Class used for extracting Speeded Up Robust Features (SURF) from an image.
class SURF_GPU
{
public:
enum KeypointLayout
{
X_ROW = 0,
Y_ROW,
LAPLACIAN_ROW,
OCTAVE_ROW,
SIZE_ROW,
ANGLE_ROW,
HESSIAN_ROW,
ROWS_COUNT
};
//! the default constructor
SURF_GPU();
//! the full constructor taking all the necessary parameters
explicit SURF_GPU(double _hessianThreshold, int _nOctaves=4,
int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f);
//! returns the descriptor size in float's (64 or 128)
int descriptorSize() const;
//! upload host keypoints to device memory
void uploadKeypoints(const vector<KeyPoint>& keypoints,
GpuMat& keypointsGPU);
//! download keypoints from device to host memory
void downloadKeypoints(const GpuMat& keypointsGPU,
vector<KeyPoint>& keypoints);
//! download descriptors from device to host memory
void downloadDescriptors(const GpuMat& descriptorsGPU,
vector<float>& descriptors);
void operator()(const GpuMat& img, const GpuMat& mask,
GpuMat& keypoints);
void operator()(const GpuMat& img, const GpuMat& mask,
GpuMat& keypoints, GpuMat& descriptors,
bool useProvidedKeypoints = false,
bool calcOrientation = true);
void operator()(const GpuMat& img, const GpuMat& mask,
std::vector<KeyPoint>& keypoints);
void operator()(const GpuMat& img, const GpuMat& mask,
std::vector<KeyPoint>& keypoints, GpuMat& descriptors,
bool useProvidedKeypoints = false,
bool calcOrientation = true);
void operator()(const GpuMat& img, const GpuMat& mask,
std::vector<KeyPoint>& keypoints,
std::vector<float>& descriptors,
bool useProvidedKeypoints = false,
bool calcOrientation = true);
void releaseMemory();
// SURF parameters
double hessianThreshold;
int nOctaves;
int nOctaveLayers;
bool extended;
bool upright;
//! max keypoints = keypointsRatio * img.size().area()
float keypointsRatio;
GpuMat sum, mask1, maskSum, intBuffer;
GpuMat det, trace;
GpuMat maxPosBuffer;
};
The class SURF_GPU implements Speeded Up Robust Features descriptor. There is a fast multi-scale Hessian keypoint detector that can be used to find the keypoints (which is the default option). But the descriptors can also be computed for the user-specified keypoints. Only 8-bit grayscale images are supported.
The class SURF_GPU can store results in the GPU and CPU memory. It provides functions to convert results between CPU and GPU version ( uploadKeypoints, downloadKeypoints, downloadDescriptors ). The format of CPU results is the same as SURF results. GPU results are stored in GpuMat. The keypoints matrix is matrix with the CV_32FC1 type.
The descriptors matrix is matrix with the CV_32FC1 type.
The class SURF_GPU uses some buffers and provides access to it. All buffers can be safely released between function calls.
See also
Class used for corner detection using the FAST algorithm.
class FAST_GPU
{
public:
enum
{
LOCATION_ROW = 0,
RESPONSE_ROW,
ROWS_COUNT
};
// all features have same size
static const int FEATURE_SIZE = 7;
explicit FAST_GPU(int threshold, bool nonmaxSupression = true,
double keypointsRatio = 0.05);
void operator ()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
void operator ()(const GpuMat& image, const GpuMat& mask,
std::vector<KeyPoint>& keypoints);
void downloadKeypoints(const GpuMat& d_keypoints,
std::vector<KeyPoint>& keypoints);
void convertKeypoints(const Mat& h_keypoints,
std::vector<KeyPoint>& keypoints);
void release();
bool nonmaxSupression;
int threshold;
double keypointsRatio;
int calcKeyPointsLocation(const GpuMat& image, const GpuMat& mask);
int getKeyPoints(GpuMat& keypoints);
};
The class FAST_GPU implements FAST corner detection algorithm.
See also
Constructor.
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Finds the keypoints using FAST detector.
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Download keypoints from GPU to CPU memory.
Converts keypoints from GPU representation to vector of KeyPoint.
Find keypoints and compute it’s response if nonmaxSupression is true.
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The function returns count of detected keypoints.
Gets final array of keypoints.
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The function performs non-max suppression if needed and returns final count of keypoints.
Class for extracting ORB features and descriptors from an image.
class ORB_GPU
{
public:
enum
{
X_ROW = 0,
Y_ROW,
RESPONSE_ROW,
ANGLE_ROW,
OCTAVE_ROW,
SIZE_ROW,
ROWS_COUNT
};
enum
{
DEFAULT_FAST_THRESHOLD = 20
};
explicit ORB_GPU(int nFeatures = 500, float scaleFactor = 1.2f,
int nLevels = 8, int edgeThreshold = 31,
int firstLevel = 0, int WTA_K = 2,
int scoreType = 0, int patchSize = 31);
void operator()(const GpuMat& image, const GpuMat& mask,
std::vector<KeyPoint>& keypoints);
void operator()(const GpuMat& image, const GpuMat& mask, GpuMat& keypoints);
void operator()(const GpuMat& image, const GpuMat& mask,
std::vector<KeyPoint>& keypoints, GpuMat& descriptors);
void operator()(const GpuMat& image, const GpuMat& mask,
GpuMat& keypoints, GpuMat& descriptors);
void downloadKeyPoints(GpuMat& d_keypoints, std::vector<KeyPoint>& keypoints);
void convertKeyPoints(Mat& d_keypoints, std::vector<KeyPoint>& keypoints);
int descriptorSize() const;
void setParams(size_t n_features, const ORB::CommonParams& detector_params);
void setFastParams(int threshold, bool nonmaxSupression = true);
void release();
bool blurForDescriptor;
};
The class implements ORB feature detection and description algorithm.
Constructor.
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Detects keypoints and computes descriptors for them.
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Download keypoints from GPU to CPU memory.
Converts keypoints from GPU representation to vector of KeyPoint.
Brute-force descriptor matcher. For each descriptor in the first set, this matcher finds the closest descriptor in the second set by trying each one. This descriptor matcher supports masking permissible matches between descriptor sets.
template<class Distance>
class BruteForceMatcher_GPU
{
public:
// 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 no train descriptors in collection.
bool empty() const;
// Return true if the matcher supports mask in match methods.
bool isMaskSupported() const;
void matchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance,
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
static void matchDownload(const GpuMat& trainIdx,
const GpuMat& distance, std::vector<DMatch>& matches);
static void matchConvert(const Mat& trainIdx,
const Mat& distance, std::vector<DMatch>& matches);
void match(const GpuMat& query, const GpuMat& train,
std::vector<DMatch>& matches, const GpuMat& mask = GpuMat());
void makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection,
const vector<GpuMat>& masks = std::vector<GpuMat>());
void matchCollection(const GpuMat& query, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& maskCollection, Stream& stream = Stream::Null());
static void matchDownload(const GpuMat& trainIdx, GpuMat& imgIdx,
const GpuMat& distance, std::vector<DMatch>& matches);
static void matchConvert(const Mat& trainIdx, const Mat& imgIdx,
const Mat& distance, std::vector<DMatch>& matches);
void match(const GpuMat& query, std::vector<DMatch>& matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>());
void knnMatchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance, GpuMat& allDist, int k,
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
static void knnMatchDownload(const GpuMat& trainIdx, const GpuMat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
static void knnMatchConvert(const Mat& trainIdx, const Mat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
void knnMatch(const GpuMat& query, const GpuMat& train,
std::vector< std::vector<DMatch> >& matches, int k,
const GpuMat& mask = GpuMat(), bool compactResult = false);
void knnMatch2Collection(const GpuMat& query, const GpuMat& trainCollection,
GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance,
const GpuMat& maskCollection = GpuMat(), Stream& stream = Stream::Null());
static void knnMatch2Download(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
static void knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
void knnMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
bool compactResult = false);
void radiusMatchSingle(const GpuMat& query, const GpuMat& train,
GpuMat& trainIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
const GpuMat& mask = GpuMat(), Stream& stream = Stream::Null());
static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& distance, const GpuMat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
static void radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
void radiusMatch(const GpuMat& query, const GpuMat& train,
std::vector< std::vector<DMatch> >& matches, float maxDistance,
const GpuMat& mask = GpuMat(), bool compactResult = false);
void radiusMatchCollection(const GpuMat& query, GpuMat& trainIdx, GpuMat& imgIdx, GpuMat& distance, GpuMat& nMatches, float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), Stream& stream = Stream::Null());
static void radiusMatchDownload(const GpuMat& trainIdx, const GpuMat& imgIdx, const GpuMat& distance, const GpuMat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
static void radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches,
std::vector< std::vector<DMatch> >& matches, bool compactResult = false);
void radiusMatch(const GpuMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(), bool compactResult = false);
private:
std::vector<GpuMat> trainDescCollection;
};
The class BruteForceMatcher_GPU has an interface similar to the class DescriptorMatcher. It has two groups of match methods: for matching descriptors of one image with another image or with an image set. Also, all functions have an alternative to save results either to the GPU memory or to the CPU memory. The Distance template parameter is kept for CPU/GPU interfaces similarity. BruteForceMatcher_GPU supports only the L1<float>, L2<float>, and Hamming distance types.
See also
DescriptorMatcher, BruteForceMatcher
Finds the best match for each descriptor from a query set with train descriptors.
See also
Performs a GPU collection of train descriptors and masks in a suitable format for the gpu::BruteForceMatcher_GPU::matchCollection() function.
Downloads matrices obtained via gpu::BruteForceMatcher_GPU::matchSingle() or gpu::BruteForceMatcher_GPU::matchCollection() to vector with DMatch.
Converts matrices obtained via gpu::BruteForceMatcher_GPU::matchSingle() or gpu::BruteForceMatcher_GPU::matchCollection() to vector with DMatch.
Finds the k best matches for each descriptor from a query set with train descriptors.
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The function returns detected k (or less if not possible) matches in the increasing order by distance.
The third variant of the method stores the results in GPU memory.
See also
Downloads matrices obtained via gpu::BruteForceMatcher_GPU::knnMatchSingle() or gpu::BruteForceMatcher_GPU::knnMatch2Collection() to vector with DMatch.
If compactResult is true , the matches vector does not contain matches for fully masked-out query descriptors.
Converts matrices obtained via gpu::BruteForceMatcher_GPU::knnMatchSingle() or gpu::BruteForceMatcher_GPU::knnMatch2Collection() to CPU vector with DMatch.
If compactResult is true , the matches vector does not contain matches for fully masked-out query descriptors.
For each query descriptor, finds the best matches with a distance less than a given threshold.
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The function returns detected matches in the increasing order by distance.
The methods work only on devices with the compute capability 1.1.
The third variant of the method stores the results in GPU memory and does not store the points by the distance.
See also
Downloads matrices obtained via gpu::BruteForceMatcher_GPU::radiusMatchSingle() or gpu::BruteForceMatcher_GPU::radiusMatchCollection() to vector with DMatch.
If compactResult is true , the matches vector does not contain matches for fully masked-out query descriptors.
Converts matrices obtained via gpu::BruteForceMatcher_GPU::radiusMatchSingle() or gpu::BruteForceMatcher_GPU::radiusMatchCollection() to vector with DMatch.
If compactResult is true , the matches vector does not contain matches for fully masked-out query descriptors.