Object Detection

ocl::OclCascadeClassifier

class ocl::OclCascadeClassifier : public CascadeClassifier

Cascade classifier class used for object detection. Supports HAAR cascade classifier in the form of cross link

class CV_EXPORTS OclCascadeClassifier : public CascadeClassifier
{
public:
      OclCascadeClassifier() {};
      ~OclCascadeClassifier() {};
       CvSeq *oclHaarDetectObjects(oclMat &gimg, CvMemStorage *storage,
                                  double scaleFactor,int minNeighbors,
                                  int flags, CvSize minSize = cvSize(0, 0),
                                  CvSize maxSize = cvSize(0, 0));
};

ocl::OclCascadeClassifier::oclHaarDetectObjects

Returns the detected objects by a list of rectangles

C++: CvSeq* ocl::OclCascadeClassifier::oclHaarDetectObjects(oclMat& gimg, CvMemStorage* storage, double scaleFactor, int minNeighbors, int flags, CvSize minSize=cvSize(0, 0), CvSize maxSize=cvSize(0, 0))

Parameters:

• image – Matrix of type CV_8U containing an image where objects should be detected.
• imageobjectsBuff – Buffer to store detected objects (rectangles). If it is empty, it is allocated with the defaultsize. If not empty, the function searches not more than N objects, where N = sizeof(objectsBufers data)/sizeof(cv::Rect).
• scaleFactor – Parameter specifying how much the image size is reduced at each image scale.
• minNeighbors – Parameter specifying how many neighbors each candidate rectangle should have to retain it.
• minSize – Minimum possible object size. Objects smaller than that are ignored.

Detects objects of different sizes in the input image,only tested for face detection now. The function returns the number of detected objects.

ocl::MatchTemplateBuf

struct ocl::MatchTemplateBuf

Class providing memory buffers for ocl::matchTemplate() function, plus it allows to adjust some specific parameters.

struct CV_EXPORTS MatchTemplateBuf
{
    Size user_block_size;
    oclMat imagef, templf;
    std::vector<oclMat> images;
    std::vector<oclMat> image_sums;
    std::vector<oclMat> image_sqsums;
};

You can use field user_block_size to set specific block size for ocl::matchTemplate() function. If you leave its default value Size(0,0) then automatic estimation of block size will be used (which is optimized for speed). By varying user_block_size you can reduce memory requirements at the cost of speed.

ocl::matchTemplate

Computes a proximity map for a raster template and an image where the template is searched for.

C++: void ocl::matchTemplate(const oclMat& image, const oclMat& templ, oclMat& result, int method)

C++: void ocl::matchTemplate(const oclMat& image, const oclMat& templ, oclMat& result, int method, MatchTemplateBuf& buf)

Parameters:

• image – Source image. CV_32F and CV_8U depth images (1..4 channels) are supported for now.
• templ – Template image with the size and type the same as image .
• result – Map containing comparison results ( CV_32FC1 ). If image is W x H and templ is w x h, then result must be W-w+1 x H-h+1.
• method – Specifies the way to compare the template with the image.
• buf – Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See ocl::MatchTemplateBuf.

The following methods are supported for the CV_8U depth images for now:

• CV_TM_SQDIFF
• CV_TM_SQDIFF_NORMED
• CV_TM_CCORR
• CV_TM_CCORR_NORMED
• CV_TM_CCOEFF
• CV_TM_CCOEFF_NORMED

The following methods are supported for the CV_32F images for now:

• CV_TM_SQDIFF
• CV_TM_CCORR

See also

matchTemplate()

ocl::SURF_OCL

class ocl::SURF_OCL

Class used for extracting Speeded Up Robust Features (SURF) from an image.

class SURF_OCL
{
public:
    enum KeypointLayout
    {
        X_ROW = 0,
        Y_ROW,
        LAPLACIAN_ROW,
        OCTAVE_ROW,
        SIZE_ROW,
        ANGLE_ROW,
        HESSIAN_ROW,
        ROWS_COUNT
    };

    //! the default constructor
    SURF_OCL();
    //! the full constructor taking all the necessary parameters
    explicit SURF_OCL(double _hessianThreshold, int _nOctaves=4,
         int _nOctaveLayers=2, bool _extended=false, float _keypointsRatio=0.01f, bool _upright = false);

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

    //! download descriptors from device to host memory
    void downloadDescriptors(const oclMat& descriptorsocl,
        vector<float>& descriptors);

    void operator()(const oclMat& img, const oclMat& mask,
        oclMat& keypoints);

    void operator()(const oclMat& img, const oclMat& mask,
        oclMat& keypoints, oclMat& descriptors,
        bool useProvidedKeypoints = false);

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

    void operator()(const oclMat& img, const oclMat& mask,
        std::vector<KeyPoint>& keypoints, oclMat& descriptors,
        bool useProvidedKeypoints = false);

    void operator()(const oclMat& img, const oclMat& mask,
        std::vector<KeyPoint>& keypoints,
        std::vector<float>& descriptors,
        bool useProvidedKeypoints = false);

    void releaseMemory();

    // SURF parameters
    double hessianThreshold;
    int nOctaves;
    int nOctaveLayers;
    bool extended;
    bool upright;

    //! max keypoints = min(keypointsRatio * img.size().area(), 65535)
    float keypointsRatio;

    oclMat sum, mask1, maskSum, intBuffer;

    oclMat det, trace;

    oclMat maxPosBuffer;
};

The class SURF_OCL 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_OCL 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 oclMat. The keypoints matrix is matrix with the CV_32FC1 type.

• keypoints.ptr<float>(X_ROW)[i] contains x coordinate of the i-th feature.
• keypoints.ptr<float>(Y_ROW)[i] contains y coordinate of the i-th feature.
• keypoints.ptr<float>(LAPLACIAN_ROW)[i] contains the laplacian sign of the i-th feature.
• keypoints.ptr<float>(OCTAVE_ROW)[i] contains the octave of the i-th feature.
• keypoints.ptr<float>(SIZE_ROW)[i] contains the size of the i-th feature.
• keypoints.ptr<float>(ANGLE_ROW)[i] contain orientation of the i-th feature.
• keypoints.ptr<float>(HESSIAN_ROW)[i] contains the response of the i-th feature.

The descriptors matrix is matrix with the CV_32FC1 type.

The class SURF_OCL uses some buffers and provides access to it. All buffers can be safely released between function calls.

See also

SURF