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Merge pull request #1010 from jet47:gpufilters-refactoring

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Roman Donchenko 2013-06-21 14:44:00 +04:00 committed by OpenCV Buildbot
commit 84639e444b
21 changed files with 1666 additions and 2355 deletions
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2
modules/gpuarithm/doc/arithm.rst
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@ -157,8 +157,6 @@ Computes a convolution (or cross-correlation) of two images.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`gpu::filter2D`
gpu::createConvolution

2
modules/gpubgsegm/include/opencv2/gpubgsegm.hpp
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@ -321,7 +321,7 @@ private:
GpuMat colors_;
GpuMat weights_;
Ptr<FilterEngine_GPU> boxFilter_;
Ptr<gpu::Filter> boxFilter_;
GpuMat buf_;
};

16
modules/gpubgsegm/src/fgd.cpp
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@ -228,11 +228,10 @@ private:
cv::gpu::GpuMat countBuf_;
cv::gpu::GpuMat buf_;
cv::gpu::GpuMat filterBuf_;
cv::gpu::GpuMat filterBrd_;
cv::Ptr<cv::gpu::FilterEngine_GPU> dilateFilter_;
cv::Ptr<cv::gpu::FilterEngine_GPU> erodeFilter_;
cv::Ptr<cv::gpu::Filter> dilateFilter_;
cv::Ptr<cv::gpu::Filter> erodeFilter_;
CvMemStorage* storage_;
};
@ -305,8 +304,8 @@ void cv::gpu::FGDStatModel::Impl::create(const cv::gpu::GpuMat& firstFrame, cons
cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(1 + params_.perform_morphing * 2, 1 + params_.perform_morphing * 2));
cv::Point anchor(params_.perform_morphing, params_.perform_morphing);
dilateFilter_ = cv::gpu::createMorphologyFilter_GPU(cv::MORPH_DILATE, CV_8UC1, kernel, filterBuf_, anchor);
erodeFilter_ = cv::gpu::createMorphologyFilter_GPU(cv::MORPH_ERODE, CV_8UC1, kernel, filterBuf_, anchor);
dilateFilter_ = cv::gpu::createMorphologyFilter(cv::MORPH_DILATE, CV_8UC1, kernel, anchor);
erodeFilter_ = cv::gpu::createMorphologyFilter(cv::MORPH_ERODE, CV_8UC1, kernel, anchor);
}
}
@ -326,7 +325,6 @@ void cv::gpu::FGDStatModel::Impl::release()
countBuf_.release();
buf_.release();
filterBuf_.release();
filterBrd_.release();
}
@ -488,14 +486,14 @@ namespace
namespace
{
void morphology(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, cv::gpu::GpuMat& filterBrd, int brd, cv::Ptr<cv::gpu::FilterEngine_GPU>& filter, cv::Scalar brdVal)
void morphology(const cv::gpu::GpuMat& src, cv::gpu::GpuMat& dst, cv::gpu::GpuMat& filterBrd, int brd, cv::Ptr<cv::gpu::Filter>& filter, cv::Scalar brdVal)
{
cv::gpu::copyMakeBorder(src, filterBrd, brd, brd, brd, brd, cv::BORDER_CONSTANT, brdVal);
filter->apply(filterBrd(cv::Rect(brd, brd, src.cols, src.rows)), dst, cv::Rect(0, 0, src.cols, src.rows));
filter->apply(filterBrd(cv::Rect(brd, brd, src.cols, src.rows)), dst);
}
void smoothForeground(cv::gpu::GpuMat& foreground, cv::gpu::GpuMat& filterBrd, cv::gpu::GpuMat& buf,
cv::Ptr<cv::gpu::FilterEngine_GPU>& erodeFilter, cv::Ptr<cv::gpu::FilterEngine_GPU>& dilateFilter,
cv::Ptr<cv::gpu::Filter>& erodeFilter, cv::Ptr<cv::gpu::Filter>& dilateFilter,
const cv::gpu::FGDStatModel::Params& params)
{
const int brd = params.perform_morphing;

4
modules/gpubgsegm/src/gmg.cpp
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@ -100,7 +100,7 @@ void cv::gpu::GMG_GPU::initialize(cv::Size frameSize, float min, float max)
nfeatures_.setTo(cv::Scalar::all(0));
if (smoothingRadius > 0)
boxFilter_ = cv::gpu::createBoxFilter_GPU(CV_8UC1, CV_8UC1, cv::Size(smoothingRadius, smoothingRadius));
boxFilter_ = cv::gpu::createBoxFilter(CV_8UC1, -1, cv::Size(smoothingRadius, smoothingRadius));
loadConstants(frameSize_.width, frameSize_.height, minVal_, maxVal_, quantizationLevels, backgroundPrior, decisionThreshold, maxFeatures, numInitializationFrames);
}
@ -141,7 +141,7 @@ void cv::gpu::GMG_GPU::operator ()(const cv::gpu::GpuMat& frame, cv::gpu::GpuMat
// medianBlur
if (smoothingRadius > 0)
{
boxFilter_->apply(fgmask, buf_, cv::Rect(0,0,-1,-1), stream);
boxFilter_->apply(fgmask, buf_, stream);
int minCount = (smoothingRadius * smoothingRadius + 1) / 2;
double thresh = 255.0 * minCount / (smoothingRadius * smoothingRadius);
cv::gpu::threshold(buf_, fgmask, thresh, 255.0, cv::THRESH_BINARY, stream);

2
modules/gpufeatures2d/include/opencv2/gpufeatures2d.hpp
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@ -351,7 +351,7 @@ private:
FAST_GPU fastDetector_;
Ptr<FilterEngine_GPU> blurFilter;
Ptr<gpu::Filter> blurFilter;
GpuMat d_keypoints_;
};

4
modules/gpufeatures2d/src/orb.cpp
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@ -468,7 +468,7 @@ cv::gpu::ORB_GPU::ORB_GPU(int nFeatures, float scaleFactor, int nLevels, int edg
pattern_.upload(h_pattern);
blurFilter = createGaussianFilter_GPU(CV_8UC1, Size(7, 7), 2, 2, BORDER_REFLECT_101);
blurFilter = gpu::createGaussianFilter(CV_8UC1, -1, Size(7, 7), 2, 2, BORDER_REFLECT_101);
blurForDescriptor = false;
}
@ -632,7 +632,7 @@ void cv::gpu::ORB_GPU::computeDescriptors(GpuMat& descriptors)
{
// preprocess the resized image
ensureSizeIsEnough(imagePyr_[level].size(), imagePyr_[level].type(), buf_);
blurFilter->apply(imagePyr_[level], buf_, Rect(0, 0, imagePyr_[level].cols, imagePyr_[level].rows));
blurFilter->apply(imagePyr_[level], buf_);
}
computeOrbDescriptor_gpu(blurForDescriptor ? buf_ : imagePyr_[level], keyPointsPyr_[level].ptr<short2>(0), keyPointsPyr_[level].ptr<float>(2),

2
modules/gpufilters/CMakeLists.txt
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@ -6,4 +6,4 @@ set(the_description "GPU-accelerated Image Filtering")
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4127 /wd4324 /wd4512 -Wundef -Wmissing-declarations)
ocv_define_module(gpufilters opencv_imgproc OPTIONAL opencv_gpuarithm)
ocv_define_module(gpufilters opencv_imgproc opencv_gpuarithm)

887
modules/gpufilters/doc/filtering.rst
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@ -7,346 +7,236 @@ Functions and classes described in this section are used to perform various line
gpu::BaseRowFilter_GPU
----------------------
.. ocv:class:: gpu::BaseRowFilter_GPU
gpu::Filter
-----------
.. ocv:class:: gpu::Filter
Base class for linear or non-linear filters that processes rows of 2D arrays. Such filters are used for the "horizontal" filtering passes in separable filters. ::
Common interface for all GPU filters ::
class BaseRowFilter_GPU
class CV_EXPORTS Filter : public Algorithm
{
public:
BaseRowFilter_GPU(int ksize_, int anchor_);
virtual ~BaseRowFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
int ksize, anchor;
virtual void apply(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0;
};
.. note:: This class does not allocate memory for a destination image. Usually this class is used inside :ocv:class:`gpu::FilterEngine_GPU`.
gpu::BaseColumnFilter_GPU
-------------------------
.. ocv:class:: gpu::BaseColumnFilter_GPU
Base class for linear or non-linear filters that processes columns of 2D arrays. Such filters are used for the "vertical" filtering passes in separable filters. ::
class BaseColumnFilter_GPU
{
public:
BaseColumnFilter_GPU(int ksize_, int anchor_);
virtual ~BaseColumnFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
int ksize, anchor;
};
.. note:: This class does not allocate memory for a destination image. Usually this class is used inside :ocv:class:`gpu::FilterEngine_GPU`.
gpu::BaseFilter_GPU
-------------------
.. ocv:class:: gpu::BaseFilter_GPU
Base class for non-separable 2D filters. ::
class CV_EXPORTS BaseFilter_GPU
{
public:
BaseFilter_GPU(const Size& ksize_, const Point& anchor_);
virtual ~BaseFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
Size ksize;
Point anchor;
};
.. note:: This class does not allocate memory for a destination image. Usually this class is used inside :ocv:class:`gpu::FilterEngine_GPU`.
gpu::FilterEngine_GPU
---------------------
.. ocv:class:: gpu::FilterEngine_GPU
Base class for the Filter Engine. ::
class CV_EXPORTS FilterEngine_GPU
{
public:
virtual ~FilterEngine_GPU() {}
virtual void apply(const GpuMat& src, GpuMat& dst,
Rect roi = Rect(0,0,-1,-1), Stream& stream = Stream::Null()) = 0;
};
The class can be used to apply an arbitrary filtering operation to an image. It contains all the necessary intermediate buffers. Pointers to the initialized ``FilterEngine_GPU`` instances are returned by various ``create*Filter_GPU`` functions (see below), and they are used inside high-level functions such as :ocv:func:`gpu::filter2D`, :ocv:func:`gpu::erode`, :ocv:func:`gpu::Sobel` , and others.
By using ``FilterEngine_GPU`` instead of functions you can avoid unnecessary memory allocation for intermediate buffers and get better performance: ::
while (...)
{
gpu::GpuMat src = getImg();
gpu::GpuMat dst;
// Allocate and release buffers at each iterations
gpu::GaussianBlur(src, dst, ksize, sigma1);
}
// Allocate buffers only once
cv::Ptr<gpu::FilterEngine_GPU> filter =
gpu::createGaussianFilter_GPU(CV_8UC4, ksize, sigma1);
while (...)
{
gpu::GpuMat src = getImg();
gpu::GpuMat dst;
filter->apply(src, dst, cv::Rect(0, 0, src.cols, src.rows));
}
// Release buffers only once
filter.release();
``FilterEngine_GPU`` can process a rectangular sub-region of an image. By default, if ``roi == Rect(0,0,-1,-1)`` , ``FilterEngine_GPU`` processes the inner region of an image ( ``Rect(anchor.x, anchor.y, src_size.width - ksize.width, src_size.height - ksize.height)`` ) because some filters do not check whether indices are outside the image for better performance. See below to understand which filters support processing the whole image and which do not and identify image type limitations.
.. note:: The GPU filters do not support the in-place mode.
.. seealso:: :ocv:class:`gpu::BaseRowFilter_GPU`, :ocv:class:`gpu::BaseColumnFilter_GPU`, :ocv:class:`gpu::BaseFilter_GPU`, :ocv:func:`gpu::createFilter2D_GPU`, :ocv:func:`gpu::createSeparableFilter_GPU`, :ocv:func:`gpu::createBoxFilter_GPU`, :ocv:func:`gpu::createMorphologyFilter_GPU`, :ocv:func:`gpu::createLinearFilter_GPU`, :ocv:func:`gpu::createSeparableLinearFilter_GPU`, :ocv:func:`gpu::createDerivFilter_GPU`, :ocv:func:`gpu::createGaussianFilter_GPU`
gpu::createFilter2D_GPU
---------------------------
Creates a non-separable filter engine with the specified filter.
.. ocv:function:: Ptr<FilterEngine_GPU> gpu::createFilter2D_GPU( const Ptr<BaseFilter_GPU>& filter2D, int srcType, int dstType)
:param filter2D: Non-separable 2D filter.
:param srcType: Input image type. It must be supported by ``filter2D`` .
:param dstType: Output image type. It must be supported by ``filter2D`` .
Usually this function is used inside such high-level functions as :ocv:func:`gpu::createLinearFilter_GPU`, :ocv:func:`gpu::createBoxFilter_GPU`.
gpu::createSeparableFilter_GPU
----------------------------------
Creates a separable filter engine with the specified filters.
.. ocv:function:: Ptr<FilterEngine_GPU> gpu::createSeparableFilter_GPU( const Ptr<BaseRowFilter_GPU>& rowFilter, const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType)
:param rowFilter: "Horizontal" 1D filter.
:param columnFilter: "Vertical" 1D filter.
:param srcType: Input image type. It must be supported by ``rowFilter`` .
:param bufType: Buffer image type. It must be supported by ``rowFilter`` and ``columnFilter`` .
:param dstType: Output image type. It must be supported by ``columnFilter`` .
Usually this function is used inside such high-level functions as :ocv:func:`gpu::createSeparableLinearFilter_GPU`.
gpu::getRowSumFilter_GPU
----------------------------
Creates a horizontal 1D box filter.
.. ocv:function:: Ptr<BaseRowFilter_GPU> gpu::getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1)
:param srcType: Input image type. Only ``CV_8UC1`` type is supported for now.
:param sumType: Output image type. Only ``CV_32FC1`` type is supported for now.
:param ksize: Kernel size.
:param anchor: Anchor point. The default value (-1) means that the anchor is at the kernel center.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
gpu::getColumnSumFilter_GPU
-------------------------------
Creates a vertical 1D box filter.
.. ocv:function:: Ptr<BaseColumnFilter_GPU> gpu::getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1)
:param sumType: Input image type. Only ``CV_8UC1`` type is supported for now.
:param dstType: Output image type. Only ``CV_32FC1`` type is supported for now.
:param ksize: Kernel size.
:param anchor: Anchor point. The default value (-1) means that the anchor is at the kernel center.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
gpu::createBoxFilter_GPU
----------------------------
Creates a normalized 2D box filter.
.. ocv:function:: Ptr<FilterEngine_GPU> gpu::createBoxFilter_GPU(int srcType, int dstType, const Size& ksize, const Point& anchor = Point(-1,-1))
.. ocv:function:: Ptr<BaseFilter_GPU> gpu::getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1))
:param srcType: Input image type supporting ``CV_8UC1`` and ``CV_8UC4`` .
:param dstType: Output image type. It supports only the same values as the source type.
:param ksize: Kernel size.
:param anchor: Anchor point. The default value ``Point(-1, -1)`` means that the anchor is at the kernel center.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
.. seealso:: :ocv:func:`boxFilter`
gpu::boxFilter
gpu::Filter::apply
------------------
Smooths the image using the normalized box filter.
Applies the specified filter to the image.
.. ocv:function:: void gpu::boxFilter(const GpuMat& src, GpuMat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null())
.. ocv:function:: void gpu::Filter::apply(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0
:param src: Input image. ``CV_8UC1`` and ``CV_8UC4`` source types are supported.
:param src: Input image.
:param dst: Output image type. The size and type is the same as ``src`` .
:param ddepth: Output image depth. If -1, the output image has the same depth as the input one. The only values allowed here are ``CV_8U`` and -1.
:param ksize: Kernel size.
:param anchor: Anchor point. The default value ``Point(-1, -1)`` means that the anchor is at the kernel center.
:param dst: Output image.
:param stream: Stream for the asynchronous version.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
gpu::createBoxFilter
--------------------
Creates a normalized 2D box filter.
.. ocv:function:: Ptr<Filter> gpu::createBoxFilter(int srcType, int dstType, Size ksize, Point anchor = Point(-1,-1), int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0))
:param srcType: Input image type. Only ``CV_8UC1`` and ``CV_8UC4`` are supported for now.
:param dstType: Output image type. Only the same type as ``src`` is supported for now.
:param ksize: Kernel size.
:param anchor: Anchor point. The default value ``Point(-1, -1)`` means that the anchor is at the kernel center.
:param borderMode: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` .
:param borderVal: Default border value.
.. seealso:: :ocv:func:`boxFilter`
gpu::blur
-------------
Acts as a synonym for the normalized box filter.
gpu::createLinearFilter
-----------------------
Creates a non-separable linear 2D filter.
.. ocv:function:: void gpu::blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null())
.. ocv:function:: Ptr<Filter> gpu::createLinearFilter(int srcType, int dstType, InputArray kernel, Point anchor = Point(-1,-1), int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0))
:param src: Input image. ``CV_8UC1`` and ``CV_8UC4`` source types are supported.
:param srcType: Input image type. Supports ``CV_8U`` , ``CV_16U`` and ``CV_32F`` one and four channel image.
:param dst: Output image type with the same size and type as ``src`` .
:param dstType: Output image type. Only the same type as ``src`` is supported for now.
:param ksize: Kernel size.
:param kernel: 2D array of filter coefficients.
:param anchor: Anchor point. The default value Point(-1, -1) means that the anchor is at the kernel center.
:param stream: Stream for the asynchronous version.
:param borderMode: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` .
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
:param borderVal: Default border value.
.. seealso:: :ocv:func:`blur`, :ocv:func:`gpu::boxFilter`
.. seealso:: :ocv:func:`filter2D`
gpu::createMorphologyFilter_GPU
-----------------------------------
gpu::createLaplacianFilter
--------------------------
Creates a Laplacian operator.
.. ocv:function:: Ptr<Filter> gpu::createLaplacianFilter(int srcType, int dstType, int ksize = 1, double scale = 1, int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0))
:param srcType: Input image type. Supports ``CV_8U`` , ``CV_16U`` and ``CV_32F`` one and four channel image.
:param dstType: Output image type. Only the same type as ``src`` is supported for now.
:param ksize: Aperture size used to compute the second-derivative filters (see :ocv:func:`getDerivKernels`). It must be positive and odd. Only ``ksize`` = 1 and ``ksize`` = 3 are supported.
:param scale: Optional scale factor for the computed Laplacian values. By default, no scaling is applied (see :ocv:func:`getDerivKernels` ).
:param borderMode: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` .
:param borderVal: Default border value.
.. seealso:: :ocv:func:`Laplacian`
gpu::createSeparableLinearFilter
--------------------------------
Creates a separable linear filter.
.. ocv:function:: Ptr<Filter> gpu::createSeparableLinearFilter(int srcType, int dstType, InputArray rowKernel, InputArray columnKernel, Point anchor = Point(-1,-1), int rowBorderMode = BORDER_DEFAULT, int columnBorderMode = -1)
:param srcType: Source array type.
:param dstType: Destination array type.
:param rowKernel: Horizontal filter coefficients. Support kernels with ``size <= 32`` .
:param columnKernel: Vertical filter coefficients. Support kernels with ``size <= 32`` .
:param anchor: Anchor position within the kernel. Negative values mean that anchor is positioned at the aperture center.
:param rowBorderMode: Pixel extrapolation method in the vertical direction For details, see :ocv:func:`borderInterpolate`.
:param columnBorderMode: Pixel extrapolation method in the horizontal direction.
.. seealso:: :ocv:func:`sepFilter2D`
gpu::createDerivFilter
----------------------
Creates a generalized Deriv operator.
.. ocv:function:: Ptr<Filter> gpu::createDerivFilter(int srcType, int dstType, int dx, int dy, int ksize, bool normalize = false, double scale = 1, int rowBorderMode = BORDER_DEFAULT, int columnBorderMode = -1)
:param srcType: Source image type.
:param dstType: Destination array type.
:param dx: Derivative order in respect of x.
:param dy: Derivative order in respect of y.
:param ksize: Aperture size. See :ocv:func:`getDerivKernels` for details.
:param normalize: Flag indicating whether to normalize (scale down) the filter coefficients or not. See :ocv:func:`getDerivKernels` for details.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see :ocv:func:`getDerivKernels` .
:param rowBorderMode: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
:param columnBorderMode: Pixel extrapolation method in the horizontal direction.
gpu::createSobelFilter
----------------------
Creates a Sobel operator.
.. ocv:function:: Ptr<Filter> gpu::createSobelFilter(int srcType, int dstType, int dx, int dy, int ksize = 3, double scale = 1, int rowBorderMode = BORDER_DEFAULT, int columnBorderMode = -1)
:param srcType: Source image type.
:param dstType: Destination array type.
:param dx: Derivative order in respect of x.
:param dy: Derivative order in respect of y.
:param ksize: Size of the extended Sobel kernel. Possible values are 1, 3, 5 or 7.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see :ocv:func:`getDerivKernels` .
:param rowBorderMode: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
:param columnBorderMode: Pixel extrapolation method in the horizontal direction.
.. seealso:: :ocv:func:`Sobel`
gpu::createScharrFilter
-----------------------
Creates a vertical or horizontal Scharr operator.
.. ocv:function:: Ptr<Filter> gpu::createScharrFilter(int srcType, int dstType, int dx, int dy, double scale = 1, int rowBorderMode = BORDER_DEFAULT, int columnBorderMode = -1)
:param srcType: Source image type.
:param dstType: Destination array type.
:param dx: Order of the derivative x.
:param dy: Order of the derivative y.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. See :ocv:func:`getDerivKernels` for details.
:param rowBorderMode: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
:param columnBorderMode: Pixel extrapolation method in the horizontal direction.
.. seealso:: :ocv:func:`Scharr`
gpu::createGaussianFilter
-------------------------
Creates a Gaussian filter.
.. ocv:function:: Ptr<Filter> gpu::createGaussianFilter(int srcType, int dstType, Size ksize, double sigma1, double sigma2 = 0, int rowBorderMode = BORDER_DEFAULT, int columnBorderMode = -1)
:param srcType: Source image type.
:param dstType: Destination array type.
:param ksize: Aperture size. See :ocv:func:`getGaussianKernel` for details.
:param sigma1: Gaussian sigma in the horizontal direction. See :ocv:func:`getGaussianKernel` for details.
:param sigma2: Gaussian sigma in the vertical direction. If 0, then :math:`\texttt{sigma2}\leftarrow\texttt{sigma1}` .
:param rowBorderMode: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
:param columnBorderMode: Pixel extrapolation method in the horizontal direction.
.. seealso:: :ocv:func:`GaussianBlur`
gpu::createMorphologyFilter
---------------------------
Creates a 2D morphological filter.
.. ocv:function:: Ptr<FilterEngine_GPU> gpu::createMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Point& anchor = Point(-1,-1), int iterations = 1)
.. ocv:function:: Ptr<BaseFilter_GPU> gpu::getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize, Point anchor=Point(-1,-1))
:param op: Morphology operation id. Only ``MORPH_ERODE`` and ``MORPH_DILATE`` are supported.
:param type: Input/output image type. Only ``CV_8UC1`` and ``CV_8UC4`` are supported.
:param kernel: 2D 8-bit structuring element for the morphological operation.
:param ksize: Size of a horizontal or vertical structuring element used for separable morphological operations.
:param anchor: Anchor position within the structuring element. Negative values mean that the anchor is at the center.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
.. seealso:: :ocv:func:`createMorphologyFilter`
gpu::erode
--------------
Erodes an image by using a specific structuring element.
.. ocv:function:: void gpu::erode( const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor=Point(-1, -1), int iterations=1 )
.. ocv:function:: void gpu::erode( const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf, Point anchor=Point(-1, -1), int iterations=1, Stream& stream=Stream::Null() )
:param src: Source image. Only ``CV_8UC1`` and ``CV_8UC4`` types are supported.
:param dst: Destination image with the same size and type as ``src`` .
:param kernel: Structuring element used for erosion. If ``kernel=Mat()``, a 3x3 rectangular structuring element is used.
:param anchor: Position of an anchor within the element. The default value ``(-1, -1)`` means that the anchor is at the element center.
:param iterations: Number of times erosion to be applied.
:param stream: Stream for the asynchronous version.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
.. seealso:: :ocv:func:`erode`
gpu::dilate
---------------
Dilates an image by using a specific structuring element.
.. ocv:function:: void gpu::dilate( const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor=Point(-1, -1), int iterations=1 )
.. ocv:function:: void gpu::dilate( const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf, Point anchor=Point(-1, -1), int iterations=1, Stream& stream=Stream::Null() )
:param src: Source image. ``CV_8UC1`` and ``CV_8UC4`` source types are supported.
:param dst: Destination image with the same size and type as ``src``.
:param kernel: Structuring element used for dilation. If ``kernel=Mat()``, a 3x3 rectangular structuring element is used.
:param anchor: Position of an anchor within the element. The default value ``(-1, -1)`` means that the anchor is at the element center.
:param iterations: Number of times dilation to be applied.
:param stream: Stream for the asynchronous version.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
.. seealso:: :ocv:func:`dilate`
gpu::morphologyEx
---------------------
Applies an advanced morphological operation to an image.
.. ocv:function:: void gpu::morphologyEx( const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, Point anchor=Point(-1, -1), int iterations=1 )
.. ocv:function:: void gpu::morphologyEx( const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, GpuMat& buf1, GpuMat& buf2, Point anchor=Point(-1, -1), int iterations=1, Stream& stream=Stream::Null() )
:param src: Source image. ``CV_8UC1`` and ``CV_8UC4`` source types are supported.
:param dst: Destination image with the same size and type as ``src`` .
.. ocv:function:: Ptr<Filter> gpu::createMorphologyFilter(int op, int srcType, InputArray kernel, Point anchor = Point(-1, -1), int iterations = 1)
:param op: Type of morphological operation. The following types are possible:
* **MORPH_ERODE** erode
* **MORPH_DILATE** dilate
* **MORPH_OPEN** opening
* **MORPH_CLOSE** closing
@ -357,363 +247,88 @@ Applies an advanced morphological operation to an image.
* **MORPH_BLACKHAT** "black hat"
:param kernel: Structuring element.
:param srcType: Input/output image type. Only ``CV_8UC1`` and ``CV_8UC4`` are supported.
:param anchor: Position of an anchor within the element. The default value ``Point(-1, -1)`` means that the anchor is at the element center.
:param kernel: 2D 8-bit structuring element for the morphological operation.
:param anchor: Anchor position within the structuring element. Negative values mean that the anchor is at the center.
:param iterations: Number of times erosion and dilation to be applied.
:param stream: Stream for the asynchronous version.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
.. seealso:: :ocv:func:`morphologyEx`
gpu::createLinearFilter_GPU
-------------------------------
Creates a non-separable linear filter.
.. ocv:function:: Ptr<FilterEngine_GPU> gpu::createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT)
:param srcType: Input image type. Supports ``CV_8U`` , ``CV_16U`` and ``CV_32F`` one and four channel image.
: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:class:`gpu::Convolution`.
:param anchor: Anchor point. The default value Point(-1, -1) means that the anchor is at the kernel center.
:param borderType: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` .
.. seealso:: :ocv:func:`createLinearFilter`
gpu::filter2D
-----------------
Applies the non-separable 2D linear filter to an image.
.. ocv:function:: void gpu::filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null())
:param src: Source image. Supports ``CV_8U`` , ``CV_16U`` and ``CV_32F`` one and four channel image.
:param dst: Destination image. The size and the number of channels is the same as ``src`` .
:param ddepth: Desired depth of the destination image. If it is negative, it is the same as ``src.depth()`` . It supports only the same depth as the source image depth.
:param kernel: 2D array of filter coefficients.
:param anchor: Anchor of the kernel that indicates the relative position of a filtered point within the kernel. The anchor resides within the kernel. The special default value (-1,-1) means that the anchor is at the kernel center.
:param borderType: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` .
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`filter2D`, :ocv:class:`gpu::Convolution`
gpu::Laplacian
------------------
Applies the Laplacian operator to an image.
.. ocv:function:: void gpu::Laplacian(const GpuMat& src, GpuMat& dst, int ddepth, int ksize = 1, double scale = 1, int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null())
:param src: Source image. ``CV_8UC1`` and ``CV_8UC4`` source types are supported.
:param dst: Destination image. The size and number of channels is the same as ``src`` .
:param ddepth: Desired depth of the destination image. It supports only the same depth as the source image depth.
:param ksize: Aperture size used to compute the second-derivative filters (see :ocv:func:`getDerivKernels`). It must be positive and odd. Only ``ksize`` = 1 and ``ksize`` = 3 are supported.
:param scale: Optional scale factor for the computed Laplacian values. By default, no scaling is applied (see :ocv:func:`getDerivKernels` ).
:param borderType: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` .
:param stream: Stream for the asynchronous version.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
.. seealso:: :ocv:func:`Laplacian`, :ocv:func:`gpu::filter2D`
gpu::getLinearRowFilter_GPU
-------------------------------
Creates a primitive row filter with the specified kernel.
.. ocv:function:: Ptr<BaseRowFilter_GPU> gpu::getLinearRowFilter_GPU( int srcType, int bufType, const Mat& rowKernel, int anchor=-1, int borderType=BORDER_DEFAULT )
:param srcType: Source array type. Only ``CV_8UC1`` , ``CV_8UC4`` , ``CV_16SC1`` , ``CV_16SC2`` , ``CV_16SC3`` , ``CV_32SC1`` , ``CV_32FC1`` source types are supported.
:param bufType: Intermediate buffer type with as many channels as ``srcType`` .
:param rowKernel: Filter coefficients. Support kernels with ``size <= 16`` .
:param anchor: Anchor position within the kernel. Negative values mean that the anchor is positioned at the aperture center.
:param borderType: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate`. For details on limitations, see below.
There are two versions of the algorithm: NPP and OpenCV.
* NPP version is called when ``srcType == CV_8UC1`` or ``srcType == CV_8UC4`` and ``bufType == srcType`` . Otherwise, the OpenCV version is called. NPP supports only ``BORDER_CONSTANT`` border type and does not check indices outside the image.
* OpenCV version supports only ``CV_32F`` buffer depth and ``BORDER_REFLECT101`` , ``BORDER_REPLICATE`` , and ``BORDER_CONSTANT`` border types. It checks indices outside the image.
.. seealso:: :ocv:func:`createSeparableLinearFilter` .
gpu::getLinearColumnFilter_GPU
----------------------------------
Creates a primitive column filter with the specified kernel.
.. ocv:function:: Ptr<BaseColumnFilter_GPU> gpu::getLinearColumnFilter_GPU( int bufType, int dstType, const Mat& columnKernel, int anchor=-1, int borderType=BORDER_DEFAULT )
:param bufType: Intermediate buffer type with as many channels as ``dstType`` .
:param dstType: Destination array type. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_16SC1`` , ``CV_16SC2`` , ``CV_16SC3`` , ``CV_32SC1`` , ``CV_32FC1`` destination types are supported.
:param columnKernel: Filter coefficients. Support kernels with ``size <= 16`` .
:param anchor: Anchor position within the kernel. Negative values mean that the anchor is positioned at the aperture center.
:param borderType: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` . For details on limitations, see below.
There are two versions of the algorithm: NPP and OpenCV.
* NPP version is called when ``dstType == CV_8UC1`` or ``dstType == CV_8UC4`` and ``bufType == dstType`` . Otherwise, the OpenCV version is called. NPP supports only ``BORDER_CONSTANT`` border type and does not check indices outside the image.
* OpenCV version supports only ``CV_32F`` buffer depth and ``BORDER_REFLECT101`` , ``BORDER_REPLICATE`` , and ``BORDER_CONSTANT`` border types. It checks indices outside image.
.. seealso:: :ocv:func:`gpu::getLinearRowFilter_GPU`, :ocv:func:`createSeparableLinearFilter`
gpu::createSeparableLinearFilter_GPU
----------------------------------------
Creates a separable linear filter engine.
.. ocv:function:: Ptr<FilterEngine_GPU> gpu::createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel, const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1)
:param srcType: Source array type. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_16SC1`` , ``CV_16SC2`` , ``CV_16SC3`` , ``CV_32SC1`` , ``CV_32FC1`` source types are supported.
:param dstType: Destination array type. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_16SC1`` , ``CV_16SC2`` , ``CV_16SC3`` , ``CV_32SC1`` , ``CV_32FC1`` destination types are supported.
:param rowKernel: Horizontal filter coefficients. Support kernels with ``size <= 16`` .
:param columnKernel: Vertical filter coefficients. Support kernels with ``size <= 16`` .
:param anchor: Anchor position within the kernel. Negative values mean that anchor is positioned at the aperture center.
:param rowBorderType: Pixel extrapolation method in the vertical direction For details, see :ocv:func:`borderInterpolate`. For details on limitations, see :ocv:func:`gpu::getLinearRowFilter_GPU`, cpp:ocv:func:`gpu::getLinearColumnFilter_GPU`.
:param columnBorderType: Pixel extrapolation method in the horizontal direction.
.. seealso:: :ocv:func:`gpu::getLinearRowFilter_GPU`, :ocv:func:`gpu::getLinearColumnFilter_GPU`, :ocv:func:`createSeparableLinearFilter`
gpu::sepFilter2D
--------------------
Applies a separable 2D linear filter to an image.
.. ocv:function:: void gpu::sepFilter2D( const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, Point anchor=Point(-1,-1), int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1 )
.. ocv:function:: void gpu::sepFilter2D( const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, GpuMat& buf, Point anchor=Point(-1,-1), int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, Stream& stream=Stream::Null() )
:param src: Source image. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_16SC1`` , ``CV_16SC2`` , ``CV_32SC1`` , ``CV_32FC1`` source types are supported.
:param dst: Destination image with the same size and number of channels as ``src`` .
:param ddepth: Destination image depth. ``CV_8U`` , ``CV_16S`` , ``CV_32S`` , and ``CV_32F`` are supported.
:param kernelX: Horizontal filter coefficients.
:param kernelY: Vertical filter coefficients.
:param anchor: Anchor position within the kernel. The default value ``(-1, 1)`` means that the anchor is at the kernel center.
:param rowBorderType: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
:param columnBorderType: Pixel extrapolation method in the horizontal direction.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`gpu::createSeparableLinearFilter_GPU`, :ocv:func:`sepFilter2D`
gpu::createDerivFilter_GPU
------------------------------
Creates a filter engine for the generalized Sobel operator.
.. ocv:function:: Ptr<FilterEngine_GPU> gpu::createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize, int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1)
:param srcType: Source image type. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_16SC1`` , ``CV_16SC2`` , ``CV_16SC3`` , ``CV_32SC1`` , ``CV_32FC1`` source types are supported.
:param dstType: Destination image type with as many channels as ``srcType`` , ``CV_8U`` , ``CV_16S`` , ``CV_32S`` , and ``CV_32F`` depths are supported.
:param dx: Derivative order in respect of x.
:param dy: Derivative order in respect of y.
:param ksize: Aperture size. See :ocv:func:`getDerivKernels` for details.
:param rowBorderType: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
:param columnBorderType: Pixel extrapolation method in the horizontal direction.
.. seealso:: :ocv:func:`gpu::createSeparableLinearFilter_GPU`, :ocv:func:`createDerivFilter`
gpu::Sobel
--------------
Applies the generalized Sobel operator to an image.
.. ocv:function:: void gpu::Sobel( const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize=3, double scale=1, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1 )
.. ocv:function:: void gpu::Sobel( const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, int ksize=3, double scale=1, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, Stream& stream=Stream::Null() )
:param src: Source image. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_16SC1`` , ``CV_16SC2`` , ``CV_16SC3`` , ``CV_32SC1`` , ``CV_32FC1`` source types are supported.
:param dst: Destination image with the same size and number of channels as source image.
:param ddepth: Destination image depth. ``CV_8U`` , ``CV_16S`` , ``CV_32S`` , and ``CV_32F`` are supported.
:param dx: Derivative order in respect of x.
:param dy: Derivative order in respect of y.
:param ksize: Size of the extended Sobel kernel. Possible values are 1, 3, 5 or 7.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. For details, see :ocv:func:`getDerivKernels` .
:param rowBorderType: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
:param columnBorderType: Pixel extrapolation method in the horizontal direction.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`gpu::createSeparableLinearFilter_GPU`, :ocv:func:`Sobel`
gpu::Scharr
---------------
Calculates the first x- or y- image derivative using the Scharr operator.
.. ocv:function:: void gpu::Scharr( const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale=1, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1 )
.. ocv:function:: void gpu::Scharr( const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, double scale=1, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, Stream& stream=Stream::Null() )
:param src: Source image. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_16SC1`` , ``CV_16SC2`` , ``CV_16SC3`` , ``CV_32SC1`` , ``CV_32FC1`` source types are supported.
:param dst: Destination image with the same size and number of channels as ``src`` has.
:param ddepth: Destination image depth. ``CV_8U`` , ``CV_16S`` , ``CV_32S`` , and ``CV_32F`` are supported.
:param dx: Order of the derivative x.
:param dy: Order of the derivative y.
:param scale: Optional scale factor for the computed derivative values. By default, no scaling is applied. See :ocv:func:`getDerivKernels` for details.
:param rowBorderType: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
:param columnBorderType: Pixel extrapolation method in the horizontal direction.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`gpu::createSeparableLinearFilter_GPU`, :ocv:func:`Scharr`
gpu::createGaussianFilter_GPU
---------------------------------
Creates a Gaussian filter engine.
.. ocv:function:: Ptr<FilterEngine_GPU> gpu::createGaussianFilter_GPU( int type, Size ksize, double sigma1, double sigma2=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1 )
:param type: Source and destination image type. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_16SC1`` , ``CV_16SC2`` , ``CV_16SC3`` , ``CV_32SC1`` , ``CV_32FC1`` are supported.
:param ksize: Aperture size. See :ocv:func:`getGaussianKernel` for details.
:param sigma1: Gaussian sigma in the horizontal direction. See :ocv:func:`getGaussianKernel` for details.
:param sigma2: Gaussian sigma in the vertical direction. If 0, then :math:`\texttt{sigma2}\leftarrow\texttt{sigma1}` .
:param rowBorderType: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
:param columnBorderType: Pixel extrapolation method in the horizontal direction.
.. seealso:: :ocv:func:`gpu::createSeparableLinearFilter_GPU`, :ocv:func:`createGaussianFilter`
gpu::GaussianBlur
---------------------
Smooths an image using the Gaussian filter.
.. ocv:function:: void gpu::GaussianBlur( const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1 )
.. ocv:function:: void gpu::GaussianBlur( const GpuMat& src, GpuMat& dst, Size ksize, GpuMat& buf, double sigma1, double sigma2=0, int rowBorderType=BORDER_DEFAULT, int columnBorderType=-1, Stream& stream=Stream::Null() )
:param src: Source image. ``CV_8UC1`` , ``CV_8UC4`` , ``CV_16SC1`` , ``CV_16SC2`` , ``CV_16SC3`` , ``CV_32SC1`` , ``CV_32FC1`` source types are supported.
:param dst: Destination image with the same size and type as ``src`` .
:param ksize: Gaussian kernel size. ``ksize.width`` and ``ksize.height`` can differ but they both must be positive and odd. If they are zeros, they are computed from ``sigma1`` and ``sigma2`` .
:param sigma1: Gaussian kernel standard deviation in X direction.
:param sigma2: Gaussian kernel standard deviation in Y direction. If ``sigma2`` is zero, it is set to be equal to ``sigma1`` . If they are both zeros, they are computed from ``ksize.width`` and ``ksize.height``, respectively. See :ocv:func:`getGaussianKernel` for details. To fully control the result regardless of possible future modification of all this semantics, you are recommended to specify all of ``ksize`` , ``sigma1`` , and ``sigma2`` .
:param rowBorderType: Pixel extrapolation method in the vertical direction. For details, see :ocv:func:`borderInterpolate`.
:param columnBorderType: Pixel extrapolation method in the horizontal direction.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`gpu::createGaussianFilter_GPU`, :ocv:func:`GaussianBlur`
gpu::getMaxFilter_GPU
-------------------------
gpu::createBoxMaxFilter
-----------------------
Creates the maximum filter.
.. ocv:function:: Ptr<BaseFilter_GPU> gpu::getMaxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1))
.. ocv:function:: Ptr<Filter> gpu::createBoxMaxFilter(int srcType, Size ksize, Point anchor = Point(-1, -1), int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0))
:param srcType: Input image type. Only ``CV_8UC1`` and ``CV_8UC4`` are supported.
:param dstType: Output image type. It supports only the same type as the source type.
:param srcType: Input/output image type. Only ``CV_8UC1`` and ``CV_8UC4`` are supported.
:param ksize: Kernel size.
:param anchor: Anchor point. The default value (-1) means that the anchor is at the kernel center.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
:param borderMode: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` .
:param borderVal: Default border value.
gpu::getMinFilter_GPU
-------------------------
gpu::createBoxMinFilter
-----------------------
Creates the minimum filter.
.. ocv:function:: Ptr<BaseFilter_GPU> gpu::getMinFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1))
.. ocv:function:: Ptr<Filter> gpu::createBoxMinFilter(int srcType, Size ksize, Point anchor = Point(-1, -1), int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0))
:param srcType: Input image type. Only ``CV_8UC1`` and ``CV_8UC4`` are supported.
:param dstType: Output image type. It supports only the same type as the source type.
:param srcType: Input/output image type. Only ``CV_8UC1`` and ``CV_8UC4`` are supported.
:param ksize: Kernel size.
:param anchor: Anchor point. The default value (-1) means that the anchor is at the kernel center.
.. note:: This filter does not check out-of-border accesses, so only a proper sub-matrix of a bigger matrix has to be passed to it.
:param borderMode: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` .
:param borderVal: Default border value.
gpu::createRowSumFilter
-----------------------
Creates a horizontal 1D box filter.
.. ocv:function:: Ptr<Filter> gpu::createRowSumFilter(int srcType, int dstType, int ksize, int anchor = -1, int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0))
:param srcType: Input image type. Only ``CV_8UC1`` type is supported for now.
:param sumType: Output image type. Only ``CV_32FC1`` type is supported for now.
:param ksize: Kernel size.
:param anchor: Anchor point. The default value (-1) means that the anchor is at the kernel center.
:param borderMode: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` .
:param borderVal: Default border value.
gpu::createColumnSumFilter
--------------------------
Creates a vertical 1D box filter.
.. ocv:function:: Ptr<Filter> gpu::createColumnSumFilter(int srcType, int dstType, int ksize, int anchor = -1, int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0))
:param srcType: Input image type. Only ``CV_8UC1`` type is supported for now.
:param sumType: Output image type. Only ``CV_32FC1`` type is supported for now.
:param ksize: Kernel size.
:param anchor: Anchor point. The default value (-1) means that the anchor is at the kernel center.
:param borderMode: Pixel extrapolation method. For details, see :ocv:func:`borderInterpolate` .
:param borderVal: Default border value.

282
modules/gpufilters/include/opencv2/gpufilters.hpp
View File

@ -48,221 +48,101 @@
#endif
#include "opencv2/core/gpu.hpp"
#include "opencv2/core/base.hpp"
#include "opencv2/imgproc.hpp"
namespace cv { namespace gpu {
/*!
The Base Class for 1D or Row-wise Filters
This is the base class for linear or non-linear filters that process 1D data.
In particular, such filters are used for the "horizontal" filtering parts in separable filters.
*/
class CV_EXPORTS BaseRowFilter_GPU
class CV_EXPORTS Filter : public Algorithm
{
public:
BaseRowFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
virtual ~BaseRowFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
int ksize, anchor;
virtual void apply(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0;
};
/*!
The Base Class for Column-wise Filters
////////////////////////////////////////////////////////////////////////////////////////////////////
// Box Filter
This is the base class for linear or non-linear filters that process columns of 2D arrays.
Such filters are used for the "vertical" filtering parts in separable filters.
*/
class CV_EXPORTS BaseColumnFilter_GPU
{
public:
BaseColumnFilter_GPU(int ksize_, int anchor_) : ksize(ksize_), anchor(anchor_) {}
virtual ~BaseColumnFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
int ksize, anchor;
};
/*!
The Base Class for Non-Separable 2D Filters.
This is the base class for linear or non-linear 2D filters.
*/
class CV_EXPORTS BaseFilter_GPU
{
public:
BaseFilter_GPU(const Size& ksize_, const Point& anchor_) : ksize(ksize_), anchor(anchor_) {}
virtual ~BaseFilter_GPU() {}
virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null()) = 0;
Size ksize;
Point anchor;
};
/*!
The Base Class for Filter Engine.
The class can be used to apply an arbitrary filtering operation to an image.
It contains all the necessary intermediate buffers.
*/
class CV_EXPORTS FilterEngine_GPU
{
public:
virtual ~FilterEngine_GPU() {}
virtual void apply(const GpuMat& src, GpuMat& dst, Rect roi = Rect(0,0,-1,-1), Stream& stream = Stream::Null()) = 0;
};
//! returns the non-separable filter engine with the specified filter
CV_EXPORTS Ptr<FilterEngine_GPU> createFilter2D_GPU(const Ptr<BaseFilter_GPU>& filter2D, int srcType, int dstType);
//! returns the separable filter engine with the specified filters
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType);
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableFilter_GPU(const Ptr<BaseRowFilter_GPU>& rowFilter,
const Ptr<BaseColumnFilter_GPU>& columnFilter, int srcType, int bufType, int dstType, GpuMat& buf);
//! returns horizontal 1D box filter
//! supports only CV_8UC1 source type and CV_32FC1 sum type
CV_EXPORTS Ptr<BaseRowFilter_GPU> getRowSumFilter_GPU(int srcType, int sumType, int ksize, int anchor = -1);
//! returns vertical 1D box filter
//! supports only CV_8UC1 sum type and CV_32FC1 dst type
CV_EXPORTS Ptr<BaseColumnFilter_GPU> getColumnSumFilter_GPU(int sumType, int dstType, int ksize, int anchor = -1);
//! returns 2D box filter
//! supports CV_8UC1 and CV_8UC4 source type, dst type must be the same as source type
CV_EXPORTS Ptr<BaseFilter_GPU> getBoxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1, -1));
//! returns box filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createBoxFilter_GPU(int srcType, int dstType, const Size& ksize,
const Point& anchor = Point(-1,-1));
//! returns 2D morphological filter
//! only MORPH_ERODE and MORPH_DILATE are supported
//! supports CV_8UC1 and CV_8UC4 types
//! kernel must have CV_8UC1 type, one rows and cols == ksize.width * ksize.height
CV_EXPORTS Ptr<BaseFilter_GPU> getMorphologyFilter_GPU(int op, int type, const Mat& kernel, const Size& ksize,
Point anchor=Point(-1,-1));
//! returns morphological filter engine. Only MORPH_ERODE and MORPH_DILATE are supported.
CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel,
const Point& anchor = Point(-1,-1), int iterations = 1);
CV_EXPORTS Ptr<FilterEngine_GPU> createMorphologyFilter_GPU(int op, int type, const Mat& kernel, GpuMat& buf,
const Point& anchor = Point(-1,-1), int iterations = 1);
//! returns 2D filter with the specified kernel
//! supports CV_8U, CV_16U and CV_32F one and four channel image
CV_EXPORTS Ptr<BaseFilter_GPU> getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, Point anchor = Point(-1, -1), int borderType = BORDER_DEFAULT);
//! returns the non-separable linear filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel,
Point anchor = Point(-1,-1), int borderType = BORDER_DEFAULT);
//! returns the primitive row filter with the specified kernel.
//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 source type.
//! there are two version of algorithm: NPP and OpenCV.
//! NPP calls when srcType == CV_8UC1 or srcType == CV_8UC4 and bufType == srcType,
//! otherwise calls OpenCV version.
//! NPP supports only BORDER_CONSTANT border type.
//! OpenCV version supports only CV_32F as buffer depth and
//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
CV_EXPORTS Ptr<BaseRowFilter_GPU> getLinearRowFilter_GPU(int srcType, int bufType, const Mat& rowKernel,
int anchor = -1, int borderType = BORDER_DEFAULT);
//! returns the primitive column filter with the specified kernel.
//! supports only CV_8UC1, CV_8UC4, CV_16SC1, CV_16SC2, CV_32SC1, CV_32FC1 dst type.
//! there are two version of algorithm: NPP and OpenCV.
//! NPP calls when dstType == CV_8UC1 or dstType == CV_8UC4 and bufType == dstType,
//! otherwise calls OpenCV version.
//! NPP supports only BORDER_CONSTANT border type.
//! OpenCV version supports only CV_32F as buffer depth and
//! BORDER_REFLECT101, BORDER_REPLICATE and BORDER_CONSTANT border types.
CV_EXPORTS Ptr<BaseColumnFilter_GPU> getLinearColumnFilter_GPU(int bufType, int dstType, const Mat& columnKernel,
int anchor = -1, int borderType = BORDER_DEFAULT);
//! returns the separable linear filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
const Mat& columnKernel, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
int columnBorderType = -1);
CV_EXPORTS Ptr<FilterEngine_GPU> createSeparableLinearFilter_GPU(int srcType, int dstType, const Mat& rowKernel,
const Mat& columnKernel, GpuMat& buf, const Point& anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT,
int columnBorderType = -1);
//! returns filter engine for the generalized Sobel operator
CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS Ptr<FilterEngine_GPU> createDerivFilter_GPU(int srcType, int dstType, int dx, int dy, int ksize, GpuMat& buf,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
//! returns the Gaussian filter engine
CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, double sigma1, double sigma2 = 0,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS Ptr<FilterEngine_GPU> createGaussianFilter_GPU(int type, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
//! returns maximum filter
CV_EXPORTS Ptr<BaseFilter_GPU> getMaxFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
//! returns minimum filter
CV_EXPORTS Ptr<BaseFilter_GPU> getMinFilter_GPU(int srcType, int dstType, const Size& ksize, Point anchor = Point(-1,-1));
//! smooths the image using the normalized box filter
//! creates a normalized 2D box filter
//! supports CV_8UC1, CV_8UC4 types
CV_EXPORTS void boxFilter(const GpuMat& src, GpuMat& dst, int ddepth, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null());
CV_EXPORTS Ptr<Filter> createBoxFilter(int srcType, int dstType, Size ksize, Point anchor = Point(-1,-1),
int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0));
//! a synonym for normalized box filter
static inline void blur(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1), Stream& stream = Stream::Null())
{
boxFilter(src, dst, -1, ksize, anchor, stream);
}
////////////////////////////////////////////////////////////////////////////////////////////////////
// Linear Filter
//! erodes the image (applies the local minimum operator)
CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
CV_EXPORTS void erode(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
Point anchor = Point(-1, -1), int iterations = 1,
Stream& stream = Stream::Null());
//! Creates a non-separable linear 2D filter
//! supports 1 and 4 channel CV_8U, CV_16U and CV_32F input
CV_EXPORTS Ptr<Filter> createLinearFilter(int srcType, int dstType, InputArray kernel, Point anchor = Point(-1,-1),
int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0));
//! dilates the image (applies the local maximum operator)
CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
CV_EXPORTS void dilate(const GpuMat& src, GpuMat& dst, const Mat& kernel, GpuMat& buf,
Point anchor = Point(-1, -1), int iterations = 1,
Stream& stream = Stream::Null());
////////////////////////////////////////////////////////////////////////////////////////////////////
// Laplacian Filter
//! applies an advanced morphological operation to the image
CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, Point anchor = Point(-1, -1), int iterations = 1);
CV_EXPORTS void morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& kernel, GpuMat& buf1, GpuMat& buf2,
Point anchor = Point(-1, -1), int iterations = 1, Stream& stream = Stream::Null());
//! applies non-separable 2D linear filter to the image
CV_EXPORTS void filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
//! applies separable 2D linear filter to the image
CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY,
Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, GpuMat& buf,
Point anchor = Point(-1,-1), int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1,
Stream& stream = Stream::Null());
//! applies generalized Sobel operator to the image
CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, int ksize = 3, double scale = 1,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void Sobel(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, int ksize = 3, double scale = 1,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
//! applies the vertical or horizontal Scharr operator to the image
CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, double scale = 1,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void Scharr(const GpuMat& src, GpuMat& dst, int ddepth, int dx, int dy, GpuMat& buf, double scale = 1,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
//! smooths the image using Gaussian filter.
CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2 = 0,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1);
CV_EXPORTS void GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, GpuMat& buf, double sigma1, double sigma2 = 0,
int rowBorderType = BORDER_DEFAULT, int columnBorderType = -1, Stream& stream = Stream::Null());
//! applies Laplacian operator to the image
//! creates a Laplacian operator
//! supports only ksize = 1 and ksize = 3
CV_EXPORTS void Laplacian(const GpuMat& src, GpuMat& dst, int ddepth, int ksize = 1, double scale = 1, int borderType = BORDER_DEFAULT, Stream& stream = Stream::Null());
CV_EXPORTS Ptr<Filter> createLaplacianFilter(int srcType, int dstType, int ksize = 1, double scale = 1,
int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0));
////////////////////////////////////////////////////////////////////////////////////////////////////
// Separable Linear Filter
//! creates a separable linear filter
CV_EXPORTS Ptr<Filter> createSeparableLinearFilter(int srcType, int dstType, InputArray rowKernel, InputArray columnKernel,
Point anchor = Point(-1,-1), int rowBorderMode = BORDER_DEFAULT, int columnBorderMode = -1);
////////////////////////////////////////////////////////////////////////////////////////////////////
// Deriv Filter
//! creates a generalized Deriv operator
CV_EXPORTS Ptr<Filter> createDerivFilter(int srcType, int dstType, int dx, int dy,
int ksize, bool normalize = false, double scale = 1,
int rowBorderMode = BORDER_DEFAULT, int columnBorderMode = -1);
//! creates a Sobel operator
CV_EXPORTS Ptr<Filter> createSobelFilter(int srcType, int dstType, int dx, int dy, int ksize = 3,
double scale = 1, int rowBorderMode = BORDER_DEFAULT, int columnBorderMode = -1);
//! creates a vertical or horizontal Scharr operator
CV_EXPORTS Ptr<Filter> createScharrFilter(int srcType, int dstType, int dx, int dy,
double scale = 1, int rowBorderMode = BORDER_DEFAULT, int columnBorderMode = -1);
////////////////////////////////////////////////////////////////////////////////////////////////////
// Gaussian Filter
//! creates a Gaussian filter
CV_EXPORTS Ptr<Filter> createGaussianFilter(int srcType, int dstType, Size ksize,
double sigma1, double sigma2 = 0,
int rowBorderMode = BORDER_DEFAULT, int columnBorderMode = -1);
////////////////////////////////////////////////////////////////////////////////////////////////////
// Morphology Filter
//! creates a 2D morphological filter
//! supports CV_8UC1 and CV_8UC4 types
CV_EXPORTS Ptr<Filter> createMorphologyFilter(int op, int srcType, InputArray kernel, Point anchor = Point(-1, -1), int iterations = 1);
////////////////////////////////////////////////////////////////////////////////////////////////////
// Image Rank Filter
//! result pixel value is the maximum of pixel values under the rectangular mask region
CV_EXPORTS Ptr<Filter> createBoxMaxFilter(int srcType, Size ksize,
Point anchor = Point(-1, -1),
int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0));
//! result pixel value is the maximum of pixel values under the rectangular mask region
CV_EXPORTS Ptr<Filter> createBoxMinFilter(int srcType, Size ksize,
Point anchor = Point(-1, -1),
int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0));
////////////////////////////////////////////////////////////////////////////////////////////////////
// 1D Sum Filter
//! creates a horizontal 1D box filter
//! supports only CV_8UC1 source type and CV_32FC1 sum type
CV_EXPORTS Ptr<Filter> createRowSumFilter(int srcType, int dstType, int ksize, int anchor = -1, int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0));
//! creates a vertical 1D box filter
//! supports only CV_8UC1 sum type and CV_32FC1 dst type
CV_EXPORTS Ptr<Filter> createColumnSumFilter(int srcType, int dstType, int ksize, int anchor = -1, int borderMode = BORDER_DEFAULT, Scalar borderVal = Scalar::all(0));
}} // namespace cv { namespace gpu {

177
modules/gpufilters/perf/perf_filters.cpp
View File

@ -70,7 +70,9 @@ PERF_TEST_P(Sz_Type_KernelSz, Blur,
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::blur(d_src, dst, cv::Size(ksize, ksize));
cv::Ptr<cv::gpu::Filter> blurFilter = cv::gpu::createBoxFilter(d_src.type(), -1, cv::Size(ksize, ksize));
TEST_CYCLE() blurFilter->apply(d_src, dst);
GPU_SANITY_CHECK(dst, 1);
}
@ -84,6 +86,79 @@ PERF_TEST_P(Sz_Type_KernelSz, Blur,
}
}
//////////////////////////////////////////////////////////////////////
// Filter2D
PERF_TEST_P(Sz_Type_KernelSz, Filter2D, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4), Values(3, 5, 7, 9, 11, 13, 15)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int ksize = GET_PARAM(2);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
cv::Mat kernel(ksize, ksize, CV_32FC1);
declare.in(kernel, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::Ptr<cv::gpu::Filter> filter2D = cv::gpu::createLinearFilter(d_src.type(), -1, kernel);
TEST_CYCLE() filter2D->apply(d_src, dst);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::filter2D(src, dst, -1, kernel);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Laplacian
PERF_TEST_P(Sz_Type_KernelSz, Laplacian, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4), Values(1, 3)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int ksize = GET_PARAM(2);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::Ptr<cv::gpu::Filter> laplacian = cv::gpu::createLaplacianFilter(d_src.type(), -1, ksize);
TEST_CYCLE() laplacian->apply(d_src, dst);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::Laplacian(src, dst, -1, ksize);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Sobel
@ -102,9 +177,10 @@ PERF_TEST_P(Sz_Type_KernelSz, Sobel, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8U
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::Sobel(d_src, dst, -1, 1, 1, d_buf, ksize);
cv::Ptr<cv::gpu::Filter> sobel = cv::gpu::createSobelFilter(d_src.type(), -1, 1, 1, ksize);
TEST_CYCLE() sobel->apply(d_src, dst);
GPU_SANITY_CHECK(dst);
}
@ -135,9 +211,10 @@ PERF_TEST_P(Sz_Type, Scharr, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::Scharr(d_src, dst, -1, 1, 0, d_buf);
cv::Ptr<cv::gpu::Filter> scharr = cv::gpu::createScharrFilter(d_src.type(), -1, 1, 0);
TEST_CYCLE() scharr->apply(d_src, dst);
GPU_SANITY_CHECK(dst);
}
@ -169,9 +246,10 @@ PERF_TEST_P(Sz_Type_KernelSz, GaussianBlur, Combine(GPU_TYPICAL_MAT_SIZES, Value
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::GaussianBlur(d_src, dst, cv::Size(ksize, ksize), d_buf, 0.5);
cv::Ptr<cv::gpu::Filter> gauss = cv::gpu::createGaussianFilter(d_src.type(), -1, cv::Size(ksize, ksize), 0.5);
TEST_CYCLE() gauss->apply(d_src, dst);
GPU_SANITY_CHECK(dst);
}
@ -185,39 +263,6 @@ PERF_TEST_P(Sz_Type_KernelSz, GaussianBlur, Combine(GPU_TYPICAL_MAT_SIZES, Value
}
}
//////////////////////////////////////////////////////////////////////
// Laplacian
PERF_TEST_P(Sz_Type_KernelSz, Laplacian, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4), Values(1, 3)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int ksize = GET_PARAM(2);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::Laplacian(d_src, dst, -1, ksize);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::Laplacian(src, dst, -1, ksize);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Erode
@ -237,9 +282,10 @@ PERF_TEST_P(Sz_Type, Erode, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8U
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::erode(d_src, dst, ker, d_buf);
cv::Ptr<cv::gpu::Filter> erode = cv::gpu::createMorphologyFilter(cv::MORPH_ERODE, src.type(), ker);
TEST_CYCLE() erode->apply(d_src, dst);
GPU_SANITY_CHECK(dst);
}
@ -272,9 +318,10 @@ PERF_TEST_P(Sz_Type, Dilate, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf;
TEST_CYCLE() cv::gpu::dilate(d_src, dst, ker, d_buf);
cv::Ptr<cv::gpu::Filter> dilate = cv::gpu::createMorphologyFilter(cv::MORPH_DILATE, src.type(), ker);
TEST_CYCLE() dilate->apply(d_src, dst);
GPU_SANITY_CHECK(dst);
}
@ -312,10 +359,10 @@ PERF_TEST_P(Sz_Type_Op, MorphologyEx, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::GpuMat d_buf1;
cv::gpu::GpuMat d_buf2;
TEST_CYCLE() cv::gpu::morphologyEx(d_src, dst, morphOp, ker, d_buf1, d_buf2);
cv::Ptr<cv::gpu::Filter> morph = cv::gpu::createMorphologyFilter(morphOp, src.type(), ker);
TEST_CYCLE() morph->apply(d_src, dst);
GPU_SANITY_CHECK(dst);
}
@ -328,39 +375,3 @@ PERF_TEST_P(Sz_Type_Op, MorphologyEx, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Filter2D
PERF_TEST_P(Sz_Type_KernelSz, Filter2D, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC1, CV_8UC4, CV_32FC1, CV_32FC4), Values(3, 5, 7, 9, 11, 13, 15)))
{
declare.time(20.0);
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int ksize = GET_PARAM(2);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
cv::Mat kernel(ksize, ksize, CV_32FC1);
declare.in(kernel, WARMUP_RNG);
if (PERF_RUN_GPU())
{
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE() cv::gpu::filter2D(d_src, dst, -1, kernel);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::filter2D(src, dst, -1, kernel);
CPU_SANITY_CHECK(dst);
}
}

189
modules/gpufilters/src/cuda/filter2d.cu
View File

@ -48,111 +48,104 @@
namespace cv { namespace gpu { namespace cudev
{
namespace imgproc
template <class SrcPtr, typename D>
__global__ void filter2D(const SrcPtr src, PtrStepSz<D> dst,
const float* __restrict__ kernel,
const int kWidth, const int kHeight,
const int anchorX, const int anchorY)
{
#define FILTER2D_MAX_KERNEL_SIZE 16
typedef typename TypeVec<float, VecTraits<D>::cn>::vec_type sum_t;
__constant__ float c_filter2DKernel[FILTER2D_MAX_KERNEL_SIZE * FILTER2D_MAX_KERNEL_SIZE];
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
template <class SrcT, typename D>
__global__ void filter2D(const SrcT src, PtrStepSz<D> dst, const int kWidth, const int kHeight, const int anchorX, const int anchorY)
if (x >= dst.cols || y >= dst.rows)
return;
sum_t res = VecTraits<sum_t>::all(0);
int kInd = 0;
for (int i = 0; i < kHeight; ++i)
{
typedef typename TypeVec<float, VecTraits<D>::cn>::vec_type sum_t;
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= dst.cols || y >= dst.rows)
return;
sum_t res = VecTraits<sum_t>::all(0);
int kInd = 0;
for (int i = 0; i < kHeight; ++i)
{
for (int j = 0; j < kWidth; ++j)
res = res + src(y - anchorY + i, x - anchorX + j) * c_filter2DKernel[kInd++];
}
dst(y, x) = saturate_cast<D>(res);
for (int j = 0; j < kWidth; ++j)
res = res + src(y - anchorY + i, x - anchorX + j) * kernel[kInd++];
}
template <typename T, typename D, template <typename> class Brd> struct Filter2DCaller;
#define IMPLEMENT_FILTER2D_TEX_READER(type) \
texture< type , cudaTextureType2D, cudaReadModeElementType> tex_filter2D_ ## type (0, cudaFilterModePoint, cudaAddressModeClamp); \
struct tex_filter2D_ ## type ## _reader \
{ \
typedef type elem_type; \
typedef int index_type; \
const int xoff; \
const int yoff; \
tex_filter2D_ ## type ## _reader (int xoff_, int yoff_) : xoff(xoff_), yoff(yoff_) {} \
__device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const \
{ \
return tex2D(tex_filter2D_ ## type , x + xoff, y + yoff); \
} \
}; \
template <typename D, template <typename> class Brd> struct Filter2DCaller< type , D, Brd> \
{ \
static void call(const PtrStepSz< type > srcWhole, int xoff, int yoff, PtrStepSz<D> dst, \
int kWidth, int kHeight, int anchorX, int anchorY, const float* borderValue, cudaStream_t stream) \
{ \
typedef typename TypeVec<float, VecTraits< type >::cn>::vec_type work_type; \
dim3 block(16, 16); \
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y)); \
bindTexture(&tex_filter2D_ ## type , srcWhole); \
tex_filter2D_ ## type ##_reader texSrc(xoff, yoff); \
Brd<work_type> brd(dst.rows, dst.cols, VecTraits<work_type>::make(borderValue)); \
BorderReader< tex_filter2D_ ## type ##_reader, Brd<work_type> > brdSrc(texSrc, brd); \
filter2D<<<grid, block, 0, stream>>>(brdSrc, dst, kWidth, kHeight, anchorX, anchorY); \
cudaSafeCall( cudaGetLastError() ); \
if (stream == 0) \
cudaSafeCall( cudaDeviceSynchronize() ); \
} \
};
IMPLEMENT_FILTER2D_TEX_READER(uchar);
IMPLEMENT_FILTER2D_TEX_READER(uchar4);
IMPLEMENT_FILTER2D_TEX_READER(ushort);
IMPLEMENT_FILTER2D_TEX_READER(ushort4);
IMPLEMENT_FILTER2D_TEX_READER(float);
IMPLEMENT_FILTER2D_TEX_READER(float4);
#undef IMPLEMENT_FILTER2D_TEX_READER
template <typename T, typename D>
void filter2D_gpu(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst,
int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel,
int borderMode, const float* borderValue, cudaStream_t stream)
{
typedef void (*func_t)(const PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSz<D> dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* borderValue, cudaStream_t stream);
static const func_t funcs[] =
{
Filter2DCaller<T, D, BrdConstant>::call,
Filter2DCaller<T, D, BrdReplicate>::call,
Filter2DCaller<T, D, BrdReflect>::call,
Filter2DCaller<T, D, BrdWrap>::call,
Filter2DCaller<T, D, BrdReflect101>::call
};
if (stream == 0)
cudaSafeCall( cudaMemcpyToSymbol(c_filter2DKernel, kernel, kWidth * kHeight * sizeof(float), 0, cudaMemcpyDeviceToDevice) );
else
cudaSafeCall( cudaMemcpyToSymbolAsync(c_filter2DKernel, kernel, kWidth * kHeight * sizeof(float), 0, cudaMemcpyDeviceToDevice, stream) );
funcs[borderMode](static_cast< PtrStepSz<T> >(srcWhole), ofsX, ofsY, static_cast< PtrStepSz<D> >(dst), kWidth, kHeight, anchorX, anchorY, borderValue, stream);
}
template void filter2D_gpu<uchar, uchar>(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D_gpu<uchar4, uchar4>(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D_gpu<ushort, ushort>(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D_gpu<ushort4, ushort4>(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D_gpu<float, float>(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D_gpu<float4, float4>(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, int kWidth, int kHeight, int anchorX, int anchorY, const float* kernel, int borderMode, const float* borderValue, cudaStream_t stream);
dst(y, x) = saturate_cast<D>(res);
}
template <typename T, typename D, template <typename> class Brd> struct Filter2DCaller;
#define IMPLEMENT_FILTER2D_TEX_READER(type) \
texture< type , cudaTextureType2D, cudaReadModeElementType> tex_filter2D_ ## type (0, cudaFilterModePoint, cudaAddressModeClamp); \
struct tex_filter2D_ ## type ## _reader \
{ \
typedef type elem_type; \
typedef int index_type; \
const int xoff; \
const int yoff; \
tex_filter2D_ ## type ## _reader (int xoff_, int yoff_) : xoff(xoff_), yoff(yoff_) {} \
__device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const \
{ \
return tex2D(tex_filter2D_ ## type , x + xoff, y + yoff); \
} \
}; \
template <typename D, template <typename> class Brd> struct Filter2DCaller< type , D, Brd> \
{ \
static void call(const PtrStepSz< type > srcWhole, int xoff, int yoff, PtrStepSz<D> dst, const float* kernel, \
int kWidth, int kHeight, int anchorX, int anchorY, const float* borderValue, cudaStream_t stream) \
{ \
typedef typename TypeVec<float, VecTraits< type >::cn>::vec_type work_type; \
dim3 block(16, 16); \
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y)); \
bindTexture(&tex_filter2D_ ## type , srcWhole); \
tex_filter2D_ ## type ##_reader texSrc(xoff, yoff); \
Brd<work_type> brd(dst.rows, dst.cols, VecTraits<work_type>::make(borderValue)); \
BorderReader< tex_filter2D_ ## type ##_reader, Brd<work_type> > brdSrc(texSrc, brd); \
filter2D<<<grid, block, 0, stream>>>(brdSrc, dst, kernel, kWidth, kHeight, anchorX, anchorY); \
cudaSafeCall( cudaGetLastError() ); \
if (stream == 0) \
cudaSafeCall( cudaDeviceSynchronize() ); \
} \
};
IMPLEMENT_FILTER2D_TEX_READER(uchar);
IMPLEMENT_FILTER2D_TEX_READER(uchar4);
IMPLEMENT_FILTER2D_TEX_READER(ushort);
IMPLEMENT_FILTER2D_TEX_READER(ushort4);
IMPLEMENT_FILTER2D_TEX_READER(float);
IMPLEMENT_FILTER2D_TEX_READER(float4);
#undef IMPLEMENT_FILTER2D_TEX_READER
template <typename T, typename D>
void filter2D(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, const float* kernel,
int kWidth, int kHeight, int anchorX, int anchorY,
int borderMode, const float* borderValue, cudaStream_t stream)
{
typedef void (*func_t)(const PtrStepSz<T> srcWhole, int xoff, int yoff, PtrStepSz<D> dst, const float* kernel,
int kWidth, int kHeight, int anchorX, int anchorY, const float* borderValue, cudaStream_t stream);
static const func_t funcs[] =
{
Filter2DCaller<T, D, BrdConstant>::call,
Filter2DCaller<T, D, BrdReplicate>::call,
Filter2DCaller<T, D, BrdReflect>::call,
Filter2DCaller<T, D, BrdWrap>::call,
Filter2DCaller<T, D, BrdReflect101>::call
};
funcs[borderMode]((PtrStepSz<T>) srcWhole, ofsX, ofsY, (PtrStepSz<D>) dst, kernel,
kWidth, kHeight, anchorX, anchorY, borderValue, stream);
}
template void filter2D<uchar , uchar >(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, const float* kernel, int kWidth, int kHeight, int anchorX, int anchorY, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D<uchar4 , uchar4 >(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, const float* kernel, int kWidth, int kHeight, int anchorX, int anchorY, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D<ushort , ushort >(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, const float* kernel, int kWidth, int kHeight, int anchorX, int anchorY, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D<ushort4, ushort4>(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, const float* kernel, int kWidth, int kHeight, int anchorX, int anchorY, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D<float , float >(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, const float* kernel, int kWidth, int kHeight, int anchorX, int anchorY, int borderMode, const float* borderValue, cudaStream_t stream);
template void filter2D<float4 , float4 >(PtrStepSzb srcWhole, int ofsX, int ofsY, PtrStepSzb dst, const float* kernel, int kWidth, int kHeight, int anchorX, int anchorY, int borderMode, const float* borderValue, cudaStream_t stream);
}}}
#endif // CUDA_DISABLER

1820
modules/gpufilters/src/filtering.cpp
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File diff suppressed because it is too large Load Diff

7
modules/gpufilters/src/precomp.hpp
View File

@ -46,14 +46,9 @@
#include <limits>
#include "opencv2/gpufilters.hpp"
#include "opencv2/gpuarithm.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/core/private.gpu.hpp"
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_GPUARITHM
# include "opencv2/gpuarithm.hpp"
#endif
#endif /* __OPENCV_PRECOMP_H__ */

327
modules/gpufilters/test/test_filters.cpp
View File

@ -70,13 +70,14 @@ namespace
/////////////////////////////////////////////////////////////////////////////////////////////////
// Blur
PARAM_TEST_CASE(Blur, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor, UseRoi)
PARAM_TEST_CASE(Blur, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
cv::Size ksize;
cv::Point anchor;
int borderType;
bool useRoi;
virtual void SetUp()
@ -86,7 +87,8 @@ PARAM_TEST_CASE(Blur, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor, Use
type = GET_PARAM(2);
ksize = GET_PARAM(3);
anchor = GET_PARAM(4);
useRoi = GET_PARAM(5);
borderType = GET_PARAM(5);
useRoi = GET_PARAM(6);
cv::gpu::setDevice(devInfo.deviceID());
}
@ -96,13 +98,15 @@ GPU_TEST_P(Blur, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Ptr<cv::gpu::Filter> blurFilter = cv::gpu::createBoxFilter(src.type(), -1, ksize, anchor, borderType);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::blur(loadMat(src, useRoi), dst, ksize, anchor);
blurFilter->apply(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::blur(src, dst_gold, ksize, anchor);
cv::blur(src, dst_gold, ksize, anchor, borderType);
EXPECT_MAT_NEAR(getInnerROI(dst_gold, ksize), getInnerROI(dst, ksize), 1.0);
EXPECT_MAT_NEAR(dst_gold, dst, 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Filters, Blur, testing::Combine(
@ -111,6 +115,173 @@ INSTANTIATE_TEST_CASE_P(GPU_Filters, Blur, testing::Combine(
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4)),
testing::Values(KSize(cv::Size(3, 3)), KSize(cv::Size(5, 5)), KSize(cv::Size(7, 7))),
testing::Values(Anchor(cv::Point(-1, -1)), Anchor(cv::Point(0, 0)), Anchor(cv::Point(2, 2))),
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT)),
WHOLE_SUBMAT));
/////////////////////////////////////////////////////////////////////////////////////////////////
// Filter2D
PARAM_TEST_CASE(Filter2D, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
cv::Size ksize;
cv::Point anchor;
int borderType;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
ksize = GET_PARAM(3);
anchor = GET_PARAM(4);
borderType = GET_PARAM(5);
useRoi = GET_PARAM(6);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Filter2D, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Mat kernel = randomMat(cv::Size(ksize.width, ksize.height), CV_32FC1, 0.0, 1.0);
cv::Ptr<cv::gpu::Filter> filter2D = cv::gpu::createLinearFilter(src.type(), -1, kernel, anchor, borderType);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
filter2D->apply(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::filter2D(src, dst_gold, -1, kernel, anchor, 0, borderType);
EXPECT_MAT_NEAR(dst_gold, dst, CV_MAT_DEPTH(type) == CV_32F ? 1e-1 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Filters, Filter2D, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC4)),
testing::Values(KSize(cv::Size(3, 3)), KSize(cv::Size(5, 5)), KSize(cv::Size(7, 7)), KSize(cv::Size(11, 11)), KSize(cv::Size(13, 13)), KSize(cv::Size(15, 15))),
testing::Values(Anchor(cv::Point(-1, -1)), Anchor(cv::Point(0, 0)), Anchor(cv::Point(2, 2))),
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT)),
WHOLE_SUBMAT));
/////////////////////////////////////////////////////////////////////////////////////////////////
// Laplacian
PARAM_TEST_CASE(Laplacian, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
cv::Size ksize;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
ksize = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Laplacian, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Ptr<cv::gpu::Filter> laplacian = cv::gpu::createLaplacianFilter(src.type(), -1, ksize.width);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
laplacian->apply(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::Laplacian(src, dst_gold, -1, ksize.width);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() < CV_32F ? 0.0 : 1e-3);
}
INSTANTIATE_TEST_CASE_P(GPU_Filters, Laplacian, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_32FC1)),
testing::Values(KSize(cv::Size(1, 1)), KSize(cv::Size(3, 3))),
WHOLE_SUBMAT));
/////////////////////////////////////////////////////////////////////////////////////////////////
// SeparableLinearFilter
PARAM_TEST_CASE(SeparableLinearFilter, cv::gpu::DeviceInfo, cv::Size, MatDepth, Channels, KSize, Anchor, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int depth;
int cn;
cv::Size ksize;
cv::Point anchor;
int borderType;
bool useRoi;
int type;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth = GET_PARAM(2);
cn = GET_PARAM(3);
ksize = GET_PARAM(4);
anchor = GET_PARAM(5);
borderType = GET_PARAM(6);
useRoi = GET_PARAM(7);
cv::gpu::setDevice(devInfo.deviceID());
type = CV_MAKE_TYPE(depth, cn);
}
};
GPU_TEST_P(SeparableLinearFilter, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Mat rowKernel = randomMat(Size(ksize.width, 1), CV_32FC1, 0.0, 1.0);
cv::Mat columnKernel = randomMat(Size(ksize.height, 1), CV_32FC1, 0.0, 1.0);
cv::Ptr<cv::gpu::Filter> filter = cv::gpu::createSeparableLinearFilter(src.type(), -1, rowKernel, columnKernel, anchor, borderType);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
filter->apply(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::sepFilter2D(src, dst_gold, -1, rowKernel, columnKernel, anchor, 0, borderType);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() < CV_32F ? 1.0 : 1e-2);
}
INSTANTIATE_TEST_CASE_P(GPU_Filters, SeparableLinearFilter, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatDepth(CV_8U), MatDepth(CV_16U), MatDepth(CV_16S), MatDepth(CV_32F)),
IMAGE_CHANNELS,
testing::Values(KSize(cv::Size(3, 3)),
KSize(cv::Size(7, 7)),
KSize(cv::Size(13, 13)),
KSize(cv::Size(15, 15)),
KSize(cv::Size(17, 17)),
KSize(cv::Size(23, 15)),
KSize(cv::Size(31, 3))),
testing::Values(Anchor(cv::Point(-1, -1)), Anchor(cv::Point(0, 0)), Anchor(cv::Point(2, 2))),
testing::Values(BorderType(cv::BORDER_REFLECT101),
BorderType(cv::BORDER_REPLICATE),
BorderType(cv::BORDER_CONSTANT),
BorderType(cv::BORDER_REFLECT)),
WHOLE_SUBMAT));
/////////////////////////////////////////////////////////////////////////////////////////////////
@ -155,13 +326,15 @@ GPU_TEST_P(Sobel, Accuracy)
cv::Mat src = randomMat(size, type);
cv::Ptr<cv::gpu::Filter> sobel = cv::gpu::createSobelFilter(src.type(), -1, dx, dy, ksize.width, 1.0, borderType);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::Sobel(loadMat(src, useRoi), dst, -1, dx, dy, ksize.width, 1.0, borderType);
sobel->apply(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::Sobel(src, dst_gold, -1, dx, dy, ksize.width, 1.0, 0.0, borderType);
EXPECT_MAT_NEAR(getInnerROI(dst_gold, ksize), getInnerROI(dst, ksize), CV_MAT_DEPTH(type) < CV_32F ? 0.0 : 0.1);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() < CV_32F ? 0.0 : 0.1);
}
INSTANTIATE_TEST_CASE_P(GPU_Filters, Sobel, testing::Combine(
@ -218,13 +391,15 @@ GPU_TEST_P(Scharr, Accuracy)
cv::Mat src = randomMat(size, type);
cv::Ptr<cv::gpu::Filter> scharr = cv::gpu::createScharrFilter(src.type(), -1, dx, dy, 1.0, borderType);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::Scharr(loadMat(src, useRoi), dst, -1, dx, dy, 1.0, borderType);
scharr->apply(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::Scharr(src, dst_gold, -1, dx, dy, 1.0, 0.0, borderType);
EXPECT_MAT_NEAR(getInnerROI(dst_gold, cv::Size(3, 3)), getInnerROI(dst, cv::Size(3, 3)), CV_MAT_DEPTH(type) < CV_32F ? 0.0 : 0.1);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() < CV_32F ? 0.0 : 0.1);
}
INSTANTIATE_TEST_CASE_P(GPU_Filters, Scharr, testing::Combine(
@ -277,28 +452,15 @@ GPU_TEST_P(GaussianBlur, Accuracy)
double sigma1 = randomDouble(0.1, 1.0);
double sigma2 = randomDouble(0.1, 1.0);
if (ksize.height > 16 && !supportFeature(devInfo, cv::gpu::FEATURE_SET_COMPUTE_20))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::GaussianBlur(loadMat(src), dst, ksize, sigma1, sigma2, borderType);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsNotImplemented, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::GaussianBlur(loadMat(src, useRoi), dst, ksize, sigma1, sigma2, borderType);
cv::Ptr<cv::gpu::Filter> gauss = cv::gpu::createGaussianFilter(src.type(), -1, ksize, sigma1, sigma2, borderType);
cv::Mat dst_gold;
cv::GaussianBlur(src, dst_gold, ksize, sigma1, sigma2, borderType);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
gauss->apply(loadMat(src, useRoi), dst);
EXPECT_MAT_NEAR(dst_gold, dst, 4.0);
}
cv::Mat dst_gold;
cv::GaussianBlur(src, dst_gold, ksize, sigma1, sigma2, borderType);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() < CV_32F ? 4.0 : 1e-4);
}
INSTANTIATE_TEST_CASE_P(GPU_Filters, GaussianBlur, testing::Combine(
@ -327,49 +489,6 @@ INSTANTIATE_TEST_CASE_P(GPU_Filters, GaussianBlur, testing::Combine(
BorderType(cv::BORDER_REFLECT)),
WHOLE_SUBMAT));
/////////////////////////////////////////////////////////////////////////////////////////////////
// Laplacian
PARAM_TEST_CASE(Laplacian, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
cv::Size ksize;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
ksize = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Laplacian, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::Laplacian(loadMat(src, useRoi), dst, -1, ksize.width);
cv::Mat dst_gold;
cv::Laplacian(src, dst_gold, -1, ksize.width);
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() < CV_32F ? 0.0 : 1e-3);
}
INSTANTIATE_TEST_CASE_P(GPU_Filters, Laplacian, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_32FC1)),
testing::Values(KSize(cv::Size(1, 1)), KSize(cv::Size(3, 3))),
WHOLE_SUBMAT));
/////////////////////////////////////////////////////////////////////////////////////////////////
// Erode
@ -400,8 +519,10 @@ GPU_TEST_P(Erode, Accuracy)
cv::Mat src = randomMat(size, type);
cv::Mat kernel = cv::Mat::ones(3, 3, CV_8U);
cv::Ptr<cv::gpu::Filter> erode = cv::gpu::createMorphologyFilter(cv::MORPH_ERODE, src.type(), kernel, anchor, iterations);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::erode(loadMat(src, useRoi), dst, kernel, anchor, iterations);
erode->apply(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::erode(src, dst_gold, kernel, anchor, iterations);
@ -449,8 +570,10 @@ GPU_TEST_P(Dilate, Accuracy)
cv::Mat src = randomMat(size, type);
cv::Mat kernel = cv::Mat::ones(3, 3, CV_8U);
cv::Ptr<cv::gpu::Filter> dilate = cv::gpu::createMorphologyFilter(cv::MORPH_DILATE, src.type(), kernel, anchor, iterations);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::dilate(loadMat(src, useRoi), dst, kernel, anchor, iterations);
dilate->apply(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::dilate(src, dst_gold, kernel, anchor, iterations);
@ -502,8 +625,10 @@ GPU_TEST_P(MorphEx, Accuracy)
cv::Mat src = randomMat(size, type);
cv::Mat kernel = cv::Mat::ones(3, 3, CV_8U);
cv::Ptr<cv::gpu::Filter> morph = cv::gpu::createMorphologyFilter(morphOp, src.type(), kernel, anchor, iterations);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::morphologyEx(loadMat(src, useRoi), dst, morphOp, kernel, anchor, iterations);
morph->apply(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::morphologyEx(src, dst_gold, morphOp, kernel, anchor, iterations);
@ -522,56 +647,4 @@ INSTANTIATE_TEST_CASE_P(GPU_Filters, MorphEx, testing::Combine(
testing::Values(Iterations(1), Iterations(2), Iterations(3)),
WHOLE_SUBMAT));
/////////////////////////////////////////////////////////////////////////////////////////////////
// Filter2D
PARAM_TEST_CASE(Filter2D, cv::gpu::DeviceInfo, cv::Size, MatType, KSize, Anchor, BorderType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
cv::Size size;
int type;
cv::Size ksize;
cv::Point anchor;
int borderType;
bool useRoi;
cv::Mat img;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
ksize = GET_PARAM(3);
anchor = GET_PARAM(4);
borderType = GET_PARAM(5);
useRoi = GET_PARAM(6);
cv::gpu::setDevice(devInfo.deviceID());
}
};
GPU_TEST_P(Filter2D, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Mat kernel = randomMat(cv::Size(ksize.width, ksize.height), CV_32FC1, 0.0, 1.0);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::filter2D(loadMat(src, useRoi), dst, -1, kernel, anchor, borderType);
cv::Mat dst_gold;
cv::filter2D(src, dst_gold, -1, kernel, anchor, 0, borderType);
EXPECT_MAT_NEAR(dst_gold, dst, CV_MAT_DEPTH(type) == CV_32F ? 1e-1 : 1.0);
}
INSTANTIATE_TEST_CASE_P(GPU_Filters, Filter2D, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC4), MatType(CV_16UC1), MatType(CV_16UC4), MatType(CV_32FC1), MatType(CV_32FC4)),
testing::Values(KSize(cv::Size(3, 3)), KSize(cv::Size(5, 5)), KSize(cv::Size(7, 7)), KSize(cv::Size(11, 11)), KSize(cv::Size(13, 13)), KSize(cv::Size(15, 15))),
testing::Values(Anchor(cv::Point(-1, -1)), Anchor(cv::Point(0, 0)), Anchor(cv::Point(2, 2))),
testing::Values(BorderType(cv::BORDER_REFLECT101), BorderType(cv::BORDER_REPLICATE), BorderType(cv::BORDER_CONSTANT), BorderType(cv::BORDER_REFLECT)),
WHOLE_SUBMAT));
#endif // HAVE_CUDA

2
modules/gpuimgproc/include/opencv2/gpuimgproc.hpp
View File

@ -158,7 +158,7 @@ struct CV_EXPORTS CannyBuf
GpuMat mag;
GpuMat map;
GpuMat st1, st2;
Ptr<FilterEngine_GPU> filterDX, filterDY;
Ptr<Filter> filterDX, filterDY;
};
CV_EXPORTS void Canny(const GpuMat& image, GpuMat& edges, double low_thresh, double high_thresh, int apperture_size = 3, bool L2gradient = false);

8
modules/gpuimgproc/src/canny.cpp
View File

@ -65,8 +65,8 @@ void cv::gpu::CannyBuf::create(const Size& image_size, int apperture_size)
if (apperture_size != 3)
{
filterDX = createDerivFilter_GPU(CV_8UC1, CV_32S, 1, 0, apperture_size, BORDER_REPLICATE);
filterDY = createDerivFilter_GPU(CV_8UC1, CV_32S, 0, 1, apperture_size, BORDER_REPLICATE);
filterDX = createDerivFilter(CV_8UC1, CV_32S, 1, 0, apperture_size, false, 1, BORDER_REPLICATE);
filterDY = createDerivFilter(CV_8UC1, CV_32S, 0, 1, apperture_size, false, 1, BORDER_REPLICATE);
}
}
@ -150,8 +150,8 @@ void cv::gpu::Canny(const GpuMat& src, CannyBuf& buf, GpuMat& dst, double low_th
}
else
{
buf.filterDX->apply(src, buf.dx, Rect(0, 0, src.cols, src.rows));
buf.filterDY->apply(src, buf.dy, Rect(0, 0, src.cols, src.rows));
buf.filterDX->apply(src, buf.dx);
buf.filterDY->apply(src, buf.dy);
calcMagnitude(buf.dx, buf.dy, buf.mag, L2gradient);
}

15
modules/gpuimgproc/src/corners.cpp
View File

@ -70,6 +70,8 @@ namespace
{
void extractCovData(const GpuMat& src, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, int borderType, Stream& stream)
{
(void) buf;
double scale = static_cast<double>(1 << ((ksize > 0 ? ksize : 3) - 1)) * blockSize;
if (ksize < 0)
@ -83,16 +85,21 @@ namespace
Dx.create(src.size(), CV_32F);
Dy.create(src.size(), CV_32F);
Ptr<gpu::Filter> filterDx, filterDy;
if (ksize > 0)
{
Sobel(src, Dx, CV_32F, 1, 0, buf, ksize, scale, borderType, -1, stream);
Sobel(src, Dy, CV_32F, 0, 1, buf, ksize, scale, borderType, -1, stream);
filterDx = gpu::createSobelFilter(src.type(), CV_32F, 1, 0, ksize, scale, borderType);
filterDy = gpu::createSobelFilter(src.type(), CV_32F, 0, 1, ksize, scale, borderType);
}
else
{
Scharr(src, Dx, CV_32F, 1, 0, buf, scale, borderType, -1, stream);
Scharr(src, Dy, CV_32F, 0, 1, buf, scale, borderType, -1, stream);
filterDx = gpu::createScharrFilter(src.type(), CV_32F, 1, 0, scale, borderType);
filterDy = gpu::createScharrFilter(src.type(), CV_32F, 0, 1, scale, borderType);
}
filterDx->apply(src, Dx);
filterDy->apply(src, Dy);
}
}

8
modules/superres/src/btv_l1_gpu.cpp
View File

@ -230,7 +230,7 @@ namespace
Ptr<DenseOpticalFlowExt> opticalFlow_;
private:
std::vector<Ptr<FilterEngine_GPU> > filters_;
std::vector<Ptr<gpu::Filter> > filters_;
int curBlurKernelSize_;
double curBlurSigma_;
int curSrcType_;
@ -299,7 +299,7 @@ namespace
{
filters_.resize(src.size());
for (size_t i = 0; i < src.size(); ++i)
filters_[i] = createGaussianFilter_GPU(src[0].type(), Size(blurKernelSize_, blurKernelSize_), blurSigma_);
filters_[i] = gpu::createGaussianFilter(src[0].type(), -1, Size(blurKernelSize_, blurKernelSize_), blurSigma_);
curBlurKernelSize_ = blurKernelSize_;
curBlurSigma_ = blurSigma_;
curSrcType_ = src[0].type();
@ -346,7 +346,7 @@ namespace
// a = M * Ih
gpu::remap(highRes_, a_[k], backwardMaps_[k].first, backwardMaps_[k].second, INTER_NEAREST, BORDER_REPLICATE, Scalar(), streams_[k]);
// b = HM * Ih
filters_[k]->apply(a_[k], b_[k], Rect(0,0,-1,-1), streams_[k]);
filters_[k]->apply(a_[k], b_[k], streams_[k]);
// c = DHF * Ih
gpu::resize(b_[k], c_[k], lowResSize, 0, 0, INTER_NEAREST, streams_[k]);
@ -355,7 +355,7 @@ namespace
// a = Dt * diff
upscale(c_[k], a_[k], scale_, streams_[k]);
// b = HtDt * diff
filters_[k]->apply(a_[k], b_[k], Rect(0,0,-1,-1), streams_[k]);
filters_[k]->apply(a_[k], b_[k], streams_[k]);
// diffTerm = MtHtDt * diff
gpu::remap(b_[k], diffTerms_[k], forwardMaps_[k].first, forwardMaps_[k].second, INTER_NEAREST, BORDER_REPLICATE, Scalar(), streams_[k]);
}

24
samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp
View File

@ -308,6 +308,8 @@ Scalar getMSSIM_GPU( const Mat& i1, const Mat& i2)
gpu::split(tmp2, vI2);
Scalar mssim;
Ptr<gpu::Filter> gauss = gpu::createGaussianFilter(vI2[0].type(), -1, Size(11, 11), 1.5);
for( int i = 0; i < gI1.channels(); ++i )
{
gpu::GpuMat I2_2, I1_2, I1_I2;
@ -318,8 +320,8 @@ Scalar getMSSIM_GPU( const Mat& i1, const Mat& i2)
/*************************** END INITS **********************************/
gpu::GpuMat mu1, mu2; // PRELIMINARY COMPUTING
gpu::GaussianBlur(vI1[i], mu1, Size(11, 11), 1.5);
gpu::GaussianBlur(vI2[i], mu2, Size(11, 11), 1.5);
gauss->apply(vI1[i], mu1);
gauss->apply(vI2[i], mu2);
gpu::GpuMat mu1_2, mu2_2, mu1_mu2;
gpu::multiply(mu1, mu1, mu1_2);
@ -328,13 +330,13 @@ Scalar getMSSIM_GPU( const Mat& i1, const Mat& i2)
gpu::GpuMat sigma1_2, sigma2_2, sigma12;
gpu::GaussianBlur(I1_2, sigma1_2, Size(11, 11), 1.5);
gauss->apply(I1_2, sigma1_2);
gpu::subtract(sigma1_2, mu1_2, sigma1_2); // sigma1_2 -= mu1_2;
gpu::GaussianBlur(I2_2, sigma2_2, Size(11, 11), 1.5);
gauss->apply(I2_2, sigma2_2);
gpu::subtract(sigma2_2, mu2_2, sigma2_2); // sigma2_2 -= mu2_2;
gpu::GaussianBlur(I1_I2, sigma12, Size(11, 11), 1.5);
gauss->apply(I1_I2, sigma12);
gpu::subtract(sigma12, mu1_mu2, sigma12); // sigma12 -= mu1_mu2;
///////////////////////////////// FORMULA ////////////////////////////////
@ -375,7 +377,7 @@ Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
gpu::split(b.t2, b.vI2, stream);
Scalar mssim;
gpu::GpuMat buf;
Ptr<gpu::Filter> gauss = gpu::createGaussianFilter(b.vI1[0].type(), -1, Size(11, 11), 1.5);
for( int i = 0; i < b.gI1.channels(); ++i )
{
@ -383,22 +385,22 @@ Scalar getMSSIM_GPU_optimized( const Mat& i1, const Mat& i2, BufferMSSIM& b)
gpu::multiply(b.vI1[i], b.vI1[i], b.I1_2, 1, -1, stream); // I1^2
gpu::multiply(b.vI1[i], b.vI2[i], b.I1_I2, 1, -1, stream); // I1 * I2
gpu::GaussianBlur(b.vI1[i], b.mu1, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gpu::GaussianBlur(b.vI2[i], b.mu2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gauss->apply(b.vI1[i], b.mu1, stream);
gauss->apply(b.vI2[i], b.mu2, stream);
gpu::multiply(b.mu1, b.mu1, b.mu1_2, 1, -1, stream);
gpu::multiply(b.mu2, b.mu2, b.mu2_2, 1, -1, stream);
gpu::multiply(b.mu1, b.mu2, b.mu1_mu2, 1, -1, stream);
gpu::GaussianBlur(b.I1_2, b.sigma1_2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gauss->apply(b.I1_2, b.sigma1_2, stream);
gpu::subtract(b.sigma1_2, b.mu1_2, b.sigma1_2, gpu::GpuMat(), -1, stream);
//b.sigma1_2 -= b.mu1_2; - This would result in an extra data transfer operation
gpu::GaussianBlur(b.I2_2, b.sigma2_2, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gauss->apply(b.I2_2, b.sigma2_2, stream);
gpu::subtract(b.sigma2_2, b.mu2_2, b.sigma2_2, gpu::GpuMat(), -1, stream);
//b.sigma2_2 -= b.mu2_2;
gpu::GaussianBlur(b.I1_I2, b.sigma12, Size(11, 11), buf, 1.5, 0, BORDER_DEFAULT, -1, stream);
gauss->apply(b.I1_I2, b.sigma12, stream);
gpu::subtract(b.sigma12, b.mu1_mu2, b.sigma12, gpu::GpuMat(), -1, stream);
//b.sigma12 -= b.mu1_mu2;

226
samples/gpu/morphology.cpp
View File

@ -1,120 +1,186 @@
#include <iostream>
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/gpu/gpu.hpp"
#include <stdlib.h>
#include <stdio.h>
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/gpufilters.hpp"
#include "opencv2/gpuimgproc.hpp"
using namespace std;
using namespace cv;
using namespace cv::gpu;
static void help()
class App
{
public:
App(int argc, const char* argv[]);
printf("\nShow off image morphology: erosion, dialation, open and close\n"
"Call:\n morphology2 [image]\n"
"This program also shows use of rect, elipse and cross kernels\n\n");
printf( "Hot keys: \n"
"\tESC - quit the program\n"
"\tr - use rectangle structuring element\n"
"\te - use elliptic structuring element\n"
"\tc - use cross-shaped structuring element\n"
"\tSPACE - loop through all the options\n" );
}
int run();
GpuMat src, dst;
private:
void help();
int element_shape = MORPH_RECT;
void OpenClose();
void ErodeDilate();
//the address of variable which receives trackbar position update
int max_iters = 10;
int open_close_pos = 0;
int erode_dilate_pos = 0;
static void OpenCloseCallback(int, void*);
static void ErodeDilateCallback(int, void*);
// callback function for open/close trackbar
static void OpenClose(int, void*)
gpu::GpuMat src, dst;
int element_shape;
int max_iters;
int open_close_pos;
int erode_dilate_pos;
};
App::App(int argc, const char* argv[])
{
int n = open_close_pos - max_iters;
int an = n > 0 ? n : -n;
Mat element = getStructuringElement(element_shape, Size(an*2+1, an*2+1), Point(an, an) );
if( n < 0 )
cv::gpu::morphologyEx(src, dst, MORPH_OPEN, element);
else
cv::gpu::morphologyEx(src, dst, MORPH_CLOSE, element);
imshow("Open/Close",(Mat)dst);
}
element_shape = MORPH_RECT;
open_close_pos = erode_dilate_pos = max_iters = 10;
// callback function for erode/dilate trackbar
static void ErodeDilate(int, void*)
{
int n = erode_dilate_pos - max_iters;
int an = n > 0 ? n : -n;
Mat element = getStructuringElement(element_shape, Size(an*2+1, an*2+1), Point(an, an) );
if( n < 0 )
cv::gpu::erode(src, dst, element);
else
cv::gpu::dilate(src, dst, element);
imshow("Erode/Dilate",(Mat)dst);
}
int main( int argc, char** argv )
{
char* filename = argc == 2 ? argv[1] : (char*)"baboon.jpg";
if (string(argv[1]) == "--help")
if (argc == 2 && String(argv[1]) == "--help")
{
help();
return -1;
exit(0);
}
src.upload(imread(filename, 1));
if (src.empty())
String filename = argc == 2 ? argv[1] : "baboon.jpg";
Mat img = imread(filename);
if (img.empty())
{
help();
return -1;
cerr << "Can't open image " << filename.c_str() << endl;
exit(-1);
}
cv::gpu::printShortCudaDeviceInfo(cv::gpu::getDevice());
help();
src.upload(img);
if (src.channels() == 3)
{
// gpu support only 4th channel images
GpuMat src4ch;
cv::gpu::cvtColor(src, src4ch, COLOR_BGR2BGRA);
gpu::GpuMat src4ch;
gpu::cvtColor(src, src4ch, COLOR_BGR2BGRA);
src = src4ch;
}
//create windows for output images
namedWindow("Open/Close",1);
namedWindow("Erode/Dilate",1);
help();
open_close_pos = erode_dilate_pos = max_iters;
createTrackbar("iterations", "Open/Close",&open_close_pos,max_iters*2+1,OpenClose);
createTrackbar("iterations", "Erode/Dilate",&erode_dilate_pos,max_iters*2+1,ErodeDilate);
gpu::printShortCudaDeviceInfo(gpu::getDevice());
}
int App::run()
{
// create windows for output images
namedWindow("Open/Close");
namedWindow("Erode/Dilate");
createTrackbar("iterations", "Open/Close", &open_close_pos, max_iters * 2 + 1, OpenCloseCallback, this);
createTrackbar("iterations", "Erode/Dilate", &erode_dilate_pos, max_iters * 2 + 1, ErodeDilateCallback, this);
for(;;)
{
int c;
OpenClose();
ErodeDilate();
OpenClose(open_close_pos, 0);
ErodeDilate(erode_dilate_pos, 0);
c = waitKey();
char c = (char) waitKey();
if( (char)c == 27 )
switch (c)
{
case 27:
return 0;
break;
if( (char)c == 'e' )
case 'e':
element_shape = MORPH_ELLIPSE;
else if( (char)c == 'r' )
break;
case 'r':
element_shape = MORPH_RECT;
else if( (char)c == 'c' )
break;
case 'c':
element_shape = MORPH_CROSS;
else if( (char)c == ' ' )
break;
case ' ':
element_shape = (element_shape + 1) % 3;
break;
}
}
}
void App::help()
{
cout << "Show off image morphology: erosion, dialation, open and close \n";
cout << "Call: \n";
cout << " gpu-example-morphology [image] \n";
cout << "This program also shows use of rect, elipse and cross kernels \n" << endl;
cout << "Hot keys: \n";
cout << "\tESC - quit the program \n";
cout << "\tr - use rectangle structuring element \n";
cout << "\te - use elliptic structuring element \n";
cout << "\tc - use cross-shaped structuring element \n";
cout << "\tSPACE - loop through all the options \n" << endl;
}
void App::OpenClose()
{
int n = open_close_pos - max_iters;
int an = n > 0 ? n : -n;
Mat element = getStructuringElement(element_shape, Size(an*2+1, an*2+1), Point(an, an));
if (n < 0)
{
Ptr<gpu::Filter> openFilter = gpu::createMorphologyFilter(MORPH_OPEN, src.type(), element);
openFilter->apply(src, dst);
}
else
{
Ptr<gpu::Filter> closeFilter = gpu::createMorphologyFilter(MORPH_CLOSE, src.type(), element);
closeFilter->apply(src, dst);
}
return 0;
Mat h_dst(dst);
imshow("Open/Close", h_dst);
}
void App::ErodeDilate()
{
int n = erode_dilate_pos - max_iters;
int an = n > 0 ? n : -n;
Mat element = getStructuringElement(element_shape, Size(an*2+1, an*2+1), Point(an, an));
if (n < 0)
{
Ptr<gpu::Filter> erodeFilter = gpu::createMorphologyFilter(MORPH_ERODE, src.type(), element);
erodeFilter->apply(src, dst);
}
else
{
Ptr<gpu::Filter> dilateFilter = gpu::createMorphologyFilter(MORPH_DILATE, src.type(), element);
dilateFilter->apply(src, dst);
}
Mat h_dst(dst);
imshow("Erode/Dilate", h_dst);
}
void App::OpenCloseCallback(int, void* data)
{
App* thiz = (App*) data;
thiz->OpenClose();
}
void App::ErodeDilateCallback(int, void* data)
{
App* thiz = (App*) data;
thiz->ErodeDilate();
}
int main(int argc, const char* argv[])
{
App app(argc, argv);
return app.run();
}

17
samples/gpu/performance/tests.cpp
View File

@ -746,10 +746,12 @@ TEST(erode)
d_src.upload(src);
gpu::erode(d_src, d_dst, ker, d_buf);
Ptr<gpu::Filter> erode = gpu::createMorphologyFilter(MORPH_ERODE, d_src.type(), ker);
erode->apply(d_src, d_dst);
GPU_ON;
gpu::erode(d_src, d_dst, ker, d_buf);
erode->apply(d_src, d_dst);
GPU_OFF;
}
}
@ -929,10 +931,12 @@ TEST(GaussianBlur)
gpu::GpuMat d_dst(src.size(), src.type());
gpu::GpuMat d_buf;
gpu::GaussianBlur(d_src, d_dst, Size(3, 3), d_buf, 1);
cv::Ptr<cv::gpu::Filter> gauss = cv::gpu::createGaussianFilter(d_src.type(), -1, cv::Size(3, 3), 1);
gauss->apply(d_src, d_dst);
GPU_ON;
gpu::GaussianBlur(d_src, d_dst, Size(3, 3), d_buf, 1);
gauss->apply(d_src, d_dst);
GPU_OFF;
}
}
@ -961,10 +965,11 @@ TEST(filter2D)
gpu::GpuMat d_src(src);
gpu::GpuMat d_dst;
gpu::filter2D(d_src, d_dst, -1, kernel);
Ptr<gpu::Filter> filter2D = gpu::createLinearFilter(d_src.type(), -1, kernel);
filter2D->apply(d_src, d_dst);
GPU_ON;
gpu::filter2D(d_src, d_dst, -1, kernel);
filter2D->apply(d_src, d_dst);
GPU_OFF;
}
}