CFeatureExtraction.h

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   |                     Mobile Robot Programming Toolkit (MRPT)               |
   |                          http://www.mrpt.org/                             |
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   | Copyright (c) 2005-2017, Individual contributors, see AUTHORS file        |
   | See: http://www.mrpt.org/Authors - All rights reserved.                   |
   | Released under BSD License. See details in http://www.mrpt.org/License    |
   +---------------------------------------------------------------------------+
   */
#ifndef CFeatureExtraction_H
#define CFeatureExtraction_H
 
#include <mrpt/utils/CImage.h>
#include <mrpt/utils/CTicTac.h>
#include <mrpt/vision/utils.h>
#include <mrpt/vision/CFeature.h>
#include <mrpt/vision/TSimpleFeature.h>
 
namespace mrpt
{
namespace vision
{
/** The central class from which images can be analyzed in search of different
  *kinds of interest points and descriptors computed for them.
  *  To extract features from an image, create an instance of
  *CFeatureExtraction,
  *   fill out its CFeatureExtraction::options field, including the algorithm to
  *use (see
  *   CFeatureExtraction::TOptions::featsType), and call
  *CFeatureExtraction::detectFeatures.
  *  This will return a set of features of the class mrpt::vision::CFeature,
  *which include
  *   details for each interest point as well as the desired descriptors and/or
  *patches.
  *
  *  By default, a 21x21 patch is extracted for each detected feature. If the
  *patch is not needed,
  *   set patchSize to 0 in CFeatureExtraction::options
  *
  *  The implemented <b>detection</b> algorithms are (see
  *CFeatureExtraction::TOptions::featsType):
  *             - KLT (Kanade-Lucas-Tomasi): A detector (no descriptor vector).
  *             - Harris: A detector (no descriptor vector).
  *             - BCD (Binary Corner Detector): A detector (no descriptor vector) (Not
  *implemented yet).
  *             - SIFT: An implementation of the SIFT detector and descriptor. The
  *implemention may be selected with
  *CFeatureExtraction::TOptions::SIFTOptions::implementation.
  *             - SURF: OpenCV's implementation of SURF detector and descriptor.
  *             - The FAST feature detector (OpenCV's implementation)
  *             - The FASTER (9,10,12) detectors (Edward Rosten's libcvd implementation
  *optimized for SSE2).
  *
  *  Additionally, given a list of interest points onto an image, the following
  *   <b>descriptors</b> can be computed for each point by calling
  *CFeatureExtraction::computeDescriptors :
  *             - SIFT descriptor (Lowe's descriptors).
  *             - SURF descriptor (OpenCV's implementation - Requires OpenCV 1.1.0 from
  *SVN
  *or later).
  *             - Intensity-domain spin images (SpinImage): Creates a vector descriptor
  *with the 2D histogram as a single row.
  *             - A circular patch in polar coordinates (Polar images): The matrix
  *descriptor is a 2D polar image centered at the interest point.
  *             - A log-polar image patch (Log-polar images): The matrix descriptor is
  *the
  *2D log-polar image centered at the interest point.
  *
  *
  *  Apart from the normal entry point \a detectFeatures(), these other
  *low-level static methods are provided for convenience:
  *   - CFeatureExtraction::detectFeatures_SSE2_FASTER9()
  *   - CFeatureExtraction::detectFeatures_SSE2_FASTER10()
  *   - CFeatureExtraction::detectFeatures_SSE2_FASTER12()
  *
  * \note The descriptor "Intensity-domain spin images" is described in "A
  *sparse texture representation using affine-invariant regions", S Lazebnik, C
  *Schmid, J Ponce, 2003 IEEE Computer Society Conference on Computer Vision.
  * \sa mrpt::vision::CFeature
  * \ingroup mrptvision_features
  */
class CFeatureExtraction
{
   public:
        enum TSIFTImplementation
        {
                LoweBinary = 0,
                CSBinary,
                VedaldiBinary,
                Hess,
                OpenCV
        };
 
        /** The set of parameters for all the detectors & descriptor algorithms */
        struct TOptions : public utils::CLoadableOptions
        {
                /** Initalizer */
                TOptions(const TFeatureType featsType = featKLT);
 
                void loadFromConfigFile(
                        const mrpt::utils::CConfigFileBase& source,
                        const std::string& section) override;  // See base docs
                void dumpToTextStream(
                        mrpt::utils::CStream& out) const override;  // See base docs
 
                /** Type of the extracted features
                */
                TFeatureType featsType;
 
                /** Size of the patch to extract, or 0 if no patch is desired
                 * (default=21).
                  */
                unsigned int patchSize;
 
                /** Whether to use a mask for determining the regions where not to look
                 * for keypoints (default=false).
                  */
                bool useMask;
 
                /** Whether to add the found features to the input feature list or clear
                 * it before adding them (default=false).
                  */
                bool addNewFeatures;
 
                /** Indicates if subpixel accuracy is desired for the extracted points
                 * (only applicable to KLT and Harris features)
                  */
                bool FIND_SUBPIXEL;
 
                /** KLT Options */
                struct TKLTOptions
                {
                        int radius;  // size of the block of pixels used
                        float threshold;  // (default=0.1) for rejecting weak local maxima
                        // (with min_eig < threshold*max(eig_image))
                        float min_distance;  // minimum distance between features
                        bool tile_image;  // splits the image into 8 tiles and search for
                        // the best points in all of them (distribute the
                        // features over all the image)
                } KLTOptions;
 
                /** Harris Options */
                struct THarrisOptions
                {
                        float threshold;  // (default=0.005) for rejecting weak local maxima
                        // (with min_eig < threshold*max(eig_image))
                        float k;  // k factor for the Harris algorithm
                        float sigma;  // standard deviation for the gaussian smoothing
                        // function
                        int radius;  // size of the block of pixels used
                        float min_distance;  // minimum distance between features
                        bool tile_image;  // splits the image into 8 tiles and search for
                        // the best points in all of them (distribute the
                        // features over all the image)
                } harrisOptions;
 
                /** BCD Options */
                struct TBCDOptions
                {
                } BCDOptions;
 
                /** FAST and FASTER Options */
                struct TFASTOptions
                {
                        int threshold;  //!< default= 20
                        float min_distance;  //!< (default=5) minimum distance between
                        //! features (in pixels)
                        bool nonmax_suppression;  //!< Default = true
                        bool use_KLT_response;  //!< (default=false) If true, use
                        //! CImage::KLT_response to compute the
                        //! response at each point instead of the
                        //! FAST "standard response".
                } FASTOptions;
 
                /** ORB Options */
                struct TORBOptions
                {
                        TORBOptions()
                                : n_levels(8),
                                  min_distance(0),
                                  scale_factor(1.2f),
                                  extract_patch(false)
                        {
                        }
 
                        size_t n_levels;
                        size_t min_distance;
                        float scale_factor;
                        bool extract_patch;
                } ORBOptions;
 
                /** SIFT Options  */
                struct TSIFTOptions
                {
                        TSIFTOptions() : threshold(0.04), edgeThreshold(10) {}
                        TSIFTImplementation implementation;  //!< Default: Hess (OpenCV
                        //! should be preferred, but its
                        //! nonfree module is not always
                        //! available by default in all
                        //! systems)
                        double threshold;  //!< default= 0.04
                        double edgeThreshold;  //!< default= 10
                } SIFTOptions;
 
                struct TSURFOptions
                {
                        TSURFOptions()
                                : rotation_invariant(true),
                                  hessianThreshold(600),
                                  nOctaves(2),
                                  nLayersPerOctave(4)
                        {
                        }
 
                        /** SURF Options
                          */
                        bool rotation_invariant;  //!< Compute the rotation invariant SURF
                        //!(dim=128) if set to true (default), or
                        //! the smaller uSURF otherwise (dim=64)
                        int hessianThreshold;  //!< Default: 600
                        int nOctaves;  //!< Default: 2
                        int nLayersPerOctave;  //!< Default: 4
                } SURFOptions;
 
                struct TSpinImagesOptions
                {
                        /** SpinImages Options
                          */
                        unsigned int hist_size_intensity;  //!< Number of bins in the
                        //!"intensity" axis of the 2D
                        //! histogram (default=10).
                        unsigned int hist_size_distance;  //!< Number of bins in the
                        //!"distance" axis of the 2D
                        //! histogram (default=10).
                        float std_dist;  //!< Standard deviation in "distance", used for the
                        //!"soft histogram" (default=0.4 pixels)
                        float std_intensity;  //!< Standard deviation in "intensity", used
                        //! for the "soft histogram" (default=20 units
                        //![0,255])
                        unsigned int radius;  //!< Maximum radius of the area of which the
                        //! histogram is built, in pixel units
                        //!(default=20 pixels)
                } SpinImagesOptions;
 
                /** PolarImagesOptions Options
                  */
                struct TPolarImagesOptions
                {
                        unsigned int bins_angle;  //!< Number of bins in the "angular" axis
                        //! of the polar image (default=8).
                        unsigned int bins_distance;  //!< Number of bins in the "distance"
                        //! axis of the polar image (default=6).
                        unsigned int radius;  //!< Maximum radius of the area of which the
                        //! polar image is built, in pixel units
                        //!(default=20 pixels)
                } PolarImagesOptions;
 
                /** LogPolarImagesOptions Options
                  */
                struct TLogPolarImagesOptions
                {
                        unsigned int radius;  //!< Maximum radius of the area of which the
                        //! log polar image is built, in pixel units
                        //!(default=30 pixels)
                        unsigned int num_angles;  //!< (default=16) Log-Polar image patch
                        //! will have dimensions WxH, with:
                        //! W=num_angles,  H= rho_scale *
                        //! log(radius)
                        double rho_scale;  //!< (default=5) Log-Polar image patch will have
                        //! dimensions WxH, with:  W=num_angles,  H=
                        //! rho_scale * log(radius)
                } LogPolarImagesOptions;
 
                // # added by Raghavender Sahdev
                /** AKAZEOptions Options
                 */
                struct TAKAZEOptions
                {
                        int descriptor_type;
                        int descriptor_size;
                        int descriptor_channels;
                        float threshold;
                        int nOctaves;
                        int nOctaveLayers;
                        int diffusivity;
                } AKAZEOptions;
 
                /** LSDOptions Options
                 */
                struct TLSDOptions
                {
                        int scale;
                        int nOctaves;
                } LSDOptions;
 
                /** BLDOptions Descriptor Options
                 */
                struct TBLDOptions
                {
                        int ksize_;
                        int reductionRatio;
                        int widthOfBand;
                        int numOfOctave;
 
                } BLDOptions;
 
                /** LATCHOptions Descriptor
                 */
                struct TLATCHOptions
                {
                        int bytes;  // = 32,
                        bool rotationInvariance;  // = true,
                        int half_ssd_size;  // = 3
                } LATCHOptions;
        };
 
        TOptions options;  //!< Set all the parameters of the desired method here
        //! before calling "detectFeatures"
 
        /** Constructor
        */
        CFeatureExtraction();
 
        /** Virtual destructor.
        */
        virtual ~CFeatureExtraction();
 
        /** Extract features from the image based on the method defined in TOptions.
        * \param img (input) The image from where to extract the images.
        * \param feats (output) A complete list of features (containing a patch for
        * each one of them if options.patchsize > 0).
        * \param nDesiredFeatures (op. input) Number of features to be extracted.
        * Default: all possible.
        *
        * \sa computeDescriptors
        */
        void detectFeatures(
                const mrpt::utils::CImage& img, CFeatureList& feats,
                const unsigned int init_ID = 0, const unsigned int nDesiredFeatures = 0,
                const TImageROI& ROI = TImageROI()) const;
 
        /** Compute one (or more) descriptors for the given set of interest points
        * onto the image, which may have been filled out manually or from \a
        * detectFeatures
        * \param in_img (input) The image from where to compute the descriptors.
        * \param inout_features (input/output) The list of features whose
        * descriptors are going to be computed.
        * \param in_descriptor_list (input) The bitwise OR of one or several
        * descriptors defined in TDescriptorType.
        *
        *  Each value in "in_descriptor_list" represents one descriptor to be
        * computed, for example:
        *  \code
        *    // This call will compute both, SIFT and Spin-Image descriptors for a
        * list of feature points lstFeats.
        *    fext.computeDescriptors(img, lstFeats, descSIFT | descSpinImages );
        *  \endcode
        *
        * \note The SIFT descriptors for already located features can only be
        * computed through the Hess and
        *        CSBinary implementations which may be specified in
        * CFeatureExtraction::TOptions::SIFTOptions.
        *
        * \note This call will also use additional parameters from \a options
        */
        void computeDescriptors(
                const mrpt::utils::CImage& in_img, CFeatureList& inout_features,
                TDescriptorType in_descriptor_list) const;
 
#if 0  // Delete? see comments in .cpp
                        /** Extract more features from the image (apart from the provided ones) based on the method defined in TOptions.
                        * \param img (input) The image from where to extract the images.
                        * \param inList (input) The actual features in the image.
                        * \param outList (output) The list of new features (containing a patch for each one of them if options.patchsize > 0).
                        * \param nDesiredFeatures (op. input) Number of features to be extracted. Default: all possible.
                        *
                        *  \sa The more powerful class: mrpt::vision::CGenericFeatureTracker
                        */
                        void  findMoreFeatures( const mrpt::utils::CImage &img,
                                                                        const CFeatureList &inList,
                                                                        CFeatureList &outList,
                                                                        unsigned int nDesiredFeats = 0) const;
#endif
 
        /** @name Static methods with low-level detector functionality
                @{ */
 
        /** A SSE2-optimized implementation of FASTER-9 (requires img to be
         * grayscale). If SSE2 is not available, it gratefully falls back to a
         * non-optimized version.
          *
          *  Only the pt.{x,y} fields are filled out for each feature: the rest of
         * fields are left <b>uninitialized</b> and their content is
         * <b>undefined</b>.
          *  Note that (x,y) are already scaled to the 0-level image coordinates if
         * octave>0, by means of:
          *
          *  \code
          *    pt.x = detected.x << octave;
          *    pt.y = detected.y << octave;
          *  \endcode
          *
          * If \a append_to_list is true, the \a corners list is not cleared before
         * adding the newly detected feats.
          *
          * If a valid pointer is provided for \a out_feats_index_by_row, upon
         * return you will find a vector with
          *  as many entries as rows in the image (the real number of rows,
         * disregarding the value of \a octave).
          * The number in each entry is the 0-based index (in \a corners) of
          *  the first feature that falls in that line of the image. This index can
         * be used to fasten looking for correspondences.
          *
          * \ingroup mrptvision_features
          */
        static void detectFeatures_SSE2_FASTER9(
                const mrpt::utils::CImage& img, TSimpleFeatureList& corners,
                const int threshold = 20, bool append_to_list = false,
                uint8_t octave = 0,
                std::vector<size_t>* out_feats_index_by_row = nullptr);
 
        /** Just like \a detectFeatures_SSE2_FASTER9() for another version of the
         * detector.
          * \ingroup mrptvision_features */
        static void detectFeatures_SSE2_FASTER10(
                const mrpt::utils::CImage& img, TSimpleFeatureList& corners,
                const int threshold = 20, bool append_to_list = false,
                uint8_t octave = 0,
                std::vector<size_t>* out_feats_index_by_row = nullptr);
 
        /** Just like \a detectFeatures_SSE2_FASTER9() for another version of the
         * detector.
          * \ingroup mrptvision_features */
        static void detectFeatures_SSE2_FASTER12(
                const mrpt::utils::CImage& img, TSimpleFeatureList& corners,
                const int threshold = 20, bool append_to_list = false,
                uint8_t octave = 0,
                std::vector<size_t>* out_feats_index_by_row = nullptr);
 
        /** @} */
 
   private:
        /** Compute the SIFT descriptor of the provided features into the input
        image
        * \param in_img (input) The image from where to compute the descriptors.
        * \param in_features (input/output) The list of features whose descriptors
        are going to be computed.
        *
        * \note The SIFT descriptors for already located features can only be
        computed through the Hess and
                        CSBinary implementations which may be specified in
        CFeatureExtraction::TOptions::SIFTOptions.
        */
        void internal_computeSiftDescriptors(
                const mrpt::utils::CImage& in_img, CFeatureList& in_features) const;
 
        /** Compute the SURF descriptor of the provided features into the input
        * image
        * \param in_img (input) The image from where to compute the descriptors.
        * \param in_features (input/output) The list of features whose descriptors
        * are going to be computed.
        */
        void internal_computeSurfDescriptors(
                const mrpt::utils::CImage& in_img, CFeatureList& in_features) const;
 
        /** Compute the ORB descriptor of the provided features into the input image
        * \param in_img (input) The image from where to compute the descriptors.
        * \param in_features (input/output) The list of features whose descriptors
        * are going to be computed.
        */
        void internal_computeORBDescriptors(
                const mrpt::utils::CImage& in_img, CFeatureList& in_features) const;
 
        /** Compute the intensity-domain spin images descriptor of the provided
        * features into the input image
        * \param in_img (input) The image from where to compute the descriptors.
        * \param in_features (input/output) The list of features whose descriptors
        * are going to be computed.
        *
        * \note Additional parameters from
        * CFeatureExtraction::TOptions::SpinImagesOptions are used in this method.
        */
        void internal_computeSpinImageDescriptors(
                const mrpt::utils::CImage& in_img, CFeatureList& in_features) const;
 
        /** Compute a polar-image descriptor of the provided features into the input
        * image
        * \param in_img (input) The image from where to compute the descriptors.
        * \param in_features (input/output) The list of features whose descriptors
        * are going to be computed.
        *
        * \note Additional parameters from
        * CFeatureExtraction::TOptions::PolarImagesOptions are used in this method.
        */
        void internal_computePolarImageDescriptors(
                const mrpt::utils::CImage& in_img, CFeatureList& in_features) const;
 
        /** Compute a log-polar image descriptor of the provided features into the
        * input image
        * \param in_img (input) The image from where to compute the descriptors.
        * \param in_features (input/output) The list of features whose descriptors
        * are going to be computed.
        *
        * \note Additional parameters from
        * CFeatureExtraction::TOptions::LogPolarImagesOptions are used in this
        * method.
        */
        void internal_computeLogPolarImageDescriptors(
                const mrpt::utils::CImage& in_img, CFeatureList& in_features) const;
        /** Compute a BLD descriptor of the provided features into the input image
        * \param in_img (input) The image from where to compute the descriptors.
        * \param in_features (input/output) The list of features whose descriptors
        * are going to be computed.
        *
        * \note Additional parameters from
        * CFeatureExtraction::TOptions::LogPolarImagesOptions are used in this
        * method.
        */
        void internal_computeBLDLineDescriptors(
                const mrpt::utils::CImage& in_img, CFeatureList& in_features) const;
        /** Compute a LATCH descriptor of the provided features into the input image
        * \param in_img (input) The image from where to compute the descriptors.
        * \param in_features (input/output) The list of features whose descriptors
        * are going to be computed.
        *
        * \note Additional parameters from
        * CFeatureExtraction::TOptions::LogPolarImagesOptions are used in this
        * method.
        */
        void internal_computeLATCHDescriptors(
                const mrpt::utils::CImage& in_img, CFeatureList& in_features) const;
 
#if 0  // Delete? see comments in .cpp
                        /** Select good features using the openCV implementation of the KLT method.
                        * \param img (input) The image from where to select extract the images.
                        * \param feats (output) A complete list of features (containing a patch for each one of them if options.patchsize > 0).
                        * \param nDesiredFeatures (op. input) Number of features to be extracted. Default: all possible.
                        */
                        void  selectGoodFeaturesKLT(
                                const mrpt::utils::CImage       &inImg,
                                CFeatureList            &feats,
                                unsigned int            init_ID = 0,
                                unsigned int            nDesiredFeatures = 0) const;
#endif
 
        /** Extract features from the image based on the KLT method.
        * \param img The image from where to extract the images.
        * \param feats The list of extracted features.
        * \param nDesiredFeatures Number of features to be extracted. Default:
        * authomatic.
        */
        void extractFeaturesKLT(
                const mrpt::utils::CImage& img, CFeatureList& feats,
                unsigned int init_ID = 0, unsigned int nDesiredFeatures = 0,
                const TImageROI& ROI = TImageROI()) const;
 
        // ------------------------------------------------------------------------------------
        //                                                                                      BCD
        // ------------------------------------------------------------------------------------
        /** Extract features from the image based on the BCD method.
        * \param img The image from where to extract the images.
        * \param feats The list of extracted features.
        * \param nDesiredFeatures Number of features to be extracted. Default:
        * authomatic.
        * \param ROI (op. input) Region of Interest. Default: All the image.
        */
        void extractFeaturesBCD(
                const mrpt::utils::CImage& img, CFeatureList& feats,
                unsigned int init_ID = 0, unsigned int nDesiredFeatures = 0,
                const TImageROI& ROI = TImageROI()) const;
 
        // ------------------------------------------------------------------------------------
        //                                                                                      SIFT
        // ------------------------------------------------------------------------------------
        /** Extract features from the image based on the SIFT method.
        * \param img The image from where to extract the images.
        * \param feats The list of extracted features.
        * \param nDesiredFeatures Number of features to be extracted. Default:
        * authomatic.
        * \param ROI (op. input) Region of Interest. Default: All the image.
        */
        void extractFeaturesSIFT(
                const mrpt::utils::CImage& img, CFeatureList& feats,
                unsigned int init_ID = 0, unsigned int nDesiredFeatures = 0,
                const TImageROI& ROI = TImageROI()) const;
 
        // ------------------------------------------------------------------------------------
        //                                                                                      ORB
        // ------------------------------------------------------------------------------------
        /** Extract features from the image based on the ORB method.
        * \param img The image from where to extract the images.
        * \param feats The list of extracted features.
        * \param nDesiredFeatures Number of features to be extracted. Default:
        * authomatic.
        */
        void extractFeaturesORB(
                const mrpt::utils::CImage& img, CFeatureList& feats,
                const unsigned int init_ID = 0, const unsigned int nDesiredFeatures = 0,
                const TImageROI& ROI = TImageROI()) const;
 
        // ------------------------------------------------------------------------------------
        //                                                                                      SURF
        // ------------------------------------------------------------------------------------
        /** Extract features from the image based on the SURF method.
        * \param img The image from where to extract the images.
        * \param feats The list of extracted features.
        * \param nDesiredFeatures Number of features to be extracted. Default:
        * authomatic.
        */
        void extractFeaturesSURF(
                const mrpt::utils::CImage& img, CFeatureList& feats,
                unsigned int init_ID = 0, unsigned int nDesiredFeatures = 0,
                const TImageROI& ROI = TImageROI()) const;
 
        // ------------------------------------------------------------------------------------
        //                                                                                      FAST
        // ------------------------------------------------------------------------------------
        /** Extract features from the image based on the FAST method.
        * \param img The image from where to extract the images.
        * \param feats The list of extracted features.
        * \param nDesiredFeatures Number of features to be extracted. Default:
        * authomatic.
        */
        void extractFeaturesFAST(
                const mrpt::utils::CImage& img, CFeatureList& feats,
                unsigned int init_ID = 0, unsigned int nDesiredFeatures = 0,
                const TImageROI& ROI = TImageROI(),
                const mrpt::math::CMatrixBool* mask = nullptr) const;
 
        /** Edward's "FASTER & Better" detector, N=9,10,12 */
        void extractFeaturesFASTER_N(
                const int N, const mrpt::utils::CImage& img, CFeatureList& feats,
                unsigned int init_ID = 0, unsigned int nDesiredFeatures = 0,
                const TImageROI& ROI = TImageROI()) const;
 
        // # added by Raghavender Sahdev
        //-------------------------------------------------------------------------------------
        //                               AKAZE
        //-------------------------------------------------------------------------------------
        /** Extract features from the image based on the AKAZE method.
        * \param img The image from where to extract the images.
        * \param feats The list of extracted features.
        * \param nDesiredFeatures Number of features to be extracted. Default:
        * authomatic.
        */
        void extractFeaturesAKAZE(
                const mrpt::utils::CImage& inImg, CFeatureList& feats,
                unsigned int init_ID, unsigned int nDesiredFeatures,
                const TImageROI& ROI = TImageROI()) const;
 
        //-------------------------------------------------------------------------------------
        //                               LSD
        //-------------------------------------------------------------------------------------
        /** Extract features from the image based on the LSD method.
        * \param img The image from where to extract the images.
        * \param feats The list of extracted features.
        * \param nDesiredFeatures Number of features to be extracted. Default:
        * authomatic.
        */
        void extractFeaturesLSD(
                const mrpt::utils::CImage& inImg, CFeatureList& feats,
                unsigned int init_ID, unsigned int nDesiredFeatures,
                const TImageROI& ROI = TImageROI()) const;
 
        // ------------------------------------------------------------------------------------
        //                                                              my_scale_space_extrema
        // ------------------------------------------------------------------------------------
        /** Computes extrema in the scale space.
        * \param dog_pyr Pyramid of images.
        * \param octvs Number of considered octaves.
        * \param intvls Number of intervales in octaves.
        */
        void* my_scale_space_extrema(
                CFeatureList& featList, void* dog_pyr, int octvs, int intvls,
                double contr_thr, int curv_thr, void* storage) const;
 
        /** Adjust scale if the image was initially doubled.
        * \param features The sequence of features.
        */
        void my_adjust_for_img_dbl(void* features) const;
 
        /** Gets the number of times that a point in the image is higher or lower
        * than the surroundings in the image-scale space
        * \param dog_pyr Pyramid of images.
        * \param octvs Number of considered octaves.
        * \param intvls Number of intervales in octaves.
        * \param row The row of the feature in the original image.
        * \param col The column of the feature in the original image.
        * \param nMin [out]: Times that the feature is lower than the surroundings.
        * \param nMax [out]: Times that the feature is higher than the surroundings.
        */
        void getTimesExtrema(
                void* dog_pyr, int octvs, int intvls, float row, float col,
                unsigned int& nMin, unsigned int& nMax) const;
 
        /** Computes the Laplacian value of the feature in the corresponing image in
        * the pyramid.
        * \param dog_pyr Pyramid of images.
        * \param octvs Number of considered octaves.
        * \param intvls Number of intervales in octaves.
        * \param row The row of the feature in the original image.
        * \param col The column of the feature in the original image.
        */
        double getLaplacianValue(
                void* dog_pyr, int octvs, int intvls, float row, float col) const;
 
        /** Append a sequence of openCV features into an MRPT feature list.
        * \param features The sequence of features.
        * \param list [in-out] The list of MRPT features.
        * \param init_ID [in] The initial ID for the new features.
        */
        void insertCvSeqInCFeatureList(
                void* features, CFeatureList& list, unsigned int init_ID = 0) const;
 
        /** Converts a sequence of openCV features into an MRPT feature list.
        * \param features The sequence of features.
        * \param list [in-out] The list of MRPT features.
        * \param init_ID [in][optional] The initial ID for the features (default =
        * 0).
        * \param ROI [in][optional] The initial ID for the features (default = empty
        * ROI -> not used).
        */
        void convertCvSeqInCFeatureList(
                void* features, CFeatureList& list, unsigned int init_ID = 0,
                const TImageROI& ROI = TImageROI()) const;
 
};  // end of class
}  // end of namespace
}  // end of namespace
#endif