opencv/modules/gpu/src/brute_force_matcher.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other GpuMaterials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or bpied warranties, including, but not limited to, the bpied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::gpu;
using namespace std;
#if !defined (HAVE_CUDA)
cv::gpu::BruteForceMatcher_GPU_base::BruteForceMatcher_GPU_base(DistType) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::add(const vector<GpuMat>&) { throw_nogpu(); }
const vector<GpuMat>& cv::gpu::BruteForceMatcher_GPU_base::getTrainDescriptors() const { throw_nogpu(); return trainDescCollection; }
void cv::gpu::BruteForceMatcher_GPU_base::clear() { throw_nogpu(); }
bool cv::gpu::BruteForceMatcher_GPU_base::empty() const { throw_nogpu(); return true; }
bool cv::gpu::BruteForceMatcher_GPU_base::isMaskSupported() const { throw_nogpu(); return true; }
void cv::gpu::BruteForceMatcher_GPU_base::matchSingle(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::BruteForceMatcher_GPU_base::matchDownload(const GpuMat&, const GpuMat&, vector<DMatch>&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::matchConvert(const Mat&, const Mat&, vector<DMatch>&) { throw_nogpu(); }
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void cv::gpu::BruteForceMatcher_GPU_base::match(const GpuMat&, const GpuMat&, vector<DMatch>&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::makeGpuCollection(GpuMat&, GpuMat&, const vector<GpuMat>&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::matchCollection(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::matchDownload(const GpuMat&, const GpuMat&, const GpuMat&, vector<DMatch>&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::matchConvert(const Mat&, const Mat&, const Mat&, vector<DMatch>&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::match(const GpuMat&, vector<DMatch>&, const vector<GpuMat>&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::knnMatchSingle(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, int, const GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::knnMatchDownload(const GpuMat&, const GpuMat&, vector< vector<DMatch> >&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::knnMatchConvert(const Mat&, const Mat&, vector< vector<DMatch> >&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch(const GpuMat&, const GpuMat&, vector< vector<DMatch> >&, int, const GpuMat&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch2Collection(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch2Download(const GpuMat&, const GpuMat&, const GpuMat&, vector< vector<DMatch> >&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch2Convert(const Mat&, const Mat&, const Mat&, vector< vector<DMatch> >&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch(const GpuMat&, vector< vector<DMatch> >&, int, const vector<GpuMat>&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchSingle(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, GpuMat&, float, const GpuMat&, Stream&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchDownload(const GpuMat&, const GpuMat&, const GpuMat&, vector< vector<DMatch> >&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchConvert(const Mat&, const Mat&, const Mat&, vector< vector<DMatch> >&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatch(const GpuMat&, const GpuMat&, vector< vector<DMatch> >&, float, const GpuMat&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchCollection(const GpuMat&, GpuMat&, GpuMat&, GpuMat&, GpuMat&, float, const vector<GpuMat>&, Stream&) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchDownload(const GpuMat&, const GpuMat&, const GpuMat&, const GpuMat&, vector< vector<DMatch> >&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchConvert(const Mat&, const Mat&, const Mat&, const Mat&, vector< vector<DMatch> >&, bool) { throw_nogpu(); }
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatch(const GpuMat&, vector< vector<DMatch> >&, float, const vector<GpuMat>&, bool) { throw_nogpu(); }
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#else /* !defined (HAVE_CUDA) */
namespace cv { namespace gpu { namespace bf_match
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{
template <typename T> void matchL1_gpu(const DevMem2D& query, const DevMem2D& train, const DevMem2D& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream);
template <typename T> void matchL2_gpu(const DevMem2D& query, const DevMem2D& train, const DevMem2D& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const DevMem2D& query, const DevMem2D& train, const DevMem2D& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream);
template <typename T> void matchL1_gpu(const DevMem2D& query, const DevMem2D& trains, const DevMem2D_<PtrStep>& masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream);
template <typename T> void matchL2_gpu(const DevMem2D& query, const DevMem2D& trains, const DevMem2D_<PtrStep>& masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const DevMem2D& query, const DevMem2D& trains, const DevMem2D_<PtrStep>& masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream);
}}}
namespace cv { namespace gpu { namespace bf_knnmatch
{
template <typename T> void matchL1_gpu(const DevMem2D& query, const DevMem2D& train, int k, const DevMem2D& mask,
const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2Df& allDist,
int cc, cudaStream_t stream);
template <typename T> void matchL2_gpu(const DevMem2D& query, const DevMem2D& train, int k, const DevMem2D& mask,
const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2Df& allDist,
int cc, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const DevMem2D& query, const DevMem2D& train, int k, const DevMem2D& mask,
const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2Df& allDist,
int cc, cudaStream_t stream);
template <typename T> void match2L1_gpu(const DevMem2D& query, const DevMem2D& trains, const DevMem2D_<PtrStep>& masks,
const DevMem2D& trainIdx, const DevMem2D& imgIdx, const DevMem2D& distance,
int cc, cudaStream_t stream);
template <typename T> void match2L2_gpu(const DevMem2D& query, const DevMem2D& trains, const DevMem2D_<PtrStep>& masks,
const DevMem2D& trainIdx, const DevMem2D& imgIdx, const DevMem2D& distance,
int cc, cudaStream_t stream);
template <typename T> void match2Hamming_gpu(const DevMem2D& query, const DevMem2D& trains, const DevMem2D_<PtrStep>& masks,
const DevMem2D& trainIdx, const DevMem2D& imgIdx, const DevMem2D& distance,
int cc, cudaStream_t stream);
}}}
namespace cv { namespace gpu { namespace bf_radius_match
{
template <typename T> void matchL1_gpu(const DevMem2D& query, const DevMem2D& train, float maxDistance, const DevMem2D& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream);
template <typename T> void matchL2_gpu(const DevMem2D& query, const DevMem2D& train, float maxDistance, const DevMem2D& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const DevMem2D& query, const DevMem2D& train, float maxDistance, const DevMem2D& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream);
template <typename T> void matchL1_gpu(const DevMem2D& query, const DevMem2D* trains, int n, float maxDistance, const DevMem2D* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream);
template <typename T> void matchL2_gpu(const DevMem2D& query, const DevMem2D* trains, int n, float maxDistance, const DevMem2D* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream);
template <typename T> void matchHamming_gpu(const DevMem2D& query, const DevMem2D* trains, int n, float maxDistance, const DevMem2D* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream);
}}}
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cv::gpu::BruteForceMatcher_GPU_base::BruteForceMatcher_GPU_base(DistType distType_) : distType(distType_)
{
}
////////////////////////////////////////////////////////////////////
// Train collection
void cv::gpu::BruteForceMatcher_GPU_base::add(const vector<GpuMat>& descCollection)
{
trainDescCollection.insert(trainDescCollection.end(), descCollection.begin(), descCollection.end());
}
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const vector<GpuMat>& cv::gpu::BruteForceMatcher_GPU_base::getTrainDescriptors() const
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{
return trainDescCollection;
}
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void cv::gpu::BruteForceMatcher_GPU_base::clear()
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{
trainDescCollection.clear();
}
bool cv::gpu::BruteForceMatcher_GPU_base::empty() const
{
return trainDescCollection.empty();
}
bool cv::gpu::BruteForceMatcher_GPU_base::isMaskSupported() const
{
return true;
}
////////////////////////////////////////////////////////////////////
// Match
void cv::gpu::BruteForceMatcher_GPU_base::matchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance,
const GpuMat& mask, Stream& stream)
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{
if (query.empty() || train.empty())
return;
using namespace cv::gpu::bf_match;
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typedef void (*caller_t)(const DevMem2D& query, const DevMem2D& train, const DevMem2D& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream);
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static const caller_t callers[3][6] =
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{
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
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},
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
},
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
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}
};
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CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.cols == query.cols && train.type() == query.type());
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const int nQuery = query.rows;
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ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32F, distance);
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caller_t func = callers[distType][query.depth()];
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CV_Assert(func != 0);
DeviceInfo info;
int cc = info.majorVersion() * 10 + info.minorVersion();
func(query, train, mask, trainIdx, distance, cc, StreamAccessor::getStream(stream));
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}
void cv::gpu::BruteForceMatcher_GPU_base::matchDownload(const GpuMat& trainIdx, const GpuMat& distance, vector<DMatch>& matches)
{
if (trainIdx.empty() || distance.empty())
return;
Mat trainIdxCPU = trainIdx;
Mat distanceCPU = distance;
matchConvert(trainIdxCPU, distanceCPU, matches);
}
void cv::gpu::BruteForceMatcher_GPU_base::matchConvert(const Mat& trainIdx, const Mat& distance, vector<DMatch>& matches)
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{
if (trainIdx.empty() || distance.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(distance.type() == CV_32FC1 && distance.cols == trainIdx.cols);
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const int nQuery = trainIdx.cols;
matches.clear();
matches.reserve(nQuery);
const int* trainIdx_ptr = trainIdx.ptr<int>();
const float* distance_ptr = distance.ptr<float>();
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for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
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if (trainIdx == -1)
continue;
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, 0, distance);
matches.push_back(m);
}
}
void cv::gpu::BruteForceMatcher_GPU_base::match(const GpuMat& query, const GpuMat& train,
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vector<DMatch>& matches, const GpuMat& mask)
{
GpuMat trainIdx, distance;
matchSingle(query, train, trainIdx, distance, mask);
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matchDownload(trainIdx, distance, matches);
}
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void cv::gpu::BruteForceMatcher_GPU_base::makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection,
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const vector<GpuMat>& masks)
{
if (empty())
return;
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if (masks.empty())
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{
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Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(DevMem2D)));
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DevMem2D* trainCollectionCPU_ptr = trainCollectionCPU.ptr<DevMem2D>();
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for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr)
*trainCollectionCPU_ptr = trainDescCollection[i];
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trainCollection.upload(trainCollectionCPU);
maskCollection.release();
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}
else
{
CV_Assert(masks.size() == trainDescCollection.size());
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Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(DevMem2D)));
Mat maskCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStep)));
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DevMem2D* trainCollectionCPU_ptr = trainCollectionCPU.ptr<DevMem2D>();
PtrStep* maskCollectionCPU_ptr = maskCollectionCPU.ptr<PtrStep>();
for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr, ++maskCollectionCPU_ptr)
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{
const GpuMat& train = trainDescCollection[i];
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const GpuMat& mask = masks[i];
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.cols == train.rows));
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*trainCollectionCPU_ptr = train;
*maskCollectionCPU_ptr = mask;
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}
trainCollection.upload(trainCollectionCPU);
maskCollection.upload(maskCollectionCPU);
}
}
void cv::gpu::BruteForceMatcher_GPU_base::matchCollection(const GpuMat& query, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& masks, Stream& stream)
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{
if (query.empty() || trainCollection.empty())
return;
using namespace cv::gpu::bf_match;
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typedef void (*caller_t)(const DevMem2D& query, const DevMem2D& trains, const DevMem2D_<PtrStep>& masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance,
int cc, cudaStream_t stream);
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static const caller_t callers[3][6] =
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{
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
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},
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
},
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
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}
};
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CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
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const int nQuery = query.rows;
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ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32S, imgIdx);
ensureSizeIsEnough(1, nQuery, CV_32F, distance);
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caller_t func = callers[distType][query.depth()];
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CV_Assert(func != 0);
DeviceInfo info;
int cc = info.majorVersion() * 10 + info.minorVersion();
func(query, trainCollection, masks, trainIdx, imgIdx, distance, cc, StreamAccessor::getStream(stream));
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}
void cv::gpu::BruteForceMatcher_GPU_base::matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, vector<DMatch>& matches)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
Mat trainIdxCPU = trainIdx;
Mat imgIdxCPU = imgIdx;
Mat distanceCPU = distance;
matchConvert(trainIdxCPU, imgIdxCPU, distanceCPU, matches);
}
void cv::gpu::BruteForceMatcher_GPU_base::matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches)
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{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(imgIdx.type() == CV_32SC1 && imgIdx.cols == trainIdx.cols);
CV_Assert(distance.type() == CV_32FC1 && distance.cols == trainIdx.cols);
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const int nQuery = trainIdx.cols;
matches.clear();
matches.reserve(nQuery);
const int* trainIdx_ptr = trainIdx.ptr<int>();
const int* imgIdx_ptr = imgIdx.ptr<int>();
const float* distance_ptr = distance.ptr<float>();
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for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
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if (trainIdx == -1)
continue;
int imgIdx = *imgIdx_ptr;
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, imgIdx, distance);
matches.push_back(m);
}
}
void cv::gpu::BruteForceMatcher_GPU_base::match(const GpuMat& query, vector<DMatch>& matches, const vector<GpuMat>& masks)
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{
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GpuMat trainCollection;
GpuMat maskCollection;
makeGpuCollection(trainCollection, maskCollection, masks);
GpuMat trainIdx, imgIdx, distance;
matchCollection(query, trainCollection, trainIdx, imgIdx, distance, maskCollection);
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matchDownload(trainIdx, imgIdx, distance, matches);
}
////////////////////////////////////////////////////////////////////
// KnnMatch
void cv::gpu::BruteForceMatcher_GPU_base::knnMatchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
const GpuMat& mask, Stream& stream)
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{
if (query.empty() || train.empty())
return;
using namespace cv::gpu::bf_knnmatch;
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typedef void (*caller_t)(const DevMem2D& query, const DevMem2D& train, int k, const DevMem2D& mask,
const DevMem2D& trainIdx, const DevMem2D& distance, const DevMem2Df& allDist,
int cc, cudaStream_t stream);
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static const caller_t callers[3][6] =
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{
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
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},
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
},
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
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}
};
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CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.type() == query.type() && train.cols == query.cols);
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const int nQuery = query.rows;
const int nTrain = train.rows;
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if (k == 2)
{
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
}
else
{
ensureSizeIsEnough(nQuery, k, CV_32S, trainIdx);
ensureSizeIsEnough(nQuery, k, CV_32F, distance);
ensureSizeIsEnough(nQuery, nTrain, CV_32FC1, allDist);
}
if (stream)
stream.enqueueMemSet(trainIdx, Scalar::all(-1));
else
trainIdx.setTo(Scalar::all(-1));
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caller_t func = callers[distType][query.depth()];
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CV_Assert(func != 0);
DeviceInfo info;
int cc = info.majorVersion() * 10 + info.minorVersion();
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func(query, train, k, mask, trainIdx, distance, allDist, cc, StreamAccessor::getStream(stream));
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}
void cv::gpu::BruteForceMatcher_GPU_base::knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
vector< vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty())
return;
Mat trainIdxCPU = trainIdx;
Mat distanceCPU = distance;
knnMatchConvert(trainIdxCPU, distanceCPU, matches, compactResult);
}
void cv::gpu::BruteForceMatcher_GPU_base::knnMatchConvert(const Mat& trainIdx, const Mat& distance,
vector< vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC2 || trainIdx.type() == CV_32SC1);
CV_Assert(distance.type() == CV_32FC2 || distance.type() == CV_32FC1);
CV_Assert(distance.size() == trainIdx.size());
CV_Assert(trainIdx.isContinuous() && distance.isContinuous());
const int nQuery = trainIdx.type() == CV_32SC2 ? trainIdx.cols : trainIdx.rows;
const int k = trainIdx.type() == CV_32SC2 ? 2 :trainIdx.cols;
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matches.clear();
matches.reserve(nQuery);
const int* trainIdx_ptr = trainIdx.ptr<int>();
const float* distance_ptr = distance.ptr<float>();
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for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
matches.push_back(vector<DMatch>());
vector<DMatch>& curMatches = matches.back();
curMatches.reserve(k);
for (int i = 0; i < k; ++i, ++trainIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
if (trainIdx != -1)
{
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, 0, distance);
curMatches.push_back(m);
}
}
if (compactResult && curMatches.empty())
matches.pop_back();
}
}
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch(const GpuMat& query, const GpuMat& train,
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vector< vector<DMatch> >& matches, int k, const GpuMat& mask, bool compactResult)
{
GpuMat trainIdx, distance, allDist;
knnMatchSingle(query, train, trainIdx, distance, allDist, k, mask);
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knnMatchDownload(trainIdx, distance, matches, compactResult);
}
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& maskCollection, Stream& stream)
{
if (query.empty() || trainCollection.empty())
return;
using namespace cv::gpu::bf_knnmatch;
typedef void (*caller_t)(const DevMem2D& query, const DevMem2D& trains, const DevMem2D_<PtrStep>& masks,
const DevMem2D& trainIdx, const DevMem2D& imgIdx, const DevMem2D& distance,
int cc, cudaStream_t stream);
static const caller_t callers[3][6] =
{
{
match2L1_gpu<unsigned char>, 0/*match2L1_gpu<signed char>*/,
match2L1_gpu<unsigned short>, match2L1_gpu<short>,
match2L1_gpu<int>, match2L1_gpu<float>
},
{
0/*match2L2_gpu<unsigned char>*/, 0/*match2L2_gpu<signed char>*/,
0/*match2L2_gpu<unsigned short>*/, 0/*match2L2_gpu<short>*/,
0/*match2L2_gpu<int>*/, match2L2_gpu<float>
},
{
match2Hamming_gpu<unsigned char>, 0/*match2Hamming_gpu<signed char>*/,
match2Hamming_gpu<unsigned short>, 0/*match2Hamming_gpu<short>*/,
match2Hamming_gpu<int>, 0/*match2Hamming_gpu<float>*/
}
};
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
const int nQuery = query.rows;
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32SC2, imgIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
if (stream)
stream.enqueueMemSet(trainIdx, Scalar::all(-1));
else
trainIdx.setTo(Scalar::all(-1));
caller_t func = callers[distType][query.depth()];
CV_Assert(func != 0);
DeviceInfo info;
int cc = info.majorVersion() * 10 + info.minorVersion();
func(query, trainCollection, maskCollection, trainIdx, imgIdx, distance, cc, StreamAccessor::getStream(stream));
}
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
vector< vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
Mat trainIdxCPU = trainIdx;
Mat imgIdxCPU = imgIdx;
Mat distanceCPU = distance;
knnMatch2Convert(trainIdxCPU, imgIdxCPU, distanceCPU, matches, compactResult);
}
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
vector< vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC2);
CV_Assert(imgIdx.type() == CV_32SC2 && imgIdx.cols == trainIdx.cols);
CV_Assert(distance.type() == CV_32FC2 && distance.cols == trainIdx.cols);
const int nQuery = trainIdx.cols;
matches.clear();
matches.reserve(nQuery);
const int* trainIdx_ptr = trainIdx.ptr<int>();
const int* imgIdx_ptr = imgIdx.ptr<int>();
const float* distance_ptr = distance.ptr<float>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
matches.push_back(vector<DMatch>());
vector<DMatch>& curMatches = matches.back();
curMatches.reserve(2);
for (int i = 0; i < 2; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
if (trainIdx != -1)
{
int imgIdx = *imgIdx_ptr;
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, imgIdx, distance);
curMatches.push_back(m);
}
}
if (compactResult && curMatches.empty())
matches.pop_back();
}
}
namespace
{
struct ImgIdxSetter
{
explicit inline ImgIdxSetter(int imgIdx_) : imgIdx(imgIdx_) {}
inline void operator()(DMatch& m) const {m.imgIdx = imgIdx;}
int imgIdx;
};
}
void cv::gpu::BruteForceMatcher_GPU_base::knnMatch(const GpuMat& query, vector< vector<DMatch> >& matches, int k,
const vector<GpuMat>& masks, bool compactResult)
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{
if (k == 2)
{
GpuMat trainCollection;
GpuMat maskCollection;
makeGpuCollection(trainCollection, maskCollection, masks);
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GpuMat trainIdx, imgIdx, distance;
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knnMatch2Collection(query, trainCollection, trainIdx, imgIdx, distance, maskCollection);
knnMatch2Download(trainIdx, imgIdx, distance, matches);
}
else
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{
if (query.empty() || empty())
return;
vector< vector<DMatch> > curMatches;
vector<DMatch> temp;
temp.reserve(2 * k);
matches.resize(query.rows);
for_each(matches.begin(), matches.end(), bind2nd(mem_fun_ref(&vector<DMatch>::reserve), k));
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for (size_t imgIdx = 0, size = trainDescCollection.size(); imgIdx < size; ++imgIdx)
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{
knnMatch(query, trainDescCollection[imgIdx], curMatches, k, masks.empty() ? GpuMat() : masks[imgIdx]);
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for (int queryIdx = 0; queryIdx < query.rows; ++queryIdx)
{
vector<DMatch>& localMatch = curMatches[queryIdx];
vector<DMatch>& globalMatch = matches[queryIdx];
for_each(localMatch.begin(), localMatch.end(), ImgIdxSetter(static_cast<int>(imgIdx)));
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temp.clear();
merge(globalMatch.begin(), globalMatch.end(), localMatch.begin(), localMatch.end(), back_inserter(temp));
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globalMatch.clear();
const size_t count = std::min((size_t)k, temp.size());
copy(temp.begin(), temp.begin() + count, back_inserter(globalMatch));
}
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}
if (compactResult)
{
vector< vector<DMatch> >::iterator new_end = remove_if(matches.begin(), matches.end(), mem_fun_ref(&vector<DMatch>::empty));
matches.erase(new_end, matches.end());
}
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}
}
////////////////////////////////////////////////////////////////////
// RadiusMatch
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
const GpuMat& mask, Stream& stream)
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{
if (query.empty() || train.empty())
return;
using namespace cv::gpu::bf_radius_match;
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typedef void (*caller_t)(const DevMem2D& query, const DevMem2D& train, float maxDistance, const DevMem2D& mask,
const DevMem2Di& trainIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream);
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static const caller_t callers[3][6] =
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{
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
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},
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
},
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
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}
};
DeviceInfo info;
int cc = info.majorVersion() * 10 + info.minorVersion();
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CV_Assert(TargetArchs::builtWith(GLOBAL_ATOMICS) && info.supports(GLOBAL_ATOMICS));
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const int nQuery = query.rows;
const int nTrain = train.rows;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.type() == query.type() && train.cols == query.cols);
CV_Assert(trainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size()));
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ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches);
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if (trainIdx.empty())
{
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32SC1, trainIdx);
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32FC1, distance);
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}
caller_t func = callers[distType][query.depth()];
CV_Assert(func != 0);
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func(query, train, maxDistance, mask, trainIdx, distance, nMatches, cc, StreamAccessor::getStream(stream));
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}
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
vector< vector<DMatch> >& matches, bool compactResult)
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{
if (trainIdx.empty() || distance.empty() || nMatches.empty())
return;
Mat trainIdxCPU = trainIdx;
Mat distanceCPU = distance;
Mat nMatchesCPU = nMatches;
radiusMatchConvert(trainIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
}
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
vector< vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || distance.empty() || nMatches.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(distance.type() == CV_32FC1 && distance.size() == trainIdx.size());
CV_Assert(nMatches.type() == CV_32SC1 && nMatches.cols == trainIdx.rows);
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const int nQuery = trainIdx.rows;
matches.clear();
matches.reserve(nQuery);
const int* nMatches_ptr = nMatches.ptr<int>();
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for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
{
const int* trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
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)
{
if (!compactResult)
matches.push_back(vector<DMatch>());
continue;
}
matches.push_back(vector<DMatch>(nMatches));
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vector<DMatch>& curMatches = matches.back();
for (int i = 0; i < nMatches; ++i, ++trainIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
float distance = *distance_ptr;
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DMatch m(queryIdx, trainIdx, 0, distance);
curMatches[i] = m;
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}
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sort(curMatches.begin(), curMatches.end());
}
}
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatch(const GpuMat& query, const GpuMat& train,
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vector< vector<DMatch> >& matches, float maxDistance, const GpuMat& mask, bool compactResult)
{
GpuMat trainIdx, distance, nMatches;
radiusMatchSingle(query, train, trainIdx, distance, nMatches, maxDistance, mask);
radiusMatchDownload(trainIdx, distance, nMatches, matches, compactResult);
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}
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches,
float maxDistance, const vector<GpuMat>& masks, Stream& stream)
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{
if (query.empty() || empty())
return;
using namespace cv::gpu::bf_radius_match;
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typedef void (*caller_t)(const DevMem2D& query, const DevMem2D* trains, int n, float maxDistance, const DevMem2D* masks,
const DevMem2Di& trainIdx, const DevMem2Di& imgIdx, const DevMem2Df& distance, const DevMem2D_<unsigned int>& nMatches,
int cc, cudaStream_t stream);
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static const caller_t callers[3][6] =
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{
{
matchL1_gpu<unsigned char>, 0/*matchL1_gpu<signed char>*/,
matchL1_gpu<unsigned short>, matchL1_gpu<short>,
matchL1_gpu<int>, matchL1_gpu<float>
},
{
0/*matchL2_gpu<unsigned char>*/, 0/*matchL2_gpu<signed char>*/,
0/*matchL2_gpu<unsigned short>*/, 0/*matchL2_gpu<short>*/,
0/*matchL2_gpu<int>*/, matchL2_gpu<float>
},
{
matchHamming_gpu<unsigned char>, 0/*matchHamming_gpu<signed char>*/,
matchHamming_gpu<unsigned short>, 0/*matchHamming_gpu<short>*/,
matchHamming_gpu<int>, 0/*matchHamming_gpu<float>*/
}
};
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DeviceInfo info;
int cc = info.majorVersion() * 10 + info.minorVersion();
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CV_Assert(TargetArchs::builtWith(GLOBAL_ATOMICS) && info.supports(GLOBAL_ATOMICS));
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const int nQuery = query.rows;
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(trainIdx.empty() || (trainIdx.rows == nQuery && trainIdx.size() == distance.size() && trainIdx.size() == imgIdx.size()));
ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches);
if (trainIdx.empty())
{
ensureSizeIsEnough(nQuery, std::max((nQuery / 100), 10), CV_32SC1, trainIdx);
ensureSizeIsEnough(nQuery, std::max((nQuery / 100), 10), CV_32SC1, imgIdx);
ensureSizeIsEnough(nQuery, std::max((nQuery / 100), 10), CV_32FC1, distance);
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}
caller_t func = callers[distType][query.depth()];
CV_Assert(func != 0);
vector<DevMem2D> trains_(trainDescCollection.begin(), trainDescCollection.end());
vector<DevMem2D> masks_(masks.begin(), masks.end());
func(query, &trains_[0], static_cast<int>(trains_.size()), maxDistance, masks_.size() == 0 ? 0 : &masks_[0],
trainIdx, imgIdx, distance, nMatches, cc, StreamAccessor::getStream(stream));
}
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
vector< vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty() || nMatches.empty())
return;
Mat trainIdxCPU = trainIdx;
Mat imgIdxCPU = imgIdx;
Mat distanceCPU = distance;
Mat nMatchesCPU = nMatches;
radiusMatchConvert(trainIdxCPU, imgIdxCPU, distanceCPU, nMatchesCPU, matches, compactResult);
}
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
vector< vector<DMatch> >& matches, bool compactResult)
{
if (trainIdx.empty() || imgIdx.empty() || distance.empty() || nMatches.empty())
return;
CV_Assert(trainIdx.type() == CV_32SC1);
CV_Assert(imgIdx.type() == CV_32SC1 && imgIdx.size() == trainIdx.size());
CV_Assert(distance.type() == CV_32FC1 && distance.size() == trainIdx.size());
CV_Assert(nMatches.type() == CV_32SC1 && nMatches.cols == trainIdx.rows);
const int nQuery = trainIdx.rows;
matches.clear();
matches.reserve(nQuery);
const int* nMatches_ptr = nMatches.ptr<int>();
for (int queryIdx = 0; queryIdx < nQuery; ++queryIdx)
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{
const int* trainIdx_ptr = trainIdx.ptr<int>(queryIdx);
const int* imgIdx_ptr = imgIdx.ptr<int>(queryIdx);
const float* distance_ptr = distance.ptr<float>(queryIdx);
const int nMatches = std::min(nMatches_ptr[queryIdx], trainIdx.cols);
if (nMatches == 0)
{
if (!compactResult)
matches.push_back(vector<DMatch>());
continue;
}
matches.push_back(vector<DMatch>());
vector<DMatch>& curMatches = matches.back();
curMatches.reserve(nMatches);
for (int i = 0; i < nMatches; ++i, ++trainIdx_ptr, ++imgIdx_ptr, ++distance_ptr)
{
int trainIdx = *trainIdx_ptr;
int imgIdx = *imgIdx_ptr;
float distance = *distance_ptr;
DMatch m(queryIdx, trainIdx, imgIdx, distance);
curMatches.push_back(m);
}
sort(curMatches.begin(), curMatches.end());
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}
}
void cv::gpu::BruteForceMatcher_GPU_base::radiusMatch(const GpuMat& query, vector< vector<DMatch> >& matches,
float maxDistance, const vector<GpuMat>& masks, bool compactResult)
{
GpuMat trainIdx, imgIdx, distance, nMatches;
radiusMatchCollection(query, trainIdx, imgIdx, distance, nMatches, maxDistance, masks);
radiusMatchDownload(trainIdx, imgIdx, distance, nMatches, matches, compactResult);
}
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#endif /* !defined (HAVE_CUDA) */