Commit 8891acb6 authored by Vladislav Vinogradov's avatar Vladislav Vinogradov

added BruteForceMatcher_GPU

parent 77027f60
set(name "gpu")
set(DEPS "opencv_core" "opencv_imgproc" "opencv_objdetect")
set(DEPS "opencv_core" "opencv_imgproc" "opencv_objdetect" "opencv_features2d" "opencv_flann")
set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} opencv_gpu)
......
......@@ -48,6 +48,7 @@
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/gpu/devmem2d.hpp"
#include "opencv2/features2d/features2d.hpp"
namespace cv
{
......@@ -1119,6 +1120,151 @@ namespace cv
// Gradients conputation results
GpuMat grad, qangle;
};
////////////////////////////////// BruteForceMatcher //////////////////////////////////
class CV_EXPORTS BruteForceMatcher_GPU_base
{
public:
enum DistType {L1Dist = 0, L2Dist};
explicit BruteForceMatcher_GPU_base(DistType distType = L2Dist);
// Add descriptors to train descriptor collection.
void add(const std::vector<GpuMat>& descCollection);
// Get train descriptors collection.
const std::vector<GpuMat>& getTrainDescriptors() const;
// Clear train descriptors collection.
void clear();
// Return true if there are not train descriptors in collection.
bool empty() const;
// Return true if the matcher supports mask in match methods.
bool isMaskSupported() const;
// Find one best match for each query descriptor.
// trainIdx.at<int>(0, queryIdx) will contain best train index for queryIdx
// distance.at<float>(0, queryIdx) will contain distance
void matchSingle(const GpuMat& queryDescs, const GpuMat& trainDescs,
GpuMat& trainIdx, GpuMat& distance,
const GpuMat& mask = GpuMat());
// Download trainIdx and distance to CPU vector with DMatch
static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
// Find one best match for each query descriptor.
void match(const GpuMat& queryDescs, const GpuMat& trainDescs, std::vector<DMatch>& matches,
const GpuMat& mask = GpuMat());
// Make gpu collection of trains and masks in suitable format for matchCollection function
void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection,
const vector<GpuMat>& masks = std::vector<GpuMat>());
// Find one best match from train collection for each query descriptor.
// trainIdx.at<int>(0, queryIdx) will contain best train index for queryIdx
// imgIdx.at<int>(0, queryIdx) will contain best image index for queryIdx
// distance.at<float>(0, queryIdx) will contain distance
void matchCollection(const GpuMat& queryDescs, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& maskCollection);
// Download trainIdx, imgIdx and distance to CPU vector with DMatch
static void matchDownload(const GpuMat& trainIdx, GpuMat& imgIdx, const GpuMat& distance,
std::vector<DMatch>& matches);
// Find one best match from train collection for each query descriptor.
void match(const GpuMat& queryDescs, std::vector<DMatch>& matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>());
// Find k best matches for each query descriptor (in increasing order of distances).
// trainIdx.at<int>(queryIdx, i) will contain index of i'th best trains (i < k).
// distance.at<float>(queryIdx, i) will contain distance.
// allDist is a buffer to store all distance between query descriptors and train descriptors
// it have size (nQuery,nTrain) and CV_32F type
// allDist.at<float>(queryIdx, trainIdx) will contain FLT_MAX, if trainIdx is one from k best,
// otherwise it will contain distance between queryIdx and trainIdx descriptors
void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k, const GpuMat& mask = GpuMat());
// Download trainIdx and distance to CPU vector with DMatch
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
// Find k best matches for each query descriptor (in increasing order of distances).
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
void knnMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
bool compactResult = false);
// Find k best matches for each query descriptor (in increasing order of distances).
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
void knnMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >& matches, int knn,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false );
// Find best matches for each query descriptor which have distance less than maxDistance.
// nMatches.at<unsigned int>(0, queruIdx) will contain matches count for queryIdx.
// carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
// because it didn't have enough memory.
// trainIdx.at<int>(queruIdx, i) will contain ith train index (i < min(nMatches.at<unsigned int>(0, queruIdx), trainIdx.cols))
// distance.at<int>(queruIdx, i) will contain ith distance (i < min(nMatches.at<unsigned int>(0, queruIdx), trainIdx.cols))
// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x nTrain,
// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
// Matches doesn't sorted.
void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
GpuMat& trainIdx, GpuMat& nMatches, GpuMat& distance, float maxDistance,
const GpuMat& mask = GpuMat());
// Download trainIdx, nMatches and distance to CPU vector with DMatch.
// matches will be sorted in increasing order of distances.
// compactResult is used when mask is not empty. If compactResult is false matches
// vector will have the same size as queryDescriptors rows. If compactResult is true
// matches vector will not contain matches for fully masked out query descriptors.
static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& nMatches, const GpuMat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
// Find best matches for each query descriptor which have distance less than maxDistance
// in increasing order of distances).
void radiusMatch(const GpuMat& queryDescs, const GpuMat& trainDescs,
std::vector< std::vector<DMatch> >& matches, float maxDistance,
const GpuMat& mask = GpuMat(), bool compactResult = false);
// Find best matches from train collection for each query descriptor which have distance less than
// maxDistance (in increasing order of distances).
void radiusMatch(const GpuMat& queryDescs, std::vector< std::vector<DMatch> >& matches, float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
private:
DistType distType;
std::vector<GpuMat> trainDescCollection;
};
template <class Distance>
class CV_EXPORTS BruteForceMatcher_GPU;
template <typename T>
class CV_EXPORTS BruteForceMatcher_GPU< L1<T> > : public BruteForceMatcher_GPU_base
{
public:
explicit BruteForceMatcher_GPU(L1<T> d = L1<T>()) : BruteForceMatcher_GPU_base(L1Dist) {}
};
template <typename T>
class CV_EXPORTS BruteForceMatcher_GPU< L2<T> > : public BruteForceMatcher_GPU_base
{
public:
explicit BruteForceMatcher_GPU(L2<T> d = L2<T>()) : BruteForceMatcher_GPU_base(L2Dist) {}
};
}
......
This diff is collapsed.
This diff is collapsed.
/*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.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "gputest.hpp"
using namespace cv;
using namespace cv::gpu;
using namespace std;
class CV_GpuBruteForceMatcherTest : public CvTest
{
public:
CV_GpuBruteForceMatcherTest() : CvTest( "GPU-BruteForceMatcher", "BruteForceMatcher" ) {}
protected:
void run(int)
{
try
{
BruteForceMatcher< L2<float> > matcherCPU;
BruteForceMatcher_GPU< L2<float> > matcherGPU;
vector<DMatch> matchesCPU, matchesGPU;
vector< vector<DMatch> > knnMatchesCPU, knnMatchesGPU;
vector< vector<DMatch> > radiusMatchesCPU, radiusMatchesGPU;
RNG rng(*ts->get_rng());
const int desc_len = rng.uniform(40, 300);
Mat queryCPU(rng.uniform(100, 300), desc_len, CV_32F);
rng.fill(queryCPU, cv::RNG::UNIFORM, cv::Scalar::all(0.0), cv::Scalar::all(1.0));
GpuMat queryGPU(queryCPU);
const int nTrains = rng.uniform(1, 5);
vector<Mat> trainsCPU(nTrains);
vector<GpuMat> trainsGPU(nTrains);
vector<Mat> masksCPU(nTrains);
vector<GpuMat> masksGPU(nTrains);
for (int i = 0; i < nTrains; ++i)
{
Mat train(rng.uniform(100, 300), desc_len, CV_32F);
rng.fill(train, cv::RNG::UNIFORM, cv::Scalar::all(0.0), cv::Scalar::all(1.0));
trainsCPU[i] = train;
trainsGPU[i].upload(train);
bool with_mask = rng.uniform(0, 10) < 5;
if (with_mask)
{
Mat mask(queryCPU.rows, train.rows, CV_8U, Scalar::all(1));
rng.fill(mask, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(200));
masksCPU[i] = mask;
masksGPU[i].upload(mask);
}
}
matcherCPU.add(trainsCPU);
matcherGPU.add(trainsGPU);
matcherCPU.match(queryCPU, matchesCPU, masksCPU);
matcherGPU.match(queryGPU, matchesGPU, masksGPU);
if (!compareMatches(matchesCPU, matchesGPU))
{
ts->set_failed_test_info(CvTS::FAIL_MISMATCH);
return;
}
const int knn = rng.uniform(3, 10);
matcherCPU.knnMatch(queryCPU, knnMatchesCPU, knn, masksCPU);
matcherGPU.knnMatch(queryGPU, knnMatchesGPU, knn, masksGPU);
if (!compareMatches(knnMatchesCPU, knnMatchesGPU))
{
ts->set_failed_test_info(CvTS::FAIL_MISMATCH);
return;
}
const float maxDistance = rng.uniform(0.01f, 0.3f);
matcherCPU.radiusMatch(queryCPU, radiusMatchesCPU, maxDistance, masksCPU);
matcherGPU.radiusMatch(queryGPU, radiusMatchesGPU, maxDistance, masksGPU);
if (!compareMatches(radiusMatchesCPU, radiusMatchesGPU))
{
ts->set_failed_test_info(CvTS::FAIL_MISMATCH);
return;
}
}
catch (const cv::Exception& e)
{
if (!check_and_treat_gpu_exception(e, ts))
throw;
return;
}
ts->set_failed_test_info(CvTS::OK);
}
private:
static void convertMatches(const vector< vector<DMatch> >& knnMatches, vector<DMatch>& matches)
{
matches.clear();
for (size_t i = 0; i < knnMatches.size(); ++i)
copy(knnMatches[i].begin(), knnMatches[i].end(), back_inserter(matches));
}
static bool compareMatches(const vector<DMatch>& matches1, const vector<DMatch>& matches2)
{
if (matches1.size() != matches2.size())
return false;
struct DMatchEqual : public binary_function<DMatch, DMatch, bool>
{
bool operator()(const DMatch& m1, const DMatch& m2)
{
return m1.imgIdx == m2.imgIdx && m1.queryIdx == m2.queryIdx && m1.trainIdx == m2.trainIdx;
}
};
return equal(matches1.begin(), matches1.end(), matches2.begin(), DMatchEqual());
}
static bool compareMatches(const vector< vector<DMatch> >& matches1, const vector< vector<DMatch> >& matches2)
{
vector<DMatch> m1, m2;
convertMatches(matches1, m1);
convertMatches(matches2, m2);
return compareMatches(m1, m2);
}
} brute_force_matcher_test;
\ No newline at end of file
......@@ -51,6 +51,7 @@
#include <opencv2/gpu/gpu.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/features2d/features2d.hpp>
#include "cxts.h"
/****************************************************************************************/
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment