generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Merge pull request #978 from jet47:gpuarithm-refactoring

Browse Source
This commit is contained in:
Roman Donchenko 2013-06-14 16:24:03 +04:00 committed by OpenCV Buildbot
commit 2fc49ac564
27 changed files with 3103 additions and 3041 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
modules/gpu/src/cascadeclassifier.cpp
View File

@ -458,7 +458,7 @@ public:
// generate integral for scale
gpu::resize(image, src, level.sFrame, 0, 0, cv::INTER_LINEAR);
gpu::integralBuffered(src, sint, buff);
gpu::integral(src, sint, buff);
// calculate job
int totalWidth = level.workArea.width / step;

105
modules/gpuarithm/doc/arithm.rst
View File

@ -6,10 +6,10 @@ Arithm Operations on Matrices
gpu::gemm
------------------
---------
Performs generalized matrix multiplication.
.. ocv:function:: void gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const GpuMat& src3, double beta, GpuMat& dst, int flags = 0, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::gemm(InputArray src1, InputArray src2, double alpha, InputArray src3, double beta, OutputArray dst, int flags = 0, Stream& stream = Stream::Null())
:param src1: First multiplied input matrix that should have ``CV_32FC1`` , ``CV_64FC1`` , ``CV_32FC2`` , or ``CV_64FC2`` type.
@ -44,38 +44,40 @@ The function performs generalized matrix multiplication similar to the ``gemm``
gpu::mulSpectrums
---------------------
-----------------
Performs a per-element multiplication of two Fourier spectrums.
.. ocv:function:: void gpu::mulSpectrums( const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::mulSpectrums(InputArray src1, InputArray src2, OutputArray dst, int flags, bool conjB=false, Stream& stream = Stream::Null())
:param a: First spectrum.
:param src1: First spectrum.
:param b: Second spectrum with the same size and type as ``a`` .
:param src2: Second spectrum with the same size and type as ``a`` .
:param c: Destination spectrum.
:param dst: Destination spectrum.
:param flags: Mock parameter used for CPU/GPU interfaces similarity.
:param conjB: Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.
Only full (not packed) ``CV_32FC2`` complex spectrums in the interleaved format are supported for now.
:param stream: Stream for the asynchronous version.
Only full (not packed) ``CV_32FC2`` complex spectrums in the interleaved format are supported for now.
.. seealso:: :ocv:func:`mulSpectrums`
gpu::mulAndScaleSpectrums
-----------------------------
-------------------------
Performs a per-element multiplication of two Fourier spectrums and scales the result.
.. ocv:function:: void gpu::mulAndScaleSpectrums( const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, float scale, bool conjB=false, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::mulAndScaleSpectrums(InputArray src1, InputArray src2, OutputArray dst, int flags, float scale, bool conjB=false, Stream& stream = Stream::Null())
:param a: First spectrum.
:param src1: First spectrum.
:param b: Second spectrum with the same size and type as ``a`` .
:param src2: Second spectrum with the same size and type as ``a`` .
:param c: Destination spectrum.
:param dst: Destination spectrum.
:param flags: Mock parameter used for CPU/GPU interfaces similarity.
@ -83,17 +85,17 @@ Performs a per-element multiplication of two Fourier spectrums and scales the re
:param conjB: Optional flag to specify if the second spectrum needs to be conjugated before the multiplication.
Only full (not packed) ``CV_32FC2`` complex spectrums in the interleaved format are supported for now.
Only full (not packed) ``CV_32FC2`` complex spectrums in the interleaved format are supported for now.
.. seealso:: :ocv:func:`mulSpectrums`
gpu::dft
------------
--------
Performs a forward or inverse discrete Fourier transform (1D or 2D) of the floating point matrix.
.. ocv:function:: void gpu::dft( const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::dft(InputArray src, OutputArray dst, Size dft_size, int flags=0, Stream& stream = Stream::Null())
:param src: Source matrix (real or complex).
@ -125,46 +127,25 @@ The source matrix should be continuous, otherwise reallocation and data copying
gpu::ConvolveBuf
gpu::Convolution
----------------
.. ocv:struct:: gpu::ConvolveBuf
.. ocv:class:: gpu::Convolution : public Algorithm
Class providing a memory buffer for :ocv:func:`gpu::convolve` function, plus it allows to adjust some specific parameters. ::
Base class for convolution (or cross-correlation) operator. ::
struct CV_EXPORTS ConvolveBuf
class CV_EXPORTS Convolution : public Algorithm
{
Size result_size;
Size block_size;
Size user_block_size;
Size dft_size;
int spect_len;
GpuMat image_spect, templ_spect, result_spect;
GpuMat image_block, templ_block, result_data;
void create(Size image_size, Size templ_size);
static Size estimateBlockSize(Size result_size, Size templ_size);
public:
virtual void convolve(InputArray image, InputArray templ, OutputArray result, bool ccorr = false, Stream& stream = Stream::Null()) = 0;
};
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.
gpu::ConvolveBuf::create
------------------------
.. ocv:function:: gpu::ConvolveBuf::create(Size image_size, Size templ_size)
Constructs a buffer for :ocv:func:`gpu::convolve` function with respective arguments.
gpu::convolve
-----------------
gpu::Convolution::convolve
---------------------------
Computes a convolution (or cross-correlation) of two images.
.. ocv:function:: void gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr=false)
.. ocv:function:: void gpu::convolve( const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::Convolution::convolve(InputArray image, InputArray templ, OutputArray result, bool ccorr = false, Stream& stream = Stream::Null())
:param image: Source image. Only ``CV_32FC1`` images are supported for now.
@ -174,38 +155,16 @@ Computes a convolution (or cross-correlation) of two images.
:param ccorr: Flags to evaluate cross-correlation instead of convolution.
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:struct:`gpu::ConvolveBuf`.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`gpu::filter2D`
gpu::integral
-----------------
Computes an integral image.
gpu::createConvolution
----------------------
Creates implementation for :ocv:class:`gpu::Convolution` .
.. ocv:function:: void gpu::integral(const GpuMat& src, GpuMat& sum, Stream& stream = Stream::Null())
.. ocv:function:: Ptr<Convolution> createConvolution(Size user_block_size = Size())
:param src: Source image. Only ``CV_8UC1`` images are supported for now.
:param sum: Integral image containing 32-bit unsigned integer values packed into ``CV_32SC1`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`integral`
gpu::sqrIntegral
--------------------
Computes a squared integral image.
.. ocv:function:: void gpu::sqrIntegral(const GpuMat& src, GpuMat& sqsum, Stream& stream = Stream::Null())
:param src: Source image. Only ``CV_8UC1`` images are supported for now.
:param sqsum: Squared integral image containing 64-bit unsigned integer values packed into ``CV_64FC1`` .
:param stream: Stream for the asynchronous version.
:param user_block_size: Block size. If you leave 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.

156
modules/gpuarithm/doc/core.rst
View File

@ -6,12 +6,12 @@ Core Operations on Matrices
gpu::merge
--------------
----------
Makes a multi-channel matrix out of several single-channel matrices.
.. ocv:function:: void gpu::merge(const GpuMat* src, size_t n, GpuMat& dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::merge(const GpuMat* src, size_t n, OutputArray dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::merge(const vector<GpuMat>& src, GpuMat& dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::merge(const std::vector<GpuMat>& src, OutputArray dst, Stream& stream = Stream::Null())
:param src: Array/vector of source matrices.
@ -26,12 +26,12 @@ Makes a multi-channel matrix out of several single-channel matrices.
gpu::split
--------------
----------
Copies each plane of a multi-channel matrix into an array.
.. ocv:function:: void gpu::split(const GpuMat& src, GpuMat* dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::split(InputArray src, GpuMat* dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::split(const GpuMat& src, vector<GpuMat>& dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::split(InputArray src, vector<GpuMat>& dst, Stream& stream = Stream::Null())
:param src: Source matrix.
@ -43,15 +43,95 @@ Copies each plane of a multi-channel matrix into an array.
gpu::transpose
--------------
Transposes a matrix.
.. ocv:function:: void gpu::transpose(InputArray src1, OutputArray dst, Stream& stream = Stream::Null())
:param src1: Source matrix. 1-, 4-, 8-byte element sizes are supported for now.
:param dst: Destination matrix.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`transpose`
gpu::flip
---------
Flips a 2D matrix around vertical, horizontal, or both axes.
.. ocv:function:: void gpu::flip(InputArray src, OutputArray dst, int flipCode, Stream& stream = Stream::Null())
:param src: Source matrix. Supports 1, 3 and 4 channels images with ``CV_8U``, ``CV_16U``, ``CV_32S`` or ``CV_32F`` depth.
:param dst: Destination matrix.
:param flipCode: Flip mode for the source:
* ``0`` Flips around x-axis.
* ``> 0`` Flips around y-axis.
* ``< 0`` Flips around both axes.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`flip`
gpu::LookUpTable
----------------
.. ocv:class:: gpu::LookUpTable : public Algorithm
Base class for transform using lookup table. ::
class CV_EXPORTS LookUpTable : public Algorithm
{
public:
virtual void transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0;
};
.. seealso:: :ocv:func:`LUT`
gpu::LookUpTable::transform
---------------------------
Transforms the source matrix into the destination matrix using the given look-up table: ``dst(I) = lut(src(I))`` .
.. ocv:function:: void gpu::LookUpTable::transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
:param src: Source matrix. ``CV_8UC1`` and ``CV_8UC3`` matrices are supported for now.
:param dst: Destination matrix.
:param stream: Stream for the asynchronous version.
gpu::createLookUpTable
----------------------
Creates implementation for :ocv:class:`gpu::LookUpTable` .
.. ocv:function:: Ptr<LookUpTable> createLookUpTable(InputArray lut)
:param lut: Look-up table of 256 elements. It is a continuous ``CV_8U`` matrix.
gpu::copyMakeBorder
-----------------------
Forms a border around an image.
.. 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())
.. ocv:function:: void gpu::copyMakeBorder(InputArray src, OutputArray dst, int top, int bottom, int left, int right, int borderType, Scalar value = Scalar(), Stream& stream = Stream::Null())
:param src: Source image. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_32SC1`` , and ``CV_32FC1`` types are supported.
:param src: Source image. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_32SC1`` , and ``CV_32FC1`` types are supported.
:param dst: Destination image with the same type as ``src``. The size is ``Size(src.cols+left+right, src.rows+top+bottom)`` .
:param dst: Destination image with the same type as ``src``. The size is ``Size(src.cols+left+right, src.rows+top+bottom)`` .
:param top:
@ -68,61 +148,3 @@ Forms a border around an image.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`copyMakeBorder`
gpu::transpose
------------------
Transposes a matrix.
.. ocv:function:: void gpu::transpose( const GpuMat& src1, GpuMat& dst, Stream& stream=Stream::Null() )
:param src1: Source matrix. 1-, 4-, 8-byte element sizes are supported for now (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc).
:param dst: Destination matrix.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`transpose`
gpu::flip
-------------
Flips a 2D matrix around vertical, horizontal, or both axes.
.. ocv:function:: void gpu::flip( const GpuMat& a, GpuMat& b, int flipCode, Stream& stream=Stream::Null() )
:param a: Source matrix. Supports 1, 3 and 4 channels images with ``CV_8U``, ``CV_16U``, ``CV_32S`` or ``CV_32F`` depth.
:param b: Destination matrix.
:param flipCode: Flip mode for the source:
* ``0`` Flips around x-axis.
* ``>0`` Flips around y-axis.
* ``<0`` Flips around both axes.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`flip`
gpu::LUT
------------
Transforms the source matrix into the destination matrix using the given look-up table: ``dst(I) = lut(src(I))``
.. ocv:function:: void gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. ``CV_8UC1`` and ``CV_8UC3`` matrices are supported for now.
:param lut: Look-up table of 256 elements. It is a continuous ``CV_8U`` matrix.
:param dst: Destination matrix with the same depth as ``lut`` and the same number of channels as ``src`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`LUT`

671
modules/gpuarithm/doc/element_operations.rst
View File

@ -6,20 +6,16 @@ Per-element Operations
gpu::add
------------
--------
Computes a matrix-matrix or matrix-scalar sum.
.. ocv:function:: void gpu::add( const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask=GpuMat(), int dtype=-1, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::add(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), int dtype = -1, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::add( const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask=GpuMat(), int dtype=-1, Stream& stream=Stream::Null() )
:param src1: First source matrix or scalar.
:param a: First source matrix.
:param src2: Second source matrix or scalar. Matrix should have the same size and type as ``src1`` .
:param b: Second source matrix to be added to ``a`` . Matrix should have the same size and type as ``a`` .
:param sc: A scalar to be added to ``a`` .
:param c: Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by ``dtype`` or ``a`` depth.
:param dst: Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by ``dtype`` or ``src1`` depth.
:param mask: Optional operation mask, 8-bit single channel array, that specifies elements of the destination array to be changed.
@ -32,20 +28,16 @@ Computes a matrix-matrix or matrix-scalar sum.
gpu::subtract
-----------------
-------------
Computes a matrix-matrix or matrix-scalar difference.
.. ocv:function:: void gpu::subtract( const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask=GpuMat(), int dtype=-1, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::subtract(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), int dtype = -1, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::subtract( const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask=GpuMat(), int dtype=-1, Stream& stream=Stream::Null() )
:param src1: First source matrix or scalar.
:param a: First source matrix.
:param src2: Second source matrix or scalar. Matrix should have the same size and type as ``src1`` .
:param b: Second source matrix to be added to ``a`` . Matrix should have the same size and type as ``a`` .
:param sc: A scalar to be added to ``a`` .
:param c: Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by ``dtype`` or ``a`` depth.
:param dst: Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by ``dtype`` or ``src1`` depth.
:param mask: Optional operation mask, 8-bit single channel array, that specifies elements of the destination array to be changed.
@ -58,20 +50,16 @@ Computes a matrix-matrix or matrix-scalar difference.
gpu::multiply
-----------------
-------------
Computes a matrix-matrix or matrix-scalar per-element product.
.. ocv:function:: void gpu::multiply( const GpuMat& a, const GpuMat& b, GpuMat& c, double scale=1, int dtype=-1, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::multiply(InputArray src1, InputArray src2, OutputArray dst, double scale = 1, int dtype = -1, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::multiply( const GpuMat& a, const Scalar& sc, GpuMat& c, double scale=1, int dtype=-1, Stream& stream=Stream::Null() )
:param src1: First source matrix or scalar.
:param a: First source matrix.
:param src2: Second source matrix or scalar.
:param b: Second source matrix to be multiplied by ``a`` elements.
:param sc: A scalar to be multiplied by ``a`` elements.
:param c: Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by ``dtype`` or ``a`` depth.
:param dst: Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by ``dtype`` or ``src1`` depth.
:param scale: Optional scale factor.
@ -87,19 +75,15 @@ gpu::divide
-----------
Computes a matrix-matrix or matrix-scalar division.
.. ocv:function:: void gpu::divide( const GpuMat& a, const GpuMat& b, GpuMat& c, double scale=1, int dtype=-1, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::divide(InputArray src1, InputArray src2, OutputArray dst, double scale = 1, int dtype = -1, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::divide(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::divide(double src1, InputArray src2, OutputArray dst, int dtype = -1, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::divide( double scale, const GpuMat& b, GpuMat& c, int dtype=-1, Stream& stream=Stream::Null() )
:param src1: First source matrix or a scalar.
:param a: First source matrix or a scalar.
:param src2: Second source matrix or scalar.
:param b: Second source matrix. The ``a`` elements are divided by it.
:param sc: A scalar to be divided by the elements of ``a`` matrix.
:param c: Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by ``dtype`` or ``a`` depth.
:param dst: Destination matrix that has the same size and number of channels as the input array(s). The depth is defined by ``dtype`` or ``src1`` depth.
:param scale: Optional scale factor.
@ -113,11 +97,296 @@ This function, in contrast to :ocv:func:`divide`, uses a round-down rounding mod
gpu::absdiff
------------
Computes per-element absolute difference of two matrices (or of a matrix and scalar).
.. ocv:function:: void gpu::absdiff(InputArray src1, InputArray src2, OutputArray dst, Stream& stream = Stream::Null())
:param src1: First source matrix or scalar.
:param src2: Second source matrix or scalar.
:param dst: Destination matrix that has the same size and type as the input array(s).
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`absdiff`
gpu::abs
--------
Computes an absolute value of each matrix element.
.. ocv:function:: void gpu::abs(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
:param src: Source matrix.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`abs`
gpu::sqr
--------
Computes a square value of each matrix element.
.. ocv:function:: void gpu::sqr(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
:param src: Source matrix.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
gpu::sqrt
---------
Computes a square root of each matrix element.
.. ocv:function:: void gpu::sqrt(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
:param src: Source matrix.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`sqrt`
gpu::exp
--------
Computes an exponent of each matrix element.
.. ocv:function:: void gpu::exp(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
:param src: Source matrix.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`exp`
gpu::log
--------
Computes a natural logarithm of absolute value of each matrix element.
.. ocv:function:: void gpu::log(InputArray src, OutputArray dst, Stream& stream = Stream::Null())
:param src: Source matrix.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`log`
gpu::pow
--------
Raises every matrix element to a power.
.. ocv:function:: void gpu::pow(InputArray src, double power, OutputArray dst, Stream& stream = Stream::Null())
:param src: Source matrix.
:param power: Exponent of power.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
The function ``pow`` raises every element of the input matrix to ``power`` :
.. math::
\texttt{dst} (I) = \fork{\texttt{src}(I)^power}{if \texttt{power} is integer}{|\texttt{src}(I)|^power}{otherwise}
.. seealso:: :ocv:func:`pow`
gpu::compare
------------
Compares elements of two matrices (or of a matrix and scalar).
.. ocv:function:: void gpu::compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop, Stream& stream = Stream::Null())
:param src1: First source matrix or scalar.
:param src2: Second source matrix or scalar.
:param dst: Destination matrix that has the same size and type as the input array(s).
:param cmpop: Flag specifying the relation between the elements to be checked:
* **CMP_EQ:** ``a(.) == b(.)``
* **CMP_GT:** ``a(.) < b(.)``
* **CMP_GE:** ``a(.) <= b(.)``
* **CMP_LT:** ``a(.) < b(.)``
* **CMP_LE:** ``a(.) <= b(.)``
* **CMP_NE:** ``a(.) != b(.)``
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`compare`
gpu::bitwise_not
----------------
Performs a per-element bitwise inversion.
.. ocv:function:: void gpu::bitwise_not(InputArray src, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null())
:param src: Source matrix.
:param dst: Destination matrix with the same size and type as ``src`` .
:param mask: Optional operation mask. 8-bit single channel image.
:param stream: Stream for the asynchronous version.
gpu::bitwise_or
---------------
Performs a per-element bitwise disjunction of two matrices (or of matrix and scalar).
.. ocv:function:: void gpu::bitwise_or(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null())
:param src1: First source matrix or scalar.
:param src2: Second source matrix or scalar.
:param dst: Destination matrix that has the same size and type as the input array(s).
:param mask: Optional operation mask. 8-bit single channel image.
:param stream: Stream for the asynchronous version.
gpu::bitwise_and
----------------
Performs a per-element bitwise conjunction of two matrices (or of matrix and scalar).
.. ocv:function:: void gpu::bitwise_and(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null())
:param src1: First source matrix or scalar.
:param src2: Second source matrix or scalar.
:param dst: Destination matrix that has the same size and type as the input array(s).
:param mask: Optional operation mask. 8-bit single channel image.
:param stream: Stream for the asynchronous version.
gpu::bitwise_xor
----------------
Performs a per-element bitwise ``exclusive or`` operation of two matrices (or of matrix and scalar).
.. ocv:function:: void gpu::bitwise_xor(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null())
:param src1: First source matrix or scalar.
:param src2: Second source matrix or scalar.
:param dst: Destination matrix that has the same size and type as the input array(s).
:param mask: Optional operation mask. 8-bit single channel image.
:param stream: Stream for the asynchronous version.
gpu::rshift
-----------
Performs pixel by pixel right shift of an image by a constant value.
.. ocv:function:: void gpu::rshift(InputArray src, Scalar_<int> val, OutputArray dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports 1, 3 and 4 channels images with integers elements.
:param val: Constant values, one per channel.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
gpu::lshift
-----------
Performs pixel by pixel right left of an image by a constant value.
.. ocv:function:: void gpu::lshift(InputArray src, Scalar_<int> val, OutputArray dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports 1, 3 and 4 channels images with ``CV_8U`` , ``CV_16U`` or ``CV_32S`` depth.
:param val: Constant values, one per channel.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
gpu::min
--------
Computes the per-element minimum of two matrices (or a matrix and a scalar).
.. ocv:function:: void gpu::min(InputArray src1, InputArray src2, OutputArray dst, Stream& stream = Stream::Null())
:param src1: First source matrix or scalar.
:param src2: Second source matrix or scalar.
:param dst: Destination matrix that has the same size and type as the input array(s).
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`min`
gpu::max
--------
Computes the per-element maximum of two matrices (or a matrix and a scalar).
.. ocv:function:: void gpu::max(InputArray src1, InputArray src2, OutputArray dst, Stream& stream = Stream::Null())
:param src1: First source matrix or scalar.
:param src2: Second source matrix or scalar.
:param dst: Destination matrix that has the same size and type as the input array(s).
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`max`
gpu::addWeighted
----------------
Computes the weighted sum of two arrays.
.. ocv:function:: void gpu::addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst, int dtype = -1, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::addWeighted(InputArray src1, double alpha, InputArray src2, double beta, double gamma, OutputArray dst, int dtype = -1, Stream& stream = Stream::Null())
:param src1: First source array.
@ -147,311 +416,11 @@ where ``I`` is a multi-dimensional index of array elements. In case of multi-cha
gpu::abs
------------
Computes an absolute value of each matrix element.
.. ocv:function:: void gpu::abs(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports ``CV_16S`` and ``CV_32F`` depth.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`abs`
gpu::sqr
------------
Computes a square value of each matrix element.
.. ocv:function:: void gpu::sqr(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports ``CV_8U`` , ``CV_16U`` , ``CV_16S`` and ``CV_32F`` depth.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
gpu::sqrt
------------
Computes a square root of each matrix element.
.. ocv:function:: void gpu::sqrt(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports ``CV_8U`` , ``CV_16U`` , ``CV_16S`` and ``CV_32F`` depth.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`sqrt`
gpu::exp
------------
Computes an exponent of each matrix element.
.. ocv:function:: void gpu::exp( const GpuMat& a, GpuMat& b, Stream& stream=Stream::Null() )
:param a: Source matrix. Supports ``CV_8U`` , ``CV_16U`` , ``CV_16S`` and ``CV_32F`` depth.
:param b: Destination matrix with the same size and type as ``a`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`exp`
gpu::log
------------
Computes a natural logarithm of absolute value of each matrix element.
.. ocv:function:: void gpu::log( const GpuMat& a, GpuMat& b, Stream& stream=Stream::Null() )
:param a: Source matrix. Supports ``CV_8U`` , ``CV_16U`` , ``CV_16S`` and ``CV_32F`` depth.
:param b: Destination matrix with the same size and type as ``a`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`log`
gpu::pow
------------
Raises every matrix element to a power.
.. ocv:function:: void gpu::pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null())
:param src: Source matrix. Supports all type, except ``CV_64F`` depth.
:param power: Exponent of power.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
The function ``pow`` raises every element of the input matrix to ``p`` :
.. math::
\texttt{dst} (I) = \fork{\texttt{src}(I)^p}{if \texttt{p} is integer}{|\texttt{src}(I)|^p}{otherwise}
.. seealso:: :ocv:func:`pow`
gpu::absdiff
----------------
Computes per-element absolute difference of two matrices (or of a matrix and scalar).
.. ocv:function:: void gpu::absdiff( const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::absdiff( const GpuMat& a, const Scalar& s, GpuMat& c, Stream& stream=Stream::Null() )
:param a: First source matrix.
:param b: Second source matrix to be added to ``a`` .
:param s: A scalar to be added to ``a`` .
:param c: Destination matrix with the same size and type as ``a`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`absdiff`
gpu::compare
----------------
Compares elements of two matrices.
.. ocv:function:: void gpu::compare( const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::compare(const GpuMat& a, Scalar sc, GpuMat& c, int cmpop, Stream& stream = Stream::Null())
:param a: First source matrix.
:param b: Second source matrix with the same size and type as ``a`` .
:param sc: A scalar to be compared with ``a`` .
:param c: Destination matrix with the same size as ``a`` and the ``CV_8UC1`` type.
:param cmpop: Flag specifying the relation between the elements to be checked:
* **CMP_EQ:** ``a(.) == b(.)``
* **CMP_GT:** ``a(.) < b(.)``
* **CMP_GE:** ``a(.) <= b(.)``
* **CMP_LT:** ``a(.) < b(.)``
* **CMP_LE:** ``a(.) <= b(.)``
* **CMP_NE:** ``a(.) != b(.)``
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`compare`
gpu::bitwise_not
--------------------
Performs a per-element bitwise inversion.
.. ocv:function:: void gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null())
:param src: Source matrix.
:param dst: Destination matrix with the same size and type as ``src`` .
:param mask: Optional operation mask. 8-bit single channel image.
:param stream: Stream for the asynchronous version.
gpu::bitwise_or
-------------------
Performs a per-element bitwise disjunction of two matrices or of matrix and scalar.
.. ocv:function:: void gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null())
.. ocv:function:: void gpu::bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null())
:param src1: First source matrix.
:param src2: Second source matrix with the same size and type as ``src1`` .
:param dst: Destination matrix with the same size and type as ``src1`` .
:param mask: Optional operation mask. 8-bit single channel image.
:param stream: Stream for the asynchronous version.
gpu::bitwise_and
--------------------
Performs a per-element bitwise conjunction of two matrices or of matrix and scalar.
.. ocv:function:: void gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null())
.. ocv:function:: void gpu::bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null())
:param src1: First source matrix.
:param src2: Second source matrix with the same size and type as ``src1`` .
:param dst: Destination matrix with the same size and type as ``src1`` .
:param mask: Optional operation mask. 8-bit single channel image.
:param stream: Stream for the asynchronous version.
gpu::bitwise_xor
--------------------
Performs a per-element bitwise ``exclusive or`` operation of two matrices of matrix and scalar.
.. ocv:function:: void gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null())
.. ocv:function:: void gpu::bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null())
:param src1: First source matrix.
:param src2: Second source matrix with the same size and type as ``src1`` .
:param dst: Destination matrix with the same size and type as ``src1`` .
:param mask: Optional operation mask. 8-bit single channel image.
:param stream: Stream for the asynchronous version.
gpu::rshift
--------------------
Performs pixel by pixel right shift of an image by a constant value.
.. ocv:function:: void gpu::rshift( const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream=Stream::Null() )
:param src: Source matrix. Supports 1, 3 and 4 channels images with integers elements.
:param sc: Constant values, one per channel.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
gpu::lshift
--------------------
Performs pixel by pixel right left of an image by a constant value.
.. ocv:function:: void gpu::lshift( const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream=Stream::Null() )
:param src: Source matrix. Supports 1, 3 and 4 channels images with ``CV_8U`` , ``CV_16U`` or ``CV_32S`` depth.
:param sc: Constant values, one per channel.
:param dst: Destination matrix with the same size and type as ``src`` .
:param stream: Stream for the asynchronous version.
gpu::min
------------
Computes the per-element minimum of two matrices (or a matrix and a scalar).
.. ocv:function:: void gpu::min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::min(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null())
:param src1: First source matrix.
:param src2: Second source matrix or a scalar to compare ``src1`` elements with.
:param dst: Destination matrix with the same size and type as ``src1`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`min`
gpu::max
------------
Computes the per-element maximum of two matrices (or a matrix and a scalar).
.. ocv:function:: void gpu::max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::max(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null())
:param src1: First source matrix.
:param src2: Second source matrix or a scalar to compare ``src1`` elements with.
:param dst: Destination matrix with the same size and type as ``src1`` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`max`
gpu::threshold
------------------
--------------
Applies a fixed-level threshold to each array element.
.. ocv:function:: double gpu::threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream = Stream::Null())
.. ocv:function:: double gpu::threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type, Stream& stream = Stream::Null())
:param src: Source array (single-channel).
@ -470,12 +439,12 @@ Applies a fixed-level threshold to each array element.
gpu::magnitude
------------------
--------------
Computes magnitudes of complex matrix elements.
.. ocv:function:: void gpu::magnitude( const GpuMat& xy, GpuMat& magnitude, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::magnitude(InputArray xy, OutputArray magnitude, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::magnitude(InputArray x, InputArray y, OutputArray magnitude, Stream& stream = Stream::Null())
:param xy: Source complex matrix in the interleaved format ( ``CV_32FC2`` ).
@ -492,12 +461,12 @@ Computes magnitudes of complex matrix elements.
gpu::magnitudeSqr
---------------------
-----------------
Computes squared magnitudes of complex matrix elements.
.. ocv:function:: void gpu::magnitudeSqr( const GpuMat& xy, GpuMat& magnitude, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::magnitudeSqr(InputArray xy, OutputArray magnitude, Stream& stream=Stream::Null() )
.. ocv:function:: void gpu::magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::magnitudeSqr(InputArray x, InputArray y, OutputArray magnitude, Stream& stream = Stream::Null())
:param xy: Source complex matrix in the interleaved format ( ``CV_32FC2`` ).
@ -512,10 +481,10 @@ Computes squared magnitudes of complex matrix elements.
gpu::phase
--------------
----------
Computes polar angles of complex matrix elements.
.. ocv:function:: void gpu::phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees=false, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::phase(InputArray x, InputArray y, OutputArray angle, bool angleInDegrees = false, Stream& stream = Stream::Null())
:param x: Source matrix containing real components ( ``CV_32FC1`` ).
@ -532,10 +501,10 @@ Computes polar angles of complex matrix elements.
gpu::cartToPolar
--------------------
----------------
Converts Cartesian coordinates into polar.
.. ocv:function:: void gpu::cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees=false, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::cartToPolar(InputArray x, InputArray y, OutputArray magnitude, OutputArray angle, bool angleInDegrees = false, Stream& stream = Stream::Null())
:param x: Source matrix containing real components ( ``CV_32FC1`` ).
@ -554,10 +523,10 @@ Converts Cartesian coordinates into polar.
gpu::polarToCart
--------------------
----------------
Converts polar coordinates into Cartesian.
.. ocv:function:: void gpu::polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees=false, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::polarToCart(InputArray magnitude, InputArray angle, OutputArray x, OutputArray y, bool angleInDegrees = false, Stream& stream = Stream::Null())
:param magnitude: Source matrix containing magnitudes ( ``CV_32FC1`` ).

164
modules/gpuarithm/doc/reductions.rst
View File

@ -6,16 +6,16 @@ Matrix Reductions
gpu::norm
-------------
---------
Returns the norm of a matrix (or difference of two matrices).
.. ocv:function:: double gpu::norm(const GpuMat& src1, int normType=NORM_L2)
.. ocv:function:: double gpu::norm(InputArray src1, int normType)
.. ocv:function:: double gpu::norm(const GpuMat& src1, int normType, GpuMat& buf)
.. ocv:function:: double gpu::norm(InputArray src1, int normType, GpuMat& buf)
.. ocv:function:: double gpu::norm(const GpuMat& src1, int normType, const GpuMat& mask, GpuMat& buf)
.. ocv:function:: double gpu::norm(InputArray src1, int normType, InputArray mask, GpuMat& buf)
.. ocv:function:: double gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType=NORM_L2)
.. ocv:function:: double gpu::norm(InputArray src1, InputArray src2, int normType=NORM_L2)
:param src1: Source matrix. Any matrices except 64F are supported.
@ -32,14 +32,14 @@ Returns the norm of a matrix (or difference of two matrices).
gpu::sum
------------
--------
Returns the sum of matrix elements.
.. ocv:function:: Scalar gpu::sum(const GpuMat& src)
.. ocv:function:: Scalar gpu::sum(InputArray src)
.. ocv:function:: Scalar gpu::sum(const GpuMat& src, GpuMat& buf)
.. ocv:function:: Scalar gpu::sum(InputArray src, GpuMat& buf)
.. ocv:function:: Scalar gpu::sum(const GpuMat& src, const GpuMat& mask, GpuMat& buf)
.. ocv:function:: Scalar gpu::sum(InputArray src, InputArray mask, GpuMat& buf)
:param src: Source image of any depth except for ``CV_64F`` .
@ -52,14 +52,14 @@ Returns the sum of matrix elements.
gpu::absSum
---------------
-----------
Returns the sum of absolute values for matrix elements.
.. ocv:function:: Scalar gpu::absSum(const GpuMat& src)
.. ocv:function:: Scalar gpu::absSum(InputArray src)
.. ocv:function:: Scalar gpu::absSum(const GpuMat& src, GpuMat& buf)
.. ocv:function:: Scalar gpu::absSum(InputArray src, GpuMat& buf)
.. ocv:function:: Scalar gpu::absSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf)
.. ocv:function:: Scalar gpu::absSum(InputArray src, InputArray mask, GpuMat& buf)
:param src: Source image of any depth except for ``CV_64F`` .
@ -70,14 +70,14 @@ Returns the sum of absolute values for matrix elements.
gpu::sqrSum
---------------
-----------
Returns the squared sum of matrix elements.
.. ocv:function:: Scalar gpu::sqrSum(const GpuMat& src)
.. ocv:function:: Scalar gpu::sqrSum(InputArray src)
.. ocv:function:: Scalar gpu::sqrSum(const GpuMat& src, GpuMat& buf)
.. ocv:function:: Scalar gpu::sqrSum(InputArray src, GpuMat& buf)
.. ocv:function:: Scalar gpu::sqrSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf)
.. ocv:function:: Scalar gpu::sqrSum(InputArray src, InputArray mask, GpuMat& buf)
:param src: Source image of any depth except for ``CV_64F`` .
@ -88,12 +88,12 @@ Returns the squared sum of matrix elements.
gpu::minMax
---------------
-----------
Finds global minimum and maximum matrix elements and returns their values.
.. ocv:function:: void gpu::minMax(const GpuMat& src, double* minVal, double* maxVal=0, const GpuMat& mask=GpuMat())
.. ocv:function:: void gpu::minMax(InputArray src, double* minVal, double* maxVal=0, InputArray mask=noArray())
.. ocv:function:: void gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf)
.. ocv:function:: void gpu::minMax(InputArray src, double* minVal, double* maxVal, InputArray mask, GpuMat& buf)
:param src: Single-channel source image.
@ -112,12 +112,12 @@ The function does not work with ``CV_64F`` images on GPUs with the compute capab
gpu::minMaxLoc
------------------
--------------
Finds global minimum and maximum matrix elements and returns their values with locations.
.. ocv:function:: void gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0, const GpuMat& mask=GpuMat())
.. ocv:function:: void gpu::minMaxLoc(InputArray src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0, InputArray mask=noArray())
.. ocv:function:: void gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf)
.. ocv:function:: void gpu::minMaxLoc(InputArray src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, InputArray mask, GpuMat& valbuf, GpuMat& locbuf)
:param src: Single-channel source image.
@ -142,12 +142,12 @@ Finds global minimum and maximum matrix elements and returns their values with l
gpu::countNonZero
---------------------
-----------------
Counts non-zero matrix elements.
.. ocv:function:: int gpu::countNonZero(const GpuMat& src)
.. ocv:function:: int gpu::countNonZero(InputArray src)
.. ocv:function:: int gpu::countNonZero(const GpuMat& src, GpuMat& buf)
.. ocv:function:: int gpu::countNonZero(InputArray src, GpuMat& buf)
:param src: Single-channel source image.
@ -163,7 +163,7 @@ gpu::reduce
-----------
Reduces a matrix to a vector.
.. ocv:function:: void gpu::reduce(const GpuMat& mtx, GpuMat& vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::reduce(InputArray mtx, OutputArray vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null())
:param mtx: Source 2D matrix.
@ -183,48 +183,20 @@ Reduces a matrix to a vector.
:param dtype: When it is negative, the destination vector will have the same type as the source matrix. Otherwise, its type will be ``CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), mtx.channels())`` .
:param stream: Stream for the asynchronous version.
The function ``reduce`` reduces the matrix to a vector by treating the matrix rows/columns as a set of 1D vectors and performing the specified operation on the vectors until a single row/column is obtained. For example, the function can be used to compute horizontal and vertical projections of a raster image. In case of ``CV_REDUCE_SUM`` and ``CV_REDUCE_AVG`` , the output may have a larger element bit-depth to preserve accuracy. And multi-channel arrays are also supported in these two reduction modes.
.. seealso:: :ocv:func:`reduce`
gpu::normalize
--------------
Normalizes the norm or value range of an array.
.. ocv:function:: void gpu::normalize(const GpuMat& src, GpuMat& dst, double alpha = 1, double beta = 0, int norm_type = NORM_L2, int dtype = -1, const GpuMat& mask = GpuMat())
.. ocv:function:: void gpu::normalize(const GpuMat& src, GpuMat& dst, double a, double b, int norm_type, int dtype, const GpuMat& mask, GpuMat& norm_buf, GpuMat& cvt_buf)
:param src: input array.
:param dst: output array of the same size as ``src`` .
:param alpha: norm value to normalize to or the lower range boundary in case of the range normalization.
:param beta: upper range boundary in case of the range normalization; it is not used for the norm normalization.
:param normType: normalization type (see the details below).
:param dtype: when negative, the output array has the same type as ``src``; otherwise, it has the same number of channels as ``src`` and the depth ``=CV_MAT_DEPTH(dtype)``.
:param mask: optional operation mask.
:param norm_buf: Optional buffer to avoid extra memory allocations. It is resized automatically.
:param cvt_buf: Optional buffer to avoid extra memory allocations. It is resized automatically.
.. seealso:: :ocv:func:`normalize`
gpu::meanStdDev
-------------------
---------------
Computes a mean value and a standard deviation of matrix elements.
.. ocv:function:: void gpu::meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev)
.. ocv:function:: void gpu::meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf)
.. ocv:function:: void gpu::meanStdDev(InputArray mtx, Scalar& mean, Scalar& stddev)
.. ocv:function:: void gpu::meanStdDev(InputArray mtx, Scalar& mean, Scalar& stddev, GpuMat& buf)
:param mtx: Source matrix. ``CV_8UC1`` matrices are supported for now.
@ -239,10 +211,10 @@ Computes a mean value and a standard deviation of matrix elements.
gpu::rectStdDev
-------------------
---------------
Computes a standard deviation of integral images.
.. ocv:function:: void gpu::rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::rectStdDev(InputArray src, InputArray sqr, OutputArray dst, Rect rect, Stream& stream = Stream::Null())
:param src: Source image. Only the ``CV_32SC1`` type is supported.
@ -253,3 +225,71 @@ Computes a standard deviation of integral images.
:param rect: Rectangular window.
:param stream: Stream for the asynchronous version.
gpu::normalize
--------------
Normalizes the norm or value range of an array.
.. ocv:function:: void gpu::normalize(InputArray src, OutputArray dst, double alpha = 1, double beta = 0, int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray())
.. ocv:function:: void gpu::normalize(InputArray src, OutputArray dst, double alpha, double beta, int norm_type, int dtype, InputArray mask, GpuMat& norm_buf, GpuMat& cvt_buf)
:param src: Input array.
:param dst: Output array of the same size as ``src`` .
:param alpha: Norm value to normalize to or the lower range boundary in case of the range normalization.
:param beta: Upper range boundary in case of the range normalization; it is not used for the norm normalization.
:param normType: Normalization type ( ``NORM_MINMAX`` , ``NORM_L2`` , ``NORM_L1`` or ``NORM_INF`` ).
:param dtype: When negative, the output array has the same type as ``src``; otherwise, it has the same number of channels as ``src`` and the depth ``=CV_MAT_DEPTH(dtype)``.
:param mask: Optional operation mask.
:param norm_buf: Optional buffer to avoid extra memory allocations. It is resized automatically.
:param cvt_buf: Optional buffer to avoid extra memory allocations. It is resized automatically.
.. seealso:: :ocv:func:`normalize`
gpu::integral
-------------
Computes an integral image.
.. ocv:function:: void gpu::integral(InputArray src, OutputArray sum, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::integral(InputArray src, OutputArray sum, GpuMat& buffer, Stream& stream = Stream::Null())
:param src: Source image. Only ``CV_8UC1`` images are supported for now.
:param sum: Integral image containing 32-bit unsigned integer values packed into ``CV_32SC1`` .
:param buffer: Optional buffer to avoid extra memory allocations. It is resized automatically.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`integral`
gpu::sqrIntegral
----------------
Computes a squared integral image.
.. ocv:function:: void gpu::sqrIntegral(InputArray src, OutputArray sqsum, Stream& stream = Stream::Null())
.. ocv:function:: void gpu::sqrIntegral(InputArray src, OutputArray sqsum, GpuMat& buf, Stream& stream = Stream::Null())
:param src: Source image. Only ``CV_8UC1`` images are supported for now.
:param sqsum: Squared integral image containing 64-bit unsigned integer values packed into ``CV_64FC1`` .
:param buf: Optional buffer to avoid extra memory allocations. It is resized automatically.
:param stream: Stream for the asynchronous version.

439
modules/gpuarithm/include/opencv2/gpuarithm.hpp
View File

@ -49,263 +49,317 @@
#include "opencv2/core/gpu.hpp"
#if defined __GNUC__
#define __OPENCV_GPUARITHM_DEPR_BEFORE__
#define __OPENCV_GPUARITHM_DEPR_AFTER__ __attribute__ ((deprecated))
#elif (defined WIN32 || defined _WIN32)
#define __OPENCV_GPUARITHM_DEPR_BEFORE__ __declspec(deprecated)
#define __OPENCV_GPUARITHM_DEPR_AFTER__
#else
#define __OPENCV_GPUARITHM_DEPR_BEFORE__
#define __OPENCV_GPUARITHM_DEPR_AFTER__
#endif
namespace cv { namespace gpu {
//! adds one matrix to another (c = a + b)
CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
//! adds scalar to a matrix (c = a + s)
CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
//! adds one matrix to another (dst = src1 + src2)
CV_EXPORTS void add(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), int dtype = -1, Stream& stream = Stream::Null());
//! subtracts one matrix from another (c = a - b)
CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
//! subtracts scalar from a matrix (c = a - s)
CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c, const GpuMat& mask = GpuMat(), int dtype = -1, Stream& stream = Stream::Null());
//! subtracts one matrix from another (dst = src1 - src2)
CV_EXPORTS void subtract(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), int dtype = -1, Stream& stream = Stream::Null());
//! computes element-wise weighted product of the two arrays (c = scale * a * b)
CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
//! weighted multiplies matrix to a scalar (c = scale * a * s)
CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
//! computes element-wise weighted product of the two arrays (dst = scale * src1 * src2)
CV_EXPORTS void multiply(InputArray src1, InputArray src2, OutputArray dst, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
//! computes element-wise weighted quotient of the two arrays (dst = scale * (src1 / src2))
CV_EXPORTS void divide(InputArray src1, InputArray src2, OutputArray dst, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
//! computes element-wise weighted quotient of the two arrays (c = a / b)
CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
//! computes element-wise weighted quotient of matrix and scalar (c = a / s)
CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
//! computes element-wise weighted reciprocal of an array (dst = scale/src2)
CV_EXPORTS void divide(double scale, const GpuMat& b, GpuMat& c, int dtype = -1, Stream& stream = Stream::Null());
static inline void divide(double src1, InputArray src2, OutputArray dst, int dtype = -1, Stream& stream = Stream::Null())
{
divide(src1, src2, dst, 1.0, dtype, stream);
}
//! computes element-wise absolute difference of two arrays (dst = abs(src1 - src2))
CV_EXPORTS void absdiff(InputArray src1, InputArray src2, OutputArray dst, Stream& stream = Stream::Null());
//! computes absolute value of each matrix element
CV_EXPORTS void abs(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
//! computes square of each pixel in an image
CV_EXPORTS void sqr(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
//! computes square root of each pixel in an image
CV_EXPORTS void sqrt(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
//! computes exponent of each matrix element
CV_EXPORTS void exp(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
//! computes natural logarithm of absolute value of each matrix element
CV_EXPORTS void log(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
//! computes power of each matrix element:
//! (dst(i,j) = pow( src(i,j) , power), if src.type() is integer
//! (dst(i,j) = pow(fabs(src(i,j)), power), otherwise
CV_EXPORTS void pow(InputArray src, double power, OutputArray dst, Stream& stream = Stream::Null());
//! compares elements of two arrays (dst = src1 <cmpop> src2)
CV_EXPORTS void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop, Stream& stream = Stream::Null());
//! performs per-elements bit-wise inversion
CV_EXPORTS void bitwise_not(InputArray src, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise disjunction of two arrays
CV_EXPORTS void bitwise_or(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise conjunction of two arrays
CV_EXPORTS void bitwise_and(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise "exclusive or" operation
CV_EXPORTS void bitwise_xor(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
//! pixel by pixel right shift of an image by a constant value
//! supports 1, 3 and 4 channels images with integers elements
CV_EXPORTS void rshift(InputArray src, Scalar_<int> val, OutputArray dst, Stream& stream = Stream::Null());
//! pixel by pixel left shift of an image by a constant value
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void lshift(InputArray src, Scalar_<int> val, OutputArray dst, Stream& stream = Stream::Null());
//! computes per-element minimum of two arrays (dst = min(src1, src2))
CV_EXPORTS void min(InputArray src1, InputArray src2, OutputArray dst, Stream& stream = Stream::Null());
//! computes per-element maximum of two arrays (dst = max(src1, src2))
CV_EXPORTS void max(InputArray src1, InputArray src2, OutputArray dst, Stream& stream = Stream::Null());
//! computes the weighted sum of two arrays (dst = alpha*src1 + beta*src2 + gamma)
CV_EXPORTS void addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst,
CV_EXPORTS void addWeighted(InputArray src1, double alpha, InputArray src2, double beta, double gamma, OutputArray dst,
int dtype = -1, Stream& stream = Stream::Null());
//! adds scaled array to another one (dst = alpha*src1 + src2)
static inline void scaleAdd(const GpuMat& src1, double alpha, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null())
static inline void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst, Stream& stream = Stream::Null())
{
addWeighted(src1, alpha, src2, 1.0, 0.0, dst, -1, stream);
}
//! computes element-wise absolute difference of two arrays (c = abs(a - b))
CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c, Stream& stream = Stream::Null());
//! computes element-wise absolute difference of array and scalar (c = abs(a - s))
CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c, Stream& stream = Stream::Null());
//! applies fixed threshold to the image
CV_EXPORTS double threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
//! computes absolute value of each matrix element
//! supports CV_16S and CV_32F depth
CV_EXPORTS void abs(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! computes magnitude of complex (x(i).re, x(i).im) vector
//! supports only CV_32FC2 type
CV_EXPORTS void magnitude(InputArray xy, OutputArray magnitude, Stream& stream = Stream::Null());
//! computes square of each pixel in an image
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void sqr(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! computes squared magnitude of complex (x(i).re, x(i).im) vector
//! supports only CV_32FC2 type
CV_EXPORTS void magnitudeSqr(InputArray xy, OutputArray magnitude, Stream& stream = Stream::Null());
//! computes square root of each pixel in an image
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void sqrt(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());
//! computes magnitude of each (x(i), y(i)) vector
//! supports only floating-point source
CV_EXPORTS void magnitude(InputArray x, InputArray y, OutputArray magnitude, Stream& stream = Stream::Null());
//! computes exponent of each matrix element (b = e**a)
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void exp(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
//! computes squared magnitude of each (x(i), y(i)) vector
//! supports only floating-point source
CV_EXPORTS void magnitudeSqr(InputArray x, InputArray y, OutputArray magnitude, Stream& stream = Stream::Null());
//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
//! supports CV_8U, CV_16U, CV_16S and CV_32F depth
CV_EXPORTS void log(const GpuMat& a, GpuMat& b, Stream& stream = Stream::Null());
//! computes angle of each (x(i), y(i)) vector
//! supports only floating-point source
CV_EXPORTS void phase(InputArray x, InputArray y, OutputArray angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
//! computes power of each matrix element:
// (dst(i,j) = pow( src(i,j) , power), if src.type() is integer
// (dst(i,j) = pow(fabs(src(i,j)), power), otherwise
//! supports all, except depth == CV_64F
CV_EXPORTS void pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream = Stream::Null());
//! converts Cartesian coordinates to polar
//! supports only floating-point source
CV_EXPORTS void cartToPolar(InputArray x, InputArray y, OutputArray magnitude, OutputArray angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
//! compares elements of two arrays (c = a <cmpop> b)
CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop, Stream& stream = Stream::Null());
CV_EXPORTS void compare(const GpuMat& a, Scalar sc, GpuMat& c, int cmpop, Stream& stream = Stream::Null());
//! converts polar coordinates to Cartesian
//! supports only floating-point source
CV_EXPORTS void polarToCart(InputArray magnitude, InputArray angle, OutputArray x, OutputArray y, bool angleInDegrees = false, Stream& stream = Stream::Null());
//! performs per-elements bit-wise inversion
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! makes multi-channel array out of several single-channel arrays
CV_EXPORTS void merge(const GpuMat* src, size_t n, OutputArray dst, Stream& stream = Stream::Null());
CV_EXPORTS void merge(const std::vector<GpuMat>& src, OutputArray dst, Stream& stream = Stream::Null());
//! calculates per-element bit-wise disjunction of two arrays
CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise disjunction of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! calculates per-element bit-wise conjunction of two arrays
CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise conjunction of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! calculates per-element bit-wise "exclusive or" operation
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise "exclusive or" of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! pixel by pixel right shift of an image by a constant value
//! supports 1, 3 and 4 channels images with integers elements
CV_EXPORTS void rshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());
//! pixel by pixel left shift of an image by a constant value
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void lshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());
//! computes per-element minimum of two arrays (dst = min(src1, src2))
CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
//! computes per-element minimum of array and scalar (dst = min(src1, src2))
CV_EXPORTS void min(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
//! computes per-element maximum of two arrays (dst = max(src1, src2))
CV_EXPORTS void max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream = Stream::Null());
//! computes per-element maximum of array and scalar (dst = max(src1, src2))
CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream = Stream::Null());
//! implements generalized matrix product algorithm GEMM from BLAS
CV_EXPORTS void gemm(const GpuMat& src1, const GpuMat& src2, double alpha,
const GpuMat& src3, double beta, GpuMat& dst, int flags = 0, Stream& stream = Stream::Null());
//! copies each plane of a multi-channel array to a dedicated array
CV_EXPORTS void split(InputArray src, GpuMat* dst, Stream& stream = Stream::Null());
CV_EXPORTS void split(InputArray src, std::vector<GpuMat>& dst, Stream& stream = Stream::Null());
//! transposes the matrix
//! supports matrix with element size = 1, 4 and 8 bytes (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc)
CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst, Stream& stream = Stream::Null());
CV_EXPORTS void transpose(InputArray src1, OutputArray dst, Stream& stream = Stream::Null());
//! reverses the order of the rows, columns or both in a matrix
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth
CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode, Stream& stream = Stream::Null());
CV_EXPORTS void flip(InputArray src, OutputArray dst, int flipCode, Stream& stream = Stream::Null());
//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
//! destination array will have the depth type as lut and the same channels number as source
//! supports CV_8UC1, CV_8UC3 types
CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& stream = Stream::Null());
class CV_EXPORTS LookUpTable : public Algorithm
{
public:
virtual void transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0;
};
CV_EXPORTS Ptr<LookUpTable> createLookUpTable(InputArray lut);
//! makes multi-channel array out of several single-channel arrays
CV_EXPORTS void merge(const GpuMat* src, size_t n, GpuMat& dst, Stream& stream = Stream::Null());
__OPENCV_GPUARITHM_DEPR_BEFORE__ void LUT(InputArray src, InputArray lut, OutputArray dst, Stream& stream = Stream::Null()) __OPENCV_GPUARITHM_DEPR_AFTER__;
inline void LUT(InputArray src, InputArray lut, OutputArray dst, Stream& stream)
{
createLookUpTable(lut)->transform(src, dst, stream);
}
//! makes multi-channel array out of several single-channel arrays
CV_EXPORTS void merge(const std::vector<GpuMat>& src, GpuMat& dst, Stream& stream = Stream::Null());
//! copies each plane of a multi-channel array to a dedicated array
CV_EXPORTS void split(const GpuMat& src, GpuMat* dst, Stream& stream = Stream::Null());
//! copies each plane of a multi-channel array to a dedicated array
CV_EXPORTS void split(const GpuMat& src, std::vector<GpuMat>& dst, Stream& stream = Stream::Null());
//! computes magnitude of complex (x(i).re, x(i).im) vector
//! supports only CV_32FC2 type
CV_EXPORTS void magnitude(const GpuMat& xy, GpuMat& magnitude, Stream& stream = Stream::Null());
//! computes squared magnitude of complex (x(i).re, x(i).im) vector
//! supports only CV_32FC2 type
CV_EXPORTS void magnitudeSqr(const GpuMat& xy, GpuMat& magnitude, Stream& stream = Stream::Null());
//! computes magnitude of each (x(i), y(i)) vector
//! supports only floating-point source
CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
//! computes squared magnitude of each (x(i), y(i)) vector
//! supports only floating-point source
CV_EXPORTS void magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, Stream& stream = Stream::Null());
//! computes angle (angle(i)) of each (x(i), y(i)) vector
//! supports only floating-point source
CV_EXPORTS void phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
//! converts Cartesian coordinates to polar
//! supports only floating-point source
CV_EXPORTS void cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& magnitude, GpuMat& angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
//! converts polar coordinates to Cartesian
//! supports only floating-point source
CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees = false, Stream& stream = Stream::Null());
//! scales and shifts array elements so that either the specified norm (alpha) or the minimum (alpha) and maximum (beta) array values get the specified values
CV_EXPORTS void normalize(const GpuMat& src, GpuMat& dst, double alpha = 1, double beta = 0,
int norm_type = NORM_L2, int dtype = -1, const GpuMat& mask = GpuMat());
CV_EXPORTS void normalize(const GpuMat& src, GpuMat& dst, double a, double b,
int norm_type, int dtype, const GpuMat& mask, GpuMat& norm_buf, GpuMat& cvt_buf);
//! copies 2D array to a larger destination array and pads borders with user-specifiable constant
CV_EXPORTS void copyMakeBorder(InputArray src, OutputArray dst, int top, int bottom, int left, int right, int borderType,
Scalar value = Scalar(), Stream& stream = Stream::Null());
//! computes norm of array
//! supports NORM_INF, NORM_L1, NORM_L2
//! supports all matrices except 64F
CV_EXPORTS double norm(const GpuMat& src1, int normType=NORM_L2);
CV_EXPORTS double norm(const GpuMat& src1, int normType, GpuMat& buf);
CV_EXPORTS double norm(const GpuMat& src1, int normType, const GpuMat& mask, GpuMat& buf);
CV_EXPORTS double norm(InputArray src1, int normType, InputArray mask, GpuMat& buf);
static inline double norm(InputArray src, int normType)
{
GpuMat buf;
return norm(src, normType, GpuMat(), buf);
}
static inline double norm(InputArray src, int normType, GpuMat& buf)
{
return norm(src, normType, GpuMat(), buf);
}
//! computes norm of the difference between two arrays
//! supports NORM_INF, NORM_L1, NORM_L2
//! supports only CV_8UC1 type
CV_EXPORTS double norm(const GpuMat& src1, const GpuMat& src2, int normType=NORM_L2);
CV_EXPORTS double norm(InputArray src1, InputArray src2, GpuMat& buf, int normType=NORM_L2);
static inline double norm(InputArray src1, InputArray src2, int normType=NORM_L2)
{
GpuMat buf;
return norm(src1, src2, buf, normType);
}
//! computes sum of array elements
//! supports only single channel images
CV_EXPORTS Scalar sum(const GpuMat& src);
CV_EXPORTS Scalar sum(const GpuMat& src, GpuMat& buf);
CV_EXPORTS Scalar sum(const GpuMat& src, const GpuMat& mask, GpuMat& buf);
CV_EXPORTS Scalar sum(InputArray src, InputArray mask, GpuMat& buf);
static inline Scalar sum(InputArray src)
{
GpuMat buf;
return sum(src, GpuMat(), buf);
}
static inline Scalar sum(InputArray src, GpuMat& buf)
{
return sum(src, GpuMat(), buf);
}
//! computes sum of array elements absolute values
//! supports only single channel images
CV_EXPORTS Scalar absSum(const GpuMat& src);
CV_EXPORTS Scalar absSum(const GpuMat& src, GpuMat& buf);
CV_EXPORTS Scalar absSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf);
CV_EXPORTS Scalar absSum(InputArray src, InputArray mask, GpuMat& buf);
static inline Scalar absSum(InputArray src)
{
GpuMat buf;
return absSum(src, GpuMat(), buf);
}
static inline Scalar absSum(InputArray src, GpuMat& buf)
{
return absSum(src, GpuMat(), buf);
}
//! computes squared sum of array elements
//! supports only single channel images
CV_EXPORTS Scalar sqrSum(const GpuMat& src);
CV_EXPORTS Scalar sqrSum(const GpuMat& src, GpuMat& buf);
CV_EXPORTS Scalar sqrSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf);
CV_EXPORTS Scalar sqrSum(InputArray src, InputArray mask, GpuMat& buf);
static inline Scalar sqrSum(InputArray src)
{
GpuMat buf;
return sqrSum(src, GpuMat(), buf);
}
static inline Scalar sqrSum(InputArray src, GpuMat& buf)
{
return sqrSum(src, GpuMat(), buf);
}
//! finds global minimum and maximum array elements and returns their values
CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal=0, const GpuMat& mask=GpuMat());
CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf);
CV_EXPORTS void minMax(InputArray src, double* minVal, double* maxVal, InputArray mask, GpuMat& buf);
static inline void minMax(InputArray src, double* minVal, double* maxVal=0, InputArray mask=noArray())
{
GpuMat buf;
minMax(src, minVal, maxVal, mask, buf);
}
//! finds global minimum and maximum array elements and returns their values with locations
CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0,
const GpuMat& mask=GpuMat());
CV_EXPORTS void minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
const GpuMat& mask, GpuMat& valbuf, GpuMat& locbuf);
CV_EXPORTS void minMaxLoc(InputArray src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
InputArray mask, GpuMat& valbuf, GpuMat& locbuf);
static inline void minMaxLoc(InputArray src, double* minVal, double* maxVal=0, Point* minLoc=0, Point* maxLoc=0,
InputArray mask=noArray())
{
GpuMat valBuf, locBuf;
minMaxLoc(src, minVal, maxVal, minLoc, maxLoc, mask, valBuf, locBuf);
}
//! counts non-zero array elements
CV_EXPORTS int countNonZero(const GpuMat& src);
CV_EXPORTS int countNonZero(const GpuMat& src, GpuMat& buf);
CV_EXPORTS int countNonZero(InputArray src, GpuMat& buf);
static inline int countNonZero(const GpuMat& src)
{
GpuMat buf;
return countNonZero(src, buf);
}
//! reduces a matrix to a vector
CV_EXPORTS void reduce(const GpuMat& mtx, GpuMat& vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null());
CV_EXPORTS void reduce(InputArray mtx, OutputArray vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null());
//! computes mean value and standard deviation of all or selected array elements
//! supports only CV_8UC1 type
CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev);
//! buffered version
CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev, GpuMat& buf);
CV_EXPORTS void meanStdDev(InputArray mtx, Scalar& mean, Scalar& stddev, GpuMat& buf);
static inline void meanStdDev(InputArray src, Scalar& mean, Scalar& stddev)
{
GpuMat buf;
meanStdDev(src, mean, stddev, buf);
}
//! computes the standard deviation of integral images
//! supports only CV_32SC1 source type and CV_32FC1 sqr type
//! output will have CV_32FC1 type
CV_EXPORTS void rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& stream = Stream::Null());
CV_EXPORTS void rectStdDev(InputArray src, InputArray sqr, OutputArray dst, Rect rect, Stream& stream = Stream::Null());
//! copies 2D array to a larger destination array and pads borders with user-specifiable constant
CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType,
const Scalar& value = Scalar(), Stream& stream = Stream::Null());
//! applies fixed threshold to the image
CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
//! scales and shifts array elements so that either the specified norm (alpha) or the minimum (alpha) and maximum (beta) array values get the specified values
CV_EXPORTS void normalize(InputArray src, OutputArray dst, double alpha, double beta,
int norm_type, int dtype, InputArray mask, GpuMat& norm_buf, GpuMat& cvt_buf);
static inline void normalize(InputArray src, OutputArray dst, double alpha = 1, double beta = 0,
int norm_type = NORM_L2, int dtype = -1, InputArray mask = noArray())
{
GpuMat norm_buf;
GpuMat cvt_buf;
normalize(src, dst, alpha, beta, norm_type, dtype, mask, norm_buf, cvt_buf);
}
//! computes the integral image
//! sum will have CV_32S type, but will contain unsigned int values
//! supports only CV_8UC1 source type
CV_EXPORTS void integral(const GpuMat& src, GpuMat& sum, Stream& stream = Stream::Null());
//! buffered version
CV_EXPORTS void integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, Stream& stream = Stream::Null());
CV_EXPORTS void integral(InputArray src, OutputArray sum, GpuMat& buffer, Stream& stream = Stream::Null());
static inline void integralBuffered(InputArray src, OutputArray sum, GpuMat& buffer, Stream& stream = Stream::Null())
{
integral(src, sum, buffer, stream);
}
static inline void integral(InputArray src, OutputArray sum, Stream& stream = Stream::Null())
{
GpuMat buffer;
integral(src, sum, buffer, stream);
}
//! computes squared integral image
//! result matrix will have 64F type, but will contain 64U values
//! supports source images of 8UC1 type only
CV_EXPORTS void sqrIntegral(const GpuMat& src, GpuMat& sqsum, Stream& stream = Stream::Null());
CV_EXPORTS void sqrIntegral(InputArray src, OutputArray sqsum, GpuMat& buf, Stream& stream = Stream::Null());
static inline void sqrIntegral(InputArray src, OutputArray sqsum, Stream& stream = Stream::Null())
{
GpuMat buffer;
sqrIntegral(src, sqsum, buffer, stream);
}
CV_EXPORTS void gemm(InputArray src1, InputArray src2, double alpha,
InputArray src3, double beta, OutputArray dst, int flags = 0, Stream& stream = Stream::Null());
//! performs per-element multiplication of two full (not packed) Fourier spectrums
//! supports 32FC2 matrixes only (interleaved format)
CV_EXPORTS void mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB=false, Stream& stream = Stream::Null());
CV_EXPORTS void mulSpectrums(InputArray src1, InputArray src2, OutputArray dst, int flags, bool conjB=false, Stream& stream = Stream::Null());
//! performs per-element multiplication of two full (not packed) Fourier spectrums
//! supports 32FC2 matrixes only (interleaved format)
CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, float scale, bool conjB=false, Stream& stream = Stream::Null());
CV_EXPORTS void mulAndScaleSpectrums(InputArray src1, InputArray src2, OutputArray dst, int flags, float scale, bool conjB=false, Stream& stream = Stream::Null());
//! Performs a forward or inverse discrete Fourier transform (1D or 2D) of floating point matrix.
//! Param dft_size is the size of DFT transform.
@ -318,9 +372,25 @@ CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c
//! in CUFFT's format. Result as full complex matrix for such kind of transform cannot be retrieved.
//!
//! For complex-to-real transform it is assumed that the source matrix is packed in CUFFT's format.
CV_EXPORTS void dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0, Stream& stream = Stream::Null());
CV_EXPORTS void dft(InputArray src, OutputArray dst, Size dft_size, int flags=0, Stream& stream = Stream::Null());
struct CV_EXPORTS ConvolveBuf
//! computes convolution (or cross-correlation) of two images using discrete Fourier transform
//! supports source images of 32FC1 type only
//! result matrix will have 32FC1 type
class CV_EXPORTS Convolution : public Algorithm
{
public:
virtual void convolve(InputArray image, InputArray templ, OutputArray result, bool ccorr = false, Stream& stream = Stream::Null()) = 0;
};
CV_EXPORTS Ptr<Convolution> createConvolution(Size user_block_size = Size());
__OPENCV_GPUARITHM_DEPR_BEFORE__ void convolve(InputArray image, InputArray templ, OutputArray result, bool ccorr = false, Stream& stream = Stream::Null()) __OPENCV_GPUARITHM_DEPR_AFTER__;
inline void convolve(InputArray image, InputArray templ, OutputArray result, bool ccorr , Stream& stream)
{
createConvolution()->convolve(image, templ, result, ccorr, stream);
}
struct ConvolveBuf
{
Size result_size;
Size block_size;
@ -331,16 +401,19 @@ struct CV_EXPORTS ConvolveBuf
GpuMat image_spect, templ_spect, result_spect;
GpuMat image_block, templ_block, result_data;
void create(Size image_size, Size templ_size);
static Size estimateBlockSize(Size result_size, Size templ_size);
void create(Size, Size){}
static Size estimateBlockSize(Size, Size){ return Size(); }
};
//! computes convolution (or cross-correlation) of two images using discrete Fourier transform
//! supports source images of 32FC1 type only
//! result matrix will have 32FC1 type
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr = false);
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream = Stream::Null());
__OPENCV_GPUARITHM_DEPR_BEFORE__ void convolve(InputArray image, InputArray templ, OutputArray result, bool ccorr, ConvolveBuf& buf, Stream& stream = Stream::Null()) __OPENCV_GPUARITHM_DEPR_AFTER__;
inline void convolve(InputArray image, InputArray templ, OutputArray result, bool ccorr, ConvolveBuf& buf, Stream& stream)
{
createConvolution(buf.user_block_size)->convolve(image, templ, result, ccorr, stream);
}
}} // namespace cv { namespace gpu {
#undef __OPENCV_GPUARITHM_DEPR_BEFORE__
#undef __OPENCV_GPUARITHM_DEPR_AFTER__
#endif /* __OPENCV_GPUARITHM_HPP__ */

13
modules/gpuarithm/perf/perf_arithm.cpp
View File

@ -228,10 +228,11 @@ PERF_TEST_P(Sz_KernelSz_Ccorr, Convolve,
cv::gpu::GpuMat d_templ = cv::gpu::createContinuous(templ_size, templ_size, CV_32FC1);
d_templ.upload(templ);
cv::gpu::GpuMat dst;
cv::gpu::ConvolveBuf d_buf;
cv::Ptr<cv::gpu::Convolution> convolution = cv::gpu::createConvolution();
TEST_CYCLE() cv::gpu::convolve(d_image, d_templ, dst, ccorr, d_buf);
cv::gpu::GpuMat dst;
TEST_CYCLE() convolution->convolve(d_image, d_templ, dst, ccorr);
GPU_SANITY_CHECK(dst);
}
@ -265,7 +266,7 @@ PERF_TEST_P(Sz, Integral,
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::integralBuffered(d_src, dst, d_buf);
TEST_CYCLE() cv::gpu::integral(d_src, dst, d_buf);
GPU_SANITY_CHECK(dst);
}
@ -293,9 +294,9 @@ PERF_TEST_P(Sz, IntegralSqr,
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat dst, buf;
TEST_CYCLE() cv::gpu::sqrIntegral(d_src, dst);
TEST_CYCLE() cv::gpu::sqrIntegral(d_src, dst, buf);
GPU_SANITY_CHECK(dst);
}

8
modules/gpuarithm/perf/perf_core.cpp
View File

@ -224,10 +224,12 @@ PERF_TEST_P(Sz_Type, LutOneChannel,
if (PERF_RUN_GPU())
{
cv::Ptr<cv::gpu::LookUpTable> lutAlg = cv::gpu::createLookUpTable(lut);
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::LUT(d_src, lut, dst);
TEST_CYCLE() lutAlg->transform(d_src, dst);
GPU_SANITY_CHECK(dst);
}
@ -259,10 +261,12 @@ PERF_TEST_P(Sz_Type, LutMultiChannel,
if (PERF_RUN_GPU())
{
cv::Ptr<cv::gpu::LookUpTable> lutAlg = cv::gpu::createLookUpTable(lut);
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::LUT(d_src, lut, dst);
TEST_CYCLE() lutAlg->transform(d_src, dst);
GPU_SANITY_CHECK(dst);
}

3
modules/gpuarithm/perf/perf_reductions.cpp
View File

@ -108,9 +108,10 @@ PERF_TEST_P(Sz_Norm, NormDiff,
{
const cv::gpu::GpuMat d_src1(src1);
const cv::gpu::GpuMat d_src2(src2);
cv::gpu::GpuMat d_buf;
double gpu_dst;
TEST_CYCLE() gpu_dst = cv::gpu::norm(d_src1, d_src2, normType);
TEST_CYCLE() gpu_dst = cv::gpu::norm(d_src1, d_src2, d_buf, normType);
SANITY_CHECK(gpu_dst);

531
modules/gpuarithm/src/arithm.cpp
View File

@ -47,21 +47,14 @@ using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::gpu::gemm(const GpuMat&, const GpuMat&, double, const GpuMat&, double, GpuMat&, int, Stream&) { throw_no_cuda(); }
void cv::gpu::gemm(InputArray, InputArray, double, InputArray, double, OutputArray, int, Stream&) { throw_no_cuda(); }
void cv::gpu::integral(const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::integralBuffered(const GpuMat&, GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::mulSpectrums(InputArray, InputArray, OutputArray, int, bool, Stream&) { throw_no_cuda(); }
void cv::gpu::mulAndScaleSpectrums(InputArray, InputArray, OutputArray, int, float, bool, Stream&) { throw_no_cuda(); }
void cv::gpu::sqrIntegral(const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::dft(InputArray, OutputArray, Size, int, Stream&) { throw_no_cuda(); }
void cv::gpu::mulSpectrums(const GpuMat&, const GpuMat&, GpuMat&, int, bool, Stream&) { throw_no_cuda(); }
void cv::gpu::mulAndScaleSpectrums(const GpuMat&, const GpuMat&, GpuMat&, int, float, bool, Stream&) { throw_no_cuda(); }
void cv::gpu::dft(const GpuMat&, GpuMat&, Size, int, Stream&) { throw_no_cuda(); }
void cv::gpu::ConvolveBuf::create(Size, Size) { throw_no_cuda(); }
void cv::gpu::convolve(const GpuMat&, const GpuMat&, GpuMat&, bool) { throw_no_cuda(); }
void cv::gpu::convolve(const GpuMat&, const GpuMat&, GpuMat&, bool, ConvolveBuf&, Stream&) { throw_no_cuda(); }
Ptr<Convolution> cv::gpu::createConvolution(Size) { throw_no_cuda(); return Ptr<Convolution>(); }
#else /* !defined (HAVE_CUDA) */
@ -169,23 +162,27 @@ namespace
////////////////////////////////////////////////////////////////////////
// gemm
void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const GpuMat& src3, double beta, GpuMat& dst, int flags, Stream& stream)
void cv::gpu::gemm(InputArray _src1, InputArray _src2, double alpha, InputArray _src3, double beta, OutputArray _dst, int flags, Stream& stream)
{
#ifndef HAVE_CUBLAS
(void)src1;
(void)src2;
(void)alpha;
(void)src3;
(void)beta;
(void)dst;
(void)flags;
(void)stream;
CV_Error(cv::Error::StsNotImplemented, "The library was build without CUBLAS");
(void) _src1;
(void) _src2;
(void) alpha;
(void) _src3;
(void) beta;
(void) _dst;
(void) flags;
(void) stream;
CV_Error(:Error::StsNotImplemented, "The library was build without CUBLAS");
#else
// CUBLAS works with column-major matrices
CV_Assert(src1.type() == CV_32FC1 || src1.type() == CV_32FC2 || src1.type() == CV_64FC1 || src1.type() == CV_64FC2);
CV_Assert(src2.type() == src1.type() && (src3.empty() || src3.type() == src1.type()));
GpuMat src1 = _src1.getGpuMat();
GpuMat src2 = _src2.getGpuMat();
GpuMat src3 = _src3.getGpuMat();
CV_Assert( src1.type() == CV_32FC1 || src1.type() == CV_32FC2 || src1.type() == CV_64FC1 || src1.type() == CV_64FC2 );
CV_Assert( src2.type() == src1.type() && (src3.empty() || src3.type() == src1.type()) );
if (src1.depth() == CV_64F)
{
@ -208,10 +205,11 @@ void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const G
Size src3Size = tr3 ? Size(src3.rows, src3.cols) : src3.size();
Size dstSize(src2Size.width, src1Size.height);
CV_Assert(src1Size.width == src2Size.height);
CV_Assert(src3.empty() || src3Size == dstSize);
CV_Assert( src1Size.width == src2Size.height );
CV_Assert( src3.empty() || src3Size == dstSize );
dst.create(dstSize, src1.type());
_dst.create(dstSize, src1.type());
GpuMat dst = _dst.getGpuMat();
if (beta != 0)
{
@ -294,116 +292,6 @@ void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const G
#endif
}
////////////////////////////////////////////////////////////////////////
// integral
void cv::gpu::integral(const GpuMat& src, GpuMat& sum, Stream& s)
{
GpuMat buffer;
gpu::integralBuffered(src, sum, buffer, s);
}
namespace cv { namespace gpu { namespace cudev
{
namespace imgproc
{
void shfl_integral_gpu(const PtrStepSzb& img, PtrStepSz<unsigned int> integral, cudaStream_t stream);
}
}}}
void cv::gpu::integralBuffered(const GpuMat& src, GpuMat& sum, GpuMat& buffer, Stream& s)
{
CV_Assert(src.type() == CV_8UC1);
cudaStream_t stream = StreamAccessor::getStream(s);
cv::Size whole;
cv::Point offset;
src.locateROI(whole, offset);
if (deviceSupports(WARP_SHUFFLE_FUNCTIONS) && src.cols <= 2048
&& offset.x % 16 == 0 && ((src.cols + 63) / 64) * 64 <= (static_cast<int>(src.step) - offset.x))
{
ensureSizeIsEnough(((src.rows + 7) / 8) * 8, ((src.cols + 63) / 64) * 64, CV_32SC1, buffer);
cv::gpu::cudev::imgproc::shfl_integral_gpu(src, buffer, stream);
sum.create(src.rows + 1, src.cols + 1, CV_32SC1);
sum.setTo(Scalar::all(0), s);
GpuMat inner = sum(Rect(1, 1, src.cols, src.rows));
GpuMat res = buffer(Rect(0, 0, src.cols, src.rows));
res.copyTo(inner, s);
}
else
{
#ifndef HAVE_OPENCV_GPULEGACY
throw_no_cuda();
#else
sum.create(src.rows + 1, src.cols + 1, CV_32SC1);
NcvSize32u roiSize;
roiSize.width = src.cols;
roiSize.height = src.rows;
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::gpu::getDevice()) );
Ncv32u bufSize;
ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
ensureSizeIsEnough(1, bufSize, CV_8UC1, buffer);
NppStStreamHandler h(stream);
ncvSafeCall( nppiStIntegral_8u32u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>()), static_cast<int>(src.step),
sum.ptr<Ncv32u>(), static_cast<int>(sum.step), roiSize, buffer.ptr<Ncv8u>(), bufSize, prop) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
#endif
}
}
//////////////////////////////////////////////////////////////////////////////
// sqrIntegral
void cv::gpu::sqrIntegral(const GpuMat& src, GpuMat& sqsum, Stream& s)
{
#ifndef HAVE_OPENCV_GPULEGACY
(void) src;
(void) sqsum;
(void) s;
throw_no_cuda();
#else
CV_Assert(src.type() == CV_8U);
NcvSize32u roiSize;
roiSize.width = src.cols;
roiSize.height = src.rows;
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::gpu::getDevice()) );
Ncv32u bufSize;
ncvSafeCall(nppiStSqrIntegralGetSize_8u64u(roiSize, &bufSize, prop));
GpuMat buf(1, bufSize, CV_8U);
cudaStream_t stream = StreamAccessor::getStream(s);
NppStStreamHandler h(stream);
sqsum.create(src.rows + 1, src.cols + 1, CV_64F);
ncvSafeCall(nppiStSqrIntegral_8u64u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>(0)), static_cast<int>(src.step),
sqsum.ptr<Ncv64u>(0), static_cast<int>(sqsum.step), roiSize, buf.ptr<Ncv8u>(0), bufSize, prop));
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
#endif
}
//////////////////////////////////////////////////////////////////////////////
// mulSpectrums
@ -418,12 +306,12 @@ namespace cv { namespace gpu { namespace cudev
#endif
void cv::gpu::mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, bool conjB, Stream& stream)
void cv::gpu::mulSpectrums(InputArray _src1, InputArray _src2, OutputArray _dst, int flags, bool conjB, Stream& stream)
{
#ifndef HAVE_CUFFT
(void) a;
(void) b;
(void) c;
(void) _src1;
(void) _src2;
(void) _dst;
(void) flags;
(void) conjB;
(void) stream;
@ -432,16 +320,19 @@ void cv::gpu::mulSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flag
(void) flags;
typedef void (*Caller)(const PtrStep<cufftComplex>, const PtrStep<cufftComplex>, PtrStepSz<cufftComplex>, cudaStream_t stream);
static Caller callers[] = { cudev::mulSpectrums, cudev::mulSpectrums_CONJ };
CV_Assert(a.type() == b.type() && a.type() == CV_32FC2);
CV_Assert(a.size() == b.size());
GpuMat src1 = _src1.getGpuMat();
GpuMat src2 = _src2.getGpuMat();
c.create(a.size(), CV_32FC2);
CV_Assert( src1.type() == src2.type() && src1.type() == CV_32FC2 );
CV_Assert( src1.size() == src2.size() );
_dst.create(src1.size(), CV_32FC2);
GpuMat dst = _dst.getGpuMat();
Caller caller = callers[(int)conjB];
caller(a, b, c, StreamAccessor::getStream(stream));
caller(src1, src2, dst, StreamAccessor::getStream(stream));
#endif
}
@ -459,12 +350,12 @@ namespace cv { namespace gpu { namespace cudev
#endif
void cv::gpu::mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c, int flags, float scale, bool conjB, Stream& stream)
void cv::gpu::mulAndScaleSpectrums(InputArray _src1, InputArray _src2, OutputArray _dst, int flags, float scale, bool conjB, Stream& stream)
{
#ifndef HAVE_CUFFT
(void) a;
(void) b;
(void) c;
(void) _src1;
(void) _src2;
(void) _dst;
(void) flags;
(void) scale;
(void) conjB;
@ -476,53 +367,57 @@ void cv::gpu::mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c,
typedef void (*Caller)(const PtrStep<cufftComplex>, const PtrStep<cufftComplex>, float scale, PtrStepSz<cufftComplex>, cudaStream_t stream);
static Caller callers[] = { cudev::mulAndScaleSpectrums, cudev::mulAndScaleSpectrums_CONJ };
CV_Assert(a.type() == b.type() && a.type() == CV_32FC2);
CV_Assert(a.size() == b.size());
GpuMat src1 = _src1.getGpuMat();
GpuMat src2 = _src2.getGpuMat();
c.create(a.size(), CV_32FC2);
CV_Assert( src1.type() == src2.type() && src1.type() == CV_32FC2);
CV_Assert( src1.size() == src2.size() );
_dst.create(src1.size(), CV_32FC2);
GpuMat dst = _dst.getGpuMat();
Caller caller = callers[(int)conjB];
caller(a, b, scale, c, StreamAccessor::getStream(stream));
caller(src1, src2, scale, dst, StreamAccessor::getStream(stream));
#endif
}
//////////////////////////////////////////////////////////////////////////////
// dft
void cv::gpu::dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags, Stream& stream)
void cv::gpu::dft(InputArray _src, OutputArray _dst, Size dft_size, int flags, Stream& stream)
{
#ifndef HAVE_CUFFT
(void) src;
(void) dst;
(void) _src;
(void) _dst;
(void) dft_size;
(void) flags;
(void) stream;
throw_no_cuda();
#else
GpuMat src = _src.getGpuMat();
CV_Assert(src.type() == CV_32F || src.type() == CV_32FC2);
CV_Assert( src.type() == CV_32FC1 || src.type() == CV_32FC2 );
// We don't support unpacked output (in the case of real input)
CV_Assert(!(flags & DFT_COMPLEX_OUTPUT));
CV_Assert( !(flags & DFT_COMPLEX_OUTPUT) );
bool is_1d_input = (dft_size.height == 1) || (dft_size.width == 1);
int is_row_dft = flags & DFT_ROWS;
int is_scaled_dft = flags & DFT_SCALE;
int is_inverse = flags & DFT_INVERSE;
bool is_complex_input = src.channels() == 2;
bool is_complex_output = !(flags & DFT_REAL_OUTPUT);
const bool is_1d_input = (dft_size.height == 1) || (dft_size.width == 1);
const bool is_row_dft = (flags & DFT_ROWS) != 0;
const bool is_scaled_dft = (flags & DFT_SCALE) != 0;
const bool is_inverse = (flags & DFT_INVERSE) != 0;
const bool is_complex_input = src.channels() == 2;
const bool is_complex_output = !(flags & DFT_REAL_OUTPUT);
// We don't support real-to-real transform
CV_Assert(is_complex_input || is_complex_output);
CV_Assert( is_complex_input || is_complex_output );
GpuMat src_data;
GpuMat src_cont = src;
// Make sure here we work with the continuous input,
// as CUFFT can't handle gaps
src_data = src;
createContinuous(src.rows, src.cols, src.type(), src_data);
if (src_data.data != src.data)
src.copyTo(src_data);
createContinuous(src.rows, src.cols, src.type(), src_cont);
if (src_cont.data != src.data)
src.copyTo(src_cont, stream);
Size dft_size_opt = dft_size;
if (is_1d_input && !is_row_dft)
@ -532,17 +427,17 @@ void cv::gpu::dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags, Stre
dft_size_opt.height = std::min(dft_size.width, dft_size.height);
}
CV_Assert( dft_size_opt.width > 1 );
cufftType dft_type = CUFFT_R2C;
if (is_complex_input)
dft_type = is_complex_output ? CUFFT_C2C : CUFFT_C2R;
CV_Assert(dft_size_opt.width > 1);
cufftHandle plan;
if (is_1d_input || is_row_dft)
cufftPlan1d(&plan, dft_size_opt.width, dft_type, dft_size_opt.height);
cufftSafeCall( cufftPlan1d(&plan, dft_size_opt.width, dft_type, dft_size_opt.height) );
else
cufftPlan2d(&plan, dft_size_opt.height, dft_size_opt.width, dft_type);
cufftSafeCall( cufftPlan2d(&plan, dft_size_opt.height, dft_size_opt.width, dft_type) );
cufftSafeCall( cufftSetStream(plan, StreamAccessor::getStream(stream)) );
@ -550,171 +445,191 @@ void cv::gpu::dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags, Stre
{
if (is_complex_output)
{
createContinuous(dft_size, CV_32FC2, dst);
createContinuous(dft_size, CV_32FC2, _dst);
GpuMat dst = _dst.getGpuMat();
cufftSafeCall(cufftExecC2C(
plan, src_data.ptr<cufftComplex>(), dst.ptr<cufftComplex>(),
plan, src_cont.ptr<cufftComplex>(), dst.ptr<cufftComplex>(),
is_inverse ? CUFFT_INVERSE : CUFFT_FORWARD));
}
else
{
createContinuous(dft_size, CV_32F, dst);
createContinuous(dft_size, CV_32F, _dst);
GpuMat dst = _dst.getGpuMat();
cufftSafeCall(cufftExecC2R(
plan, src_data.ptr<cufftComplex>(), dst.ptr<cufftReal>()));
plan, src_cont.ptr<cufftComplex>(), dst.ptr<cufftReal>()));
}
}
else
{
// We could swap dft_size for efficiency. Here we must reflect it
if (dft_size == dft_size_opt)
createContinuous(Size(dft_size.width / 2 + 1, dft_size.height), CV_32FC2, dst);
createContinuous(Size(dft_size.width / 2 + 1, dft_size.height), CV_32FC2, _dst);
else
createContinuous(Size(dft_size.width, dft_size.height / 2 + 1), CV_32FC2, dst);
createContinuous(Size(dft_size.width, dft_size.height / 2 + 1), CV_32FC2, _dst);
GpuMat dst = _dst.getGpuMat();
cufftSafeCall(cufftExecR2C(
plan, src_data.ptr<cufftReal>(), dst.ptr<cufftComplex>()));
plan, src_cont.ptr<cufftReal>(), dst.ptr<cufftComplex>()));
}
cufftSafeCall(cufftDestroy(plan));
cufftSafeCall( cufftDestroy(plan) );
if (is_scaled_dft)
multiply(dst, Scalar::all(1. / dft_size.area()), dst, 1, -1, stream);
gpu::multiply(_dst, Scalar::all(1. / dft_size.area()), _dst, 1, -1, stream);
#endif
}
//////////////////////////////////////////////////////////////////////////////
// convolve
// Convolution
void cv::gpu::ConvolveBuf::create(Size image_size, Size templ_size)
#ifdef HAVE_CUFFT
namespace
{
result_size = Size(image_size.width - templ_size.width + 1,
image_size.height - templ_size.height + 1);
block_size = user_block_size;
if (user_block_size.width == 0 || user_block_size.height == 0)
block_size = estimateBlockSize(result_size, templ_size);
dft_size.width = 1 << int(ceil(std::log(block_size.width + templ_size.width - 1.) / std::log(2.)));
dft_size.height = 1 << int(ceil(std::log(block_size.height + templ_size.height - 1.) / std::log(2.)));
// CUFFT has hard-coded kernels for power-of-2 sizes (up to 8192),
// see CUDA Toolkit 4.1 CUFFT Library Programming Guide
if (dft_size.width > 8192)
dft_size.width = getOptimalDFTSize(block_size.width + templ_size.width - 1);
if (dft_size.height > 8192)
dft_size.height = getOptimalDFTSize(block_size.height + templ_size.height - 1);
// To avoid wasting time doing small DFTs
dft_size.width = std::max(dft_size.width, 512);
dft_size.height = std::max(dft_size.height, 512);
createContinuous(dft_size, CV_32F, image_block);
createContinuous(dft_size, CV_32F, templ_block);
createContinuous(dft_size, CV_32F, result_data);
spect_len = dft_size.height * (dft_size.width / 2 + 1);
createContinuous(1, spect_len, CV_32FC2, image_spect);
createContinuous(1, spect_len, CV_32FC2, templ_spect);
createContinuous(1, spect_len, CV_32FC2, result_spect);
// Use maximum result matrix block size for the estimated DFT block size
block_size.width = std::min(dft_size.width - templ_size.width + 1, result_size.width);
block_size.height = std::min(dft_size.height - templ_size.height + 1, result_size.height);
}
Size cv::gpu::ConvolveBuf::estimateBlockSize(Size result_size, Size /*templ_size*/)
{
int width = (result_size.width + 2) / 3;
int height = (result_size.height + 2) / 3;
width = std::min(width, result_size.width);
height = std::min(height, result_size.height);
return Size(width, height);
}
void cv::gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr)
{
ConvolveBuf buf;
gpu::convolve(image, templ, result, ccorr, buf);
}
void cv::gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream)
{
#ifndef HAVE_CUFFT
(void) image;
(void) templ;
(void) result;
(void) ccorr;
(void) buf;
(void) stream;
throw_no_cuda();
#else
using namespace cv::gpu::cudev::imgproc;
CV_Assert(image.type() == CV_32F);
CV_Assert(templ.type() == CV_32F);
buf.create(image.size(), templ.size());
result.create(buf.result_size, CV_32F);
Size& block_size = buf.block_size;
Size& dft_size = buf.dft_size;
GpuMat& image_block = buf.image_block;
GpuMat& templ_block = buf.templ_block;
GpuMat& result_data = buf.result_data;
GpuMat& image_spect = buf.image_spect;
GpuMat& templ_spect = buf.templ_spect;
GpuMat& result_spect = buf.result_spect;
cufftHandle planR2C, planC2R;
cufftSafeCall(cufftPlan2d(&planC2R, dft_size.height, dft_size.width, CUFFT_C2R));
cufftSafeCall(cufftPlan2d(&planR2C, dft_size.height, dft_size.width, CUFFT_R2C));
cufftSafeCall( cufftSetStream(planR2C, StreamAccessor::getStream(stream)) );
cufftSafeCall( cufftSetStream(planC2R, StreamAccessor::getStream(stream)) );
GpuMat templ_roi(templ.size(), CV_32F, templ.data, templ.step);
gpu::copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0,
templ_block.cols - templ_roi.cols, 0, Scalar(), stream);
cufftSafeCall(cufftExecR2C(planR2C, templ_block.ptr<cufftReal>(),
templ_spect.ptr<cufftComplex>()));
// Process all blocks of the result matrix
for (int y = 0; y < result.rows; y += block_size.height)
class ConvolutionImpl : public Convolution
{
for (int x = 0; x < result.cols; x += block_size.width)
{
Size image_roi_size(std::min(x + dft_size.width, image.cols) - x,
std::min(y + dft_size.height, image.rows) - y);
GpuMat image_roi(image_roi_size, CV_32F, (void*)(image.ptr<float>(y) + x),
image.step);
gpu::copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows,
0, image_block.cols - image_roi.cols, 0, Scalar(), stream);
public:
explicit ConvolutionImpl(Size user_block_size_) : user_block_size(user_block_size_) {}
cufftSafeCall(cufftExecR2C(planR2C, image_block.ptr<cufftReal>(),
image_spect.ptr<cufftComplex>()));
gpu::mulAndScaleSpectrums(image_spect, templ_spect, result_spect, 0,
1.f / dft_size.area(), ccorr, stream);
cufftSafeCall(cufftExecC2R(planC2R, result_spect.ptr<cufftComplex>(),
result_data.ptr<cufftReal>()));
void convolve(InputArray image, InputArray templ, OutputArray result, bool ccorr = false, Stream& stream = Stream::Null());
Size result_roi_size(std::min(x + block_size.width, result.cols) - x,
std::min(y + block_size.height, result.rows) - y);
GpuMat result_roi(result_roi_size, result.type(),
(void*)(result.ptr<float>(y) + x), result.step);
GpuMat result_block(result_roi_size, result_data.type(),
result_data.ptr(), result_data.step);
private:
void create(Size image_size, Size templ_size);
static Size estimateBlockSize(Size result_size);
result_block.copyTo(result_roi, stream);
}
Size result_size;
Size block_size;
Size user_block_size;
Size dft_size;
int spect_len;
GpuMat image_spect, templ_spect, result_spect;
GpuMat image_block, templ_block, result_data;
};
void ConvolutionImpl::create(Size image_size, Size templ_size)
{
result_size = Size(image_size.width - templ_size.width + 1,
image_size.height - templ_size.height + 1);
block_size = user_block_size;
if (user_block_size.width == 0 || user_block_size.height == 0)
block_size = estimateBlockSize(result_size);
dft_size.width = 1 << int(ceil(std::log(block_size.width + templ_size.width - 1.) / std::log(2.)));
dft_size.height = 1 << int(ceil(std::log(block_size.height + templ_size.height - 1.) / std::log(2.)));
// CUFFT has hard-coded kernels for power-of-2 sizes (up to 8192),
// see CUDA Toolkit 4.1 CUFFT Library Programming Guide
if (dft_size.width > 8192)
dft_size.width = getOptimalDFTSize(block_size.width + templ_size.width - 1);
if (dft_size.height > 8192)
dft_size.height = getOptimalDFTSize(block_size.height + templ_size.height - 1);
// To avoid wasting time doing small DFTs
dft_size.width = std::max(dft_size.width, 512);
dft_size.height = std::max(dft_size.height, 512);
createContinuous(dft_size, CV_32F, image_block);
createContinuous(dft_size, CV_32F, templ_block);
createContinuous(dft_size, CV_32F, result_data);
spect_len = dft_size.height * (dft_size.width / 2 + 1);
createContinuous(1, spect_len, CV_32FC2, image_spect);
createContinuous(1, spect_len, CV_32FC2, templ_spect);
createContinuous(1, spect_len, CV_32FC2, result_spect);
// Use maximum result matrix block size for the estimated DFT block size
block_size.width = std::min(dft_size.width - templ_size.width + 1, result_size.width);
block_size.height = std::min(dft_size.height - templ_size.height + 1, result_size.height);
}
cufftSafeCall(cufftDestroy(planR2C));
cufftSafeCall(cufftDestroy(planC2R));
Size ConvolutionImpl::estimateBlockSize(Size result_size)
{
int width = (result_size.width + 2) / 3;
int height = (result_size.height + 2) / 3;
width = std::min(width, result_size.width);
height = std::min(height, result_size.height);
return Size(width, height);
}
void ConvolutionImpl::convolve(InputArray _image, InputArray _templ, OutputArray _result, bool ccorr, Stream& _stream)
{
GpuMat image = _image.getGpuMat();
GpuMat templ = _templ.getGpuMat();
CV_Assert( image.type() == CV_32FC1 );
CV_Assert( templ.type() == CV_32FC1 );
create(image.size(), templ.size());
_result.create(result_size, CV_32FC1);
GpuMat result = _result.getGpuMat();
cudaStream_t stream = StreamAccessor::getStream(_stream);
cufftHandle planR2C, planC2R;
cufftSafeCall( cufftPlan2d(&planC2R, dft_size.height, dft_size.width, CUFFT_C2R) );
cufftSafeCall( cufftPlan2d(&planR2C, dft_size.height, dft_size.width, CUFFT_R2C) );
cufftSafeCall( cufftSetStream(planR2C, stream) );
cufftSafeCall( cufftSetStream(planC2R, stream) );
GpuMat templ_roi(templ.size(), CV_32FC1, templ.data, templ.step);
gpu::copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0,
templ_block.cols - templ_roi.cols, 0, Scalar(), _stream);
cufftSafeCall( cufftExecR2C(planR2C, templ_block.ptr<cufftReal>(), templ_spect.ptr<cufftComplex>()) );
// Process all blocks of the result matrix
for (int y = 0; y < result.rows; y += block_size.height)
{
for (int x = 0; x < result.cols; x += block_size.width)
{
Size image_roi_size(std::min(x + dft_size.width, image.cols) - x,
std::min(y + dft_size.height, image.rows) - y);
GpuMat image_roi(image_roi_size, CV_32F, (void*)(image.ptr<float>(y) + x),
image.step);
gpu::copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows,
0, image_block.cols - image_roi.cols, 0, Scalar(), _stream);
cufftSafeCall(cufftExecR2C(planR2C, image_block.ptr<cufftReal>(),
image_spect.ptr<cufftComplex>()));
gpu::mulAndScaleSpectrums(image_spect, templ_spect, result_spect, 0,
1.f / dft_size.area(), ccorr, _stream);
cufftSafeCall(cufftExecC2R(planC2R, result_spect.ptr<cufftComplex>(),
result_data.ptr<cufftReal>()));
Size result_roi_size(std::min(x + block_size.width, result.cols) - x,
std::min(y + block_size.height, result.rows) - y);
GpuMat result_roi(result_roi_size, result.type(),
(void*)(result.ptr<float>(y) + x), result.step);
GpuMat result_block(result_roi_size, result_data.type(),
result_data.ptr(), result_data.step);
result_block.copyTo(result_roi, _stream);
}
}
cufftSafeCall( cufftDestroy(planR2C) );
cufftSafeCall( cufftDestroy(planC2R) );
}
}
#endif
Ptr<Convolution> cv::gpu::createConvolution(Size user_block_size)
{
#ifndef HAVE_CUBLAS
(void) user_block_size;
CV_Error(cv::Error::StsNotImplemented, "The library was build without CUFFT");
return Ptr<BLAS>();
#else
return new ConvolutionImpl(user_block_size);
#endif
}

382
modules/gpuarithm/src/core.cpp
View File

@ -47,19 +47,19 @@ using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::gpu::merge(const GpuMat* /*src*/, size_t /*count*/, GpuMat& /*dst*/, Stream& /*stream*/) { throw_no_cuda(); }
void cv::gpu::merge(const std::vector<GpuMat>& /*src*/, GpuMat& /*dst*/, Stream& /*stream*/) { throw_no_cuda(); }
void cv::gpu::merge(const GpuMat*, size_t, OutputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::merge(const std::vector<GpuMat>&, OutputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::split(const GpuMat& /*src*/, GpuMat* /*dst*/, Stream& /*stream*/) { throw_no_cuda(); }
void cv::gpu::split(const GpuMat& /*src*/, std::vector<GpuMat>& /*dst*/, Stream& /*stream*/) { throw_no_cuda(); }
void cv::gpu::split(InputArray, GpuMat*, Stream&) { throw_no_cuda(); }
void cv::gpu::split(InputArray, std::vector<GpuMat>&, Stream&) { throw_no_cuda(); }
void cv::gpu::transpose(const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::transpose(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::flip(const GpuMat&, GpuMat&, int, Stream&) { throw_no_cuda(); }
void cv::gpu::flip(InputArray, OutputArray, int, Stream&) { throw_no_cuda(); }
void cv::gpu::LUT(const GpuMat&, const Mat&, GpuMat&, Stream&) { throw_no_cuda(); }
Ptr<LookUpTable> cv::gpu::createLookUpTable(InputArray) { throw_no_cuda(); return Ptr<LookUpTable>(); }
void cv::gpu::copyMakeBorder(const GpuMat&, GpuMat&, int, int, int, int, int, const Scalar&, Stream&) { throw_no_cuda(); }
void cv::gpu::copyMakeBorder(InputArray, OutputArray, int, int, int, int, int, Scalar, Stream&) { throw_no_cuda(); }
#else /* !defined (HAVE_CUDA) */
@ -70,22 +70,27 @@ namespace cv { namespace gpu { namespace cudev
{
namespace split_merge
{
void merge_caller(const PtrStepSzb* src, PtrStepSzb& dst, int total_channels, size_t elem_size, const cudaStream_t& stream);
void split_caller(const PtrStepSzb& src, PtrStepSzb* dst, int num_channels, size_t elem_size1, const cudaStream_t& stream);
void merge(const PtrStepSzb* src, PtrStepSzb& dst, int total_channels, size_t elem_size, const cudaStream_t& stream);
void split(const PtrStepSzb& src, PtrStepSzb* dst, int num_channels, size_t elem_size1, const cudaStream_t& stream);
}
}}}
namespace
{
void merge(const GpuMat* src, size_t n, GpuMat& dst, const cudaStream_t& stream)
void merge_caller(const GpuMat* src, size_t n, OutputArray _dst, Stream& stream)
{
using namespace ::cv::gpu::cudev::split_merge;
CV_Assert( src != 0 );
CV_Assert( n > 0 && n <= 4 );
CV_Assert(src);
CV_Assert(n > 0);
const int depth = src[0].depth();
const Size size = src[0].size();
int depth = src[0].depth();
Size size = src[0].size();
for (size_t i = 0; i < n; ++i)
{
CV_Assert( src[i].size() == size );
CV_Assert( src[i].depth() == depth );
CV_Assert( src[i].channels() == 1 );
}
if (depth == CV_64F)
{
@ -93,43 +98,32 @@ namespace
CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
}
bool single_channel_only = true;
int total_channels = 0;
for (size_t i = 0; i < n; ++i)
if (n == 1)
{
CV_Assert(src[i].size() == size);
CV_Assert(src[i].depth() == depth);
single_channel_only = single_channel_only && src[i].channels() == 1;
total_channels += src[i].channels();
src[0].copyTo(_dst, stream);
}
CV_Assert(single_channel_only);
CV_Assert(total_channels <= 4);
if (total_channels == 1)
src[0].copyTo(dst);
else
{
dst.create(size, CV_MAKETYPE(depth, total_channels));
_dst.create(size, CV_MAKE_TYPE(depth, (int)n));
GpuMat dst = _dst.getGpuMat();
PtrStepSzb src_as_devmem[4];
for(size_t i = 0; i < n; ++i)
src_as_devmem[i] = src[i];
PtrStepSzb dst_as_devmem(dst);
merge_caller(src_as_devmem, dst_as_devmem, total_channels, CV_ELEM_SIZE(depth), stream);
cv::gpu::cudev::split_merge::merge(src_as_devmem, dst_as_devmem, (int)n, CV_ELEM_SIZE(depth), StreamAccessor::getStream(stream));
}
}
void split(const GpuMat& src, GpuMat* dst, const cudaStream_t& stream)
void split_caller(const GpuMat& src, GpuMat* dst, Stream& stream)
{
using namespace ::cv::gpu::cudev::split_merge;
CV_Assert( dst != 0 );
CV_Assert(dst);
const int depth = src.depth();
const int num_channels = src.channels();
int depth = src.depth();
int num_channels = src.channels();
CV_Assert( num_channels <= 4 );
if (depth == CV_64F)
{
@ -139,45 +133,45 @@ namespace
if (num_channels == 1)
{
src.copyTo(dst[0]);
src.copyTo(dst[0], stream);
return;
}
for (int i = 0; i < num_channels; ++i)
dst[i].create(src.size(), depth);
CV_Assert(num_channels <= 4);
PtrStepSzb dst_as_devmem[4];
for (int i = 0; i < num_channels; ++i)
dst_as_devmem[i] = dst[i];
PtrStepSzb src_as_devmem(src);
split_caller(src_as_devmem, dst_as_devmem, num_channels, src.elemSize1(), stream);
cv::gpu::cudev::split_merge::split(src_as_devmem, dst_as_devmem, num_channels, src.elemSize1(), StreamAccessor::getStream(stream));
}
}
void cv::gpu::merge(const GpuMat* src, size_t n, GpuMat& dst, Stream& stream)
void cv::gpu::merge(const GpuMat* src, size_t n, OutputArray dst, Stream& stream)
{
::merge(src, n, dst, StreamAccessor::getStream(stream));
merge_caller(src, n, dst, stream);
}
void cv::gpu::merge(const std::vector<GpuMat>& src, GpuMat& dst, Stream& stream)
void cv::gpu::merge(const std::vector<GpuMat>& src, OutputArray dst, Stream& stream)
{
::merge(&src[0], src.size(), dst, StreamAccessor::getStream(stream));
merge_caller(&src[0], src.size(), dst, stream);
}
void cv::gpu::split(const GpuMat& src, GpuMat* dst, Stream& stream)
void cv::gpu::split(InputArray _src, GpuMat* dst, Stream& stream)
{
::split(src, dst, StreamAccessor::getStream(stream));
GpuMat src = _src.getGpuMat();
split_caller(src, dst, stream);
}
void cv::gpu::split(const GpuMat& src, std::vector<GpuMat>& dst, Stream& stream)
void cv::gpu::split(InputArray _src, std::vector<GpuMat>& dst, Stream& stream)
{
GpuMat src = _src.getGpuMat();
dst.resize(src.channels());
if(src.channels() > 0)
::split(src, &dst[0], StreamAccessor::getStream(stream));
split_caller(src, &dst[0], stream);
}
////////////////////////////////////////////////////////////////////////
@ -188,13 +182,16 @@ namespace arithm
template <typename T> void transpose(PtrStepSz<T> src, PtrStepSz<T> dst, cudaStream_t stream);
}
void cv::gpu::transpose(const GpuMat& src, GpuMat& dst, Stream& s)
void cv::gpu::transpose(InputArray _src, OutputArray _dst, Stream& _stream)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.elemSize() == 1 || src.elemSize() == 4 || src.elemSize() == 8 );
dst.create( src.cols, src.rows, src.type() );
_dst.create( src.cols, src.rows, src.type() );
GpuMat dst = _dst.getGpuMat();
cudaStream_t stream = StreamAccessor::getStream(s);
cudaStream_t stream = StreamAccessor::getStream(_stream);
if (src.elemSize() == 1)
{
@ -266,7 +263,7 @@ namespace
};
}
void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& stream)
void cv::gpu::flip(InputArray _src, OutputArray _dst, int flipCode, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream);
static const func_t funcs[6][4] =
@ -279,10 +276,13 @@ void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& stream)
{NppMirror<CV_32F, nppiMirror_32f_C1R>::call, 0, NppMirror<CV_32F, nppiMirror_32f_C3R>::call, NppMirror<CV_32F, nppiMirror_32f_C4R>::call}
};
GpuMat src = _src.getGpuMat();
CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S || src.depth() == CV_32F);
CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);
dst.create(src.size(), src.type());
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
funcs[src.depth()][src.channels() - 1](src, dst, flipCode, StreamAccessor::getStream(stream));
}
@ -290,93 +290,214 @@ void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& stream)
////////////////////////////////////////////////////////////////////////
// LUT
void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& s)
#if (CUDA_VERSION >= 5000)
namespace
{
const int cn = src.channels();
CV_Assert( src.type() == CV_8UC1 || src.type() == CV_8UC3 );
CV_Assert( lut.depth() == CV_8U );
CV_Assert( lut.channels() == 1 || lut.channels() == cn );
CV_Assert( lut.rows * lut.cols == 256 && lut.isContinuous() );
dst.create(src.size(), CV_MAKE_TYPE(lut.depth(), cn));
NppiSize sz;
sz.height = src.rows;
sz.width = src.cols;
Mat nppLut;
lut.convertTo(nppLut, CV_32S);
int nValues3[] = {256, 256, 256};
Npp32s pLevels[256];
for (int i = 0; i < 256; ++i)
pLevels[i] = i;
const Npp32s* pLevels3[3];
#if (CUDA_VERSION <= 4020)
pLevels3[0] = pLevels3[1] = pLevels3[2] = pLevels;
#else
GpuMat d_pLevels;
d_pLevels.upload(Mat(1, 256, CV_32S, pLevels));
pLevels3[0] = pLevels3[1] = pLevels3[2] = d_pLevels.ptr<Npp32s>();
#endif
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
if (src.type() == CV_8UC1)
{
#if (CUDA_VERSION <= 4020)
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, nppLut.ptr<Npp32s>(), pLevels, 256) );
#else
GpuMat d_nppLut(Mat(1, 256, CV_32S, nppLut.data));
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, d_nppLut.ptr<Npp32s>(), d_pLevels.ptr<Npp32s>(), 256) );
#endif
}
else
class LookUpTableImpl : public LookUpTable
{
public:
LookUpTableImpl(InputArray lut);
void transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
private:
int lut_cn;
int nValues3[3];
const Npp32s* pValues3[3];
const Npp32s* pLevels3[3];
Mat nppLut3[3];
if (nppLut.channels() == 1)
GpuMat d_pLevels;
GpuMat d_nppLut;
GpuMat d_nppLut3[3];
};
LookUpTableImpl::LookUpTableImpl(InputArray _lut)
{
nValues3[0] = nValues3[1] = nValues3[2] = 256;
Npp32s pLevels[256];
for (int i = 0; i < 256; ++i)
pLevels[i] = i;
d_pLevels.upload(Mat(1, 256, CV_32S, pLevels));
pLevels3[0] = pLevels3[1] = pLevels3[2] = d_pLevels.ptr<Npp32s>();
GpuMat lut;
if (_lut.kind() == _InputArray::GPU_MAT)
{
lut = _lut.getGpuMat();
}
else
{
Mat hLut = _lut.getMat();
CV_Assert( hLut.total() == 256 && hLut.isContinuous() );
lut.upload(Mat(1, 256, hLut.type(), hLut.data));
}
lut_cn = lut.channels();
CV_Assert( lut.depth() == CV_8U );
CV_Assert( lut.rows == 1 && lut.cols == 256 );
lut.convertTo(d_nppLut, CV_32S);
if (lut_cn == 1)
{
#if (CUDA_VERSION <= 4020)
pValues3[0] = pValues3[1] = pValues3[2] = nppLut.ptr<Npp32s>();
#else
GpuMat d_nppLut(Mat(1, 256, CV_32S, nppLut.data));
pValues3[0] = pValues3[1] = pValues3[2] = d_nppLut.ptr<Npp32s>();
#endif
}
else
{
gpu::split(d_nppLut, d_nppLut3);
pValues3[0] = d_nppLut3[0].ptr<Npp32s>();
pValues3[1] = d_nppLut3[1].ptr<Npp32s>();
pValues3[2] = d_nppLut3[2].ptr<Npp32s>();
}
}
void LookUpTableImpl::transform(InputArray _src, OutputArray _dst, Stream& _stream)
{
GpuMat src = _src.getGpuMat();
const int cn = src.channels();
CV_Assert( src.type() == CV_8UC1 || src.type() == CV_8UC3 );
CV_Assert( lut_cn == 1 || lut_cn == cn );
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStreamHandler h(stream);
NppiSize sz;
sz.height = src.rows;
sz.width = src.cols;
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, d_nppLut.ptr<Npp32s>(), d_pLevels.ptr<Npp32s>(), 256) );
}
else
{
nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, pLevels3, nValues3) );
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
#else // (CUDA_VERSION >= 5000)
namespace
{
class LookUpTableImpl : public LookUpTable
{
public:
LookUpTableImpl(InputArray lut);
void transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
private:
int lut_cn;
Npp32s pLevels[256];
int nValues3[3];
const Npp32s* pValues3[3];
const Npp32s* pLevels3[3];
Mat nppLut;
Mat nppLut3[3];
};
LookUpTableImpl::LookUpTableImpl(InputArray _lut)
{
nValues3[0] = nValues3[1] = nValues3[2] = 256;
for (int i = 0; i < 256; ++i)
pLevels[i] = i;
pLevels3[0] = pLevels3[1] = pLevels3[2] = pLevels;
Mat lut;
if (_lut.kind() == _InputArray::GPU_MAT)
{
lut = Mat(_lut.getGpuMat());
}
else
{
Mat hLut = _lut.getMat();
CV_Assert( hLut.total() == 256 && hLut.isContinuous() );
lut = hLut;
}
lut_cn = lut.channels();
CV_Assert( lut.depth() == CV_8U );
CV_Assert( lut.rows == 1 && lut.cols == 256 );
lut.convertTo(nppLut, CV_32S);
if (lut_cn == 1)
{
pValues3[0] = pValues3[1] = pValues3[2] = nppLut.ptr<Npp32s>();
}
else
{
cv::split(nppLut, nppLut3);
#if (CUDA_VERSION <= 4020)
pValues3[0] = nppLut3[0].ptr<Npp32s>();
pValues3[1] = nppLut3[1].ptr<Npp32s>();
pValues3[2] = nppLut3[2].ptr<Npp32s>();
#else
GpuMat d_nppLut0(Mat(1, 256, CV_32S, nppLut3[0].data));
GpuMat d_nppLut1(Mat(1, 256, CV_32S, nppLut3[1].data));
GpuMat d_nppLut2(Mat(1, 256, CV_32S, nppLut3[2].data));
pValues3[0] = d_nppLut0.ptr<Npp32s>();
pValues3[1] = d_nppLut1.ptr<Npp32s>();
pValues3[2] = d_nppLut2.ptr<Npp32s>();
#endif
}
nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, pLevels3, nValues3) );
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
void LookUpTableImpl::transform(InputArray _src, OutputArray _dst, Stream& _stream)
{
GpuMat src = _src.getGpuMat();
const int cn = src.channels();
CV_Assert( src.type() == CV_8UC1 || src.type() == CV_8UC3 );
CV_Assert( lut_cn == 1 || lut_cn == cn );
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStreamHandler h(stream);
NppiSize sz;
sz.height = src.rows;
sz.width = src.cols;
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, nppLut.ptr<Npp32s>(), pLevels, 256) );
}
else
{
nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, pLevels3, nValues3) );
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
#endif // (CUDA_VERSION >= 5000)
Ptr<LookUpTable> cv::gpu::createLookUpTable(InputArray lut)
{
return new LookUpTableImpl(lut);
}
////////////////////////////////////////////////////////////////////////
@ -408,14 +529,17 @@ typedef Npp32s __attribute__((__may_alias__)) Npp32s_a;
typedef Npp32s Npp32s_a;
#endif
void cv::gpu::copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType, const Scalar& value, Stream& s)
void cv::gpu::copyMakeBorder(InputArray _src, OutputArray _dst, int top, int bottom, int left, int right, int borderType, Scalar value, Stream& _stream)
{
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
CV_Assert(borderType == BORDER_REFLECT_101 || borderType == BORDER_REPLICATE || borderType == BORDER_CONSTANT || borderType == BORDER_REFLECT || borderType == BORDER_WRAP);
GpuMat src = _src.getGpuMat();
dst.create(src.rows + top + bottom, src.cols + left + right, src.type());
CV_Assert( src.depth() <= CV_32F && src.channels() <= 4 );
CV_Assert( borderType == BORDER_REFLECT_101 || borderType == BORDER_REPLICATE || borderType == BORDER_CONSTANT || borderType == BORDER_REFLECT || borderType == BORDER_WRAP );
cudaStream_t stream = StreamAccessor::getStream(s);
_dst.create(src.rows + top + bottom, src.cols + left + right, src.type());
GpuMat dst = _dst.getGpuMat();
cudaStream_t stream = StreamAccessor::getStream(_stream);
if (borderType == BORDER_CONSTANT && (src.type() == CV_8UC1 || src.type() == CV_8UC4 || src.type() == CV_32SC1 || src.type() == CV_32FC1))
{

144
modules/gpuarithm/src/cuda/div_inv.cu
View File

@ -1,144 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#if !defined CUDA_DISABLER
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/functional.hpp"
#include "opencv2/core/cuda/transform.hpp"
#include "opencv2/core/cuda/saturate_cast.hpp"
#include "opencv2/core/cuda/simd_functions.hpp"
#include "arithm_func_traits.hpp"
using namespace cv::gpu;
using namespace cv::gpu::cudev;
namespace arithm
{
template <typename T, typename S, typename D> struct DivInv : unary_function<T, D>
{
S val;
__host__ explicit DivInv(S val_) : val(val_) {}
__device__ __forceinline__ D operator ()(T a) const
{
return a != 0 ? saturate_cast<D>(val / a) : 0;
}
};
}
namespace cv { namespace gpu { namespace cudev
{
template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::DivInv<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
}}}
namespace arithm
{
template <typename T, typename S, typename D>
void divInv(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream)
{
DivInv<T, S, D> op(static_cast<S>(val));
cudev::transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
}
template void divInv<uchar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<uchar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<uchar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<uchar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<uchar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<uchar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<uchar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<schar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<schar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<schar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<schar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<schar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<schar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<schar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<ushort, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<ushort, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<ushort, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<ushort, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<ushort, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<ushort, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<ushort, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<short, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<short, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<short, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<short, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<short, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<short, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<short, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<int, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<int, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<int, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<int, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<int, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<int, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<int, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<float, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<float, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<float, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<float, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<float, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<float, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<float, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<double, double, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<double, double, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<double, double, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<double, double, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<double, double, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divInv<double, double, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divInv<double, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
}
#endif // CUDA_DISABLER

128
modules/gpuarithm/src/cuda/div_scalar.cu
View File

@ -66,6 +66,18 @@ namespace arithm
return saturate_cast<D>(a / val);
}
};
template <typename T, typename S, typename D> struct DivScalarInv : unary_function<T, D>
{
S val;
explicit DivScalarInv(S val_) : val(val_) {}
__device__ __forceinline__ D operator ()(T a) const
{
return a != 0 ? saturate_cast<D>(val / a) : 0;
}
};
}
namespace cv { namespace gpu { namespace cudev
@ -73,72 +85,84 @@ namespace cv { namespace gpu { namespace cudev
template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::DivScalar<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
template <typename T, typename S, typename D> struct TransformFunctorTraits< arithm::DivScalarInv<T, S, D> > : arithm::ArithmFuncTraits<sizeof(T), sizeof(D)>
{
};
}}}
namespace arithm
{
template <typename T, typename S, typename D>
void divScalar(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream)
void divScalar(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream)
{
DivScalar<T, S, D> op(static_cast<S>(val));
cudev::transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
if (inv)
{
DivScalarInv<T, S, D> op(static_cast<S>(val));
cudev::transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
}
else
{
DivScalar<T, S, D> op(static_cast<S>(val));
cudev::transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
}
}
template void divScalar<uchar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<uchar, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<schar, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<ushort, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<ushort, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<ushort, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<ushort, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<ushort, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<ushort, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<ushort, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<ushort, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<ushort, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<ushort, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<ushort, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<ushort, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<ushort, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<ushort, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<short, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<short, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<short, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<short, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<short, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<short, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<short, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<short, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<short, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<short, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<short, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<short, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<short, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<short, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<int, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<int, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<int, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<int, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<int, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<int, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<int, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<int, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<int, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<int, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<int, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<int, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<int, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<int, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<float, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<float, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<float, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<float, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<float, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<float, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<float, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<float, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<float, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<float, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<float, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<float, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<float, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<float, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, short>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, int>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, float>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<double, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
//template void divScalar<double, double, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
template void divScalar<double, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, cudaStream_t stream);
}
#endif // CUDA_DISABLER

4
modules/gpuarithm/src/cuda/split_merge.cu
View File

@ -278,7 +278,7 @@ namespace cv { namespace gpu { namespace cudev
}
void merge_caller(const PtrStepSzb* src, PtrStepSzb& dst,
void merge(const PtrStepSzb* src, PtrStepSzb& dst,
int total_channels, size_t elem_size,
const cudaStream_t& stream)
{
@ -487,7 +487,7 @@ namespace cv { namespace gpu { namespace cudev
}
void split_caller(const PtrStepSzb& src, PtrStepSzb* dst, int num_channels, size_t elem_size1, const cudaStream_t& stream)
void split(const PtrStepSzb& src, PtrStepSzb* dst, int num_channels, size_t elem_size1, const cudaStream_t& stream)
{
static SplitFunction split_func_tbl[] =
{

107
modules/gpuarithm/src/cuda/sub_scalar.cu
View File

@ -58,12 +58,13 @@ namespace arithm
template <typename T, typename S, typename D> struct SubScalar : unary_function<T, D>
{
S val;
int scale;
__host__ explicit SubScalar(S val_) : val(val_) {}
__host__ SubScalar(S val_, int scale_) : val(val_), scale(scale_) {}
__device__ __forceinline__ D operator ()(T a) const
{
return saturate_cast<D>(a - val);
return saturate_cast<D>(scale * (a - val));
}
};
}
@ -78,9 +79,9 @@ namespace cv { namespace gpu { namespace cudev
namespace arithm
{
template <typename T, typename S, typename D>
void subScalar(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
void subScalar(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream)
{
SubScalar<T, S, D> op(static_cast<S>(val));
SubScalar<T, S, D> op(static_cast<S>(val), inv ? -1 : 1);
if (mask.data)
cudev::transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, mask, stream);
@ -88,61 +89,61 @@ namespace arithm
cudev::transform((PtrStepSz<T>) src1, (PtrStepSz<D>) dst, op, WithOutMask(), stream);
}
template void subScalar<uchar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<uchar, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<schar, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<ushort, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<ushort, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<ushort, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<ushort, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<ushort, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<ushort, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<ushort, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<ushort, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<ushort, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<ushort, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<ushort, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<ushort, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<ushort, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<ushort, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<short, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<short, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<short, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<short, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<short, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<short, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<short, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<short, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<short, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<short, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<short, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<short, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<short, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<short, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<int, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<int, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<int, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<int, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<int, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<int, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<int, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<int, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<int, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<int, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<int, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<int, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<int, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<int, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<float, float, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<float, float, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<float, float, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<float, float, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<float, float, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<float, float, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<float, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<float, float, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<float, float, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<float, float, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<float, float, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<float, float, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<float, float, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<float, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, uchar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, schar>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, ushort>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, short>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, int>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, float>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<double, double, double>(PtrStepSzb src1, double val, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, uchar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, schar>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, ushort>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, short>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, int>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
//template void subScalar<double, double, float>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
template void subScalar<double, double, double>(PtrStepSzb src1, double val, bool inv, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
}
#endif // CUDA_DISABLER

2420
modules/gpuarithm/src/element_operations.cpp
View File

File diff suppressed because it is too large Load Diff

302
modules/gpuarithm/src/reductions.cpp
View File

@ -47,41 +47,28 @@ using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
double cv::gpu::norm(const GpuMat&, int) { throw_no_cuda(); return 0.0; }
double cv::gpu::norm(const GpuMat&, int, GpuMat&) { throw_no_cuda(); return 0.0; }
double cv::gpu::norm(const GpuMat&, int, const GpuMat&, GpuMat&) { throw_no_cuda(); return 0.0; }
double cv::gpu::norm(const GpuMat&, const GpuMat&, int) { throw_no_cuda(); return 0.0; }
double cv::gpu::norm(InputArray, int, InputArray, GpuMat&) { throw_no_cuda(); return 0.0; }
double cv::gpu::norm(InputArray, InputArray, GpuMat&, int) { throw_no_cuda(); return 0.0; }
Scalar cv::gpu::sum(const GpuMat&) { throw_no_cuda(); return Scalar(); }
Scalar cv::gpu::sum(const GpuMat&, GpuMat&) { throw_no_cuda(); return Scalar(); }
Scalar cv::gpu::sum(const GpuMat&, const GpuMat&, GpuMat&) { throw_no_cuda(); return Scalar(); }
Scalar cv::gpu::sum(InputArray, InputArray, GpuMat&) { throw_no_cuda(); return Scalar(); }
Scalar cv::gpu::absSum(InputArray, InputArray, GpuMat&) { throw_no_cuda(); return Scalar(); }
Scalar cv::gpu::sqrSum(InputArray, InputArray, GpuMat&) { throw_no_cuda(); return Scalar(); }
Scalar cv::gpu::absSum(const GpuMat&) { throw_no_cuda(); return Scalar(); }
Scalar cv::gpu::absSum(const GpuMat&, GpuMat&) { throw_no_cuda(); return Scalar(); }
Scalar cv::gpu::absSum(const GpuMat&, const GpuMat&, GpuMat&) { throw_no_cuda(); return Scalar(); }
void cv::gpu::minMax(InputArray, double*, double*, InputArray, GpuMat&) { throw_no_cuda(); }
void cv::gpu::minMaxLoc(InputArray, double*, double*, Point*, Point*, InputArray, GpuMat&, GpuMat&) { throw_no_cuda(); }
Scalar cv::gpu::sqrSum(const GpuMat&) { throw_no_cuda(); return Scalar(); }
Scalar cv::gpu::sqrSum(const GpuMat&, GpuMat&) { throw_no_cuda(); return Scalar(); }
Scalar cv::gpu::sqrSum(const GpuMat&, const GpuMat&, GpuMat&) { throw_no_cuda(); return Scalar(); }
int cv::gpu::countNonZero(InputArray, GpuMat&) { throw_no_cuda(); return 0; }
void cv::gpu::minMax(const GpuMat&, double*, double*, const GpuMat&) { throw_no_cuda(); }
void cv::gpu::minMax(const GpuMat&, double*, double*, const GpuMat&, GpuMat&) { throw_no_cuda(); }
void cv::gpu::reduce(InputArray, OutputArray, int, int, int, Stream&) { throw_no_cuda(); }
void cv::gpu::minMaxLoc(const GpuMat&, double*, double*, Point*, Point*, const GpuMat&) { throw_no_cuda(); }
void cv::gpu::minMaxLoc(const GpuMat&, double*, double*, Point*, Point*, const GpuMat&, GpuMat&, GpuMat&) { throw_no_cuda(); }
void cv::gpu::meanStdDev(InputArray, Scalar&, Scalar&, GpuMat&) { throw_no_cuda(); }
int cv::gpu::countNonZero(const GpuMat&) { throw_no_cuda(); return 0; }
int cv::gpu::countNonZero(const GpuMat&, GpuMat&) { throw_no_cuda(); return 0; }
void cv::gpu::rectStdDev(InputArray, InputArray, OutputArray, Rect, Stream&) { throw_no_cuda(); }
void cv::gpu::reduce(const GpuMat&, GpuMat&, int, int, int, Stream&) { throw_no_cuda(); }
void cv::gpu::normalize(InputArray, OutputArray, double, double, int, int, InputArray, GpuMat&, GpuMat&) { throw_no_cuda(); }
void cv::gpu::meanStdDev(const GpuMat&, Scalar&, Scalar&) { throw_no_cuda(); }
void cv::gpu::meanStdDev(const GpuMat&, Scalar&, Scalar&, GpuMat&) { throw_no_cuda(); }
void cv::gpu::rectStdDev(const GpuMat&, const GpuMat&, GpuMat&, const Rect&, Stream&) { throw_no_cuda(); }
void cv::gpu::normalize(const GpuMat&, GpuMat&, double, double, int, int, const GpuMat&) { throw_no_cuda(); }
void cv::gpu::normalize(const GpuMat&, GpuMat&, double, double, int, int, const GpuMat&, GpuMat&, GpuMat&) { throw_no_cuda(); }
void cv::gpu::integral(InputArray, OutputArray, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::sqrIntegral(InputArray, OutputArray, GpuMat&, Stream&) { throw_no_cuda(); }
#else
@ -124,21 +111,13 @@ namespace
////////////////////////////////////////////////////////////////////////
// norm
double cv::gpu::norm(const GpuMat& src, int normType)
double cv::gpu::norm(InputArray _src, int normType, InputArray _mask, GpuMat& buf)
{
GpuMat buf;
return gpu::norm(src, normType, GpuMat(), buf);
}
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
double cv::gpu::norm(const GpuMat& src, int normType, GpuMat& buf)
{
return gpu::norm(src, normType, GpuMat(), buf);
}
double cv::gpu::norm(const GpuMat& src, int normType, const GpuMat& mask, GpuMat& buf)
{
CV_Assert(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2);
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size() && src.channels() == 1));
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size() && src.channels() == 1) );
GpuMat src_single_channel = src.reshape(1);
@ -154,13 +133,11 @@ double cv::gpu::norm(const GpuMat& src, int normType, const GpuMat& mask, GpuMat
return std::max(std::abs(min_val), std::abs(max_val));
}
double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
double cv::gpu::norm(InputArray _src1, InputArray _src2, GpuMat& buf, int normType)
{
CV_Assert(src1.type() == CV_8UC1);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2);
#if CUDA_VERSION < 5050
(void) buf;
typedef NppStatus (*func_t)(const Npp8u* pSrc1, int nSrcStep1, const Npp8u* pSrc2, int nSrcStep2, NppiSize oSizeROI, Npp64f* pRetVal);
static const func_t funcs[] = {nppiNormDiff_Inf_8u_C1R, nppiNormDiff_L1_8u_C1R, nppiNormDiff_L2_8u_C1R};
@ -175,13 +152,18 @@ double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
static const buf_size_func_t buf_size_funcs[] = {nppiNormDiffInfGetBufferHostSize_8u_C1R, nppiNormDiffL1GetBufferHostSize_8u_C1R, nppiNormDiffL2GetBufferHostSize_8u_C1R};
#endif
GpuMat src1 = _src1.getGpuMat();
GpuMat src2 = _src2.getGpuMat();
CV_Assert( src1.type() == CV_8UC1 );
CV_Assert( src1.size() == src2.size() && src1.type() == src2.type() );
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
int funcIdx = normType >> 1;
double retVal;
const int funcIdx = normType >> 1;
DeviceBuffer dbuf;
@ -191,13 +173,14 @@ double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
int bufSize;
buf_size_funcs[funcIdx](sz, &bufSize);
GpuMat buf(1, bufSize, CV_8UC1);
ensureSizeIsEnough(1, bufSize, CV_8UC1, buf);
nppSafeCall( funcs[funcIdx](src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step), sz, dbuf, buf.data) );
#endif
cudaSafeCall( cudaDeviceSynchronize() );
double retVal;
dbuf.download(&retVal);
return retVal;
@ -220,19 +203,11 @@ namespace sum
void runSqr(PtrStepSzb src, void* buf, double* sum, PtrStepSzb mask);
}
Scalar cv::gpu::sum(const GpuMat& src)
Scalar cv::gpu::sum(InputArray _src, InputArray _mask, GpuMat& buf)
{
GpuMat buf;
return gpu::sum(src, GpuMat(), buf);
}
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf)
{
return gpu::sum(src, GpuMat(), buf);
}
Scalar cv::gpu::sum(const GpuMat& src, const GpuMat& mask, GpuMat& buf)
{
typedef void (*func_t)(PtrStepSzb src, void* buf, double* sum, PtrStepSzb mask);
static const func_t funcs[7][5] =
{
@ -266,19 +241,11 @@ Scalar cv::gpu::sum(const GpuMat& src, const GpuMat& mask, GpuMat& buf)
return Scalar(result[0], result[1], result[2], result[3]);
}
Scalar cv::gpu::absSum(const GpuMat& src)
Scalar cv::gpu::absSum(InputArray _src, InputArray _mask, GpuMat& buf)
{
GpuMat buf;
return gpu::absSum(src, GpuMat(), buf);
}
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
Scalar cv::gpu::absSum(const GpuMat& src, GpuMat& buf)
{
return gpu::absSum(src, GpuMat(), buf);
}
Scalar cv::gpu::absSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf)
{
typedef void (*func_t)(PtrStepSzb src, void* buf, double* sum, PtrStepSzb mask);
static const func_t funcs[7][5] =
{
@ -312,19 +279,11 @@ Scalar cv::gpu::absSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf)
return Scalar(result[0], result[1], result[2], result[3]);
}
Scalar cv::gpu::sqrSum(const GpuMat& src)
Scalar cv::gpu::sqrSum(InputArray _src, InputArray _mask, GpuMat& buf)
{
GpuMat buf;
return gpu::sqrSum(src, GpuMat(), buf);
}
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
Scalar cv::gpu::sqrSum(const GpuMat& src, GpuMat& buf)
{
return gpu::sqrSum(src, GpuMat(), buf);
}
Scalar cv::gpu::sqrSum(const GpuMat& src, const GpuMat& mask, GpuMat& buf)
{
typedef void (*func_t)(PtrStepSzb src, void* buf, double* sum, PtrStepSzb mask);
static const func_t funcs[7][5] =
{
@ -369,14 +328,11 @@ namespace minMax
void run(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
}
void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask)
void cv::gpu::minMax(InputArray _src, double* minVal, double* maxVal, InputArray _mask, GpuMat& buf)
{
GpuMat buf;
gpu::minMax(src, minVal, maxVal, mask, buf);
}
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf)
{
typedef void (*func_t)(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
static const func_t funcs[] =
{
@ -419,15 +375,12 @@ namespace minMaxLoc
void run(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
}
void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, const GpuMat& mask)
void cv::gpu::minMaxLoc(InputArray _src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
InputArray _mask, GpuMat& valBuf, GpuMat& locBuf)
{
GpuMat valBuf, locBuf;
gpu::minMaxLoc(src, minVal, maxVal, minLoc, maxLoc, mask, valBuf, locBuf);
}
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
const GpuMat& mask, GpuMat& valBuf, GpuMat& locBuf)
{
typedef void (*func_t)(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, int* minloc, int* maxloc, PtrStepb valbuf, PtrStep<unsigned int> locbuf);
static const func_t funcs[] =
{
@ -472,14 +425,10 @@ namespace countNonZero
int run(const PtrStepSzb src, PtrStep<unsigned int> buf);
}
int cv::gpu::countNonZero(const GpuMat& src)
int cv::gpu::countNonZero(InputArray _src, GpuMat& buf)
{
GpuMat buf;
return countNonZero(src, buf);
}
GpuMat src = _src.getGpuMat();
int cv::gpu::countNonZero(const GpuMat& src, GpuMat& buf)
{
typedef int (*func_t)(const PtrStepSzb src, PtrStep<unsigned int> buf);
static const func_t funcs[] =
{
@ -521,8 +470,10 @@ namespace reduce
void cols(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
}
void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int dtype, Stream& stream)
void cv::gpu::reduce(InputArray _src, OutputArray _dst, int dim, int reduceOp, int dtype, Stream& stream)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.channels() <= 4 );
CV_Assert( dim == 0 || dim == 1 );
CV_Assert( reduceOp == REDUCE_SUM || reduceOp == REDUCE_AVG || reduceOp == REDUCE_MAX || reduceOp == REDUCE_MIN );
@ -530,7 +481,8 @@ void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int
if (dtype < 0)
dtype = src.depth();
dst.create(1, dim == 0 ? src.cols : src.rows, CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()));
_dst.create(1, dim == 0 ? src.cols : src.rows, CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()));
GpuMat dst = _dst.getGpuMat();
if (dim == 0)
{
@ -691,15 +643,11 @@ void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int
////////////////////////////////////////////////////////////////////////
// meanStdDev
void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev)
void cv::gpu::meanStdDev(InputArray _src, Scalar& mean, Scalar& stddev, GpuMat& buf)
{
GpuMat buf;
meanStdDev(src, mean, stddev, buf);
}
GpuMat src = _src.getGpuMat();
void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev, GpuMat& buf)
{
CV_Assert(src.type() == CV_8UC1);
CV_Assert( src.type() == CV_8UC1 );
if (!deviceSupports(FEATURE_SET_COMPUTE_13))
CV_Error(cv::Error::StsNotImplemented, "Not sufficient compute capebility");
@ -730,11 +678,15 @@ void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev, GpuMat
//////////////////////////////////////////////////////////////////////////////
// rectStdDev
void cv::gpu::rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& s)
void cv::gpu::rectStdDev(InputArray _src, InputArray _sqr, OutputArray _dst, Rect rect, Stream& _stream)
{
CV_Assert(src.type() == CV_32SC1 && sqr.type() == CV_64FC1);
GpuMat src = _src.getGpuMat();
GpuMat sqr = _sqr.getGpuMat();
dst.create(src.size(), CV_32FC1);
CV_Assert( src.type() == CV_32SC1 && sqr.type() == CV_64FC1 );
_dst.create(src.size(), CV_32FC1);
GpuMat dst = _dst.getGpuMat();
NppiSize sz;
sz.width = src.cols;
@ -746,7 +698,7 @@ void cv::gpu::rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, cons
nppRect.x = rect.x;
nppRect.y = rect.y;
cudaStream_t stream = StreamAccessor::getStream(s);
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStreamHandler h(stream);
@ -760,16 +712,12 @@ void cv::gpu::rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, cons
////////////////////////////////////////////////////////////////////////
// normalize
void cv::gpu::normalize(const GpuMat& src, GpuMat& dst, double a, double b, int norm_type, int dtype, const GpuMat& mask)
void cv::gpu::normalize(InputArray _src, OutputArray dst, double a, double b, int norm_type, int dtype, InputArray mask, GpuMat& norm_buf, GpuMat& cvt_buf)
{
GpuMat norm_buf;
GpuMat cvt_buf;
normalize(src, dst, a, b, norm_type, dtype, mask, norm_buf, cvt_buf);
}
GpuMat src = _src.getGpuMat();
void cv::gpu::normalize(const GpuMat& src, GpuMat& dst, double a, double b, int norm_type, int dtype, const GpuMat& mask, GpuMat& norm_buf, GpuMat& cvt_buf)
{
double scale = 1, shift = 0;
if (norm_type == NORM_MINMAX)
{
double smin = 0, smax = 0;
@ -800,4 +748,116 @@ void cv::gpu::normalize(const GpuMat& src, GpuMat& dst, double a, double b, int
}
}
////////////////////////////////////////////////////////////////////////
// integral
namespace cv { namespace gpu { namespace cudev
{
namespace imgproc
{
void shfl_integral_gpu(const PtrStepSzb& img, PtrStepSz<unsigned int> integral, cudaStream_t stream);
}
}}}
void cv::gpu::integral(InputArray _src, OutputArray _dst, GpuMat& buffer, Stream& _stream)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == CV_8UC1 );
cudaStream_t stream = StreamAccessor::getStream(_stream);
cv::Size whole;
cv::Point offset;
src.locateROI(whole, offset);
if (deviceSupports(WARP_SHUFFLE_FUNCTIONS) && src.cols <= 2048
&& offset.x % 16 == 0 && ((src.cols + 63) / 64) * 64 <= (static_cast<int>(src.step) - offset.x))
{
ensureSizeIsEnough(((src.rows + 7) / 8) * 8, ((src.cols + 63) / 64) * 64, CV_32SC1, buffer);
cv::gpu::cudev::imgproc::shfl_integral_gpu(src, buffer, stream);
_dst.create(src.rows + 1, src.cols + 1, CV_32SC1);
GpuMat dst = _dst.getGpuMat();
dst.setTo(Scalar::all(0), _stream);
GpuMat inner = dst(Rect(1, 1, src.cols, src.rows));
GpuMat res = buffer(Rect(0, 0, src.cols, src.rows));
res.copyTo(inner, _stream);
}
else
{
#ifndef HAVE_OPENCV_GPULEGACY
throw_no_cuda();
#else
_dst.create(src.rows + 1, src.cols + 1, CV_32SC1);
GpuMat dst = _dst.getGpuMat();
NcvSize32u roiSize;
roiSize.width = src.cols;
roiSize.height = src.rows;
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::gpu::getDevice()) );
Ncv32u bufSize;
ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
ensureSizeIsEnough(1, bufSize, CV_8UC1, buffer);
NppStStreamHandler h(stream);
ncvSafeCall( nppiStIntegral_8u32u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>()), static_cast<int>(src.step),
dst.ptr<Ncv32u>(), static_cast<int>(dst.step), roiSize, buffer.ptr<Ncv8u>(), bufSize, prop) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
#endif
}
}
//////////////////////////////////////////////////////////////////////////////
// sqrIntegral
void cv::gpu::sqrIntegral(InputArray _src, OutputArray _dst, GpuMat& buf, Stream& _stream)
{
#ifndef HAVE_OPENCV_GPULEGACY
(void) _src;
(void) _dst;
(void) _stream;
throw_no_cuda();
#else
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == CV_8U );
NcvSize32u roiSize;
roiSize.width = src.cols;
roiSize.height = src.rows;
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::gpu::getDevice()) );
Ncv32u bufSize;
ncvSafeCall(nppiStSqrIntegralGetSize_8u64u(roiSize, &bufSize, prop));
ensureSizeIsEnough(1, bufSize, CV_8U, buf);
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStStreamHandler h(stream);
_dst.create(src.rows + 1, src.cols + 1, CV_64F);
GpuMat dst = _dst.getGpuMat();
ncvSafeCall(nppiStSqrIntegral_8u64u_C1R(const_cast<Ncv8u*>(src.ptr<Ncv8u>(0)), static_cast<int>(src.step),
dst.ptr<Ncv64u>(0), static_cast<int>(dst.step), roiSize, buf.ptr<Ncv8u>(0), bufSize, prop));
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
#endif
}
#endif

4
modules/gpuarithm/test/test_arithm.cpp
View File

@ -419,8 +419,10 @@ GPU_TEST_P(Convolve, Accuracy)
cv::Mat src = randomMat(size, CV_32FC1, 0.0, 100.0);
cv::Mat kernel = randomMat(cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0);
cv::Ptr<cv::gpu::Convolution> conv = cv::gpu::createConvolution();
cv::gpu::GpuMat dst;
cv::gpu::convolve(loadMat(src), loadMat(kernel), dst, ccorr);
conv->convolve(loadMat(src), loadMat(kernel), dst, ccorr);
cv::Mat dst_gold;
convolveDFT(src, kernel, dst_gold, ccorr);

8
modules/gpuarithm/test/test_core.cpp
View File

@ -323,8 +323,10 @@ GPU_TEST_P(LUT, OneChannel)
cv::Mat src = randomMat(size, type);
cv::Mat lut = randomMat(cv::Size(256, 1), CV_8UC1);
cv::Ptr<cv::gpu::LookUpTable> lutAlg = cv::gpu::createLookUpTable(lut);
cv::gpu::GpuMat dst = createMat(size, CV_MAKE_TYPE(lut.depth(), src.channels()));
cv::gpu::LUT(loadMat(src, useRoi), lut, dst);
lutAlg->transform(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::LUT(src, lut, dst_gold);
@ -337,8 +339,10 @@ GPU_TEST_P(LUT, MultiChannel)
cv::Mat src = randomMat(size, type);
cv::Mat lut = randomMat(cv::Size(256, 1), CV_MAKE_TYPE(CV_8U, src.channels()));
cv::Ptr<cv::gpu::LookUpTable> lutAlg = cv::gpu::createLookUpTable(lut);
cv::gpu::GpuMat dst = createMat(size, CV_MAKE_TYPE(lut.depth(), src.channels()), useRoi);
cv::gpu::LUT(loadMat(src, useRoi), lut, dst);
lutAlg->transform(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::LUT(src, lut, dst_gold);

514
modules/gpuarithm/test/test_element_operations.cpp
View File

@ -261,6 +261,94 @@ INSTANTIATE_TEST_CASE_P(GPU_Arithm, Add_Scalar, testing::Combine(
DEPTH_PAIRS,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Add_Scalar_First
PARAM_TEST_CASE(Add_Scalar_First, cv::gpu::DeviceInfo, cv::Size, std::pair<MatDepth, MatDepth>, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
std::pair<MatDepth, MatDepth> depth;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Add_Scalar_First, WithOutMask)
{
cv::Mat mat = randomMat(size, depth.first);
cv::Scalar val = randomScalar(0, 255);
if ((depth.first == CV_64F || depth.second == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::add(val, loadMat(mat), dst, cv::gpu::GpuMat(), depth.second);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth.second, useRoi);
dst.setTo(cv::Scalar::all(0));
cv::gpu::add(val, loadMat(mat, useRoi), dst, cv::gpu::GpuMat(), depth.second);
cv::Mat dst_gold(size, depth.second, cv::Scalar::all(0));
cv::add(val, mat, dst_gold, cv::noArray(), depth.second);
EXPECT_MAT_NEAR(dst_gold, dst, depth.first >= CV_32F || depth.second >= CV_32F ? 1e-4 : 0.0);
}
}
GPU_TEST_P(Add_Scalar_First, WithMask)
{
cv::Mat mat = randomMat(size, depth.first);
cv::Scalar val = randomScalar(0, 255);
cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
if ((depth.first == CV_64F || depth.second == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::add(val, loadMat(mat), dst, cv::gpu::GpuMat(), depth.second);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth.second, useRoi);
dst.setTo(cv::Scalar::all(0));
cv::gpu::add(val, loadMat(mat, useRoi), dst, loadMat(mask, useRoi), depth.second);
cv::Mat dst_gold(size, depth.second, cv::Scalar::all(0));
cv::add(val, mat, dst_gold, mask, depth.second);
EXPECT_MAT_NEAR(dst_gold, dst, depth.first >= CV_32F || depth.second >= CV_32F ? 1e-4 : 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Arithm, Add_Scalar_First, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
DEPTH_PAIRS,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Subtract_Array
@ -476,6 +564,94 @@ INSTANTIATE_TEST_CASE_P(GPU_Arithm, Subtract_Scalar, testing::Combine(
DEPTH_PAIRS,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Subtract_Scalar_First
PARAM_TEST_CASE(Subtract_Scalar_First, cv::gpu::DeviceInfo, cv::Size, std::pair<MatDepth, MatDepth>, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
std::pair<MatDepth, MatDepth> depth;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Subtract_Scalar_First, WithOutMask)
{
cv::Mat mat = randomMat(size, depth.first);
cv::Scalar val = randomScalar(0, 255);
if ((depth.first == CV_64F || depth.second == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::subtract(val, loadMat(mat), dst, cv::gpu::GpuMat(), depth.second);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth.second, useRoi);
dst.setTo(cv::Scalar::all(0));
cv::gpu::subtract(val, loadMat(mat, useRoi), dst, cv::gpu::GpuMat(), depth.second);
cv::Mat dst_gold(size, depth.second, cv::Scalar::all(0));
cv::subtract(val, mat, dst_gold, cv::noArray(), depth.second);
EXPECT_MAT_NEAR(dst_gold, dst, depth.first >= CV_32F || depth.second >= CV_32F ? 1e-4 : 0.0);
}
}
GPU_TEST_P(Subtract_Scalar_First, WithMask)
{
cv::Mat mat = randomMat(size, depth.first);
cv::Scalar val = randomScalar(0, 255);
cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
if ((depth.first == CV_64F || depth.second == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::subtract(val, loadMat(mat), dst, cv::gpu::GpuMat(), depth.second);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth.second, useRoi);
dst.setTo(cv::Scalar::all(0));
cv::gpu::subtract(val, loadMat(mat, useRoi), dst, loadMat(mask, useRoi), depth.second);
cv::Mat dst_gold(size, depth.second, cv::Scalar::all(0));
cv::subtract(val, mat, dst_gold, mask, depth.second);
EXPECT_MAT_NEAR(dst_gold, dst, depth.first >= CV_32F || depth.second >= CV_32F ? 1e-4 : 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Arithm, Subtract_Scalar_First, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
DEPTH_PAIRS,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Multiply_Array
@ -756,6 +932,93 @@ INSTANTIATE_TEST_CASE_P(GPU_Arithm, Multiply_Scalar, testing::Combine(
DEPTH_PAIRS,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Multiply_Scalar_First
PARAM_TEST_CASE(Multiply_Scalar_First, cv::gpu::DeviceInfo, cv::Size, std::pair<MatDepth, MatDepth>, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
std::pair<MatDepth, MatDepth> depth;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Multiply_Scalar_First, WithOutScale)
{
cv::Mat mat = randomMat(size, depth.first);
cv::Scalar val = randomScalar(0, 255);
if ((depth.first == CV_64F || depth.second == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::multiply(val, loadMat(mat), dst, 1, depth.second);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth.second, useRoi);
cv::gpu::multiply(val, loadMat(mat, useRoi), dst, 1, depth.second);
cv::Mat dst_gold;
cv::multiply(val, mat, dst_gold, 1, depth.second);
EXPECT_MAT_NEAR(dst_gold, dst, 1.0);
}
}
GPU_TEST_P(Multiply_Scalar_First, WithScale)
{
cv::Mat mat = randomMat(size, depth.first);
cv::Scalar val = randomScalar(0, 255);
double scale = randomDouble(0.0, 255.0);
if ((depth.first == CV_64F || depth.second == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::multiply(val, loadMat(mat), dst, scale, depth.second);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth.second, useRoi);
cv::gpu::multiply(val, loadMat(mat, useRoi), dst, scale, depth.second);
cv::Mat dst_gold;
cv::multiply(val, mat, dst_gold, scale, depth.second);
EXPECT_MAT_NEAR(dst_gold, dst, 1.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Arithm, Multiply_Scalar_First, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
DEPTH_PAIRS,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Divide_Array
@ -1036,9 +1299,9 @@ INSTANTIATE_TEST_CASE_P(GPU_Arithm, Divide_Scalar, testing::Combine(
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Divide_Scalar_Inv
// Divide_Scalar_First
PARAM_TEST_CASE(Divide_Scalar_Inv, cv::gpu::DeviceInfo, cv::Size, std::pair<MatDepth, MatDepth>, UseRoi)
PARAM_TEST_CASE(Divide_Scalar_First, cv::gpu::DeviceInfo, cv::Size, std::pair<MatDepth, MatDepth>, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
@ -1056,7 +1319,7 @@ PARAM_TEST_CASE(Divide_Scalar_Inv, cv::gpu::DeviceInfo, cv::Size, std::pair<MatD
}
};
GPU_TEST_P(Divide_Scalar_Inv, Accuracy)
GPU_TEST_P(Divide_Scalar_First, Accuracy)
{
double scale = randomDouble(0.0, 255.0);
cv::Mat mat = randomMat(size, depth.first, 1.0, 255.0);
@ -1085,7 +1348,7 @@ GPU_TEST_P(Divide_Scalar_Inv, Accuracy)
}
}
INSTANTIATE_TEST_CASE_P(GPU_Arithm, Divide_Scalar_Inv, testing::Combine(
INSTANTIATE_TEST_CASE_P(GPU_Arithm, Divide_Scalar_First, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
DEPTH_PAIRS,
@ -1170,6 +1433,35 @@ GPU_TEST_P(AbsDiff, Scalar)
}
}
GPU_TEST_P(AbsDiff, Scalar_First)
{
cv::Mat src = randomMat(size, depth);
cv::Scalar val = randomScalar(0.0, 255.0);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::absdiff(val, loadMat(src), dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::absdiff(val, loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::absdiff(val, src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, depth <= CV_32F ? 1.0 : 1e-5);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Arithm, AbsDiff, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
@ -1478,6 +1770,65 @@ INSTANTIATE_TEST_CASE_P(GPU_Arithm, Exp, testing::Combine(
MatDepth(CV_32F)),
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Pow
PARAM_TEST_CASE(Pow, cv::gpu::DeviceInfo, cv::Size, MatDepth, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int depth;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Pow, Accuracy)
{
cv::Mat src = randomMat(size, depth, 0.0, 10.0);
double power = randomDouble(2.0, 4.0);
if (src.depth() < CV_32F)
power = static_cast<int>(power);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::pow(loadMat(src), power, dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::pow(loadMat(src, useRoi), power, dst);
cv::Mat dst_gold;
cv::pow(src, power, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, depth < CV_32F ? 0.0 : 1e-1);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Arithm, Pow, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_DEPTH,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Compare_Array
@ -2110,65 +2461,6 @@ INSTANTIATE_TEST_CASE_P(GPU_Arithm, Max, testing::Combine(
ALL_DEPTH,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Pow
PARAM_TEST_CASE(Pow, cv::gpu::DeviceInfo, cv::Size, MatDepth, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int depth;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Pow, Accuracy)
{
cv::Mat src = randomMat(size, depth, 0.0, 10.0);
double power = randomDouble(2.0, 4.0);
if (src.depth() < CV_32F)
power = static_cast<int>(power);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::pow(loadMat(src), power, dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::pow(loadMat(src, useRoi), power, dst);
cv::Mat dst_gold;
cv::pow(src, power, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, depth < CV_32F ? 0.0 : 1e-1);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Arithm, Pow, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_DEPTH,
WHOLE_SUBMAT));
//////////////////////////////////////////////////////////////////////////////
// AddWeighted
@ -2234,6 +2526,54 @@ INSTANTIATE_TEST_CASE_P(GPU_Arithm, AddWeighted, testing::Combine(
ALL_DEPTH,
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Threshold
CV_ENUM(ThreshOp, cv::THRESH_BINARY, cv::THRESH_BINARY_INV, cv::THRESH_TRUNC, cv::THRESH_TOZERO, cv::THRESH_TOZERO_INV)
#define ALL_THRESH_OPS testing::Values(ThreshOp(cv::THRESH_BINARY), ThreshOp(cv::THRESH_BINARY_INV), ThreshOp(cv::THRESH_TRUNC), ThreshOp(cv::THRESH_TOZERO), ThreshOp(cv::THRESH_TOZERO_INV))
PARAM_TEST_CASE(Threshold, cv::gpu::DeviceInfo, cv::Size, MatType, ThreshOp, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
int threshOp;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
threshOp = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Threshold, Accuracy)
{
cv::Mat src = randomMat(size, type);
double maxVal = randomDouble(20.0, 127.0);
double thresh = randomDouble(0.0, maxVal);
cv::gpu::GpuMat dst = createMat(src.size(), src.type(), useRoi);
cv::gpu::threshold(loadMat(src, useRoi), dst, thresh, maxVal, threshOp);
cv::Mat dst_gold;
cv::threshold(src, dst_gold, thresh, maxVal, threshOp);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Arithm, Threshold, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_16SC1), MatType(CV_32FC1)),
ALL_THRESH_OPS,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Magnitude
@ -2452,52 +2792,4 @@ INSTANTIATE_TEST_CASE_P(GPU_Arithm, PolarToCart, testing::Combine(
testing::Values(AngleInDegrees(false), AngleInDegrees(true)),
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Threshold
CV_ENUM(ThreshOp, cv::THRESH_BINARY, cv::THRESH_BINARY_INV, cv::THRESH_TRUNC, cv::THRESH_TOZERO, cv::THRESH_TOZERO_INV)
#define ALL_THRESH_OPS testing::Values(ThreshOp(cv::THRESH_BINARY), ThreshOp(cv::THRESH_BINARY_INV), ThreshOp(cv::THRESH_TRUNC), ThreshOp(cv::THRESH_TOZERO), ThreshOp(cv::THRESH_TOZERO_INV))
PARAM_TEST_CASE(Threshold, cv::gpu::DeviceInfo, cv::Size, MatType, ThreshOp, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
int threshOp;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
threshOp = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Threshold, Accuracy)
{
cv::Mat src = randomMat(size, type);
double maxVal = randomDouble(20.0, 127.0);
double thresh = randomDouble(0.0, maxVal);
cv::gpu::GpuMat dst = createMat(src.size(), src.type(), useRoi);
cv::gpu::threshold(loadMat(src, useRoi), dst, thresh, maxVal, threshOp);
cv::Mat dst_gold;
cv::threshold(src, dst_gold, thresh, maxVal, threshOp);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Arithm, Threshold, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_16SC1), MatType(CV_32FC1)),
ALL_THRESH_OPS,
WHOLE_SUBMAT));
#endif // HAVE_CUDA

4
modules/gpufilters/doc/filtering.rst
View File

@ -381,7 +381,7 @@ Creates a non-separable linear filter.
:param dstType: Output image type. The same type as ``src`` is supported.
:param kernel: 2D array of filter coefficients. Floating-point coefficients will be converted to fixed-point representation before the actual processing. Supports size up to 16. For larger kernels use :ocv:func:`gpu::convolve`.
:param kernel: 2D array of filter coefficients. Floating-point coefficients will be converted to fixed-point representation before the actual processing. Supports size up to 16. For larger kernels use :ocv:class:`gpu::Convolution`.
:param anchor: Anchor point. The default value Point(-1, -1) means that the anchor is at the kernel center.
@ -411,7 +411,7 @@ Applies the non-separable 2D linear filter to an image.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`filter2D`, :ocv:func:`gpu::convolve`
.. seealso:: :ocv:func:`filter2D`, :ocv:class:`gpu::Convolution`

2
modules/gpuimgproc/src/hough.cpp
View File

@ -761,7 +761,7 @@ namespace
{
buildRTable_gpu(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
r_table, r_sizes.ptr<int>(), make_short2(templCenter.x, templCenter.y), levels);
min(r_sizes, maxSize, r_sizes);
gpu::min(r_sizes, maxSize, r_sizes);
}
}

11
modules/gpuimgproc/src/match_template.cpp
View File

@ -172,15 +172,16 @@ namespace
return;
}
gpu::ConvolveBuf convolve_buf;
convolve_buf.user_block_size = buf.user_block_size;
Ptr<gpu::Convolution> conv = gpu::createConvolution(buf.user_block_size);
if (image.channels() == 1)
gpu::convolve(image.reshape(1), templ.reshape(1), result, true, convolve_buf, stream);
{
conv->convolve(image.reshape(1), templ.reshape(1), result, true, stream);
}
else
{
GpuMat result_;
gpu::convolve(image.reshape(1), templ.reshape(1), result_, true, convolve_buf, stream);
conv->convolve(image.reshape(1), templ.reshape(1), result_, true, stream);
extractFirstChannel_32F(result_, result, image.channels(), StreamAccessor::getStream(stream));
}
}
@ -268,7 +269,7 @@ namespace
buf.image_sums.resize(1);
gpu::integral(image, buf.image_sums[0], stream);
unsigned int templ_sum = (unsigned int)sum(templ)[0];
unsigned int templ_sum = (unsigned int)gpu::sum(templ)[0];
matchTemplatePrepared_CCOFF_8U(templ.cols, templ.rows, buf.image_sums[0], templ_sum, result, StreamAccessor::getStream(stream));
}
else

6
modules/nonfree/src/surf_gpu.cpp
View File

@ -142,13 +142,13 @@ namespace
bindImgTex(img);
gpu::integralBuffered(img, surf_.sum, surf_.intBuffer);
gpu::integral(img, surf_.sum, surf_.intBuffer);
sumOffset = bindSumTex(surf_.sum);
if (use_mask)
{
min(mask, 1.0, surf_.mask1);
gpu::integralBuffered(surf_.mask1, surf_.maskSum, surf_.intBuffer);
gpu::min(mask, 1.0, surf_.mask1);
gpu::integral(surf_.mask1, surf_.maskSum, surf_.intBuffer);
maskOffset = bindMaskSumTex(surf_.maskSum);
}
}

6
samples/gpu/driver_api_multi.cpp
View File

@ -130,15 +130,15 @@ void Worker::operator()(int device_id) const
rng.fill(src, RNG::UNIFORM, 0, 1);
// CPU works
transpose(src, dst);
cv::transpose(src, dst);
// GPU works
GpuMat d_src(src);
GpuMat d_dst;
transpose(d_src, d_dst);
gpu::transpose(d_src, d_dst);
// Check results
bool passed = norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
bool passed = cv::norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
std::cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): "
<< (passed ? "passed" : "FAILED") << endl;

4
samples/gpu/farneback_optical_flow.cpp
View File

@ -22,9 +22,9 @@ inline T mapVal(T x, T a, T b, T c, T d)
static void colorizeFlow(const Mat &u, const Mat &v, Mat &dst)
{
double uMin, uMax;
minMaxLoc(u, &uMin, &uMax, 0, 0);
cv::minMaxLoc(u, &uMin, &uMax, 0, 0);
double vMin, vMax;
minMaxLoc(v, &vMin, &vMax, 0, 0);
cv::minMaxLoc(v, &vMin, &vMax, 0, 0);
uMin = ::abs(uMin); uMax = ::abs(uMax);
vMin = ::abs(vMin); vMax = ::abs(vMax);
float dMax = static_cast<float>(::max(::max(uMin, uMax), ::max(vMin, vMax)));

6
samples/gpu/multi.cpp
View File

@ -87,15 +87,15 @@ void Worker::operator()(int device_id) const
rng.fill(src, RNG::UNIFORM, 0, 1);
// CPU works
transpose(src, dst);
cv::transpose(src, dst);
// GPU works
GpuMat d_src(src);
GpuMat d_dst;
transpose(d_src, d_dst);
gpu::transpose(d_src, d_dst);
// Check results
bool passed = norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
bool passed = cv::norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
std::cout << "GPU #" << device_id << " (" << DeviceInfo().name() << "): "
<< (passed ? "passed" : "FAILED") << endl;