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/cudafilters.hpp"

namespace cv { namespace cuda {

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.
    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
    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;
};

class CV_EXPORTS FAST_CUDA
{
public:
    enum
    {
        LOCATION_ROW = 0,
        RESPONSE_ROW,
        ROWS_COUNT
    };

    // all features have same size
    static const int FEATURE_SIZE = 7;

    explicit FAST_CUDA(int threshold, bool nonmaxSuppression = true, double keypointsRatio = 0.05);

    //! finds the keypoints using FAST detector
    //! supports only CV_8UC1 images
    void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
    void operator ()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);

    //! download keypoints from device to host memory
    static void downloadKeypoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);

    //! convert keypoints to KeyPoint vector
    static void convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints);

    //! release temporary buffer's memory
    void release();

    bool nonmaxSuppression;

    int threshold;

    //! max keypoints = keypointsRatio * img.size().area()
    double keypointsRatio;

    //! find keypoints and compute it's response if nonmaxSuppression is true
    //! return count of detected keypoints
    int calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask);

    //! get final array of keypoints
    //! performs nonmax suppression if needed
    //! return final count of keypoints
    int getKeyPoints(GpuMat& keypoints);

private:
    GpuMat kpLoc_;
    int count_;

    GpuMat score_;

    GpuMat d_keypoints_;
};

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

    enum
    {
        DEFAULT_FAST_THRESHOLD = 20
    };

    //! Constructor
    explicit ORB_CUDA(int nFeatures = 500, float scaleFactor = 1.2f, int nLevels = 8, int edgeThreshold = 31,
                     int firstLevel = 0, int WTA_K = 2, int scoreType = 0, int patchSize = 31);

    //! Compute the ORB features on an image
    //! image - the image to compute the features (supports only CV_8UC1 images)
    //! mask - the mask to apply
    //! keypoints - the resulting keypoints
    void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
    void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);

    //! Compute the ORB features and descriptors on an image
    //! image - the image to compute the features (supports only CV_8UC1 images)
    //! mask - the mask to apply
    //! keypoints - the resulting keypoints
    //! descriptors - descriptors array
    void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors);
    void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors);

    //! download keypoints from device to host memory
    static void downloadKeyPoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
    //! convert keypoints to KeyPoint vector
    static void convertKeyPoints(const Mat& d_keypoints, std::vector<KeyPoint>& keypoints);

    //! returns the descriptor size in bytes
    inline int descriptorSize() const { return kBytes; }

    inline void setFastParams(int threshold, bool nonmaxSuppression = true)
    {
        fastDetector_.threshold = threshold;
        fastDetector_.nonmaxSuppression = nonmaxSuppression;
    }

    //! release temporary buffer's memory
    void release();

    //! if true, image will be blurred before descriptors calculation
    bool blurForDescriptor;

private:
    enum { kBytes = 32 };

    void buildScalePyramids(const GpuMat& image, const GpuMat& mask);

    void computeKeyPointsPyramid();

    void computeDescriptors(GpuMat& descriptors);

    void mergeKeyPoints(GpuMat& keypoints);

    int nFeatures_;
    float scaleFactor_;
    int nLevels_;
    int edgeThreshold_;
    int firstLevel_;
    int WTA_K_;
    int scoreType_;
    int patchSize_;

    // The number of desired features per scale
    std::vector<size_t> n_features_per_level_;

    // Points to compute BRIEF descriptors from
    GpuMat pattern_;

    std::vector<GpuMat> imagePyr_;
    std::vector<GpuMat> maskPyr_;

    GpuMat buf_;

    std::vector<GpuMat> keyPointsPyr_;
    std::vector<int> keyPointsCount_;

    FAST_CUDA fastDetector_;

    Ptr<cuda::Filter> blurFilter;

    GpuMat d_keypoints_;
};

}} // namespace cv { namespace cuda {

#endif /* __OPENCV_CUDAFEATURES2D_HPP__ */