Image Processing

cuda::CannyEdgeDetector

class cuda::CannyEdgeDetector : public Algorithm

Base class for Canny Edge Detector.

class CV_EXPORTS CannyEdgeDetector : public Algorithm
{
public:
    virtual void detect(InputArray image, OutputArray edges) = 0;
    virtual void detect(InputArray dx, InputArray dy, OutputArray edges) = 0;

    virtual void setLowThreshold(double low_thresh) = 0;
    virtual double getLowThreshold() const = 0;

    virtual void setHighThreshold(double high_thresh) = 0;
    virtual double getHighThreshold() const = 0;

    virtual void setAppertureSize(int apperture_size) = 0;
    virtual int getAppertureSize() const = 0;

    virtual void setL2Gradient(bool L2gradient) = 0;
    virtual bool getL2Gradient() const = 0;
};

cuda::CannyEdgeDetector::detect

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

C++: void cuda::CannyEdgeDetector::detect(InputArray image, OutputArray edges)

C++: void cuda::CannyEdgeDetector::detect(InputArray dx, InputArray dy, OutputArray edges)

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 .

cuda::createCannyEdgeDetector

Creates implementation for cuda::CannyEdgeDetector .

C++: Ptr<CannyEdgeDetector> cuda::createCannyEdgeDetector(double low_thresh, double high_thresh, int apperture_size=3, bool L2gradient=false)

Parameters:

• 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 ).

cuda::meanShiftFiltering

Performs mean-shift filtering for each point of the source image.

C++: void cuda::meanShiftFiltering(InputArray src, OutputArray dst, int sp, int sr, TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1), Stream& stream=Stream::Null())

Parameters:

• src – Source image. Only CV_8UC4 images are supported for now.
• dst – Destination image containing the color of mapped points. It has the same size and type as src .
• sp – Spatial window radius.
• sr – Color window radius.
• criteria – Termination criteria. See TermCriteria.

It maps each point of the source image into another point. As a result, you have a new color and new position of each point.

cuda::meanShiftProc

Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images.

C++: void cuda::meanShiftProc(InputArray src, OutputArray dstr, OutputArray dstsp, int sp, int sr, TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1), Stream& stream=Stream::Null())

Parameters:

• src – Source image. Only CV_8UC4 images are supported for now.
• dstr – Destination image containing the color of mapped points. The size and type is the same as src .
• dstsp – Destination image containing the position of mapped points. The size is the same as src size. The type is CV_16SC2 .
• sp – Spatial window radius.
• sr – Color window radius.
• criteria – Termination criteria. See TermCriteria.

See also

cuda::meanShiftFiltering()

cuda::meanShiftSegmentation

Performs a mean-shift segmentation of the source image and eliminates small segments.

C++: void cuda::meanShiftSegmentation(InputArray src, OutputArray dst, int sp, int sr, int minsize, TermCriteria criteria=TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1))

Parameters:

• src – Source image. Only CV_8UC4 images are supported for now.
• dst – Segmented image with the same size and type as src (host memory).
• sp – Spatial window radius.
• sr – Color window radius.
• minsize – Minimum segment size. Smaller segments are merged.
• criteria – Termination criteria. See TermCriteria.

cuda::TemplateMatching

class cuda::TemplateMatching : public Algorithm

Base class for Template Matching.

class CV_EXPORTS TemplateMatching : public Algorithm
{
public:
    virtual void match(InputArray image, InputArray templ, OutputArray result, Stream& stream = Stream::Null()) = 0;
};

cuda::TemplateMatching::match

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

C++: void cuda::TemplateMatching::match(InputArray image, InputArray templ, OutputArray result, Stream& stream=Stream::Null())

Parameters:

• image – Source image.
• 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.
• stream – Stream for the asynchronous version.

cuda::createTemplateMatching

Creates implementation for cuda::TemplateMatching .

C++: Ptr<TemplateMatching> cuda::createTemplateMatching(int srcType, int method, Size user_block_size=Size())

Parameters:

• srcType – Input source type. CV_32F and CV_8U depth images (1..4 channels) are supported for now.
• method – Specifies the way to compare the template with the image.
• user_block_size – You can use field user_block_size to set specific block size. 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.

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()

cuda::bilateralFilter

Performs bilateral filtering of passed image

C++: void cuda::bilateralFilter(InputArray src, OutputArray dst, int kernel_size, float sigma_color, float sigma_spatial, int borderMode=BORDER_DEFAULT, Stream& stream=Stream::Null())

Parameters:

• src – Source image. Supports only (channles != 2 && depth() != CV_8S && depth() != CV_32S && depth() != CV_64F).
• dst – Destination imagwe.
• kernel_size – Kernel window size.
• sigma_color – Filter sigma in the color space.
• sigma_spatial – Filter sigma in the coordinate space.
• borderMode – Border type. See borderInterpolate() for details. BORDER_REFLECT101 , BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
• stream – Stream for the asynchronous version.

See also

bilateralFilter()

cuda::blendLinear

Performs linear blending of two images.

C++: void cuda::blendLinear(InputArray img1, InputArray img2, InputArray weights1, InputArray weights2, OutputArray result, Stream& stream=Stream::Null())

Parameters:

• img1 – First image. Supports only CV_8U and CV_32F depth.
• img2 – Second image. Must have the same size and the same type as img1 .
• weights1 – Weights for first image. Must have tha same size as img1 . Supports only CV_32F type.
• weights2 – Weights for second image. Must have tha same size as img2 . Supports only CV_32F type.
• result – Destination image.
• stream – Stream for the asynchronous version.