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added BruteForceMatcher_GPU

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This commit is contained in:
Vladislav Vinogradov
2010-12-06 12:06:51 +00:00
parent 77027f6075
commit 8891acb67a
6 changed files with 2135 additions and 3 deletions
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148
modules/gpu/include/opencv2/gpu/gpu.hpp
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@@ -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
{
@@ -1118,7 +1119,152 @@ 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) {}
};
}