tracking.cpp

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
   |                                                                        |
   | Copyright (c) 2005-2019, Individual contributors, see AUTHORS file     |
   | See: https://www.mrpt.org/Authors - All rights reserved.               |
   | Released under BSD License. See: https://www.mrpt.org/License          |
   +------------------------------------------------------------------------+ */

#include "vision-precomp.h"  // Precompiled headers

#include <mrpt/math/ops_matrices.h>
#include <mrpt/otherlibs/do_opencv_includes.h>
#include <mrpt/vision/CFeatureExtraction.h>
#include <mrpt/vision/tracking.h>

using namespace mrpt;
using namespace mrpt::vision;
using namespace mrpt::img;
using namespace mrpt::tfest;
using namespace mrpt::math;
using namespace std;

// ------------------------------- internal helper templates
// ---------------------------------
namespace mrpt::vision::detail
{
template <typename FEATLIST>
inline void trackFeatures_checkResponses(
    FEATLIST& featureList, const CImage& cur_gray,
    const float minimum_KLT_response, const unsigned int KLT_response_half_win,
    const unsigned int max_x, const unsigned int max_y);

template <>
inline void trackFeatures_checkResponses<CFeatureList>(
    CFeatureList& featureList, const CImage& cur_gray,
    const float minimum_KLT_response, const unsigned int KLT_response_half_win,
    const unsigned int max_x, const unsigned int max_y)
{
    for (auto& ft : featureList)
    {
        if (ft.track_status != status_TRACKED)
            continue;  // Skip if it's not correctly tracked.

        const unsigned int x = ft.keypoint.pt.x;
        const unsigned int y = ft.keypoint.pt.y;
        if (x > KLT_response_half_win && y > KLT_response_half_win &&
            x < max_x && y < max_y)
        {  // Update response:
            ft.response = cur_gray.KLT_response(x, y, KLT_response_half_win);

            // Is it good enough?
            // http://grooveshark.com/s/Goonies+Are+Good+Enough/2beBfO?src=5
            if (ft.response < minimum_KLT_response)
            {  // Nope!
                ft.track_status = status_LOST;
            }
        }
        else
        {  // Out of bounds
            ft.response = 0;
            ft.track_status = status_OOB;
        }
    }
}  // end of trackFeatures_checkResponses<>

template <class FEAT_LIST>
inline void trackFeatures_checkResponses_impl_simple(
    FEAT_LIST& featureList, const CImage& cur_gray,
    const float minimum_KLT_response, const unsigned int KLT_response_half_win,
    const unsigned int max_x_, const unsigned int max_y_)
{
    if (featureList.empty()) return;

    using pixel_coord_t = typename FEAT_LIST::feature_t::pixel_coord_t;
    const auto half_win = static_cast<pixel_coord_t>(KLT_response_half_win);
    const auto max_x = static_cast<pixel_coord_t>(max_x_);
    const auto max_y = static_cast<pixel_coord_t>(max_y_);

    for (int N = featureList.size() - 1; N >= 0; --N)
    {
        typename FEAT_LIST::feature_t& ft = featureList[N];
        if (ft.track_status != status_TRACKED)
            continue;  // Skip if it's not correctly tracked.

        if (ft.pt.x > half_win && ft.pt.y > half_win && ft.pt.x < max_x &&
            ft.pt.y < max_y)
        {  // Update response:
            ft.response =
                cur_gray.KLT_response(ft.pt.x, ft.pt.y, KLT_response_half_win);

            // Is it good enough?
            // http://grooveshark.com/s/Goonies+Are+Good+Enough/2beBfO?src=5
            if (ft.response < minimum_KLT_response)
            {  // Nope!
                ft.track_status = status_LOST;
            }
        }
        else
        {  // Out of bounds
            ft.response = 0;
            ft.track_status = status_OOB;
        }
    }
}  // end of trackFeatures_checkResponses<>

template <>
inline void trackFeatures_checkResponses<TKeyPointList>(
    TKeyPointList& featureList, const CImage& cur_gray,
    const float minimum_KLT_response, const unsigned int KLT_response_half_win,
    const unsigned int max_x, const unsigned int max_y)
{
    trackFeatures_checkResponses_impl_simple<TKeyPointList>(
        featureList, cur_gray, minimum_KLT_response, KLT_response_half_win,
        max_x, max_y);
}
template <>
inline void trackFeatures_checkResponses<TKeyPointfList>(
    TKeyPointfList& featureList, const CImage& cur_gray,
    const float minimum_KLT_response, const unsigned int KLT_response_half_win,
    const unsigned int max_x, const unsigned int max_y)
{
    trackFeatures_checkResponses_impl_simple<TKeyPointfList>(
        featureList, cur_gray, minimum_KLT_response, KLT_response_half_win,
        max_x, max_y);
}

template <typename FEATLIST>
inline void trackFeatures_updatePatch(
    FEATLIST& featureList, const CImage& cur_gray);

template <>
inline void trackFeatures_updatePatch<CFeatureList>(
    CFeatureList& featureList, const CImage& cur_gray)
{
    for (auto& ft : featureList)
    {
        if (ft.track_status != status_TRACKED)
            continue;  // Skip if it's not correctly tracked.

        const size_t patch_width = ft.patch->getWidth();
        const size_t patch_height = ft.patch->getHeight();
        if (patch_width > 0 && patch_height > 0)
        {
            try
            {
                const int offset = (int)patch_width / 2;  // + 1;
                ft.patch.emplace();
                cur_gray.extract_patch(
                    *ft.patch, round(ft.keypoint.pt.x) - offset,
                    round(ft.keypoint.pt.y) - offset, patch_width,
                    patch_height);
            }
            catch (std::exception&)
            {
                ft.track_status = status_OOB;  // Out of bounds!
            }
        }
    }
}  // end of trackFeatures_updatePatch<>
template <>
inline void trackFeatures_updatePatch<TKeyPointList>(
    TKeyPointList& featureList, const CImage& cur_gray)
{
    MRPT_UNUSED_PARAM(featureList);
    MRPT_UNUSED_PARAM(cur_gray);
    // This list type does not have patch stored explicitly
}  // end of trackFeatures_updatePatch<>
template <>
inline void trackFeatures_updatePatch<TKeyPointfList>(
    TKeyPointfList& featureList, const CImage& cur_gray)
{
    MRPT_UNUSED_PARAM(featureList);
    MRPT_UNUSED_PARAM(cur_gray);
    // This list type does not have patch stored explicitly
}  // end of trackFeatures_updatePatch<>

template <typename FEATLIST>
inline void trackFeatures_addNewFeats(
    FEATLIST& featureList, const TKeyPointList& new_feats,
    const std::vector<size_t>& sorted_indices, const size_t nNewToCheck,
    const size_t maxNumFeatures, const float minimum_KLT_response_to_add,
    const double threshold_sqr_dist_to_add_new, const size_t patchSize,
    const CImage& cur_gray, TFeatureID& max_feat_ID_at_input);

template <>
inline void trackFeatures_addNewFeats<CFeatureList>(
    CFeatureList& featureList, const TKeyPointList& new_feats,
    const std::vector<size_t>& sorted_indices, const size_t nNewToCheck,
    const size_t maxNumFeatures, const float minimum_KLT_response_to_add,
    const double threshold_sqr_dist_to_add_new, const size_t patchSize,
    const CImage& cur_gray, TFeatureID& max_feat_ID_at_input)
{
    const TImageSize imgSize = cur_gray.getSize();
    const int offset = (int)patchSize / 2 + 1;
    const int w_off = int(imgSize.x - offset);
    const int h_off = int(imgSize.y - offset);

    for (size_t i = 0; i < nNewToCheck && featureList.size() < maxNumFeatures;
         i++)
    {
        const TKeyPoint& feat = new_feats[sorted_indices[i]];

        if (feat.response < minimum_KLT_response_to_add) continue;

        double min_dist_sqr = square(10000);

        if (!featureList.empty())
        {
            min_dist_sqr =
                featureList.kdTreeClosestPoint2DsqrError(feat.pt.x, feat.pt.y);
        }

        if (min_dist_sqr > threshold_sqr_dist_to_add_new &&
            feat.pt.x > offset && feat.pt.y > offset && feat.pt.x < w_off &&
            feat.pt.y < h_off)
        {
            // Add new feature:
            CFeature ft;
            ft.type = featFAST;
            ft.keypoint.ID = ++max_feat_ID_at_input;
            ft.keypoint.pt.x = feat.pt.x;
            ft.keypoint.pt.y = feat.pt.y;
            ft.response = feat.response;
            ft.orientation = 0;
            ft.keypoint.octave = 1;
            ft.patchSize = patchSize;  // The size of the feature patch

            // Image patch surronding the feature
            if (patchSize > 0)
            {
                ft.patch.emplace();
                cur_gray.extract_patch(
                    *ft.patch, round(feat.pt.x) - offset,
                    round(feat.pt.y) - offset, patchSize, patchSize);
            }

            featureList.emplace_back(std::move(ft));
        }
    }
}  // end of trackFeatures_addNewFeats<>

template <class FEAT_LIST>
inline void trackFeatures_addNewFeats_simple_list(
    FEAT_LIST& featureList, const TKeyPointList& new_feats,
    const std::vector<size_t>& sorted_indices, const size_t nNewToCheck,
    const size_t maxNumFeatures, const float minimum_KLT_response_to_add,
    const double threshold_sqr_dist_to_add_new, const size_t patchSize,
    const CImage& cur_gray, TFeatureID& max_feat_ID_at_input)
{
    MRPT_UNUSED_PARAM(patchSize);
    MRPT_UNUSED_PARAM(cur_gray);
    // Version with KD-tree
    CFeatureListKDTree<typename FEAT_LIST::feature_t> kdtree(
        featureList.getVector());

    for (size_t i = 0; i < nNewToCheck && featureList.size() < maxNumFeatures;
         i++)
    {
        const TKeyPoint& feat = new_feats[sorted_indices[i]];
        if (feat.response < minimum_KLT_response_to_add) break;  // continue;

        // Check the min-distance:
        double min_dist_sqr = std::numeric_limits<double>::max();

        if (!featureList.empty())
        {
            min_dist_sqr =
                kdtree.kdTreeClosestPoint2DsqrError(feat.pt.x, feat.pt.y);
        }

        if (min_dist_sqr > threshold_sqr_dist_to_add_new)
        {
            // OK: accept it
            featureList.emplace_back(feat.pt.x, feat.pt.y);
            kdtree.mark_as_outdated();

            // Fill out the rest of data:
            typename FEAT_LIST::feature_t& newFeat = featureList.back();

            newFeat.ID = ++max_feat_ID_at_input;
            newFeat.response = feat.response;
            newFeat.octave = 0;
            /** Inactive: right after detection, and before being tried to track
             */
            newFeat.track_status = status_IDLE;
        }
    }
}  // end of trackFeatures_addNewFeats<>

template <>
inline void trackFeatures_addNewFeats<TKeyPointList>(
    TKeyPointList& featureList, const TKeyPointList& new_feats,
    const std::vector<size_t>& sorted_indices, const size_t nNewToCheck,
    const size_t maxNumFeatures, const float minimum_KLT_response_to_add,
    const double threshold_sqr_dist_to_add_new, const size_t patchSize,
    const CImage& cur_gray, TFeatureID& max_feat_ID_at_input)
{
    trackFeatures_addNewFeats_simple_list<TKeyPointList>(
        featureList, new_feats, sorted_indices, nNewToCheck, maxNumFeatures,
        minimum_KLT_response_to_add, threshold_sqr_dist_to_add_new, patchSize,
        cur_gray, max_feat_ID_at_input);
}
template <>
inline void trackFeatures_addNewFeats<TKeyPointfList>(
    TKeyPointfList& featureList, const TKeyPointList& new_feats,
    const std::vector<size_t>& sorted_indices, const size_t nNewToCheck,
    const size_t maxNumFeatures, const float minimum_KLT_response_to_add,
    const double threshold_sqr_dist_to_add_new, const size_t patchSize,
    const CImage& cur_gray, TFeatureID& max_feat_ID_at_input)
{
    trackFeatures_addNewFeats_simple_list<TKeyPointfList>(
        featureList, new_feats, sorted_indices, nNewToCheck, maxNumFeatures,
        minimum_KLT_response_to_add, threshold_sqr_dist_to_add_new, patchSize,
        cur_gray, max_feat_ID_at_input);
}

// Return the number of removed features
template <typename FEATLIST>
inline size_t trackFeatures_deleteOOB(
    FEATLIST& trackedFeats, const size_t img_width, const size_t img_height,
    const int MIN_DIST_MARGIN_TO_STOP_TRACKING);

template <typename FEATLIST>
inline size_t trackFeatures_deleteOOB_impl_simple_feat(
    FEATLIST& trackedFeats, const size_t img_width, const size_t img_height,
    const int MIN_DIST_MARGIN_TO_STOP_TRACKING)
{
    if (trackedFeats.empty()) return 0;

    std::vector<size_t> survival_idxs;
    const size_t N = trackedFeats.size();

    // 1st: Build list of survival indexes:
    survival_idxs.reserve(N);
    for (size_t i = 0; i < N; i++)
    {
        const typename FEATLIST::feature_t& ft = trackedFeats[i];
        const TFeatureTrackStatus status = ft.track_status;
        bool eras = (status_TRACKED != status && status_IDLE != status);
        if (!eras)
        {
            // Also, check if it's too close to the image border:
            const int x = ft.pt.x;
            const int y = ft.pt.y;
            if (x < MIN_DIST_MARGIN_TO_STOP_TRACKING ||
                y < MIN_DIST_MARGIN_TO_STOP_TRACKING ||
                x > static_cast<int>(
                        img_width - MIN_DIST_MARGIN_TO_STOP_TRACKING) ||
                y > static_cast<int>(
                        img_height - MIN_DIST_MARGIN_TO_STOP_TRACKING))
            {
                eras = true;
            }
        }
        if (!eras) survival_idxs.push_back(i);
    }

    // 2nd: Build updated list:
    const size_t N2 = survival_idxs.size();
    const size_t n_removed = N - N2;
    for (size_t i = 0; i < N2; i++)
    {
        if (survival_idxs[i] != i)
            trackedFeats[i] = trackedFeats[survival_idxs[i]];
    }
    trackedFeats.resize(N2);
    return n_removed;
}  // end of trackFeatures_deleteOOB

template <>
inline size_t trackFeatures_deleteOOB(
    TKeyPointList& trackedFeats, const size_t img_width,
    const size_t img_height, const int MIN_DIST_MARGIN_TO_STOP_TRACKING)
{
    return trackFeatures_deleteOOB_impl_simple_feat<TKeyPointList>(
        trackedFeats, img_width, img_height, MIN_DIST_MARGIN_TO_STOP_TRACKING);
}
template <>
inline size_t trackFeatures_deleteOOB(
    TKeyPointfList& trackedFeats, const size_t img_width,
    const size_t img_height, const int MIN_DIST_MARGIN_TO_STOP_TRACKING)
{
    return trackFeatures_deleteOOB_impl_simple_feat<TKeyPointfList>(
        trackedFeats, img_width, img_height, MIN_DIST_MARGIN_TO_STOP_TRACKING);
}

template <>
inline size_t trackFeatures_deleteOOB(
    CFeatureList& trackedFeats, const size_t img_width, const size_t img_height,
    const int MIN_DIST_MARGIN_TO_STOP_TRACKING)
{
    auto itFeat = trackedFeats.begin();
    size_t n_removed = 0;
    while (itFeat != trackedFeats.end())
    {
        const TFeatureTrackStatus status = itFeat->track_status;
        bool eras = (status_TRACKED != status && status_IDLE != status);
        if (!eras)
        {
            // Also, check if it's too close to the image border:
            const float x = itFeat->keypoint.pt.x;
            const float y = itFeat->keypoint.pt.y;
            if (x < MIN_DIST_MARGIN_TO_STOP_TRACKING ||
                y < MIN_DIST_MARGIN_TO_STOP_TRACKING ||
                x > (img_width - MIN_DIST_MARGIN_TO_STOP_TRACKING) ||
                y > (img_height - MIN_DIST_MARGIN_TO_STOP_TRACKING))
            {
                eras = true;
            }
        }
        if (eras)  // Erase or keep?
        {
            itFeat = trackedFeats.erase(itFeat);
            n_removed++;
        }
        else
            ++itFeat;
    }
    return n_removed;
}  // end of trackFeatures_deleteOOB
}  // namespace mrpt::vision::detail
// ---------------------------- end of internal helper templates
// -------------------------------

void CGenericFeatureTracker::trackFeatures_impl(
    const CImage& old_img, const CImage& new_img,
    TKeyPointfList& inout_featureList)
{
    MRPT_UNUSED_PARAM(old_img);
    MRPT_UNUSED_PARAM(new_img);
    MRPT_UNUSED_PARAM(inout_featureList);
    THROW_EXCEPTION("Method not implemented by derived class!");
}

/** Perform feature tracking from "old_img" to "new_img", with a (possibly
 *empty) list of previously tracked features "featureList".
 *  This is a list of parameters (in "extraParams") accepted by ALL
 *implementations of feature tracker (see each derived class for more specific
 *parameters).
 *      - "add_new_features" (Default=0). If set to "1", new features will be
 *also
 *added to the existing ones in areas of the image poor of features.
 * This method actually first call the pure virtual "trackFeatures_impl"
 *method, then implements the optional detection of new features if
 *"add_new_features"!=0.
 */
template <typename FEATLIST>
void CGenericFeatureTracker::internal_trackFeatures(
    const CImage& old_img, const CImage& new_img, FEATLIST& featureList)
{
    mrpt::system::CTimeLoggerEntry tleg(m_timlog, "CGenericFeatureTracker");

    const size_t img_width = new_img.getWidth();
    const size_t img_height = new_img.getHeight();

    // Take the maximum ID of "old" features so new feats (if
    // "add_new_features==true") will be id+1, id+2, ...
    TFeatureID max_feat_ID_at_input = 0;
    if (!featureList.empty()) max_feat_ID_at_input = featureList.getMaxID();

    // Grayscale images
    // =========================================
    m_timlog.enter("CGenericFeatureTracker.to_grayscale");

    const CImage prev_gray(old_img, FAST_REF_OR_CONVERT_TO_GRAY);
    const CImage cur_gray(new_img, FAST_REF_OR_CONVERT_TO_GRAY);

    m_timlog.leave("CGenericFeatureTracker.to_grayscale");

    // =================================
    // (1st STEP)  Do the actual tracking
    // =================================
    m_newly_detected_feats.clear();

    m_timlog.enter("CGenericFeatureTracker.trackFeatures_impl");

    trackFeatures_impl(prev_gray, cur_gray, featureList);

    m_timlog.leave("CGenericFeatureTracker.trackFeatures_impl");

    // ========================================================
    // (2nd STEP) For successfully followed features, check their KLT response??
    // ========================================================
    const int check_KLT_response_every =
        extra_params.getWithDefaultVal("check_KLT_response_every", 1);
    const float minimum_KLT_response =
        extra_params.getWithDefaultVal("minimum_KLT_response", 30.f);
    const unsigned int KLT_response_half_win =
        extra_params.getWithDefaultVal("KLT_response_half_win", 8U);

    if (check_KLT_response_every > 0 &&
        ++m_check_KLT_counter >= size_t(check_KLT_response_every))
    {
        m_timlog.enter("CGenericFeatureTracker.check_KLT_responses");
        m_check_KLT_counter = 0;

        const unsigned int max_x = img_width - KLT_response_half_win;
        const unsigned int max_y = img_height - KLT_response_half_win;

        detail::trackFeatures_checkResponses(
            featureList, cur_gray, minimum_KLT_response, KLT_response_half_win,
            max_x, max_y);

        m_timlog.leave("CGenericFeatureTracker.check_KLT_responses");

    }  // end check_KLT_response_every

    // ============================================================
    // (3rd STEP)  Remove Out-of-bounds or badly tracked features
    //   or those marked as "bad" by their low KLT response
    // ============================================================
    const bool remove_lost_features =
        extra_params.getWithDefaultVal("remove_lost_features", 0) != 0;

    if (remove_lost_features)
    {
        m_timlog.enter("CGenericFeatureTracker.OOB_remove");

        static const int MIN_DIST_MARGIN_TO_STOP_TRACKING = 10;

        const size_t nRemoved = detail::trackFeatures_deleteOOB(
            featureList, img_width, img_height,
            MIN_DIST_MARGIN_TO_STOP_TRACKING);

        m_timlog.leave("CGenericFeatureTracker.OOB_remove");

        last_execution_extra_info.num_deleted_feats = nRemoved;
    }
    else
    {
        last_execution_extra_info.num_deleted_feats = 0;
    }

    // ========================================================
    // (4th STEP) For successfully followed features, update its patch:
    // ========================================================
    const int update_patches_every =
        extra_params.getWithDefaultVal("update_patches_every", 0);

    if (update_patches_every > 0 &&
        ++m_update_patches_counter >= size_t(update_patches_every))
    {
        mrpt::system::CTimeLoggerEntry tle(
            m_timlog, "CGenericFeatureTracker.update_patches");

        m_update_patches_counter = 0;

        // Update the patch for each valid feature:
        detail::trackFeatures_updatePatch(featureList, cur_gray);

    }  // end if update_patches_every

    // ========================================================
    // (5th STEP) Do detection of new features??
    // ========================================================
    const bool add_new_features =
        extra_params.getWithDefaultVal("add_new_features", 1) != 0;
    const double threshold_dist_to_add_new =
        extra_params.getWithDefaultVal("add_new_feat_min_separation", 15);

    // Additional operation: if "add_new_features==true", find new features and
    // add them in areas spare of valid features:
    if (add_new_features)
    {
        mrpt::system::CTimeLoggerEntry tle(
            m_timlog, "CGenericFeatureTracker.add_new_features");

        // Look for new features and save in "m_newly_detected_feats", if
        // they're not already computed:
        if (m_newly_detected_feats.empty())
        {
            // Do the detection
            const int fast_v = extra_params.getWithDefaultVal(
                "add_new_features_FAST_version", 10);

            switch (fast_v)
            {
                case 9:
                    CFeatureExtraction::detectFeatures_SSE2_FASTER9(
                        cur_gray, m_newly_detected_feats,
                        m_detector_adaptive_thres);
                    break;
                case 10:
                    CFeatureExtraction::detectFeatures_SSE2_FASTER10(
                        cur_gray, m_newly_detected_feats,
                        m_detector_adaptive_thres);
                    break;
                case 12:
                    CFeatureExtraction::detectFeatures_SSE2_FASTER12(
                        cur_gray, m_newly_detected_feats,
                        m_detector_adaptive_thres);
                    break;
                default:
                    THROW_EXCEPTION(
                        "Invalid value for `add_new_features_FAST_version`: "
                        "valid are 9,10,12");
            }
        }

        // Extra out info.
        const size_t N = m_newly_detected_feats.size();
        last_execution_extra_info.raw_FAST_feats_detected = N;

        // Update the adaptive threshold.
        const size_t desired_num_features = extra_params.getWithDefaultVal(
            "desired_num_features_adapt",
            size_t((img_width * img_height) >> 9));
        updateAdaptiveNewFeatsThreshold(N, desired_num_features);

        // Use KLT response instead of the OpenCV's original "response" field:
        {
            const unsigned int max_x = img_width - KLT_response_half_win;
            const unsigned int max_y = img_height - KLT_response_half_win;
            for (size_t i = 0; i < N; i++)
            {
                const unsigned int x = m_newly_detected_feats[i].pt.x;
                const unsigned int y = m_newly_detected_feats[i].pt.y;
                if (x > KLT_response_half_win && y > KLT_response_half_win &&
                    x < max_x && y < max_y)
                    m_newly_detected_feats[i].response =
                        cur_gray.KLT_response(x, y, KLT_response_half_win);
                else
                    m_newly_detected_feats[i].response = 0;  // Out of bounds
            }
        }

        //  Sort them by "response": It's ~100 times faster to sort a list of
        //      indices "sorted_indices" than sorting directly the actual list
        //      of features "m_newly_detected_feats"
        std::vector<size_t> sorted_indices(N);
        for (size_t i = 0; i < N; i++) sorted_indices[i] = i;

        std::sort(
            sorted_indices.begin(), sorted_indices.end(),
            KeypointResponseSorter<TKeyPointList>(m_newly_detected_feats));

        // For each new good feature, add it to the list of tracked ones only if
        // it's pretty
        //  isolated:

        const size_t nNewToCheck = std::min(size_t(1500), N);
        const double threshold_sqr_dist_to_add_new =
            square(threshold_dist_to_add_new);
        const size_t maxNumFeatures =
            extra_params.getWithDefaultVal("add_new_feat_max_features", 100U);
        const size_t patchSize =
            extra_params.getWithDefaultVal("add_new_feat_patch_size", 0U);
        const float minimum_KLT_response_to_add =
            extra_params.getWithDefaultVal("minimum_KLT_response_to_add", 70.f);

        // Do it:
        detail::trackFeatures_addNewFeats(
            featureList, m_newly_detected_feats, sorted_indices, nNewToCheck,
            maxNumFeatures, minimum_KLT_response_to_add,
            threshold_sqr_dist_to_add_new, patchSize, cur_gray,
            max_feat_ID_at_input);
    }

}  // end of CGenericFeatureTracker::trackFeatures

void CGenericFeatureTracker::trackFeatures(
    const CImage& old_img, const CImage& new_img, TKeyPointList& featureList)
{
    internal_trackFeatures<TKeyPointList>(old_img, new_img, featureList);
}

void CGenericFeatureTracker::trackFeatures(
    const CImage& old_img, const CImage& new_img, TKeyPointfList& featureList)
{
    internal_trackFeatures<TKeyPointfList>(old_img, new_img, featureList);
}

void CGenericFeatureTracker::updateAdaptiveNewFeatsThreshold(
    const size_t nNewlyDetectedFeats, const size_t desired_num_features)
{
    const size_t hysteresis_min_num_feats = desired_num_features * 0.9;
    const size_t hysteresis_max_num_feats = desired_num_features * 1.1;

    if (nNewlyDetectedFeats < hysteresis_min_num_feats)
        m_detector_adaptive_thres = std::max(
            2.0, std::min(
                     m_detector_adaptive_thres - 1.0,
                     m_detector_adaptive_thres * 0.8));
    else if (nNewlyDetectedFeats > hysteresis_max_num_feats)
        m_detector_adaptive_thres = std::max(
            m_detector_adaptive_thres + 1.0, m_detector_adaptive_thres * 1.2);
}