opencv/modules/features2d/include/opencv2/features2d/features2d.hpp

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#ifndef __OPENCV_FEATURES_2D_HPP__
#define __OPENCV_FEATURES_2D_HPP__

#include "opencv2/core/core.hpp"
#include "opencv2/flann/flann.hpp"

#ifdef __cplusplus
#include <limits>

extern "C" {
#endif

typedef struct CvSURFPoint
{
    CvPoint2D32f pt;
    int laplacian;
    int size;
    float dir;
    float hessian;
} CvSURFPoint;

CV_INLINE CvSURFPoint cvSURFPoint( CvPoint2D32f pt, int laplacian,
                                   int size, float dir CV_DEFAULT(0),
                                   float hessian CV_DEFAULT(0))
{
    CvSURFPoint kp;
    kp.pt = pt;
    kp.laplacian = laplacian;
    kp.size = size;
    kp.dir = dir;
    kp.hessian = hessian;
    return kp;
}

typedef struct CvSURFParams
{
    int extended;
    double hessianThreshold;

    int nOctaves;
    int nOctaveLayers;
} CvSURFParams;

CVAPI(CvSURFParams) cvSURFParams( double hessianThreshold, int extended CV_DEFAULT(0) );

// If useProvidedKeyPts!=0, keypoints are not detected, but descriptors are computed
//  at the locations provided in keypoints (a CvSeq of CvSURFPoint).
CVAPI(void) cvExtractSURF( const CvArr* img, const CvArr* mask,
                           CvSeq** keypoints, CvSeq** descriptors,
                           CvMemStorage* storage, CvSURFParams params, int useProvidedKeyPts CV_DEFAULT(0)  );

/*!
 Maximal Stable Regions Parameters
*/
typedef struct CvMSERParams
{
    //! delta, in the code, it compares (size_{i}-size_{i-delta})/size_{i-delta}
    int delta;
    //! prune the area which bigger than maxArea
    int maxArea;
    //! prune the area which smaller than minArea
    int minArea;
    //! prune the area have simliar size to its children
    float maxVariation;
    //! trace back to cut off mser with diversity < min_diversity
    float minDiversity;
    
    /////// the next few params for MSER of color image
    
    //! for color image, the evolution steps
    int maxEvolution;
    //! the area threshold to cause re-initialize
    double areaThreshold;
    //! ignore too small margin
    double minMargin;
    //! the aperture size for edge blur
    int edgeBlurSize;
} CvMSERParams;

CVAPI(CvMSERParams) cvMSERParams( int delta CV_DEFAULT(5), int min_area CV_DEFAULT(60),
                           int max_area CV_DEFAULT(14400), float max_variation CV_DEFAULT(.25f),
                           float min_diversity CV_DEFAULT(.2f), int max_evolution CV_DEFAULT(200),
                           double area_threshold CV_DEFAULT(1.01),
                           double min_margin CV_DEFAULT(.003),
                           int edge_blur_size CV_DEFAULT(5) );

// Extracts the contours of Maximally Stable Extremal Regions
CVAPI(void) cvExtractMSER( CvArr* _img, CvArr* _mask, CvSeq** contours, CvMemStorage* storage, CvMSERParams params );


typedef struct CvStarKeypoint
{
    CvPoint pt;
    int size;
    float response;
} CvStarKeypoint;

CV_INLINE CvStarKeypoint cvStarKeypoint(CvPoint pt, int size, float response)
{
    CvStarKeypoint kpt;
    kpt.pt = pt;
    kpt.size = size;
    kpt.response = response;
    return kpt;
}

typedef struct CvStarDetectorParams
{
    int maxSize;
    int responseThreshold;
    int lineThresholdProjected;
    int lineThresholdBinarized;
    int suppressNonmaxSize;
} CvStarDetectorParams;

CV_INLINE CvStarDetectorParams cvStarDetectorParams(
    int maxSize CV_DEFAULT(45),
    int responseThreshold CV_DEFAULT(30),
    int lineThresholdProjected CV_DEFAULT(10),
    int lineThresholdBinarized CV_DEFAULT(8),
    int suppressNonmaxSize CV_DEFAULT(5))
{
    CvStarDetectorParams params;
    params.maxSize = maxSize;
    params.responseThreshold = responseThreshold;
    params.lineThresholdProjected = lineThresholdProjected;
    params.lineThresholdBinarized = lineThresholdBinarized;
    params.suppressNonmaxSize = suppressNonmaxSize;

    return params;
}

CVAPI(CvSeq*) cvGetStarKeypoints( const CvArr* img, CvMemStorage* storage,
        CvStarDetectorParams params CV_DEFAULT(cvStarDetectorParams()));

#ifdef __cplusplus
}

namespace cv
{
    struct CV_EXPORTS DefaultRngAuto
    {
        const uint64 old_state;

        DefaultRngAuto() : old_state(theRNG().state) { theRNG().state = (uint64)-1; }
        ~DefaultRngAuto() { theRNG().state = old_state; }

        DefaultRngAuto& operator=(const DefaultRngAuto&);
    };


// CvAffinePose: defines a parameterized affine transformation of an image patch.
// An image patch is rotated on angle phi (in degrees), then scaled lambda1 times
// along horizontal and lambda2 times along vertical direction, and then rotated again
// on angle (theta - phi).
class CV_EXPORTS CvAffinePose
{
public:
    float phi;
    float theta;
    float lambda1;
    float lambda2;
};

/*!
 The Keypoint Class
 
 The class instance stores a keypoint, i.e. a point feature found by one of many available keypoint detectors, such as
 Harris corner detector, cv::FAST, cv::StarDetector, cv::SURF, cv::SIFT, cv::LDetector etc.
 
 The keypoint is characterized by the 2D position, scale
 (proportional to the diameter of the neighborhood that needs to be taken into account),
 orientation and some other parameters. The keypoint neighborhood is then analyzed by another algorithm that builds a descriptor
 (usually represented as a feature vector). The keypoints representing the same object in different images can then be matched using
 cv::KDTree or another method.
*/
class CV_EXPORTS_W_SIMPLE KeyPoint
{
public:
    //! the default constructor
    CV_WRAP KeyPoint() : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {}
    //! the full constructor
    KeyPoint(Point2f _pt, float _size, float _angle=-1,
            float _response=0, int _octave=0, int _class_id=-1)
            : pt(_pt), size(_size), angle(_angle),
            response(_response), octave(_octave), class_id(_class_id) {}
    //! another form of the full constructor
    CV_WRAP KeyPoint(float x, float y, float _size, float _angle=-1,
            float _response=0, int _octave=0, int _class_id=-1)
            : pt(x, y), size(_size), angle(_angle),
            response(_response), octave(_octave), class_id(_class_id) {}
    //! converts vector of keypoints to vector of points
    static void convert(const std::vector<KeyPoint>& keypoints,
                        CV_OUT std::vector<Point2f>& points2f,
                        const std::vector<int>& keypointIndexes=std::vector<int>());
    //! converts vector of points to the vector of keypoints, where each keypoint is assigned the same size and the same orientation
    static void convert(const std::vector<Point2f>& points2f,
                        CV_OUT std::vector<KeyPoint>& keypoints,
                        float size=1, float response=1, int octave=0, int class_id=-1);

    //! computes overlap for pair of keypoints;
    //! overlap is a ratio between area of keypoint regions intersection and
    //! area of keypoint regions union (now keypoint region is circle)
    static float overlap(const KeyPoint& kp1, const KeyPoint& kp2);

    CV_PROP_RW Point2f pt; //!< coordinates of the keypoints
    CV_PROP_RW float size; //!< diameter of the meaningfull keypoint neighborhood
    CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable)
    CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling
    CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted
    CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to) 
};

//! writes vector of keypoints to the file storage
CV_EXPORTS void write(FileStorage& fs, const string& name, const vector<KeyPoint>& keypoints);
//! reads vector of keypoints from the specified file storage node
CV_EXPORTS void read(const FileNode& node, CV_OUT vector<KeyPoint>& keypoints);    

/*!
 SIFT implementation.
 
 The class implements SIFT algorithm by D. Lowe.
*/
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 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:
    CommonParams commParams;
    DetectorParams detectorParams;
    DescriptorParams descriptorParams;
};

    
/*!
 SURF implementation.
 
 The class implements SURF algorithm by H. Bay et al.
 */
class CV_EXPORTS_W SURF : public CvSURFParams
{
public:
    //! the default constructor
    CV_WRAP SURF();
    //! the full constructor taking all the necessary parameters
    CV_WRAP SURF(double _hessianThreshold, int _nOctaves=4,
         int _nOctaveLayers=2, bool _extended=false);

    //! returns the descriptor size in float's (64 or 128)
    CV_WRAP int descriptorSize() const;
    //! finds the keypoints using fast hessian detector used in SURF
    CV_WRAP_AS(detect) void operator()(const Mat& img, const Mat& mask,
                    CV_OUT vector<KeyPoint>& keypoints) const;
    //! finds the keypoints and computes their descriptors. Optionally it can compute descriptors for the user-provided keypoints
    CV_WRAP_AS(detect) void operator()(const Mat& img, const Mat& mask,
                    CV_OUT vector<KeyPoint>& keypoints,
                    CV_OUT vector<float>& descriptors,
                    bool useProvidedKeypoints=false) const;
};

/*!
 Maximal Stable Extremal Regions class.
 
 The class implements MSER algorithm introduced by J. Matas.
 Unlike SIFT, SURF and many other detectors in OpenCV, this is salient region detector,
 not the salient point detector.
 
 It returns the regions, each of those is encoded as a contour.
*/
class CV_EXPORTS_W MSER : public CvMSERParams
{
public:
    //! the default constructor
    CV_WRAP MSER();
    //! the full constructor
    CV_WRAP MSER( int _delta, int _min_area, int _max_area,
          double _max_variation, double _min_diversity,
          int _max_evolution, double _area_threshold,
          double _min_margin, int _edge_blur_size );
    //! the operator that extracts the MSERs from the image or the specific part of it
    CV_WRAP_AS(detect) void operator()( const Mat& image,
        CV_OUT vector<vector<Point> >& msers, const Mat& mask ) const;
};

/*!
 The "Star" Detector.
 
 The class implements the keypoint detector introduced by K. Konolige.
*/
class CV_EXPORTS_W StarDetector : public CvStarDetectorParams
{
public:
    //! the default constructor
    CV_WRAP StarDetector();
    //! the full constructor
    CV_WRAP StarDetector(int _maxSize, int _responseThreshold,
                 int _lineThresholdProjected,
                 int _lineThresholdBinarized,
                 int _suppressNonmaxSize);
    //! finds the keypoints in the image
    CV_WRAP_AS(detect) void operator()(const Mat& image,
                CV_OUT vector<KeyPoint>& keypoints) const;
};

//! detects corners using FAST algorithm by E. Rosten
CV_EXPORTS void FAST( const Mat& image, CV_OUT vector<KeyPoint>& keypoints,
                      int threshold, bool nonmaxSupression=true );

/*!
 The Patch Generator class 
*/
class CV_EXPORTS PatchGenerator
{
public:
    PatchGenerator();
    PatchGenerator(double _backgroundMin, double _backgroundMax,
                   double _noiseRange, bool _randomBlur=true,
                   double _lambdaMin=0.6, double _lambdaMax=1.5,
                   double _thetaMin=-CV_PI, double _thetaMax=CV_PI,
                   double _phiMin=-CV_PI, double _phiMax=CV_PI );
    void operator()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const;
    void operator()(const Mat& image, const Mat& transform, Mat& patch,
                    Size patchSize, RNG& rng) const;
    void warpWholeImage(const Mat& image, Mat& matT, Mat& buf,
                        CV_OUT Mat& warped, int border, RNG& rng) const;
    void generateRandomTransform(Point2f srcCenter, Point2f dstCenter,
                                 CV_OUT Mat& transform, RNG& rng,
                                 bool inverse=false) const;
    void setAffineParam(double lambda, double theta, double phi);
    
    double backgroundMin, backgroundMax;
    double noiseRange;
    bool randomBlur;
    double lambdaMin, lambdaMax;
    double thetaMin, thetaMax;
    double phiMin, phiMax;
};


class CV_EXPORTS LDetector
{
public:
    LDetector();
    LDetector(int _radius, int _threshold, int _nOctaves,
              int _nViews, double _baseFeatureSize, double _clusteringDistance);
    void operator()(const Mat& image,
                    CV_OUT vector<KeyPoint>& keypoints,
                    int maxCount=0, bool scaleCoords=true) const;
    void operator()(const vector<Mat>& pyr,
                    CV_OUT vector<KeyPoint>& keypoints,
                    int maxCount=0, bool scaleCoords=true) const;
    void getMostStable2D(const Mat& image, CV_OUT vector<KeyPoint>& keypoints,
                         int maxCount, const PatchGenerator& patchGenerator) const;
    void setVerbose(bool verbose);
    
    void read(const FileNode& node);
    void write(FileStorage& fs, const String& name=String()) const;
    
    int radius;
    int threshold;
    int nOctaves;
    int nViews;
    bool verbose;
    
    double baseFeatureSize;
    double clusteringDistance;
};

typedef LDetector YAPE;

class CV_EXPORTS FernClassifier
{
public:
    FernClassifier();
    FernClassifier(const FileNode& node);
    FernClassifier(const vector<vector<Point2f> >& points,
                   const vector<Mat>& refimgs,
                   const vector<vector<int> >& labels=vector<vector<int> >(),
                   int _nclasses=0, int _patchSize=PATCH_SIZE,
                   int _signatureSize=DEFAULT_SIGNATURE_SIZE,
                   int _nstructs=DEFAULT_STRUCTS,
                   int _structSize=DEFAULT_STRUCT_SIZE,
                   int _nviews=DEFAULT_VIEWS,
                   int _compressionMethod=COMPRESSION_NONE,
                   const PatchGenerator& patchGenerator=PatchGenerator());
    virtual ~FernClassifier();
    virtual void read(const FileNode& n);
    virtual void write(FileStorage& fs, const String& name=String()) const;
    virtual void trainFromSingleView(const Mat& image,
                                     const vector<KeyPoint>& keypoints,
                                     int _patchSize=PATCH_SIZE,
                                     int _signatureSize=DEFAULT_SIGNATURE_SIZE,
                                     int _nstructs=DEFAULT_STRUCTS,
                                     int _structSize=DEFAULT_STRUCT_SIZE,
                                     int _nviews=DEFAULT_VIEWS,
                                     int _compressionMethod=COMPRESSION_NONE,
                                     const PatchGenerator& patchGenerator=PatchGenerator());
    virtual void train(const vector<vector<Point2f> >& points,
                       const vector<Mat>& refimgs,
                       const vector<vector<int> >& labels=vector<vector<int> >(),
                       int _nclasses=0, int _patchSize=PATCH_SIZE,
                       int _signatureSize=DEFAULT_SIGNATURE_SIZE,
                       int _nstructs=DEFAULT_STRUCTS,
                       int _structSize=DEFAULT_STRUCT_SIZE,
                       int _nviews=DEFAULT_VIEWS,
                       int _compressionMethod=COMPRESSION_NONE,
                       const PatchGenerator& patchGenerator=PatchGenerator());
    virtual int operator()(const Mat& img, Point2f kpt, vector<float>& signature) const;
    virtual int operator()(const Mat& patch, vector<float>& signature) const;
    virtual void clear();
    void setVerbose(bool verbose);
    
    int getClassCount() const;
    int getStructCount() const;
    int getStructSize() const;
    int getSignatureSize() const;
    int getCompressionMethod() const;
    Size getPatchSize() const;
    
    struct Feature
    {
        uchar x1, y1, x2, y2;
        Feature() : x1(0), y1(0), x2(0), y2(0) {}
        Feature(int _x1, int _y1, int _x2, int _y2)
        : x1((uchar)_x1), y1((uchar)_y1), x2((uchar)_x2), y2((uchar)_y2)
        {}
        template<typename _Tp> bool operator ()(const Mat_<_Tp>& patch) const
        { return patch(y1,x1) > patch(y2, x2); }
    };
    
    enum
    {
        PATCH_SIZE = 31,
        DEFAULT_STRUCTS = 50,
        DEFAULT_STRUCT_SIZE = 9,
        DEFAULT_VIEWS = 5000,
        DEFAULT_SIGNATURE_SIZE = 176,
        COMPRESSION_NONE = 0,
        COMPRESSION_RANDOM_PROJ = 1,
        COMPRESSION_PCA = 2,
        DEFAULT_COMPRESSION_METHOD = COMPRESSION_NONE
    };
    
protected:
    virtual void prepare(int _nclasses, int _patchSize, int _signatureSize,
                         int _nstructs, int _structSize,
                         int _nviews, int _compressionMethod);
    virtual void finalize(RNG& rng);
    virtual int getLeaf(int fidx, const Mat& patch) const;
    
    bool verbose;
    int nstructs;
    int structSize;
    int nclasses;
    int signatureSize;
    int compressionMethod;
    int leavesPerStruct;
    Size patchSize;
    vector<Feature> features;
    vector<int> classCounters;
    vector<float> posteriors;
};


class CV_EXPORTS PlanarObjectDetector
{
public:
    PlanarObjectDetector();
    PlanarObjectDetector(const FileNode& node);
    PlanarObjectDetector(const vector<Mat>& pyr, int _npoints=300,
                         int _patchSize=FernClassifier::PATCH_SIZE,
                         int _nstructs=FernClassifier::DEFAULT_STRUCTS,
                         int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
                         int _nviews=FernClassifier::DEFAULT_VIEWS,
                         const LDetector& detector=LDetector(),
                         const PatchGenerator& patchGenerator=PatchGenerator());
    virtual ~PlanarObjectDetector();
    virtual void train(const vector<Mat>& pyr, int _npoints=300,
                       int _patchSize=FernClassifier::PATCH_SIZE,
                       int _nstructs=FernClassifier::DEFAULT_STRUCTS,
                       int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
                       int _nviews=FernClassifier::DEFAULT_VIEWS,
                       const LDetector& detector=LDetector(),
                       const PatchGenerator& patchGenerator=PatchGenerator());
    virtual void train(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
                       int _patchSize=FernClassifier::PATCH_SIZE,
                       int _nstructs=FernClassifier::DEFAULT_STRUCTS,
                       int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
                       int _nviews=FernClassifier::DEFAULT_VIEWS,
                       const LDetector& detector=LDetector(),
                       const PatchGenerator& patchGenerator=PatchGenerator());
    Rect getModelROI() const;
    vector<KeyPoint> getModelPoints() const;
    const LDetector& getDetector() const;
    const FernClassifier& getClassifier() const;
    void setVerbose(bool verbose);
    
    void read(const FileNode& node);
    void write(FileStorage& fs, const String& name=String()) const;
    bool operator()(const Mat& image, CV_OUT Mat& H, CV_OUT vector<Point2f>& corners) const;
    bool operator()(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
                                       CV_OUT Mat& H, CV_OUT vector<Point2f>& corners,
                                       CV_OUT vector<int>* pairs=0) const;
    
protected:
    bool verbose;
    Rect modelROI;
    vector<KeyPoint> modelPoints;
    LDetector ldetector;
    FernClassifier fernClassifier;
};

/****************************************************************************************\
*                                 Calonder Classifier                                    *
\****************************************************************************************/

struct RTreeNode;

struct CV_EXPORTS BaseKeypoint
{
  int x;
  int y;
  IplImage* image;

  BaseKeypoint()
    : x(0), y(0), image(NULL)
  {}

  BaseKeypoint(int x, int y, IplImage* image)
    : x(x), y(y), image(image)
  {}
};

class CV_EXPORTS RandomizedTree
{
public:
  friend class RTreeClassifier;

  static const uchar PATCH_SIZE = 32;
  static const int DEFAULT_DEPTH = 9;
  static const int DEFAULT_VIEWS = 5000;
  static const size_t DEFAULT_REDUCED_NUM_DIM = 176;
  static float GET_LOWER_QUANT_PERC() { return .03f; }
  static float GET_UPPER_QUANT_PERC() { return .92f; }

  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);

  // following two funcs 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);
  const uchar* getPosterior2(uchar* patch_data) const;

  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 setKeepFloatPosteriors(bool b) { keep_float_posteriors_ = b; }
  void discardFloatPosteriors() { freePosteriors(1); }

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

  // debug
  void savePosteriors(std::string url, bool append=false);
  void savePosteriors2(std::string url, bool append=false);

private:
  int classes_;
  int depth_;
  int num_leaves_;
  std::vector<RTreeNode> nodes_;
  float **posteriors_;        // 16-bytes aligned posteriors
  uchar **posteriors2_;     // 16-bytes 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 const float* getPosteriorByIndex(int index) const;
  inline uchar* getPosteriorByIndex2(int index);
  inline const uchar* getPosteriorByIndex2(int index) const;
  //void makeRandomMeasMatrix(float *cs_phi, PHI_DISTR_TYPE dt, size_t reduced_num_dim);
  void convertPosteriorsToChar();
  void makePosteriors2(int num_quant_bits);
  void compressLeaves(size_t reduced_num_dim);
  void estimateQuantPercForPosteriors(float perc[2]);
};


inline uchar* getData(IplImage* image)
{
  return reinterpret_cast<uchar*>(image->imageData);
}

inline float* RandomizedTree::getPosteriorByIndex(int index)
{
  return const_cast<float*>(const_cast<const RandomizedTree*>(this)->getPosteriorByIndex(index));
}

inline const float* RandomizedTree::getPosteriorByIndex(int index) const
{
  return posteriors_[index];
}

inline uchar* RandomizedTree::getPosteriorByIndex2(int index)
{
  return const_cast<uchar*>(const_cast<const RandomizedTree*>(this)->getPosteriorByIndex2(index));
}

inline const uchar* RandomizedTree::getPosteriorByIndex2(int index) const
{
  return posteriors2_[index];
}

struct CV_EXPORTS RTreeNode
{
  short offset1, offset2;

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

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

  // sig must point to a memory block of at least classes()*sizeof(float|uchar) bytes
  void getSignature(IplImage *patch, uchar *sig) const;
  void getSignature(IplImage *patch, float *sig) const;
  void getSparseSignature(IplImage *patch, float *sig, float thresh) const;
  // TODO: deprecated in favor of getSignature overload, remove
  void getFloatSignature(IplImage *patch, float *sig) const { getSignature(patch, sig); }

  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() const { return classes_; }
  inline int original_num_classes() const { 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;

  // experimental and debug
  void saveAllFloatPosteriors(std::string file_url);
  void saveAllBytePosteriors(std::string file_url);
  void setFloatPosteriorsFromTextfile_176(std::string url);
  float countZeroElements();

  std::vector<RandomizedTree> trees_;

private:
  int classes_;
  int num_quant_bits_;
  mutable uchar **posteriors_;
  mutable unsigned short *ptemp_;
  int original_num_classes_;
  bool keep_floats_;
};

/****************************************************************************************\
*                                     One-Way Descriptor                                 *
\****************************************************************************************/

class CV_EXPORTS OneWayDescriptor
{
public:
    OneWayDescriptor();
    ~OneWayDescriptor();

    // allocates memory for given descriptor parameters
    void Allocate(int pose_count, CvSize size, int nChannels);

    // GenerateSamples: generates affine transformed patches with averaging them over small transformation variations.
    // If external poses and transforms were specified, uses them instead of generating random ones
    // - pose_count: the number of poses to be generated
    // - frontal: the input patch (can be a roi in a larger image)
    // - norm: if nonzero, normalizes the output patch so that the sum of pixel intensities is 1
    void GenerateSamples(int pose_count, IplImage* frontal, int norm = 0);

    // GenerateSamplesFast: generates affine transformed patches with averaging them over small transformation variations.
    // Uses precalculated transformed pca components.
    // - frontal: the input patch (can be a roi in a larger image)
    // - pca_hr_avg: pca average vector
    // - pca_hr_eigenvectors: pca eigenvectors
    // - pca_descriptors: an array of precomputed descriptors of pca components containing their affine transformations
    //   pca_descriptors[0] corresponds to the average, pca_descriptors[1]-pca_descriptors[pca_dim] correspond to eigenvectors
    void GenerateSamplesFast(IplImage* frontal, CvMat* pca_hr_avg,
                             CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors);

    // sets the poses and corresponding transforms
    void SetTransforms(CvAffinePose* poses, CvMat** transforms);

    // Initialize: builds a descriptor.
    // - pose_count: the number of poses to build. If poses were set externally, uses them rather than generating random ones
    // - frontal: input patch. Can be a roi in a larger image
    // - feature_name: the feature name to be associated with the descriptor
    // - norm: if 1, the affine transformed patches are normalized so that their sum is 1
    void Initialize(int pose_count, IplImage* frontal, const char* feature_name = 0, int norm = 0);

    // InitializeFast: builds a descriptor using precomputed descriptors of pca components
    // - pose_count: the number of poses to build
    // - frontal: input patch. Can be a roi in a larger image
    // - feature_name: the feature name to be associated with the descriptor
    // - pca_hr_avg: average vector for PCA
    // - pca_hr_eigenvectors: PCA eigenvectors (one vector per row)
    // - pca_descriptors: precomputed descriptors of PCA components, the first descriptor for the average vector
    // followed by the descriptors for eigenvectors
    void InitializeFast(int pose_count, IplImage* frontal, const char* feature_name,
                        CvMat* pca_hr_avg, CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors);

    // ProjectPCASample: unwarps an image patch into a vector and projects it into PCA space
    // - patch: input image patch
    // - avg: PCA average vector
    // - eigenvectors: PCA eigenvectors, one per row
    // - pca_coeffs: output PCA coefficients
    void ProjectPCASample(IplImage* patch, CvMat* avg, CvMat* eigenvectors, CvMat* pca_coeffs) const;

    // InitializePCACoeffs: projects all warped patches into PCA space
    // - avg: PCA average vector
    // - eigenvectors: PCA eigenvectors, one per row
    void InitializePCACoeffs(CvMat* avg, CvMat* eigenvectors);

    // EstimatePose: finds the closest match between an input patch and a set of patches with different poses
    // - patch: input image patch
    // - pose_idx: the output index of the closest pose
    // - distance: the distance to the closest pose (L2 distance)
    void EstimatePose(IplImage* patch, int& pose_idx, float& distance) const;

    // EstimatePosePCA: finds the closest match between an input patch and a set of patches with different poses.
    // The distance between patches is computed in PCA space
    // - patch: input image patch
    // - pose_idx: the output index of the closest pose
    // - distance: distance to the closest pose (L2 distance in PCA space)
    // - avg: PCA average vector. If 0, matching without PCA is used
    // - eigenvectors: PCA eigenvectors, one per row
    void EstimatePosePCA(CvArr* patch, int& pose_idx, float& distance, CvMat* avg, CvMat* eigenvalues) const;

    // GetPatchSize: returns the size of each image patch after warping (2 times smaller than the input patch)
    CvSize GetPatchSize() const
    {
        return m_patch_size;
    }

    // GetInputPatchSize: returns the required size of the patch that the descriptor is built from
    // (2 time larger than the patch after warping)
    CvSize GetInputPatchSize() const
    {
        return cvSize(m_patch_size.width*2, m_patch_size.height*2);
    }

    // GetPatch: returns a patch corresponding to specified pose index
    // - index: pose index
    // - return value: the patch corresponding to specified pose index
    IplImage* GetPatch(int index);

    // GetPose: returns a pose corresponding to specified pose index
    // - index: pose index
    // - return value: the pose corresponding to specified pose index
    CvAffinePose GetPose(int index) const;

    // Save: saves all patches with different poses to a specified path
    void Save(const char* path);

    // ReadByName: reads a descriptor from a file storage
    // - fs: file storage
    // - parent: parent node
    // - name: node name
    // - return value: 1 if succeeded, 0 otherwise
    int ReadByName(CvFileStorage* fs, CvFileNode* parent, const char* name);

    // ReadByName: reads a descriptor from a file node
    // - parent: parent node
    // - name: node name
    // - return value: 1 if succeeded, 0 otherwise
    int ReadByName(const FileNode &parent, const char* name);

    // Write: writes a descriptor into a file storage
    // - fs: file storage
    // - name: node name
    void Write(CvFileStorage* fs, const char* name);

    // GetFeatureName: returns a name corresponding to a feature
    const char* GetFeatureName() const;

    // GetCenter: returns the center of the feature
    CvPoint GetCenter() const;

    void SetPCADimHigh(int pca_dim_high) {m_pca_dim_high = pca_dim_high;};
    void SetPCADimLow(int pca_dim_low) {m_pca_dim_low = pca_dim_low;};

    int GetPCADimLow() const;
    int GetPCADimHigh() const;

    CvMat** GetPCACoeffs() const {return m_pca_coeffs;}

protected:
    int m_pose_count; // the number of poses
    CvSize m_patch_size; // size of each image
    IplImage** m_samples; // an array of length m_pose_count containing the patch in different poses
    IplImage* m_input_patch;
    IplImage* m_train_patch;
    CvMat** m_pca_coeffs; // an array of length m_pose_count containing pca decomposition of the patch in different poses
    CvAffinePose* m_affine_poses; // an array of poses
    CvMat** m_transforms; // an array of affine transforms corresponding to poses

    string m_feature_name; // the name of the feature associated with the descriptor
    CvPoint m_center; // the coordinates of the feature (the center of the input image ROI)

    int m_pca_dim_high; // the number of descriptor pca components to use for generating affine poses
    int m_pca_dim_low; // the number of pca components to use for comparison
};


// OneWayDescriptorBase: encapsulates functionality for training/loading a set of one way descriptors
// and finding the nearest closest descriptor to an input feature
class CV_EXPORTS OneWayDescriptorBase
{
public:

    // creates an instance of OneWayDescriptor from a set of training files
    // - patch_size: size of the input (large) patch
    // - pose_count: the number of poses to generate for each descriptor
    // - train_path: path to training files
    // - pca_config: the name of the file that contains PCA for small patches (2 times smaller
    // than patch_size each dimension
    // - pca_hr_config: the name of the file that contains PCA for large patches (of patch_size size)
    // - pca_desc_config: the name of the file that contains descriptors of PCA components
    OneWayDescriptorBase(CvSize patch_size, int pose_count, const char* train_path = 0, const char* pca_config = 0,
                         const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1,
                         int pca_dim_high = 100, int pca_dim_low = 100);

    OneWayDescriptorBase(CvSize patch_size, int pose_count, const string &pca_filename, const string &train_path = string(), const string &images_list = string(),
                         float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1,
                         int pca_dim_high = 100, int pca_dim_low = 100);


    virtual ~OneWayDescriptorBase();
    void clear ();


    // Allocate: allocates memory for a given number of descriptors
    void Allocate(int train_feature_count);

    // AllocatePCADescriptors: allocates memory for pca descriptors
    void AllocatePCADescriptors();

    // returns patch size
    CvSize GetPatchSize() const {return m_patch_size;};
    // returns the number of poses for each descriptor
    int GetPoseCount() const {return m_pose_count;};

    // returns the number of pyramid levels
    int GetPyrLevels() const {return m_pyr_levels;};

    // returns the number of descriptors
    int GetDescriptorCount() const {return m_train_feature_count;};

    // CreateDescriptorsFromImage: creates descriptors for each of the input features
    // - src: input image
    // - features: input features
    // - pyr_levels: the number of pyramid levels
    void CreateDescriptorsFromImage(IplImage* src, const std::vector<cv::KeyPoint>& features);

    // CreatePCADescriptors: generates descriptors for PCA components, needed for fast generation of feature descriptors
    void CreatePCADescriptors();

    // returns a feature descriptor by feature index
    const OneWayDescriptor* GetDescriptor(int desc_idx) const {return &m_descriptors[desc_idx];};

    // FindDescriptor: finds the closest descriptor
    // - patch: input image patch
    // - desc_idx: output index of the closest descriptor to the input patch
    // - pose_idx: output index of the closest pose of the closest descriptor to the input patch
    // - distance: distance from the input patch to the closest feature pose
    // - _scales: scales of the input patch for each descriptor
    // - scale_ranges: input scales variation (float[2])
    void FindDescriptor(IplImage* patch, int& desc_idx, int& pose_idx, float& distance, float* _scale = 0, float* scale_ranges = 0) const;

    // - patch: input image patch
    // - n: number of the closest indexes
    // - desc_idxs: output indexes of the closest descriptor to the input patch (n)
    // - pose_idx: output indexes of the closest pose of the closest descriptor to the input patch (n)
    // - distances: distance from the input patch to the closest feature pose (n)
    // - _scales: scales of the input patch
    // - scale_ranges: input scales variation (float[2])
    void FindDescriptor(IplImage* patch, int n, std::vector<int>& desc_idxs, std::vector<int>& pose_idxs,
                        std::vector<float>& distances, std::vector<float>& _scales, float* scale_ranges = 0) const;

    // FindDescriptor: finds the closest descriptor
    // - src: input image
    // - pt: center of the feature
    // - desc_idx: output index of the closest descriptor to the input patch
    // - pose_idx: output index of the closest pose of the closest descriptor to the input patch
    // - distance: distance from the input patch to the closest feature pose
    void FindDescriptor(IplImage* src, cv::Point2f pt, int& desc_idx, int& pose_idx, float& distance) const;

    // InitializePoses: generates random poses
    void InitializePoses();

    // InitializeTransformsFromPoses: generates 2x3 affine matrices from poses (initializes m_transforms)
    void InitializeTransformsFromPoses();

    // InitializePoseTransforms: subsequently calls InitializePoses and InitializeTransformsFromPoses
    void InitializePoseTransforms();

    // InitializeDescriptor: initializes a descriptor
    // - desc_idx: descriptor index
    // - train_image: image patch (ROI is supported)
    // - feature_label: feature textual label
    void InitializeDescriptor(int desc_idx, IplImage* train_image, const char* feature_label);

    void InitializeDescriptor(int desc_idx, IplImage* train_image, const cv::KeyPoint& keypoint, const char* feature_label);

    // InitializeDescriptors: load features from an image and create descriptors for each of them
    void InitializeDescriptors(IplImage* train_image, const vector<cv::KeyPoint>& features,
                               const char* feature_label = "", int desc_start_idx = 0);

    // Write: writes this object to a file storage
    // - fs: output filestorage
    void Write (FileStorage &fs) const;
    
    // Read: reads OneWayDescriptorBase object from a file node
    // - fn: input file node    
    void Read (const FileNode &fn);

    // LoadPCADescriptors: loads PCA descriptors from a file
    // - filename: input filename
    int LoadPCADescriptors(const char* filename);

    // LoadPCADescriptors: loads PCA descriptors from a file node
    // - fn: input file node
    int LoadPCADescriptors(const FileNode &fn);

    // SavePCADescriptors: saves PCA descriptors to a file
    // - filename: output filename
    void SavePCADescriptors(const char* filename);

    // SavePCADescriptors: saves PCA descriptors to a file storage
    // - fs: output file storage
    void SavePCADescriptors(CvFileStorage* fs) const;

    // GeneratePCA: calculate and save PCA components and descriptors
    // - img_path: path to training PCA images directory
    // - images_list: filename with filenames of training PCA images
    void GeneratePCA(const char* img_path, const char* images_list, int pose_count=500);

    // SetPCAHigh: sets the high resolution pca matrices (copied to internal structures)
    void SetPCAHigh(CvMat* avg, CvMat* eigenvectors);

    // SetPCALow: sets the low resolution pca matrices (copied to internal structures)
    void SetPCALow(CvMat* avg, CvMat* eigenvectors);

    int GetLowPCA(CvMat** avg, CvMat** eigenvectors)
    {
        *avg = m_pca_avg;
        *eigenvectors = m_pca_eigenvectors;
        return m_pca_dim_low;
    };

    int GetPCADimLow() const {return m_pca_dim_low;};
    int GetPCADimHigh() const {return m_pca_dim_high;};

    void ConvertDescriptorsArrayToTree(); // Converting pca_descriptors array to KD tree

    // GetPCAFilename: get default PCA filename
    static string GetPCAFilename () { return "pca.yml"; }

protected:
    CvSize m_patch_size; // patch size
    int m_pose_count; // the number of poses for each descriptor
    int m_train_feature_count; // the number of the training features
    OneWayDescriptor* m_descriptors; // array of train feature descriptors
    CvMat* m_pca_avg; // PCA average Vector for small patches
    CvMat* m_pca_eigenvectors; // PCA eigenvectors for small patches
    CvMat* m_pca_hr_avg; // PCA average Vector for large patches
    CvMat* m_pca_hr_eigenvectors; // PCA eigenvectors for large patches
    OneWayDescriptor* m_pca_descriptors; // an array of PCA descriptors

    cv::flann::Index* m_pca_descriptors_tree;
    CvMat* m_pca_descriptors_matrix;

    CvAffinePose* m_poses; // array of poses
    CvMat** m_transforms; // array of affine transformations corresponding to poses

    int m_pca_dim_high;
    int m_pca_dim_low;

    int m_pyr_levels;
    float scale_min;
    float scale_max;
    float scale_step;

    // SavePCAall: saves PCA components and descriptors to a file storage
    // - fs: output file storage
    void SavePCAall (FileStorage &fs) const;

    // LoadPCAall: loads PCA components and descriptors from a file node
    // - fn: input file node
    void LoadPCAall (const FileNode &fn);
};

class CV_EXPORTS OneWayDescriptorObject : public OneWayDescriptorBase
{
public:
    // creates an instance of OneWayDescriptorObject from a set of training files
    // - patch_size: size of the input (large) patch
    // - pose_count: the number of poses to generate for each descriptor
    // - train_path: path to training files
    // - pca_config: the name of the file that contains PCA for small patches (2 times smaller
    // than patch_size each dimension
    // - pca_hr_config: the name of the file that contains PCA for large patches (of patch_size size)
    // - pca_desc_config: the name of the file that contains descriptors of PCA components
    OneWayDescriptorObject(CvSize patch_size, int pose_count, const char* train_path, const char* pca_config,
                           const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1);

    OneWayDescriptorObject(CvSize patch_size, int pose_count, const string &pca_filename,
                           const string &train_path = string (), const string &images_list = string (),
                           float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1);


    virtual ~OneWayDescriptorObject();

    // Allocate: allocates memory for a given number of features
    // - train_feature_count: the total number of features
    // - object_feature_count: the number of features extracted from the object
    void Allocate(int train_feature_count, int object_feature_count);


    void SetLabeledFeatures(const vector<cv::KeyPoint>& features) {m_train_features = features;};
    vector<cv::KeyPoint>& GetLabeledFeatures() {return m_train_features;};
    const vector<cv::KeyPoint>& GetLabeledFeatures() const {return m_train_features;};
    vector<cv::KeyPoint> _GetLabeledFeatures() const;

    // IsDescriptorObject: returns 1 if descriptor with specified index is positive, otherwise 0
    int IsDescriptorObject(int desc_idx) const;

    // MatchPointToPart: returns the part number of a feature if it matches one of the object parts, otherwise -1
    int MatchPointToPart(CvPoint pt) const;

    // GetDescriptorPart: returns the part number of the feature corresponding to a specified descriptor
    // - desc_idx: descriptor index
    int GetDescriptorPart(int desc_idx) const;


    void InitializeObjectDescriptors(IplImage* train_image, const vector<cv::KeyPoint>& features,
                                     const char* feature_label, int desc_start_idx = 0, float scale = 1.0f,
                                     int is_background = 0);

    // GetObjectFeatureCount: returns the number of object features
    int GetObjectFeatureCount() const {return m_object_feature_count;};

protected:
    int* m_part_id; // contains part id for each of object descriptors
    vector<cv::KeyPoint> m_train_features; // train features
    int m_object_feature_count; // the number of the positive features

};


/****************************************************************************************\
*                                    FeatureDetector                                     *
\****************************************************************************************/

/*
 * Abstract base class for 2D image feature detectors.
 */
class CV_EXPORTS FeatureDetector
{
public:
    virtual ~FeatureDetector() {}
    
    /*
     * Detect keypoints in an image. Must be implemented by the subclass.
     *
     * image        The image.
     * keypoints    The detected keypoints.
     * mask         Mask specifying where to look for keypoints (optional). Must be a char
     *              matrix with non-zero values in the region of interest.
     */
    virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const = 0;
    
    /*
     * Detect keypoints in an image set.
     *
     * images           Image collection.
     * pointCollection  Collection of keypoints detected in an input images.
     * masks            Masks for each input image.
     */
    void detect( const vector<Mat>& imageCollection, vector<vector<KeyPoint> >& pointCollection, const vector<Mat>& masks=vector<Mat>() ) const;

    virtual void read( const FileNode& ) {}
    virtual void write( FileStorage& ) const {}

protected:
    /*
     * Remove keypoints that are not in the mask.
     *
     * Helper function, useful when wrapping a library call for keypoint detection that
     * does not support a mask argument.
     */
    static void removeInvalidPoints( const Mat& mask, vector<KeyPoint>& keypoints );
};

class CV_EXPORTS FastFeatureDetector : public FeatureDetector
{
public:
    FastFeatureDetector( int _threshold=1, bool _nonmaxSuppression=true );
    virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;

    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;

protected:
    int threshold;
    bool nonmaxSuppression;
};


class CV_EXPORTS GoodFeaturesToTrackDetector : public FeatureDetector
{
public:
    GoodFeaturesToTrackDetector( int _maxCorners, double _qualityLevel, double _minDistance,
                                 int _blockSize=3, bool _useHarrisDetector=false, double _k=0.04 );
    virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;

    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;

protected:
    int maxCorners;
    double qualityLevel;
    double minDistance;
    int blockSize;
    bool useHarrisDetector;
    double k;
};

class CV_EXPORTS MserFeatureDetector : public FeatureDetector
{
public:
    MserFeatureDetector( CvMSERParams params=cvMSERParams () );
    MserFeatureDetector( int delta, int minArea, int maxArea, double maxVariation, double minDiversity,
                         int maxEvolution, double areaThreshold, double minMargin, int edgeBlurSize );
    virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;

    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;

protected:
    MSER mser;
};

class CV_EXPORTS StarFeatureDetector : public FeatureDetector
{
public:
    StarFeatureDetector( int maxSize=16, int responseThreshold=30, int lineThresholdProjected = 10,
                         int lineThresholdBinarized=8, int suppressNonmaxSize=5 );
    virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;

    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;

protected:
    StarDetector star;
};

class CV_EXPORTS SiftFeatureDetector : public FeatureDetector
{
public:
    SiftFeatureDetector( double threshold=SIFT::DetectorParams::GET_DEFAULT_THRESHOLD(),
                         double edgeThreshold=SIFT::DetectorParams::GET_DEFAULT_EDGE_THRESHOLD(),
                         int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
                         int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
                         int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
                         int angleMode=SIFT::CommonParams::FIRST_ANGLE );
    virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;

    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;

protected:
    SIFT sift;
};

class CV_EXPORTS SurfFeatureDetector : public FeatureDetector
{
public:
    SurfFeatureDetector( double hessianThreshold = 400., int octaves = 3, int octaveLayers = 4 );
    virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;

protected:
    SURF surf;
};

class CV_EXPORTS DenseFeatureDetector : public FeatureDetector
{
public:
    DenseFeatureDetector() : initFeatureScale(1), featureScaleLevels(1), featureScaleMul(0.1f),
                             initXyStep(6), initImgBound(0),  varyXyStepWithScale(true), varyImgBoundWithScale(false) {}
    DenseFeatureDetector( float _initFeatureScale, int _featureScaleLevels=1, float _featureScaleMul=0.1f,
                          int _initXyStep=6, int _initImgBound=0, bool _varyXyStepWithScale=true, bool _varyImgBoundWithScale=false ) :
        initFeatureScale(_initFeatureScale), featureScaleLevels(_featureScaleLevels), featureScaleMul(_featureScaleMul),
        initXyStep(_initXyStep), initImgBound(_initImgBound), varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale) {}
    virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
    // todo read/write
protected:
    float initFeatureScale;
    int featureScaleLevels;
    float featureScaleMul;

    int initXyStep;
    int initImgBound;

    bool varyXyStepWithScale;
    bool varyImgBoundWithScale;
};

/*
 * Adapts a detector to partition the source image into a grid and detect
 * points in each cell.
 */
class CV_EXPORTS GridAdaptedFeatureDetector : public FeatureDetector
{
public:
    /*
     * detector            Detector that will be adapted.
     * maxTotalKeypoints   Maximum count of keypoints detected on the image. Only the strongest keypoints
     *                      will be keeped.
     * gridRows            Grid rows count.
     * gridCols            Grid column count.
     */
    GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int maxTotalKeypoints,
                                int gridRows=4, int gridCols=4 );
    virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;

    // todo read/write
    virtual void read( const FileNode& ) {}
    virtual void write( FileStorage& ) const {}

protected:
    Ptr<FeatureDetector> detector;
    int maxTotalKeypoints;
    int gridRows;
    int gridCols;
};

/*
 * Adapts a detector to detect points over multiple levels of a Gaussian
 * pyramid. Useful for detectors that are not inherently scaled.
 */
class CV_EXPORTS PyramidAdaptedFeatureDetector : public FeatureDetector
{
public:
    PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int levels=2 );
    virtual void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;

    // todo read/write
    virtual void read( const FileNode& ) {}
    virtual void write( FileStorage& ) const {}

protected:
    Ptr<FeatureDetector> detector;
    int levels;
};

CV_EXPORTS Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
                                     float maxDeltaX, float maxDeltaY );

CV_EXPORTS Ptr<FeatureDetector> createFeatureDetector( const string& detectorType );
/****************************************************************************************\
*                                 DescriptorExtractor                                    *
\****************************************************************************************/

/*
 * Abstract base class for computing descriptors for image keypoints.
 *
 * In this interface we assume a keypoint descriptor can be represented as a
 * dense, fixed-dimensional vector of some basic type. Most descriptors used
 * in practice follow this pattern, as it makes it very easy to compute
 * distances between descriptors. Therefore we represent a collection of
 * descriptors as a cv::Mat, where each row is one keypoint descriptor.
 */
class CV_EXPORTS DescriptorExtractor
{
public:
    virtual ~DescriptorExtractor() {}
    /*
     * Compute the descriptors for a set of keypoints in an image.
     * Must be implemented by the subclass.
     *
     * image        The image.
     * keypoints    The keypoints. Keypoints for which a descriptor cannot be computed are removed.
     * descriptors  The descriptors. Row i is the descriptor for keypoint i.
     */
    virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const = 0;

    /*
     * Compute the descriptors for a keypoints collection detected in image collection.
     *
     * imageCollection      Image collection.
     * pointCollection      Keypoints collection. pointCollection[i] is keypoints detected in imageCollection[i].
     * descCollection       Descriptor collection. descCollection[i] is descriptors computed for pointCollection[i].
     */
    void compute( const vector<Mat>& imageCollection, vector<vector<KeyPoint> >& pointCollection, vector<Mat>& descCollection ) const;

    virtual void read( const FileNode& ) {}
    virtual void write( FileStorage& ) const {}

    virtual int descriptorSize() const = 0;
    virtual int descriptorType() const = 0;

protected:
    /*
     * Remove keypoints within border_pixels of an image edge.
     */
    static void removeBorderKeypoints( vector<KeyPoint>& keypoints,
                                       Size imageSize, int borderPixels );
};

class CV_EXPORTS SiftDescriptorExtractor : public DescriptorExtractor
{
public:
    SiftDescriptorExtractor( double magnification=SIFT::DescriptorParams::GET_DEFAULT_MAGNIFICATION(),
                             bool isNormalize=true, bool recalculateAngles=true,
                             int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
                             int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
                             int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
                             int angleMode=SIFT::CommonParams::FIRST_ANGLE );

    virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
    virtual void read( const FileNode &fn );
    virtual void write( FileStorage &fs ) const;

    virtual int descriptorSize() const { return sift.descriptorSize(); }
    virtual int descriptorType() const { return CV_32FC1; }

protected:
    SIFT sift;
};

class CV_EXPORTS SurfDescriptorExtractor : public DescriptorExtractor
{
public:
    SurfDescriptorExtractor( int nOctaves=4,
                             int nOctaveLayers=2, bool extended=false );

    virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
    virtual void read( const FileNode &fn );
    virtual void write( FileStorage &fs ) const;

    virtual int descriptorSize() const { return surf.descriptorSize(); }
    virtual int descriptorType() const { return CV_32FC1; }

protected:
    SURF surf;
};

template<typename T>
class CV_EXPORTS CalonderDescriptorExtractor : public DescriptorExtractor
{
public:
    CalonderDescriptorExtractor( const string& classifierFile );

    virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
    virtual void read( const FileNode &fn );
    virtual void write( FileStorage &fs ) const;

    virtual int descriptorSize() const { return classifier_.classes(); }
    virtual int descriptorType() const { return DataType<T>::type; }

protected:
    RTreeClassifier classifier_;
    static const int BORDER_SIZE = 16;
};

template<typename T>
CalonderDescriptorExtractor<T>::CalonderDescriptorExtractor(const std::string& classifier_file)
{
    classifier_.read( classifier_file.c_str() );
}

template<typename T>
void CalonderDescriptorExtractor<T>::compute( const cv::Mat& image,
                                              std::vector<cv::KeyPoint>& keypoints,
                                              cv::Mat& descriptors) const
{
  // Cannot compute descriptors for keypoints on the image border.
  removeBorderKeypoints(keypoints, image.size(), BORDER_SIZE);

  /// @todo Check 16-byte aligned
  descriptors.create(keypoints.size(), classifier_.classes(), cv::DataType<T>::type);

  int patchSize = RandomizedTree::PATCH_SIZE;
  int offset = patchSize / 2;
  for (size_t i = 0; i < keypoints.size(); ++i) {
    cv::Point2f pt = keypoints[i].pt;
    IplImage ipl = image( Rect((int)(pt.x - offset), (int)(pt.y - offset), patchSize, patchSize) );
    classifier_.getSignature( &ipl, descriptors.ptr<T>(i));
  }
}

template<typename T>
void CalonderDescriptorExtractor<T>::read( const FileNode& )
{}

template<typename T>
void CalonderDescriptorExtractor<T>::write( FileStorage& ) const
{}

/*
 * Adapts a descriptor extractor to compute descripors in Opponent Color Space
 * (refer to van de Sande et al., CGIV 2008 "Color Descriptors for Object Category Recognition").
 * Input RGB image is transformed in Opponent Color Space. Then unadapted descriptor extractor
 * (set in constructor) computes descriptors on each of the three channel and concatenate
 * them into a single color descriptor.
 */
class CV_EXPORTS OpponentColorDescriptorExtractor : public DescriptorExtractor
{
public:
    OpponentColorDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor );

    virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;

    virtual void read( const FileNode& );
    virtual void write( FileStorage& ) const;

    virtual int descriptorSize() const { return 3*dextractor->descriptorSize(); }
    virtual int descriptorType() const { return dextractor->descriptorType(); }

protected:
    Ptr<DescriptorExtractor> dextractor;
};

CV_EXPORTS Ptr<DescriptorExtractor> createDescriptorExtractor( const string& descriptorExtractorType );

/****************************************************************************************\
*                                          Distance                                      *
\****************************************************************************************/
template<typename T>
struct CV_EXPORTS Accumulator
{
    typedef T Type;
};

template<> struct Accumulator<unsigned char>  { typedef unsigned int Type; };
template<> struct Accumulator<unsigned short> { typedef unsigned int Type; };
template<> struct Accumulator<char>   { typedef int Type; };
template<> struct Accumulator<short>  { typedef int Type; };

/*
 * Squared Euclidean distance functor
 */
template<class T>
struct CV_EXPORTS L2
{
    typedef T ValueType;
    typedef typename Accumulator<T>::Type ResultType;

    ResultType operator()( const T* a, const T* b, int size ) const
    {
        ResultType result = ResultType();
        for( int i = 0; i < size; i++ )
        {
            ResultType diff = a[i] - b[i];
            result += diff*diff;
        }
        return sqrt(result);
    }
};

/*
 * Manhattan distance (city block distance) functor
 */
template<class T>
struct CV_EXPORTS L1
{
    typedef T ValueType;
    typedef typename Accumulator<T>::Type ResultType;

    ResultType operator()( const T* a, const T* b, int size ) const
    {
        ResultType result = ResultType();
        for( int i = 0; i < size; i++ )
        {
            ResultType diff = a[i] - b[i];
            result += fabs( diff );
        }
        return result;
    }
};


/****************************************************************************************\
*                                      DMatch                                            *
\****************************************************************************************/
/*
 * Struct for matching: query descriptor index, train descriptor index, train image index and distance between descriptors.
 */
struct CV_EXPORTS DMatch
{
    DMatch() : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(std::numeric_limits<float>::max()) {}
    DMatch( int _queryIdx, int _trainIdx, float _distance ) :
            queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {}
    DMatch( int _queryIdx, int _trainIdx, int _imgIdx, float _distance ) :
            queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {}

    int queryIdx; // query descriptor index
    int trainIdx; // train descriptor index
    int imgIdx; // train image index

    float distance;

    // less is better
    bool operator<( const DMatch &m) const
    {
        return distance < m.distance;
    }
};

/****************************************************************************************\
*                                  DescriptorMatcher                                     *
\****************************************************************************************/
/*
 * Abstract base class for matching two sets of descriptors.
 */
class CV_EXPORTS DescriptorMatcher
{
public:
    virtual ~DescriptorMatcher() {}
    /*
     * Add descriptors to train descriptor collection.
     * descCollection       Descriptors to add. Each descCollection[i] is from one image.
     */
    virtual void add( const vector<Mat>& descCollection );
    /*
     * Get descriptor collection.
     */
    const vector<Mat>& getTrainDescCollection() const { return trainDescCollection; }
    /*
     * Clear inner data (train image collection).
     */
    virtual void clear();

    virtual bool supportMask() = 0;

    /*
     * Train matcher (e.g. train flann index)
     */
    virtual void train() = 0;
    /*
     * Group of methods to match descriptors from image pair.
     */
    // Find one best match for each query descriptor (if mask is empty).
    void match( const Mat& queryDescs, const Mat& trainDescs, vector<DMatch>& matches,
                const Mat& mask=Mat() ) const;
    // Find knn best matches for each query descriptor (in increasing order of distances).
    // compactResult is used when mask is not empty. If compactResult is false matches vector will have the same size as queryDescs rows.
    // If compactResult is true matches vector will not contain matches for fully masked out query descriptors.
    void knnMatch( const Mat& queryDescs, const Mat& trainDescs, vector<vector<DMatch> >& matches, int knn,
                   const Mat& mask=Mat(), bool compactResult=false ) const;
    // Find best matches for each query descriptor which have distance less than maxDistance (in increasing order of distances).
    void radiusMatch( const Mat& queryDescs, const Mat& trainDescs, vector<vector<DMatch> >& matches, float maxDistance,
                      const Mat& mask=Mat(), bool compactResult=false ) const;
    /*
     * Group of methods to match descriptors from one image to image set.
     * See description of similar methods for matching image pair above.
     */
    void match( const Mat& queryDescs, vector<DMatch>& matches,
                const vector<Mat>& masks=vector<Mat>() );
    void knnMatch( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
                   const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
    void radiusMatch( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
                      const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );

    // Reads matcher object from a file node
    virtual void read( const FileNode& ) {}

    // Writes matcher object to a file storage
    virtual void write( FileStorage& ) const {}

protected:
    /*
     * Class to work with descriptors from several images as with one merged matrix.
     * It is used e.g. in FlannBasedMatcher
     */
    class CV_EXPORTS DescriptorCollection
    {
    public:
        DescriptorCollection() {}
        virtual ~DescriptorCollection() {}

        // descCollection will be merged to dmatrix here
        void set( const vector<Mat>& descCollection );
        virtual void clear();

        const Mat& getDescriptors() const { return dmatrix; }
        const Mat getDescriptor( int imgIdx, int localDescIdx ) const;
        const Mat getDescriptor( int globalDescIdx ) const;
        void getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const;

        int size() const { return dmatrix.rows; }

    protected:
        Mat dmatrix;
        vector<int> startIdxs;
    };

    // create matcher clone with current parameters but with empty data
    virtual Ptr<DescriptorMatcher> cloneWithoutData() const = 0;

    virtual void knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
                               const vector<Mat>& masks, bool compactResult ) = 0;
    virtual void radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
                                  const vector<Mat>& masks, bool compactResult ) = 0;


    static bool possibleMatch( const Mat& mask, int index_1, int index_2 )
    {
        return mask.empty() || mask.at<uchar>(index_1, index_2);
    }

    static bool maskedOut( const vector<Mat>& masks, int queryDescIdx )
    {
        size_t outCount = 0;
        for( size_t i = 0; i < masks.size(); i++ )
        {
            if( !masks[i].empty() && (countNonZero(masks[i].row(queryDescIdx)) == 0) )
                outCount++;
        }

        return !masks.empty() && outCount == masks.size() ;
    }

    vector<Mat> trainDescCollection;
};

/*
 * Brute-force descriptor matcher.
 *
 * For each descriptor in the first set, this matcher finds the closest
 * descriptor in the second set by trying each one.
 *
 * For efficiency, BruteForceMatcher is templated on the distance metric.
 * For float descriptors, a common choice would be cv::L2<float>.
 */
template<class Distance>
class CV_EXPORTS BruteForceMatcher : public DescriptorMatcher
{
public:
    BruteForceMatcher( Distance d = Distance() ) : distance(d) {}
    virtual ~BruteForceMatcher() {}

    virtual void train() {}
    virtual bool supportMask() { return true; }

protected:
    virtual Ptr<DescriptorMatcher> cloneWithoutData() const { return new BruteForceMatcher(distance); }

    virtual void knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
                               const vector<Mat>& masks, bool compactResult );
    virtual void radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
                                  const vector<Mat>& masks, bool compactResult );
    Distance distance;

private:
    /*
     * Next two methods are used to implement specialization
     */
    static void bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher,
                                const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
                                const vector<Mat>& masks, bool compactResult );
    static void bfRadiusMatchImpl( BruteForceMatcher<Distance>& matcher,
                                   const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
                                   const vector<Mat>& masks, bool compactResult );
};

template<class Distance>
void BruteForceMatcher<Distance>::knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
                                                const vector<Mat>& masks, bool compactResult )
{
    bfKnnMatchImpl( *this, queryDescs, matches, knn, masks, compactResult );
}

template<class Distance>
void BruteForceMatcher<Distance>::radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
                                                   const vector<Mat>& masks, bool compactResult )
{
    bfRadiusMatchImpl( *this, queryDescs, matches, maxDistance, masks, compactResult );
}

template<class Distance>
inline void BruteForceMatcher<Distance>::bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher,
                          const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
                          const vector<Mat>& masks, bool compactResult )
 {
     typedef typename Distance::ValueType ValueType;
     typedef typename Distance::ResultType DistanceType;
     CV_Assert( DataType<ValueType>::type == queryDescs.type() ||  queryDescs.empty() );
     CV_Assert( masks.empty() || masks.size() == matcher.trainDescCollection.size() );
     int dimension = queryDescs.cols;
     matches.reserve(queryDescs.rows);

     size_t imgCount = matcher.trainDescCollection.size();
     vector<Mat> allDists( imgCount ); // distances between one query descriptor and all train descriptors
     for( size_t i = 0; i < imgCount; i++ )
         allDists[i] = Mat( 1, matcher.trainDescCollection[i].rows, DataType<DistanceType>::type );

     for( int qIdx = 0; qIdx < queryDescs.rows; qIdx++ )
     {
         if( matcher.maskedOut( masks, qIdx ) )
         {
             if( !compactResult ) // push empty vector
                 matches.push_back( vector<DMatch>() );
         }
         else
         {
             // 1. compute distances between i-th query descriptor and all train descriptors
             for( size_t iIdx = 0; iIdx < imgCount; iIdx++ )
             {
                 CV_Assert( masks.empty() || masks[iIdx].empty() ||
                            ( masks[iIdx].rows == queryDescs.rows && masks[iIdx].cols == matcher.trainDescCollection[iIdx].rows &&
                              masks[iIdx].type() == CV_8UC1 ) );
                 CV_Assert( DataType<ValueType>::type == matcher.trainDescCollection[iIdx].type() ||  matcher.trainDescCollection[iIdx].empty() );
                 CV_Assert( queryDescs.cols == matcher.trainDescCollection[iIdx].cols );

                 const ValueType* d1 = (const ValueType*)(queryDescs.data + queryDescs.step*qIdx);
                 allDists[iIdx].setTo( Scalar::all(std::numeric_limits<DistanceType>::max()) );
                 for( int tIdx = 0; tIdx < matcher.trainDescCollection[iIdx].rows; tIdx++ )
                 {
                     if( masks.empty() || matcher.possibleMatch(masks[iIdx], qIdx, tIdx) )
                     {
                         const ValueType* d2 = (const ValueType*)(matcher.trainDescCollection[iIdx].data +
                                                                  matcher.trainDescCollection[iIdx].step*tIdx);
                         allDists[iIdx].at<DistanceType>(0, tIdx) = matcher.distance(d1, d2, dimension);
                     }
                 }
             }

             // 2. choose knn nearest matches for query[i]
             matches.push_back( vector<DMatch>() );
             vector<vector<DMatch> >::reverse_iterator curMatches = matches.rbegin();
             for( int k = 0; k < knn; k++ )
             {
                 DMatch bestMatch;
                 bestMatch.distance = std::numeric_limits<DistanceType>::max();
                 for( size_t iIdx = 0; iIdx < imgCount; iIdx++ )
                 {
                     double minVal;
                     Point minLoc;
                     minMaxLoc( allDists[iIdx], &minVal, 0, &minLoc, 0 );
                     if( minVal < bestMatch.distance )
                         bestMatch = DMatch( qIdx, minLoc.x, iIdx, minVal );
                 }
                 if( bestMatch.trainIdx == -1 )
                     break;

                 allDists[bestMatch.imgIdx].at<DistanceType>(0, bestMatch.trainIdx) = std::numeric_limits<DistanceType>::max();
                 curMatches->push_back( bestMatch );
             }
             //TODO should already be sorted at this point?
             std::sort( curMatches->begin(), curMatches->end() );
         }
     }
}

template<class Distance>
inline void BruteForceMatcher<Distance>::bfRadiusMatchImpl( BruteForceMatcher<Distance>& matcher,
                             const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
                             const vector<Mat>& masks, bool compactResult )
{
    typedef typename Distance::ValueType ValueType;
    typedef typename Distance::ResultType DistanceType;
    CV_Assert( DataType<ValueType>::type == queryDescs.type() ||  queryDescs.empty() );
    CV_Assert( masks.empty() || masks.size() == matcher.trainDescCollection.size() );

    int dimension = queryDescs.cols;
    matches.reserve(queryDescs.rows);

    size_t imgCount = matcher.trainDescCollection.size();
    for( int qIdx = 0; qIdx < queryDescs.rows; qIdx++ )
    {
        if( matcher.maskedOut( masks, qIdx ) )
        {
            if( !compactResult ) // push empty vector
                matches.push_back( vector<DMatch>() );
        }
        else
        {
            matches.push_back( vector<DMatch>() );
            vector<vector<DMatch> >::reverse_iterator curMatches = matches.rbegin();
            for( size_t iIdx = 0; iIdx < imgCount; iIdx++ )
            {
                CV_Assert( masks.empty() || masks[iIdx].empty() ||
                           ( masks[iIdx].rows == queryDescs.rows && masks[iIdx].cols == matcher.trainDescCollection[iIdx].rows &&
                             masks[iIdx].type() == CV_8UC1 ) );
                CV_Assert( DataType<ValueType>::type == matcher.trainDescCollection[iIdx].type() ||
                           matcher.trainDescCollection[iIdx].empty() );
                CV_Assert( queryDescs.cols == matcher.trainDescCollection[iIdx].cols );

                const ValueType* d1 = (const ValueType*)(queryDescs.data + queryDescs.step*qIdx);
                for( int tIdx = 0; tIdx < matcher.trainDescCollection[iIdx].rows; tIdx++ )
                {
                    if( masks.empty() || matcher.possibleMatch(masks[iIdx], qIdx, tIdx) )
                    {
                        const ValueType* d2 = (const ValueType*)(matcher.trainDescCollection[iIdx].data +
                                                                 matcher.trainDescCollection[iIdx].step*tIdx);
                        DistanceType d = matcher.distance(d1, d2, dimension);
                        if( d < maxDistance )
                            curMatches->push_back( DMatch( qIdx, tIdx, iIdx, d ) );
                    }
                }
            }
            std::sort( curMatches->begin(), curMatches->end() );
        }
    }
}

/*
 * BruteForceMatcher L2 specialization
 */
template<>
void BruteForceMatcher<L2<float> >::knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
                                                  const vector<Mat>& masks, bool compactResult );
template<>
void BruteForceMatcher<L2<float> >::radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
                                                   const vector<Mat>& masks, bool compactResult );

/*
 * Flann based matcher
 */
class CV_EXPORTS FlannBasedMatcher : public DescriptorMatcher
{
public:
    FlannBasedMatcher( const Ptr<flann::IndexParams>& _indexParams=new flann::KDTreeIndexParams(),
                       const Ptr<flann::SearchParams>& _searchParams=new flann::SearchParams() );

    virtual void add( const vector<Mat>& descCollection );
    virtual void clear();

    virtual void train();
    virtual bool supportMask() { return false; }
protected:
    virtual Ptr<DescriptorMatcher> cloneWithoutData() const { return new FlannBasedMatcher(indexParams, searchParams); }

    // masks is ignored (unsupported)
    virtual void knnMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, int knn,
                               const vector<Mat>& masks, bool compactResult );
    virtual void radiusMatchImpl( const Mat& queryDescs, vector<vector<DMatch> >& matches, float maxDistance,
                                  const vector<Mat>& masks, bool compactResult );

    static void convertToDMatches( const DescriptorCollection& collection, const Mat& indices, const Mat& dists,
                                   vector<vector<DMatch> >& matches );

    Ptr<flann::IndexParams> indexParams;
    Ptr<flann::SearchParams> searchParams;
    Ptr<flann::Index> flannIndex;

    DescriptorCollection mergedDescriptors;
    int addedDescCount;
};


CV_EXPORTS Ptr<DescriptorMatcher> createDescriptorMatcher( const string& descriptorMatcherType );

/****************************************************************************************\
*                                GenericDescriptorMatcher                                *
\****************************************************************************************/
/*
 *   Abstract interface for a keypoint descriptor
 */
class GenericDescriptorMatcher;
typedef GenericDescriptorMatcher GenericDescriptorMatch;

class CV_EXPORTS GenericDescriptorMatcher
{
public:
    GenericDescriptorMatcher() {}
    virtual ~GenericDescriptorMatcher() {}

    /*
     * Set train collection: images and keypoints from them.
     * imgCollection    Image collection.
     * pointCollection  Keypoint collection detected on imgCollection.
     */
    virtual void add( const vector<Mat>& imgCollection,
                      vector<vector<KeyPoint> >& pointCollection );

    const vector<Mat>& getTrainImgCollection() const { return trainPointCollection.getImages(); }
    const vector<vector<KeyPoint> >& getTrainPointCollection() const { return trainPointCollection.getKeypoints(); }

    // Clears keypoints storing in collection
    virtual void clear();

    virtual void train() = 0;

    virtual bool supportMask() = 0;

    /*
     * Classifies query keypoints.
     * queryImage    The query image
     * queryPoints   Keypoints from the query image
     * trainImage    The train image
     * trainPoints   Keypoints from the train image
     */
    // Classify keypoints from query image under one train image.
    virtual void classify( const Mat& queryImage, vector<KeyPoint>& queryPoints,
                           const Mat& trainImage, vector<KeyPoint>& trainPoints ) const;
    // Classify keypoints from query image under train image collection.
    virtual void classify( const Mat& queryImage, vector<KeyPoint>& queryPoints );

    /*
     * Group of methods to match keypoints from image pair.
     */
    // Find one best match for each query descriptor (if mask is empty).
    void match( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                const Mat& trainImg, vector<KeyPoint>& trainPoints,
                vector<DMatch>& matches, const Mat& mask=Mat() ) const;
    // Find knn best matches for each query keypoint (in increasing order of distances).
    // compactResult is used when mask is not empty. If compactResult is false matches vector will have the same size as queryDescs rows.
    // If compactResult is true matches vector will not contain matches for fully masked out query descriptors.
    void knnMatch( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                   const Mat& trainImg, vector<KeyPoint>& trainPoints,
                   vector<vector<DMatch> >& matches, int knn, const Mat& mask=Mat(), bool compactResult=false ) const;
    // Find best matches for each query descriptor which have distance less than maxDistance (in increasing order of distances).
    void radiusMatch( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                      const Mat& trainImg, vector<KeyPoint>& trainPoints,
                      vector<vector<DMatch> >& matches, float maxDistance, const Mat& mask=Mat(), bool compactResult=false ) const;
    /*
     * Group of methods to match keypoints from one image to image set.
     * See description of similar methods for matching image pair above.
     */
    void match( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                vector<DMatch>& matches, const vector<Mat>& masks=vector<Mat>() );
    void knnMatch( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                   vector<vector<DMatch> >& matches, int knn, const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );
    void radiusMatch( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                      vector<vector<DMatch> >& matches, float maxDistance, const vector<Mat>& masks=vector<Mat>(), bool compactResult=false );

    // Reads matcher object from a file node
    virtual void read( const FileNode& ) {}
    
    // Writes matcher object to a file storage
    virtual void write( FileStorage& ) const {}

protected:
    virtual Ptr<GenericDescriptorMatcher> createEmptyMatcherCopy() const = 0;

    virtual void knnMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                               vector<vector<DMatch> >& matches, int knn,
                               const vector<Mat>& masks, bool compactResult ) = 0;
    virtual void radiusMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                                  vector<vector<DMatch> >& matches, float maxDistance,
                                  const vector<Mat>& masks, bool compactResult ) = 0;
    /*
     * A storage for sets of keypoints together with corresponding images and class IDs
     */
    class CV_EXPORTS KeyPointCollection
    {
    public:
        KeyPointCollection() : size(0) {}
        void add( const vector<Mat>& _images, const vector<vector<KeyPoint> >& _points );
        void clear();

        // Returns the total number of keypoints in the collection
        size_t pointCount() const { return size; }
        size_t imageCount() const { return images.size(); }

        const vector<vector<KeyPoint> >& getKeypoints() const { return points; }
        const vector<KeyPoint>& getKeypoints( int imgIdx ) const { CV_Assert( imgIdx < (int)imageCount() ); return points[imgIdx]; }
        const KeyPoint& getKeyPoint( int imgIdx, int localPointIdx ) const;
        const KeyPoint& getKeyPoint( int globalPointIdx ) const;
        void getLocalIdx( int globalPointIdx, int& imgIdx, int& localPointIdx ) const;

        const vector<Mat>& getImages() const { return images; }
        const Mat& getImage( int imgIdx ) const { CV_Assert( imgIdx < (int)imageCount() ); return images[imgIdx]; }

    protected:
        int size;

        vector<Mat> images;
        vector<vector<KeyPoint> > points;

        // global indices of the first points in each image,
        // startIndices.size() = points.size()
        vector<int> startIndices;
    };

    KeyPointCollection trainPointCollection;
};

/*
 *  OneWayDescriptorMatcher
 */
class OneWayDescriptorMatcher;
typedef OneWayDescriptorMatcher OneWayDescriptorMatch;

class CV_EXPORTS OneWayDescriptorMatcher : public GenericDescriptorMatcher
{
public:
    class Params
    {
    public:
        static const int POSE_COUNT = 500;
        static const int PATCH_WIDTH = 24;
        static const int PATCH_HEIGHT = 24;
        static float GET_MIN_SCALE() { return 0.7f; }
        static float GET_MAX_SCALE() { return 1.5f; }
        static float GET_STEP_SCALE() { return 1.2f; }

        Params( int _poseCount = POSE_COUNT,
                Size _patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
                string _pcaFilename = string(),
                string _trainPath = string(),
                string _trainImagesList = string(),
                float _minScale = GET_MIN_SCALE(), float _maxScale = GET_MAX_SCALE(),
                float _stepScale = GET_STEP_SCALE() ) :
        poseCount(_poseCount), patchSize(_patchSize), pcaFilename(_pcaFilename),
        trainPath(_trainPath), trainImagesList(_trainImagesList),
        minScale(_minScale), maxScale(_maxScale), stepScale(_stepScale) {}

        int poseCount;
        Size patchSize;
        string pcaFilename;
        string trainPath;
        string trainImagesList;

        float minScale, maxScale, stepScale;
    };

    // Equivalent to calling PointMatchOneWay() followed by Initialize(_params)
    OneWayDescriptorMatcher( const Params& _params=Params() );
    virtual ~OneWayDescriptorMatcher();

    void initialize( const Params& _params, const Ptr<OneWayDescriptorBase>& _base=Ptr<OneWayDescriptorBase>() );

    // Clears keypoints storing in collection and OneWayDescriptorBase
    virtual void clear ();

    virtual void train();

    virtual bool supportMask() { return false; }

    // Reads match object from a file node
    virtual void read( const FileNode &fn );
    
    // Writes match object to a file storage
    virtual void write( FileStorage& fs ) const;

protected:
    virtual Ptr<GenericDescriptorMatcher> createEmptyMatcherCopy() const { return new OneWayDescriptorMatcher( params ); }

    // Matches a set of keypoints from a single image of the training set. A rectangle with a center in a keypoint
    // and size (patch_width/2*scale, patch_height/2*scale) is cropped from the source image for each
    // keypoint. scale is iterated from DescriptorOneWayParams::min_scale to DescriptorOneWayParams::max_scale.
    // The minimum distance to each training patch with all its affine poses is found over all scales.
    // The class ID of a match is returned for each keypoint. The distance is calculated over PCA components
    // loaded with DescriptorOneWay::Initialize, kd tree is used for finding minimum distances.
    virtual void knnMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                               vector<vector<DMatch> >& matches, int knn,
                               const vector<Mat>& masks, bool compactResult );
    virtual void radiusMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                                  vector<vector<DMatch> >& matches, float maxDistance,
                                  const vector<Mat>& masks, bool compactResult );

    Ptr<OneWayDescriptorBase> base;
    Params params;
    int prevTrainCount;
};

/*
 *  FernDescriptorMatcher
 */
class FernDescriptorMatcher;
typedef FernDescriptorMatcher FernDescriptorMatch;

class CV_EXPORTS FernDescriptorMatcher : public GenericDescriptorMatcher
{
public:
    class CV_EXPORTS Params
    {
    public:
        Params( int _nclasses=0,
                int _patchSize=FernClassifier::PATCH_SIZE,
                int _signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
                int _nstructs=FernClassifier::DEFAULT_STRUCTS,
                int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
                int _nviews=FernClassifier::DEFAULT_VIEWS,
                int _compressionMethod=FernClassifier::COMPRESSION_NONE,
                const PatchGenerator& patchGenerator=PatchGenerator() );

        Params( const string& _filename );

        int nclasses;
        int patchSize;
        int signatureSize;
        int nstructs;
        int structSize;
        int nviews;
        int compressionMethod;
        PatchGenerator patchGenerator;

        string filename;
    };

    FernDescriptorMatcher( const Params& _params=Params() );
    virtual ~FernDescriptorMatcher();

    virtual void clear();

    virtual void train();

    virtual bool supportMask() { return false; }

    virtual void read( const FileNode &fn );
    virtual void write( FileStorage& fs ) const;
    
protected:
    virtual Ptr<GenericDescriptorMatcher> createEmptyMatcherCopy() const { return new FernDescriptorMatcher( params ); }

    virtual void knnMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                               vector<vector<DMatch> >& matches, int knn,
                               const vector<Mat>& masks, bool compactResult );
    virtual void radiusMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                                  vector<vector<DMatch> >& matches, float maxDistance,
                                  const vector<Mat>& masks, bool compactResult );

    void trainFernClassifier();
    void calcBestProbAndMatchIdx( const Mat& image, const Point2f& pt,
                                  float& bestProb, int& bestMatchIdx, vector<float>& signature );
    Ptr<FernClassifier> classifier;
    Params params;
    int prevTrainCount;
};

CV_EXPORTS Ptr<GenericDescriptorMatcher> createGenericDescriptorMatcher( const string& genericDescritptorMatcherType,
                                                                         const string &paramsFilename = string () );

/****************************************************************************************\
*                                VectorDescriptorMatcher                                 *
\****************************************************************************************/

/*
 *  A class used for matching descriptors that can be described as vectors in a finite-dimensional space
 */
class VectorDescriptorMatcher;
typedef VectorDescriptorMatcher VectorDescriptorMatch;

class CV_EXPORTS VectorDescriptorMatcher : public GenericDescriptorMatcher
{
public:
    VectorDescriptorMatcher( const Ptr<DescriptorExtractor>& _extractor, const Ptr<DescriptorMatcher>& _matcher )
                        : extractor( _extractor ), matcher( _matcher ) { CV_Assert( !extractor.empty() && !matcher.empty() ); }
    virtual ~VectorDescriptorMatcher() {}

    virtual void add( const vector<Mat>& imgCollection,
                      vector<vector<KeyPoint> >& pointCollection );

    virtual void clear();

    virtual void train();

    virtual bool supportMask() { return matcher->supportMask(); }

    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;

protected:
    virtual Ptr<GenericDescriptorMatcher> createEmptyMatcherCopy() const { return new VectorDescriptorMatcher(extractor, matcher); }

    virtual void knnMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                               vector<vector<DMatch> >& matches, int knn,
                               const vector<Mat>& masks, bool compactResult );
    virtual void radiusMatchImpl( const Mat& queryImg, vector<KeyPoint>& queryPoints,
                                  vector<vector<DMatch> >& matches, float maxDistance,
                                  const vector<Mat>& masks, bool compactResult );

    Ptr<DescriptorExtractor> extractor;
    Ptr<DescriptorMatcher> matcher;
};

/****************************************************************************************\
*                                   Drawing functions                                    *
\****************************************************************************************/
struct CV_EXPORTS DrawMatchesFlags
{
    enum{ DEFAULT = 0, // Output image matrix will be created (Mat::create),
                       // i.e. existing memory of output image may be reused.
                       // Two source image, matches and single keypoints will be drawn.
                       // For each keypoint only the center point will be drawn (without
                       // the circle around keypoint with keypoint size and orientation).
          DRAW_OVER_OUTIMG = 1, // Output image matrix will not be created (Mat::create).
                                // Matches will be drawn on existing content of output image.
          NOT_DRAW_SINGLE_POINTS = 2, // Single keypoints will not be drawn.
          DRAW_RICH_KEYPOINTS = 4 // For each keypoint the circle around keypoint with keypoint size and
                                  // orientation will be drawn.
        };
};

// Draw keypoints.
CV_EXPORTS void drawKeypoints( const Mat& image, const vector<KeyPoint>& keypoints, Mat& outImg,
                               const Scalar& color=Scalar::all(-1), int flags=DrawMatchesFlags::DEFAULT );

// Draws matches of keypints from two images on output image.
CV_EXPORTS void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,
                             const Mat& img2, const vector<KeyPoint>& keypoints2,
                             const vector<DMatch>& matches1to2, Mat& outImg,
                             const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
                             const vector<char>& matchesMask=vector<char>(), int flags=DrawMatchesFlags::DEFAULT );

CV_EXPORTS void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,
                             const Mat& img2, const vector<KeyPoint>& keypoints2,
                             const vector<vector<DMatch> >& matches1to2, Mat& outImg,
                             const Scalar& matchColor=Scalar::all(-1), const Scalar& singlePointColor=Scalar::all(-1),
                             const vector<vector<char> >& matchesMask=vector<vector<char> >(), int flags=DrawMatchesFlags::DEFAULT );

/****************************************************************************************\
*   Functions to evaluate the feature detectors and [generic] descriptor extractors      *
\****************************************************************************************/

CV_EXPORTS void evaluateFeatureDetector( const Mat& img1, const Mat& img2, const Mat& H1to2,
                                         vector<KeyPoint>* keypoints1, vector<KeyPoint>* keypoints2,
                                         float& repeatability, int& correspCount,
                                         const Ptr<FeatureDetector>& fdetector=Ptr<FeatureDetector>() );

CV_EXPORTS void computeRecallPrecisionCurve( const vector<vector<DMatch> >& matches1to2,
                                             const vector<vector<uchar> >& correctMatches1to2Mask,
                                             vector<Point2f>& recallPrecisionCurve );
CV_EXPORTS float getRecall( const vector<Point2f>& recallPrecisionCurve, float l_precision );

CV_EXPORTS void evaluateGenericDescriptorMatcher( const Mat& img1, const Mat& img2, const Mat& H1to2,
                                                  vector<KeyPoint>& keypoints1, vector<KeyPoint>& keypoints2,
                                                  vector<vector<DMatch> >* matches1to2, vector<vector<uchar> >* correctMatches1to2Mask,
                                                  vector<Point2f>& recallPrecisionCurve,
                                                  const Ptr<GenericDescriptorMatcher>& dmatch=Ptr<GenericDescriptorMatcher>() );


/****************************************************************************************\
*                                     Bag of visual words                                *
\****************************************************************************************/
/*
 * Abstract base class for training of a 'bag of visual words' vocabulary from a set of descriptors
 */
class CV_EXPORTS BOWTrainer
{
public:
    BOWTrainer(){}
    virtual ~BOWTrainer(){}

    void add( const Mat& descriptors );
    const vector<Mat>& getDescriptors() const { return descriptors; }
    int descripotorsCount() const { return descriptors.empty() ? 0 : size; }

    virtual void clear();

    /*
     * Train visual words vocabulary, that is cluster training descriptors and
     * compute cluster centers.
     * Returns cluster centers.
     *
     * descriptors      Training descriptors computed on images keypoints.
     */
    virtual Mat cluster() const = 0;
    virtual Mat cluster( const Mat& descriptors ) const = 0;

protected:
    vector<Mat> descriptors;
    int size;
};

/*
 * This is BOWTrainer using cv::kmeans to get vocabulary.
 */
class CV_EXPORTS BOWKMeansTrainer : public BOWTrainer
{
public:
    BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
                      int attempts=3, int flags=KMEANS_PP_CENTERS );
    virtual ~BOWKMeansTrainer(){}

    // Returns trained vocabulary (i.e. cluster centers).
    virtual Mat cluster() const;
    virtual Mat cluster( const Mat& descriptors ) const;

protected:

    int clusterCount;
    TermCriteria termcrit;
    int attempts;
    int flags;
};

/*
 * Class to compute image descriptor using bad of visual words.
 */
class CV_EXPORTS BOWImgDescriptorExtractor
{
public:
    BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
                               const Ptr<DescriptorMatcher>& dmatcher );
    virtual ~BOWImgDescriptorExtractor(){}

    void setVocabulary( const Mat& vocabulary );
    const Mat& getVocabulary() const { return vocabulary; }
    void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor,
                  vector<vector<int> >* pointIdxsOfClusters=0, Mat* descriptors=0 ); //not constant because DescriptorMatcher::match is not constant
    int descriptorSize() const { return vocabulary.empty() ? 0 : vocabulary.rows; }
    int descriptorType() const { return CV_32FC1; }

protected:
    Mat vocabulary;
    Ptr<DescriptorExtractor> dextractor;
    Ptr<DescriptorMatcher> dmatcher;
};

} /* namespace cv */

#endif /* __cplusplus */

#endif

/* End of file. */