/*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*/ #include "precomp.hpp" using namespace cv; using namespace cv::cuda; #if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) Ptr cv::cuda::ORB::create(int, float, int, int, int, int, int, int, int, bool) { throw_no_cuda(); return Ptr(); } #else /* !defined (HAVE_CUDA) */ namespace cv { namespace cuda { namespace device { namespace orb { int cull_gpu(int* loc, float* response, int size, int n_points, cudaStream_t stream); void HarrisResponses_gpu(PtrStepSzb img, const short2* loc, float* response, const int npoints, int blockSize, float harris_k, cudaStream_t stream); void loadUMax(const int* u_max, int count); void IC_Angle_gpu(PtrStepSzb image, const short2* loc, float* angle, int npoints, int half_k, cudaStream_t stream); void computeOrbDescriptor_gpu(PtrStepb img, const short2* loc, const float* angle, const int npoints, const int* pattern_x, const int* pattern_y, PtrStepb desc, int dsize, int WTA_K, cudaStream_t stream); void mergeLocation_gpu(const short2* loc, float* x, float* y, int npoints, float scale, cudaStream_t stream); } }}} namespace { const float HARRIS_K = 0.04f; const int DESCRIPTOR_SIZE = 32; const int bit_pattern_31_[256 * 4] = { 8,-3, 9,5/*mean (0), correlation (0)*/, 4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/, -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/, 7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/, 2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/, 1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/, -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/, -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/, -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/, 10,4, 11,9/*mean (0.122065), correlation (0.093285)*/, -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/, -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/, 7,7, 12,6/*mean (0.160583), correlation (0.130064)*/, -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/, -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/, -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/, 12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/, -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/, -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/, 11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/, 4,7, 5,1/*mean (0.205106), correlation (0.186848)*/, 5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/, 3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/, -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/, -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/, -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/, -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/, -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/, -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/, 5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/, 5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/, 1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/, 9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/, 4,7, 4,12/*mean (0.131005), correlation (0.257622)*/, 2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/, -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/, -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/, 4,11, 9,12/*mean (0.226226), correlation (0.258255)*/, 0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/, -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/, -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/, -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/, 8,12, 10,7/*mean (0.225337), correlation (0.282851)*/, 0,9, 1,3/*mean (0.226687), correlation (0.278734)*/, 7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/, -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/, 10,7, 12,1/*mean (0.125517), correlation (0.31089)*/, -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/, 10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/, -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/, -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/, 3,3, 7,8/*mean (0.177755), correlation (0.309394)*/, 5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/, -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/, 3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/, 2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/, -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/, -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/, -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/, -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/, 6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/, -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/, -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/, -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/, 3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/, -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/, -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/, 2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/, -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/, -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/, 5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/, -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/, -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/, -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/, 10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/, 7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/, -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/, -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/, 7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/, -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/, -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/, -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/, 7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/, -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/, 1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/, 2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/, -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/, -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/, 7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/, 1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/, 9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/, -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/, -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/, 7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/, 12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/, 6,3, 7,11/*mean (0.1074), correlation (0.413224)*/, 5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/, 2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/, 3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/, 2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/, 9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/, -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/, -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/, 1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/, 6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/, 2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/, 6,3, 11,0/*mean (0.204588), correlation (0.411762)*/, 3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/, 7,8, 9,3/*mean (0.213237), correlation (0.409306)*/, -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/, -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/, -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/, -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/, 8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/, 4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/, -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/, 4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/, -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/, -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/, 7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/, -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/, -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/, 8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/, -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/, 1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/, 7,-4, 9,1/*mean (0.132692), correlation (0.454)*/, -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/, 11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/, -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/, 3,7, 7,12/*mean (0.147627), correlation (0.456643)*/, 5,5, 10,8/*mean (0.152901), correlation (0.455036)*/, 0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/, -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/, 0,7, 2,12/*mean (0.18312), correlation (0.433855)*/, -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/, 5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/, 3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/, -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/, -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/, -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/, 6,5, 8,0/*mean (0.1972), correlation (0.450481)*/, -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/, -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/, 1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/, 4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/, -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/, 2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/, -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/, 4,1, 9,3/*mean (0.23962), correlation (0.444824)*/, -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/, -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/, 7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/, 4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/, -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/, 7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/, 7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/, -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/, -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/, -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/, 2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/, 10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/, -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/, 8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/, 2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/, -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/, -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/, -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/, 5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/, -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/, -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/, -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/, -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/, -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/, 2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/, -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/, -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/, -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/, -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/, 6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/, -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/, 11,11, 12,6/*mean (0.16652), correlation (0.497632)*/, 7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/, -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/, -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/, -7,1, -6,7/*mean (0.175), correlation (0.500024)*/, -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/, -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/, -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/, -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/, -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/, 1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/, 1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/, 9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/, 5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/, -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/, -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/, -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/, -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/, 8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/, 2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/, 7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/, -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/, -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/, 4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/, 3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/, -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/, 5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/, 4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/, -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/, 0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/, -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/, 3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/, -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/, 8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/, -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/, 2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/, 10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/, 6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/, -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/, -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/, -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/, -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/, -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/, 4,2, 12,12/*mean (0.01778), correlation (0.546921)*/, 2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/, 6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/, 3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/, 11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/, -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/, 4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/, 2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/, -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/, -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/, -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/, 6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/, 0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/, -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/, -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/, -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/, 5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/, 2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/, -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/, 9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/, 11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/, 3,0, 3,5/*mean (0.101147), correlation (0.525576)*/, -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/, 3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/, -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/, 5,8, 12,11/*mean (0.114181), correlation (0.555793)*/, 8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/, 7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/, -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/, 7,3, 12,4/*mean (0.122582), correlation (0.555825)*/, 9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/, 7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/, -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/ }; class ORB_Impl : public cv::cuda::ORB { public: ORB_Impl(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize, int fastThreshold, bool blurForDescriptor); virtual void detectAndCompute(InputArray _image, InputArray _mask, std::vector& keypoints, OutputArray _descriptors, bool useProvidedKeypoints); virtual void detectAndComputeAsync(InputArray _image, InputArray _mask, OutputArray _keypoints, OutputArray _descriptors, bool useProvidedKeypoints, Stream& stream); virtual void convert(InputArray _gpu_keypoints, std::vector& keypoints); virtual int descriptorSize() const { return cv::ORB::kBytes; } virtual int descriptorType() const { return CV_8U; } virtual int defaultNorm() const { return NORM_HAMMING; } virtual void setMaxFeatures(int maxFeatures) { nFeatures_ = maxFeatures; } virtual int getMaxFeatures() const { return nFeatures_; } virtual void setScaleFactor(double scaleFactor) { scaleFactor_ = scaleFactor; } virtual double getScaleFactor() const { return scaleFactor_; } virtual void setNLevels(int nlevels) { nLevels_ = nlevels; } virtual int getNLevels() const { return nLevels_; } virtual void setEdgeThreshold(int edgeThreshold) { edgeThreshold_ = edgeThreshold; } virtual int getEdgeThreshold() const { return edgeThreshold_; } virtual void setFirstLevel(int firstLevel) { firstLevel_ = firstLevel; } virtual int getFirstLevel() const { return firstLevel_; } virtual void setWTA_K(int wta_k) { WTA_K_ = wta_k; } virtual int getWTA_K() const { return WTA_K_; } virtual void setScoreType(int scoreType) { scoreType_ = scoreType; } virtual int getScoreType() const { return scoreType_; } virtual void setPatchSize(int patchSize) { patchSize_ = patchSize; } virtual int getPatchSize() const { return patchSize_; } virtual void setFastThreshold(int fastThreshold) { fastThreshold_ = fastThreshold; } virtual int getFastThreshold() const { return fastThreshold_; } virtual void setBlurForDescriptor(bool blurForDescriptor) { blurForDescriptor_ = blurForDescriptor; } virtual bool getBlurForDescriptor() const { return blurForDescriptor_; } private: int nFeatures_; float scaleFactor_; int nLevels_; int edgeThreshold_; int firstLevel_; int WTA_K_; int scoreType_; int patchSize_; int fastThreshold_; bool blurForDescriptor_; private: void buildScalePyramids(InputArray _image, InputArray _mask, Stream& stream); void computeKeyPointsPyramid(Stream& stream); void computeDescriptors(OutputArray _descriptors, Stream& stream); void mergeKeyPoints(OutputArray _keypoints, Stream& stream); private: Ptr fastDetector_; //! The number of desired features per scale std::vector n_features_per_level_; //! Points to compute BRIEF descriptors from GpuMat pattern_; std::vector imagePyr_; std::vector maskPyr_; GpuMat buf_; std::vector keyPointsPyr_; std::vector keyPointsCount_; Ptr blurFilter_; GpuMat d_keypoints_; }; static void initializeOrbPattern(const Point* pattern0, Mat& pattern, int ntuples, int tupleSize, int poolSize) { RNG rng(0x12345678); pattern.create(2, ntuples * tupleSize, CV_32SC1); pattern.setTo(Scalar::all(0)); int* pattern_x_ptr = pattern.ptr(0); int* pattern_y_ptr = pattern.ptr(1); for (int i = 0; i < ntuples; i++) { for (int k = 0; k < tupleSize; k++) { for(;;) { int idx = rng.uniform(0, poolSize); Point pt = pattern0[idx]; int k1; for (k1 = 0; k1 < k; k1++) if (pattern_x_ptr[tupleSize * i + k1] == pt.x && pattern_y_ptr[tupleSize * i + k1] == pt.y) break; if (k1 == k) { pattern_x_ptr[tupleSize * i + k] = pt.x; pattern_y_ptr[tupleSize * i + k] = pt.y; break; } } } } } static void makeRandomPattern(int patchSize, Point* pattern, int npoints) { // we always start with a fixed seed, // to make patterns the same on each run RNG rng(0x34985739); for (int i = 0; i < npoints; i++) { pattern[i].x = rng.uniform(-patchSize / 2, patchSize / 2 + 1); pattern[i].y = rng.uniform(-patchSize / 2, patchSize / 2 + 1); } } ORB_Impl::ORB_Impl(int nFeatures, float scaleFactor, int nLevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize, int fastThreshold, bool blurForDescriptor) : nFeatures_(nFeatures), scaleFactor_(scaleFactor), nLevels_(nLevels), edgeThreshold_(edgeThreshold), firstLevel_(firstLevel), WTA_K_(WTA_K), scoreType_(scoreType), patchSize_(patchSize), fastThreshold_(fastThreshold), blurForDescriptor_(blurForDescriptor) { CV_Assert( patchSize_ >= 2 ); CV_Assert( WTA_K_ == 2 || WTA_K_ == 3 || WTA_K_ == 4 ); fastDetector_ = cuda::FastFeatureDetector::create(fastThreshold_); // fill the extractors and descriptors for the corresponding scales float factor = 1.0f / scaleFactor_; float n_desired_features_per_scale = nFeatures_ * (1.0f - factor) / (1.0f - std::pow(factor, nLevels_)); n_features_per_level_.resize(nLevels_); size_t sum_n_features = 0; for (int level = 0; level < nLevels_ - 1; ++level) { n_features_per_level_[level] = cvRound(n_desired_features_per_scale); sum_n_features += n_features_per_level_[level]; n_desired_features_per_scale *= factor; } n_features_per_level_[nLevels_ - 1] = nFeatures - sum_n_features; // pre-compute the end of a row in a circular patch int half_patch_size = patchSize_ / 2; std::vector u_max(half_patch_size + 2); for (int v = 0; v <= half_patch_size * std::sqrt(2.f) / 2 + 1; ++v) { u_max[v] = cvRound(std::sqrt(static_cast(half_patch_size * half_patch_size - v * v))); } // Make sure we are symmetric for (int v = half_patch_size, v_0 = 0; v >= half_patch_size * std::sqrt(2.f) / 2; --v) { while (u_max[v_0] == u_max[v_0 + 1]) ++v_0; u_max[v] = v_0; ++v_0; } CV_Assert( u_max.size() < 32 ); cv::cuda::device::orb::loadUMax(&u_max[0], static_cast(u_max.size())); // Calc pattern const int npoints = 512; Point pattern_buf[npoints]; const Point* pattern0 = (const Point*)bit_pattern_31_; if (patchSize_ != 31) { pattern0 = pattern_buf; makeRandomPattern(patchSize_, pattern_buf, npoints); } Mat h_pattern; if (WTA_K_ == 2) { h_pattern.create(2, npoints, CV_32SC1); int* pattern_x_ptr = h_pattern.ptr(0); int* pattern_y_ptr = h_pattern.ptr(1); for (int i = 0; i < npoints; ++i) { pattern_x_ptr[i] = pattern0[i].x; pattern_y_ptr[i] = pattern0[i].y; } } else { int ntuples = descriptorSize() * 4; initializeOrbPattern(pattern0, h_pattern, ntuples, WTA_K_, npoints); } pattern_.upload(h_pattern); blurFilter_ = cuda::createGaussianFilter(CV_8UC1, -1, Size(7, 7), 2, 2, BORDER_REFLECT_101); } static float getScale(float scaleFactor, int firstLevel, int level) { return pow(scaleFactor, level - firstLevel); } void ORB_Impl::detectAndCompute(InputArray _image, InputArray _mask, std::vector& keypoints, OutputArray _descriptors, bool useProvidedKeypoints) { using namespace cv::cuda::device::orb; if (useProvidedKeypoints) { d_keypoints_.release(); keyPointsPyr_.clear(); int j, level, nkeypoints = (int)keypoints.size(); nLevels_ = 0; for( j = 0; j < nkeypoints; j++ ) { level = keypoints[j].octave; CV_Assert(level >= 0); nLevels_ = std::max(nLevels_, level); } nLevels_ ++; std::vector > oKeypoints(nLevels_); for( j = 0; j < nkeypoints; j++ ) { level = keypoints[j].octave; oKeypoints[level].push_back(keypoints[j]); } if (!keypoints.empty()) { keyPointsPyr_.resize(nLevels_); keyPointsCount_.resize(nLevels_); int t; for(t = 0; t < nLevels_; t++) { const std::vector& ks = oKeypoints[t]; if (!ks.empty()){ Mat h_keypoints(ROWS_COUNT, static_cast(ks.size()), CV_32FC1); float sf = getScale(scaleFactor_, firstLevel_, t); float locScale = t != firstLevel_ ? sf : 1.0f; float scale = 1.f/locScale; short2* x_loc_row = h_keypoints.ptr(0); float* x_kp_hessian = h_keypoints.ptr(1); float* x_kp_dir = h_keypoints.ptr(2); for (size_t i = 0, size = ks.size(); i < size; ++i) { const KeyPoint& kp = ks[i]; x_kp_hessian[i] = kp.response; x_loc_row[i].x = cvRound(kp.pt.x * scale); x_loc_row[i].y = cvRound(kp.pt.y * scale); x_kp_dir[i] = kp.angle; } keyPointsPyr_[t].upload(h_keypoints.rowRange(0,3)); keyPointsCount_[t] = h_keypoints.cols; } } } } detectAndComputeAsync(_image, _mask, d_keypoints_, _descriptors, useProvidedKeypoints, Stream::Null()); if (!useProvidedKeypoints) { convert(d_keypoints_, keypoints); } } void ORB_Impl::detectAndComputeAsync(InputArray _image, InputArray _mask, OutputArray _keypoints, OutputArray _descriptors, bool useProvidedKeypoints, Stream& stream) { buildScalePyramids(_image, _mask, stream); if (!useProvidedKeypoints) { computeKeyPointsPyramid(stream); } if (_descriptors.needed()) { computeDescriptors(_descriptors, stream); } if (!useProvidedKeypoints) { mergeKeyPoints(_keypoints, stream); } } void ORB_Impl::buildScalePyramids(InputArray _image, InputArray _mask, Stream& stream) { const GpuMat image = _image.getGpuMat(); const GpuMat mask = _mask.getGpuMat(); CV_Assert( image.type() == CV_8UC1 ); CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()) ); imagePyr_.resize(nLevels_); maskPyr_.resize(nLevels_); for (int level = 0; level < nLevels_; ++level) { float scale = 1.0f / getScale(scaleFactor_, firstLevel_, level); Size sz(cvRound(image.cols * scale), cvRound(image.rows * scale)); ensureSizeIsEnough(sz, image.type(), imagePyr_[level]); ensureSizeIsEnough(sz, CV_8UC1, maskPyr_[level]); maskPyr_[level].setTo(Scalar::all(255)); // Compute the resized image if (level != firstLevel_) { if (level < firstLevel_) { cuda::resize(image, imagePyr_[level], sz, 0, 0, INTER_LINEAR, stream); if (!mask.empty()) cuda::resize(mask, maskPyr_[level], sz, 0, 0, INTER_LINEAR, stream); } else { cuda::resize(imagePyr_[level - 1], imagePyr_[level], sz, 0, 0, INTER_LINEAR, stream); if (!mask.empty()) { cuda::resize(maskPyr_[level - 1], maskPyr_[level], sz, 0, 0, INTER_LINEAR, stream); cuda::threshold(maskPyr_[level], maskPyr_[level], 254, 0, THRESH_TOZERO, stream); } } } else { image.copyTo(imagePyr_[level], stream); if (!mask.empty()) mask.copyTo(maskPyr_[level], stream); } // Filter keypoints by image border ensureSizeIsEnough(sz, CV_8UC1, buf_); buf_.setTo(Scalar::all(0), stream); Rect inner(edgeThreshold_, edgeThreshold_, sz.width - 2 * edgeThreshold_, sz.height - 2 * edgeThreshold_); buf_(inner).setTo(Scalar::all(255), stream); cuda::bitwise_and(maskPyr_[level], buf_, maskPyr_[level], cv::noArray(), stream); } } // takes keypoints and culls them by the response static void cull(GpuMat& keypoints, int& count, int n_points, Stream& stream) { using namespace cv::cuda::device::orb; //this is only necessary if the keypoints size is greater than the number of desired points. if (count > n_points) { if (n_points == 0) { keypoints.release(); return; } count = cull_gpu(keypoints.ptr(cuda::FastFeatureDetector::LOCATION_ROW), keypoints.ptr(cuda::FastFeatureDetector::RESPONSE_ROW), count, n_points, StreamAccessor::getStream(stream)); } } void ORB_Impl::computeKeyPointsPyramid(Stream& stream) { using namespace cv::cuda::device::orb; int half_patch_size = patchSize_ / 2; keyPointsPyr_.resize(nLevels_); keyPointsCount_.resize(nLevels_); fastDetector_->setThreshold(fastThreshold_); for (int level = 0; level < nLevels_; ++level) { fastDetector_->setMaxNumPoints(0.05 * imagePyr_[level].size().area()); GpuMat fastKpRange; fastDetector_->detectAsync(imagePyr_[level], fastKpRange, maskPyr_[level], stream); keyPointsCount_[level] = fastKpRange.cols; if (keyPointsCount_[level] == 0) continue; ensureSizeIsEnough(3, keyPointsCount_[level], fastKpRange.type(), keyPointsPyr_[level]); fastKpRange.copyTo(keyPointsPyr_[level].rowRange(0, 2), stream); const int n_features = static_cast(n_features_per_level_[level]); if (scoreType_ == cv::ORB::HARRIS_SCORE) { // Keep more points than necessary as FAST does not give amazing corners cull(keyPointsPyr_[level], keyPointsCount_[level], 2 * n_features, stream); // Compute the Harris cornerness (better scoring than FAST) HarrisResponses_gpu(imagePyr_[level], keyPointsPyr_[level].ptr(0), keyPointsPyr_[level].ptr(1), keyPointsCount_[level], 7, HARRIS_K, StreamAccessor::getStream(stream)); } //cull to the final desired level, using the new Harris scores or the original FAST scores. cull(keyPointsPyr_[level], keyPointsCount_[level], n_features, stream); // Compute orientation IC_Angle_gpu(imagePyr_[level], keyPointsPyr_[level].ptr(0), keyPointsPyr_[level].ptr(2), keyPointsCount_[level], half_patch_size, StreamAccessor::getStream(stream)); } } void ORB_Impl::computeDescriptors(OutputArray _descriptors, Stream& stream) { using namespace cv::cuda::device::orb; int nAllkeypoints = 0; for (int level = 0; level < nLevels_; ++level) nAllkeypoints += keyPointsCount_[level]; if (nAllkeypoints == 0) { _descriptors.release(); return; } ensureSizeIsEnough(nAllkeypoints, descriptorSize(), CV_8UC1, _descriptors); GpuMat descriptors = _descriptors.getGpuMat(); int offset = 0; for (int level = 0; level < nLevels_; ++level) { if (keyPointsCount_[level] == 0) continue; GpuMat descRange = descriptors.rowRange(offset, offset + keyPointsCount_[level]); if (blurForDescriptor_) { // preprocess the resized image ensureSizeIsEnough(imagePyr_[level].size(), imagePyr_[level].type(), buf_); blurFilter_->apply(imagePyr_[level], buf_, stream); } computeOrbDescriptor_gpu(blurForDescriptor_ ? buf_ : imagePyr_[level], keyPointsPyr_[level].ptr(0), keyPointsPyr_[level].ptr(2), keyPointsCount_[level], pattern_.ptr(0), pattern_.ptr(1), descRange, descriptorSize(), WTA_K_, StreamAccessor::getStream(stream)); offset += keyPointsCount_[level]; } } void ORB_Impl::mergeKeyPoints(OutputArray _keypoints, Stream& stream) { using namespace cv::cuda::device::orb; int nAllkeypoints = 0; for (int level = 0; level < nLevels_; ++level) nAllkeypoints += keyPointsCount_[level]; if (nAllkeypoints == 0) { _keypoints.release(); return; } ensureSizeIsEnough(ROWS_COUNT, nAllkeypoints, CV_32FC1, _keypoints); GpuMat& keypoints = _keypoints.getGpuMatRef(); int offset = 0; for (int level = 0; level < nLevels_; ++level) { if (keyPointsCount_[level] == 0) continue; float sf = getScale(scaleFactor_, firstLevel_, level); GpuMat keyPointsRange = keypoints.colRange(offset, offset + keyPointsCount_[level]); float locScale = level != firstLevel_ ? sf : 1.0f; mergeLocation_gpu(keyPointsPyr_[level].ptr(0), keyPointsRange.ptr(0), keyPointsRange.ptr(1), keyPointsCount_[level], locScale, StreamAccessor::getStream(stream)); GpuMat range = keyPointsRange.rowRange(2, 4); keyPointsPyr_[level](Range(1, 3), Range(0, keyPointsCount_[level])).copyTo(range, stream); keyPointsRange.row(4).setTo(Scalar::all(level), stream); keyPointsRange.row(5).setTo(Scalar::all(patchSize_ * sf), stream); offset += keyPointsCount_[level]; } } void ORB_Impl::convert(InputArray _gpu_keypoints, std::vector& keypoints) { if (_gpu_keypoints.empty()) { keypoints.clear(); return; } Mat h_keypoints; if (_gpu_keypoints.kind() == _InputArray::CUDA_GPU_MAT) { _gpu_keypoints.getGpuMat().download(h_keypoints); } else { h_keypoints = _gpu_keypoints.getMat(); } CV_Assert( h_keypoints.rows == ROWS_COUNT ); CV_Assert( h_keypoints.type() == CV_32FC1 ); const int npoints = h_keypoints.cols; keypoints.resize(npoints); const float* x_ptr = h_keypoints.ptr(X_ROW); const float* y_ptr = h_keypoints.ptr(Y_ROW); const float* response_ptr = h_keypoints.ptr(RESPONSE_ROW); const float* angle_ptr = h_keypoints.ptr(ANGLE_ROW); const float* octave_ptr = h_keypoints.ptr(OCTAVE_ROW); const float* size_ptr = h_keypoints.ptr(SIZE_ROW); for (int i = 0; i < npoints; ++i) { KeyPoint kp; kp.pt.x = x_ptr[i]; kp.pt.y = y_ptr[i]; kp.response = response_ptr[i]; kp.angle = angle_ptr[i]; kp.octave = static_cast(octave_ptr[i]); kp.size = size_ptr[i]; keypoints[i] = kp; } } } Ptr cv::cuda::ORB::create(int nfeatures, float scaleFactor, int nlevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize, int fastThreshold, bool blurForDescriptor) { return makePtr(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize, fastThreshold, blurForDescriptor); } #endif /* !defined (HAVE_CUDA) */