gpu.hpp

/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                           License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's in binary form must reproduce the above copyright notice,
//     this list of conditions and the following disclaimer in the documentation
//     and/or other materials provided with the distribution.
//
//   * The name of the copyright holders may not be used to endorse or promote products
//     derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/

#ifndef __OPENCV_GPU_HPP__
#define __OPENCV_GPU_HPP__

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

#include "opencv2/core/gpu.hpp"

#if !defined(__OPENCV_BUILD) && !defined(OPENCV_GPU_SKIP_INCLUDE)
    #include "opencv2/opencv_modules.hpp"

    #ifdef HAVE_OPENCV_GPUARITHM
        #include "opencv2/gpuarithm.hpp"
    #endif

    #ifdef HAVE_OPENCV_GPUWARPING
        #include "opencv2/gpuwarping.hpp"
    #endif

    #ifdef HAVE_OPENCV_GPUFILTERS
        #include "opencv2/gpufilters.hpp"
    #endif

    #ifdef HAVE_OPENCV_GPUIMGPROC
        #include "opencv2/gpuimgproc.hpp"
    #endif

    #ifdef HAVE_OPENCV_GPUFEATURES2D
        #include "opencv2/gpufeatures2d.hpp"
    #endif

    #ifdef HAVE_OPENCV_GPUOPTFLOW
        #include "opencv2/gpuoptflow.hpp"
    #endif

    #ifdef HAVE_OPENCV_GPUBGSEGM
        #include "opencv2/gpubgsegm.hpp"
    #endif

    #ifdef HAVE_OPENCV_GPUSTEREO
        #include "opencv2/gpustereo.hpp"
    #endif

    #ifdef HAVE_OPENCV_GPUCODEC
        #include "opencv2/gpucodec.hpp"
    #endif
#endif

namespace cv { namespace gpu {

//////////////// 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;
};

//////////////////////////// CascadeClassifier ////////////////////////////

// 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;
};

//////////////////////////// Labeling ////////////////////////////

//!performs labeling via graph cuts of a 2D regular 4-connected graph.
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& bottom, GpuMat& labels,
                         GpuMat& buf, Stream& stream = Stream::Null());

//!performs labeling via graph cuts of a 2D regular 8-connected graph.
CV_EXPORTS void graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
                         GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight,
                         GpuMat& labels,
                         GpuMat& buf, Stream& stream = Stream::Null());

//! compute mask for Generalized Flood fill componetns labeling.
CV_EXPORTS void connectivityMask(const GpuMat& image, GpuMat& mask, const cv::Scalar& lo, const cv::Scalar& hi, Stream& stream = Stream::Null());

//! performs connected componnents labeling.
CV_EXPORTS void labelComponents(const GpuMat& mask, GpuMat& components, int flags = 0, Stream& stream = Stream::Null());

//////////////////////////// Calib3d ////////////////////////////

CV_EXPORTS void transformPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
                                GpuMat& dst, Stream& stream = Stream::Null());

CV_EXPORTS void projectPoints(const GpuMat& src, const Mat& rvec, const Mat& tvec,
                              const Mat& camera_mat, const Mat& dist_coef, GpuMat& dst,
                              Stream& stream = Stream::Null());

CV_EXPORTS void solvePnPRansac(const Mat& object, const Mat& image, const Mat& camera_mat,
                               const Mat& dist_coef, Mat& rvec, Mat& tvec, bool use_extrinsic_guess=false,
                               int num_iters=100, float max_dist=8.0, int min_inlier_count=100,
                               std::vector<int>* inliers=NULL);

//////////////////////////// VStab ////////////////////////////

//! 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 cv { namespace gpu {

#endif /* __OPENCV_GPU_HPP__ */