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Open Source Computer Vision Library
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775 lines
34 KiB
775 lines
34 KiB
/*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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cv::cuda::ORB_CUDA::ORB_CUDA(int, float, int, int, int, int, int, int) : fastDetector_(20) { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::operator()(const GpuMat&, const GpuMat&, std::vector<KeyPoint>&) { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::operator()(const GpuMat&, const GpuMat&, GpuMat&) { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::operator()(const GpuMat&, const GpuMat&, std::vector<KeyPoint>&, GpuMat&) { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::operator()(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&) { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::downloadKeyPoints(const GpuMat&, std::vector<KeyPoint>&) { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::convertKeyPoints(const Mat&, std::vector<KeyPoint>&) { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::release() { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::buildScalePyramids(const GpuMat&, const GpuMat&) { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::computeKeyPointsPyramid() { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::computeDescriptors(GpuMat&) { throw_no_cuda(); } |
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void cv::cuda::ORB_CUDA::mergeKeyPoints(GpuMat&) { throw_no_cuda(); } |
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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); |
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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)*/, |
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-13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/, |
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-10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/, |
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-3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/, |
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2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/, |
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-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)*/, |
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-6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/, |
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6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/, |
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-13,11, -5,5/*mean (0.162427), correlation (0.501907)*/, |
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11,11, 12,6/*mean (0.16652), correlation (0.497632)*/, |
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7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/, |
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-1,12, 0,7/*mean (0.169456), correlation (0.495339)*/, |
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-4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/, |
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-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)*/, |
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-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)*/, |
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-10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/, |
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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)*/ |
|
}; |
|
|
|
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; |
|
} |
|
} |
|
} |
|
} |
|
} |
|
|
|
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); |
|
} |
|
} |
|
} |
|
|
|
cv::cuda::ORB_CUDA::ORB_CUDA(int nFeatures, float scaleFactor, int nLevels, int edgeThreshold, int firstLevel, int WTA_K, int scoreType, int patchSize) : |
|
nFeatures_(nFeatures), scaleFactor_(scaleFactor), nLevels_(nLevels), edgeThreshold_(edgeThreshold), firstLevel_(firstLevel), WTA_K_(WTA_K), |
|
scoreType_(scoreType), patchSize_(patchSize), |
|
fastDetector_(DEFAULT_FAST_THRESHOLD) |
|
{ |
|
CV_Assert(patchSize_ >= 2); |
|
|
|
// 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); |
|
} |
|
|
|
CV_Assert(WTA_K_ == 2 || WTA_K_ == 3 || WTA_K_ == 4); |
|
|
|
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); |
|
|
|
blurForDescriptor = false; |
|
} |
|
|
|
namespace |
|
{ |
|
inline float getScale(float scaleFactor, int firstLevel, int level) |
|
{ |
|
return pow(scaleFactor, level - firstLevel); |
|
} |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::buildScalePyramids(const GpuMat& image, const GpuMat& mask) |
|
{ |
|
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); |
|
|
|
if (!mask.empty()) |
|
cuda::resize(mask, maskPyr_[level], sz, 0, 0, INTER_LINEAR); |
|
} |
|
else |
|
{ |
|
cuda::resize(imagePyr_[level - 1], imagePyr_[level], sz, 0, 0, INTER_LINEAR); |
|
|
|
if (!mask.empty()) |
|
{ |
|
cuda::resize(maskPyr_[level - 1], maskPyr_[level], sz, 0, 0, INTER_LINEAR); |
|
cuda::threshold(maskPyr_[level], maskPyr_[level], 254, 0, THRESH_TOZERO); |
|
} |
|
} |
|
} |
|
else |
|
{ |
|
image.copyTo(imagePyr_[level]); |
|
|
|
if (!mask.empty()) |
|
mask.copyTo(maskPyr_[level]); |
|
} |
|
|
|
// Filter keypoints by image border |
|
ensureSizeIsEnough(sz, CV_8UC1, buf_); |
|
buf_.setTo(Scalar::all(0)); |
|
Rect inner(edgeThreshold_, edgeThreshold_, sz.width - 2 * edgeThreshold_, sz.height - 2 * edgeThreshold_); |
|
buf_(inner).setTo(Scalar::all(255)); |
|
|
|
cuda::bitwise_and(maskPyr_[level], buf_, maskPyr_[level]); |
|
} |
|
} |
|
|
|
namespace |
|
{ |
|
//takes keypoints and culls them by the response |
|
void cull(GpuMat& keypoints, int& count, int n_points) |
|
{ |
|
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>(FAST_CUDA::LOCATION_ROW), keypoints.ptr<float>(FAST_CUDA::RESPONSE_ROW), count, n_points); |
|
} |
|
} |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::computeKeyPointsPyramid() |
|
{ |
|
using namespace cv::cuda::device::orb; |
|
|
|
int half_patch_size = patchSize_ / 2; |
|
|
|
keyPointsPyr_.resize(nLevels_); |
|
keyPointsCount_.resize(nLevels_); |
|
|
|
for (int level = 0; level < nLevels_; ++level) |
|
{ |
|
keyPointsCount_[level] = fastDetector_.calcKeyPointsLocation(imagePyr_[level], maskPyr_[level]); |
|
|
|
if (keyPointsCount_[level] == 0) |
|
continue; |
|
|
|
ensureSizeIsEnough(3, keyPointsCount_[level], CV_32FC1, keyPointsPyr_[level]); |
|
|
|
GpuMat fastKpRange = keyPointsPyr_[level].rowRange(0, 2); |
|
keyPointsCount_[level] = fastDetector_.getKeyPoints(fastKpRange); |
|
|
|
if (keyPointsCount_[level] == 0) |
|
continue; |
|
|
|
int n_features = static_cast<int>(n_features_per_level_[level]); |
|
|
|
if (scoreType_ == ORB::HARRIS_SCORE) |
|
{ |
|
// Keep more points than necessary as FAST does not give amazing corners |
|
cull(keyPointsPyr_[level], keyPointsCount_[level], 2 * n_features); |
|
|
|
// 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, 0); |
|
} |
|
|
|
//cull to the final desired level, using the new Harris scores or the original FAST scores. |
|
cull(keyPointsPyr_[level], keyPointsCount_[level], n_features); |
|
|
|
// Compute orientation |
|
IC_Angle_gpu(imagePyr_[level], keyPointsPyr_[level].ptr<short2>(0), keyPointsPyr_[level].ptr<float>(2), keyPointsCount_[level], half_patch_size, 0); |
|
} |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::computeDescriptors(GpuMat& descriptors) |
|
{ |
|
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); |
|
|
|
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_); |
|
} |
|
|
|
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_, 0); |
|
|
|
offset += keyPointsCount_[level]; |
|
} |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::mergeKeyPoints(GpuMat& keypoints) |
|
{ |
|
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); |
|
|
|
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, 0); |
|
|
|
GpuMat range = keyPointsRange.rowRange(2, 4); |
|
keyPointsPyr_[level](Range(1, 3), Range(0, keyPointsCount_[level])).copyTo(range); |
|
|
|
keyPointsRange.row(4).setTo(Scalar::all(level)); |
|
keyPointsRange.row(5).setTo(Scalar::all(patchSize_ * sf)); |
|
|
|
offset += keyPointsCount_[level]; |
|
} |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::downloadKeyPoints(const GpuMat &d_keypoints, std::vector<KeyPoint>& keypoints) |
|
{ |
|
if (d_keypoints.empty()) |
|
{ |
|
keypoints.clear(); |
|
return; |
|
} |
|
|
|
Mat h_keypoints(d_keypoints); |
|
|
|
convertKeyPoints(h_keypoints, keypoints); |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::convertKeyPoints(const Mat &d_keypoints, std::vector<KeyPoint>& keypoints) |
|
{ |
|
if (d_keypoints.empty()) |
|
{ |
|
keypoints.clear(); |
|
return; |
|
} |
|
|
|
CV_Assert(d_keypoints.type() == CV_32FC1 && d_keypoints.rows == ROWS_COUNT); |
|
|
|
const float* x_ptr = d_keypoints.ptr<float>(X_ROW); |
|
const float* y_ptr = d_keypoints.ptr<float>(Y_ROW); |
|
const float* response_ptr = d_keypoints.ptr<float>(RESPONSE_ROW); |
|
const float* angle_ptr = d_keypoints.ptr<float>(ANGLE_ROW); |
|
const float* octave_ptr = d_keypoints.ptr<float>(OCTAVE_ROW); |
|
const float* size_ptr = d_keypoints.ptr<float>(SIZE_ROW); |
|
|
|
keypoints.resize(d_keypoints.cols); |
|
|
|
for (int i = 0; i < d_keypoints.cols; ++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; |
|
} |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints) |
|
{ |
|
buildScalePyramids(image, mask); |
|
computeKeyPointsPyramid(); |
|
mergeKeyPoints(keypoints); |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors) |
|
{ |
|
buildScalePyramids(image, mask); |
|
computeKeyPointsPyramid(); |
|
computeDescriptors(descriptors); |
|
mergeKeyPoints(keypoints); |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints) |
|
{ |
|
(*this)(image, mask, d_keypoints_); |
|
downloadKeyPoints(d_keypoints_, keypoints); |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors) |
|
{ |
|
(*this)(image, mask, d_keypoints_, descriptors); |
|
downloadKeyPoints(d_keypoints_, keypoints); |
|
} |
|
|
|
void cv::cuda::ORB_CUDA::release() |
|
{ |
|
imagePyr_.clear(); |
|
maskPyr_.clear(); |
|
|
|
buf_.release(); |
|
|
|
keyPointsPyr_.clear(); |
|
|
|
fastDetector_.release(); |
|
|
|
d_keypoints_.release(); |
|
} |
|
|
|
#endif /* !defined (HAVE_CUDA) */
|
|
|