add bruteForceMatcher to ocl module
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yao
@@ -946,6 +946,186 @@ namespace cv
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oclMat maxPosBuffer;
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};
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////////////////////////////////// BruteForceMatcher //////////////////////////////////
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class CV_EXPORTS BruteForceMatcher_OCL_base
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{
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public:
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enum DistType {L1Dist = 0, L2Dist, HammingDist};
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explicit BruteForceMatcher_OCL_base(DistType distType = L2Dist);
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// Add descriptors to train descriptor collection
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void add(const std::vector<oclMat>& descCollection);
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// Get train descriptors collection
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const std::vector<oclMat>& getTrainDescriptors() const;
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// Clear train descriptors collection
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void clear();
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// Return true if there are not train descriptors in collection
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bool empty() const;
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// Return true if the matcher supports mask in match methods
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bool isMaskSupported() const;
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// Find one best match for each query descriptor
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void matchSingle(const oclMat& query, const oclMat& train,
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oclMat& trainIdx, oclMat& distance,
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const oclMat& mask = oclMat());
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// Download trainIdx and distance and convert it to CPU vector with DMatch
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static void matchDownload(const oclMat& trainIdx, const oclMat& distance, std::vector<DMatch>& matches);
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// Convert trainIdx and distance to vector with DMatch
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static void matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches);
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// Find one best match for each query descriptor
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void match(const oclMat& query, const oclMat& train, std::vector<DMatch>& matches, const oclMat& mask = oclMat());
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// Make gpu collection of trains and masks in suitable format for matchCollection function
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void makeGpuCollection(oclMat& trainCollection, oclMat& maskCollection, const std::vector<oclMat>& masks = std::vector<oclMat>());
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// Find one best match from train collection for each query descriptor
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void matchCollection(const oclMat& query, const oclMat& trainCollection,
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oclMat& trainIdx, oclMat& imgIdx, oclMat& distance,
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const oclMat& masks = oclMat());
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// Download trainIdx, imgIdx and distance and convert it to vector with DMatch
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static void matchDownload(const oclMat& trainIdx, const oclMat& imgIdx, const oclMat& distance, std::vector<DMatch>& matches);
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// Convert trainIdx, imgIdx and distance to vector with DMatch
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static void matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches);
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// Find one best match from train collection for each query descriptor.
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void match(const oclMat& query, std::vector<DMatch>& matches, const std::vector<oclMat>& masks = std::vector<oclMat>());
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// Find k best matches for each query descriptor (in increasing order of distances)
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void knnMatchSingle(const oclMat& query, const oclMat& train,
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oclMat& trainIdx, oclMat& distance, oclMat& allDist, int k,
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const oclMat& mask = oclMat());
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// Download trainIdx and distance and convert it to vector with DMatch
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// compactResult is used when mask is not empty. If compactResult is false matches
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// vector will have the same size as queryDescriptors rows. If compactResult is true
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// matches vector will not contain matches for fully masked out query descriptors.
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static void knnMatchDownload(const oclMat& trainIdx, const oclMat& distance,
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std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
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// Convert trainIdx and distance to vector with DMatch
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static void knnMatchConvert(const Mat& trainIdx, const Mat& distance,
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std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
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// Find k best matches for each query descriptor (in increasing order of distances).
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// compactResult is used when mask is not empty. If compactResult is false matches
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// vector will have the same size as queryDescriptors rows. If compactResult is true
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// matches vector will not contain matches for fully masked out query descriptors.
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void knnMatch(const oclMat& query, const oclMat& train,
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std::vector< std::vector<DMatch> >& matches, int k, const oclMat& mask = oclMat(),
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bool compactResult = false);
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// Find k best matches from train collection for each query descriptor (in increasing order of distances)
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void knnMatch2Collection(const oclMat& query, const oclMat& trainCollection,
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oclMat& trainIdx, oclMat& imgIdx, oclMat& distance,
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const oclMat& maskCollection = oclMat());
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// Download trainIdx and distance and convert it to vector with DMatch
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// compactResult is used when mask is not empty. If compactResult is false matches
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// vector will have the same size as queryDescriptors rows. If compactResult is true
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// matches vector will not contain matches for fully masked out query descriptors.
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static void knnMatch2Download(const oclMat& trainIdx, const oclMat& imgIdx, const oclMat& distance,
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std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
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// Convert trainIdx and distance to vector with DMatch
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static void knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
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std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
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// Find k best matches for each query descriptor (in increasing order of distances).
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// compactResult is used when mask is not empty. If compactResult is false matches
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// vector will have the same size as queryDescriptors rows. If compactResult is true
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// matches vector will not contain matches for fully masked out query descriptors.
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void knnMatch(const oclMat& query, std::vector< std::vector<DMatch> >& matches, int k,
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const std::vector<oclMat>& masks = std::vector<oclMat>(), bool compactResult = false);
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// Find best matches for each query descriptor which have distance less than maxDistance.
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// nMatches.at<int>(0, queryIdx) will contain matches count for queryIdx.
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// carefully nMatches can be greater than trainIdx.cols - it means that matcher didn't find all matches,
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// because it didn't have enough memory.
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// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nTrain / 100), 10),
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// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
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// Matches doesn't sorted.
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void radiusMatchSingle(const oclMat& query, const oclMat& train,
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oclMat& trainIdx, oclMat& distance, oclMat& nMatches, float maxDistance,
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const oclMat& mask = oclMat());
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// Download trainIdx, nMatches and distance and convert it to vector with DMatch.
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// matches will be sorted in increasing order of distances.
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// compactResult is used when mask is not empty. If compactResult is false matches
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// vector will have the same size as queryDescriptors rows. If compactResult is true
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// matches vector will not contain matches for fully masked out query descriptors.
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static void radiusMatchDownload(const oclMat& trainIdx, const oclMat& distance, const oclMat& nMatches,
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std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
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// Convert trainIdx, nMatches and distance to vector with DMatch.
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static void radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
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std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
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// Find best matches for each query descriptor which have distance less than maxDistance
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// in increasing order of distances).
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void radiusMatch(const oclMat& query, const oclMat& train,
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std::vector< std::vector<DMatch> >& matches, float maxDistance,
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const oclMat& mask = oclMat(), bool compactResult = false);
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// Find best matches for each query descriptor which have distance less than maxDistance.
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// If trainIdx is empty, then trainIdx and distance will be created with size nQuery x max((nQuery / 100), 10),
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// otherwize user can pass own allocated trainIdx and distance with size nQuery x nMaxMatches
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// Matches doesn't sorted.
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void radiusMatchCollection(const oclMat& query, oclMat& trainIdx, oclMat& imgIdx, oclMat& distance, oclMat& nMatches, float maxDistance,
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const std::vector<oclMat>& masks = std::vector<oclMat>());
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// Download trainIdx, imgIdx, nMatches and distance and convert it to vector with DMatch.
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// matches will be sorted in increasing order of distances.
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// compactResult is used when mask is not empty. If compactResult is false matches
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// vector will have the same size as queryDescriptors rows. If compactResult is true
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// matches vector will not contain matches for fully masked out query descriptors.
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static void radiusMatchDownload(const oclMat& trainIdx, const oclMat& imgIdx, const oclMat& distance, const oclMat& nMatches,
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std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
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// Convert trainIdx, nMatches and distance to vector with DMatch.
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static void radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
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std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
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// Find best matches from train collection for each query descriptor which have distance less than
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// maxDistance (in increasing order of distances).
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void radiusMatch(const oclMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
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const std::vector<oclMat>& masks = std::vector<oclMat>(), bool compactResult = false);
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DistType distType;
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private:
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std::vector<oclMat> trainDescCollection;
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};
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template <class Distance>
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class CV_EXPORTS BruteForceMatcher_OCL;
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template <typename T>
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class CV_EXPORTS BruteForceMatcher_OCL< L1<T> > : public BruteForceMatcher_OCL_base
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{
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public:
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explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L1Dist) {}
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explicit BruteForceMatcher_OCL(L1<T> /*d*/) : BruteForceMatcher_OCL_base(L1Dist) {}
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};
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template <typename T>
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class CV_EXPORTS BruteForceMatcher_OCL< L2<T> > : public BruteForceMatcher_OCL_base
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{
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public:
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explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(L2Dist) {}
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explicit BruteForceMatcher_OCL(L2<T> /*d*/) : BruteForceMatcher_OCL_base(L2Dist) {}
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};
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template <> class CV_EXPORTS BruteForceMatcher_OCL< Hamming > : public BruteForceMatcher_OCL_base
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{
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public:
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explicit BruteForceMatcher_OCL() : BruteForceMatcher_OCL_base(HammingDist) {}
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explicit BruteForceMatcher_OCL(Hamming /*d*/) : BruteForceMatcher_OCL_base(HammingDist) {}
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};
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}
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}
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#include "opencv2/ocl/matrix_operations.hpp"
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