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931 lines
41 KiB
C++
931 lines
41 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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using namespace cv;
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using namespace cv::cuda;
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#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
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Ptr<cv::cuda::ORB> cv::cuda::ORB::create(int, float, int, int, int, int, int, int, int, bool) { throw_no_cuda(); return Ptr<cv::cuda::ORB>(); }
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#else /* !defined (HAVE_CUDA) */
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namespace cv { namespace cuda { namespace device
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{
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namespace orb
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{
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int cull_gpu(int* loc, float* response, int size, int n_points, cudaStream_t stream);
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void HarrisResponses_gpu(PtrStepSzb img, const short2* loc, float* response, const int npoints, int blockSize, float harris_k, cudaStream_t stream);
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void loadUMax(const int* u_max, int count);
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void IC_Angle_gpu(PtrStepSzb image, const short2* loc, float* angle, int npoints, int half_k, cudaStream_t stream);
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void computeOrbDescriptor_gpu(PtrStepb img, const short2* loc, const float* angle, const int npoints,
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const int* pattern_x, const int* pattern_y, PtrStepb desc, int dsize, int WTA_K, cudaStream_t stream);
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void mergeLocation_gpu(const short2* loc, float* x, float* y, int npoints, float scale, cudaStream_t stream);
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}
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}}}
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namespace
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{
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const float HARRIS_K = 0.04f;
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const int DESCRIPTOR_SIZE = 32;
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const int bit_pattern_31_[256 * 4] =
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{
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8,-3, 9,5/*mean (0), correlation (0)*/,
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4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
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-11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
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7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
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2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
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1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
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-2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
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-13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
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-13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
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10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
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-13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
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-11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
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7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
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-4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
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-13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
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-9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
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12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
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-3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
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-6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
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11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
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4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
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5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
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3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
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-8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
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-2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
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-13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
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-7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
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-4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
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-10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
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5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
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5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
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1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
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9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
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4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
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2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
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-4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
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-8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
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4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
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0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
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-13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
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-3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
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-6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
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8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
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0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
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7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
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-13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
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10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
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-6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
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10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
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-13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
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-13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
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3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
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5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
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-1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
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3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
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2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
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-13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
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-13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
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-13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
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-7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
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6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
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-9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
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-2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
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-12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
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3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
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-7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
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-3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
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2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
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-11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
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-1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
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5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
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-4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
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-9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
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-12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
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10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
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7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
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-7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
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-4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
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7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
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-7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
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-13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
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-3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
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7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
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-13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
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1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
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2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
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-4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
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-1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
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7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
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1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
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9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
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-1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
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-13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
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7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
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12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
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6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
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5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
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2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
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3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
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2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
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9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
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-8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
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-11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
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1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
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6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
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2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
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6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
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3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
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7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
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-11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
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-10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
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-5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
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-10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
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8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
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4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
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-10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
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4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
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-2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
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-5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
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7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
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-9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
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-5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
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8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
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-9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
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1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
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7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
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-2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
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11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
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-12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
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3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
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5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
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0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
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-9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
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0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
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-1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
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5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
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3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
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-13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
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-5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
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-4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
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6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
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-7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
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-13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
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1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
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4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
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-2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
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2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
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-2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
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4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
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-6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
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-3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
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7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
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4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
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-13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
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7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
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7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
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|
-7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
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|
-8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
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-13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
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2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
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10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
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|
-6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
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|
8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
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|
2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
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|
-11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
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|
-12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
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|
-11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
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|
5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
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|
-2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
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|
-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)*/,
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|
2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
|
|
-9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
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|
-4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
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|
-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)*/,
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|
-13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
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|
-7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
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|
-8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
|
|
-5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
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|
-13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
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|
1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
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|
1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
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|
9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
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|
5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
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|
-1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
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|
-9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
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|
-1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
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|
-13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
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|
8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
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|
2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
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|
7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
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-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<KeyPoint>& 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<KeyPoint>& 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<cv::cuda::FastFeatureDetector> fastDetector_;
|
|
|
|
//! 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_;
|
|
|
|
Ptr<cuda::Filter> 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<int>(0);
|
|
int* pattern_y_ptr = pattern.ptr<int>(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<int> 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<float>(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<int>(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<int>(0);
|
|
int* pattern_y_ptr = h_pattern.ptr<int>(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<KeyPoint>& 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<std::vector<KeyPoint> > 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<KeyPoint>& ks = oKeypoints[t];
|
|
if (!ks.empty()){
|
|
|
|
Mat h_keypoints(ROWS_COUNT, static_cast<int>(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<short2>(0);
|
|
float* x_kp_hessian = h_keypoints.ptr<float>(1);
|
|
float* x_kp_dir = h_keypoints.ptr<float>(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<int>(cuda::FastFeatureDetector::LOCATION_ROW), keypoints.ptr<float>(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<int>(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<short2>(0), keyPointsPyr_[level].ptr<float>(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<short2>(0), keyPointsPyr_[level].ptr<float>(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<short2>(0), keyPointsPyr_[level].ptr<float>(2),
|
|
keyPointsCount_[level], pattern_.ptr<int>(0), pattern_.ptr<int>(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<short2>(0), keyPointsRange.ptr<float>(0), keyPointsRange.ptr<float>(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<KeyPoint>& 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<float>(X_ROW);
|
|
const float* y_ptr = h_keypoints.ptr<float>(Y_ROW);
|
|
const float* response_ptr = h_keypoints.ptr<float>(RESPONSE_ROW);
|
|
const float* angle_ptr = h_keypoints.ptr<float>(ANGLE_ROW);
|
|
const float* octave_ptr = h_keypoints.ptr<float>(OCTAVE_ROW);
|
|
const float* size_ptr = h_keypoints.ptr<float>(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<int>(octave_ptr[i]);
|
|
kp.size = size_ptr[i];
|
|
|
|
keypoints[i] = kp;
|
|
}
|
|
}
|
|
}
|
|
|
|
Ptr<cv::cuda::ORB> 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<ORB_Impl>(nfeatures, scaleFactor, nlevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize, fastThreshold, blurForDescriptor);
|
|
}
|
|
|
|
#endif /* !defined (HAVE_CUDA) */
|