opencv/modules/features2d/doc/feature_detection_and_description.rst

Feature Detection and Description
=================================

.. highlight:: cpp

FAST
--------
Detects corners using the FAST algorithm

.. ocv:function:: void FAST( const Mat& image, vector<KeyPoint>& keypoints,            int threshold, bool nonmaxSupression=true )

    :param image: Image where keypoints (corners) are detected.

    :param keypoints: Keypoints detected on the image.

    :param threshold: Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel. See the algorithm description below.

    :param nonmaxSupression: If it is true, non-maximum supression is applied to detected corners (keypoints).

Detects corners using the FAST algorithm by E. Rosten (*Machine Learning for High-speed Corner Detection*, 2006).


MSER
----
.. ocv:class:: MSER

Maximally stable extremal region extractor. ::

    class MSER : public CvMSERParams
    {
    public:
        // default constructor
        MSER();
        // constructor that initializes all the algorithm parameters
        MSER( int _delta, int _min_area, int _max_area,
              float _max_variation, float _min_diversity,
              int _max_evolution, double _area_threshold,
              double _min_margin, int _edge_blur_size );
        // runs the extractor on the specified image; returns the MSERs,
        // each encoded as a contour (vector<Point>, see findContours)
        // the optional mask marks the area where MSERs are searched for
        void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
    };

The class encapsulates all the parameters of the MSER extraction algorithm (see
http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions). Also see http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER for usefull comments and parameters description.


StarDetector
------------
.. ocv:class:: StarDetector

Class implementing the ``Star`` keypoint detector, a modified version of the ``CenSurE`` keypoint detector described in [Agrawal08]_.

.. [Agrawal08] Agrawal, M. and Konolige, K. and Blas, M.R. "CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching", ECCV08, 2008

StarDetector::StarDetector
--------------------------
The Star Detector constructor

.. ocv:function:: StarDetector::StarDetector()

.. ocv:function:: StarDetector::StarDetector(int maxSize, int responseThreshold, int lineThresholdProjected, int lineThresholdBinarized, int suppressNonmaxSize)

.. ocv:pyfunction:: cv2.StarDetector(maxSize, responseThreshold, lineThresholdProjected, lineThresholdBinarized, suppressNonmaxSize) -> <StarDetector object>

    :param maxSize: maximum size of the features. The following values are supported: 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128. In the case of a different value the result is undefined.
    
    :param responseThreshold: threshold for the approximated laplacian, used to eliminate weak features. The larger it is, the less features will be retrieved
    
    :param lineThresholdProjected: another threshold for the laplacian to eliminate edges    

    :param lineThresholdBinarized: yet another threshold for the feature size to eliminate edges. The larger the 2nd threshold, the more points you get.

StarDetector::operator()
------------------------
Finds keypoints in an image
        
.. ocv:function:: void StarDetector::operator()(const Mat& image, vector<KeyPoint>& keypoints)

.. ocv:pyfunction:: cv2.StarDetector.detect(image) -> keypoints

.. ocv:cfunction:: CvSeq* cvGetStarKeypoints( const CvArr* image, CvMemStorage* storage, CvStarDetectorParams params=cvStarDetectorParams() )

.. ocv:pyoldfunction:: cv.GetStarKeypoints(image, storage, params)-> keypoints

    :param image: The input 8-bit grayscale image
    
    :param keypoints: The output vector of keypoints
    
    :param storage: The memory storage used to store the keypoints (OpenCV 1.x API only)
    
    :param params: The algorithm parameters stored in ``CvStarDetectorParams`` (OpenCV 1.x API only)


SIFT
----
.. ocv:class:: SIFT

Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) approach. ::

    class CV_EXPORTS SIFT
    {
    public:
        struct CommonParams
        {
            static const int DEFAULT_NOCTAVES = 4;
            static const int DEFAULT_NOCTAVE_LAYERS = 3;
            static const int DEFAULT_FIRST_OCTAVE = -1;
            enum{ FIRST_ANGLE = 0, AVERAGE_ANGLE = 1 };

            CommonParams();
            CommonParams( int _nOctaves, int _nOctaveLayers, int _firstOctave,
                                              int _angleMode );
            int nOctaves, nOctaveLayers, firstOctave;
            int angleMode;
        };

        struct DetectorParams
        {
            static double GET_DEFAULT_THRESHOLD()
              { return 0.04 / SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS / 2.0; }
            static double GET_DEFAULT_EDGE_THRESHOLD() { return 10.0; }

            DetectorParams();
            DetectorParams( double _threshold, double _edgeThreshold );
            double threshold, edgeThreshold;
        };

        struct DescriptorParams
        {
            static double GET_DEFAULT_MAGNIFICATION() { return 3.0; }
            static const bool DEFAULT_IS_NORMALIZE = true;
            static const int DESCRIPTOR_SIZE = 128;

            DescriptorParams();
            DescriptorParams( double _magnification, bool _isNormalize,
                                                      bool _recalculateAngles );
            double magnification;
            bool isNormalize;
            bool recalculateAngles;
        };

        SIFT();
        //! sift-detector constructor
        SIFT( double _threshold, double _edgeThreshold,
              int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
              int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
              int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
              int _angleMode=CommonParams::FIRST_ANGLE );
        //! sift-descriptor constructor
        SIFT( double _magnification, bool _isNormalize=true,
              bool _recalculateAngles = true,
              int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
              int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
              int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
              int _angleMode=CommonParams::FIRST_ANGLE );
        SIFT( const CommonParams& _commParams,
              const DetectorParams& _detectorParams = DetectorParams(),
              const DescriptorParams& _descriptorParams = DescriptorParams() );

        //! returns the descriptor size in floats (128)
        int descriptorSize() const { return DescriptorParams::DESCRIPTOR_SIZE; }
        //! finds the keypoints using the SIFT algorithm
        void operator()(const Mat& img, const Mat& mask,
                        vector<KeyPoint>& keypoints) const;
        //! finds the keypoints and computes descriptors for them using SIFT algorithm.
        //! Optionally it can compute descriptors for the user-provided keypoints
        void operator()(const Mat& img, const Mat& mask,
                        vector<KeyPoint>& keypoints,
                        Mat& descriptors,
                        bool useProvidedKeypoints=false) const;

        CommonParams getCommonParams () const { return commParams; }
        DetectorParams getDetectorParams () const { return detectorParams; }
        DescriptorParams getDescriptorParams () const { return descriptorParams; }
    protected:
        ...
    };




SURF
----
.. ocv:class:: SURF

Class for extracting Speeded Up Robust Features from an image [Bay06]_. The class is derived from ``CvSURFParams`` structure, which specifies the algorithm parameters:

    .. ocv:member:: int extended
    
        * 0 means that the basic descriptors (64 elements each) shall be computed
        * 1 means that the extended descriptors (128 elements each) shall be computed
       
    .. ocv:member:: int upright
    
        * 0 means that detector computes orientation of each feature.
        * 1 means that the orientation is not computed (which is much, much faster). For example, if you match images from a stereo pair, or do image stitching, the matched features likely have very similar angles, and you can speed up feature extraction by setting ``upright=1``.
        
    .. ocv:member:: double hessianThreshold
    
        Threshold for the keypoint detector. Only features, whose hessian is larger than ``hessianThreshold`` are retained by the detector. Therefore, the larger the value, the less keypoints you will get. A good default value could be from 300 to 500, depending from the image contrast.
        
    .. ocv:member:: int nOctaves
    
        The number of a gaussian pyramid octaves that the detector uses. It is set to 4 by default. If you want to get very large features, use the larger value. If you want just small features, decrease it.
        
    .. ocv:member:: int nOctaveLayers
    
        The number of images within each octave of a gaussian pyramid. It is set to 2 by default.


.. [Bay06] Bay, H. and Tuytelaars, T. and Van Gool, L. "SURF: Speeded Up Robust Features", 9th European Conference on Computer Vision, 2006


SURF::SURF
----------
The SURF extractor constructors.

.. ocv:function:: SURF::SURF()

.. ocv:function:: SURF::SURF(double hessianThreshold, int nOctaves=4, int nOctaveLayers=2, bool extended=false, bool upright=false)

.. ocv:pyfunction:: cv2.SURF(_hessianThreshold[, _nOctaves[, _nOctaveLayers[, _extended[, _upright]]]]) -> <SURF object>

    :param hessianThreshold: Threshold for hessian keypoint detector used in SURF.
    
    :param nOctaves: Number of pyramid octaves the keypoint detector will use.
    
    :param nOctaveLayers: Number of octave layers within each octave.
    
    :param extended: Extended descriptor flag (true - use extended 128-element descriptors; false - use 64-element descriptors).
    
    :param upright: Up-right or rotated features flag (true - do not compute orientation of features; false - compute orientation).


SURF::operator()
----------------
Detects keypoints and computes SURF descriptors for them.

.. ocv:function:: void SURF::operator()(const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints)
.. ocv:function:: void SURF::operator()(const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints, vector<float>& descriptors, bool useProvidedKeypoints=false)

.. ocv:pyfunction:: cv2.SURF.detect(img, mask) -> keypoints
.. ocv:pyfunction:: cv2.SURF.detect(img, mask[, useProvidedKeypoints]) -> keypoints, descriptors

.. ocv:cfunction:: void cvExtractSURF( const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, CvMemStorage* storage, CvSURFParams params )

.. ocv:pyoldfunction:: cv.ExtractSURF(image, mask, storage, params)-> (keypoints, descriptors)

    :param image: Input 8-bit grayscale image
    
    :param mask: Optional input mask that marks the regions where we should detect features.
    
    :param keypoints: The input/output vector of keypoints
    
    :param descriptors: The output concatenated vectors of descriptors. Each descriptor is 64- or 128-element vector, as returned by ``SURF::descriptorSize()``. So the total size of ``descriptors`` will be ``keypoints.size()*descriptorSize()``.
    
    :param useProvidedKeypoints: Boolean flag. If it is true, the keypoint detector is not run. Instead, the provided vector of keypoints is used and the algorithm just computes their descriptors.
    
    :param storage: Memory storage for the output keypoints and descriptors in OpenCV 1.x API.
    
    :param params: SURF algorithm parameters in OpenCV 1.x API.


ORB
----
.. ocv:class:: ORB

Class for extracting ORB features and descriptors from an image. ::

    class ORB
    {
    public:
        /** The patch sizes that can be used (only one right now) */
        struct CommonParams
        {
            enum { DEFAULT_N_LEVELS = 3, DEFAULT_FIRST_LEVEL = 0};

            /** default constructor */
            CommonParams(float scale_factor = 1.2f, unsigned int n_levels = DEFAULT_N_LEVELS,
                 int edge_threshold = 31, unsigned int first_level = DEFAULT_FIRST_LEVEL);
            void read(const FileNode& fn);
            void write(FileStorage& fs) const;

            /** Coefficient by which we divide the dimensions from one scale pyramid level to the next */
            float scale_factor_;
            /** The number of levels in the scale pyramid */
            unsigned int n_levels_;
            /** The level at which the image is given
             * if 1, that means we will also look at the image scale_factor_ times bigger
             */
            unsigned int first_level_;
            /** How far from the boundary the points should be */
            int edge_threshold_;
        };

        // c:function::default constructor
        ORB();
        // constructor that initializes all the algorithm parameters
        ORB( const CommonParams detector_params );
        // returns the number of elements in each descriptor (32 bytes)
        int descriptorSize() const;
        // detects keypoints using ORB
        void operator()(const Mat& img, const Mat& mask,
                        vector<KeyPoint>& keypoints) const;
        // detects ORB keypoints and computes the ORB descriptors for them;
        // output vector "descriptors" stores elements of descriptors and has size
        // equal descriptorSize()*keypoints.size() as each descriptor is
        // descriptorSize() elements of this vector.
        void operator()(const Mat& img, const Mat& mask,
                        vector<KeyPoint>& keypoints,
                        cv::Mat& descriptors,
                        bool useProvidedKeypoints=false) const;
    };

The class implements ORB.





RandomizedTree
--------------
.. ocv:class:: RandomizedTree

Class containing a base structure for ``RTreeClassifier``. ::

    class CV_EXPORTS RandomizedTree
    {
    public:
            friend class RTreeClassifier;

            RandomizedTree();
            ~RandomizedTree();

            void train(std::vector<BaseKeypoint> const& base_set,
                     RNG &rng, int depth, int views,
                     size_t reduced_num_dim, int num_quant_bits);
            void train(std::vector<BaseKeypoint> const& base_set,
                     RNG &rng, PatchGenerator &make_patch, int depth,
                     int views, size_t reduced_num_dim, int num_quant_bits);

            // next two functions are EXPERIMENTAL
            //(do not use unless you know exactly what you do)
            static void quantizeVector(float *vec, int dim, int N, float bnds[2],
                     int clamp_mode=0);
            static void quantizeVector(float *src, int dim, int N, float bnds[2],
                     uchar *dst);

            // patch_data must be a 32x32 array (no row padding)
            float* getPosterior(uchar* patch_data);
            const float* getPosterior(uchar* patch_data) const;
            uchar* getPosterior2(uchar* patch_data);

            void read(const char* file_name, int num_quant_bits);
            void read(std::istream &is, int num_quant_bits);
            void write(const char* file_name) const;
            void write(std::ostream &os) const;

            int classes() { return classes_; }
            int depth() { return depth_; }

            void discardFloatPosteriors() { freePosteriors(1); }

            inline void applyQuantization(int num_quant_bits)
                     { makePosteriors2(num_quant_bits); }

    private:
            int classes_;
            int depth_;
            int num_leaves_;
            std::vector<RTreeNode> nodes_;
            float **posteriors_;        // 16-byte aligned posteriors
            uchar **posteriors2_;     // 16-byte aligned posteriors
            std::vector<int> leaf_counts_;

            void createNodes(int num_nodes, RNG &rng);
            void allocPosteriorsAligned(int num_leaves, int num_classes);
            void freePosteriors(int which);
                     // which: 1=posteriors_, 2=posteriors2_, 3=both
            void init(int classes, int depth, RNG &rng);
            void addExample(int class_id, uchar* patch_data);
            void finalize(size_t reduced_num_dim, int num_quant_bits);
            int getIndex(uchar* patch_data) const;
            inline float* getPosteriorByIndex(int index);
            inline uchar* getPosteriorByIndex2(int index);
            inline const float* getPosteriorByIndex(int index) const;
            void convertPosteriorsToChar();
            void makePosteriors2(int num_quant_bits);
            void compressLeaves(size_t reduced_num_dim);
            void estimateQuantPercForPosteriors(float perc[2]);
    };



RandomizedTree::train
-------------------------
Trains a randomized tree using an input set of keypoints.

.. ocv:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)

.. ocv:function:: void train(std::vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)

    :param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
    
    :param rng: Random-number generator used for training.
    
    :param make_patch: Patch generator used for training.
    
    :param depth: Maximum tree depth.

    :param views: Number of random views of each keypoint neighborhood to generate.

    :param reduced_num_dim: Number of dimensions used in the compressed signature.
    
    :param num_quant_bits: Number of bits used for quantization.



RandomizedTree::read
------------------------
Reads a pre-saved randomized tree from a file or stream.

.. ocv:function:: read(const char* file_name, int num_quant_bits)

.. ocv:function:: read(std::istream &is, int num_quant_bits)

    :param file_name: Name of the file that contains randomized tree data.

    :param is: Input stream associated with the file that contains randomized tree data.

    :param num_quant_bits: Number of bits used for quantization.



RandomizedTree::write
-------------------------
Writes the current randomized tree to a file or stream.

.. ocv:function:: void write(const char* file_name) const

.. ocv:function:: void write(std::ostream &os) const

    :param file_name: Name of the file where randomized tree data is stored.

    :param is: Output stream associated with the file where randomized tree data is stored.



RandomizedTree::applyQuantization
-------------------------------------
.. ocv:function:: void applyQuantization(int num_quant_bits)

    Applies quantization to the current randomized tree.

    :param num_quant_bits: Number of bits used for quantization.


RTreeNode
---------
.. ocv:class:: RTreeNode

Class containing a base structure for ``RandomizedTree``. ::

    struct RTreeNode
    {
            short offset1, offset2;

            RTreeNode() {}

            RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
                    : offset1(y1*PATCH_SIZE + x1),
                    offset2(y2*PATCH_SIZE + x2)
            {}

            //! Left child on 0, right child on 1
            inline bool operator() (uchar* patch_data) const
            {
                    return patch_data[offset1] > patch_data[offset2];
            }
    };



RTreeClassifier
---------------
.. ocv:class:: RTreeClassifier

Class containing ``RTreeClassifier``. It represents the Calonder descriptor originally introduced by Michael Calonder. ::

    class CV_EXPORTS RTreeClassifier
    {
    public:
            static const int DEFAULT_TREES = 48;
            static const size_t DEFAULT_NUM_QUANT_BITS = 4;

            RTreeClassifier();

            void train(std::vector<BaseKeypoint> const& base_set,
                    RNG &rng,
                    int num_trees = RTreeClassifier::DEFAULT_TREES,
                    int depth = DEFAULT_DEPTH,
                    int views = DEFAULT_VIEWS,
                    size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
                    int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
                             bool print_status = true);
            void train(std::vector<BaseKeypoint> const& base_set,
                    RNG &rng,
                    PatchGenerator &make_patch,
                    int num_trees = RTreeClassifier::DEFAULT_TREES,
                    int depth = DEFAULT_DEPTH,
                    int views = DEFAULT_VIEWS,
                    size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
                    int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
                     bool print_status = true);

            // sig must point to a memory block of at least
            //classes()*sizeof(float|uchar) bytes
            void getSignature(IplImage *patch, uchar *sig);
            void getSignature(IplImage *patch, float *sig);
            void getSparseSignature(IplImage *patch, float *sig,
                     float thresh);

            static int countNonZeroElements(float *vec, int n, double tol=1e-10);
            static inline void safeSignatureAlloc(uchar **sig, int num_sig=1,
                            int sig_len=176);
            static inline uchar* safeSignatureAlloc(int num_sig=1,
                             int sig_len=176);

            inline int classes() { return classes_; }
            inline int original_num_classes()
                     { return original_num_classes_; }

            void setQuantization(int num_quant_bits);
            void discardFloatPosteriors();

            void read(const char* file_name);
            void read(std::istream &is);
            void write(const char* file_name) const;
            void write(std::ostream &os) const;

            std::vector<RandomizedTree> trees_;

    private:
            int classes_;
            int num_quant_bits_;
            uchar **posteriors_;
            ushort *ptemp_;
            int original_num_classes_;
            bool keep_floats_;
    };



RTreeClassifier::train
--------------------------
Trains a randomized tree classifier using an input set of keypoints.

.. ocv:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, int num_trees = RTreeClassifier::DEFAULT_TREES,                         int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)

.. ocv:function:: void train(vector<BaseKeypoint> const& base_set, RNG& rng, PatchGenerator& make_patch, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,                         int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)

    :param base_set: Vector of the ``BaseKeypoint``  type. It contains image keypoints used for training.
    
    :param rng: Random-number generator used for training.
    
    :param make_patch: Patch generator used for training.
    
    :param num_trees: Number of randomized trees used in ``RTreeClassificator`` .
    
    :param depth: Maximum tree depth.

    :param views: Number of random views of each keypoint neighborhood to generate.

    :param reduced_num_dim: Number of dimensions used in the compressed signature.
    
    :param num_quant_bits: Number of bits used for quantization.
    
    :param print_status: Current status of training printed on the console.



RTreeClassifier::getSignature
---------------------------------
Returns a signature for an image patch.

.. ocv:function:: void getSignature(IplImage *patch, uchar *sig)

.. ocv:function:: void getSignature(IplImage *patch, float *sig)

    :param patch: Image patch to calculate the signature for.
    :param sig: Output signature (array dimension is ``reduced_num_dim)`` .



RTreeClassifier::getSparseSignature
--------------------------------------- 
Returns a sparse signature for an image patch

.. ocv:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)

    :param patch: Image patch to calculate the signature for.
    
    :param sig: Output signature (array dimension is ``reduced_num_dim)`` .
    
    :param thresh: Threshold used for compressing the signature.

    Returns a signature for an image patch similarly to ``getSignature``  but uses a threshold for removing all signature elements below the threshold so that the signature is compressed.


RTreeClassifier::countNonZeroElements
-----------------------------------------
Returns the number of non-zero elements in an input array.

.. ocv:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)

    :param vec: Input vector containing float elements.

    :param n: Input vector size.

    :param tol: Threshold used for counting elements. All elements less than ``tol``  are considered as zero elements.



RTreeClassifier::read
-------------------------
Reads a pre-saved ``RTreeClassifier`` from a file or stream.

.. ocv:function:: read(const char* file_name)

.. ocv:function:: read(std::istream& is)

    :param file_name: Name of the file that contains randomized tree data.

    :param is: Input stream associated with the file that contains randomized tree data.



RTreeClassifier::write
--------------------------
Writes the current ``RTreeClassifier`` to a file or stream.

.. ocv:function:: void write(const char* file_name) const

.. ocv:function:: void write(std::ostream &os) const

    :param file_name: Name of the file where randomized tree data is stored.

    :param os: Output stream associated with the file where randomized tree data is stored.



RTreeClassifier::setQuantization
------------------------------------
Applies quantization to the current randomized tree.

.. ocv:function:: void setQuantization(int num_quant_bits)

    :param num_quant_bits: Number of bits used for quantization.

The example below demonstrates the usage of ``RTreeClassifier`` for matching the features. The features are extracted from the test and train images with SURF. Output is
:math:`best\_corr` and
:math:`best\_corr\_idx` arrays that keep the best probabilities and corresponding features indices for every train feature. ::

    CvMemStorage* storage = cvCreateMemStorage(0);
    CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
    CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
    CvSURFParams params = cvSURFParams(500, 1);
    cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors,
                     storage, params );
    cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors,
                     storage, params );

    RTreeClassifier detector;
    int patch_width = PATCH_SIZE;
    iint patch_height = PATCH_SIZE;
    vector<BaseKeypoint> base_set;
    int i=0;
    CvSURFPoint* point;
    for (i=0;i<(n_points > 0 ? n_points : objectKeypoints->total);i++)
    {
            point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,i);
            base_set.push_back(
                    BaseKeypoint(point->pt.x,point->pt.y,train_image));
    }

            //Detector training
     RNG rng( cvGetTickCount() );
    PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3,
                            -CV_PI/3,CV_PI/3);

    printf("RTree Classifier training...n");
    detector.train(base_set,rng,gen,24,DEFAULT_DEPTH,2000,
            (int)base_set.size(), detector.DEFAULT_NUM_QUANT_BITS);
    printf("Donen");

    float* signature = new float[detector.original_num_classes()];
    float* best_corr;
    int* best_corr_idx;
    if (imageKeypoints->total > 0)
    {
            best_corr = new float[imageKeypoints->total];
            best_corr_idx = new int[imageKeypoints->total];
    }

    for(i=0; i < imageKeypoints->total; i++)
    {
            point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,i);
            int part_idx = -1;
            float prob = 0.0f;

            CvRect roi = cvRect((int)(point->pt.x) - patch_width/2,
                    (int)(point->pt.y) - patch_height/2,
                     patch_width, patch_height);
            cvSetImageROI(test_image, roi);
            roi = cvGetImageROI(test_image);
            if(roi.width != patch_width || roi.height != patch_height)
            {
                    best_corr_idx[i] = part_idx;
                    best_corr[i] = prob;
            }
            else
            {
                    cvSetImageROI(test_image, roi);
                    IplImage* roi_image =
                             cvCreateImage(cvSize(roi.width, roi.height),
                             test_image->depth, test_image->nChannels);
                    cvCopy(test_image,roi_image);

                    detector.getSignature(roi_image, signature);
                    for (int j = 0; j< detector.original_num_classes();j++)
                    {
                            if (prob < signature[j])
                            {
                                    part_idx = j;
                                    prob = signature[j];
                            }
                    }

                    best_corr_idx[i] = part_idx;
                    best_corr[i] = prob;

                    if (roi_image)
                            cvReleaseImage(&roi_image);
            }
            cvResetImageROI(test_image);
    }

..