opencv/modules/cudafeatures2d/include/opencv2/cudafeatures2d.hpp

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#ifndef __OPENCV_CUDAFEATURES2D_HPP__
#define __OPENCV_CUDAFEATURES2D_HPP__

#ifndef __cplusplus
#  error cudafeatures2d.hpp header must be compiled as C++
#endif

#include "opencv2/core/cuda.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/cudafilters.hpp"

/**
  @addtogroup cuda
  @{
    @defgroup cudafeatures2d Feature Detection and Description
  @}
 */

namespace cv { namespace cuda {

//! @addtogroup cudafeatures2d
//! @{

/** @brief 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. This descriptor matcher supports masking permissible matches between descriptor
sets.

The class BFMatcher_CUDA has an interface similar to the class DescriptorMatcher. It has two groups
of match methods: for matching descriptors of one image with another image or with an image set.
Also, all functions have an alternative to save results either to the GPU memory or to the CPU
memory.

@sa DescriptorMatcher, BFMatcher
 */
class CV_EXPORTS BFMatcher_CUDA
{
public:
    explicit BFMatcher_CUDA(int norm = cv::NORM_L2);

    //! Add descriptors to train descriptor collection
    void add(const std::vector<GpuMat>& descCollection);

    //! Get train descriptors collection
    const std::vector<GpuMat>& getTrainDescriptors() const;

    //! Clear train descriptors collection
    void clear();

    //! Return true if there are not train descriptors in collection
    bool empty() const;

    //! Return true if the matcher supports mask in match methods
    bool isMaskSupported() const;

    //! Find one best match for each query descriptor
    void matchSingle(const GpuMat& query, const GpuMat& train,
        GpuMat& trainIdx, GpuMat& distance,
        const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());

    //! Download trainIdx and distance and convert it to CPU vector with DMatch
    static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
    //! Convert trainIdx and distance to vector with DMatch
    static void matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches);

    //! Find one best match for each query descriptor
    void match(const GpuMat& query, const GpuMat& train, std::vector<DMatch>& matches, const GpuMat& mask = GpuMat());

    //! Make gpu collection of trains and masks in suitable format for matchCollection function
    void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection, const std::vector<GpuMat>& masks = std::vector<GpuMat>());

    //! Find one best match from train collection for each query descriptor
    void matchCollection(const GpuMat& query, const GpuMat& trainCollection,
        GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
        const GpuMat& masks = GpuMat(), Stream& stream = Stream::Null());

    //! Download trainIdx, imgIdx and distance and convert it to vector with DMatch
    static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches);
    //! Convert trainIdx, imgIdx and distance to vector with DMatch
    static void matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches);

    //! Find one best match from train collection for each query descriptor.
    void match(const GpuMat& query, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks = std::vector<GpuMat>());

    //! Find k best matches for each query descriptor (in increasing order of distances)
    void knnMatchSingle(const GpuMat& query, const GpuMat& train,
        GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
        const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());

    //! Download trainIdx and distance and convert it to vector with DMatch
    //! compactResult is used when mask is not empty. If compactResult is false matches
    //! vector will have the same size as queryDescriptors rows. If compactResult is true
    //! matches vector will not contain matches for fully masked out query descriptors.
    static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
    //! Convert trainIdx and distance to vector with DMatch
    static void knnMatchConvert(const Mat& trainIdx, const Mat& distance,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);

    //! Find k 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 queryDescriptors rows. If compactResult is true
    //! matches vector will not contain matches for fully masked out query descriptors.
    void knnMatch(const GpuMat& query, const GpuMat& train,
        std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
        bool compactResult = false);

    //! Find k best matches from train collection for each query descriptor (in increasing order of distances)
    void knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
        GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
        const GpuMat& maskCollection = GpuMat(), Stream& stream = Stream::Null());

    //! Download trainIdx and distance and convert it to vector with DMatch
    //! compactResult is used when mask is not empty. If compactResult is false matches
    //! vector will have the same size as queryDescriptors rows. If compactResult is true
    //! matches vector will not contain matches for fully masked out query descriptors.
    //! @see BFMatcher_CUDA::knnMatchDownload
    static void knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
    //! Convert trainIdx and distance to vector with DMatch
    //! @see BFMatcher_CUDA::knnMatchConvert
    static void knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);

    //! Find k 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 queryDescriptors rows. If compactResult is true
    //! matches vector will not contain matches for fully masked out query descriptors.
    void knnMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k,
        const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);

    //! Find best matches for each query descriptor which have distance less than maxDistance.
    //! nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
    //! carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
    //! because it didn't have enough memory.
    //! If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
    //! otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
    //! Matches doesn't sorted.
    void radiusMatchSingle(const GpuMat& query, const GpuMat& train,
        GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
        const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());

    //! Download trainIdx, nMatches and distance and convert it to vector with DMatch.
    //! matches will be sorted 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 queryDescriptors rows. If compactResult is true
    //! matches vector will not contain matches for fully masked out query descriptors.
    static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
    //! Convert trainIdx, nMatches and distance to vector with DMatch.
    static void radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);

    //! Find best matches for each query descriptor which have distance less than maxDistance
    //! in increasing order of distances).
    void radiusMatch(const GpuMat& query, const GpuMat& train,
        std::vector< std::vector<DMatch> >& matches, float maxDistance,
        const GpuMat& mask = GpuMat(), bool compactResult = false);

    //! Find best matches for each query descriptor which have distance less than maxDistance.
    //! If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
    //! otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
    //! Matches doesn't sorted.
    void radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
        const std::vector<GpuMat>& masks = std::vector<GpuMat>(), Stream& stream = Stream::Null());

    //! Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
    //! matches will be sorted 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 queryDescriptors rows. If compactResult is true
    //! matches vector will not contain matches for fully masked out query descriptors.
    static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
    //! Convert trainIdx, nMatches and distance to vector with DMatch.
    static void radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
        std::vector< std::vector<DMatch> >& matches, bool compactResult = false);

    //! Find best matches from train collection for each query descriptor which have distance less than
    //! maxDistance (in increasing order of distances).
    void radiusMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
        const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);

    int norm;

private:
    std::vector<GpuMat> trainDescCollection;
};

//
// Feature2DAsync
//

class CV_EXPORTS Feature2DAsync
{
public:
    virtual ~Feature2DAsync();

    virtual void detectAsync(InputArray image,
                             OutputArray keypoints,
                             InputArray mask = noArray(),
                             Stream& stream = Stream::Null());

    virtual void computeAsync(InputArray image,
                              OutputArray keypoints,
                              OutputArray descriptors,
                              Stream& stream = Stream::Null());

    virtual void detectAndComputeAsync(InputArray image,
                                       InputArray mask,
                                       OutputArray keypoints,
                                       OutputArray descriptors,
                                       bool useProvidedKeypoints=false,
                                       Stream& stream = Stream::Null());

    virtual void convert(InputArray gpu_keypoints,
                         std::vector<KeyPoint>& keypoints) = 0;
};

//
// FastFeatureDetector
//

class CV_EXPORTS FastFeatureDetector : public cv::FastFeatureDetector, public Feature2DAsync
{
public:
    enum
    {
        LOCATION_ROW = 0,
        RESPONSE_ROW,
        ROWS_COUNT,

        FEATURE_SIZE = 7
    };

    static Ptr<FastFeatureDetector> create(int threshold=10,
                                           bool nonmaxSuppression=true,
                                           int type=FastFeatureDetector::TYPE_9_16,
                                           int max_npoints = 5000);

    virtual void setMaxNumPoints(int max_npoints) = 0;
    virtual int getMaxNumPoints() const = 0;
};

//
// ORB
//

class CV_EXPORTS ORB : public cv::ORB, public Feature2DAsync
{
public:
    enum
    {
        X_ROW = 0,
        Y_ROW,
        RESPONSE_ROW,
        ANGLE_ROW,
        OCTAVE_ROW,
        SIZE_ROW,
        ROWS_COUNT
    };

    static Ptr<ORB> create(int nfeatures=500,
                           float scaleFactor=1.2f,
                           int nlevels=8,
                           int edgeThreshold=31,
                           int firstLevel=0,
                           int WTA_K=2,
                           int scoreType=ORB::HARRIS_SCORE,
                           int patchSize=31,
                           int fastThreshold=20,
                           bool blurForDescriptor=false);

    //! if true, image will be blurred before descriptors calculation
    virtual void setBlurForDescriptor(bool blurForDescriptor) = 0;
    virtual bool getBlurForDescriptor() const = 0;
};

//! @}

}} // namespace cv { namespace cuda {

#endif /* __OPENCV_CUDAFEATURES2D_HPP__ */