opencv/modules/gpu/include/opencv2/gpu.hpp

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#ifndef __OPENCV_GPU_HPP__
#define __OPENCV_GPU_HPP__

#ifndef SKIP_INCLUDES
#include <vector>
#include <memory>
#include <iosfwd>
#endif

#include "opencv2/core/gpumat.hpp"
#include "opencv2/gpuarithm.hpp"
#include "opencv2/gpufilters.hpp"
#include "opencv2/gpuimgproc.hpp"
#include "opencv2/gpufeatures2d.hpp"
#include "opencv2/gpuvideo.hpp"
#include "opencv2/gpucalib3d.hpp"

#include "opencv2/imgproc.hpp"
#include "opencv2/objdetect.hpp"
#include "opencv2/features2d.hpp"

namespace cv { namespace gpu {
////////////////////////////// Image processing //////////////////////////////




///////////////////////////// Calibration 3D //////////////////////////////////

//////////////////////////////// Image Labeling ////////////////////////////////



////////////////////////////////// Histograms //////////////////////////////////



//////////////////////////////// StereoBM_GPU ////////////////////////////////



////////////////////////// StereoBeliefPropagation ///////////////////////////


/////////////////////////// StereoConstantSpaceBP ///////////////////////////



/////////////////////////// DisparityBilateralFilter ///////////////////////////



//////////////// HOG (Histogram-of-Oriented-Gradients) Descriptor and Object Detector //////////////
struct CV_EXPORTS HOGConfidence
{
   double scale;
   std::vector<Point> locations;
   std::vector<double> confidences;
   std::vector<double> part_scores[4];
};

struct CV_EXPORTS HOGDescriptor
{
    enum { DEFAULT_WIN_SIGMA = -1 };
    enum { DEFAULT_NLEVELS = 64 };
    enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };

    HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
                  Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
                  int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
                  double threshold_L2hys=0.2, bool gamma_correction=true,
                  int nlevels=DEFAULT_NLEVELS);

    size_t getDescriptorSize() const;
    size_t getBlockHistogramSize() const;

    void setSVMDetector(const std::vector<float>& detector);

    static std::vector<float> getDefaultPeopleDetector();
    static std::vector<float> getPeopleDetector48x96();
    static std::vector<float> getPeopleDetector64x128();

    void detect(const GpuMat& img, std::vector<Point>& found_locations,
                double hit_threshold=0, Size win_stride=Size(),
                Size padding=Size());

    void detectMultiScale(const GpuMat& img, std::vector<Rect>& found_locations,
                          double hit_threshold=0, Size win_stride=Size(),
                          Size padding=Size(), double scale0=1.05,
                          int group_threshold=2);

    void computeConfidence(const GpuMat& img, std::vector<Point>& hits, double hit_threshold,
                                                Size win_stride, Size padding, std::vector<Point>& locations, std::vector<double>& confidences);

    void computeConfidenceMultiScale(const GpuMat& img, std::vector<Rect>& found_locations,
                                                                    double hit_threshold, Size win_stride, Size padding,
                                                                    std::vector<HOGConfidence> &conf_out, int group_threshold);

    void getDescriptors(const GpuMat& img, Size win_stride,
                        GpuMat& descriptors,
                        int descr_format=DESCR_FORMAT_COL_BY_COL);

    Size win_size;
    Size block_size;
    Size block_stride;
    Size cell_size;
    int nbins;
    double win_sigma;
    double threshold_L2hys;
    bool gamma_correction;
    int nlevels;

protected:
    void computeBlockHistograms(const GpuMat& img);
    void computeGradient(const GpuMat& img, GpuMat& grad, GpuMat& qangle);

    double getWinSigma() const;
    bool checkDetectorSize() const;

    static int numPartsWithin(int size, int part_size, int stride);
    static Size numPartsWithin(Size size, Size part_size, Size stride);

    // Coefficients of the separating plane
    float free_coef;
    GpuMat detector;

    // Results of the last classification step
    GpuMat labels, labels_buf;
    Mat labels_host;

    // Results of the last histogram evaluation step
    GpuMat block_hists, block_hists_buf;

    // Gradients conputation results
    GpuMat grad, qangle, grad_buf, qangle_buf;

    // returns subbuffer with required size, reallocates buffer if nessesary.
    static GpuMat getBuffer(const Size& sz, int type, GpuMat& buf);
    static GpuMat getBuffer(int rows, int cols, int type, GpuMat& buf);

    std::vector<GpuMat> image_scales;
};


////////////////////////////////// BruteForceMatcher //////////////////////////////////



template <class Distance>
class CV_EXPORTS BruteForceMatcher_GPU;

template <typename T>
class CV_EXPORTS BruteForceMatcher_GPU< L1<T> > : public BFMatcher_GPU
{
public:
    explicit BruteForceMatcher_GPU() : BFMatcher_GPU(NORM_L1) {}
    explicit BruteForceMatcher_GPU(L1<T> /*d*/) : BFMatcher_GPU(NORM_L1) {}
};
template <typename T>
class CV_EXPORTS BruteForceMatcher_GPU< L2<T> > : public BFMatcher_GPU
{
public:
    explicit BruteForceMatcher_GPU() : BFMatcher_GPU(NORM_L2) {}
    explicit BruteForceMatcher_GPU(L2<T> /*d*/) : BFMatcher_GPU(NORM_L2) {}
};
template <> class CV_EXPORTS BruteForceMatcher_GPU< Hamming > : public BFMatcher_GPU
{
public:
    explicit BruteForceMatcher_GPU() : BFMatcher_GPU(NORM_HAMMING) {}
    explicit BruteForceMatcher_GPU(Hamming /*d*/) : BFMatcher_GPU(NORM_HAMMING) {}
};

////////////////////////////////// CascadeClassifier_GPU //////////////////////////////////////////
// The cascade classifier class for object detection: supports old haar and new lbp xlm formats and nvbin for haar cascades olny.
class CV_EXPORTS CascadeClassifier_GPU
{
public:
    CascadeClassifier_GPU();
    CascadeClassifier_GPU(const String& filename);
    ~CascadeClassifier_GPU();

    bool empty() const;
    bool load(const String& filename);
    void release();

    /* returns number of detected objects */
    int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4, Size minSize = Size());
    int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4);

    bool findLargestObject;
    bool visualizeInPlace;

    Size getClassifierSize() const;

private:
    struct CascadeClassifierImpl;
    CascadeClassifierImpl* impl;
    struct HaarCascade;
    struct LbpCascade;
    friend class CascadeClassifier_GPU_LBP;
};

////////////////////////////////// FAST //////////////////////////////////////////



////////////////////////////////// ORB //////////////////////////////////////////





















//! removes points (CV_32FC2, single row matrix) with zero mask value
CV_EXPORTS void compactPoints(GpuMat &points0, GpuMat &points1, const GpuMat &mask);

CV_EXPORTS void calcWobbleSuppressionMaps(
        int left, int idx, int right, Size size, const Mat &ml, const Mat &mr,
        GpuMat &mapx, GpuMat &mapy);

} // namespace gpu

} // namespace cv

#endif /* __OPENCV_GPU_HPP__ */