gpufeatures2d module 2d for feature detection and matching
This commit is contained in:
Vladislav Vinogradov
@@ -53,6 +53,7 @@
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#include "opencv2/gpuarithm.hpp"
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#include "opencv2/gpufilters.hpp"
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#include "opencv2/gpuimgproc.hpp"
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#include "opencv2/gpufeatures2d.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/objdetect.hpp"
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@@ -368,156 +369,7 @@ protected:
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////////////////////////////////// BruteForceMatcher //////////////////////////////////
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class CV_EXPORTS BFMatcher_GPU
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{
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public:
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explicit BFMatcher_GPU(int norm = cv::NORM_L2);
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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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const std::vector<GpuMat>& 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 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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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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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 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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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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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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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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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 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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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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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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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 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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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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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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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 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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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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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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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 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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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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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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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 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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int norm;
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private:
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std::vector<GpuMat> trainDescCollection;
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};
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template <class Distance>
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class CV_EXPORTS BruteForceMatcher_GPU;
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@@ -575,160 +427,11 @@ private:
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////////////////////////////////// FAST //////////////////////////////////////////
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class CV_EXPORTS FAST_GPU
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{
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public:
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enum
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{
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LOCATION_ROW = 0,
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RESPONSE_ROW,
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ROWS_COUNT
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};
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// all features have same size
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static const int FEATURE_SIZE = 7;
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explicit FAST_GPU(int threshold, bool nonmaxSupression = 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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void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
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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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static void downloadKeypoints(const GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
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//! convert keypoints to KeyPoint vector
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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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void release();
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bool nonmaxSupression;
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int threshold;
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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 nonmaxSupression is true
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//! return count of detected keypoints
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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 supression if needed
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//! return final count of keypoints
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int getKeyPoints(GpuMat& keypoints);
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private:
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GpuMat kpLoc_;
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int count_;
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GpuMat score_;
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GpuMat d_keypoints_;
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};
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////////////////////////////////// ORB //////////////////////////////////////////
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class CV_EXPORTS ORB_GPU
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{
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public:
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enum
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{
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X_ROW = 0,
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Y_ROW,
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RESPONSE_ROW,
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ANGLE_ROW,
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OCTAVE_ROW,
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SIZE_ROW,
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ROWS_COUNT
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};
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enum
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{
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DEFAULT_FAST_THRESHOLD = 20
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};
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//! Constructor
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explicit ORB_GPU(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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void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints);
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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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void operator()(const GpuMat& image, const GpuMat& mask, std::vector<KeyPoint>& keypoints, GpuMat& descriptors);
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void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints, GpuMat& descriptors);
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//! download keypoints from device to host memory
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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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static void convertKeyPoints(const Mat& d_keypoints, std::vector<KeyPoint>& keypoints);
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//! returns the descriptor size in bytes
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inline int descriptorSize() const { return kBytes; }
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inline void setFastParams(int threshold, bool nonmaxSupression = true)
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{
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fastDetector_.threshold = threshold;
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fastDetector_.nonmaxSupression = nonmaxSupression;
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}
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//! release temporary buffer's memory
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void release();
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//! if true, image will be blurred before descriptors calculation
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bool blurForDescriptor;
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private:
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enum { kBytes = 32 };
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void buildScalePyramids(const GpuMat& image, const GpuMat& mask);
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void computeKeyPointsPyramid();
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void computeDescriptors(GpuMat& descriptors);
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void mergeKeyPoints(GpuMat& keypoints);
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int nFeatures_;
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float scaleFactor_;
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int nLevels_;
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int edgeThreshold_;
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int firstLevel_;
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int WTA_K_;
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int scoreType_;
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int patchSize_;
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// The number of desired features per scale
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std::vector<size_t> n_features_per_level_;
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// Points to compute BRIEF descriptors from
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GpuMat pattern_;
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std::vector<GpuMat> imagePyr_;
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std::vector<GpuMat> maskPyr_;
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GpuMat buf_;
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std::vector<GpuMat> keyPointsPyr_;
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std::vector<int> keyPointsCount_;
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FAST_GPU fastDetector_;
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Ptr<FilterEngine_GPU> blurFilter;
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GpuMat d_keypoints_;
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};
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////////////////////////////////// Optical Flow //////////////////////////////////////////
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