Image Processing ================ .. highlight:: cpp cuda::CannyEdgeDetector ----------------------- .. ocv: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. .. ocv:function:: void cuda::CannyEdgeDetector::detect(InputArray image, OutputArray edges) .. ocv:function:: void cuda::CannyEdgeDetector::detect(InputArray dx, InputArray dy, OutputArray edges) :param image: Single-channel 8-bit input image. :param dx: First derivative of image in the vertical direction. Support only ``CV_32S`` type. :param dy: First derivative of image in the horizontal direction. Support only ``CV_32S`` type. :param edges: Output edge map. It has the same size and type as ``image`` . cuda::createCannyEdgeDetector ----------------------------- Creates implementation for :ocv:class:`cuda::CannyEdgeDetector` . .. ocv:function:: Ptr cuda::createCannyEdgeDetector(double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false) :param low_thresh: First threshold for the hysteresis procedure. :param high_thresh: Second threshold for the hysteresis procedure. :param apperture_size: Aperture size for the :ocv:func:`Sobel` operator. :param L2gradient: Flag indicating whether a more accurate :math:`L_2` norm :math:`=\sqrt{(dI/dx)^2 + (dI/dy)^2}` should be used to compute the image gradient magnitude ( ``L2gradient=true`` ), or a faster default :math:`L_1` norm :math:`=|dI/dx|+|dI/dy|` is enough ( ``L2gradient=false`` ). cuda::meanShiftFiltering ------------------------ Performs mean-shift filtering for each point of the source image. .. ocv:function:: 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()) :param src: Source image. Only ``CV_8UC4`` images are supported for now. :param dst: Destination image containing the color of mapped points. It has the same size and type as ``src`` . :param sp: Spatial window radius. :param sr: Color window radius. :param criteria: Termination criteria. See :ocv:class:`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. .. ocv:function:: 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()) :param src: Source image. Only ``CV_8UC4`` images are supported for now. :param dstr: Destination image containing the color of mapped points. The size and type is the same as ``src`` . :param dstsp: Destination image containing the position of mapped points. The size is the same as ``src`` size. The type is ``CV_16SC2`` . :param sp: Spatial window radius. :param sr: Color window radius. :param criteria: Termination criteria. See :ocv:class:`TermCriteria`. .. seealso:: :ocv:func:`cuda::meanShiftFiltering` cuda::meanShiftSegmentation --------------------------- Performs a mean-shift segmentation of the source image and eliminates small segments. .. ocv:function:: void cuda::meanShiftSegmentation(InputArray src, OutputArray dst, int sp, int sr, int minsize, TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1)) :param src: Source image. Only ``CV_8UC4`` images are supported for now. :param dst: Segmented image with the same size and type as ``src`` (host memory). :param sp: Spatial window radius. :param sr: Color window radius. :param minsize: Minimum segment size. Smaller segments are merged. :param criteria: Termination criteria. See :ocv:class:`TermCriteria`. cuda::TemplateMatching ---------------------- .. ocv: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. .. ocv:function:: void cuda::TemplateMatching::match(InputArray image, InputArray templ, OutputArray result, Stream& stream = Stream::Null()) :param image: Source image. :param templ: Template image with the size and type the same as ``image`` . :param 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*. :param stream: Stream for the asynchronous version. cuda::createTemplateMatching ---------------------------- Creates implementation for :ocv:class:`cuda::TemplateMatching` . .. ocv:function:: Ptr cuda::createTemplateMatching(int srcType, int method, Size user_block_size = Size()) :param srcType: Input source type. ``CV_32F`` and ``CV_8U`` depth images (1..4 channels) are supported for now. :param method: Specifies the way to compare the template with the image. :param 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`` .. seealso:: :ocv:func:`matchTemplate` cuda::bilateralFilter --------------------- Performs bilateral filtering of passed image .. ocv:function:: void cuda::bilateralFilter(InputArray src, OutputArray dst, int kernel_size, float sigma_color, float sigma_spatial, int borderMode=BORDER_DEFAULT, Stream& stream=Stream::Null()) :param src: Source image. Supports only (channles != 2 && depth() != CV_8S && depth() != CV_32S && depth() != CV_64F). :param dst: Destination imagwe. :param kernel_size: Kernel window size. :param sigma_color: Filter sigma in the color space. :param sigma_spatial: Filter sigma in the coordinate space. :param borderMode: Border type. See :ocv:func:`borderInterpolate` for details. ``BORDER_REFLECT101`` , ``BORDER_REPLICATE`` , ``BORDER_CONSTANT`` , ``BORDER_REFLECT`` and ``BORDER_WRAP`` are supported for now. :param stream: Stream for the asynchronous version. .. seealso:: :ocv:func:`bilateralFilter` cuda::blendLinear ----------------- Performs linear blending of two images. .. ocv:function:: void cuda::blendLinear(InputArray img1, InputArray img2, InputArray weights1, InputArray weights2, OutputArray result, Stream& stream = Stream::Null()) :param img1: First image. Supports only ``CV_8U`` and ``CV_32F`` depth. :param img2: Second image. Must have the same size and the same type as ``img1`` . :param weights1: Weights for first image. Must have tha same size as ``img1`` . Supports only ``CV_32F`` type. :param weights2: Weights for second image. Must have tha same size as ``img2`` . Supports only ``CV_32F`` type. :param result: Destination image. :param stream: Stream for the asynchronous version.