Open Source Computer Vision Library https://opencv.org/
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Image Processing
================
.. highlight:: cpp
gpu::CannyEdgeDetector
----------------------
.. ocv:class:: gpu::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;
};
gpu::CannyEdgeDetector::detect
------------------------------
Finds edges in an image using the [Canny86]_ algorithm.
.. ocv:function:: void gpu::CannyEdgeDetector::detect(InputArray image, OutputArray edges)
.. ocv:function:: void gpu::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`` .
gpu::createCannyEdgeDetector
----------------------------
Creates implementation for :ocv:class:`gpu::CannyEdgeDetector` .
.. ocv:function:: Ptr<CannyEdgeDetector> gpu::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`` ).
gpu::meanShiftFiltering
-----------------------
Performs mean-shift filtering for each point of the source image.
.. ocv:function:: void gpu::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.
gpu::meanShiftProc
------------------
Performs a mean-shift procedure and stores information about processed points (their colors and positions) in two images.
.. ocv:function:: void gpu::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:`gpu::meanShiftFiltering`
gpu::meanShiftSegmentation
--------------------------
Performs a mean-shift segmentation of the source image and eliminates small segments.
.. ocv:function:: void gpu::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`.
gpu::TemplateMatching
---------------------
.. ocv:class:: gpu::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;
};
gpu::TemplateMatching::match
----------------------------
Computes a proximity map for a raster template and an image where the template is searched for.
.. ocv:function:: void gpu::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.
gpu::createTemplateMatching
---------------------------
Creates implementation for :ocv:class:`gpu::TemplateMatching` .
.. ocv:function:: Ptr<TemplateMatching> gpu::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`
gpu::bilateralFilter
--------------------
Performs bilateral filtering of passed image
.. ocv:function:: void gpu::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`
gpu::blendLinear
-------------------
Performs linear blending of two images.
.. ocv:function:: void gpu::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.