Feature Detection And Description

ocl::Canny

Finds edges in an image using the [Canny86] algorithm.

C++: void ocl::Canny(const oclMat& image, oclMat& edges, double low_thresh, double high_thresh, int apperture_size=3, bool L2gradient=false)

C++: void ocl::Canny(const oclMat& image, CannyBuf& buf, oclMat& edges, double low_thresh, double high_thresh, int apperture_size=3, bool L2gradient=false)

C++: void ocl::Canny(const oclMat& dx, const oclMat& dy, oclMat& edges, double low_thresh, double high_thresh, bool L2gradient=false)

C++: void ocl::Canny(const oclMat& dx, const oclMat& dy, CannyBuf& buf, oclMat& edges, double low_thresh, double high_thresh, bool L2gradient=false)

Parameters:

• image – Single-channel 8-bit input image.
• dx – First derivative of image in the vertical direction. Support only CV_32S type.
• dy – First derivative of image in the horizontal direction. Support only CV_32S type.
• edges – Output edge map. It has the same size and type as image .
• low_thresh – First threshold for the hysteresis procedure.
• high_thresh – Second threshold for the hysteresis procedure.
• apperture_size – Aperture size for the Sobel() operator.
• L2gradient – Flag indicating whether a more accurate norm should be used to compute the image gradient magnitude ( L2gradient=true ), or a faster default norm is enough ( L2gradient=false ).
• buf – Optional buffer to avoid extra memory allocations (for many calls with the same sizes).

See also

Canny()

ocl::BruteForceMatcher_OCL_base

class ocl::BruteForceMatcher_OCL_base

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.

class BruteForceMatcher_OCL_base
{
public:
        enum DistType {L1Dist = 0, L2Dist, HammingDist};

    // Add descriptors to train descriptor collection.
    void add(const std::vector<oclMat>& descCollection);

    // Get train descriptors collection.
    const std::vector<oclMat>& 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 oclMat& query, const oclMat& train,
        oclMat& trainIdx, oclMat& distance,
        const oclMat& mask = oclMat());

    static void matchDownload(const oclMat& trainIdx,
        const oclMat& distance, std::vector<DMatch>& matches);
    static void matchConvert(const Mat& trainIdx,
        const Mat& distance, std::vector<DMatch>& matches);

    void match(const oclMat& query, const oclMat& train,
        std::vector<DMatch>& matches, const oclMat& mask = oclMat());

    void makeGpuCollection(oclMat& trainCollection, oclMat& maskCollection,
        const vector<oclMat>& masks = std::vector<oclMat>());

    void matchCollection(const oclMat& query, const oclMat& trainCollection,
        oclMat& trainIdx, oclMat& imgIdx, oclMat& distance,
        const oclMat& maskCollection);

    static void matchDownload(const oclMat& trainIdx, oclMat& imgIdx,
        const oclMat& distance, std::vector<DMatch>& matches);
    static void matchConvert(const Mat& trainIdx, const Mat& imgIdx,
        const Mat& distance, std::vector<DMatch>& matches);

    void match(const oclMat& query, std::vector<DMatch>& matches,
        const std::vector<oclMat>& masks = std::vector<oclMat>());

    void knnMatchSingle(const oclMat& query, const oclMat& train,
        oclMat& trainIdx, oclMat& distance, oclMat& allDist, int k,
        const oclMat& mask = oclMat());

    static void knnMatchDownload(const oclMat& trainIdx, const oclMat& 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 oclMat& query, const oclMat& train,
        std::vector< std::vector<DMatch> >& matches, int k,
        const oclMat& mask = oclMat(), bool compactResult = false);

    void knnMatch2Collection(const oclMat& query, const oclMat& trainCollection,
        oclMat& trainIdx, oclMat& imgIdx, oclMat& distance,
        const oclMat& maskCollection = oclMat());

    static void knnMatch2Download(const oclMat& trainIdx, const oclMat& imgIdx, const oclMat& 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 oclMat& query, std::vector< std::vector<DMatch> >& matches, int k,
        const std::vector<oclMat>& masks = std::vector<oclMat>(),
        bool compactResult = false);

    void radiusMatchSingle(const oclMat& query, const oclMat& train,
        oclMat& trainIdx, oclMat& distance, oclMat& nMatches, float maxDistance,
        const oclMat& mask = oclMat());

    static void radiusMatchDownload(const oclMat& trainIdx, const oclMat& distance, const oclMat& 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 oclMat& query, const oclMat& train,
        std::vector< std::vector<DMatch> >& matches, float maxDistance,
        const oclMat& mask = oclMat(), bool compactResult = false);

    void radiusMatchCollection(const oclMat& query, oclMat& trainIdx, oclMat& imgIdx, oclMat& distance, oclMat& nMatches, float maxDistance,
        const std::vector<oclMat>& masks = std::vector<oclMat>());

    static void radiusMatchDownload(const oclMat& trainIdx, const oclMat& imgIdx, const oclMat& distance, const oclMat& 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 oclMat& query, std::vector< std::vector<DMatch> >& matches, float maxDistance,
        const std::vector<oclMat>& masks = std::vector<oclMat>(), bool compactResult = false);

            DistType distType;

private:
    std::vector<oclMat> trainDescCollection;
};

The class BruteForceMatcher_OCL_base 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. BruteForceMatcher_OCL_base supports only the L1<float>, L2<float>, and Hamming distance types.

See also

DescriptorMatcher, BFMatcher

ocl::BruteForceMatcher_OCL_base::match

Finds the best match for each descriptor from a query set with train descriptors.

C++: void ocl::BruteForceMatcher_OCL_base::match(const oclMat& query, const oclMat& train, std::vector<DMatch>& matches, const oclMat& mask=oclMat())

C++: void ocl::BruteForceMatcher_OCL_base::matchSingle(const oclMat& query, const oclMat& train, oclMat& trainIdx, oclMat& distance, const oclMat& mask=oclMat())

C++: void ocl::BruteForceMatcher_OCL_base::match(const oclMat& query, std::vector<DMatch>& matches, const std::vector<oclMat>& masks=std::vector<oclMat>())

C++: void ocl::BruteForceMatcher_OCL_base::matchCollection(const oclMat& query, const oclMat& trainCollection, oclMat& trainIdx, oclMat& imgIdx, oclMat& distance, const oclMat& masks=oclMat() )

See also

DescriptorMatcher::match()

ocl::BruteForceMatcher_OCL_base::makeGpuCollection

Performs a GPU collection of train descriptors and masks in a suitable format for the ocl::BruteForceMatcher_OCL_base::matchCollection() function.

C++: void ocl::BruteForceMatcher_OCL_base::makeGpuCollection(oclMat& trainCollection, oclMat& maskCollection, const vector<oclMat>& masks=std::vector<oclMat>())

ocl::BruteForceMatcher_OCL_base::matchDownload

Downloads matrices obtained via ocl::BruteForceMatcher_OCL_base::matchSingle() or ocl::BruteForceMatcher_OCL_base::matchCollection() to vector with DMatch.

C++: static void ocl::BruteForceMatcher_OCL_base::matchDownload(const oclMat& trainIdx, const oclMat& distance, std::vector<DMatch>& matches)

C++: static void ocl::BruteForceMatcher_OCL_base::matchDownload(const oclMat& trainIdx, const oclMat& imgIdx, const oclMat& distance, std::vector<DMatch>& matches)

ocl::BruteForceMatcher_OCL_base::matchConvert

Converts matrices obtained via ocl::BruteForceMatcher_OCL_base::matchSingle() or ocl::BruteForceMatcher_OCL_base::matchCollection() to vector with DMatch.

C++: void ocl::BruteForceMatcher_OCL_base::matchConvert(const Mat& trainIdx, const Mat& distance, std::vector<DMatch>& matches)

C++: void ocl::BruteForceMatcher_OCL_base::matchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<DMatch>& matches)

ocl::BruteForceMatcher_OCL_base::knnMatch

Finds the k best matches for each descriptor from a query set with train descriptors.

C++: void ocl::BruteForceMatcher_OCL_base::knnMatch(const oclMat& query, const oclMat& train, std::vector<std::vector<DMatch>>& matches, int k, const oclMat& mask=oclMat(), bool compactResult=false)

C++: void ocl::BruteForceMatcher_OCL_base::knnMatchSingle(const oclMat& query, const oclMat& train, oclMat& trainIdx, oclMat& distance, oclMat& allDist, int k, const oclMat& mask=oclMat())

C++: void ocl::BruteForceMatcher_OCL_base::knnMatch(const oclMat& query, std::vector<std::vector<DMatch>>& matches, int k, const std::vector<oclMat>& masks=std::vector<oclMat>(), bool compactResult=false )

C++: void ocl::BruteForceMatcher_OCL_base::knnMatch2Collection(const oclMat& query, const oclMat& trainCollection, oclMat& trainIdx, oclMat& imgIdx, oclMat& distance, const oclMat& maskCollection=oclMat())

Parameters:

• query – Query set of descriptors.
• train – Training set of descriptors. It is not be added to train descriptors collection stored in the class object.
• k – Number of the best matches per each query descriptor (or less if it is not possible).
• mask – Mask specifying permissible matches between the input query and train matrices of descriptors.
• compactResult – If compactResult is true , the matches vector does not contain matches for fully masked-out query descriptors.

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

DescriptorMatcher::knnMatch()

ocl::BruteForceMatcher_OCL_base::knnMatchDownload

Downloads matrices obtained via ocl::BruteForceMatcher_OCL_base::knnMatchSingle() or ocl::BruteForceMatcher_OCL_base::knnMatch2Collection() to vector with DMatch.

C++: void ocl::BruteForceMatcher_OCL_base::knnMatchDownload(const oclMat& trainIdx, const oclMat& distance, std::vector<std::vector<DMatch>>& matches, bool compactResult=false)

C++: void ocl::BruteForceMatcher_OCL_base::knnMatch2Download(const oclMat& trainIdx, const oclMat& imgIdx, const oclMat& distance, std::vector<std::vector<DMatch>>& matches, bool compactResult=false)

If compactResult is true , the matches vector does not contain matches for fully masked-out query descriptors.

ocl::BruteForceMatcher_OCL_base::knnMatchConvert

Converts matrices obtained via ocl::BruteForceMatcher_OCL_base::knnMatchSingle() or ocl::BruteForceMatcher_OCL_base::knnMatch2Collection() to CPU vector with DMatch.

C++: void ocl::BruteForceMatcher_OCL_base::knnMatchConvert(const Mat& trainIdx, const Mat& distance, std::vector<std::vector<DMatch>>& matches, bool compactResult=false)

C++: void ocl::BruteForceMatcher_OCL_base::knnMatch2Convert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, std::vector<std::vector<DMatch>>& matches, bool compactResult=false)

If compactResult is true , the matches vector does not contain matches for fully masked-out query descriptors.

ocl::BruteForceMatcher_OCL_base::radiusMatch

For each query descriptor, finds the best matches with a distance less than a given threshold.

C++: void ocl::BruteForceMatcher_OCL_base::radiusMatch(const oclMat& query, const oclMat& train, std::vector<std::vector<DMatch>>& matches, float maxDistance, const oclMat& mask=oclMat(), bool compactResult=false)

C++: void ocl::BruteForceMatcher_OCL_base::radiusMatchSingle(const oclMat& query, const oclMat& train, oclMat& trainIdx, oclMat& distance, oclMat& nMatches, float maxDistance, const oclMat& mask=oclMat())

C++: void ocl::BruteForceMatcher_OCL_base::radiusMatch(const oclMat& query, std::vector<std::vector<DMatch>>& matches, float maxDistance, const std::vector<oclMat>& masks=std::vector<oclMat>(), bool compactResult=false)

C++: void ocl::BruteForceMatcher_OCL_base::radiusMatchCollection(const oclMat& query, oclMat& trainIdx, oclMat& imgIdx, oclMat& distance, oclMat& nMatches, float maxDistance, const std::vector<oclMat>& masks=std::vector<oclMat>())

Parameters:

• query – Query set of descriptors.
• train – Training set of descriptors. It is not added to train descriptors collection stored in the class object.
• maxDistance – Distance threshold.
• mask – Mask specifying permissible matches between the input query and train matrices of descriptors.
• compactResult – If compactResult is true , the matches vector does not contain matches for fully masked-out query descriptors.

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

DescriptorMatcher::radiusMatch()

ocl::BruteForceMatcher_OCL_base::radiusMatchDownload

Downloads matrices obtained via ocl::BruteForceMatcher_OCL_base::radiusMatchSingle() or ocl::BruteForceMatcher_OCL_base::radiusMatchCollection() to vector with DMatch.

C++: void ocl::BruteForceMatcher_OCL_base::radiusMatchDownload(const oclMat& trainIdx, const oclMat& distance, const oclMat& nMatches, std::vector<std::vector<DMatch>>& matches, bool compactResult=false)

C++: void ocl::BruteForceMatcher_OCL_base::radiusMatchDownload(const oclMat& trainIdx, const oclMat& imgIdx, const oclMat& distance, const oclMat& nMatches, std::vector<std::vector<DMatch>>& matches, bool compactResult=false)

If compactResult is true , the matches vector does not contain matches for fully masked-out query descriptors.

ocl::BruteForceMatcher_OCL_base::radiusMatchConvert

Converts matrices obtained via ocl::BruteForceMatcher_OCL_base::radiusMatchSingle() or ocl::BruteForceMatcher_OCL_base::radiusMatchCollection() to vector with DMatch.

C++: void ocl::BruteForceMatcher_OCL_base::radiusMatchConvert(const Mat& trainIdx, const Mat& distance, const Mat& nMatches, std::vector<std::vector<DMatch>>& matches, bool compactResult=false)

C++: void ocl::BruteForceMatcher_OCL_base::radiusMatchConvert(const Mat& trainIdx, const Mat& imgIdx, const Mat& distance, const Mat& nMatches, std::vector<std::vector<DMatch>>& matches, bool compactResult=false)

If compactResult is true , the matches vector does not contain matches for fully masked-out query descriptors.

ocl::FAST_OCL

class ocl::FAST_OCL

Class used for corner detection using the FAST algorithm.

class CV_EXPORTS FAST_OCL
{
public:
    enum
    {
        X_ROW = 0,
        Y_ROW,
        RESPONSE_ROW,
        ROWS_COUNT
    };

    // all features have same size
    static const int FEATURE_SIZE = 7;

    explicit FAST_OCL(int threshold, bool nonmaxSupression = true, double keypointsRatio = 0.05);

    //! finds the keypoints using FAST detector
    //! supports only CV_8UC1 images
    void operator ()(const oclMat& image, const oclMat& mask, oclMat& keypoints);
    void operator ()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints);

    //! download keypoints from device to host memory
    static void downloadKeypoints(const oclMat& d_keypoints, std::vector<KeyPoint>& keypoints);

    //! convert keypoints to KeyPoint vector
    static void convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints);

    //! release temporary buffer's memory
    void release();

    bool nonmaxSupression;

    int threshold;

    //! max keypoints = keypointsRatio * img.size().area()
    double keypointsRatio;

    //! find keypoints and compute it's response if nonmaxSupression is true
    //! return count of detected keypoints
    int calcKeyPointsLocation(const oclMat& image, const oclMat& mask);

    //! get final array of keypoints
    //! performs nonmax supression if needed
    //! return final count of keypoints
    int getKeyPoints(oclMat& keypoints);

private:
    // Hidden
};

The class FAST_OCL implements FAST corner detection algorithm.

See also

FAST()

ocl::FAST_OCL::FAST_OCL

Constructor.

C++: ocl::FAST_OCL::FAST_OCL(int threshold, bool nonmaxSupression=true, double keypointsRatio=0.05)

Parameters:

• threshold – Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel.
• nonmaxSupression – If it is true, non-maximum suppression is applied to detected corners (keypoints).
• keypointsRatio – Inner buffer size for keypoints store is determined as (keypointsRatio * image_width * image_height).

ocl::FAST_OCL::operator ()

Finds the keypoints using FAST detector.

C++: void ocl::FAST_OCL::operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints)

C++: void ocl::FAST_OCL::operator()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints)

Parameters:

• image – Image where keypoints (corners) are detected. Only 8-bit grayscale images are supported.
• mask – Optional input mask that marks the regions where we should detect features.
• keypoints –

  The output vector of keypoints. Can be stored both in host or device memory. For device memory:

  • X_ROW of keypoints will contain the horizontal coordinate of the i’th point
  • Y_ROW of keypoints will contain the vertical coordinate of the i’th point
  • RESPONSE_ROW will contain response of i’th point (if non-maximum suppression is applied)

ocl::FAST_OCL::downloadKeypoints

Download keypoints from device to host memory.

C++: void ocl::FAST_OCL::downloadKeypoints(const oclMat& d_keypoints, std::vector<KeyPoint>& keypoints)

ocl::FAST_OCL::convertKeypoints

Converts keypoints from OpenCL representation to vector of KeyPoint.

C++: void ocl::FAST_OCL::convertKeypoints(const Mat& h_keypoints, std::vector<KeyPoint>& keypoints)

ocl::FAST_OCL::release

Releases inner buffer memory.

C++: void ocl::FAST_OCL::release()

ocl::FAST_OCL::calcKeyPointsLocation

Find keypoints. If nonmaxSupression is true, responses are computed and eliminates keypoints with the smaller responses from 9-neighborhood regions.

C++: int ocl::FAST_OCL::calcKeyPointsLocation(const oclMat& image, const oclMat& mask)

Parameters:

• image – Image where keypoints (corners) are detected. Only 8-bit grayscale images are supported.
• mask – Optional input mask that marks the regions where we should detect features.

The function returns the amount of detected keypoints.

ocl::FAST_OCL::getKeyPoints

Gets final array of keypoints.

C++: int ocl::FAST_OCL::getKeyPoints(oclMat& keypoints)

Parameters:

• keypoints – The output vector of keypoints.

The function performs non-max suppression if needed and returns the final amount of keypoints.

ocl::BRIEF_OCL

class ocl::BRIEF_OCL

Class for computing BRIEF descriptors described in a paper of Calonder M., Lepetit V., Strecha C., Fua P. BRIEF: Binary Robust Independent Elementary Features , 11th European Conference on Computer Vision (ECCV), Heraklion, Crete. LNCS Springer, September 2010.

class CV_EXPORTS BRIEF_OCL
{
public:
    static const int PATCH_SIZE = 48;
    static const int KERNEL_SIZE = 9;

    explicit BRIEF_OCL(int _bytes = 32);

    //!computes the brief descriptor for a set of given keypoints
    //! supports only CV_8UC1 images
    void compute(const oclMat& image, const oclMat& keypoints, oclMat& mask, oclMat& descriptors) const;

    static int getBorderSize();
protected:
    ...
};

ocl::BRIEF_OCL::BRIEF_OCL

Constructor.

C++: ocl::BRIEF_OCL::BRIEF_OCL(int _bytes=32)

Parameters:

• bytes – The length of the descriptor in bytes. Supported values are 16, 32 or 64 bytes.

ocl::BRIEF_OCL::compute

Computes BRIEF descriptors.

C++: void ocl::BRIEF_OCL::compute(const oclMat& image, const oclMat& keypoints, oclMat& mask, oclMat& descriptors) const

Parameters:

• image – Image The input 8-bit grayscale image.
• keypoints – The keypoints.
• mask – In and output mask. If mask has same cols as keypoints, descriptors are computed for keypoints with non-zero mask element. On return it indicates for what keypoints a descriptor was computed or not(if a keypoint is near the image border).
• descriptors – The computed descriptors. It has size keypoints.cols x bytes.

ocl::BRIEF_OCL::getBorderSize

Returns the size of the image border where descriptors cannot be computed

C++: static int ocl::BRIEF_OCL::getBorderSize() const

ocl::HOGDescriptor

struct ocl::HOGDescriptor

The class implements Histogram of Oriented Gradients ([Dalal2005]) object detector.

struct CV_EXPORTS HOGDescriptor
{
    enum { DEFAULT_WIN_SIGMA = -1 };
    enum { DEFAULT_NLEVELS = 64 };
    enum { DESCR_FORMAT_ROW_BY_ROW, DESCR_FORMAT_COL_BY_COL };

    HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16),
                  Size block_stride=Size(8, 8), Size cell_size=Size(8, 8),
                  int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA,
                  double threshold_L2hys=0.2, bool gamma_correction=true,
                  int nlevels=DEFAULT_NLEVELS);

    size_t getDescriptorSize() const;
    size_t getBlockHistogramSize() const;

    void setSVMDetector(const vector<float>& detector);

    static vector<float> getDefaultPeopleDetector();
    static vector<float> getPeopleDetector48x96();
    static vector<float> getPeopleDetector64x128();

    void detect(const oclMat& img, vector<Point>& found_locations,
                double hit_threshold=0, Size win_stride=Size(),
                Size padding=Size());

    void detectMultiScale(const oclMat& img, vector<Rect>& found_locations,
                          double hit_threshold=0, Size win_stride=Size(),
                          Size padding=Size(), double scale0=1.05,
                          int group_threshold=2);

    void getDescriptors(const oclMat& img, Size win_stride,
                        oclMat& descriptors,
                        int descr_format=DESCR_FORMAT_COL_BY_COL);

    Size win_size;
    Size block_size;
    Size block_stride;
    Size cell_size;
    int nbins;
    double win_sigma;
    double threshold_L2hys;
    bool gamma_correction;
    int nlevels;

private:
    // Hidden
}

Interfaces of all methods are kept similar to the CPU HOG descriptor and detector analogues as much as possible.

Note

(Ocl) An example using the HOG descriptor can be found at opencv_source_code/samples/ocl/hog.cpp

ocl::HOGDescriptor::HOGDescriptor

Creates the HOG descriptor and detector.

C++: ocl::HOGDescriptor::HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16), Size block_stride=Size(8, 8), Size cell_size=Size(8, 8), int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA, double threshold_L2hys=0.2, bool gamma_correction=true, int nlevels=DEFAULT_NLEVELS)

Parameters:

• win_size – Detection window size. Align to block size and block stride.
• block_size – Block size in pixels. Align to cell size. Only (16,16) is supported for now.
• block_stride – Block stride. It must be a multiple of cell size.
• cell_size – Cell size. Only (8, 8) is supported for now.
• nbins – Number of bins. Only 9 bins per cell are supported for now.
• win_sigma – Gaussian smoothing window parameter.
• threshold_L2hys – L2-Hys normalization method shrinkage.
• gamma_correction – Flag to specify whether the gamma correction preprocessing is required or not.
• nlevels – Maximum number of detection window increases.

ocl::HOGDescriptor::getDescriptorSize

Returns the number of coefficients required for the classification.

C++: size_t ocl::HOGDescriptor::getDescriptorSize() const

ocl::HOGDescriptor::getBlockHistogramSize

Returns the block histogram size.

C++: size_t ocl::HOGDescriptor::getBlockHistogramSize() const

ocl::HOGDescriptor::setSVMDetector

Sets coefficients for the linear SVM classifier.

C++: void ocl::HOGDescriptor::setSVMDetector(const vector<float>& detector)

ocl::HOGDescriptor::getDefaultPeopleDetector

Returns coefficients of the classifier trained for people detection (for default window size).

C++: static vector<float> ocl::HOGDescriptor::getDefaultPeopleDetector()

ocl::HOGDescriptor::getPeopleDetector48x96

Returns coefficients of the classifier trained for people detection (for 48x96 windows).

C++: static vector<float> ocl::HOGDescriptor::getPeopleDetector48x96()

ocl::HOGDescriptor::getPeopleDetector64x128

Returns coefficients of the classifier trained for people detection (for 64x128 windows).

C++: static vector<float> ocl::HOGDescriptor::getPeopleDetector64x128()

ocl::HOGDescriptor::detect

Performs object detection without a multi-scale window.

C++: void ocl::HOGDescriptor::detect(const oclMat& img, vector<Point>& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size())

Parameters:

• img – Source image. CV_8UC1 and CV_8UC4 types are supported for now.
• found_locations – Left-top corner points of detected objects boundaries.
• hit_threshold – Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.
• win_stride – Window stride. It must be a multiple of block stride.
• padding – Mock parameter to keep the CPU interface compatibility. It must be (0,0).

ocl::HOGDescriptor::detectMultiScale

Performs object detection with a multi-scale window.

C++: void ocl::HOGDescriptor::detectMultiScale(const oclMat& img, vector<Rect>& found_locations, double hit_threshold=0, Size win_stride=Size(), Size padding=Size(), double scale0=1.05, int group_threshold=2)

Parameters:

• img – Source image. See ocl::HOGDescriptor::detect() for type limitations.
• found_locations – Detected objects boundaries.
• hit_threshold – Threshold for the distance between features and SVM classifying plane. See ocl::HOGDescriptor::detect() for details.
• win_stride – Window stride. It must be a multiple of block stride.
• padding – Mock parameter to keep the CPU interface compatibility. It must be (0,0).
• scale0 – Coefficient of the detection window increase.
• group_threshold – Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. See groupRectangles() .

ocl::HOGDescriptor::getDescriptors

Returns block descriptors computed for the whole image.

C++: void ocl::HOGDescriptor::getDescriptors(const oclMat& img, Size win_stride, oclMat& descriptors, int descr_format=DESCR_FORMAT_COL_BY_COL)

Parameters:

• img – Source image. See ocl::HOGDescriptor::detect() for type limitations.
• win_stride – Window stride. It must be a multiple of block stride.
• descriptors – 2D array of descriptors.
• descr_format –

  Descriptor storage format:

  • DESCR_FORMAT_ROW_BY_ROW - Row-major order.
  • DESCR_FORMAT_COL_BY_COL - Column-major order.

The function is mainly used to learn the classifier.

ocl::ORB_OCL

class ocl::ORB_OCL

Class for extracting ORB features and descriptors from an image.

class ORB_OCL
{
public:
    enum
    {
        X_ROW = 0,
        Y_ROW,
        RESPONSE_ROW,
        ANGLE_ROW,
        OCTAVE_ROW,
        SIZE_ROW,
        ROWS_COUNT
    };

    enum
    {
        DEFAULT_FAST_THRESHOLD = 20
    };

    explicit ORB_OCL(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 oclMat& image, const oclMat& mask,
                    std::vector<KeyPoint>& keypoints);
    void operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints);

    void operator()(const oclMat& image, const oclMat& mask,
                    std::vector<KeyPoint>& keypoints, oclMat& descriptors);
    void operator()(const oclMat& image, const oclMat& mask,
                    oclMat& keypoints, oclMat& descriptors);

    void downloadKeyPoints(oclMat& d_keypoints, std::vector<KeyPoint>& keypoints);

    void convertKeyPoints(Mat& d_keypoints, std::vector<KeyPoint>& keypoints);

    int descriptorSize() const;
    int descriptorType() const;
    int defaultNorm() const;

    void setFastParams(int threshold, bool nonmaxSupression = true);

    void release();

    bool blurForDescriptor;
};

The class implements ORB feature detection and description algorithm.

ocl::ORB_OCL::ORB_OCL

Constructor.

C++: ocl::ORB_OCL::ORB_OCL(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)

Parameters:

• nfeatures – The maximum number of features to retain.
• scaleFactor – Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
• nlevels – The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels).
• edgeThreshold – This is size of the border where the features are not detected. It should roughly match the patchSize parameter.
• firstLevel – It should be 0 in the current implementation.
• WTA_K – The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
• scoreType – The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to KeyPoint::score and is used to retain best nfeatures features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
• patchSize – size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.

ocl::ORB_OCL::operator()

Detects keypoints and computes descriptors for them.

C++: void ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints)

C++: void ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints)

C++: void ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, std::vector<KeyPoint>& keypoints, oclMat& descriptors)

C++: void ocl::ORB_OCL::operator()(const oclMat& image, const oclMat& mask, oclMat& keypoints, oclMat& descriptors)

Parameters:

• image – Input 8-bit grayscale image.
• mask – Optional input mask that marks the regions where we should detect features.
• keypoints –

  The input/output vector of keypoints. Can be stored both in host and device memory. For device memory:

  • X_ROW contains the horizontal coordinate of the i’th feature.
  • Y_ROW contains the vertical coordinate of the i’th feature.
  • RESPONSE_ROW contains the response of the i’th feature.
  • ANGLE_ROW contains the orientation of the i’th feature.
  • RESPONSE_ROW contains the octave of the i’th feature.
  • ANGLE_ROW contains the size of the i’th feature.
• descriptors – Computed descriptors. if blurForDescriptor is true, image will be blurred before descriptors calculation.

ocl::ORB_OCL::downloadKeyPoints

Download keypoints from device to host memory.

C++: static void ocl::ORB_OCL::downloadKeyPoints(const oclMat& d_keypoints, std::vector<KeyPoint>& keypoints)

ocl::ORB_OCL::convertKeyPoints

Converts keypoints from OCL representation to vector of KeyPoint.

C++: static void ocl::ORB_OCL::convertKeyPoints(const Mat& d_keypoints, std::vector<KeyPoint>& keypoints)

ocl::ORB_OCL::release

Releases inner buffer memory.

C++: void ocl::ORB_OCL::release()