSPoseEstimation();
    m_useConsensusPercentage = xmlp.getUseRansacConsensusPercentage();
    m_nbRansacIterations = xmlp.getNbRansacIterations();
    m_ransacReprojectionError = xmlp.getRansacReprojectionError();
    m_nbRansacMinInlierCount = xmlp.getNbRansacMinInlierCount();
    m_ransacThreshold = xmlp.getRansacThreshold();
    m_ransacConsensusPercentage = xmlp.getRansacConsensusPercentage();

    if (m_filterType == ratioDistanceThreshold || m_filterType == stdAndRatioDistanceThreshold) {
      m_useKnn = true;
    } else {
      m_useKnn = false;
    }

    init();
  } catch (...) {
    throw vpException(vpException::ioError, "Can't open XML file \"%s\"\n ", configFile.c_str());
  }
}

void vpKeyPoint::loadLearningData(const std::string &filename, bool binaryMode, bool append)
{
  int startClassId = 0;
  int startImageId = 0;
  if (!append) {
    m_trainKeyPoints.clear();
    m_trainPoints.clear();
    m_mapOfImageId.clear();
    m_mapOfImages.clear();
  } else {
    // In append case, find the max index of keypoint class Id
    for (std::map<int, int>::const_iterator it = m_mapOfImageId.begin(); it != m_mapOfImageId.end(); ++it) {
      if (startClassId < it->first) {
        startClassId = it->first;
      }
    }

    // In append case, find the max index of images Id
    for (std::map<int, vpImage<unsigned char> >::const_iterator it = m_mapOfImages.begin(); it != m_mapOfImages.end();
         ++it) {
      if (startImageId < it->first) {
        startImageId = it->first;
      }
    }
  }

  // Get parent directory
  std::string parent = vpIoTools::getParent(filename);
  if (!parent.empty()) {
    parent += "/";
  }

  if (binaryMode) {
    std::ifstream file(filename.c_str(), std::ifstream::binary);
    if (!file.is_open()) {
      throw vpException(vpException::ioError, "Cannot open the file.");
    }

    // Read info about training images
    int nbImgs = 0;
    vpIoTools::readBinaryValueLE(file, nbImgs);

#if !defined(VISP_HAVE_MODULE_IO)
    if (nbImgs > 0) {
      std::cout << "Warning: The learning file contains image data that will "
                   "not be loaded as visp_io module "
                   "is not available !"
                << std::endl;
    }
#endif

    for (int i = 0; i < nbImgs; i++) {
      // Read image_id
      int id = 0;
      vpIoTools::readBinaryValueLE(file, id);

      int length = 0;
      vpIoTools::readBinaryValueLE(file, length);
      // Will contain the path to the training images
      char *path = new char[length + 1]; // char path[length + 1];

      for (int cpt = 0; cpt < length; cpt++) {
        char c;
        file.read((char *)(&c), sizeof(c));
        path[cpt] = c;
      }
      path[length] = '\0';

      vpImage<unsigned char> I;
#ifdef VISP_HAVE_MODULE_IO
      if (vpIoTools::isAbsolutePathname(std::string(path))) {
        vpImageIo::read(I, path);
      } else {
        vpImageIo::read(I, parent + path);
      }

      // Add the image previously loaded only if VISP_HAVE_MODULE_IO
      m_mapOfImages[id + startImageId] = I;
#endif

      // Delete path
      delete[] path;
    }

    // Read if 3D point information are saved or not
    int have3DInfoInt = 0;
    vpIoTools::readBinaryValueLE(file, have3DInfoInt);
    bool have3DInfo = have3DInfoInt != 0;

    // Read the number of descriptors
    int nRows = 0;
    vpIoTools::readBinaryValueLE(file, nRows);

    // Read the size of the descriptor
    int nCols = 0;
    vpIoTools::readBinaryValueLE(file, nCols);

    // Read the type of the descriptor
    int descriptorType = 5; // CV_32F
    vpIoTools::readBinaryValueLE(file, descriptorType);

    cv::Mat trainDescriptorsTmp = cv::Mat(nRows, nCols, descriptorType);
    for (int i = 0; i < nRows; i++) {
      // Read information about keyPoint
      float u, v, size, angle, response;
      int octave, class_id, image_id;
      vpIoTools::readBinaryValueLE(file, u);
      vpIoTools::readBinaryValueLE(file, v);
      vpIoTools::readBinaryValueLE(file, size);
      vpIoTools::readBinaryValueLE(file, angle);
      vpIoTools::readBinaryValueLE(file, response);
      vpIoTools::readBinaryValueLE(file, octave);
      vpIoTools::readBinaryValueLE(file, class_id);
      vpIoTools::readBinaryValueLE(file, image_id);
      cv::KeyPoint keyPoint(cv::Point2f(u, v), size, angle, response, octave, (class_id + startClassId));
      m_trainKeyPoints.push_back(keyPoint);

      if (image_id != -1) {
#ifdef VISP_HAVE_MODULE_IO
        // No training images if image_id == -1
        m_mapOfImageId[m_trainKeyPoints.back().class_id] = image_id + startImageId;
#endif
      }

      if (have3DInfo) {
        // Read oX, oY, oZ
        float oX, oY, oZ;
        vpIoTools::readBinaryValueLE(file, oX);
        vpIoTools::readBinaryValueLE(file, oY);
        vpIoTools::readBinaryValueLE(file, oZ);
        m_trainPoints.push_back(cv::Point3f(oX, oY, oZ));
      }

      for (int j = 0; j < nCols; j++) {
        // Read the value of the descriptor
        switch (descriptorType) {
        case CV_8U: {
          unsigned char value;
          file.read((char *)(&value), sizeof(value));
          trainDescriptorsTmp.at<unsigned char>(i, j) = value;
        } break;

        case CV_8S: {
          char value;
          file.read((char *)(&value), sizeof(value));
          trainDescriptorsTmp.at<char>(i, j) = value;
        } break;

        case CV_16U: {
          unsigned short int value;
          vpIoTools::readBinaryValueLE(file, value);
          trainDescriptorsTmp.at<unsigned short int>(i, j) = value;
        } break;

        case CV_16S: {
          short int value;
          vpIoTools::readBinaryValueLE(file, value);
          trainDescriptorsTmp.at<short int>(i, j) = value;
        } break;

        case CV_32S: {
          int value;
          vpIoTools::readBinaryValueLE(file, value);
          trainDescriptorsTmp.at<int>(i, j) = value;
        } break;

        case CV_32F: {
          float value;
          vpIoTools::readBinaryValueLE(file, value);
          trainDescriptorsTmp.at<float>(i, j) = value;
        } break;

        case CV_64F: {
          double value;
          vpIoTools::readBinaryValueLE(file, value);
          trainDescriptorsTmp.at<double>(i, j) = value;
        } break;

        default: {
          float value;
          vpIoTools::readBinaryValueLE(file, value);
          trainDescriptorsTmp.at<float>(i, j) = value;
        } break;
        }
      }
    }

    if (!append || m_trainDescriptors.empty()) {
      trainDescriptorsTmp.copyTo(m_trainDescriptors);
    } else {
      cv::vconcat(m_trainDescriptors, trainDescriptorsTmp, m_trainDescriptors);
    }

    file.close();
  } else {
    pugi::xml_document doc;

    /*parse the file and get the DOM */
    if (!doc.load_file(filename.c_str())) {
      throw vpException(vpException::ioError, "Error with file: %s", filename.c_str());
    }

    pugi::xml_node root_element = doc.document_element();

    int descriptorType = CV_32F; // float
    int nRows = 0, nCols = 0;
    int i = 0, j = 0;

    cv::Mat trainDescriptorsTmp;

    for (pugi::xml_node first_level_node = root_element.first_child(); first_level_node; first_level_node = first_level_node.next_sibling()) {

      std::string name(first_level_node.name());
      if (first_level_node.type() == pugi::node_element && name == "TrainingImageInfo") {

        for (pugi::xml_node image_info_node = first_level_node.first_child(); image_info_node; image_info_node = image_info_node.next_sibling()) {
          name = std::string(image_info_node.name());

          if (name == "trainImg") {
            // Read image_id
            int id = image_info_node.attribute("image_id").as_int();

            vpImage<unsigned char> I;
#ifdef VISP_HAVE_MODULE_IO
            std::string path(image_info_node.text().as_string());
            // Read path to the training images
            if (vpIoTools::isAbsolutePathname(std::string(path))) {
              vpImageIo::read(I, path);
            } else {
              vpImageIo::read(I, parent + path);
            }

            // Add the image previously loaded only if VISP_HAVE_MODULE_IO
            m_mapOfImages[id + startImageId] = I;
#endif
          }
        }
      } else if (first_level_node.type() == pugi::node_element && name == "DescriptorsInfo") {
        for (pugi::xml_node descriptors_info_node = first_level_node.first_child(); descriptors_info_node;
             descriptors_info_node = descriptors_info_node.next_sibling()) {
          if (descriptors_info_node.type() == pugi::node_element) {
            name = std::string(descriptors_info_node.name());

            if (name == "nrows") {
              nRows = descriptors_info_node.text().as_int();
            } else if (name == "ncols") {
              nCols = descriptors_info_node.text().as_int();
            } else if (name == "type") {
              descriptorType = descriptors_info_node.text().as_int();
            }
          }
        }

        trainDescriptorsTmp = cv::Mat(nRows, nCols, descriptorType);
      } else if (first_level_node.type() == pugi::node_element && name == "DescriptorInfo") {
        double u = 0.0, v = 0.0, size = 0.0, angle = 0.0, response = 0.0;
        int octave = 0, class_id = 0, image_id = 0;
        double oX = 0.0, oY = 0.0, oZ = 0.0;

        std::stringstream ss;

        for (pugi::xml_node point_node = first_level_node.first_child(); point_node; point_node = point_node.next_sibling()) {
          if (point_node.type() == pugi::node_element) {
            name = std::string(point_node.name());

            // Read information about keypoints
            if (name == "u") {
              u = point_node.text().as_double();
            } else if (name == "v") {
              v = point_node.text().as_double();
            } else if (name == "size") {
              size = point_node.text().as_double();
            } else if (name == "angle") {
              angle = point_node.text().as_double();
            } else if (name == "response") {
              response = point_node.text().as_double();
            } else if (name == "octave") {
              octave = point_node.text().as_int();
            } else if (name == "class_id") {
              class_id = point_node.text().as_int();
              cv::KeyPoint keyPoint(cv::Point2f((float)u, (float)v), (float)size, (float)angle, (float)response, octave,
                                    (class_id + startClassId));
              m_trainKeyPoints.push_back(keyPoint);
            } else if (name == "image_id") {
              image_id = point_node.text().as_int();
              if (image_id != -1) {
#ifdef VISP_HAVE_MODULE_IO
                // No training images if image_id == -1
                m_mapOfImageId[m_trainKeyPoints.back().class_id] = image_id + startImageId;
#endif
              }
            } else if (name == "oX") {
              oX = point_node.text().as_double();
            } else if (name == "oY") {
              oY = point_node.text().as_double();
            } else if (name == "oZ") {
              oZ = point_node.text().as_double();
              m_trainPoints.push_back(cv::Point3f((float)oX, (float)oY, (float)oZ));
            } else if (name == "desc") {
              j = 0;

              for (pugi::xml_node descriptor_value_node = point_node.first_child(); descriptor_value_node;
                   descriptor_value_node = descriptor_value_node.next_sibling()) {

                if (descriptor_value_node.type() == pugi::node_element) {
                  // Read descriptors values
                  std::string parseStr(descriptor_value_node.text().as_string());
                  ss.clear();
                  ss.str(parseStr);

                  if (!ss.fail()) {
                    switch (descriptorType) {
                    case CV_8U: {
                      // Parse the numeric value [0 ; 255] to an int
                      int parseValue;
                      ss >> parseValue;
                      trainDescriptorsTmp.at<unsigned char>(i, j) = (unsigned char)parseValue;
                    } break;

                    case CV_8S:
                      // Parse the numeric value [-128 ; 127] to an int
                      int parseValue;
                      ss >> parseValue;
                      trainDescriptorsTmp.at<char>(i, j) = (char)parseValue;
                      break;

                    case CV_16U:
                      ss >> trainDescriptorsTmp.at<unsigned short int>(i, j);
                      break;

                    case CV_16S:
                      ss >> trainDescriptorsTmp.at<short int>(i, j);
                      break;

                    case CV_32S:
                      ss >> trainDescriptorsTmp.at<int>(i, j);
                      break;

                    case CV_32F:
                      ss >> trainDescriptorsTmp.at<float>(i, j);
                      break;

                    case CV_64F:
                      ss >> trainDescriptorsTmp.at<double>(i, j);
                      break;

                    default:
                      ss >> trainDescriptorsTmp.at<float>(i, j);
                      break;
                    }
                  } else {
                    std::cerr << "Error when converting:" << ss.str() << std::endl;
                  }

                  j++;
                }
              }
            }
          }
        }
        i++;
      }
    }

    if (!append || m_trainDescriptors.empty()) {
      trainDescriptorsTmp.copyTo(m_trainDescriptors);
    } else {
      cv::vconcat(m_trainDescriptors, trainDescriptorsTmp, m_trainDescriptors);
    }
  }

  // Convert OpenCV type to ViSP type for compatibility
  vpConvert::convertFromOpenCV(m_trainKeyPoints, referenceImagePointsList);
  vpConvert::convertFromOpenCV(this->m_trainPoints, m_trainVpPoints);

  // Add train descriptors in matcher object
  m_matcher->clear();
  m_matcher->add(std::vector<cv::Mat>(1, m_trainDescriptors));

  // Set _reference_computed to true as we load a learning file
  _reference_computed = true;

  // Set m_currentImageId
  m_currentImageId = (int)m_mapOfImages.size();
}

void vpKeyPoint::match(const cv::Mat &trainDescriptors, const cv::Mat &queryDescriptors,
                       std::vector<cv::DMatch> &matches, double &elapsedTime)
{
  double t = vpTime::measureTimeMs();

  if (m_useKnn) {
    m_knnMatches.clear();

    if (m_useMatchTrainToQuery) {
      std::vector<std::vector<cv::DMatch> > knnMatchesTmp;

      // Match train descriptors to query descriptors
      cv::Ptr<cv::DescriptorMatcher> matcherTmp = m_matcher->clone(true);
      matcherTmp->knnMatch(trainDescriptors, queryDescriptors, knnMatchesTmp, 2);

      for (std::vector<std::vector<cv::DMatch> >::const_iterator it1 = knnMatchesTmp.begin();
           it1 != knnMatchesTmp.end(); ++it1) {
        std::vector<cv::DMatch> tmp;
        for (std::vector<cv::DMatch>::const_iterator it2 = it1->begin(); it2 != it1->end(); ++it2) {
          tmp.push_back(cv::DMatch(it2->trainIdx, it2->queryIdx, it2->distance));
        }
        m_knnMatches.push_back(tmp);
      }

      matches.resize(m_knnMatches.size());
      std::transform(m_knnMatches.begin(), m_knnMatches.end(), matches.begin(), knnToDMatch);
    } else {
      // Match query descriptors to train descriptors
      m_matcher->knnMatch(queryDescriptors, m_knnMatches, 2);
      matches.resize(m_knnMatches.size());
      std::transform(m_knnMatches.begin(), m_knnMatches.end(), matches.begin(), knnToDMatch);
    }
  } else {
    matches.clear();

    if (m_useMatchTrainToQuery) {
      std::vector<cv::DMatch> matchesTmp;
      // Match train descriptors to query descriptors
      cv::Ptr<cv::DescriptorMatcher> matcherTmp = m_matcher->clone(true);
      matcherTmp->match(trainDescriptors, queryDescriptors, matchesTmp);

      for (std::vector<cv::DMatch>::const_iterator it = matchesTmp.begin(); it != matchesTmp.end(); ++it) {
        matches.push_back(cv::DMatch(it->trainIdx, it->queryIdx, it->distance));
      }
    } else {
      // Match query descriptors to train descriptors
      m_matcher->match(queryDescriptors, matches);
    }
  }
  elapsedTime = vpTime::measureTimeMs() - t;
}

unsigned int vpKeyPoint::matchPoint(const vpImage<unsigned char> &I) { return matchPoint(I, vpRect()); }

unsigned int vpKeyPoint::matchPoint(const vpImage<vpRGBa> &I_color) { return matchPoint(I_color, vpRect()); }

unsigned int vpKeyPoint::matchPoint(const vpImage<unsigned char> &I, const vpImagePoint &iP, unsigned int height,
                                    unsigned int width)
{
  return matchPoint(I, vpRect(iP, width, height));
}

unsigned int vpKeyPoint::matchPoint(const vpImage<vpRGBa> &I_color, const vpImagePoint &iP, unsigned int height,
                                    unsigned int width)
{
  return matchPoint(I_color, vpRect(iP, width, height));
}

unsigned int vpKeyPoint::matchPoint(const vpImage<unsigned char> &I, const vpRect &rectangle)
{
  if (m_trainDescriptors.empty()) {
    std::cerr << "Reference is empty." << std::endl;
    if (!_reference_computed) {
      std::cerr << "Reference is not computed." << std::endl;
    }
    std::cerr << "Matching is not possible." << std::endl;

    return 0;
  }

  if (m_useAffineDetection) {
    std::vector<std::vector<cv::KeyPoint> > listOfQueryKeyPoints;
    std::vector<cv::Mat> listOfQueryDescriptors;

    // Detect keypoints and extract descriptors on multiple images
    detectExtractAffine(I, listOfQueryKeyPoints, listOfQueryDescriptors);

    // Flatten the different train lists
    m_queryKeyPoints.clear();
    for (std::vector<std::vector<cv::KeyPoint> >::const_iterator it = listOfQueryKeyPoints.begin();
         it != listOfQueryKeyPoints.end(); ++it) {
      m_queryKeyPoints.insert(m_queryKeyPoints.end(), it->begin(), it->end());
    }

    bool first = true;
    for (std::vector<cv::Mat>::const_iterator it = listOfQueryDescriptors.begin(); it != listOfQueryDescriptors.end();
         ++it) {
      if (first) {
        first = false;
        it->copyTo(m_queryDescriptors);
      } else {
        m_queryDescriptors.push_back(*it);
      }
    }
  } else {
    detect(I, m_queryKeyPoints, m_detectionTime, rectangle);
    extract(I, m_queryKeyPoints, m_queryDescriptors, m_extractionTime);
  }

  return matchPoint(m_queryKeyPoints, m_queryDescriptors);
}

unsigned int vpKeyPoint::matchPoint(const std::vector<cv::KeyPoint> &queryKeyPoints, const cv::Mat &queryDescriptors)
{
  m_queryKeyPoints = queryKeyPoints;
  m_queryDescriptors = queryDescriptors;

  match(m_trainDescriptors, m_queryDescriptors, m_matches, m_matchingTime);

  if (m_filterType != noFilterMatching) {
    m_queryFilteredKeyPoints.clear();
    m_objectFilteredPoints.clear();
    m_filteredMatches.clear();

    filterMatches();
  } else {
    if (m_useMatchTrainToQuery) {
      // Add only query keypoints matched with a train keypoints
      m_queryFilteredKeyPoints.clear();
      m_filteredMatches.clear();
      for (std::vector<cv::DMatch>::const_iterator it = m_matches.begin(); it != m_matches.end(); ++it) {
        m_filteredMatches.push_back(cv::DMatch((int)m_queryFilteredKeyPoints.size(), it->trainIdx, it->distance));
        m_queryFilteredKeyPoints.push_back(m_queryKeyPoints[(size_t)it->queryIdx]);
      }
    } else {
      m_queryFilteredKeyPoints = m_queryKeyPoints;
      m_filteredMatches = m_matches;
    }

    if (!m_trainPoints.empty()) {
      m_objectFilteredPoints.clear();
      // Add 3D object points such as the same index in
      // m_queryFilteredKeyPoints and in m_objectFilteredPoints
      // matches to the same train object
      for (std::vector<cv::DMatch>::const_iterator it = m_matches.begin(); it != m_matches.end(); ++it) {
        // m_matches is normally ordered following the queryDescriptor index
        m_objectFilteredPoints.push_back(m_trainPoints[(size_t)it->trainIdx]);
      }
    }
  }

  // Convert OpenCV type to ViSP type for compatibility
  vpConvert::convertFromOpenCV(m_queryFilteredKeyPoints, currentImagePointsList);
  vpConvert::convertFromOpenCV(m_filteredMatches, matchedReferencePoints);

  return static_cast<unsigned int>(m_filteredMatches.size());
}

unsigned int vpKeyPoint::matchPoint(const vpImage<vpRGBa> &I_color, const vpRect &rectangle)
{
  vpImageConvert::convert(I_color, m_I);
  return matchPoint(m_I, rectangle);
}

bool vpKeyPoint::matchPoint(const vpImage<unsigned char> &I, const vpCameraParameters &cam, vpHomogeneousMatrix &cMo,
                            bool (*func)(const vpHomogeneousMatrix &), const vpRect &rectangle)
{
  double error, elapsedTime;
  return matchPoint(I, cam, cMo, error, elapsedTime, func, rectangle);
}

bool vpKeyPoint::matchPoint(const vpImage<vpRGBa> &I_color, const vpCameraParameters &cam, vpHomogeneousMatrix &cMo,
                            bool (*func)(const vpHomogeneousMatrix &), const vpRect &rectangle)
{
  double error, elapsedTime;
  return matchPoint(I_color, cam, cMo, error, elapsedTime, func, rectangle);
}

bool vpKeyPoint::matchPoint(const vpImage<unsigned char> &I, const vpCameraParameters &cam, vpHomogeneousMatrix &cMo,
                            double &error, double &elapsedTime, bool (*func)(const vpHomogeneousMatrix &),
                            const vpRect &rectangle)
{
  // Check if we have training descriptors
  if (m_trainDescriptors.empty()) {
    std::cerr << "Reference is empty." << std::endl;
    if (!_reference_computed) {
      std::cerr << "Reference is not computed." << std::endl;
    }
    std::cerr << "Matching is not possible." << std::endl;

    return false;
  }

  if (m_useAffineDetection) {
    std::vector<std::vector<cv::KeyPoint> > listOfQueryKeyPoints;
    std::vector<cv::Mat> listOfQueryDescriptors;

    // Detect keypoints and extract descriptors on multiple images
    detectExtractAffine(I, listOfQueryKeyPoints, listOfQueryDescriptors);

    // Flatten the different train lists
    m_queryKeyPoints.clear();
    for (std::vector<std::vector<cv::KeyPoint> >::const_iterator it = listOfQueryKeyPoints.begin();
         it != listOfQueryKeyPoints.end(); ++it) {
      m_queryKeyPoints.insert(m_queryKeyPoints.end(), it->begin(), it->end());
    }

    bool first = true;
    for (std::vector<cv::Mat>::const_iterator it = listOfQueryDescriptors.begin(); it != listOfQueryDescriptors.end();
         ++it) {
      if (first) {
        first = false;
        it->copyTo(m_queryDescriptors);
      } else {
        m_queryDescriptors.push_back(*it);
      }
    }
  } else {
    detect(I, m_queryKeyPoints, m_detectionTime, rectangle);
    extract(I, m_queryKeyPoints, m_queryDescriptors, m_extractionTime);
  }

  match(m_trainDescriptors, m_queryDescriptors, m_matches, m_matchingTime);

  elapsedTime = m_detectionTime + m_extractionTime + m_matchingTime;

  if (m_filterType != noFilterMatching) {
    m_queryFilteredKeyPoints.clear();
    m_objectFilteredPoints.clear();
    m_filteredMatches.clear();

    filterMatches();
  } else {
    if (m_useMatchTrainToQuery) {
      // Add only query keypoints matched with a train keypoints
      m_queryFilteredKeyPoints.clear();
      m_filteredMatches.clear();
      for (std::vector<cv::DMatch>::const_iterator it = m_matches.begin(); it != m_matches.end(); ++it) {
        m_filteredMatches.push_back(cv::DMatch((int)m_queryFilteredKeyPoints.size(), it->trainIdx, it->distance));
        m_queryFilteredKeyPoints.push_back(m_queryKeyPoints[(size_t)it->queryIdx]);
      }
    } else {
      m_queryFilteredKeyPoints = m_queryKeyPoints;
      m_filteredMatches = m_matches;
    }

    if (!m_trainPoints.empty()) {
      m_objectFilteredPoints.clear();
      // Add 3D object points such as the same index in
      // m_queryFilteredKeyPoints and in m_objectFilteredPoints
      // matches to the same train object
      for (std::vector<cv::DMatch>::const_iterator it = m_matches.begin(); it != m_matches.end(); ++it) {
        // m_matches is normally ordered following the queryDescriptor index
        m_objectFilteredPoints.push_back(m_trainPoints[(size_t)it->trainIdx]);
      }
    }
  }

  // Convert OpenCV type to ViSP type for compatibility
  vpConvert::convertFromOpenCV(m_queryFilteredKeyPoints, currentImagePointsList);
  vpConvert::convertFromOpenCV(m_filteredMatches, matchedReferencePoints);

  // error = std::numeric_limits<double>::max(); // create an error under
  // Windows. To fix it we have to add #undef max
  error = DBL_MAX;
  m_ransacInliers.clear();
  m_ransacOutliers.clear();

  if (m_useRansacVVS) {
    std::vector<vpPoint> objectVpPoints(m_objectFilteredPoints.size());
    size_t cpt = 0;
    // Create a list of vpPoint with 2D coordinates (current keypoint
    // location) + 3D coordinates (world/object coordinates)
    for (std::vector<cv::Point3f>::const_iterator it = m_objectFilteredPoints.begin();
         it != m_objectFilteredPoints.end(); ++it, cpt++) {
      vpPoint pt;
      pt.setWorldCoordinates(it->x, it->y, it->z);

      vpImagePoint imP(m_queryFilteredKeyPoints[cpt].pt.y, m_queryFilteredKeyPoints[cpt].pt.x);

      double x = 0.0, y = 0.0;
      vpPixelMeterConversion::convertPoint(cam, imP, x, y);
      pt.set_x(x);
      pt.set_y(y);

      objectVpPoints[cpt] = pt;
    }

    std::vector<vpPoint> inliers;
    std::vector<unsigned int> inlierIndex;

    bool res = computePose(objectVpPoints, cMo, inliers, inlierIndex, m_poseTime, func);

    std::map<unsigned int, bool> mapOfInlierIndex;
    m_matchRansacKeyPointsToPoints.clear();

    for (std::vector<unsigned int>::const_iterator it = inlierIndex.begin(); it != inlierIndex.end(); ++it) {
      m_matchRansacKeyPointsToPoints.push_back(std::pair<cv::KeyPoint, cv::Point3f>(
          m_queryFilteredKeyPoints[(size_t)(*it)], m_objectFilteredPoints[(size_t)(*it)]));
      mapOfInlierIndex[*it] = true;
    }

    for (size_t i = 0; i < m_queryFilteredKeyPoints.size(); i++) {
      if (mapOfInlierIndex.find((unsigned int)i) == mapOfInlierIndex.end()) {
        m_ransacOutliers.push_back(vpImagePoint(m_queryFilteredKeyPoints[i].pt.y, m_queryFilteredKeyPoints[i].pt.x));
      }
    }

    error = computePoseEstimationError(m_matchRansacKeyPointsToPoints, cam, cMo);

    m_ransacInliers.resize(m_matchRansacKeyPointsToPoints.size());
    std::transform(m_matchRansacKeyPointsToPoints.begin(), m_matchRansacKeyPointsToPoints.end(),
                   m_ransacInliers.begin(), matchRansacToVpImage);

    elapsedTime += m_poseTime;

    return res;
  } else {
    std::vector<cv::Point2f> imageFilteredPoints;
    cv::KeyPoint::convert(m_queryFilteredKeyPoints, imageFilteredPoints);
    std::vector<int> inlierIndex;
    bool res = computePose(imageFilteredPoints, m_objectFilteredPoints, cam, cMo, inlierIndex, m_poseTime);

    std::map<int, bool> mapOfInlierIndex;
    m_matchRansacKeyPointsToPoints.clear();

    for (std::vector<int>::const_iterator it = inlierIndex.begin(); it != inlierIndex.end(); ++it) {
      m_matchRansacKeyPointsToPoints.push_back(std::pair<cv::KeyPoint, cv::Point3f>(
          m_queryFilteredKeyPoints[(size_t)(*it)], m_objectFilteredPoints[(size_t)(*it)]));
      mapOfInlierIndex[*it] = true;
    }

    for (size_t i = 0; i < m_queryFilteredKeyPoints.size(); i++) {
      if (mapOfInlierIndex.find((int)i) == mapOfInlierIndex.end()) {
        m_ransacOutliers.push_back(vpImagePoint(m_queryFilteredKeyPoints[i].pt.y, m_queryFilteredKeyPoints[i].pt.x));
      }
    }

    error = computePoseEstimationError(m_matchRansacKeyPointsToPoints, cam, cMo);

    m_ransacInliers.resize(m_matchRansacKeyPointsToPoints.size());
    std::transform(m_matchRansacKeyPointsToPoints.begin(), m_matchRansacKeyPointsToPoints.end(),
                   m_ransacInliers.begin(), matchRansacToVpImage);

    elapsedTime += m_poseTime;

    return res;
  }
}

bool vpKeyPoint::matchPoint(const vpImage<vpRGBa> &I_color, const vpCameraParameters &cam, vpHomogeneousMatrix &cMo,
                            double &error, double &elapsedTime, bool (*func)(const vpHomogeneousMatrix &),
                            const vpRect &rectangle)
{
  vpImageConvert::convert(I_color, m_I);
  return (matchPoint(m_I, cam, cMo, error, elapsedTime, func, rectangle));
}


bool vpKeyPoint::matchPointAndDetect(const vpImage<unsigned char> &I, vpRect &boundingBox,
                                     vpImagePoint &centerOfGravity, const bool isPlanarObject,
                                     std::vector<vpImagePoint> *imPts1, std::vector<vpImagePoint> *imPts2,
                                     double *meanDescriptorDistance, double *detection_score, const vpRect &rectangle)
{
  if (imPts1 != NULL && imPts2 != NULL) {
    imPts1->clear();
    imPts2->clear();
  }

  matchPoint(I, rectangle);

  double meanDescriptorDistanceTmp = 0.0;
  for (std::vector<cv::DMatch>::const_iterator it = m_filteredMatches.begin(); it != m_filteredMatches.end(); ++it) {
    meanDescriptorDistanceTmp += (double)it->distance;
  }

  meanDescriptorDistanceTmp /= (double)m_filteredMatches.size();
  double score = (double)m_filteredMatches.size() / meanDescriptorDistanceTmp;

  if (meanDescriptorDistance != NULL) {
    *meanDescriptorDistance = meanDescriptorDistanceTmp;
  }
  if (detection_score != NULL) {
    *detection_score = score;
  }

  if (m_filteredMatches.size() >= 4) {
    // Training / Reference 2D points
    std::vector<cv::Point2f> points1(m_filteredMatches.size());
    // Query / Current 2D points
    std::vector<cv::Point2f> points2(m_filteredMatches.size());

    for (size_t i = 0; i < m_filteredMatches.size(); i++) {
      points1[i] = cv::Point2f(m_trainKeyPoints[(size_t)m_filteredMatches[i].trainIdx].pt);
      points2[i] = cv::Point2f(m_queryFilteredKeyPoints[(size_t)m_filteredMatches[i].queryIdx].pt);
    }

    std::vector<vpImagePoint> inliers;
    if (isPlanarObject) {
#if (VISP_HAVE_OPENCV_VERSION < 0x030000)
      cv::Mat homographyMatrix = cv::findHomography(points1, points2, CV_RANSAC);
#else
      cv::Mat homographyMatrix = cv::findHomography(points1, points2, cv::RANSAC);
#endif

      for (size_t i = 0; i < m_filteredMatches.size(); i++) {
        // Compute reprojection error
        cv::Mat realPoint = cv::Mat(3, 1, CV_64F);
        realPoint.at<double>(0, 0) = points1[i].x;
        realPoint.at<double>(1, 0) = points1[i].y;
        realPoint.at<double>(2, 0) = 1.f;

        cv::Mat reprojectedPoint = homographyMatrix * realPoint;
        double err_x = (reprojectedPoint.at<double>(0, 0) / reprojectedPoint.at<double>(2, 0)) - points2[i].x;
        double err_y = (reprojectedPoint.at<double>(1, 0) / reprojectedPoint.at<double>(2, 0)) - points2[i].y;
        double reprojectionError = std::sqrt(err_x * err_x + err_y * err_y);

        if (reprojectionError < 6.0) {
          inliers.push_back(vpImagePoint((double)points2[i].y, (double)points2[i].x));
          if (imPts1 != NULL) {
            imPts1->push_back(vpImagePoint((double)points1[i].y, (double)points1[i].x));
          }

          if (imPts2 != NULL) {
            imPts2->push_back(vpImagePoint((double)points2[i].y, (double)points2[i].x));
          }
        }
      }
    } else if (m_filteredMatches.size() >= 8) {
      cv::Mat fundamentalInliers;
      cv::Mat fundamentalMatrix = cv::findFundamentalMat(points1, points2, cv::FM_RANSAC, 3, 0.99, fundamentalInliers);

      for (size_t i = 0; i < (size_t)fundamentalInliers.rows; i++) {
        if (fundamentalInliers.at<uchar>((int)i, 0)) {
          inliers.push_back(vpImagePoint((double)points2[i].y, (double)points2[i].x));

          if (imPts1 != NULL) {
            imPts1->push_back(vpImagePoint((double)points1[i].y, (double)points1[i].x));
          }

          if (imPts2 != NULL) {
            imPts2->push_back(vpImagePoint((double)points2[i].y, (double)points2[i].x));
          }
        }
      }
    }

    if (!inliers.empty()) {
      // Build a polygon with the list of inlier keypoints detected in the
      // current image to get the bounding box
      vpPolygon polygon(inliers);
      boundingBox = polygon.getBoundingBox();

      // Compute the center of gravity
      double meanU = 0.0, meanV = 0.0;
      for (std::vector<vpImagePoint>::const_iterator it = inliers.begin(); it != inliers.end(); ++it) {
        meanU += it->get_u();
        meanV += it->get_v();
      }

      meanU /= (double)inliers.size();
      meanV /= (double)inliers.size();

      centerOfGravity.set_u(meanU);
      centerOfGravity.set_v(meanV);
    }
  } else {
    // Too few matches
    return false;
  }

  if (m_detectionMethod == detectionThreshold) {
    return meanDescriptorDistanceTmp < m_detectionThreshold;
  } else {
    return score > m_detectionScore;
  }
}

bool vpKeyPoint::matchPointAndDetect(const vpImage<unsigned char> &I, const vpCameraParameters &cam,
                                     vpHomogeneousMatrix &cMo, double &error, double &elapsedTime, vpRect &boundingBox,
                                     vpImagePoint &centerOfGravity, bool (*func)(const vpHomogeneousMatrix &),
                                     const vpRect &rectangle)
{
  bool isMatchOk = matchPoint(I, cam, cMo, error, elapsedTime, func, rectangle);
  if (isMatchOk) {
    // Use the pose estimated to project the model points in the image
    vpPoint pt;
    vpImagePoint imPt;
    std::vector<vpImagePoint> modelImagePoints(m_trainVpPoints.size());
    size_t cpt = 0;
    for (std::vector<vpPoint>::const_iterator it = m_trainVpPoints.begin(); it != m_trainVpPoints.end(); ++it, cpt++) {
      pt = *it;
      pt.project(cMo);
      vpMeterPixelConversion::convertPoint(cam, pt.get_x(), pt.get_y(), imPt);
      modelImagePoints[cpt] = imPt;
    }

    // Build a polygon with the list of model image points to get the bounding
    // box
    vpPolygon polygon(modelImagePoints);
    boundingBox = polygon.getBoundingBox();

    // Compute the center of gravity of the current inlier keypoints
    double meanU = 0.0, meanV = 0.0;
    for (std::vector<vpImagePoint>::const_iterator it = m_ransacInliers.begin(); it != m_ransacInliers.end(); ++it) {
      meanU += it->get_u();
      meanV += it->get_v();
    }

    meanU /= (double)m_ransacInliers.size();
    meanV /= (double)m_ransacInliers.size();

    centerOfGravity.set_u(meanU);
    centerOfGravity.set_v(meanV);
  }

  return isMatchOk;
}

void vpKeyPoint::detectExtractAffine(const vpImage<unsigned char> &I,
                                     std::vector<std::vector<cv::KeyPoint> > &listOfKeypoints,
                                     std::vector<cv::Mat> &listOfDescriptors,
                                     std::vector<vpImage<unsigned char> > *listOfAffineI)
{
#if 0
  cv::Mat img;
  vpImageConvert::convert(I, img);
  listOfKeypoints.clear();
  listOfDescriptors.clear();

  for (int tl = 1; tl < 6; tl++) {
    double t = pow(2, 0.5 * tl);
    for (int phi = 0; phi < 180; phi += (int)(72.0 / t)) {
      std::vector<cv::KeyPoint> keypoints;
      cv::Mat descriptors;

      cv::Mat timg, mask, Ai;
      img.copyTo(timg);

      affineSkew(t, phi, timg, mask, Ai);


      if(listOfAffineI != NULL) {
        cv::Mat img_disp;
        bitwise_and(mask, timg, img_disp);
        vpImage<unsigned char> tI;
        vpImageConvert::convert(img_disp, tI);
        listOfAffineI->push_back(tI);
      }
#if 0
      cv::Mat img_disp;
      cv::bitwise_and(mask, timg, img_disp);
      cv::namedWindow( "Skew", cv::WINDOW_AUTOSIZE ); // Create a window for display.
      cv::imshow( "Skew", img_disp );
      cv::waitKey(0);
#endif

      for(std::map<std::string, cv::Ptr<cv::FeatureDetector> >::const_iterator it = m_detectors.begin();
          it != m_detectors.end(); ++it) {
        std::vector<cv::KeyPoint> kp;
        it->second->detect(timg, kp, mask);
        keypoints.insert(keypoints.end(), kp.begin(), kp.end());
      }

      double elapsedTime;
      extract(timg, keypoints, descriptors, elapsedTime);

      for(unsigned int i = 0; i < keypoints.size(); i++) {
        cv::Point3f kpt(keypoints[i].pt.x, keypoints[i].pt.y, 1.f);
        cv::Mat kpt_t = Ai * cv::Mat(kpt);
        keypoints[i].pt.x = kpt_t.at<float>(0, 0);
        keypoints[i].pt.y = kpt_t.at<float>(1, 0);
      }

      listOfKeypoints.push_back(keypoints);
      listOfDescriptors.push_back(descriptors);
    }
  }

#else
  cv::Mat img;
  vpImageConvert::convert(I, img);

  // Create a vector for storing the affine skew parameters
  std::vector<std::pair<double, int> > listOfAffineParams;
  for (int tl = 1; tl < 6; tl++) {
    double t = pow(2, 0.5 * tl);
    for (int phi = 0; phi < 180; phi += (int)(72.0 / t)) {
      listOfAffineParams.push_back(std::pair<double, int>(t, phi));
    }
  }

  listOfKeypoints.resize(listOfAffineParams.size());
  listOfDescriptors.resize(listOfAffineParams.size());

  if (listOfAffineI != NULL) {
    listOfAffineI->resize(listOfAffineParams.size());
  }

#ifdef VISP_HAVE_OPENMP
#pragma omp parallel for
#endif
  for (int cpt = 0; cpt < static_cast<int>(listOfAffineParams.size()); cpt++) {
    std::vector<cv::KeyPoint> keypoints;
    cv::Mat descriptors;

    cv::Mat timg, mask, Ai;
    img.copyTo(timg);

    affineSkew(listOfAffineParams[(size_t)cpt].first, listOfAffineParams[(size_t)cpt].second, timg, mask, Ai);

    if (listOfAffineI != NULL) {
      cv::Mat img_disp;
      bitwise_and(mask, timg, img_disp);
      vpImage<unsigned char> tI;
      vpImageConvert::convert(img_disp, tI);
      (*listOfAffineI)[(size_t)cpt] = tI;
    }

#if 0
    cv::Mat img_disp;
    cv::bitwise_and(mask, timg, img_disp);
    cv::namedWindow( "Skew", cv::WINDOW_AUTOSIZE ); // Create a window for display.
    cv::imshow( "Skew", img_disp );
    cv::waitKey(0);
#endif

    for (std::map<std::string, cv::Ptr<cv::FeatureDetector> >::const_iterator it = m_detectors.begin();
         it != m_detectors.end(); ++it) {
      std::vector<cv::KeyPoint> kp;
      it->second->detect(timg, kp, mask);
      keypoints.insert(keypoints.end(), kp.begin(), kp.end());
    }

    double elapsedTime;
    extract(timg, keypoints, descriptors, elapsedTime);

    for (size_t i = 0; i < keypoints.size(); i++) {
      cv::Point3f kpt(keypoints[i].pt.x, keypoints[i].pt.y, 1.f);
      cv::Mat kpt_t = Ai * cv::Mat(kpt);
      keypoints[i].pt.x = kpt_t.at<float>(0, 0);
      keypoints[i].pt.y = kpt_t.at<float>(1, 0);
    }

    listOfKeypoints[(size_t)cpt] = keypoints;
    listOfDescriptors[(size_t)cpt] = descriptors;
  }
#endif
}

void vpKeyPoint::reset()
{
  // vpBasicKeyPoint class
  referenceImagePointsList.clear();
  currentImagePointsList.clear();
  matchedReferencePoints.clear();
  _reference_computed = false;

  m_computeCovariance = false;
  m_covarianceMatrix = vpMatrix();
  m_currentImageId = 0;
  m_detectionMethod = detectionScore;
  m_detectionScore = 0.15;
  m_detectionThreshold = 100.0;
  m_detectionTime = 0.0;
  m_detectorNames.clear();
  m_detectors.clear();
  m_extractionTime = 0.0;
  m_extractorNames.clear();
  m_extractors.clear();
  m_filteredMatches.clear();
  m_filterType = ratioDistanceThreshold;
  m_imageFormat = jpgImageFormat;
  m_knnMatches.clear();
  m_mapOfImageId.clear();
  m_mapOfImages.clear();
  m_matcher = cv::Ptr<cv::DescriptorMatcher>();
  m_matcherName = "BruteForce-Hamming";
  m_matches.clear();
  m_matchingFactorThreshold = 2.0;
  m_matchingRatioThreshold = 0.85;
  m_matchingTime = 0.0;
  m_matchRansacKeyPointsToPoints.clear();
  m_nbRansacIterations = 200;
  m_nbRansacMinInlierCount = 100;
  m_objectFilteredPoints.clear();
  m_poseTime = 0.0;
  m_queryDescriptors = cv::Mat();
  m_queryFilteredKeyPoints.clear();
  m_queryKeyPoints.clear();
  m_ransacConsensusPercentage = 20.0;
  m_ransacFilterFlag = vpPose::NO_FILTER;
  m_ransacInliers.clear();
  m_ransacOutliers.clear();
  m_ransacParallel = true;
  m_ransacParallelNbThreads = 0;
  m_ransacReprojectionError = 6.0;
  m_ransacThreshold = 0.01;
  m_trainDescriptors = cv::Mat();
  m_trainKeyPoints.clear();
  m_trainPoints.clear();
  m_trainVpPoints.clear();
  m_useAffineDetection = false;
#if (VISP_HAVE_OPENCV_VERSION >= 0x020400 && VISP_HAVE_OPENCV_VERSION < 0x030000)
  m_useBruteForceCrossCheck = true;
#endif
  m_useConsensusPercentage = false;
  m_useKnn = true; // as m_filterType == ratioDistanceThreshold
  m_useMatchTrainToQuery = false;
  m_useRansacVVS = true;
  m_useSingleMatchFilter = true;

  m_detectorNames.push_back("ORB");
  m_extractorNames.push_back("ORB");

  init();
}

void vpKeyPoint::saveLearningData(const std::string &filename, bool binaryMode, bool saveTrainingImages)
{
  std::string parent = vpIoTools::getParent(filename);
  if (!parent.empty()) {
    vpIoTools::makeDirectory(parent);
  }

  std::map<int, std::string> mapOfImgPath;
  if (saveTrainingImages) {
#ifdef VISP_HAVE_MODULE_IO
    // Save the training image files in the same directory
    unsigned int cpt = 0;

    for (std::map<int, vpImage<unsigned char> >::const_iterator it = m_mapOfImages.begin(); it != m_mapOfImages.end();
         ++it, cpt++) {
      if (cpt > 999) {
        throw vpException(vpException::fatalError, "The number of training images to save is too big !");
      }

      std::stringstream ss;
      ss << "train_image_" << std::setfill('0') << std::setw(3) << cpt;

      switch (m_imageFormat) {
      case jpgImageFormat:
        ss << ".jpg";
        break;

      case pngImageFormat:
        ss << ".png";
        break;

      case ppmImageFormat:
        ss << ".ppm";
        break;

      case pgmImageFormat:
        ss << ".pgm";
        break;

      default:
        ss << ".png";
        break;
      }

      std::string imgFilename = ss.str();
      mapOfImgPath[it->first] = imgFilename;
      vpImageIo::write(it->second, parent + (!parent.empty() ? "/" : "") + imgFilename);
    }
#else
    std::cout << "Warning: in vpKeyPoint::saveLearningData() training images "
                 "are not saved because "
                 "visp_io module is not available !"
              << std::endl;
#endif
  }

  bool have3DInfo = m_trainPoints.size() > 0;
  if (have3DInfo && m_trainPoints.size() != m_trainKeyPoints.size()) {
    throw vpException(vpException::fatalError, "List of keypoints and list of 3D points have different size !");
  }

  if (binaryMode) {
    // Save the learning data into little endian binary file.
    std::ofstream file(filename.c_str(), std::ofstream::binary);
    if (!file.is_open()) {
      throw vpException(vpException::ioError, "Cannot create the file.");
    }

    // Write info about training images
    int nbImgs = (int)mapOfImgPath.size();
    vpIoTools::writeBinaryValueLE(file, nbImgs);

#ifdef VISP_HAVE_MODULE_IO
    for (std::map<int, std::string>::const_iterator it = mapOfImgPath.begin(); it != mapOfImgPath.end(); ++it) {
      // Write image_id
      int id = it->first;
      vpIoTools::writeBinaryValueLE(file, id);

      // Write image path
      std::string path = it->second;
      int length = (int)path.length();
      vpIoTools::writeBinaryValueLE(file, length);

      for (int cpt = 0; cpt < length; cpt++) {
        file.write((char *)(&path[(size_t)cpt]), sizeof(path[(size_t)cpt]));
      }
    }
#endif

    // Write if we have 3D point information
    int have3DInfoInt = have3DInfo ? 1 : 0;
    vpIoTools::writeBinaryValueLE(file, have3DInfoInt);

    int nRows = m_trainDescriptors.rows, nCols = m_trainDescriptors.cols;
    int descriptorType = m_trainDescriptors.type();

    // Write the number of descriptors
    vpIoTools::writeBinaryValueLE(file, nRows);

    // Write the size of the descriptor
    vpIoTools::writeBinaryValueLE(file, nCols);

    // Write the type of the descriptor
    vpIoTools::writeBinaryValueLE(file, descriptorType);

    for (int i = 0; i < nRows; i++) {
      unsigned int i_ = (unsigned int)i;
      // Write u
      float u = m_trainKeyPoints[i_].pt.x;
      vpIoTools::writeBinaryValueLE(file, u);

      // Write v
      float v = m_trainKeyPoints[i_].pt.y;
      vpIoTools::writeBinaryValueLE(file, v);

      // Write size
      float size = m_trainKeyPoints[i_].size;
      vpIoTools::writeBinaryValueLE(file, size);

      // Write angle
      float angle = m_trainKeyPoints[i_].angle;
      vpIoTools::writeBinaryValueLE(file, angle);

      // Write response
      float response = m_trainKeyPoints[i_].response;
      vpIoTools::writeBinaryValueLE(file, response);

      // Write octave
      int octave = m_trainKeyPoints[i_].octave;
      vpIoTools::writeBinaryValueLE(file, octave);

      // Write class_id
      int class_id = m_trainKeyPoints[i_].class_id;
      vpIoTools::writeBinaryValueLE(file, class_id);

// Write image_id
#ifdef VISP_HAVE_MODULE_IO
      std::map<int, int>::const_iterator it_findImgId = m_mapOfImageId.find(m_trainKeyPoints[i_].class_id);
      int image_id = (saveTrainingImages && it_findImgId != m_mapOfImageId.end()) ? it_findImgId->second : -1;
      vpIoTools::writeBinaryValueLE(file, image_id);
#else
      int image_id = -1;
      //      file.write((char *)(&image_id), sizeof(image_id));
      vpIoTools::writeBinaryValueLE(file, image_id);
#endif

      if (have3DInfo) {
        float oX = m_trainPoints[i_].x, oY = m_trainPoints[i_].y, oZ = m_trainPoints[i_].z;
        // Write oX
        vpIoTools::writeBinaryValueLE(file, oX);

        // Write oY
        vpIoTools::writeBinaryValueLE(file, oY);

        // Write oZ
        vpIoTools::writeBinaryValueLE(file, oZ);
      }

      for (int j = 0; j < nCols; j++) {
        // Write the descriptor value
        switch (descriptorType) {
        case CV_8U:
          file.write((char *)(&m_trainDescriptors.at<unsigned char>(i, j)),
                     sizeof(m_trainDescriptors.at<unsigned char>(i, j)));
          break;

        case CV_8S:
          file.write((char *)(&m_trainDescriptors.at<char>(i, j)), sizeof(m_trainDescriptors.at<char>(i, j)));
          break;

        case CV_16U:
          vpIoTools::writeBinaryValueLE(file, m_trainDescriptors.at<unsigned short int>(i, j));
          break;

        case CV_16S:
          vpIoTools::writeBinaryValueLE(file, m_trainDescriptors.at<short int>(i, j));
          break;

        case CV_32S:
          vpIoTools::writeBinaryValueLE(file, m_trainDescriptors.at<int>(i, j));
          break;

        case CV_32F:
          vpIoTools::writeBinaryValueLE(file, m_trainDescriptors.at<float>(i, j));
          break;

        case CV_64F:
          vpIoTools::writeBinaryValueLE(file, m_trainDescriptors.at<double>(i, j));
          break;

        default:
          throw vpException(vpException::fatalError, "Problem with the data type of descriptors !");
          break;
        }
      }
    }

    file.close();
  } else {
    pugi::xml_document doc;
    pugi::xml_node node = doc.append_child(pugi::node_declaration);
    node.append_attribute("version") = "1.0";
    node.append_attribute("encoding") = "UTF-8";

    if (!doc) {
      throw vpException(vpException::ioError, "Error with file: ", filename.c_str());
    }

    pugi::xml_node root_node = doc.append_child("LearningData");

    // Write the training images info
    pugi::xml_node image_node = root_node.append_child("TrainingImageInfo");

#ifdef VISP_HAVE_MODULE_IO
    for (std::map<int, std::string>::const_iterator it = mapOfImgPath.begin(); it != mapOfImgPath.end(); ++it) {
      pugi::xml_node image_info_node = image_node.append_child("trainImg");
      image_info_node.append_child(pugi::node_pcdata).set_value(it->second.c_str());
      std::stringstream ss;
      ss << it->first;
      image_info_node.append_attribute("image_id") = ss.str().c_str();
    }
#endif

    // Write information about descriptors
    pugi::xml_node descriptors_info_node = root_node.append_child("DescriptorsInfo");

    int nRows = m_trainDescriptors.rows, nCols = m_trainDescriptors.cols;
    int descriptorType = m_trainDescriptors.type();

    // Write the number of rows
    descriptors_info_node.append_child("nrows").append_child(pugi::node_pcdata).text() = nRows;

    // Write the number of cols
    descriptors_info_node.append_child("ncols").append_child(pugi::node_pcdata).text() = nCols;

    // Write the descriptors type
    descriptors_info_node.append_child("type").append_child(pugi::node_pcdata).text() = descriptorType;

    for (int i = 0; i < nRows; i++) {
      unsigned int i_ = (unsigned int)i;
      pugi::xml_node descriptor_node = root_node.append_child("DescriptorInfo");

      descriptor_node.append_child("u").append_child(pugi::node_pcdata).text() = m_trainKeyPoints[i_].pt.x;
      descriptor_node.append_child("v").append_child(pugi::node_pcdata).text() = m_trainKeyPoints[i_].pt.y;
      descriptor_node.append_child("size").append_child(pugi::node_pcdata).text() = m_trainKeyPoints[i_].size;
      descriptor_node.append_child("angle").append_child(pugi::node_pcdata).text() = m_trainKeyPoints[i_].angle;
      descriptor_node.append_child("response").append_child(pugi::node_pcdata).text() = m_trainKeyPoints[i_].response;
      descriptor_node.append_child("octave").append_child(pugi::node_pcdata).text() = m_trainKeyPoints[i_].octave;
      descriptor_node.append_child("class_id").append_child(pugi::node_pcdata).text() = m_trainKeyPoints[i_].class_id;

#ifdef VISP_HAVE_MODULE_IO
      std::map<int, int>::const_iterator it_findImgId = m_mapOfImageId.find(m_trainKeyPoints[i_].class_id);
      descriptor_node.append_child("image_id").append_child(pugi::node_pcdata).text() =
          ((saveTrainingImages && it_findImgId != m_mapOfImageId.end()) ? it_findImgId->second : -1);
#else
      descriptor_node.append_child("image_id").append_child(pugi::node_pcdata).text() = -1;
#endif

      if (have3DInfo) {
        descriptor_node.append_child("oX").append_child(pugi::node_pcdata).text() = m_trainPoints[i_].x;
        descriptor_node.append_child("oY").append_child(pugi::node_pcdata).text() = m_trainPoints[i_].y;
        descriptor_node.append_child("oZ").append_child(pugi::node_pcdata).text() = m_trainPoints[i_].z;
      }

      pugi::xml_node desc_node = descriptor_node.append_child("desc");

      for (int j = 0; j < nCols; j++) {
        switch (descriptorType) {
        case CV_8U: {
          // Promote an unsigned char to an int
          // val_tmp holds the numeric value that will be written
          // We save the value in numeric form otherwise xml library will not be
          // able to parse  A better solution could be possible
          int val_tmp = m_trainDescriptors.at<unsigned char>(i, j);
          desc_node.append_child("val").append_child(pugi::node_pcdata).text() = val_tmp;
        } break;

        case CV_8S: {
          // Promote a char to an int
          // val_tmp holds the numeric value that will be written
          // We save the value in numeric form otherwise xml library will not be
          // able to parse  A better solution could be possible
          int val_tmp = m_trainDescriptors.at<char>(i, j);
          desc_node.append_child("val").append_child(pugi::node_pcdata).text() = val_tmp;
        } break;

        case CV_16U:
          desc_node.append_child("val").append_child(pugi::node_pcdata).text() =
              m_trainDescriptors.at<unsigned short int>(i, j);
          break;

        case CV_16S:
          desc_node.append_child("val").append_child(pugi::node_pcdata).text() =
              m_trainDescriptors.at<short int>(i, j);
          break;

        case CV_32S:
          desc_node.append_child("val").append_child(pugi::node_pcdata).text() =
              m_trainDescriptors.at<int>(i, j);
          break;

        case CV_32F:
          desc_node.append_child("val").append_child(pugi::node_pcdata).text() =
              m_trainDescriptors.at<float>(i, j);
          break;

        case CV_64F:
          desc_node.append_child("val").append_child(pugi::node_pcdata).text() =
              m_trainDescriptors.at<double>(i, j);
          break;

        default:
          throw vpException(vpException::fatalError, "Problem with the data type of descriptors !");
          break;
        }
      }
    }

    doc.save_file(filename.c_str(), PUGIXML_TEXT("  "), pugi::format_default, pugi::encoding_utf8);
  }
}

#if defined(VISP_HAVE_OPENCV) && (VISP_HAVE_OPENCV_VERSION >= 0x030000)
#ifndef DOXYGEN_SHOULD_SKIP_THIS
// From OpenCV 2.4.11 source code.
struct KeypointResponseGreaterThanThreshold {
  KeypointResponseGreaterThanThreshold(float _value) : value(_value) {}
  inline bool operator()(const cv::KeyPoint &kpt) const { return kpt.response >= value; }
  float value;
};

struct KeypointResponseGreater {
  inline bool operator()(const cv::KeyPoint &kp1, const cv::KeyPoint &kp2) const { return kp1.response > kp2.response; }
};

// takes keypoints and culls them by the response
void vpKeyPoint::KeyPointsFilter::retainBest(std::vector<cv::KeyPoint> &keypoints, int n_points)
{
  // this is only necessary if the keypoints size is greater than the number
  // of desired points.
  if (n_points >= 0 && keypoints.size() > (size_t)n_points) {
    if (n_points == 0) {
      keypoints.clear();
      return;
    }
    // first use nth element to partition the keypoints into the best and
    // worst.
    std::nth_element(keypoints.begin(), keypoints.begin() + n_points, keypoints.end(), KeypointResponseGreater());
    // this is the boundary response, and in the case of FAST may be ambiguous
    float ambiguous_response = keypoints[(size_t)(n_points - 1)].response;
    // use std::partition to grab all of the keypoints with the boundary
    // response.
    std::vector<cv::KeyPoint>::const_iterator new_end = std::partition(
        keypoints.begin() + n_points, keypoints.end(), KeypointResponseGreaterThanThreshold(ambiguous_response));
    // resize the keypoints, given this new end point. nth_element and
    // partition reordered the points inplace
    keypoints.resize((size_t)(new_end - keypoints.begin()));
  }
}

struct RoiPredicate {
  RoiPredicate(const cv::Rect &_r) : r(_r) {}

  bool operator()(const cv::KeyPoint &keyPt) const { return !r.contains(keyPt.pt); }

  cv::Rect r;
};

void vpKeyPoint::KeyPointsFilter::runByImageBorder(std::vector<cv::KeyPoint> &keypoints, cv::Size imageSize,
                                                   int borderSize)
{
  if (borderSize > 0) {
    if (imageSize.height <= borderSize * 2 || imageSize.width <= borderSize * 2)
      keypoints.clear();
    else
      keypoints.erase(std::remove_if(keypoints.begin(), keypoints.end(),
                                     RoiPredicate(cv::Rect(
                                         cv::Point(borderSize, borderSize),
                                         cv::Point(imageSize.width - borderSize, imageSize.height - borderSize)))),
                      keypoints.end());
  }
}

struct SizePredicate {
  SizePredicate(float _minSize, float _maxSize) : minSize(_minSize), maxSize(_maxSize) {}

  bool operator()(const cv::KeyPoint &keyPt) const
  {
    float size = keyPt.size;
    return (size < minSize) || (size > maxSize);
  }

  float minSize, maxSize;
};

void vpKeyPoint::KeyPointsFilter::runByKeypointSize(std::vector<cv::KeyPoint> &keypoints, float minSize, float maxSize)
{
  CV_Assert(minSize >= 0);
  CV_Assert(maxSize >= 0);
  CV_Assert(minSize <= maxSize);

  keypoints.erase(std::remove_if(keypoints.begin(), keypoints.end(), SizePredicate(minSize, maxSize)), keypoints.end());
}

class MaskPredicate
{
public:
  MaskPredicate(const cv::Mat &_mask) : mask(_mask) {}
  bool operator()(const cv::KeyPoint &key_pt) const
  {
    return mask.at<uchar>((int)(key_pt.pt.y + 0.5f), (int)(key_pt.pt.x + 0.5f)) == 0;
  }

private:
  const cv::Mat mask;
};

void vpKeyPoint::KeyPointsFilter::runByPixelsMask(std::vector<cv::KeyPoint> &keypoints, const cv::Mat &mask)
{
  if (mask.empty())
    return;

  keypoints.erase(std::remove_if(keypoints.begin(), keypoints.end(), MaskPredicate(mask)), keypoints.end());
}

struct KeyPoint_LessThan {
  KeyPoint_LessThan(const std::vector<cv::KeyPoint> &_kp) : kp(&_kp) {}
  bool operator()(/*int i, int j*/ size_t i, size_t j) const
  {
    const cv::KeyPoint &kp1 = (*kp)[/*(size_t)*/ i];
    const cv::KeyPoint &kp2 = (*kp)[/*(size_t)*/ j];
    if (!vpMath::equal(kp1.pt.x, kp2.pt.x,
                       std::numeric_limits<float>::epsilon())) { // if (kp1.pt.x !=
                                                                 // kp2.pt.x) {
      return kp1.pt.x < kp2.pt.x;
    }

    if (!vpMath::equal(kp1.pt.y, kp2.pt.y,
                       std::numeric_limits<float>::epsilon())) { // if (kp1.pt.y !=
                                                                 // kp2.pt.y) {
      return kp1.pt.y < kp2.pt.y;
    }

    if (!vpMath::equal(kp1.size, kp2.size,
                       std::numeric_limits<float>::epsilon())) { // if (kp1.size !=
                                                                 // kp2.size) {
      return kp1.size > kp2.size;
    }

    if (!vpMath::equal(kp1.angle, kp2.angle,
                       std::numeric_limits<float>::epsilon())) { // if (kp1.angle !=
                                                                 // kp2.angle) {
      return kp1.angle < kp2.angle;
    }

    if (!vpMath::equal(kp1.response, kp2.response,
                       std::numeric_limits<float>::epsilon())) { // if (kp1.response !=
                                                                 // kp2.response) {
      return kp1.response > kp2.response;
    }

    if (kp1.octave != kp2.octave) {
      return kp1.octave > kp2.octave;
    }

    if (kp1.class_id != kp2.class_id) {
      return kp1.class_id > kp2.class_id;
    }

    return i < j;
  }
  const std::vector<cv::KeyPoint> *kp;
};

void vpKeyPoint::KeyPointsFilter::removeDuplicated(std::vector<cv::KeyPoint> &keypoints)
{
  size_t i, j, n = keypoints.size();
  std::vector<size_t> kpidx(n);
  std::vector<uchar> mask(n, (uchar)1);

  for (i = 0; i < n; i++) {
    kpidx[i] = i;
  }
  std::sort(kpidx.begin(), kpidx.end(), KeyPoint_LessThan(keypoints));
  for (i = 1, j = 0; i < n; i++) {
    cv::KeyPoint &kp1 = keypoints[kpidx[i]];
    cv::KeyPoint &kp2 = keypoints[kpidx[j]];
    //    if (kp1.pt.x != kp2.pt.x || kp1.pt.y != kp2.pt.y || kp1.size !=
    //    kp2.size || kp1.angle != kp2.angle) {
    if (!vpMath::equal(kp1.pt.x, kp2.pt.x, std::numeric_limits<float>::epsilon()) ||
        !vpMath::equal(kp1.pt.y, kp2.pt.y, std::numeric_limits<float>::epsilon()) ||
        !vpMath::equal(kp1.size, kp2.size, std::numeric_limits<float>::epsilon()) ||
        !vpMath::equal(kp1.angle, kp2.angle, std::numeric_limits<float>::epsilon())) {
      j = i;
    } else {
      mask[kpidx[i]] = 0;
    }
  }

  for (i = j = 0; i < n; i++) {
    if (mask[i]) {
      if (i != j) {
        keypoints[j] = keypoints[i];
      }
      j++;
    }
  }
  keypoints.resize(j);
}

/*
 *  PyramidAdaptedFeatureDetector
 */
vpKeyPoint::PyramidAdaptedFeatureDetector::PyramidAdaptedFeatureDetector(const cv::Ptr<cv::FeatureDetector> &_detector,
                                                                         int _maxLevel)
  : detector(_detector), maxLevel(_maxLevel)
{
}

bool vpKeyPoint::PyramidAdaptedFeatureDetector::empty() const
{
  return detector.empty() || (cv::FeatureDetector *)detector->empty();
}

void vpKeyPoint::PyramidAdaptedFeatureDetector::detect(cv::InputArray image,
                                                       CV_OUT std::vector<cv::KeyPoint> &keypoints, cv::InputArray mask)
{
  detectImpl(image.getMat(), keypoints, mask.getMat());
}

void vpKeyPoint::PyramidAdaptedFeatureDetector::detectImpl(const cv::Mat &image, std::vector<cv::KeyPoint> &keypoints,
                                                           const cv::Mat &mask) const
{
  cv::Mat src = image;
  cv::Mat src_mask = mask;

  cv::Mat dilated_mask;
  if (!mask.empty()) {
    cv::dilate(mask, dilated_mask, cv::Mat());
    cv::Mat mask255(mask.size(), CV_8UC1, cv::Scalar(0));
    mask255.setTo(cv::Scalar(255), dilated_mask != 0);
    dilated_mask = mask255;
  }

  for (int l = 0, multiplier = 1; l <= maxLevel; ++l, multiplier *= 2) {
    // Detect on current level of the pyramid
    std::vector<cv::KeyPoint> new_pts;
    detector->detect(src, new_pts, src_mask);
    std::vector<cv::KeyPoint>::iterator it = new_pts.begin(), end = new_pts.end();
    for (; it != end; ++it) {
      it->pt.x *= multiplier;
      it->pt.y *= multiplier;
      it->size *= multiplier;
      it->octave = l;
    }
    keypoints.insert(keypoints.end(), new_pts.begin(), new_pts.end());

    // Downsample
    if (l < maxLevel) {
      cv::Mat dst;
      pyrDown(src, dst);
      src = dst;

      if (!mask.empty())
        resize(dilated_mask, src_mask, src.size(), 0, 0, CV_INTER_AREA);
    }
  }

  if (!mask.empty())
    vpKeyPoint::KeyPointsFilter::runByPixelsMask(keypoints, mask);
}
#endif
#endif

#elif !defined(VISP_BUILD_SHARED_LIBS)
// Work around to avoid warning: libvisp_vision.a(vpKeyPoint.cpp.o) has no
// symbols
void dummy_vpKeyPoint(){};
#endif