opencv_contrib/modules/xfeatures2d/include/opencv2/xfeatures2d.hpp

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#ifndef __OPENCV_XFEATURES2D_HPP__
#define __OPENCV_XFEATURES2D_HPP__

#include "opencv2/features2d.hpp"
#include "opencv2/xfeatures2d/nonfree.hpp"

/** @defgroup xfeatures2d Extra 2D Features Framework
@{
    @defgroup xfeatures2d_experiment Experimental 2D Features Algorithms

This section describes experimental algorithms for 2d feature detection.

    @defgroup xfeatures2d_nonfree Non-free 2D Features Algorithms

This section describes two popular algorithms for 2d feature detection, SIFT and SURF, that are
known to be patented. Use them at your own risk.

@}
*/

namespace cv
{
namespace xfeatures2d
{

//! @addtogroup xfeatures2d_experiment
//! @{

/** @brief Class implementing the FREAK (*Fast Retina Keypoint*) keypoint descriptor, described in @cite AOV12 .

The algorithm propose a novel keypoint descriptor inspired by the human visual system and more
precisely the retina, coined Fast Retina Key- point (FREAK). A cascade of binary strings is
computed by efficiently comparing image intensities over a retinal sampling pattern. FREAKs are in
general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK.
They are competitive alternatives to existing keypoints in particular for embedded applications.

@note
   -   An example on how to use the FREAK descriptor can be found at
        opencv_source_code/samples/cpp/freak_demo.cpp
 */
class CV_EXPORTS_W FREAK : public Feature2D
{
public:

    enum
    {
        NB_SCALES = 64, NB_PAIRS = 512, NB_ORIENPAIRS = 45
    };

    /**
    @param orientationNormalized Enable orientation normalization.
    @param scaleNormalized Enable scale normalization.
    @param patternScale Scaling of the description pattern.
    @param nOctaves Number of octaves covered by the detected keypoints.
    @param selectedPairs (Optional) user defined selected pairs indexes,
     */
    CV_WRAP static Ptr<FREAK> create(bool orientationNormalized = true,
                             bool scaleNormalized = true,
                             float patternScale = 22.0f,
                             int nOctaves = 4,
                             const std::vector<int>& selectedPairs = std::vector<int>());
};


/** @brief The class implements the keypoint detector introduced by @cite Agrawal08, synonym of StarDetector. :
 */
class CV_EXPORTS_W StarDetector : public Feature2D
{
public:
    //! the full constructor
    CV_WRAP static Ptr<StarDetector> create(int maxSize=45, int responseThreshold=30,
                         int lineThresholdProjected=10,
                         int lineThresholdBinarized=8,
                         int suppressNonmaxSize=5);
};

/*
 * BRIEF Descriptor
 */

/** @brief Class for computing BRIEF descriptors described in @cite calon2010 .

@note
   -   A complete BRIEF extractor sample can be found at
        opencv_source_code/samples/cpp/brief_match_test.cpp

 */
class CV_EXPORTS_W BriefDescriptorExtractor : public Feature2D
{
public:
    CV_WRAP static Ptr<BriefDescriptorExtractor> create( int bytes = 32 );
};

/** @brief Class implementing the locally uniform comparison image descriptor, described in @cite LUCID

An image descriptor that can be computed very fast, while being
about as robust as, for example, SURF or BRIEF.
 */
class CV_EXPORTS_W LUCID : public Feature2D
{
public:
    /**
     * @param lucid_kernel kernel for descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth
     * @param blur_kernel kernel for blurring image prior to descriptor construction, where 1=3x3, 2=5x5, 3=7x7 and so forth
     */
    CV_WRAP static Ptr<LUCID> create(const int lucid_kernel, const int blur_kernel);
};



//! @}

}
}

#endif