The source matrix should be continuous, otherwise reallocation and data copying is performed. The function chooses an operation mode depending on the flags, size, and channel count of the source matrix:
* If the source matrix is complex and the output is not specified as real, the destination matrix is complex and has the ``dft_size`` size and ``CV_32FC2`` type. The destination matrix contains a full result of the DFT (forward or inverse).
* If the source matrix is complex and the output is specified as real, the function assumes that its input is the result of the forward transform (see the next item). The destination matrix has the ``dft_size`` size and ``CV_32FC1`` type. It contains the result of the inverse DFT.
* If the source matrix is real (its type is ``CV_32FC1`` ), forward DFT is performed. The result of the DFT is packed into complex ( ``CV_32FC2`` ) matrix. So, the width of the destination matrix is ``dft_size.width / 2 + 1`` . But if the source is a single column, the height is reduced instead of the width.
You can use field `user_block_size` to set specific block size for :ocv:func:`gpu::convolve` 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.
You can use field `user_block_size` to set specific block size for :ocv:func:`gpu::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.
: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 interpolation:Interpolation method (see :ocv:func:`resize` ). ``INTER_NEAREST`` , ``INTER_LINEAR`` and ``INTER_CUBIC`` are supported for now.
:param borderMode:Pixel extrapolation method (see :ocv:func:`borderInterpolate` ). ``BORDER_REFLECT101`` , ``BORDER_REPLICATE`` , ``BORDER_CONSTANT`` , ``BORDER_REFLECT`` and ``BORDER_WRAP`` are supported for now.
:param borderValue:Value used in case of a constant border. By default, it is 0.
:param stream:Stream for the asynchronous version.
:param dcn:Number of channels in the destination image. If the parameter is 0, the number of the channels is derived automatically from ``src`` and the ``code`` .
:param dstOrder:Integer array describing how channel values are permutated. The n-th entry of the array contains the number of the channel that is stored in the n-th channel of the output image. E.g. Given an RGBA image, aDstOrder = [3,2,1,0] converts this to ABGR channel order.
:param stream:Stream for the asynchronous version.
The methods support arbitrary permutations of the original channels, including replication.
:param dst:Destination image with the same type as ``src`` . The size is ``dsize`` (when it is non-zero) or the size is computed from ``src.size()`` , ``fx`` , and ``fy`` .
:param flags:Combination of interpolation methods (see :ocv:func:`resize`) and the optional flag ``WARP_INVERSE_MAP`` specifying that ``M`` is an inverse transformation ( ``dst=>src`` ). Only ``INTER_NEAREST`` , ``INTER_LINEAR`` , and ``INTER_CUBIC`` interpolation methods are supported.
:param stream:Stream for the asynchronous version.
:param flags:Combination of interpolation methods (see :ocv:func:`resize` ) and the optional flag ``WARP_INVERSE_MAP`` specifying that ``M`` is the inverse transformation ( ``dst => src`` ). Only ``INTER_NEAREST`` , ``INTER_LINEAR`` , and ``INTER_CUBIC`` interpolation methods are supported.
:param stream:Stream for the asynchronous version.
..ocv:function:: void gpu::copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType, const Scalar& value = Scalar(), Stream& stream = Stream::Null())
:param right:Number of pixels in each direction from the source image rectangle to extrapolate. For example: ``top=1, bottom=1, left=1, right=1`` mean that 1 pixel-wide border needs to be built.
:param borderType:Border type. See :ocv:func:`borderInterpolate` for details. ``BORDER_REFLECT101`` , ``BORDER_REPLICATE`` , ``BORDER_CONSTANT`` , ``BORDER_REFLECT`` and ``BORDER_WRAP`` are supported for now.
:param src:Source image. ``CV_8U``, ``CV_16U``, or ``CV_16S`` depth and 1 or 4 channels are supported. For a four-channel image, all channels are processed separately.
:param src:Source image. ``CV_8U`` , ``CV_16U`` , or ``CV_16S`` depth and 1 or 4 channels are supported. For a four-channel image, all channels are processed separately.
: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.
: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.
//! Simple method, recommended for grayscale images (though it supports multichannel images)
void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
//! Processes luminance and color components separatelly
void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
};
The class implements fast approximate Non Local Means Denoising algorithm.
gpu::FastNonLocalMeansDenoising::simpleMethod()
-------------------------------------
Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising with several computational optimizations. Noise expected to be a gaussian white noise
..ocv:function:: void gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
:param src:Input 8-bit 1-channel, 2-channel or 3-channel image.
:param dst:Output image with the same size and type as ``src`` .
:param h:Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
:param search_window:Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater search_window - greater denoising time. Recommended value 21 pixels
:param block_size:Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
:param stream:Stream for the asynchronous invocations.
This function expected to be applied to grayscale images. For colored images look at ``FastNonLocalMeansDenoising::labMethod``.
..seealso::
:ocv:func:`fastNlMeansDenoising`
gpu::FastNonLocalMeansDenoising::labMethod()
-------------------------------------
Modification of ``FastNonLocalMeansDenoising::simpleMethod`` for color images
..ocv:function:: void gpu::FastNonLocalMeansDenoising::labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
:param src:Input 8-bit 3-channel image.
:param dst:Output image with the same size and type as ``src`` .
:param h_luminance:Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
:param float:The same as h but for color components. For most images value equals 10 will be enought to remove colored noise and do not distort colors
:param search_window:Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater search_window - greater denoising time. Recommended value 21 pixels
:param block_size:Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
:param stream:Stream for the asynchronous invocations.
The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using ``FastNonLocalMeansDenoising::simpleMethod`` function.
: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`` ).
:param buf:Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
:param lines:Output vector of lines. Each line is represented by a two-element vector :math:`(\rho, \theta)` . :math:`\rho` is the distance from the coordinate origin :math:`(0,0)` (top-left corner of the image). :math:`\theta` is the line rotation angle in radians ( :math:`0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}` ).
:param rho:Distance resolution of the accumulator in pixels.
:param theta:Angle resolution of the accumulator in radians.
:param threshold:Accumulator threshold parameter. Only those lines are returned that get enough votes ( :math:`>\texttt{threshold}` ).
:param doSort:Performs lines sort by votes.
:param maxLines:Maximum number of output lines.
:param buf:Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
..ocv:function:: void gpu::HoughCircles(const GpuMat& src, GpuMat& circles, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096)
..ocv:function:: void gpu::HoughCircles(const GpuMat& src, GpuMat& circles, HoughCirclesBuf& buf, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096)
:param method:Detection method to use. Currently, the only implemented method is ``CV_HOUGH_GRADIENT`` , which is basically *21HT* , described in [Yuen90]_.
:param dp:Inverse ratio of the accumulator resolution to the image resolution. For example, if ``dp=1`` , the accumulator has the same resolution as the input image. If ``dp=2`` , the accumulator has half as big width and height.
:param minDist:Minimum distance between the centers of the detected circles. If the parameter is too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
:param votesThreshold:The accumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected.
