Doxygen documentation: cuda
This commit is contained in:
Maksim Shabunin
@@ -50,150 +50,175 @@
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#include "opencv2/core/cuda.hpp"
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#include "opencv2/cudafilters.hpp"
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/**
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@addtogroup cuda
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@{
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@defgroup cudafeatures2d Feature Detection and Description
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@}
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*/
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namespace cv { namespace cuda {
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//! @addtogroup cudafeatures2d
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//! @{
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/** @brief Brute-force descriptor matcher.
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For each descriptor in the first set, this matcher finds the closest descriptor in the second set
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by trying each one. This descriptor matcher supports masking permissible matches between descriptor
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sets.
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The class BFMatcher\_CUDA has an interface similar to the class DescriptorMatcher. It has two groups
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of match methods: for matching descriptors of one image with another image or with an image set.
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Also, all functions have an alternative to save results either to the GPU memory or to the CPU
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memory.
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@sa DescriptorMatcher, BFMatcher
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*/
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class CV_EXPORTS BFMatcher_CUDA
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{
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public:
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explicit BFMatcher_CUDA(int norm = cv::NORM_L2);
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// Add descriptors to train descriptor collection
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//! Add descriptors to train descriptor collection
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void add(const std::vector<GpuMat>& descCollection);
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// Get train descriptors collection
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//! Get train descriptors collection
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const std::vector<GpuMat>& getTrainDescriptors() const;
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// Clear train descriptors collection
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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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//! 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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//! 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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//! Find one best match for each query descriptor
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void matchSingle(const GpuMat& query, const GpuMat& train,
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GpuMat& trainIdx, GpuMat& distance,
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const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
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// Download trainIdx and distance and convert it to CPU vector with DMatch
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//! Download trainIdx and distance and convert it to CPU vector with DMatch
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static void matchDownload(const GpuMat& trainIdx, const GpuMat& distance, std::vector<DMatch>& matches);
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// Convert trainIdx and distance to vector with DMatch
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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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//! Find one best match for each query descriptor
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void match(const GpuMat& query, const GpuMat& train, std::vector<DMatch>& matches, const GpuMat& mask = GpuMat());
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// Make gpu collection of trains and masks in suitable format for matchCollection function
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//! Make gpu collection of trains and masks in suitable format for matchCollection function
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void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
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// Find one best match from train collection for each query descriptor
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//! Find one best match from train collection for each query descriptor
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void matchCollection(const GpuMat& query, const GpuMat& trainCollection,
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GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
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const GpuMat& masks = GpuMat(), Stream& stream = Stream::Null());
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// Download trainIdx, imgIdx and distance and convert it to vector with DMatch
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//! Download trainIdx, imgIdx and distance and convert it to vector with DMatch
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static void matchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, std::vector<DMatch>& matches);
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// Convert trainIdx, imgIdx and distance to vector with DMatch
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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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//! Find one best match from train collection for each query descriptor.
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void match(const GpuMat& query, std::vector<DMatch>& matches, const std::vector<GpuMat>& masks = std::vector<GpuMat>());
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// Find k best matches for each query descriptor (in increasing order of distances)
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//! Find k best matches for each query descriptor (in increasing order of distances)
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void knnMatchSingle(const GpuMat& query, const GpuMat& train,
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GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
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const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
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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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//! 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 GpuMat& trainIdx, const GpuMat& 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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//! 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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//! 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 GpuMat& query, const GpuMat& train,
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std::vector< std::vector<DMatch> >& matches, int k, const GpuMat& mask = GpuMat(),
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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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//! Find k best matches from train collection for each query descriptor (in increasing order of distances)
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void knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
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GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
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const GpuMat& maskCollection = GpuMat(), Stream& stream = Stream::Null());
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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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//! 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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//! @see BFMatcher_CUDA::knnMatchDownload
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static void knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& 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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//! Convert trainIdx and distance to vector with DMatch
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//! @see BFMatcher_CUDA::knnMatchConvert
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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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//! 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 GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k,
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const std::vector<GpuMat>& masks = std::vector<GpuMat>(), 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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//! 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 GpuMat& query, const GpuMat& train,
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GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
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const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
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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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//! 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 GpuMat& trainIdx, const GpuMat& distance, const GpuMat& 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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//! 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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//! 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 GpuMat& query, const GpuMat& train,
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std::vector< std::vector<DMatch> >& matches, float maxDistance,
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const GpuMat& mask = GpuMat(), 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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//! 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 GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
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const std::vector<GpuMat>& masks = std::vector<GpuMat>(), Stream& stream = Stream::Null());
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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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//! 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 GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& 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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//! 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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//! 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 GpuMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
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const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
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@@ -203,6 +228,8 @@ private:
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std::vector<GpuMat> trainDescCollection;
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};
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/** @brief Class used for corner detection using the FAST algorithm. :
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*/
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class CV_EXPORTS FAST_CUDA
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{
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public:
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@@ -213,23 +240,45 @@ public:
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ROWS_COUNT
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};
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// all features have same size
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//! all features have same size
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static const int FEATURE_SIZE = 7;
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/** @brief Constructor.
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@param threshold Threshold on difference between intensity of the central pixel and pixels on a
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circle around this pixel.
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@param nonmaxSuppression If it is true, non-maximum suppression is applied to detected corners
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(keypoints).
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@param keypointsRatio Inner buffer size for keypoints store is determined as (keypointsRatio \*
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image\_width \* image\_height).
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*/
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explicit FAST_CUDA(int threshold, bool nonmaxSuppression = true, double keypointsRatio = 0.05);
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//! finds the keypoints using FAST detector
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//! supports only CV_8UC1 images
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/** @brief Finds the keypoints using FAST detector.
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@param image Image where keypoints (corners) are detected. Only 8-bit grayscale images are
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supported.
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@param mask Optional input mask that marks the regions where we should detect features.
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@param keypoints The output vector of keypoints. Can be stored both in CPU and GPU memory. For GPU
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memory:
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- keypoints.ptr\<Vec2s\>(LOCATION\_ROW)[i] will contain location of i'th point
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- keypoints.ptr\<float\>(RESPONSE\_ROW)[i] will contain response of i'th point (if non-maximum
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suppression is applied)
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*/
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void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
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/** @overload */
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void operator ()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
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//! download keypoints from device to host memory
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/** @brief Download keypoints from GPU to CPU memory.
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*/
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static void downloadKeypoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
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//! convert keypoints to KeyPoint vector
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/** @brief Converts keypoints from CUDA representation to vector of KeyPoint.
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*/
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static void convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints);
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//! release temporary buffer's memory
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/** @brief Releases inner buffer memory.
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*/
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void release();
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bool nonmaxSuppression;
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@@ -239,13 +288,22 @@ public:
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//! max keypoints = keypointsRatio * img.size().area()
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double keypointsRatio;
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//! find keypoints and compute it's response if nonmaxSuppression is true
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//! return count of detected keypoints
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/** @brief Find keypoints and compute it's response if nonmaxSuppression is true.
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@param image Image where keypoints (corners) are detected. Only 8-bit grayscale images are
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supported.
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@param mask Optional input mask that marks the regions where we should detect features.
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The function returns count of detected keypoints.
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*/
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int calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask);
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//! get final array of keypoints
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//! performs nonmax suppression if needed
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//! return final count of keypoints
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/** @brief Gets final array of keypoints.
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@param keypoints The output vector of keypoints.
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The function performs non-max suppression if needed and returns final count of keypoints.
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*/
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int getKeyPoints(GpuMat& keypoints);
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private:
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@@ -257,6 +315,8 @@ private:
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GpuMat d_keypoints_;
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};
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/** @brief Class for extracting ORB features and descriptors from an image. :
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*/
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class CV_EXPORTS ORB_CUDA
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{
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public:
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@@ -276,28 +336,51 @@ public:
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DEFAULT_FAST_THRESHOLD = 20
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};
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//! Constructor
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/** @brief Constructor.
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@param nFeatures The number of desired features.
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@param scaleFactor Coefficient by which we divide the dimensions from one scale pyramid level to
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the next.
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@param nLevels The number of levels in the scale pyramid.
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@param edgeThreshold How far from the boundary the points should be.
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@param firstLevel The level at which the image is given. If 1, that means we will also look at the
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image scaleFactor times bigger.
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@param WTA_K
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@param scoreType
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@param patchSize
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*/
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explicit ORB_CUDA(int nFeatures = 500, float scaleFactor = 1.2f, int nLevels = 8, int edgeThreshold = 31,
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int firstLevel = 0, int WTA_K = 2, int scoreType = 0, int patchSize = 31);
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//! Compute the ORB features on an image
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//! image - the image to compute the features (supports only CV_8UC1 images)
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//! mask - the mask to apply
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//! keypoints - the resulting keypoints
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/** @overload */
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void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
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/** @overload */
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void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
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//! Compute the ORB features and descriptors on an image
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//! image - the image to compute the features (supports only CV_8UC1 images)
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//! mask - the mask to apply
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//! keypoints - the resulting keypoints
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//! descriptors - descriptors array
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/** @brief Detects keypoints and computes descriptors for them.
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@param image Input 8-bit grayscale image.
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@param mask Optional input mask that marks the regions where we should detect features.
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@param keypoints The input/output vector of keypoints. Can be stored both in CPU and GPU memory.
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For GPU memory:
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- keypoints.ptr\<float\>(X\_ROW)[i] contains x coordinate of the i'th feature.
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- keypoints.ptr\<float\>(Y\_ROW)[i] contains y coordinate of the i'th feature.
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- keypoints.ptr\<float\>(RESPONSE\_ROW)[i] contains the response of the i'th feature.
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- keypoints.ptr\<float\>(ANGLE\_ROW)[i] contains orientation of the i'th feature.
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- keypoints.ptr\<float\>(OCTAVE\_ROW)[i] contains the octave of the i'th feature.
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- keypoints.ptr\<float\>(SIZE\_ROW)[i] contains the size of the i'th feature.
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||||
@param descriptors Computed descriptors. if blurForDescriptor is true, image will be blurred
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before descriptors calculation.
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||||
*/
|
||||
void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors);
|
||||
/** @overload */
|
||||
void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors);
|
||||
|
||||
//! download keypoints from device to host memory
|
||||
/** @brief Download keypoints from GPU to CPU memory.
|
||||
*/
|
||||
static void downloadKeyPoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
|
||||
//! convert keypoints to KeyPoint vector
|
||||
/** @brief Converts keypoints from CUDA representation to vector of KeyPoint.
|
||||
*/
|
||||
static void convertKeyPoints(const Mat& d_keypoints, std::vector<KeyPoint>& keypoints);
|
||||
|
||||
//! returns the descriptor size in bytes
|
||||
@@ -309,7 +392,8 @@ public:
|
||||
fastDetector_.nonmaxSuppression = nonmaxSuppression;
|
||||
}
|
||||
|
||||
//! release temporary buffer's memory
|
||||
/** @brief Releases inner buffer memory.
|
||||
*/
|
||||
void release();
|
||||
|
||||
//! if true, image will be blurred before descriptors calculation
|
||||
@@ -335,10 +419,10 @@ private:
|
||||
int scoreType_;
|
||||
int patchSize_;
|
||||
|
||||
// The number of desired features per scale
|
||||
//! The number of desired features per scale
|
||||
std::vector<size_t> n_features_per_level_;
|
||||
|
||||
// Points to compute BRIEF descriptors from
|
||||
//! Points to compute BRIEF descriptors from
|
||||
GpuMat pattern_;
|
||||
|
||||
std::vector<GpuMat> imagePyr_;
|
||||
@@ -356,6 +440,8 @@ private:
|
||||
GpuMat d_keypoints_;
|
||||
};
|
||||
|
||||
//! @}
|
||||
|
||||
}} // namespace cv { namespace cuda {
|
||||
|
||||
#endif /* __OPENCV_CUDAFEATURES2D_HPP__ */
|
||||
|
||||
Reference in New Issue
Block a user