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#ifndef OPENCV_FEATURES_2D_HPP

#define OPENCV_FEATURES_2D_HPP

#include "opencv2/opencv_modules.hpp"

#include "opencv2/core.hpp"

#ifdef HAVE_OPENCV_FLANN

#include "opencv2/flann/miniflann.hpp"

#endif

/**

  @defgroup features2d 2D Features Framework

  @{

    @defgroup features2d_main Feature Detection and Description

    @defgroup features2d_match Descriptor Matchers

    Matchers of keypoint descriptors in OpenCV have wrappers with a common interface that enables

    you to easily switch between different algorithms solving the same problem. This section is

    devoted to matching descriptors that are represented as vectors in a multidimensional space.

    All objects that implement vector descriptor matchers inherit the DescriptorMatcher interface.

    @defgroup features2d_draw Drawing Function of Keypoints and Matches

    @defgroup features2d_category Object Categorization

    This section describes approaches based on local 2D features and used to categorize objects.

    @defgroup feature2d_hal Hardware Acceleration Layer

    @{

        @defgroup features2d_hal_interface Interface

    @}

  @}

 */

namespace cv

{

//! @addtogroup features2d_main

//! @{

// //! writes vector of keypoints to the file storage

// CV_EXPORTS void write(FileStorage& fs, const String& name, const std::vector<KeyPoint>& keypoints);

// //! reads vector of keypoints from the specified file storage node

// CV_EXPORTS void read(const FileNode& node, CV_OUT std::vector<KeyPoint>& keypoints);

/** @brief A class filters a vector of keypoints.

 Because now it is difficult to provide a convenient interface for all usage scenarios of the

 keypoints filter class, it has only several needed by now static methods.

 */

class CV_EXPORTS KeyPointsFilter

{

public:

    KeyPointsFilter(){}

    /*

     * Remove keypoints within borderPixels of an image edge.

     */

    static void runByImageBorder( std::vector<KeyPoint>& keypoints, Size imageSize, int borderSize );

    /*

     * Remove keypoints of sizes out of range.

     */

    static void runByKeypointSize( std::vector<KeyPoint>& keypoints, float minSize,

                                   float maxSize=FLT_MAX );

    /*

     * Remove keypoints from some image by mask for pixels of this image.

     */

    static void runByPixelsMask( std::vector<KeyPoint>& keypoints, const Mat& mask );

    /*

     * Remove objects from some image and a vector of points by mask for pixels of this image

     */

    static void runByPixelsMask2VectorPoint(std::vector<KeyPoint> &keypoints, std::vector<std::vector<Point> > &removeFrom, const Mat &mask);

    /*

     * Remove duplicated keypoints.

     */

    static void removeDuplicated( std::vector<KeyPoint>& keypoints );

    /*

     * Remove duplicated keypoints and sort the remaining keypoints

     */

    static void removeDuplicatedSorted( std::vector<KeyPoint>& keypoints );

    /*

     * Retain the specified number of the best keypoints (according to the response)

     */

    static void retainBest( std::vector<KeyPoint>& keypoints, int npoints );

};

/************************************ Base Classes ************************************/

/** @brief Abstract base class for 2D image feature detectors and descriptor extractors

*/

#ifdef __EMSCRIPTEN__

class CV_EXPORTS_W Feature2D : public Algorithm

#else

class CV_EXPORTS_W Feature2D : public virtual Algorithm

#endif

{

public:

    virtual ~Feature2D();

    /** @brief Detects keypoints in an image (first variant) or image set (second variant).

    @param image Image.

    @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set

    of keypoints detected in images[i] .

    @param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer

    matrix with non-zero values in the region of interest.

     */

    CV_WRAP virtual void detect( InputArray image,

                                 CV_OUT std::vector<KeyPoint>& keypoints,

                                 InputArray mask=noArray() );

    /** @overload

    @param images Image set.

    @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set

    of keypoints detected in images[i] .

    @param masks Masks for each input image specifying where to look for keypoints (optional).

    masks[i] is a mask for images[i].

    */

    CV_WRAP virtual void detect( InputArrayOfArrays images,

                         CV_OUT std::vector<std::vector<KeyPoint> >& keypoints,

                         InputArrayOfArrays masks=noArray() );

    /** @brief Computes the descriptors for a set of keypoints detected in an image (first variant) or image set

    (second variant).

    @param image Image.

    @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be

    computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint

    with several dominant orientations (for each orientation).

    @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are

    descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the

    descriptor for keypoint j-th keypoint.

     */

    CV_WRAP virtual void compute( InputArray image,

                                  CV_OUT CV_IN_OUT std::vector<KeyPoint>& keypoints,

                                  OutputArray descriptors );

    /** @overload

    @param images Image set.

    @param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be

    computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint

    with several dominant orientations (for each orientation).

    @param descriptors Computed descriptors. In the second variant of the method descriptors[i] are

    descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the

    descriptor for keypoint j-th keypoint.

    */

    CV_WRAP virtual void compute( InputArrayOfArrays images,

                          CV_OUT CV_IN_OUT std::vector<std::vector<KeyPoint> >& keypoints,

                          OutputArrayOfArrays descriptors );

    /** Detects keypoints and computes the descriptors */

    CV_WRAP virtual void detectAndCompute( InputArray image, InputArray mask,

                                           CV_OUT std::vector<KeyPoint>& keypoints,

                                           OutputArray descriptors,

                                           bool useProvidedKeypoints=false );

    CV_WRAP virtual int descriptorSize() const;

    CV_WRAP virtual int descriptorType() const;

    CV_WRAP virtual int defaultNorm() const;

    CV_WRAP void write( const String& fileName ) const;

    CV_WRAP void read( const String& fileName );

    virtual void write( FileStorage&) const CV_OVERRIDE;

    // see corresponding cv::Algorithm method

    CV_WRAP virtual void read( const FileNode&) CV_OVERRIDE;

    //! Return true if detector object is empty

    CV_WRAP virtual bool empty() const CV_OVERRIDE;

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

    // see corresponding cv::Algorithm method

    CV_WRAP inline void write(FileStorage& fs, const String& name) const { Algorithm::write(fs, name); }

#if CV_VERSION_MAJOR < 5

    inline void write(const Ptr<FileStorage>& fs, const String& name) const { CV_Assert(fs); Algorithm::write(*fs, name); }

#endif

};

/** Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch

between different algorithms solving the same problem. All objects that implement keypoint detectors

inherit the FeatureDetector interface. */

typedef Feature2D FeatureDetector;

/** Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you

to easily switch between different algorithms solving the same problem. This section is devoted to

computing descriptors represented as vectors in a multidimensional space. All objects that implement

the vector descriptor extractors inherit the DescriptorExtractor interface.

 */

typedef Feature2D DescriptorExtractor;

/** @brief Class for implementing the wrapper which makes detectors and extractors to be affine invariant,

described as ASIFT in @cite YM11 .

*/

class CV_EXPORTS_W AffineFeature : public Feature2D

{

public:

    /**

    @param backend The detector/extractor you want to use as backend.

    @param maxTilt The highest power index of tilt factor. 5 is used in the paper as tilt sampling range n.

    @param minTilt The lowest power index of tilt factor. 0 is used in the paper.

    @param tiltStep Tilt sampling step \f$\delta_t\f$ in Algorithm 1 in the paper.

    @param rotateStepBase Rotation sampling step factor b in Algorithm 1 in the paper.

    */

    CV_WRAP static Ptr<AffineFeature> create(const Ptr<Feature2D>& backend,

        int maxTilt = 5, int minTilt = 0, float tiltStep = 1.4142135623730951f, float rotateStepBase = 72);

    CV_WRAP virtual void setViewParams(const std::vector<float>& tilts, const std::vector<float>& rolls) = 0;

    CV_WRAP virtual void getViewParams(std::vector<float>& tilts, std::vector<float>& rolls) const = 0;

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

};

typedef AffineFeature AffineFeatureDetector;

typedef AffineFeature AffineDescriptorExtractor;

/** @brief Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform

(SIFT) algorithm by D. Lowe @cite Lowe04 .

*/

class CV_EXPORTS_W SIFT : public Feature2D

{

public:

    /**

    @param nfeatures The number of best features to retain. The features are ranked by their scores

    (measured in SIFT algorithm as the local contrast)

    @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The

    number of octaves is computed automatically from the image resolution.

    @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform

    (low-contrast) regions. The larger the threshold, the less features are produced by the detector.

    @note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When

    nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set

    this argument to 0.09.

    @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning

    is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are

    filtered out (more features are retained).

    @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image

    is captured with a weak camera with soft lenses, you might want to reduce the number.

    @param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps

    index \f$\texttt{x}\f$ to \f$\texttt{2x}\f$. This prevents localization bias. The option

    is disabled by default.

    */

    CV_WRAP static Ptr<SIFT> create(int nfeatures = 0, int nOctaveLayers = 3,

        double contrastThreshold = 0.04, double edgeThreshold = 10,

        double sigma = 1.6, bool enable_precise_upscale = false);

    /** @brief Create SIFT with specified descriptorType.

    @param nfeatures The number of best features to retain. The features are ranked by their scores

    (measured in SIFT algorithm as the local contrast)

    @param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The

    number of octaves is computed automatically from the image resolution.

    @param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform

    (low-contrast) regions. The larger the threshold, the less features are produced by the detector.

    @note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When

    nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set

    this argument to 0.09.

    @param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning

    is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are

    filtered out (more features are retained).

    @param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image

    is captured with a weak camera with soft lenses, you might want to reduce the number.

    @param descriptorType The type of descriptors. Only CV_32F and CV_8U are supported.

    @param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps

    index \f$\texttt{x}\f$ to \f$\texttt{2x}\f$. This prevents localization bias. The option

    is disabled by default.

    */

    CV_WRAP static Ptr<SIFT> create(int nfeatures, int nOctaveLayers,

        double contrastThreshold, double edgeThreshold,

        double sigma, int descriptorType, bool enable_precise_upscale = false);

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

    CV_WRAP virtual void setNFeatures(int maxFeatures) = 0;

    CV_WRAP virtual int getNFeatures() const = 0;

    CV_WRAP virtual void setNOctaveLayers(int nOctaveLayers) = 0;

    CV_WRAP virtual int getNOctaveLayers() const = 0;

    CV_WRAP virtual void setContrastThreshold(double contrastThreshold) = 0;

    CV_WRAP virtual double getContrastThreshold() const = 0;

    CV_WRAP virtual void setEdgeThreshold(double edgeThreshold) = 0;

    CV_WRAP virtual double getEdgeThreshold() const = 0;

    CV_WRAP virtual void setSigma(double sigma) = 0;

    CV_WRAP virtual double getSigma() const = 0;

};

typedef SIFT SiftFeatureDetector;

typedef SIFT SiftDescriptorExtractor;

/** @brief Class implementing the BRISK keypoint detector and descriptor extractor, described in @cite LCS11 .

 */

class CV_EXPORTS_W BRISK : public Feature2D

{

public:

    /** @brief The BRISK constructor

    @param thresh AGAST detection threshold score.

    @param octaves detection octaves. Use 0 to do single scale.

    @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a

    keypoint.

     */

    CV_WRAP static Ptr<BRISK> create(int thresh=30, int octaves=3, float patternScale=1.0f);

    /** @brief The BRISK constructor for a custom pattern

    @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for

    keypoint scale 1).

    @param numberList defines the number of sampling points on the sampling circle. Must be the same

    size as radiusList..

    @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint

    scale 1).

    @param dMin threshold for the long pairings used for orientation determination (in pixels for

    keypoint scale 1).

    @param indexChange index remapping of the bits. */

    CV_WRAP static Ptr<BRISK> create(const std::vector<float> &radiusList, const std::vector<int> &numberList,

        float dMax=5.85f, float dMin=8.2f, const std::vector<int>& indexChange=std::vector<int>());

    /** @brief The BRISK constructor for a custom pattern, detection threshold and octaves

    @param thresh AGAST detection threshold score.

    @param octaves detection octaves. Use 0 to do single scale.

    @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for

    keypoint scale 1).

    @param numberList defines the number of sampling points on the sampling circle. Must be the same

    size as radiusList..

    @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint

    scale 1).

    @param dMin threshold for the long pairings used for orientation determination (in pixels for

    keypoint scale 1).

    @param indexChange index remapping of the bits. */

    CV_WRAP static Ptr<BRISK> create(int thresh, int octaves, const std::vector<float> &radiusList,

        const std::vector<int> &numberList, float dMax=5.85f, float dMin=8.2f,

        const std::vector<int>& indexChange=std::vector<int>());

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

    /** @brief Set detection threshold.

    @param threshold AGAST detection threshold score.

    */

    CV_WRAP virtual void setThreshold(int threshold) = 0;

    CV_WRAP virtual int getThreshold() const = 0;

    /** @brief Set detection octaves.

    @param octaves detection octaves. Use 0 to do single scale.

    */

    CV_WRAP virtual void setOctaves(int octaves) = 0;

    CV_WRAP virtual int getOctaves() const = 0;

    /** @brief Set detection patternScale.

    @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a

    keypoint.

    */

    CV_WRAP virtual void setPatternScale(float patternScale) = 0;

    CV_WRAP virtual float getPatternScale() const = 0;

};

/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor

described in @cite RRKB11 . The algorithm uses FAST in pyramids to detect stable keypoints, selects

the strongest features using FAST or Harris response, finds their orientation using first-order

moments and computes the descriptors using BRIEF (where the coordinates of random point pairs (or

k-tuples) are rotated according to the measured orientation).

 */

class CV_EXPORTS_W ORB : public Feature2D

{

public:

    enum ScoreType { HARRIS_SCORE=0, FAST_SCORE=1 };

    static const int kBytes = 32;

    /** @brief The ORB constructor

    @param nfeatures The maximum number of features to retain.

    @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical

    pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor

    will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor

    will mean that to cover certain scale range you will need more pyramid levels and so the speed

    will suffer.

    @param nlevels The number of pyramid levels. The smallest level will have linear size equal to

    input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).

    @param edgeThreshold This is size of the border where the features are not detected. It should

    roughly match the patchSize parameter.

    @param firstLevel The level of pyramid to put source image to. Previous layers are filled

    with upscaled source image.

    @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The

    default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,

    so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3

    random points (of course, those point coordinates are random, but they are generated from the

    pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel

    rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such

    output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,

    denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each

    bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).

    @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features

    (the score is written to KeyPoint::score and is used to retain best nfeatures features);

    FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,

    but it is a little faster to compute.

    @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller

    pyramid layers the perceived image area covered by a feature will be larger.

    @param fastThreshold the fast threshold

     */

    CV_WRAP static Ptr<ORB> create(int nfeatures=500, float scaleFactor=1.2f, int nlevels=8, int edgeThreshold=31,

        int firstLevel=0, int WTA_K=2, ORB::ScoreType scoreType=ORB::HARRIS_SCORE, int patchSize=31, int fastThreshold=20);

    CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;

    CV_WRAP virtual int getMaxFeatures() const = 0;

    CV_WRAP virtual void setScaleFactor(double scaleFactor) = 0;

    CV_WRAP virtual double getScaleFactor() const = 0;

    CV_WRAP virtual void setNLevels(int nlevels) = 0;

    CV_WRAP virtual int getNLevels() const = 0;

    CV_WRAP virtual void setEdgeThreshold(int edgeThreshold) = 0;

    CV_WRAP virtual int getEdgeThreshold() const = 0;

    CV_WRAP virtual void setFirstLevel(int firstLevel) = 0;

    CV_WRAP virtual int getFirstLevel() const = 0;

    CV_WRAP virtual void setWTA_K(int wta_k) = 0;

    CV_WRAP virtual int getWTA_K() const = 0;

    CV_WRAP virtual void setScoreType(ORB::ScoreType scoreType) = 0;

    CV_WRAP virtual ORB::ScoreType getScoreType() const = 0;

    CV_WRAP virtual void setPatchSize(int patchSize) = 0;

    CV_WRAP virtual int getPatchSize() const = 0;

    CV_WRAP virtual void setFastThreshold(int fastThreshold) = 0;

    CV_WRAP virtual int getFastThreshold() const = 0;

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

};

/** @brief Maximally stable extremal region extractor

The class encapsulates all the parameters of the %MSER extraction algorithm (see [wiki

article](http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions)).

- there are two different implementation of %MSER: one for grey image, one for color image

- the grey image algorithm is taken from: @cite nister2008linear ;  the paper claims to be faster

than union-find method; it actually get 1.5~2m/s on my centrino L7200 1.2GHz laptop.

- the color image algorithm is taken from: @cite forssen2007maximally ; it should be much slower

than grey image method ( 3~4 times )

- (Python) A complete example showing the use of the %MSER detector can be found at samples/python/mser.py

*/

class CV_EXPORTS_W MSER : public Feature2D

{

public:

    /** @brief Full constructor for %MSER detector

    @param delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$

    @param min_area prune the area which smaller than minArea

    @param max_area prune the area which bigger than maxArea

    @param max_variation prune the area have similar size to its children

    @param min_diversity for color image, trace back to cut off mser with diversity less than min_diversity

    @param max_evolution  for color image, the evolution steps

    @param area_threshold for color image, the area threshold to cause re-initialize

    @param min_margin for color image, ignore too small margin

    @param edge_blur_size for color image, the aperture size for edge blur

     */

    CV_WRAP static Ptr<MSER> create( int delta=5, int min_area=60, int max_area=14400,

          double max_variation=0.25, double min_diversity=.2,

          int max_evolution=200, double area_threshold=1.01,

          double min_margin=0.003, int edge_blur_size=5 );

    /** @brief Detect %MSER regions

    @param image input image (8UC1, 8UC3 or 8UC4, must be greater or equal than 3x3)

    @param msers resulting list of point sets

    @param bboxes resulting bounding boxes

    */

    CV_WRAP virtual void detectRegions( InputArray image,

                                        CV_OUT std::vector<std::vector<Point> >& msers,

                                        CV_OUT std::vector<Rect>& bboxes ) = 0;

    CV_WRAP virtual void setDelta(int delta) = 0;

    CV_WRAP virtual int getDelta() const = 0;

    CV_WRAP virtual void setMinArea(int minArea) = 0;

    CV_WRAP virtual int getMinArea() const = 0;

    CV_WRAP virtual void setMaxArea(int maxArea) = 0;

    CV_WRAP virtual int getMaxArea() const = 0;

    CV_WRAP virtual void setMaxVariation(double maxVariation) = 0;

    CV_WRAP virtual double getMaxVariation() const = 0;

    CV_WRAP virtual void setMinDiversity(double minDiversity) = 0;

    CV_WRAP virtual double getMinDiversity() const = 0;

    CV_WRAP virtual void setMaxEvolution(int maxEvolution) = 0;

    CV_WRAP virtual int getMaxEvolution() const = 0;

    CV_WRAP virtual void setAreaThreshold(double areaThreshold) = 0;

    CV_WRAP virtual double getAreaThreshold() const = 0;

    CV_WRAP virtual void setMinMargin(double min_margin) = 0;

    CV_WRAP virtual double getMinMargin() const = 0;

    CV_WRAP virtual void setEdgeBlurSize(int edge_blur_size) = 0;

    CV_WRAP virtual int getEdgeBlurSize() const = 0;

    CV_WRAP virtual void setPass2Only(bool f) = 0;

    CV_WRAP virtual bool getPass2Only() const = 0;

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

};

/** @brief Wrapping class for feature detection using the FAST method. :

 */

class CV_EXPORTS_W FastFeatureDetector : public Feature2D

{

public:

    enum DetectorType

    {

        TYPE_5_8 = 0, TYPE_7_12 = 1, TYPE_9_16 = 2

    };

    enum

    {

        THRESHOLD = 10000, NONMAX_SUPPRESSION=10001, FAST_N=10002

    };

    CV_WRAP static Ptr<FastFeatureDetector> create( int threshold=10,

                                                    bool nonmaxSuppression=true,

                                                    FastFeatureDetector::DetectorType type=FastFeatureDetector::TYPE_9_16 );

    CV_WRAP virtual void setThreshold(int threshold) = 0;

    CV_WRAP virtual int getThreshold() const = 0;

    CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;

    CV_WRAP virtual bool getNonmaxSuppression() const = 0;

    CV_WRAP virtual void setType(FastFeatureDetector::DetectorType type) = 0;

    CV_WRAP virtual FastFeatureDetector::DetectorType getType() const = 0;

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

};

/** @overload */

CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,

                      int threshold, bool nonmaxSuppression=true );

/** @brief Detects corners using the FAST algorithm

@param image grayscale image where keypoints (corners) are detected.

@param keypoints keypoints detected on the image.

@param threshold threshold on difference between intensity of the central pixel and pixels of a

circle around this pixel.

@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners

(keypoints).

@param type one of the three neighborhoods as defined in the paper:

FastFeatureDetector::TYPE_9_16, FastFeatureDetector::TYPE_7_12,

FastFeatureDetector::TYPE_5_8

Detects corners using the FAST algorithm by @cite Rosten06 .

@note In Python API, types are given as cv.FAST_FEATURE_DETECTOR_TYPE_5_8,

cv.FAST_FEATURE_DETECTOR_TYPE_7_12 and cv.FAST_FEATURE_DETECTOR_TYPE_9_16. For corner

detection, use cv.FAST.detect() method.

 */

CV_EXPORTS void FAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,

                      int threshold, bool nonmaxSuppression, FastFeatureDetector::DetectorType type );

/** @brief Wrapping class for feature detection using the AGAST method. :

 */

class CV_EXPORTS_W AgastFeatureDetector : public Feature2D

{

public:

    enum DetectorType

    {

        AGAST_5_8 = 0, AGAST_7_12d = 1, AGAST_7_12s = 2, OAST_9_16 = 3,

    };

    enum

    {

        THRESHOLD = 10000, NONMAX_SUPPRESSION = 10001,

    };

    CV_WRAP static Ptr<AgastFeatureDetector> create( int threshold=10,

                                                     bool nonmaxSuppression=true,

                                                     AgastFeatureDetector::DetectorType type = AgastFeatureDetector::OAST_9_16);

    CV_WRAP virtual void setThreshold(int threshold) = 0;

    CV_WRAP virtual int getThreshold() const = 0;

    CV_WRAP virtual void setNonmaxSuppression(bool f) = 0;

    CV_WRAP virtual bool getNonmaxSuppression() const = 0;

    CV_WRAP virtual void setType(AgastFeatureDetector::DetectorType type) = 0;

    CV_WRAP virtual AgastFeatureDetector::DetectorType getType() const = 0;

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

};

/** @overload */

CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,

                      int threshold, bool nonmaxSuppression=true );

/** @brief Detects corners using the AGAST algorithm

@param image grayscale image where keypoints (corners) are detected.

@param keypoints keypoints detected on the image.

@param threshold threshold on difference between intensity of the central pixel and pixels of a

circle around this pixel.

@param nonmaxSuppression if true, non-maximum suppression is applied to detected corners

(keypoints).

@param type one of the four neighborhoods as defined in the paper:

AgastFeatureDetector::AGAST_5_8, AgastFeatureDetector::AGAST_7_12d,

AgastFeatureDetector::AGAST_7_12s, AgastFeatureDetector::OAST_9_16

For non-Intel platforms, there is a tree optimised variant of AGAST with same numerical results.

The 32-bit binary tree tables were generated automatically from original code using perl script.

The perl script and examples of tree generation are placed in features2d/doc folder.

Detects corners using the AGAST algorithm by @cite mair2010_agast .

 */

CV_EXPORTS void AGAST( InputArray image, CV_OUT std::vector<KeyPoint>& keypoints,

                      int threshold, bool nonmaxSuppression, AgastFeatureDetector::DetectorType type );

/** @brief Wrapping class for feature detection using the goodFeaturesToTrack function. :

 */

class CV_EXPORTS_W GFTTDetector : public Feature2D

{

public:

    CV_WRAP static Ptr<GFTTDetector> create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1,

                                             int blockSize=3, bool useHarrisDetector=false, double k=0.04 );

    CV_WRAP static Ptr<GFTTDetector> create( int maxCorners, double qualityLevel, double minDistance,

                                             int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04 );

    CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;

    CV_WRAP virtual int getMaxFeatures() const = 0;

    CV_WRAP virtual void setQualityLevel(double qlevel) = 0;

    CV_WRAP virtual double getQualityLevel() const = 0;

    CV_WRAP virtual void setMinDistance(double minDistance) = 0;

    CV_WRAP virtual double getMinDistance() const = 0;

    CV_WRAP virtual void setBlockSize(int blockSize) = 0;

    CV_WRAP virtual int getBlockSize() const = 0;

    CV_WRAP virtual void setGradientSize(int gradientSize_) = 0;

    CV_WRAP virtual int getGradientSize() = 0;

    CV_WRAP virtual void setHarrisDetector(bool val) = 0;

    CV_WRAP virtual bool getHarrisDetector() const = 0;

    CV_WRAP virtual void setK(double k) = 0;

    CV_WRAP virtual double getK() const = 0;

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

};

/** @brief Class for extracting blobs from an image. :

The class implements a simple algorithm for extracting blobs from an image:

1.  Convert the source image to binary images by applying thresholding with several thresholds from

    minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between

    neighboring thresholds.

2.  Extract connected components from every binary image by findContours and calculate their

    centers.

3.  Group centers from several binary images by their coordinates. Close centers form one group that

    corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter.

4.  From the groups, estimate final centers of blobs and their radiuses and return as locations and

    sizes of keypoints.

This class performs several filtrations of returned blobs. You should set filterBy\* to true/false

to turn on/off corresponding filtration. Available filtrations:

-   **By color**. This filter compares the intensity of a binary image at the center of a blob to

blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs

and blobColor = 255 to extract light blobs.

-   **By area**. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive).

-   **By circularity**. Extracted blobs have circularity

(\f$\frac{4*\pi*Area}{perimeter * perimeter}\f$) between minCircularity (inclusive) and

maxCircularity (exclusive).

-   **By ratio of the minimum inertia to maximum inertia**. Extracted blobs have this ratio

between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive).

-   **By convexity**. Extracted blobs have convexity (area / area of blob convex hull) between

minConvexity (inclusive) and maxConvexity (exclusive).

Default values of parameters are tuned to extract dark circular blobs.

 */

class CV_EXPORTS_W SimpleBlobDetector : public Feature2D

{

public:

  struct CV_EXPORTS_W_SIMPLE Params

  {

      CV_WRAP Params();

      CV_PROP_RW float thresholdStep;

      CV_PROP_RW float minThreshold;

      CV_PROP_RW float maxThreshold;

      CV_PROP_RW size_t minRepeatability;

      CV_PROP_RW float minDistBetweenBlobs;

      CV_PROP_RW bool filterByColor;

      CV_PROP_RW uchar blobColor;

      CV_PROP_RW bool filterByArea;

      CV_PROP_RW float minArea, maxArea;

      CV_PROP_RW bool filterByCircularity;

      CV_PROP_RW float minCircularity, maxCircularity;

      CV_PROP_RW bool filterByInertia;

      CV_PROP_RW float minInertiaRatio, maxInertiaRatio;

      CV_PROP_RW bool filterByConvexity;

      CV_PROP_RW float minConvexity, maxConvexity;

      CV_PROP_RW bool collectContours;

      void read( const FileNode& fn );

      void write( FileStorage& fs ) const;

  };

  CV_WRAP static Ptr<SimpleBlobDetector>

    create(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());

  CV_WRAP virtual void setParams(const SimpleBlobDetector::Params& params ) = 0;

  CV_WRAP virtual SimpleBlobDetector::Params getParams() const = 0;

  CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

  CV_WRAP virtual const std::vector<std::vector<cv::Point> >& getBlobContours() const;

};

/** @brief Class implementing the KAZE keypoint detector and descriptor extractor, described in @cite ABD12 .

@note AKAZE descriptor can only be used with KAZE or AKAZE keypoints .. [ABD12] KAZE Features. Pablo

F. Alcantarilla, Adrien Bartoli and Andrew J. Davison. In European Conference on Computer Vision

(ECCV), Fiorenze, Italy, October 2012.

*/

class CV_EXPORTS_W KAZE : public Feature2D

{

public:

    enum DiffusivityType

    {

        DIFF_PM_G1 = 0,

        DIFF_PM_G2 = 1,

        DIFF_WEICKERT = 2,

        DIFF_CHARBONNIER = 3

    };

    /** @brief The KAZE constructor

    @param extended Set to enable extraction of extended (128-byte) descriptor.

    @param upright Set to enable use of upright descriptors (non rotation-invariant).

    @param threshold Detector response threshold to accept point

    @param nOctaves Maximum octave evolution of the image

    @param nOctaveLayers Default number of sublevels per scale level

    @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or

    DIFF_CHARBONNIER

     */

    CV_WRAP static Ptr<KAZE> create(bool extended=false, bool upright=false,

                                    float threshold = 0.001f,

                                    int nOctaves = 4, int nOctaveLayers = 4,

                                    KAZE::DiffusivityType diffusivity = KAZE::DIFF_PM_G2);

    CV_WRAP virtual void setExtended(bool extended) = 0;

    CV_WRAP virtual bool getExtended() const = 0;

    CV_WRAP virtual void setUpright(bool upright) = 0;

    CV_WRAP virtual bool getUpright() const = 0;

    CV_WRAP virtual void setThreshold(double threshold) = 0;

    CV_WRAP virtual double getThreshold() const = 0;

    CV_WRAP virtual void setNOctaves(int octaves) = 0;

    CV_WRAP virtual int getNOctaves() const = 0;

    CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0;

    CV_WRAP virtual int getNOctaveLayers() const = 0;

    CV_WRAP virtual void setDiffusivity(KAZE::DiffusivityType diff) = 0;

    CV_WRAP virtual KAZE::DiffusivityType getDiffusivity() const = 0;

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

};

/** @brief Class implementing the AKAZE keypoint detector and descriptor extractor, described in @cite ANB13.

@details AKAZE descriptors can only be used with KAZE or AKAZE keypoints. This class is thread-safe.

@note When you need descriptors use Feature2D::detectAndCompute, which

provides better performance. When using Feature2D::detect followed by

Feature2D::compute scale space pyramid is computed twice.

@note AKAZE implements T-API. When image is passed as UMat some parts of the algorithm

will use OpenCL.

@note [ANB13] Fast Explicit Diffusion for Accelerated Features in Nonlinear

Scale Spaces. Pablo F. Alcantarilla, Jesús Nuevo and Adrien Bartoli. In

British Machine Vision Conference (BMVC), Bristol, UK, September 2013.

*/

class CV_EXPORTS_W AKAZE : public Feature2D

{

public:

    // AKAZE descriptor type

    enum DescriptorType

    {

        DESCRIPTOR_KAZE_UPRIGHT = 2, ///< Upright descriptors, not invariant to rotation

        DESCRIPTOR_KAZE = 3,

        DESCRIPTOR_MLDB_UPRIGHT = 4, ///< Upright descriptors, not invariant to rotation

        DESCRIPTOR_MLDB = 5

    };

    /** @brief The AKAZE constructor

    @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE,

    DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.

    @param descriptor_size Size of the descriptor in bits. 0 -\> Full size

    @param descriptor_channels Number of channels in the descriptor (1, 2, 3)

    @param threshold Detector response threshold to accept point

    @param nOctaves Maximum octave evolution of the image

    @param nOctaveLayers Default number of sublevels per scale level

    @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or

    DIFF_CHARBONNIER

    @param max_points Maximum amount of returned points. In case if image contains

    more features, then the features with highest response are returned.

    Negative value means no limitation.

     */

    CV_WRAP static Ptr<AKAZE> create(AKAZE::DescriptorType descriptor_type = AKAZE::DESCRIPTOR_MLDB,

                                     int descriptor_size = 0, int descriptor_channels = 3,

                                     float threshold = 0.001f, int nOctaves = 4,

                                     int nOctaveLayers = 4, KAZE::DiffusivityType diffusivity = KAZE::DIFF_PM_G2,

                                     int max_points = -1);

    CV_WRAP virtual void setDescriptorType(AKAZE::DescriptorType dtype) = 0;

    CV_WRAP virtual AKAZE::DescriptorType getDescriptorType() const = 0;

    CV_WRAP virtual void setDescriptorSize(int dsize) = 0;

    CV_WRAP virtual int getDescriptorSize() const = 0;

    CV_WRAP virtual void setDescriptorChannels(int dch) = 0;

    CV_WRAP virtual int getDescriptorChannels() const = 0;

    CV_WRAP virtual void setThreshold(double threshold) = 0;

    CV_WRAP virtual double getThreshold() const = 0;

    CV_WRAP virtual void setNOctaves(int octaves) = 0;

    CV_WRAP virtual int getNOctaves() const = 0;

    CV_WRAP virtual void setNOctaveLayers(int octaveLayers) = 0;

    CV_WRAP virtual int getNOctaveLayers() const = 0;

    CV_WRAP virtual void setDiffusivity(KAZE::DiffusivityType diff) = 0;

    CV_WRAP virtual KAZE::DiffusivityType getDiffusivity() const = 0;

    CV_WRAP virtual String getDefaultName() const CV_OVERRIDE;

    CV_WRAP virtual void setMaxPoints(int max_points) = 0;

    CV_WRAP virtual int getMaxPoints() const = 0;

};

/****************************************************************************************\

*                                      Distance                                          *

\****************************************************************************************/

template<typename T>

struct CV_EXPORTS Accumulator

{

    typedef T Type;

};

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

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

template<> struct Accumulator<char>   { typedef float Type; };

template<> struct Accumulator<short>  { typedef float Type; };

/*

 * Squared Euclidean distance functor

 */

template<class T>

struct CV_EXPORTS SL2

{

    static const NormTypes normType = NORM_L2SQR;

    typedef T ValueType;

    typedef typename Accumulator<T>::Type ResultType;

    ResultType operator()( const T* a, const T* b, int size ) const

    {

        return normL2Sqr<ValueType, ResultType>(a, b, size);

    }

};

/*

 * Euclidean distance functor

 */

template<class T>

struct L2

{

    static const NormTypes normType = NORM_L2;

    typedef T ValueType;

    typedef typename Accumulator<T>::Type ResultType;

    ResultType operator()( const T* a, const T* b, int size ) const

    {

        return (ResultType)std::sqrt((double)normL2Sqr<ValueType, ResultType>(a, b, size));

    }

};

/*

 * Manhattan distance (city block distance) functor

 */

template<class T>

struct L1

{

    static const NormTypes normType = NORM_L1;

    typedef T ValueType;

    typedef typename Accumulator<T>::Type ResultType;

    ResultType operator()( const T* a, const T* b, int size ) const

    {

        return normL1<ValueType, ResultType>(a, b, size);

    }

};

//! @} features2d_main

/****************************************************************************************\

*                                  DescriptorMatcher                                     *

\****************************************************************************************/

//! @addtogroup features2d_match

//! @{

/** @brief Abstract base class for matching keypoint descriptors.

It has two groups of match methods: for matching descriptors of an image with another image or with

an image set.

 */

class CV_EXPORTS_W DescriptorMatcher : public Algorithm

{

public:

   enum MatcherType

    {

        FLANNBASED            = 1,

        BRUTEFORCE            = 2,

        BRUTEFORCE_L1         = 3,

        BRUTEFORCE_HAMMING    = 4,

        BRUTEFORCE_HAMMINGLUT = 5,

        BRUTEFORCE_SL2        = 6

    };

    virtual ~DescriptorMatcher();

    /** @brief Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor

    collection.

    If the collection is not empty, the new descriptors are added to existing train descriptors.

    @param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same

    train image.

     */

    CV_WRAP virtual void add( InputArrayOfArrays descriptors );

    /** @brief Returns a constant link to the train descriptor collection trainDescCollection .

     */

    CV_WRAP const std::vector<Mat>& getTrainDescriptors() const;

    /** @brief Clears the train descriptor collections.

     */

    CV_WRAP virtual void clear() CV_OVERRIDE;

    /** @brief Returns true if there are no train descriptors in the both collections.

     */

    CV_WRAP virtual bool empty() const CV_OVERRIDE;

    /** @brief Returns true if the descriptor matcher supports masking permissible matches.

     */

    CV_WRAP virtual bool isMaskSupported() const = 0;

    /** @brief Trains a descriptor matcher

    Trains a descriptor matcher (for example, the flann index). In all methods to match, the method

    train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher)

    have an empty implementation of this method. Other matchers really train their inner structures (for

    example, FlannBasedMatcher trains flann::Index ).

     */

    CV_WRAP virtual void train();

    /** @brief Finds the best match for each descriptor from a query set.

    @param queryDescriptors Query set of descriptors.

    @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors

    collection stored in the class object.

    @param matches Matches. If a query descriptor is masked out in mask , no match is added for this

    descriptor. So, matches size may be smaller than the query descriptors count.

    @param mask Mask specifying permissible matches between an input query and train matrices of

    descriptors.

    In the first variant of this method, the train descriptors are passed as an input argument. In the

    second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is

    used. Optional mask (or masks) can be passed to specify which query and training descriptors can be

    matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if

    mask.at\<uchar\>(i,j) is non-zero.

     */

    CV_WRAP void match( InputArray queryDescriptors, InputArray trainDescriptors,

                CV_OUT std::vector<DMatch>& matches, InputArray mask=noArray() ) const;

    /** @brief Finds the k best matches for each descriptor from a query set.

    @param queryDescriptors Query set of descriptors.

    @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors

    collection stored in the class object.

    @param mask Mask specifying permissible matches between an input query and train matrices of

    descriptors.

    @param matches Matches. Each matches[i] is k or less matches for the same query descriptor.

    @param k Count of best matches found per each query descriptor or less if a query descriptor has

    less than k possible matches in total.

    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is

    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,

    the matches vector does not contain matches for fully masked-out query descriptors.

    These extended variants of DescriptorMatcher::match methods find several best matches for each query

    descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match

    for the details about query and train descriptors.

     */

    CV_WRAP void knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,

                   CV_OUT std::vector<std::vector<DMatch> >& matches, int k,

                   InputArray mask=noArray(), bool compactResult=false ) const;

    /** @brief For each query descriptor, finds the training descriptors not farther than the specified distance.

    @param queryDescriptors Query set of descriptors.

    @param trainDescriptors Train set of descriptors. This set is not added to the train descriptors

    collection stored in the class object.

    @param matches Found matches.

    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is

    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,

    the matches vector does not contain matches for fully masked-out query descriptors.

    @param maxDistance Threshold for the distance between matched descriptors. Distance means here

    metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured

    in Pixels)!

    @param mask Mask specifying permissible matches between an input query and train matrices of

    descriptors.

    For each query descriptor, the methods find such training descriptors that the distance between the

    query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are

    returned in the distance increasing order.

     */

    CV_WRAP void radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors,

                      CV_OUT std::vector<std::vector<DMatch> >& matches, float maxDistance,

                      InputArray mask=noArray(), bool compactResult=false ) const;

    /** @overload

    @param queryDescriptors Query set of descriptors.

    @param matches Matches. If a query descriptor is masked out in mask , no match is added for this

    descriptor. So, matches size may be smaller than the query descriptors count.

    @param masks Set of masks. Each masks[i] specifies permissible matches between the input query

    descriptors and stored train descriptors from the i-th image trainDescCollection[i].

    */

    CV_WRAP void match( InputArray queryDescriptors, CV_OUT std::vector<DMatch>& matches,

                        InputArrayOfArrays masks=noArray() );

    /** @overload

    @param queryDescriptors Query set of descriptors.

    @param matches Matches. Each matches[i] is k or less matches for the same query descriptor.

    @param k Count of best matches found per each query descriptor or less if a query descriptor has

    less than k possible matches in total.

    @param masks Set of masks. Each masks[i] specifies permissible matches between the input query

    descriptors and stored train descriptors from the i-th image trainDescCollection[i].

    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is

    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,

    the matches vector does not contain matches for fully masked-out query descriptors.

    */

    CV_WRAP void knnMatch( InputArray queryDescriptors, CV_OUT std::vector<std::vector<DMatch> >& matches, int k,

                           InputArrayOfArrays masks=noArray(), bool compactResult=false );

    /** @overload

    @param queryDescriptors Query set of descriptors.

    @param matches Found matches.

    @param maxDistance Threshold for the distance between matched descriptors. Distance means here

    metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured

    in Pixels)!

    @param masks Set of masks. Each masks[i] specifies permissible matches between the input query

    descriptors and stored train descriptors from the i-th image trainDescCollection[i].

    @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is

    false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,