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1324 lines
44 KiB
C++
1324 lines
44 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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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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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#include <limits>
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#include "opencl_kernels_features2d.hpp"
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#if defined(HAVE_EIGEN) && EIGEN_WORLD_VERSION == 2
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#include <Eigen/Array>
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#endif
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namespace cv
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{
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/////////////////////// ocl functions for BFMatcher ///////////////////////////
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static void ensureSizeIsEnough(int rows, int cols, int type, UMat &m)
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{
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if (m.type() == type && m.rows >= rows && m.cols >= cols)
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m = m(Rect(0, 0, cols, rows));
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else
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m.create(rows, cols, type);
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}
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static bool ocl_matchSingle(InputArray query, InputArray train,
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UMat &trainIdx, UMat &distance, int distType)
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{
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if (query.empty() || train.empty())
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return false;
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const int query_rows = query.rows();
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const int query_cols = query.cols();
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ensureSizeIsEnough(1, query_rows, CV_32S, trainIdx);
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ensureSizeIsEnough(1, query_rows, CV_32F, distance);
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ocl::Device devDef = ocl::Device::getDefault();
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UMat uquery = query.getUMat(), utrain = train.getUMat();
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int kercn = 1;
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if (devDef.isIntel() &&
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(0 == (uquery.step % 4)) && (0 == (uquery.cols % 4)) && (0 == (uquery.offset % 4)) &&
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(0 == (utrain.step % 4)) && (0 == (utrain.cols % 4)) && (0 == (utrain.offset % 4)))
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kercn = 4;
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int block_size = 16;
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int max_desc_len = 0;
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bool is_cpu = devDef.type() == ocl::Device::TYPE_CPU;
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if (query_cols <= 64)
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max_desc_len = 64 / kercn;
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else if (query_cols <= 128 && !is_cpu)
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max_desc_len = 128 / kercn;
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int depth = query.depth();
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cv::String opts;
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opts = cv::format("-D T=%s -D TN=%s -D kercn=%d %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
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ocl::typeToStr(depth), ocl::typeToStr(CV_MAKETYPE(depth, kercn)), kercn, depth == CV_32F ? "-D T_FLOAT" : "", distType, block_size, max_desc_len);
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ocl::Kernel k("BruteForceMatch_Match", ocl::features2d::brute_force_match_oclsrc, opts);
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if(k.empty())
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return false;
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size_t globalSize[] = {(query.size().height + block_size - 1) / block_size * block_size, block_size};
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size_t localSize[] = {block_size, block_size};
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int idx = 0;
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idx = k.set(idx, ocl::KernelArg::PtrReadOnly(uquery));
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idx = k.set(idx, ocl::KernelArg::PtrReadOnly(utrain));
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idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
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idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
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idx = k.set(idx, uquery.rows);
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idx = k.set(idx, uquery.cols);
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idx = k.set(idx, utrain.rows);
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idx = k.set(idx, utrain.cols);
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idx = k.set(idx, (int)(uquery.step / sizeof(float)));
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return k.run(2, globalSize, localSize, false);
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}
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static bool ocl_matchConvert(const Mat &trainIdx, const Mat &distance, std::vector< std::vector<DMatch> > &matches)
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{
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if (trainIdx.empty() || distance.empty())
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return false;
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if( (trainIdx.type() != CV_32SC1) || (distance.type() != CV_32FC1 || distance.cols != trainIdx.cols) )
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return false;
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const int nQuery = trainIdx.cols;
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matches.clear();
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matches.reserve(nQuery);
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const int *trainIdx_ptr = trainIdx.ptr<int>();
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const float *distance_ptr = distance.ptr<float>();
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for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++distance_ptr)
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{
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int trainIndex = *trainIdx_ptr;
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if (trainIndex == -1)
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continue;
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float dst = *distance_ptr;
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DMatch m(queryIdx, trainIndex, 0, dst);
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std::vector<DMatch> temp;
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temp.push_back(m);
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matches.push_back(temp);
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}
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return true;
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}
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static bool ocl_matchDownload(const UMat &trainIdx, const UMat &distance, std::vector< std::vector<DMatch> > &matches)
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{
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if (trainIdx.empty() || distance.empty())
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return false;
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Mat trainIdxCPU = trainIdx.getMat(ACCESS_READ);
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Mat distanceCPU = distance.getMat(ACCESS_READ);
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return ocl_matchConvert(trainIdxCPU, distanceCPU, matches);
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}
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static bool ocl_knnMatchSingle(InputArray query, InputArray train, UMat &trainIdx,
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UMat &distance, int distType)
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{
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if (query.empty() || train.empty())
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return false;
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const int query_rows = query.rows();
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const int query_cols = query.cols();
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ensureSizeIsEnough(1, query_rows, CV_32SC2, trainIdx);
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ensureSizeIsEnough(1, query_rows, CV_32FC2, distance);
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trainIdx.setTo(Scalar::all(-1));
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ocl::Device devDef = ocl::Device::getDefault();
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UMat uquery = query.getUMat(), utrain = train.getUMat();
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int kercn = 1;
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if (devDef.isIntel() &&
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(0 == (uquery.step % 4)) && (0 == (uquery.cols % 4)) && (0 == (uquery.offset % 4)) &&
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(0 == (utrain.step % 4)) && (0 == (utrain.cols % 4)) && (0 == (utrain.offset % 4)))
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kercn = 4;
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int block_size = 16;
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int max_desc_len = 0;
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bool is_cpu = devDef.type() == ocl::Device::TYPE_CPU;
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if (query_cols <= 64)
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max_desc_len = 64 / kercn;
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else if (query_cols <= 128 && !is_cpu)
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max_desc_len = 128 / kercn;
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int depth = query.depth();
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cv::String opts;
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opts = cv::format("-D T=%s -D TN=%s -D kercn=%d %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
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ocl::typeToStr(depth), ocl::typeToStr(CV_MAKETYPE(depth, kercn)), kercn, depth == CV_32F ? "-D T_FLOAT" : "", distType, block_size, max_desc_len);
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ocl::Kernel k("BruteForceMatch_knnMatch", ocl::features2d::brute_force_match_oclsrc, opts);
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if(k.empty())
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return false;
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size_t globalSize[] = {(query_rows + block_size - 1) / block_size * block_size, block_size};
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size_t localSize[] = {block_size, block_size};
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int idx = 0;
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idx = k.set(idx, ocl::KernelArg::PtrReadOnly(uquery));
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idx = k.set(idx, ocl::KernelArg::PtrReadOnly(utrain));
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idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
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idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
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idx = k.set(idx, uquery.rows);
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idx = k.set(idx, uquery.cols);
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idx = k.set(idx, utrain.rows);
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idx = k.set(idx, utrain.cols);
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idx = k.set(idx, (int)(uquery.step / sizeof(float)));
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return k.run(2, globalSize, localSize, false);
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}
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static bool ocl_knnMatchConvert(const Mat &trainIdx, const Mat &distance, std::vector< std::vector<DMatch> > &matches, bool compactResult)
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{
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if (trainIdx.empty() || distance.empty())
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return false;
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if(trainIdx.type() != CV_32SC2 && trainIdx.type() != CV_32SC1) return false;
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if(distance.type() != CV_32FC2 && distance.type() != CV_32FC1)return false;
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if(distance.size() != trainIdx.size()) return false;
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if(!trainIdx.isContinuous() || !distance.isContinuous()) return false;
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const int nQuery = trainIdx.type() == CV_32SC2 ? trainIdx.cols : trainIdx.rows;
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const int k = trainIdx.type() == CV_32SC2 ? 2 : trainIdx.cols;
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matches.clear();
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matches.reserve(nQuery);
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const int *trainIdx_ptr = trainIdx.ptr<int>();
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const float *distance_ptr = distance.ptr<float>();
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for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
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{
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matches.push_back(std::vector<DMatch>());
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std::vector<DMatch> &curMatches = matches.back();
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curMatches.reserve(k);
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for (int i = 0; i < k; ++i, ++trainIdx_ptr, ++distance_ptr)
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{
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int trainIndex = *trainIdx_ptr;
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if (trainIndex != -1)
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{
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float dst = *distance_ptr;
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DMatch m(queryIdx, trainIndex, 0, dst);
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curMatches.push_back(m);
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}
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}
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if (compactResult && curMatches.empty())
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matches.pop_back();
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}
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return true;
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}
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static bool ocl_knnMatchDownload(const UMat &trainIdx, const UMat &distance, std::vector< std::vector<DMatch> > &matches, bool compactResult)
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{
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if (trainIdx.empty() || distance.empty())
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return false;
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Mat trainIdxCPU = trainIdx.getMat(ACCESS_READ);
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Mat distanceCPU = distance.getMat(ACCESS_READ);
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return ocl_knnMatchConvert(trainIdxCPU, distanceCPU, matches, compactResult);
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}
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static bool ocl_radiusMatchSingle(InputArray query, InputArray train,
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UMat &trainIdx, UMat &distance, UMat &nMatches, float maxDistance, int distType)
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{
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if (query.empty() || train.empty())
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return false;
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const int query_rows = query.rows();
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const int train_rows = train.rows();
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ensureSizeIsEnough(1, query_rows, CV_32SC1, nMatches);
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if (trainIdx.empty())
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{
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ensureSizeIsEnough(query_rows, std::max((train_rows / 100), 10), CV_32SC1, trainIdx);
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ensureSizeIsEnough(query_rows, std::max((train_rows / 100), 10), CV_32FC1, distance);
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}
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nMatches.setTo(Scalar::all(0));
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ocl::Device devDef = ocl::Device::getDefault();
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UMat uquery = query.getUMat(), utrain = train.getUMat();
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int kercn = 1;
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if (devDef.isIntel() &&
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(0 == (uquery.step % 4)) && (0 == (uquery.cols % 4)) && (0 == (uquery.offset % 4)) &&
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(0 == (utrain.step % 4)) && (0 == (utrain.cols % 4)) && (0 == (utrain.offset % 4)))
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kercn = 4;
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int block_size = 16;
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int depth = query.depth();
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cv::String opts;
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opts = cv::format("-D T=%s -D TN=%s -D kercn=%d %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
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ocl::typeToStr(depth), ocl::typeToStr(CV_MAKETYPE(depth, kercn)), kercn, depth == CV_32F ? "-D T_FLOAT" : "", distType, block_size);
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ocl::Kernel k("BruteForceMatch_RadiusMatch", ocl::features2d::brute_force_match_oclsrc, opts);
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if (k.empty())
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return false;
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size_t globalSize[] = {(train_rows + block_size - 1) / block_size * block_size, (query_rows + block_size - 1) / block_size * block_size};
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size_t localSize[] = {block_size, block_size};
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int idx = 0;
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idx = k.set(idx, ocl::KernelArg::PtrReadOnly(uquery));
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idx = k.set(idx, ocl::KernelArg::PtrReadOnly(utrain));
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idx = k.set(idx, maxDistance);
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idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
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idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
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idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(nMatches));
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idx = k.set(idx, uquery.rows);
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idx = k.set(idx, uquery.cols);
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idx = k.set(idx, utrain.rows);
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idx = k.set(idx, utrain.cols);
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idx = k.set(idx, trainIdx.cols);
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idx = k.set(idx, (int)(uquery.step / sizeof(float)));
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idx = k.set(idx, (int)(trainIdx.step / sizeof(int)));
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return k.run(2, globalSize, localSize, false);
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}
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static bool ocl_radiusMatchConvert(const Mat &trainIdx, const Mat &distance, const Mat &_nMatches,
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std::vector< std::vector<DMatch> > &matches, bool compactResult)
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{
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if (trainIdx.empty() || distance.empty() || _nMatches.empty())
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return false;
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if( (trainIdx.type() != CV_32SC1) ||
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(distance.type() != CV_32FC1 || distance.size() != trainIdx.size()) ||
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(_nMatches.type() != CV_32SC1 || _nMatches.cols != trainIdx.rows) )
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return false;
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const int nQuery = trainIdx.rows;
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matches.clear();
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matches.reserve(nQuery);
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const int *nMatches_ptr = _nMatches.ptr<int>();
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for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
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{
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const int *trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
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const float *distance_ptr = distance.ptr<float>(queryIdx);
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const int nMatches = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
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if (nMatches == 0)
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{
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if (!compactResult)
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matches.push_back(std::vector<DMatch>());
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continue;
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}
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matches.push_back(std::vector<DMatch>(nMatches));
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std::vector<DMatch> &curMatches = matches.back();
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for (int i = 0; i < nMatches; ++i, ++trainIdx_ptr, ++distance_ptr)
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{
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int trainIndex = *trainIdx_ptr;
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float dst = *distance_ptr;
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DMatch m(queryIdx, trainIndex, 0, dst);
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curMatches[i] = m;
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}
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std::sort(curMatches.begin(), curMatches.end());
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}
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return true;
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}
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static bool ocl_radiusMatchDownload(const UMat &trainIdx, const UMat &distance, const UMat &nMatches,
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std::vector< std::vector<DMatch> > &matches, bool compactResult)
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{
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if (trainIdx.empty() || distance.empty() || nMatches.empty())
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return false;
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Mat trainIdxCPU = trainIdx.getMat(ACCESS_READ);
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Mat distanceCPU = distance.getMat(ACCESS_READ);
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Mat nMatchesCPU = nMatches.getMat(ACCESS_READ);
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return ocl_radiusMatchConvert(trainIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
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}
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/****************************************************************************************\
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* DescriptorMatcher *
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\****************************************************************************************/
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DescriptorMatcher::DescriptorCollection::DescriptorCollection()
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{}
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DescriptorMatcher::DescriptorCollection::DescriptorCollection( const DescriptorCollection& collection )
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{
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mergedDescriptors = collection.mergedDescriptors.clone();
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std::copy( collection.startIdxs.begin(), collection.startIdxs.begin(), startIdxs.begin() );
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}
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DescriptorMatcher::DescriptorCollection::~DescriptorCollection()
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{}
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void DescriptorMatcher::DescriptorCollection::set( const std::vector<Mat>& descriptors )
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{
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clear();
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size_t imageCount = descriptors.size();
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CV_Assert( imageCount > 0 );
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startIdxs.resize( imageCount );
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int dim = -1;
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int type = -1;
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startIdxs[0] = 0;
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for( size_t i = 1; i < imageCount; i++ )
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{
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int s = 0;
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if( !descriptors[i-1].empty() )
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{
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dim = descriptors[i-1].cols;
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type = descriptors[i-1].type();
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s = descriptors[i-1].rows;
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}
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startIdxs[i] = startIdxs[i-1] + s;
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}
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if( imageCount == 1 )
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{
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if( descriptors[0].empty() ) return;
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dim = descriptors[0].cols;
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type = descriptors[0].type();
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}
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CV_Assert( dim > 0 );
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int count = startIdxs[imageCount-1] + descriptors[imageCount-1].rows;
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if( count > 0 )
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{
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mergedDescriptors.create( count, dim, type );
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for( size_t i = 0; i < imageCount; i++ )
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{
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if( !descriptors[i].empty() )
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{
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CV_Assert( descriptors[i].cols == dim && descriptors[i].type() == type );
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Mat m = mergedDescriptors.rowRange( startIdxs[i], startIdxs[i] + descriptors[i].rows );
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descriptors[i].copyTo(m);
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}
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}
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}
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}
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void DescriptorMatcher::DescriptorCollection::clear()
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{
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startIdxs.clear();
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mergedDescriptors.release();
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}
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|
|
const Mat DescriptorMatcher::DescriptorCollection::getDescriptor( int imgIdx, int localDescIdx ) const
|
|
{
|
|
CV_Assert( imgIdx < (int)startIdxs.size() );
|
|
int globalIdx = startIdxs[imgIdx] + localDescIdx;
|
|
CV_Assert( globalIdx < (int)size() );
|
|
|
|
return getDescriptor( globalIdx );
|
|
}
|
|
|
|
const Mat& DescriptorMatcher::DescriptorCollection::getDescriptors() const
|
|
{
|
|
return mergedDescriptors;
|
|
}
|
|
|
|
const Mat DescriptorMatcher::DescriptorCollection::getDescriptor( int globalDescIdx ) const
|
|
{
|
|
CV_Assert( globalDescIdx < size() );
|
|
return mergedDescriptors.row( globalDescIdx );
|
|
}
|
|
|
|
void DescriptorMatcher::DescriptorCollection::getLocalIdx( int globalDescIdx, int& imgIdx, int& localDescIdx ) const
|
|
{
|
|
CV_Assert( (globalDescIdx>=0) && (globalDescIdx < size()) );
|
|
std::vector<int>::const_iterator img_it = std::upper_bound(startIdxs.begin(), startIdxs.end(), globalDescIdx);
|
|
--img_it;
|
|
imgIdx = (int)(img_it - startIdxs.begin());
|
|
localDescIdx = globalDescIdx - (*img_it);
|
|
}
|
|
|
|
int DescriptorMatcher::DescriptorCollection::size() const
|
|
{
|
|
return mergedDescriptors.rows;
|
|
}
|
|
|
|
/*
|
|
* DescriptorMatcher
|
|
*/
|
|
static void convertMatches( const std::vector<std::vector<DMatch> >& knnMatches, std::vector<DMatch>& matches )
|
|
{
|
|
matches.clear();
|
|
matches.reserve( knnMatches.size() );
|
|
for( size_t i = 0; i < knnMatches.size(); i++ )
|
|
{
|
|
CV_Assert( knnMatches[i].size() <= 1 );
|
|
if( !knnMatches[i].empty() )
|
|
matches.push_back( knnMatches[i][0] );
|
|
}
|
|
}
|
|
|
|
DescriptorMatcher::~DescriptorMatcher()
|
|
{}
|
|
|
|
void DescriptorMatcher::add( InputArrayOfArrays _descriptors )
|
|
{
|
|
if(_descriptors.isUMatVector())
|
|
{
|
|
std::vector<UMat> descriptors;
|
|
_descriptors.getUMatVector(descriptors);
|
|
utrainDescCollection.insert( utrainDescCollection.end(), descriptors.begin(), descriptors.end() );
|
|
}
|
|
else if(_descriptors.isUMat())
|
|
{
|
|
std::vector<UMat> descriptors = std::vector<UMat>(1, _descriptors.getUMat());
|
|
utrainDescCollection.insert( utrainDescCollection.end(), descriptors.begin(), descriptors.end() );
|
|
}
|
|
else if(_descriptors.isMatVector())
|
|
{
|
|
std::vector<Mat> descriptors;
|
|
_descriptors.getMatVector(descriptors);
|
|
trainDescCollection.insert( trainDescCollection.end(), descriptors.begin(), descriptors.end() );
|
|
}
|
|
else if(_descriptors.isMat())
|
|
{
|
|
std::vector<Mat> descriptors = std::vector<Mat>(1, _descriptors.getMat());
|
|
trainDescCollection.insert( trainDescCollection.end(), descriptors.begin(), descriptors.end() );
|
|
}
|
|
else
|
|
CV_Assert( _descriptors.isUMat() || _descriptors.isUMatVector() || _descriptors.isMat() || _descriptors.isMatVector() );
|
|
}
|
|
|
|
const std::vector<Mat>& DescriptorMatcher::getTrainDescriptors() const
|
|
{
|
|
return trainDescCollection;
|
|
}
|
|
|
|
void DescriptorMatcher::clear()
|
|
{
|
|
utrainDescCollection.clear();
|
|
trainDescCollection.clear();
|
|
}
|
|
|
|
bool DescriptorMatcher::empty() const
|
|
{
|
|
return trainDescCollection.empty() && utrainDescCollection.empty();
|
|
}
|
|
|
|
void DescriptorMatcher::train()
|
|
{}
|
|
|
|
void DescriptorMatcher::match( InputArray queryDescriptors, InputArray trainDescriptors,
|
|
std::vector<DMatch>& matches, InputArray mask ) const
|
|
{
|
|
Ptr<DescriptorMatcher> tempMatcher = clone(true);
|
|
tempMatcher->add(trainDescriptors);
|
|
tempMatcher->match( queryDescriptors, matches, std::vector<Mat>(1, mask.getMat()) );
|
|
}
|
|
|
|
void DescriptorMatcher::knnMatch( InputArray queryDescriptors, InputArray trainDescriptors,
|
|
std::vector<std::vector<DMatch> >& matches, int knn,
|
|
InputArray mask, bool compactResult ) const
|
|
{
|
|
Ptr<DescriptorMatcher> tempMatcher = clone(true);
|
|
tempMatcher->add(trainDescriptors);
|
|
tempMatcher->knnMatch( queryDescriptors, matches, knn, std::vector<Mat>(1, mask.getMat()), compactResult );
|
|
}
|
|
|
|
void DescriptorMatcher::radiusMatch( InputArray queryDescriptors, InputArray trainDescriptors,
|
|
std::vector<std::vector<DMatch> >& matches, float maxDistance, InputArray mask,
|
|
bool compactResult ) const
|
|
{
|
|
Ptr<DescriptorMatcher> tempMatcher = clone(true);
|
|
tempMatcher->add(trainDescriptors);
|
|
tempMatcher->radiusMatch( queryDescriptors, matches, maxDistance, std::vector<Mat>(1, mask.getMat()), compactResult );
|
|
}
|
|
|
|
void DescriptorMatcher::match( InputArray queryDescriptors, std::vector<DMatch>& matches, InputArrayOfArrays masks )
|
|
{
|
|
std::vector<std::vector<DMatch> > knnMatches;
|
|
knnMatch( queryDescriptors, knnMatches, 1, masks, true /*compactResult*/ );
|
|
convertMatches( knnMatches, matches );
|
|
}
|
|
|
|
void DescriptorMatcher::checkMasks( InputArrayOfArrays _masks, int queryDescriptorsCount ) const
|
|
{
|
|
std::vector<Mat> masks;
|
|
_masks.getMatVector(masks);
|
|
|
|
if( isMaskSupported() && !masks.empty() )
|
|
{
|
|
// Check masks
|
|
size_t imageCount = std::max(trainDescCollection.size(), utrainDescCollection.size() );
|
|
CV_Assert( masks.size() == imageCount );
|
|
for( size_t i = 0; i < imageCount; i++ )
|
|
{
|
|
if( !masks[i].empty() && (!trainDescCollection[i].empty() || !utrainDescCollection[i].empty() ) )
|
|
{
|
|
int rows = trainDescCollection[i].empty() ? utrainDescCollection[i].rows : trainDescCollection[i].rows;
|
|
CV_Assert( masks[i].rows == queryDescriptorsCount &&
|
|
(masks[i].cols == rows || masks[i].cols == rows) &&
|
|
masks[i].type() == CV_8UC1 );
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void DescriptorMatcher::knnMatch( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, int knn,
|
|
InputArrayOfArrays masks, bool compactResult )
|
|
{
|
|
if( empty() || queryDescriptors.empty() )
|
|
return;
|
|
|
|
CV_Assert( knn > 0 );
|
|
|
|
checkMasks( masks, queryDescriptors.size().height );
|
|
|
|
train();
|
|
knnMatchImpl( queryDescriptors, matches, knn, masks, compactResult );
|
|
}
|
|
|
|
void DescriptorMatcher::radiusMatch( InputArray queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
|
|
InputArrayOfArrays masks, bool compactResult )
|
|
{
|
|
matches.clear();
|
|
if( empty() || queryDescriptors.empty() )
|
|
return;
|
|
|
|
CV_Assert( maxDistance > std::numeric_limits<float>::epsilon() );
|
|
|
|
checkMasks( masks, queryDescriptors.size().height );
|
|
|
|
train();
|
|
radiusMatchImpl( queryDescriptors, matches, maxDistance, masks, compactResult );
|
|
}
|
|
|
|
void DescriptorMatcher::read( const FileNode& )
|
|
{}
|
|
|
|
void DescriptorMatcher::write( FileStorage& ) const
|
|
{}
|
|
|
|
bool DescriptorMatcher::isPossibleMatch( InputArray _mask, int queryIdx, int trainIdx )
|
|
{
|
|
Mat mask = _mask.getMat();
|
|
return mask.empty() || mask.at<uchar>(queryIdx, trainIdx);
|
|
}
|
|
|
|
bool DescriptorMatcher::isMaskedOut( InputArrayOfArrays _masks, int queryIdx )
|
|
{
|
|
std::vector<Mat> masks;
|
|
_masks.getMatVector(masks);
|
|
|
|
size_t outCount = 0;
|
|
for( size_t i = 0; i < masks.size(); i++ )
|
|
{
|
|
if( !masks[i].empty() && (countNonZero(masks[i].row(queryIdx)) == 0) )
|
|
outCount++;
|
|
}
|
|
|
|
return !masks.empty() && outCount == masks.size() ;
|
|
}
|
|
|
|
|
|
////////////////////////////////////////////////////// BruteForceMatcher /////////////////////////////////////////////////
|
|
|
|
BFMatcher::BFMatcher( int _normType, bool _crossCheck )
|
|
{
|
|
normType = _normType;
|
|
crossCheck = _crossCheck;
|
|
}
|
|
|
|
Ptr<DescriptorMatcher> BFMatcher::clone( bool emptyTrainData ) const
|
|
{
|
|
Ptr<BFMatcher> matcher = makePtr<BFMatcher>(normType, crossCheck);
|
|
if( !emptyTrainData )
|
|
{
|
|
matcher->trainDescCollection.resize(trainDescCollection.size());
|
|
std::transform( trainDescCollection.begin(), trainDescCollection.end(),
|
|
matcher->trainDescCollection.begin(), clone_op );
|
|
}
|
|
return matcher;
|
|
}
|
|
|
|
static bool ocl_match(InputArray query, InputArray _train, std::vector< std::vector<DMatch> > &matches, int dstType)
|
|
{
|
|
UMat trainIdx, distance;
|
|
if (!ocl_matchSingle(query, _train, trainIdx, distance, dstType))
|
|
return false;
|
|
if (!ocl_matchDownload(trainIdx, distance, matches))
|
|
return false;
|
|
return true;
|
|
}
|
|
|
|
static bool ocl_knnMatch(InputArray query, InputArray _train, std::vector< std::vector<DMatch> > &matches, int k, int dstType, bool compactResult)
|
|
{
|
|
UMat trainIdx, distance;
|
|
if (k != 2)
|
|
return false;
|
|
if (!ocl_knnMatchSingle(query, _train, trainIdx, distance, dstType))
|
|
return false;
|
|
if (!ocl_knnMatchDownload(trainIdx, distance, matches, compactResult) )
|
|
return false;
|
|
return true;
|
|
}
|
|
|
|
void BFMatcher::knnMatchImpl( InputArray _queryDescriptors, std::vector<std::vector<DMatch> >& matches, int knn,
|
|
InputArrayOfArrays _masks, bool compactResult )
|
|
{
|
|
int trainDescType = trainDescCollection.empty() ? utrainDescCollection[0].type() : trainDescCollection[0].type();
|
|
CV_Assert( _queryDescriptors.type() == trainDescType );
|
|
|
|
const int IMGIDX_SHIFT = 18;
|
|
const int IMGIDX_ONE = (1 << IMGIDX_SHIFT);
|
|
|
|
if( _queryDescriptors.empty() || (trainDescCollection.empty() && utrainDescCollection.empty()))
|
|
{
|
|
matches.clear();
|
|
return;
|
|
}
|
|
|
|
std::vector<Mat> masks;
|
|
_masks.getMatVector(masks);
|
|
|
|
if(!trainDescCollection.empty() && !utrainDescCollection.empty())
|
|
{
|
|
for(int i = 0; i < (int)utrainDescCollection.size(); i++)
|
|
{
|
|
Mat tempMat;
|
|
utrainDescCollection[i].copyTo(tempMat);
|
|
trainDescCollection.push_back(tempMat);
|
|
}
|
|
utrainDescCollection.clear();
|
|
}
|
|
|
|
int trainDescVectorSize = trainDescCollection.empty() ? (int)utrainDescCollection.size() : (int)trainDescCollection.size();
|
|
Size trainDescSize = trainDescCollection.empty() ? utrainDescCollection[0].size() : trainDescCollection[0].size();
|
|
int trainDescOffset = trainDescCollection.empty() ? (int)utrainDescCollection[0].offset : 0;
|
|
|
|
if ( ocl::useOpenCL() && _queryDescriptors.isUMat() && _queryDescriptors.dims()<=2 && trainDescVectorSize == 1 &&
|
|
_queryDescriptors.type() == CV_32FC1 && _queryDescriptors.offset() == 0 && trainDescOffset == 0 &&
|
|
trainDescSize.width == _queryDescriptors.size().width && masks.size() == 1 && masks[0].total() == 0 )
|
|
{
|
|
if(knn == 1)
|
|
{
|
|
if(trainDescCollection.empty())
|
|
{
|
|
if(ocl_match(_queryDescriptors, utrainDescCollection[0], matches, normType))
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_OCL);
|
|
return;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(ocl_match(_queryDescriptors, trainDescCollection[0], matches, normType))
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_OCL);
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(trainDescCollection.empty())
|
|
{
|
|
if(ocl_knnMatch(_queryDescriptors, utrainDescCollection[0], matches, knn, normType, compactResult) )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_OCL);
|
|
return;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if(ocl_knnMatch(_queryDescriptors, trainDescCollection[0], matches, knn, normType, compactResult) )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_OCL);
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
Mat queryDescriptors = _queryDescriptors.getMat();
|
|
if(trainDescCollection.empty() && !utrainDescCollection.empty())
|
|
{
|
|
for(int i = 0; i < (int)utrainDescCollection.size(); i++)
|
|
{
|
|
Mat tempMat;
|
|
utrainDescCollection[i].copyTo(tempMat);
|
|
trainDescCollection.push_back(tempMat);
|
|
}
|
|
utrainDescCollection.clear();
|
|
}
|
|
|
|
matches.reserve(queryDescriptors.rows);
|
|
|
|
Mat dist, nidx;
|
|
|
|
int iIdx, imgCount = (int)trainDescCollection.size(), update = 0;
|
|
int dtype = normType == NORM_HAMMING || normType == NORM_HAMMING2 ||
|
|
(normType == NORM_L1 && queryDescriptors.type() == CV_8U) ? CV_32S : CV_32F;
|
|
|
|
CV_Assert( (int64)imgCount*IMGIDX_ONE < INT_MAX );
|
|
|
|
for( iIdx = 0; iIdx < imgCount; iIdx++ )
|
|
{
|
|
CV_Assert( trainDescCollection[iIdx].rows < IMGIDX_ONE );
|
|
batchDistance(queryDescriptors, trainDescCollection[iIdx], dist, dtype, nidx,
|
|
normType, knn, masks.empty() ? Mat() : masks[iIdx], update, crossCheck);
|
|
update += IMGIDX_ONE;
|
|
}
|
|
|
|
if( dtype == CV_32S )
|
|
{
|
|
Mat temp;
|
|
dist.convertTo(temp, CV_32F);
|
|
dist = temp;
|
|
}
|
|
|
|
for( int qIdx = 0; qIdx < queryDescriptors.rows; qIdx++ )
|
|
{
|
|
const float* distptr = dist.ptr<float>(qIdx);
|
|
const int* nidxptr = nidx.ptr<int>(qIdx);
|
|
|
|
matches.push_back( std::vector<DMatch>() );
|
|
std::vector<DMatch>& mq = matches.back();
|
|
mq.reserve(knn);
|
|
|
|
for( int k = 0; k < nidx.cols; k++ )
|
|
{
|
|
if( nidxptr[k] < 0 )
|
|
break;
|
|
mq.push_back( DMatch(qIdx, nidxptr[k] & (IMGIDX_ONE - 1),
|
|
nidxptr[k] >> IMGIDX_SHIFT, distptr[k]) );
|
|
}
|
|
|
|
if( mq.empty() && compactResult )
|
|
matches.pop_back();
|
|
}
|
|
}
|
|
|
|
static bool ocl_radiusMatch(InputArray query, InputArray _train, std::vector< std::vector<DMatch> > &matches,
|
|
float maxDistance, int dstType, bool compactResult)
|
|
{
|
|
UMat trainIdx, distance, nMatches;
|
|
if (!ocl_radiusMatchSingle(query, _train, trainIdx, distance, nMatches, maxDistance, dstType))
|
|
return false;
|
|
if (!ocl_radiusMatchDownload(trainIdx, distance, nMatches, matches, compactResult))
|
|
return false;
|
|
return true;
|
|
}
|
|
|
|
void BFMatcher::radiusMatchImpl( InputArray _queryDescriptors, std::vector<std::vector<DMatch> >& matches,
|
|
float maxDistance, InputArrayOfArrays _masks, bool compactResult )
|
|
{
|
|
int trainDescType = trainDescCollection.empty() ? utrainDescCollection[0].type() : trainDescCollection[0].type();
|
|
CV_Assert( _queryDescriptors.type() == trainDescType );
|
|
|
|
if( _queryDescriptors.empty() || (trainDescCollection.empty() && utrainDescCollection.empty()))
|
|
{
|
|
matches.clear();
|
|
return;
|
|
}
|
|
|
|
std::vector<Mat> masks;
|
|
_masks.getMatVector(masks);
|
|
|
|
if(!trainDescCollection.empty() && !utrainDescCollection.empty())
|
|
{
|
|
for(int i = 0; i < (int)utrainDescCollection.size(); i++)
|
|
{
|
|
Mat tempMat;
|
|
utrainDescCollection[i].copyTo(tempMat);
|
|
trainDescCollection.push_back(tempMat);
|
|
}
|
|
utrainDescCollection.clear();
|
|
}
|
|
|
|
int trainDescVectorSize = trainDescCollection.empty() ? (int)utrainDescCollection.size() : (int)trainDescCollection.size();
|
|
Size trainDescSize = trainDescCollection.empty() ? utrainDescCollection[0].size() : trainDescCollection[0].size();
|
|
int trainDescOffset = trainDescCollection.empty() ? (int)utrainDescCollection[0].offset : 0;
|
|
|
|
if ( ocl::useOpenCL() && _queryDescriptors.isUMat() && _queryDescriptors.dims()<=2 && trainDescVectorSize == 1 &&
|
|
_queryDescriptors.type() == CV_32FC1 && _queryDescriptors.offset() == 0 && trainDescOffset == 0 &&
|
|
trainDescSize.width == _queryDescriptors.size().width && masks.size() == 1 && masks[0].total() == 0 )
|
|
{
|
|
if (trainDescCollection.empty())
|
|
{
|
|
if(ocl_radiusMatch(_queryDescriptors, utrainDescCollection[0], matches, maxDistance, normType, compactResult) )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_OCL);
|
|
return;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
if (ocl_radiusMatch(_queryDescriptors, trainDescCollection[0], matches, maxDistance, normType, compactResult) )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_OCL);
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
|
|
Mat queryDescriptors = _queryDescriptors.getMat();
|
|
if(trainDescCollection.empty() && !utrainDescCollection.empty())
|
|
{
|
|
for(int i = 0; i < (int)utrainDescCollection.size(); i++)
|
|
{
|
|
Mat tempMat;
|
|
utrainDescCollection[i].copyTo(tempMat);
|
|
trainDescCollection.push_back(tempMat);
|
|
}
|
|
utrainDescCollection.clear();
|
|
}
|
|
|
|
matches.resize(queryDescriptors.rows);
|
|
Mat dist, distf;
|
|
|
|
int iIdx, imgCount = (int)trainDescCollection.size();
|
|
int dtype = normType == NORM_HAMMING ||
|
|
(normType == NORM_L1 && queryDescriptors.type() == CV_8U) ? CV_32S : CV_32F;
|
|
|
|
for( iIdx = 0; iIdx < imgCount; iIdx++ )
|
|
{
|
|
batchDistance(queryDescriptors, trainDescCollection[iIdx], dist, dtype, noArray(),
|
|
normType, 0, masks.empty() ? Mat() : masks[iIdx], 0, false);
|
|
if( dtype == CV_32S )
|
|
dist.convertTo(distf, CV_32F);
|
|
else
|
|
distf = dist;
|
|
|
|
for( int qIdx = 0; qIdx < queryDescriptors.rows; qIdx++ )
|
|
{
|
|
const float* distptr = distf.ptr<float>(qIdx);
|
|
|
|
std::vector<DMatch>& mq = matches[qIdx];
|
|
for( int k = 0; k < distf.cols; k++ )
|
|
{
|
|
if( distptr[k] <= maxDistance )
|
|
mq.push_back( DMatch(qIdx, k, iIdx, distptr[k]) );
|
|
}
|
|
}
|
|
}
|
|
|
|
int qIdx0 = 0;
|
|
for( int qIdx = 0; qIdx < queryDescriptors.rows; qIdx++ )
|
|
{
|
|
if( matches[qIdx].empty() && compactResult )
|
|
continue;
|
|
|
|
if( qIdx0 < qIdx )
|
|
std::swap(matches[qIdx], matches[qIdx0]);
|
|
|
|
std::sort( matches[qIdx0].begin(), matches[qIdx0].end() );
|
|
qIdx0++;
|
|
}
|
|
}
|
|
|
|
///////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
|
|
/*
|
|
* Factory function for DescriptorMatcher creating
|
|
*/
|
|
Ptr<DescriptorMatcher> DescriptorMatcher::create( const String& descriptorMatcherType )
|
|
{
|
|
Ptr<DescriptorMatcher> dm;
|
|
if( !descriptorMatcherType.compare( "FlannBased" ) )
|
|
{
|
|
dm = makePtr<FlannBasedMatcher>();
|
|
}
|
|
else if( !descriptorMatcherType.compare( "BruteForce" ) ) // L2
|
|
{
|
|
dm = makePtr<BFMatcher>(int(NORM_L2)); // anonymous enums can't be template parameters
|
|
}
|
|
else if( !descriptorMatcherType.compare( "BruteForce-SL2" ) ) // Squared L2
|
|
{
|
|
dm = makePtr<BFMatcher>(int(NORM_L2SQR));
|
|
}
|
|
else if( !descriptorMatcherType.compare( "BruteForce-L1" ) )
|
|
{
|
|
dm = makePtr<BFMatcher>(int(NORM_L1));
|
|
}
|
|
else if( !descriptorMatcherType.compare("BruteForce-Hamming") ||
|
|
!descriptorMatcherType.compare("BruteForce-HammingLUT") )
|
|
{
|
|
dm = makePtr<BFMatcher>(int(NORM_HAMMING));
|
|
}
|
|
else if( !descriptorMatcherType.compare("BruteForce-Hamming(2)") )
|
|
{
|
|
dm = makePtr<BFMatcher>(int(NORM_HAMMING2));
|
|
}
|
|
else
|
|
CV_Error( Error::StsBadArg, "Unknown matcher name" );
|
|
|
|
return dm;
|
|
}
|
|
|
|
|
|
/*
|
|
* Flann based matcher
|
|
*/
|
|
FlannBasedMatcher::FlannBasedMatcher( const Ptr<flann::IndexParams>& _indexParams, const Ptr<flann::SearchParams>& _searchParams )
|
|
: indexParams(_indexParams), searchParams(_searchParams), addedDescCount(0)
|
|
{
|
|
CV_Assert( _indexParams );
|
|
CV_Assert( _searchParams );
|
|
}
|
|
|
|
void FlannBasedMatcher::add( InputArrayOfArrays _descriptors )
|
|
{
|
|
DescriptorMatcher::add( _descriptors );
|
|
std::vector<UMat> descriptors;
|
|
_descriptors.getUMatVector(descriptors);
|
|
|
|
for( size_t i = 0; i < descriptors.size(); i++ )
|
|
{
|
|
addedDescCount += descriptors[i].rows;
|
|
}
|
|
}
|
|
|
|
void FlannBasedMatcher::clear()
|
|
{
|
|
DescriptorMatcher::clear();
|
|
|
|
mergedDescriptors.clear();
|
|
flannIndex.release();
|
|
|
|
addedDescCount = 0;
|
|
}
|
|
|
|
void FlannBasedMatcher::train()
|
|
{
|
|
if( !flannIndex || mergedDescriptors.size() < addedDescCount )
|
|
{
|
|
// FIXIT: Workaround for 'utrainDescCollection' issue (PR #2142)
|
|
if (!utrainDescCollection.empty())
|
|
{
|
|
CV_Assert(trainDescCollection.size() == 0);
|
|
for (size_t i = 0; i < utrainDescCollection.size(); ++i)
|
|
trainDescCollection.push_back(utrainDescCollection[i].getMat(ACCESS_READ));
|
|
}
|
|
mergedDescriptors.set( trainDescCollection );
|
|
flannIndex = makePtr<flann::Index>( mergedDescriptors.getDescriptors(), *indexParams );
|
|
}
|
|
}
|
|
|
|
void FlannBasedMatcher::read( const FileNode& fn)
|
|
{
|
|
if (!indexParams)
|
|
indexParams = makePtr<flann::IndexParams>();
|
|
|
|
FileNode ip = fn["indexParams"];
|
|
CV_Assert(ip.type() == FileNode::SEQ);
|
|
|
|
for(int i = 0; i < (int)ip.size(); ++i)
|
|
{
|
|
CV_Assert(ip[i].type() == FileNode::MAP);
|
|
String _name = (String)ip[i]["name"];
|
|
int type = (int)ip[i]["type"];
|
|
|
|
switch(type)
|
|
{
|
|
case CV_8U:
|
|
case CV_8S:
|
|
case CV_16U:
|
|
case CV_16S:
|
|
case CV_32S:
|
|
indexParams->setInt(_name, (int) ip[i]["value"]);
|
|
break;
|
|
case CV_32F:
|
|
indexParams->setFloat(_name, (float) ip[i]["value"]);
|
|
break;
|
|
case CV_64F:
|
|
indexParams->setDouble(_name, (double) ip[i]["value"]);
|
|
break;
|
|
case CV_USRTYPE1:
|
|
indexParams->setString(_name, (String) ip[i]["value"]);
|
|
break;
|
|
case CV_MAKETYPE(CV_USRTYPE1,2):
|
|
indexParams->setBool(_name, (int) ip[i]["value"] != 0);
|
|
break;
|
|
case CV_MAKETYPE(CV_USRTYPE1,3):
|
|
indexParams->setAlgorithm((int) ip[i]["value"]);
|
|
break;
|
|
};
|
|
}
|
|
|
|
if (!searchParams)
|
|
searchParams = makePtr<flann::SearchParams>();
|
|
|
|
FileNode sp = fn["searchParams"];
|
|
CV_Assert(sp.type() == FileNode::SEQ);
|
|
|
|
for(int i = 0; i < (int)sp.size(); ++i)
|
|
{
|
|
CV_Assert(sp[i].type() == FileNode::MAP);
|
|
String _name = (String)sp[i]["name"];
|
|
int type = (int)sp[i]["type"];
|
|
|
|
switch(type)
|
|
{
|
|
case CV_8U:
|
|
case CV_8S:
|
|
case CV_16U:
|
|
case CV_16S:
|
|
case CV_32S:
|
|
searchParams->setInt(_name, (int) sp[i]["value"]);
|
|
break;
|
|
case CV_32F:
|
|
searchParams->setFloat(_name, (float) ip[i]["value"]);
|
|
break;
|
|
case CV_64F:
|
|
searchParams->setDouble(_name, (double) ip[i]["value"]);
|
|
break;
|
|
case CV_USRTYPE1:
|
|
searchParams->setString(_name, (String) ip[i]["value"]);
|
|
break;
|
|
case CV_MAKETYPE(CV_USRTYPE1,2):
|
|
searchParams->setBool(_name, (int) ip[i]["value"] != 0);
|
|
break;
|
|
case CV_MAKETYPE(CV_USRTYPE1,3):
|
|
searchParams->setAlgorithm((int) ip[i]["value"]);
|
|
break;
|
|
};
|
|
}
|
|
|
|
flannIndex.release();
|
|
}
|
|
|
|
void FlannBasedMatcher::write( FileStorage& fs) const
|
|
{
|
|
fs << "indexParams" << "[";
|
|
|
|
if (indexParams)
|
|
{
|
|
std::vector<String> names;
|
|
std::vector<int> types;
|
|
std::vector<String> strValues;
|
|
std::vector<double> numValues;
|
|
|
|
indexParams->getAll(names, types, strValues, numValues);
|
|
|
|
for(size_t i = 0; i < names.size(); ++i)
|
|
{
|
|
fs << "{" << "name" << names[i] << "type" << types[i] << "value";
|
|
switch(types[i])
|
|
{
|
|
case CV_8U:
|
|
fs << (uchar)numValues[i];
|
|
break;
|
|
case CV_8S:
|
|
fs << (char)numValues[i];
|
|
break;
|
|
case CV_16U:
|
|
fs << (ushort)numValues[i];
|
|
break;
|
|
case CV_16S:
|
|
fs << (short)numValues[i];
|
|
break;
|
|
case CV_32S:
|
|
case CV_MAKETYPE(CV_USRTYPE1,2):
|
|
case CV_MAKETYPE(CV_USRTYPE1,3):
|
|
fs << (int)numValues[i];
|
|
break;
|
|
case CV_32F:
|
|
fs << (float)numValues[i];
|
|
break;
|
|
case CV_64F:
|
|
fs << (double)numValues[i];
|
|
break;
|
|
case CV_USRTYPE1:
|
|
fs << strValues[i];
|
|
break;
|
|
default:
|
|
fs << (double)numValues[i];
|
|
fs << "typename" << strValues[i];
|
|
break;
|
|
}
|
|
fs << "}";
|
|
}
|
|
}
|
|
|
|
fs << "]" << "searchParams" << "[";
|
|
|
|
if (searchParams)
|
|
{
|
|
std::vector<String> names;
|
|
std::vector<int> types;
|
|
std::vector<String> strValues;
|
|
std::vector<double> numValues;
|
|
|
|
searchParams->getAll(names, types, strValues, numValues);
|
|
|
|
for(size_t i = 0; i < names.size(); ++i)
|
|
{
|
|
fs << "{" << "name" << names[i] << "type" << types[i] << "value";
|
|
switch(types[i])
|
|
{
|
|
case CV_8U:
|
|
fs << (uchar)numValues[i];
|
|
break;
|
|
case CV_8S:
|
|
fs << (char)numValues[i];
|
|
break;
|
|
case CV_16U:
|
|
fs << (ushort)numValues[i];
|
|
break;
|
|
case CV_16S:
|
|
fs << (short)numValues[i];
|
|
break;
|
|
case CV_32S:
|
|
case CV_MAKETYPE(CV_USRTYPE1,2):
|
|
case CV_MAKETYPE(CV_USRTYPE1,3):
|
|
fs << (int)numValues[i];
|
|
break;
|
|
case CV_32F:
|
|
fs << (float)numValues[i];
|
|
break;
|
|
case CV_64F:
|
|
fs << (double)numValues[i];
|
|
break;
|
|
case CV_USRTYPE1:
|
|
fs << strValues[i];
|
|
break;
|
|
default:
|
|
fs << (double)numValues[i];
|
|
fs << "typename" << strValues[i];
|
|
break;
|
|
}
|
|
fs << "}";
|
|
}
|
|
}
|
|
fs << "]";
|
|
}
|
|
|
|
bool FlannBasedMatcher::isMaskSupported() const
|
|
{
|
|
return false;
|
|
}
|
|
|
|
Ptr<DescriptorMatcher> FlannBasedMatcher::clone( bool emptyTrainData ) const
|
|
{
|
|
Ptr<FlannBasedMatcher> matcher = makePtr<FlannBasedMatcher>(indexParams, searchParams);
|
|
if( !emptyTrainData )
|
|
{
|
|
CV_Error( Error::StsNotImplemented, "deep clone functionality is not implemented, because "
|
|
"Flann::Index has not copy constructor or clone method ");
|
|
//matcher->flannIndex;
|
|
matcher->addedDescCount = addedDescCount;
|
|
matcher->mergedDescriptors = DescriptorCollection( mergedDescriptors );
|
|
std::transform( trainDescCollection.begin(), trainDescCollection.end(),
|
|
matcher->trainDescCollection.begin(), clone_op );
|
|
}
|
|
return matcher;
|
|
}
|
|
|
|
void FlannBasedMatcher::convertToDMatches( const DescriptorCollection& collection, const Mat& indices, const Mat& dists,
|
|
std::vector<std::vector<DMatch> >& matches )
|
|
{
|
|
matches.resize( indices.rows );
|
|
for( int i = 0; i < indices.rows; i++ )
|
|
{
|
|
for( int j = 0; j < indices.cols; j++ )
|
|
{
|
|
int idx = indices.at<int>(i, j);
|
|
if( idx >= 0 )
|
|
{
|
|
int imgIdx, trainIdx;
|
|
collection.getLocalIdx( idx, imgIdx, trainIdx );
|
|
float dist = 0;
|
|
if (dists.type() == CV_32S)
|
|
dist = static_cast<float>( dists.at<int>(i,j) );
|
|
else
|
|
dist = std::sqrt(dists.at<float>(i,j));
|
|
matches[i].push_back( DMatch( i, trainIdx, imgIdx, dist ) );
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void FlannBasedMatcher::knnMatchImpl( InputArray _queryDescriptors, std::vector<std::vector<DMatch> >& matches, int knn,
|
|
InputArrayOfArrays /*masks*/, bool /*compactResult*/ )
|
|
{
|
|
Mat queryDescriptors = _queryDescriptors.getMat();
|
|
Mat indices( queryDescriptors.rows, knn, CV_32SC1 );
|
|
Mat dists( queryDescriptors.rows, knn, CV_32FC1);
|
|
flannIndex->knnSearch( queryDescriptors, indices, dists, knn, *searchParams );
|
|
|
|
convertToDMatches( mergedDescriptors, indices, dists, matches );
|
|
}
|
|
|
|
void FlannBasedMatcher::radiusMatchImpl( InputArray _queryDescriptors, std::vector<std::vector<DMatch> >& matches, float maxDistance,
|
|
InputArrayOfArrays /*masks*/, bool /*compactResult*/ )
|
|
{
|
|
Mat queryDescriptors = _queryDescriptors.getMat();
|
|
const int count = mergedDescriptors.size(); // TODO do count as param?
|
|
Mat indices( queryDescriptors.rows, count, CV_32SC1, Scalar::all(-1) );
|
|
Mat dists( queryDescriptors.rows, count, CV_32FC1, Scalar::all(-1) );
|
|
for( int qIdx = 0; qIdx < queryDescriptors.rows; qIdx++ )
|
|
{
|
|
Mat queryDescriptorsRow = queryDescriptors.row(qIdx);
|
|
Mat indicesRow = indices.row(qIdx);
|
|
Mat distsRow = dists.row(qIdx);
|
|
flannIndex->radiusSearch( queryDescriptorsRow, indicesRow, distsRow, maxDistance*maxDistance, count, *searchParams );
|
|
}
|
|
|
|
convertToDMatches( mergedDescriptors, indices, dists, matches );
|
|
}
|
|
|
|
}
|