fast-yolo4/3rdparty/opencv/inc/opencv2/xfeatures2d/cuda.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_XFEATURES2D_CUDA_HPP__
#define __OPENCV_XFEATURES2D_CUDA_HPP__

#include "opencv2/core/cuda.hpp"

namespace cv { namespace cuda {

//! @addtogroup xfeatures2d_nonfree
//! @{

/** @brief Class used for extracting Speeded Up Robust Features (SURF) from an image. :

The class SURF_CUDA implements Speeded Up Robust Features descriptor. There is a fast multi-scale
Hessian keypoint detector that can be used to find the keypoints (which is the default option). But
the descriptors can also be computed for the user-specified keypoints. Only 8-bit grayscale images
are supported.

The class SURF_CUDA can store results in the GPU and CPU memory. It provides functions to convert
results between CPU and GPU version ( uploadKeypoints, downloadKeypoints, downloadDescriptors ). The
format of CPU results is the same as SURF results. GPU results are stored in GpuMat. The keypoints
matrix is \f$\texttt{nFeatures} \times 7\f$ matrix with the CV_32FC1 type.

-   keypoints.ptr\<float\>(X_ROW)[i] contains x coordinate of the i-th feature.
-   keypoints.ptr\<float\>(Y_ROW)[i] contains y coordinate of the i-th feature.
-   keypoints.ptr\<float\>(LAPLACIAN_ROW)[i] contains the laplacian sign of the i-th feature.
-   keypoints.ptr\<float\>(OCTAVE_ROW)[i] contains the octave of the i-th feature.
-   keypoints.ptr\<float\>(SIZE_ROW)[i] contains the size of the i-th feature.
-   keypoints.ptr\<float\>(ANGLE_ROW)[i] contain orientation of the i-th feature.
-   keypoints.ptr\<float\>(HESSIAN_ROW)[i] contains the response of the i-th feature.

The descriptors matrix is \f$\texttt{nFeatures} \times \texttt{descriptorSize}\f$ matrix with the
CV_32FC1 type.

The class SURF_CUDA uses some buffers and provides access to it. All buffers can be safely released
between function calls.

@sa SURF

@note
   -   An example for using the SURF keypoint matcher on GPU can be found at
        opencv_source_code/samples/gpu/surf_keypoint_matcher.cpp

 */
class CV_EXPORTS_W SURF_CUDA
{
public:
    enum KeypointLayout
    {
        X_ROW = 0,
        Y_ROW,
        LAPLACIAN_ROW,
        OCTAVE_ROW,
        SIZE_ROW,
        ANGLE_ROW,
        HESSIAN_ROW,
        ROWS_COUNT
    };

    //! the default constructor
    SURF_CUDA();
    //! the full constructor taking all the necessary parameters
    explicit SURF_CUDA(double _hessianThreshold, int _nOctaves=4,
         int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f, bool _upright = false);

    /**
    @param _hessianThreshold Threshold for hessian keypoint detector used in SURF.
    @param _nOctaves Number of pyramid octaves the keypoint detector will use.
    @param _nOctaveLayers Number of octave layers within each octave.
    @param _extended Extended descriptor flag (true - use extended 128-element descriptors; false - use
    64-element descriptors).
    @param _keypointsRatio
    @param _upright Up-right or rotated features flag (true - do not compute orientation of features;
    false - compute orientation).
    */
    CV_WRAP static Ptr<SURF_CUDA> create(double _hessianThreshold, int _nOctaves = 4,
        int _nOctaveLayers = 2, bool _extended = false, float _keypointsRatio = 0.01f, bool _upright = false);

    //! returns the descriptor size in float's (64 or 128)
    CV_WRAP int descriptorSize() const;
    //! returns the default norm type
    CV_WRAP int defaultNorm() const;

    //! upload host keypoints to device memory
    void uploadKeypoints(const std::vector<KeyPoint>& keypoints, GpuMat& keypointsGPU);
    //! download keypoints from device to host memory
    CV_WRAP void downloadKeypoints(const GpuMat& keypointsGPU, CV_OUT std::vector<KeyPoint>& keypoints);

    //! download descriptors from device to host memory
    void downloadDescriptors(const GpuMat& descriptorsGPU, std::vector<float>& descriptors);

    //! finds the keypoints using fast hessian detector used in SURF
    //! supports CV_8UC1 images
    //! keypoints will have nFeature cols and 6 rows
    //! keypoints.ptr<float>(X_ROW)[i] will contain x coordinate of i'th feature
    //! keypoints.ptr<float>(Y_ROW)[i] will contain y coordinate of i'th feature
    //! keypoints.ptr<float>(LAPLACIAN_ROW)[i] will contain laplacian sign of i'th feature
    //! keypoints.ptr<float>(OCTAVE_ROW)[i] will contain octave of i'th feature
    //! keypoints.ptr<float>(SIZE_ROW)[i] will contain size of i'th feature
    //! keypoints.ptr<float>(ANGLE_ROW)[i] will contain orientation of i'th feature
    //! keypoints.ptr<float>(HESSIAN_ROW)[i] will contain response of i'th feature
    void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints);
    //! finds the keypoints and computes their descriptors.
    //! Optionally it can compute descriptors for the user-provided keypoints and recompute keypoints direction
    void operator()(const GpuMat& img, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors,
        bool useProvidedKeypoints = false);

    /** @brief Finds the keypoints using fast hessian detector used in SURF

    @param img Source image, currently supports only CV_8UC1 images.
    @param mask A mask image same size as src and of type CV_8UC1.
    @param keypoints Detected keypoints.
     */
    CV_WRAP inline void detect(const GpuMat& img, const GpuMat& mask, CV_OUT GpuMat& keypoints) {
        (*this)(img, mask, keypoints);
    }

    void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
    void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors,
        bool useProvidedKeypoints = false);

    /** @brief Finds the keypoints and computes their descriptors using fast hessian detector used in SURF

    @param img Source image, currently supports only CV_8UC1 images.
    @param mask A mask image same size as src and of type CV_8UC1.
    @param keypoints Detected keypoints.
    @param descriptors Keypoint descriptors.
    @param useProvidedKeypoints Compute descriptors for the user-provided keypoints and recompute keypoints direction.
     */
    CV_WRAP inline void detectWithDescriptors(const GpuMat& img, const GpuMat& mask, CV_OUT GpuMat& keypoints, CV_OUT GpuMat& descriptors,
        bool useProvidedKeypoints = false) {
        (*this)(img, mask, keypoints, descriptors, useProvidedKeypoints);
    }

    void operator()(const GpuMat& img, const GpuMat& mask, std::vector<KeyPoint>& keypoints, std::vector<float>& descriptors,
        bool useProvidedKeypoints = false);

    void releaseMemory();

    // SURF parameters
    CV_PROP double hessianThreshold;
    CV_PROP int nOctaves;
    CV_PROP int nOctaveLayers;
    CV_PROP bool extended;
    CV_PROP bool upright;

    //! max keypoints = min(keypointsRatio * img.size().area(), 65535)
    CV_PROP float keypointsRatio;

    GpuMat sum, mask1, maskSum;

    GpuMat det, trace;

    GpuMat maxPosBuffer;
};

//! @}

}} // namespace cv { namespace cuda {

#endif // __OPENCV_XFEATURES2D_CUDA_HPP__