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cuda: move CUDA modules to opencv_contrib

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OpenCV 4.0+
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Alexander Alekhin 2018-09-18 16:38:12 +00:00 committed by Alexander Alekhin
parent 86ddff0b88
commit c5920df159
458 changed files with 0 additions and 120709 deletions
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27
modules/cudaarithm/CMakeLists.txt
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if(IOS OR WINRT OR (NOT HAVE_CUDA AND NOT BUILD_CUDA_STUBS))
ocv_module_disable(cudaarithm)
endif()
set(the_description "CUDA-accelerated Operations on Matrices")
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4127 /wd4324 /wd4512 -Wundef -Wmissing-declarations -Wshadow)
ocv_add_module(cudaarithm opencv_core OPTIONAL opencv_cudev WRAP python)
ocv_module_include_directories()
ocv_glob_module_sources()
set(extra_libs "")
if(HAVE_CUBLAS)
list(APPEND extra_libs ${CUDA_cublas_LIBRARY})
endif()
if(HAVE_CUFFT)
list(APPEND extra_libs ${CUDA_cufft_LIBRARY})
endif()
ocv_create_module(${extra_libs})
ocv_add_accuracy_tests(DEPENDS_ON opencv_imgproc)
ocv_add_perf_tests(DEPENDS_ON opencv_imgproc)

878
modules/cudaarithm/include/opencv2/cudaarithm.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CUDAARITHM_HPP
#define OPENCV_CUDAARITHM_HPP
#ifndef __cplusplus
# error cudaarithm.hpp header must be compiled as C++
#endif
#include "opencv2/core/cuda.hpp"
/**
@addtogroup cuda
@{
@defgroup cudaarithm Operations on Matrices
@{
@defgroup cudaarithm_core Core Operations on Matrices
@defgroup cudaarithm_elem Per-element Operations
@defgroup cudaarithm_reduce Matrix Reductions
@defgroup cudaarithm_arithm Arithm Operations on Matrices
@}
@}
*/
namespace cv { namespace cuda {
//! @addtogroup cudaarithm
//! @{
//! @addtogroup cudaarithm_elem
//! @{
/** @brief Computes a matrix-matrix or matrix-scalar sum.
@param src1 First source matrix or scalar.
@param src2 Second source matrix or scalar. Matrix should have the same size and type as src1 .
@param dst Destination matrix that has the same size and number of channels as the input array(s).
The depth is defined by dtype or src1 depth.
@param mask Optional operation mask, 8-bit single channel array, that specifies elements of the
destination array to be changed. The mask can be used only with single channel images.
@param dtype Optional depth of the output array.
@param stream Stream for the asynchronous version.
@sa add
*/
CV_EXPORTS_W void add(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), int dtype = -1, Stream& stream = Stream::Null());
/** @brief Computes a matrix-matrix or matrix-scalar difference.
@param src1 First source matrix or scalar.
@param src2 Second source matrix or scalar. Matrix should have the same size and type as src1 .
@param dst Destination matrix that has the same size and number of channels as the input array(s).
The depth is defined by dtype or src1 depth.
@param mask Optional operation mask, 8-bit single channel array, that specifies elements of the
destination array to be changed. The mask can be used only with single channel images.
@param dtype Optional depth of the output array.
@param stream Stream for the asynchronous version.
@sa subtract
*/
CV_EXPORTS_W void subtract(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), int dtype = -1, Stream& stream = Stream::Null());
/** @brief Computes a matrix-matrix or matrix-scalar per-element product.
@param src1 First source matrix or scalar.
@param src2 Second source matrix or scalar.
@param dst Destination matrix that has the same size and number of channels as the input array(s).
The depth is defined by dtype or src1 depth.
@param scale Optional scale factor.
@param dtype Optional depth of the output array.
@param stream Stream for the asynchronous version.
@sa multiply
*/
CV_EXPORTS_W void multiply(InputArray src1, InputArray src2, OutputArray dst, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
/** @brief Computes a matrix-matrix or matrix-scalar division.
@param src1 First source matrix or a scalar.
@param src2 Second source matrix or scalar.
@param dst Destination matrix that has the same size and number of channels as the input array(s).
The depth is defined by dtype or src1 depth.
@param scale Optional scale factor.
@param dtype Optional depth of the output array.
@param stream Stream for the asynchronous version.
This function, in contrast to divide, uses a round-down rounding mode.
@sa divide
*/
CV_EXPORTS_W void divide(InputArray src1, InputArray src2, OutputArray dst, double scale = 1, int dtype = -1, Stream& stream = Stream::Null());
/** @brief Computes per-element absolute difference of two matrices (or of a matrix and scalar).
@param src1 First source matrix or scalar.
@param src2 Second source matrix or scalar.
@param dst Destination matrix that has the same size and type as the input array(s).
@param stream Stream for the asynchronous version.
@sa absdiff
*/
CV_EXPORTS_W void absdiff(InputArray src1, InputArray src2, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Computes an absolute value of each matrix element.
@param src Source matrix.
@param dst Destination matrix with the same size and type as src .
@param stream Stream for the asynchronous version.
@sa abs
*/
CV_EXPORTS_W void abs(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Computes a square value of each matrix element.
@param src Source matrix.
@param dst Destination matrix with the same size and type as src .
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void sqr(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Computes a square root of each matrix element.
@param src Source matrix.
@param dst Destination matrix with the same size and type as src .
@param stream Stream for the asynchronous version.
@sa sqrt
*/
CV_EXPORTS_W void sqrt(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Computes an exponent of each matrix element.
@param src Source matrix.
@param dst Destination matrix with the same size and type as src .
@param stream Stream for the asynchronous version.
@sa exp
*/
CV_EXPORTS_W void exp(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Computes a natural logarithm of absolute value of each matrix element.
@param src Source matrix.
@param dst Destination matrix with the same size and type as src .
@param stream Stream for the asynchronous version.
@sa log
*/
CV_EXPORTS_W void log(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Raises every matrix element to a power.
@param src Source matrix.
@param power Exponent of power.
@param dst Destination matrix with the same size and type as src .
@param stream Stream for the asynchronous version.
The function pow raises every element of the input matrix to power :
\f[\texttt{dst} (I) = \fork{\texttt{src}(I)^power}{if \texttt{power} is integer}{|\texttt{src}(I)|^power}{otherwise}\f]
@sa pow
*/
CV_EXPORTS_W void pow(InputArray src, double power, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Compares elements of two matrices (or of a matrix and scalar).
@param src1 First source matrix or scalar.
@param src2 Second source matrix or scalar.
@param dst Destination matrix that has the same size and type as the input array(s).
@param cmpop Flag specifying the relation between the elements to be checked:
- **CMP_EQ:** a(.) == b(.)
- **CMP_GT:** a(.) \> b(.)
- **CMP_GE:** a(.) \>= b(.)
- **CMP_LT:** a(.) \< b(.)
- **CMP_LE:** a(.) \<= b(.)
- **CMP_NE:** a(.) != b(.)
@param stream Stream for the asynchronous version.
@sa compare
*/
CV_EXPORTS_W void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop, Stream& stream = Stream::Null());
/** @brief Performs a per-element bitwise inversion.
@param src Source matrix.
@param dst Destination matrix with the same size and type as src .
@param mask Optional operation mask, 8-bit single channel array, that specifies elements of the
destination array to be changed. The mask can be used only with single channel images.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void bitwise_not(InputArray src, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
/** @brief Performs a per-element bitwise disjunction of two matrices (or of matrix and scalar).
@param src1 First source matrix or scalar.
@param src2 Second source matrix or scalar.
@param dst Destination matrix that has the same size and type as the input array(s).
@param mask Optional operation mask, 8-bit single channel array, that specifies elements of the
destination array to be changed. The mask can be used only with single channel images.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void bitwise_or(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
/** @brief Performs a per-element bitwise conjunction of two matrices (or of matrix and scalar).
@param src1 First source matrix or scalar.
@param src2 Second source matrix or scalar.
@param dst Destination matrix that has the same size and type as the input array(s).
@param mask Optional operation mask, 8-bit single channel array, that specifies elements of the
destination array to be changed. The mask can be used only with single channel images.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void bitwise_and(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
/** @brief Performs a per-element bitwise exclusive or operation of two matrices (or of matrix and scalar).
@param src1 First source matrix or scalar.
@param src2 Second source matrix or scalar.
@param dst Destination matrix that has the same size and type as the input array(s).
@param mask Optional operation mask, 8-bit single channel array, that specifies elements of the
destination array to be changed. The mask can be used only with single channel images.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void bitwise_xor(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
/** @brief Performs pixel by pixel right shift of an image by a constant value.
@param src Source matrix. Supports 1, 3 and 4 channels images with integers elements.
@param val Constant values, one per channel.
@param dst Destination matrix with the same size and type as src .
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS void rshift(InputArray src, Scalar_<int> val, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Performs pixel by pixel right left of an image by a constant value.
@param src Source matrix. Supports 1, 3 and 4 channels images with CV_8U , CV_16U or CV_32S
depth.
@param val Constant values, one per channel.
@param dst Destination matrix with the same size and type as src .
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS void lshift(InputArray src, Scalar_<int> val, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Computes the per-element minimum of two matrices (or a matrix and a scalar).
@param src1 First source matrix or scalar.
@param src2 Second source matrix or scalar.
@param dst Destination matrix that has the same size and type as the input array(s).
@param stream Stream for the asynchronous version.
@sa min
*/
CV_EXPORTS_W void min(InputArray src1, InputArray src2, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Computes the per-element maximum of two matrices (or a matrix and a scalar).
@param src1 First source matrix or scalar.
@param src2 Second source matrix or scalar.
@param dst Destination matrix that has the same size and type as the input array(s).
@param stream Stream for the asynchronous version.
@sa max
*/
CV_EXPORTS_W void max(InputArray src1, InputArray src2, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Computes the weighted sum of two arrays.
@param src1 First source array.
@param alpha Weight for the first array elements.
@param src2 Second source array of the same size and channel number as src1 .
@param beta Weight for the second array elements.
@param dst Destination array that has the same size and number of channels as the input arrays.
@param gamma Scalar added to each sum.
@param dtype Optional depth of the destination array. When both input arrays have the same depth,
dtype can be set to -1, which will be equivalent to src1.depth().
@param stream Stream for the asynchronous version.
The function addWeighted calculates the weighted sum of two arrays as follows:
\f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\f]
where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
channel is processed independently.
@sa addWeighted
*/
CV_EXPORTS_W void addWeighted(InputArray src1, double alpha, InputArray src2, double beta, double gamma, OutputArray dst,
int dtype = -1, Stream& stream = Stream::Null());
//! adds scaled array to another one (dst = alpha*src1 + src2)
static inline void scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst, Stream& stream = Stream::Null())
{
addWeighted(src1, alpha, src2, 1.0, 0.0, dst, -1, stream);
}
/** @brief Applies a fixed-level threshold to each array element.
@param src Source array (single-channel).
@param dst Destination array with the same size and type as src .
@param thresh Threshold value.
@param maxval Maximum value to use with THRESH_BINARY and THRESH_BINARY_INV threshold types.
@param type Threshold type. For details, see threshold . The THRESH_OTSU and THRESH_TRIANGLE
threshold types are not supported.
@param stream Stream for the asynchronous version.
@sa threshold
*/
CV_EXPORTS_W double threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type, Stream& stream = Stream::Null());
/** @brief Computes magnitudes of complex matrix elements.
@param xy Source complex matrix in the interleaved format ( CV_32FC2 ).
@param magnitude Destination matrix of float magnitudes ( CV_32FC1 ).
@param stream Stream for the asynchronous version.
@sa magnitude
*/
CV_EXPORTS_W void magnitude(InputArray xy, OutputArray magnitude, Stream& stream = Stream::Null());
/** @brief Computes squared magnitudes of complex matrix elements.
@param xy Source complex matrix in the interleaved format ( CV_32FC2 ).
@param magnitude Destination matrix of float magnitude squares ( CV_32FC1 ).
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void magnitudeSqr(InputArray xy, OutputArray magnitude, Stream& stream = Stream::Null());
/** @overload
computes magnitude of each (x(i), y(i)) vector
supports only floating-point source
@param x Source matrix containing real components ( CV_32FC1 ).
@param y Source matrix containing imaginary components ( CV_32FC1 ).
@param magnitude Destination matrix of float magnitudes ( CV_32FC1 ).
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void magnitude(InputArray x, InputArray y, OutputArray magnitude, Stream& stream = Stream::Null());
/** @overload
computes squared magnitude of each (x(i), y(i)) vector
supports only floating-point source
@param x Source matrix containing real components ( CV_32FC1 ).
@param y Source matrix containing imaginary components ( CV_32FC1 ).
@param magnitude Destination matrix of float magnitude squares ( CV_32FC1 ).
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void magnitudeSqr(InputArray x, InputArray y, OutputArray magnitude, Stream& stream = Stream::Null());
/** @brief Computes polar angles of complex matrix elements.
@param x Source matrix containing real components ( CV_32FC1 ).
@param y Source matrix containing imaginary components ( CV_32FC1 ).
@param angle Destination matrix of angles ( CV_32FC1 ).
@param angleInDegrees Flag for angles that must be evaluated in degrees.
@param stream Stream for the asynchronous version.
@sa phase
*/
CV_EXPORTS_W void phase(InputArray x, InputArray y, OutputArray angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
/** @brief Converts Cartesian coordinates into polar.
@param x Source matrix containing real components ( CV_32FC1 ).
@param y Source matrix containing imaginary components ( CV_32FC1 ).
@param magnitude Destination matrix of float magnitudes ( CV_32FC1 ).
@param angle Destination matrix of angles ( CV_32FC1 ).
@param angleInDegrees Flag for angles that must be evaluated in degrees.
@param stream Stream for the asynchronous version.
@sa cartToPolar
*/
CV_EXPORTS_W void cartToPolar(InputArray x, InputArray y, OutputArray magnitude, OutputArray angle, bool angleInDegrees = false, Stream& stream = Stream::Null());
/** @brief Converts polar coordinates into Cartesian.
@param magnitude Source matrix containing magnitudes ( CV_32FC1 ).
@param angle Source matrix containing angles ( CV_32FC1 ).
@param x Destination matrix of real components ( CV_32FC1 ).
@param y Destination matrix of imaginary components ( CV_32FC1 ).
@param angleInDegrees Flag that indicates angles in degrees.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void polarToCart(InputArray magnitude, InputArray angle, OutputArray x, OutputArray y, bool angleInDegrees = false, Stream& stream = Stream::Null());
//! @} cudaarithm_elem
//! @addtogroup cudaarithm_core
//! @{
/** @brief Makes a multi-channel matrix out of several single-channel matrices.
@param src Array/vector of source matrices.
@param n Number of source matrices.
@param dst Destination matrix.
@param stream Stream for the asynchronous version.
@sa merge
*/
CV_EXPORTS_W void merge(const GpuMat* src, size_t n, OutputArray dst, Stream& stream = Stream::Null());
/** @overload */
CV_EXPORTS_W void merge(const std::vector<GpuMat>& src, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Copies each plane of a multi-channel matrix into an array.
@param src Source matrix.
@param dst Destination array/vector of single-channel matrices.
@param stream Stream for the asynchronous version.
@sa split
*/
CV_EXPORTS_W void split(InputArray src, GpuMat* dst, Stream& stream = Stream::Null());
/** @overload */
CV_EXPORTS_W void split(InputArray src, std::vector<GpuMat>& dst, Stream& stream = Stream::Null());
/** @brief Transposes a matrix.
@param src1 Source matrix. 1-, 4-, 8-byte element sizes are supported for now.
@param dst Destination matrix.
@param stream Stream for the asynchronous version.
@sa transpose
*/
CV_EXPORTS_W void transpose(InputArray src1, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Flips a 2D matrix around vertical, horizontal, or both axes.
@param src Source matrix. Supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or
CV_32F depth.
@param dst Destination matrix.
@param flipCode Flip mode for the source:
- 0 Flips around x-axis.
- \> 0 Flips around y-axis.
- \< 0 Flips around both axes.
@param stream Stream for the asynchronous version.
@sa flip
*/
CV_EXPORTS_W void flip(InputArray src, OutputArray dst, int flipCode, Stream& stream = Stream::Null());
/** @brief Base class for transform using lookup table.
*/
class CV_EXPORTS_W LookUpTable : public Algorithm
{
public:
/** @brief Transforms the source matrix into the destination matrix using the given look-up table:
dst(I) = lut(src(I)) .
@param src Source matrix. CV_8UC1 and CV_8UC3 matrices are supported for now.
@param dst Destination matrix.
@param stream Stream for the asynchronous version.
*/
CV_WRAP virtual void transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) = 0;
};
/** @brief Creates implementation for cuda::LookUpTable .
@param lut Look-up table of 256 elements. It is a continuous CV_8U matrix.
*/
CV_EXPORTS_W Ptr<LookUpTable> createLookUpTable(InputArray lut);
/** @brief Forms a border around an image.
@param src Source image. CV_8UC1 , CV_8UC4 , CV_32SC1 , and CV_32FC1 types are supported.
@param dst Destination image with the same type as src. The size is
Size(src.cols+left+right, src.rows+top+bottom) .
@param top
@param bottom
@param left
@param right Number of pixels in each direction from the source image rectangle to extrapolate.
For example: top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs to be built.
@param borderType Border type. See borderInterpolate for details. BORDER_REFLECT101 ,
BORDER_REPLICATE , BORDER_CONSTANT , BORDER_REFLECT and BORDER_WRAP are supported for now.
@param value Border value.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void copyMakeBorder(InputArray src, OutputArray dst, int top, int bottom, int left, int right, int borderType,
Scalar value = Scalar(), Stream& stream = Stream::Null());
//! @} cudaarithm_core
//! @addtogroup cudaarithm_reduce
//! @{
/** @brief Returns the norm of a matrix (or difference of two matrices).
@param src1 Source matrix. Any matrices except 64F are supported.
@param normType Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
@sa norm
*/
CV_EXPORTS_W double norm(InputArray src1, int normType, InputArray mask = noArray());
/** @overload */
CV_EXPORTS_W void calcNorm(InputArray src, OutputArray dst, int normType, InputArray mask = noArray(), Stream& stream = Stream::Null());
/** @brief Returns the difference of two matrices.
@param src1 Source matrix. Any matrices except 64F are supported.
@param src2 Second source matrix (if any) with the same size and type as src1.
@param normType Norm type. NORM_L1 , NORM_L2 , and NORM_INF are supported for now.
@sa norm
*/
CV_EXPORTS_W double norm(InputArray src1, InputArray src2, int normType=NORM_L2);
/** @overload */
CV_EXPORTS_W void calcNormDiff(InputArray src1, InputArray src2, OutputArray dst, int normType=NORM_L2, Stream& stream = Stream::Null());
/** @brief Returns the sum of matrix elements.
@param src Source image of any depth except for CV_64F .
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
@sa sum
*/
CV_EXPORTS_W Scalar sum(InputArray src, InputArray mask = noArray());
/** @overload */
CV_EXPORTS_W void calcSum(InputArray src, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
/** @brief Returns the sum of absolute values for matrix elements.
@param src Source image of any depth except for CV_64F .
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
*/
CV_EXPORTS_W Scalar absSum(InputArray src, InputArray mask = noArray());
/** @overload */
CV_EXPORTS_W void calcAbsSum(InputArray src, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
/** @brief Returns the squared sum of matrix elements.
@param src Source image of any depth except for CV_64F .
@param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
*/
CV_EXPORTS_W Scalar sqrSum(InputArray src, InputArray mask = noArray());
/** @overload */
CV_EXPORTS_W void calcSqrSum(InputArray src, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
/** @brief Finds global minimum and maximum matrix elements and returns their values.
@param src Single-channel source image.
@param minVal Pointer to the returned minimum value. Use NULL if not required.
@param maxVal Pointer to the returned maximum value. Use NULL if not required.
@param mask Optional mask to select a sub-matrix.
The function does not work with CV_64F images on GPUs with the compute capability \< 1.3.
@sa minMaxLoc
*/
CV_EXPORTS_W void minMax(InputArray src, double* minVal, double* maxVal, InputArray mask = noArray());
/** @overload */
CV_EXPORTS_W void findMinMax(InputArray src, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
/** @brief Finds global minimum and maximum matrix elements and returns their values with locations.
@param src Single-channel source image.
@param minVal Pointer to the returned minimum value. Use NULL if not required.
@param maxVal Pointer to the returned maximum value. Use NULL if not required.
@param minLoc Pointer to the returned minimum location. Use NULL if not required.
@param maxLoc Pointer to the returned maximum location. Use NULL if not required.
@param mask Optional mask to select a sub-matrix.
The function does not work with CV_64F images on GPU with the compute capability \< 1.3.
@sa minMaxLoc
*/
CV_EXPORTS_W void minMaxLoc(InputArray src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc,
InputArray mask = noArray());
/** @overload */
CV_EXPORTS_W void findMinMaxLoc(InputArray src, OutputArray minMaxVals, OutputArray loc,
InputArray mask = noArray(), Stream& stream = Stream::Null());
/** @brief Counts non-zero matrix elements.
@param src Single-channel source image.
The function does not work with CV_64F images on GPUs with the compute capability \< 1.3.
@sa countNonZero
*/
CV_EXPORTS_W int countNonZero(InputArray src);
/** @overload */
CV_EXPORTS_W void countNonZero(InputArray src, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Reduces a matrix to a vector.
@param mtx Source 2D matrix.
@param vec Destination vector. Its size and type is defined by dim and dtype parameters.
@param dim Dimension index along which the matrix is reduced. 0 means that the matrix is reduced
to a single row. 1 means that the matrix is reduced to a single column.
@param reduceOp Reduction operation that could be one of the following:
- **CV_REDUCE_SUM** The output is the sum of all rows/columns of the matrix.
- **CV_REDUCE_AVG** The output is the mean vector of all rows/columns of the matrix.
- **CV_REDUCE_MAX** The output is the maximum (column/row-wise) of all rows/columns of the
matrix.
- **CV_REDUCE_MIN** The output is the minimum (column/row-wise) of all rows/columns of the
matrix.
@param dtype When it is negative, the destination vector will have the same type as the source
matrix. Otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), mtx.channels()) .
@param stream Stream for the asynchronous version.
The function reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of
1D vectors and performing the specified operation on the vectors until a single row/column is
obtained. For example, the function can be used to compute horizontal and vertical projections of a
raster image. In case of CV_REDUCE_SUM and CV_REDUCE_AVG , the output may have a larger element
bit-depth to preserve accuracy. And multi-channel arrays are also supported in these two reduction
modes.
@sa reduce
*/
CV_EXPORTS_W void reduce(InputArray mtx, OutputArray vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null());
/** @brief Computes a mean value and a standard deviation of matrix elements.
@param mtx Source matrix. CV_8UC1 matrices are supported for now.
@param mean Mean value.
@param stddev Standard deviation value.
@sa meanStdDev
*/
CV_EXPORTS_W void meanStdDev(InputArray mtx, Scalar& mean, Scalar& stddev);
/** @overload */
CV_EXPORTS_W void meanStdDev(InputArray mtx, OutputArray dst, Stream& stream = Stream::Null());
/** @brief Computes a standard deviation of integral images.
@param src Source image. Only the CV_32SC1 type is supported.
@param sqr Squared source image. Only the CV_32FC1 type is supported.
@param dst Destination image with the same type and size as src .
@param rect Rectangular window.
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void rectStdDev(InputArray src, InputArray sqr, OutputArray dst, Rect rect, Stream& stream = Stream::Null());
/** @brief Normalizes the norm or value range of an array.
@param src Input array.
@param dst Output array of the same size as src .
@param alpha Norm value to normalize to or the lower range boundary in case of the range
normalization.
@param beta Upper range boundary in case of the range normalization; it is not used for the norm
normalization.
@param norm_type Normalization type ( NORM_MINMAX , NORM_L2 , NORM_L1 or NORM_INF ).
@param dtype When negative, the output array has the same type as src; otherwise, it has the same
number of channels as src and the depth =CV_MAT_DEPTH(dtype).
@param mask Optional operation mask.
@param stream Stream for the asynchronous version.
@sa normalize
*/
CV_EXPORTS_W void normalize(InputArray src, OutputArray dst, double alpha, double beta,
int norm_type, int dtype, InputArray mask = noArray(),
Stream& stream = Stream::Null());
/** @brief Computes an integral image.
@param src Source image. Only CV_8UC1 images are supported for now.
@param sum Integral image containing 32-bit unsigned integer values packed into CV_32SC1 .
@param stream Stream for the asynchronous version.
@sa integral
*/
CV_EXPORTS_W void integral(InputArray src, OutputArray sum, Stream& stream = Stream::Null());
/** @brief Computes a squared integral image.
@param src Source image. Only CV_8UC1 images are supported for now.
@param sqsum Squared integral image containing 64-bit unsigned integer values packed into
CV_64FC1 .
@param stream Stream for the asynchronous version.
*/
CV_EXPORTS_W void sqrIntegral(InputArray src, OutputArray sqsum, Stream& stream = Stream::Null());
//! @} cudaarithm_reduce
//! @addtogroup cudaarithm_arithm
//! @{
/** @brief Performs generalized matrix multiplication.
@param src1 First multiplied input matrix that should have CV_32FC1 , CV_64FC1 , CV_32FC2 , or
CV_64FC2 type.
@param src2 Second multiplied input matrix of the same type as src1 .
@param alpha Weight of the matrix product.
@param src3 Third optional delta matrix added to the matrix product. It should have the same type
as src1 and src2 .
@param beta Weight of src3 .
@param dst Destination matrix. It has the proper size and the same type as input matrices.
@param flags Operation flags:
- **GEMM_1_T** transpose src1
- **GEMM_2_T** transpose src2
- **GEMM_3_T** transpose src3
@param stream Stream for the asynchronous version.
The function performs generalized matrix multiplication similar to the gemm functions in BLAS level
3. For example, gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T) corresponds to
\f[\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\f]
@note Transposition operation doesn't support CV_64FC2 input type.
@sa gemm
*/
CV_EXPORTS_W void gemm(InputArray src1, InputArray src2, double alpha,
InputArray src3, double beta, OutputArray dst, int flags = 0, Stream& stream = Stream::Null());
/** @brief Performs a per-element multiplication of two Fourier spectrums.
@param src1 First spectrum.
@param src2 Second spectrum with the same size and type as a .
@param dst Destination spectrum.
@param flags Mock parameter used for CPU/CUDA interfaces similarity.
@param conjB Optional flag to specify if the second spectrum needs to be conjugated before the
multiplication.
@param stream Stream for the asynchronous version.
Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now.
@sa mulSpectrums
*/
CV_EXPORTS_W void mulSpectrums(InputArray src1, InputArray src2, OutputArray dst, int flags, bool conjB=false, Stream& stream = Stream::Null());
/** @brief Performs a per-element multiplication of two Fourier spectrums and scales the result.
@param src1 First spectrum.
@param src2 Second spectrum with the same size and type as a .
@param dst Destination spectrum.
@param flags Mock parameter used for CPU/CUDA interfaces similarity, simply add a `0` value.
@param scale Scale constant.
@param conjB Optional flag to specify if the second spectrum needs to be conjugated before the
multiplication.
@param stream Stream for the asynchronous version.
Only full (not packed) CV_32FC2 complex spectrums in the interleaved format are supported for now.
@sa mulSpectrums
*/
CV_EXPORTS_W void mulAndScaleSpectrums(InputArray src1, InputArray src2, OutputArray dst, int flags, float scale, bool conjB=false, Stream& stream = Stream::Null());
/** @brief Performs a forward or inverse discrete Fourier transform (1D or 2D) of the floating point matrix.
@param src Source matrix (real or complex).
@param dst Destination matrix (real or complex).
@param dft_size Size of a discrete Fourier transform.
@param flags Optional flags:
- **DFT_ROWS** transforms each individual row of the source matrix.
- **DFT_SCALE** scales the result: divide it by the number of elements in the transform
(obtained from dft_size ).
- **DFT_INVERSE** inverts DFT. Use for complex-complex cases (real-complex and complex-real
cases are always forward and inverse, respectively).
- **DFT_COMPLEX_INPUT** Specifies that input is complex input with 2 channels.
- **DFT_REAL_OUTPUT** specifies the output as real. The source matrix is the result of
real-complex transform, so the destination matrix must be real.
@param stream Stream for the asynchronous version.
Use to handle real matrices ( CV32FC1 ) and complex matrices in the interleaved format ( CV32FC2 ).
The source matrix should be continuous, otherwise reallocation and data copying is performed. The
function chooses an operation mode depending on the flags, size, and channel count of the source
matrix:
- If the source matrix is complex and the output is not specified as real, the destination
matrix is complex and has the dft_size size and CV_32FC2 type. The destination matrix
contains a full result of the DFT (forward or inverse).
- If the source matrix is complex and the output is specified as real, the function assumes that
its input is the result of the forward transform (see the next item). The destination matrix
has the dft_size size and CV_32FC1 type. It contains the result of the inverse DFT.
- If the source matrix is real (its type is CV_32FC1 ), forward DFT is performed. The result of
the DFT is packed into complex ( CV_32FC2 ) matrix. So, the width of the destination matrix
is dft_size.width / 2 + 1 . But if the source is a single column, the height is reduced
instead of the width.
@sa dft
*/
CV_EXPORTS_W void dft(InputArray src, OutputArray dst, Size dft_size, int flags=0, Stream& stream = Stream::Null());
/** @brief Base class for DFT operator as a cv::Algorithm. :
*/
class CV_EXPORTS_W DFT : public Algorithm
{
public:
/** @brief Computes an FFT of a given image.
@param image Source image. Only CV_32FC1 images are supported for now.
@param result Result image.
@param stream Stream for the asynchronous version.
*/
CV_WRAP virtual void compute(InputArray image, OutputArray result, Stream& stream = Stream::Null()) = 0;
};
/** @brief Creates implementation for cuda::DFT.
@param dft_size The image size.
@param flags Optional flags:
- **DFT_ROWS** transforms each individual row of the source matrix.
- **DFT_SCALE** scales the result: divide it by the number of elements in the transform
(obtained from dft_size ).
- **DFT_INVERSE** inverts DFT. Use for complex-complex cases (real-complex and complex-real
cases are always forward and inverse, respectively).
- **DFT_COMPLEX_INPUT** Specifies that inputs will be complex with 2 channels.
- **DFT_REAL_OUTPUT** specifies the output as real. The source matrix is the result of
real-complex transform, so the destination matrix must be real.
*/
CV_EXPORTS_W Ptr<DFT> createDFT(Size dft_size, int flags);
/** @brief Base class for convolution (or cross-correlation) operator. :
*/
class CV_EXPORTS_W Convolution : public Algorithm
{
public:
/** @brief Computes a convolution (or cross-correlation) of two images.
@param image Source image. Only CV_32FC1 images are supported for now.
@param templ Template image. The size is not greater than the image size. The type is the same as
image .
@param result Result image. If image is *W x H* and templ is *w x h*, then result must be *W-w+1 x
H-h+1*.
@param ccorr Flags to evaluate cross-correlation instead of convolution.
@param stream Stream for the asynchronous version.
*/
virtual void convolve(InputArray image, InputArray templ, OutputArray result, bool ccorr = false, Stream& stream = Stream::Null()) = 0;
};
/** @brief Creates implementation for cuda::Convolution .
@param user_block_size Block size. If you leave default value Size(0,0) then automatic
estimation of block size will be used (which is optimized for speed). By varying user_block_size
you can reduce memory requirements at the cost of speed.
*/
CV_EXPORTS_W Ptr<Convolution> createConvolution(Size user_block_size = Size());
//! @} cudaarithm_arithm
//! @} cudaarithm
}} // namespace cv { namespace cuda {
#endif /* OPENCV_CUDAARITHM_HPP */

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@ -1,254 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
namespace opencv_test { namespace {
//////////////////////////////////////////////////////////////////////
// GEMM
#ifdef HAVE_CUBLAS
CV_FLAGS(GemmFlags, 0, cv::GEMM_1_T, cv::GEMM_2_T, cv::GEMM_3_T)
#define ALL_GEMM_FLAGS Values(GemmFlags(0), GemmFlags(cv::GEMM_1_T), GemmFlags(cv::GEMM_2_T), GemmFlags(cv::GEMM_3_T), \
GemmFlags(cv::GEMM_1_T | cv::GEMM_2_T), GemmFlags(cv::GEMM_1_T | cv::GEMM_3_T), GemmFlags(cv::GEMM_1_T | cv::GEMM_2_T | cv::GEMM_3_T))
DEF_PARAM_TEST(Sz_Type_Flags, cv::Size, MatType, GemmFlags);
PERF_TEST_P(Sz_Type_Flags, GEMM,
Combine(Values(cv::Size(512, 512), cv::Size(1024, 1024)),
Values(CV_32FC1, CV_32FC2, CV_64FC1),
ALL_GEMM_FLAGS))
{
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int flags = GET_PARAM(2);
cv::Mat src1(size, type);
declare.in(src1, WARMUP_RNG);
cv::Mat src2(size, type);
declare.in(src2, WARMUP_RNG);
cv::Mat src3(size, type);
declare.in(src3, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
declare.time(5.0);
const cv::cuda::GpuMat d_src1(src1);
const cv::cuda::GpuMat d_src2(src2);
const cv::cuda::GpuMat d_src3(src3);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::gemm(d_src1, d_src2, 1.0, d_src3, 1.0, dst, flags);
CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
}
else
{
declare.time(50.0);
cv::Mat dst;
TEST_CYCLE() cv::gemm(src1, src2, 1.0, src3, 1.0, dst, flags);
CPU_SANITY_CHECK(dst);
}
}
#endif
//////////////////////////////////////////////////////////////////////
// MulSpectrums
CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT)
DEF_PARAM_TEST(Sz_Flags, cv::Size, DftFlags);
PERF_TEST_P(Sz_Flags, MulSpectrums,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(0, DftFlags(cv::DFT_ROWS))))
{
const cv::Size size = GET_PARAM(0);
const int flag = GET_PARAM(1);
cv::Mat a(size, CV_32FC2);
cv::Mat b(size, CV_32FC2);
declare.in(a, b, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_a(a);
const cv::cuda::GpuMat d_b(b);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::mulSpectrums(d_a, d_b, dst, flag);
CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::mulSpectrums(a, b, dst, flag);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// MulAndScaleSpectrums
PERF_TEST_P(Sz, MulAndScaleSpectrums,
CUDA_TYPICAL_MAT_SIZES)
{
const cv::Size size = GetParam();
const float scale = 1.f / size.area();
cv::Mat src1(size, CV_32FC2);
cv::Mat src2(size, CV_32FC2);
declare.in(src1,src2, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src1(src1);
const cv::cuda::GpuMat d_src2(src2);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::mulAndScaleSpectrums(d_src1, d_src2, dst, cv::DFT_ROWS, scale, false);
CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// Dft
PERF_TEST_P(Sz_Flags, Dft,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(0, DftFlags(cv::DFT_ROWS), DftFlags(cv::DFT_INVERSE))))
{
declare.time(10.0);
const cv::Size size = GET_PARAM(0);
const int flag = GET_PARAM(1);
cv::Mat src(size, CV_32FC2);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::dft(d_src, dst, size, flag);
CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::dft(src, dst, flag);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Convolve
DEF_PARAM_TEST(Sz_KernelSz_Ccorr, cv::Size, int, bool);
PERF_TEST_P(Sz_KernelSz_Ccorr, Convolve,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(17, 27, 32, 64),
Bool()))
{
declare.time(10.0);
const cv::Size size = GET_PARAM(0);
const int templ_size = GET_PARAM(1);
const bool ccorr = GET_PARAM(2);
const cv::Mat image(size, CV_32FC1);
const cv::Mat templ(templ_size, templ_size, CV_32FC1);
declare.in(image, templ, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
cv::cuda::GpuMat d_image = cv::cuda::createContinuous(size, CV_32FC1);
d_image.upload(image);
cv::cuda::GpuMat d_templ = cv::cuda::createContinuous(templ_size, templ_size, CV_32FC1);
d_templ.upload(templ);
cv::Ptr<cv::cuda::Convolution> convolution = cv::cuda::createConvolution();
cv::cuda::GpuMat dst;
TEST_CYCLE() convolution->convolve(d_image, d_templ, dst, ccorr);
CUDA_SANITY_CHECK(dst, 1e-6, ERROR_RELATIVE);
}
else
{
if (ccorr)
FAIL_NO_CPU();
cv::Mat dst;
TEST_CYCLE() cv::filter2D(image, dst, image.depth(), templ);
CPU_SANITY_CHECK(dst);
}
}
}} // namespace

323
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@ -1,323 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
namespace opencv_test { namespace {
#define ARITHM_MAT_DEPTH Values(CV_8U, CV_16U, CV_32F, CV_64F)
//////////////////////////////////////////////////////////////////////
// Merge
DEF_PARAM_TEST(Sz_Depth_Cn, cv::Size, MatDepth, MatCn);
PERF_TEST_P(Sz_Depth_Cn, Merge,
Combine(CUDA_TYPICAL_MAT_SIZES,
ARITHM_MAT_DEPTH,
Values(2, 3, 4)))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
std::vector<cv::Mat> src(channels);
for (int i = 0; i < channels; ++i)
{
src[i].create(size, depth);
declare.in(src[i], WARMUP_RNG);
}
if (PERF_RUN_CUDA())
{
std::vector<cv::cuda::GpuMat> d_src(channels);
for (int i = 0; i < channels; ++i)
d_src[i].upload(src[i]);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::merge(d_src, dst);
CUDA_SANITY_CHECK(dst, 1e-10);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::merge(src, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Split
PERF_TEST_P(Sz_Depth_Cn, Split,
Combine(CUDA_TYPICAL_MAT_SIZES,
ARITHM_MAT_DEPTH,
Values(2, 3, 4)))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
cv::Mat src(size, CV_MAKE_TYPE(depth, channels));
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
std::vector<cv::cuda::GpuMat> dst;
TEST_CYCLE() cv::cuda::split(d_src, dst);
const cv::cuda::GpuMat& dst0 = dst[0];
const cv::cuda::GpuMat& dst1 = dst[1];
CUDA_SANITY_CHECK(dst0, 1e-10);
CUDA_SANITY_CHECK(dst1, 1e-10);
}
else
{
std::vector<cv::Mat> dst;
TEST_CYCLE() cv::split(src, dst);
const cv::Mat& dst0 = dst[0];
const cv::Mat& dst1 = dst[1];
CPU_SANITY_CHECK(dst0);
CPU_SANITY_CHECK(dst1);
}
}
//////////////////////////////////////////////////////////////////////
// Transpose
PERF_TEST_P(Sz_Type, Transpose,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8UC1, CV_8UC4, CV_16UC2, CV_16SC2, CV_32SC1, CV_32SC2, CV_64FC1)))
{
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::transpose(d_src, dst);
CUDA_SANITY_CHECK(dst, 1e-10);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::transpose(src, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Flip
enum {FLIP_BOTH = 0, FLIP_X = 1, FLIP_Y = -1};
CV_ENUM(FlipCode, FLIP_BOTH, FLIP_X, FLIP_Y)
DEF_PARAM_TEST(Sz_Depth_Cn_Code, cv::Size, MatDepth, MatCn, FlipCode);
PERF_TEST_P(Sz_Depth_Cn_Code, Flip,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32F),
CUDA_CHANNELS_1_3_4,
FlipCode::all()))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int flipCode = GET_PARAM(3);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::flip(d_src, dst, flipCode);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::flip(src, dst, flipCode);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// LutOneChannel
PERF_TEST_P(Sz_Type, LutOneChannel,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8UC1, CV_8UC3)))
{
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
cv::Mat lut(1, 256, CV_8UC1);
declare.in(lut, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cuda::LookUpTable> lutAlg = cv::cuda::createLookUpTable(lut);
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() lutAlg->transform(d_src, dst);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::LUT(src, lut, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// LutMultiChannel
PERF_TEST_P(Sz_Type, LutMultiChannel,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values<MatType>(CV_8UC3)))
{
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
cv::Mat lut(1, 256, CV_MAKE_TYPE(CV_8U, src.channels()));
declare.in(lut, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cuda::LookUpTable> lutAlg = cv::cuda::createLookUpTable(lut);
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() lutAlg->transform(d_src, dst);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::LUT(src, lut, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// CopyMakeBorder
DEF_PARAM_TEST(Sz_Depth_Cn_Border, cv::Size, MatDepth, MatCn, BorderMode);
PERF_TEST_P(Sz_Depth_Cn_Border, CopyMakeBorder,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32F),
CUDA_CHANNELS_1_3_4,
ALL_BORDER_MODES))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int borderMode = GET_PARAM(3);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::copyMakeBorder(d_src, dst, 5, 5, 5, 5, borderMode);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::copyMakeBorder(src, dst, 5, 5, 5, 5, borderMode);
CPU_SANITY_CHECK(dst);
}
}
}} // namespace

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modules/cudaarithm/perf/perf_element_operations.cpp
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47
modules/cudaarithm/perf/perf_main.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
using namespace perf;
CV_PERF_TEST_CUDA_MAIN(cudaarithm)

55
modules/cudaarithm/perf/perf_precomp.hpp
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@ -1,55 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_PERF_PRECOMP_HPP__
#define __OPENCV_PERF_PRECOMP_HPP__
#include "opencv2/ts.hpp"
#include "opencv2/ts/cuda_perf.hpp"
#include "opencv2/cudaarithm.hpp"
namespace opencv_test {
using namespace perf;
using namespace testing;
}
#endif

520
modules/cudaarithm/perf/perf_reductions.cpp
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@ -1,520 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
namespace opencv_test { namespace {
//////////////////////////////////////////////////////////////////////
// Norm
DEF_PARAM_TEST(Sz_Depth_Norm, cv::Size, MatDepth, NormType);
PERF_TEST_P(Sz_Depth_Norm, Norm,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32S, CV_32F),
Values(NormType(cv::NORM_INF), NormType(cv::NORM_L1), NormType(cv::NORM_L2))))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int normType = GET_PARAM(2);
cv::Mat src(size, depth);
if (depth == CV_8U)
cv::randu(src, 0, 254);
else
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat d_buf;
double gpu_dst;
TEST_CYCLE() gpu_dst = cv::cuda::norm(d_src, normType, d_buf);
SANITY_CHECK(gpu_dst, 1e-6, ERROR_RELATIVE);
}
else
{
double cpu_dst;
TEST_CYCLE() cpu_dst = cv::norm(src, normType);
SANITY_CHECK(cpu_dst, 1e-6, ERROR_RELATIVE);
}
}
//////////////////////////////////////////////////////////////////////
// NormDiff
DEF_PARAM_TEST(Sz_Norm, cv::Size, NormType);
PERF_TEST_P(Sz_Norm, NormDiff,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(NormType(cv::NORM_INF), NormType(cv::NORM_L1), NormType(cv::NORM_L2))))
{
const cv::Size size = GET_PARAM(0);
const int normType = GET_PARAM(1);
cv::Mat src1(size, CV_8UC1);
declare.in(src1, WARMUP_RNG);
cv::Mat src2(size, CV_8UC1);
declare.in(src2, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src1(src1);
const cv::cuda::GpuMat d_src2(src2);
double gpu_dst;
TEST_CYCLE() gpu_dst = cv::cuda::norm(d_src1, d_src2, normType);
SANITY_CHECK(gpu_dst);
}
else
{
double cpu_dst;
TEST_CYCLE() cpu_dst = cv::norm(src1, src2, normType);
SANITY_CHECK(cpu_dst);
}
}
//////////////////////////////////////////////////////////////////////
// Sum
DEF_PARAM_TEST(Sz_Depth_Cn, cv::Size, MatDepth, MatCn);
PERF_TEST_P(Sz_Depth_Cn, Sum,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32F),
CUDA_CHANNELS_1_3_4))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::Scalar gpu_dst;
TEST_CYCLE() gpu_dst = cv::cuda::sum(d_src);
SANITY_CHECK(gpu_dst, 1e-5, ERROR_RELATIVE);
}
else
{
cv::Scalar cpu_dst;
TEST_CYCLE() cpu_dst = cv::sum(src);
SANITY_CHECK(cpu_dst, 1e-6, ERROR_RELATIVE);
}
}
//////////////////////////////////////////////////////////////////////
// SumAbs
PERF_TEST_P(Sz_Depth_Cn, SumAbs,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32F),
CUDA_CHANNELS_1_3_4))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::Scalar gpu_dst;
TEST_CYCLE() gpu_dst = cv::cuda::absSum(d_src);
SANITY_CHECK(gpu_dst, 1e-6, ERROR_RELATIVE);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// SumSqr
PERF_TEST_P(Sz_Depth_Cn, SumSqr,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values<MatDepth>(CV_8U, CV_16U, CV_32F),
CUDA_CHANNELS_1_3_4))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::Scalar gpu_dst;
TEST_CYCLE() gpu_dst = cv::cuda::sqrSum(d_src);
SANITY_CHECK(gpu_dst, 1e-6, ERROR_RELATIVE);
}
else
{
FAIL_NO_CPU();
}
}
//////////////////////////////////////////////////////////////////////
// MinMax
DEF_PARAM_TEST(Sz_Depth, cv::Size, MatDepth);
PERF_TEST_P(Sz_Depth, MinMax,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32F, CV_64F)))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
cv::Mat src(size, depth);
if (depth == CV_8U)
cv::randu(src, 0, 254);
else
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
double gpu_minVal, gpu_maxVal;
TEST_CYCLE() cv::cuda::minMax(d_src, &gpu_minVal, &gpu_maxVal, cv::cuda::GpuMat());
SANITY_CHECK(gpu_minVal, 1e-10);
SANITY_CHECK(gpu_maxVal, 1e-10);
}
else
{
double cpu_minVal, cpu_maxVal;
TEST_CYCLE() cv::minMaxLoc(src, &cpu_minVal, &cpu_maxVal);
SANITY_CHECK(cpu_minVal);
SANITY_CHECK(cpu_maxVal);
}
}
//////////////////////////////////////////////////////////////////////
// MinMaxLoc
PERF_TEST_P(Sz_Depth, MinMaxLoc,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32F, CV_64F)))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
cv::Mat src(size, depth);
if (depth == CV_8U)
cv::randu(src, 0, 254);
else
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
double gpu_minVal, gpu_maxVal;
cv::Point gpu_minLoc, gpu_maxLoc;
TEST_CYCLE() cv::cuda::minMaxLoc(d_src, &gpu_minVal, &gpu_maxVal, &gpu_minLoc, &gpu_maxLoc);
SANITY_CHECK(gpu_minVal, 1e-10);
SANITY_CHECK(gpu_maxVal, 1e-10);
}
else
{
double cpu_minVal, cpu_maxVal;
cv::Point cpu_minLoc, cpu_maxLoc;
TEST_CYCLE() cv::minMaxLoc(src, &cpu_minVal, &cpu_maxVal, &cpu_minLoc, &cpu_maxLoc);
SANITY_CHECK(cpu_minVal);
SANITY_CHECK(cpu_maxVal);
}
}
//////////////////////////////////////////////////////////////////////
// CountNonZero
PERF_TEST_P(Sz_Depth, CountNonZero,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32F, CV_64F)))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
cv::Mat src(size, depth);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
int gpu_dst = 0;
TEST_CYCLE() gpu_dst = cv::cuda::countNonZero(d_src);
SANITY_CHECK(gpu_dst);
}
else
{
int cpu_dst = 0;
TEST_CYCLE() cpu_dst = cv::countNonZero(src);
SANITY_CHECK(cpu_dst);
}
}
//////////////////////////////////////////////////////////////////////
// Reduce
CV_ENUM(ReduceCode, REDUCE_SUM, REDUCE_AVG, REDUCE_MAX, REDUCE_MIN)
enum {Rows = 0, Cols = 1};
CV_ENUM(ReduceDim, Rows, Cols)
DEF_PARAM_TEST(Sz_Depth_Cn_Code_Dim, cv::Size, MatDepth, MatCn, ReduceCode, ReduceDim);
PERF_TEST_P(Sz_Depth_Cn_Code_Dim, Reduce,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_16S, CV_32F),
Values(1, 2, 3, 4),
ReduceCode::all(),
ReduceDim::all()))
{
const cv::Size size = GET_PARAM(0);
const int depth = GET_PARAM(1);
const int channels = GET_PARAM(2);
const int reduceOp = GET_PARAM(3);
const int dim = GET_PARAM(4);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::reduce(d_src, dst, dim, reduceOp, CV_32F);
dst = dst.reshape(dst.channels(), 1);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::reduce(src, dst, dim, reduceOp, CV_32F);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// Normalize
DEF_PARAM_TEST(Sz_Depth_NormType, cv::Size, MatDepth, NormType);
PERF_TEST_P(Sz_Depth_NormType, Normalize,
Combine(CUDA_TYPICAL_MAT_SIZES,
Values(CV_8U, CV_16U, CV_32F, CV_64F),
Values(NormType(cv::NORM_INF),
NormType(cv::NORM_L1),
NormType(cv::NORM_L2),
NormType(cv::NORM_MINMAX))))
{
const cv::Size size = GET_PARAM(0);
const int type = GET_PARAM(1);
const int norm_type = GET_PARAM(2);
const double alpha = 1;
const double beta = 0;
cv::Mat src(size, type);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::normalize(d_src, dst, alpha, beta, norm_type, type, cv::cuda::GpuMat());
CUDA_SANITY_CHECK(dst, 1e-6);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::normalize(src, dst, alpha, beta, norm_type, type);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// MeanStdDev
PERF_TEST_P(Sz, MeanStdDev,
CUDA_TYPICAL_MAT_SIZES)
{
const cv::Size size = GetParam();
cv::Mat src(size, CV_8UC1);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::Scalar gpu_mean;
cv::Scalar gpu_stddev;
TEST_CYCLE() cv::cuda::meanStdDev(d_src, gpu_mean, gpu_stddev);
SANITY_CHECK(gpu_mean);
SANITY_CHECK(gpu_stddev);
}
else
{
cv::Scalar cpu_mean;
cv::Scalar cpu_stddev;
TEST_CYCLE() cv::meanStdDev(src, cpu_mean, cpu_stddev);
SANITY_CHECK(cpu_mean);
SANITY_CHECK(cpu_stddev);
}
}
//////////////////////////////////////////////////////////////////////
// Integral
PERF_TEST_P(Sz, Integral,
CUDA_TYPICAL_MAT_SIZES)
{
const cv::Size size = GetParam();
cv::Mat src(size, CV_8UC1);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::integral(d_src, dst);
CUDA_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
TEST_CYCLE() cv::integral(src, dst);
CPU_SANITY_CHECK(dst);
}
}
//////////////////////////////////////////////////////////////////////
// IntegralSqr
PERF_TEST_P(Sz, IntegralSqr,
CUDA_TYPICAL_MAT_SIZES)
{
const cv::Size size = GetParam();
cv::Mat src(size, CV_8UC1);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
const cv::cuda::GpuMat d_src(src);
cv::cuda::GpuMat dst;
TEST_CYCLE() cv::cuda::sqrIntegral(d_src, dst);
CUDA_SANITY_CHECK(dst);
}
else
{
FAIL_NO_CPU();
}
}
}} // namespace

582
modules/cudaarithm/src/arithm.cpp
View File

@ -1,582 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::cuda::gemm(InputArray, InputArray, double, InputArray, double, OutputArray, int, Stream&) { throw_no_cuda(); }
void cv::cuda::mulSpectrums(InputArray, InputArray, OutputArray, int, bool, Stream&) { throw_no_cuda(); }
void cv::cuda::mulAndScaleSpectrums(InputArray, InputArray, OutputArray, int, float, bool, Stream&) { throw_no_cuda(); }
void cv::cuda::dft(InputArray, OutputArray, Size, int, Stream&) { throw_no_cuda(); }
Ptr<Convolution> cv::cuda::createConvolution(Size) { throw_no_cuda(); return Ptr<Convolution>(); }
#else /* !defined (HAVE_CUDA) */
namespace
{
#define error_entry(entry) { entry, #entry }
struct ErrorEntry
{
int code;
const char* str;
};
struct ErrorEntryComparer
{
int code;
ErrorEntryComparer(int code_) : code(code_) {}
bool operator()(const ErrorEntry& e) const { return e.code == code; }
};
String getErrorString(int code, const ErrorEntry* errors, size_t n)
{
size_t idx = std::find_if(errors, errors + n, ErrorEntryComparer(code)) - errors;
const char* msg = (idx != n) ? errors[idx].str : "Unknown error code";
String str = cv::format("%s [Code = %d]", msg, code);
return str;
}
}
#ifdef HAVE_CUBLAS
namespace
{
const ErrorEntry cublas_errors[] =
{
error_entry( CUBLAS_STATUS_SUCCESS ),
error_entry( CUBLAS_STATUS_NOT_INITIALIZED ),
error_entry( CUBLAS_STATUS_ALLOC_FAILED ),
error_entry( CUBLAS_STATUS_INVALID_VALUE ),
error_entry( CUBLAS_STATUS_ARCH_MISMATCH ),
error_entry( CUBLAS_STATUS_MAPPING_ERROR ),
error_entry( CUBLAS_STATUS_EXECUTION_FAILED ),
error_entry( CUBLAS_STATUS_INTERNAL_ERROR )
};
const size_t cublas_error_num = sizeof(cublas_errors) / sizeof(cublas_errors[0]);
static inline void ___cublasSafeCall(cublasStatus_t err, const char* file, const int line, const char* func)
{
if (CUBLAS_STATUS_SUCCESS != err)
{
String msg = getErrorString(err, cublas_errors, cublas_error_num);
cv::error(cv::Error::GpuApiCallError, msg, func, file, line);
}
}
}
#define cublasSafeCall(expr) ___cublasSafeCall(expr, __FILE__, __LINE__, CV_Func)
#endif // HAVE_CUBLAS
#ifdef HAVE_CUFFT
namespace
{
//////////////////////////////////////////////////////////////////////////
// CUFFT errors
const ErrorEntry cufft_errors[] =
{
error_entry( CUFFT_INVALID_PLAN ),
error_entry( CUFFT_ALLOC_FAILED ),
error_entry( CUFFT_INVALID_TYPE ),
error_entry( CUFFT_INVALID_VALUE ),
error_entry( CUFFT_INTERNAL_ERROR ),
error_entry( CUFFT_EXEC_FAILED ),
error_entry( CUFFT_SETUP_FAILED ),
error_entry( CUFFT_INVALID_SIZE ),
error_entry( CUFFT_UNALIGNED_DATA )
};
const int cufft_error_num = sizeof(cufft_errors) / sizeof(cufft_errors[0]);
void ___cufftSafeCall(int err, const char* file, const int line, const char* func)
{
if (CUFFT_SUCCESS != err)
{
String msg = getErrorString(err, cufft_errors, cufft_error_num);
cv::error(cv::Error::GpuApiCallError, msg, func, file, line);
}
}
}
#define cufftSafeCall(expr) ___cufftSafeCall(expr, __FILE__, __LINE__, CV_Func)
#endif
////////////////////////////////////////////////////////////////////////
// gemm
void cv::cuda::gemm(InputArray _src1, InputArray _src2, double alpha, InputArray _src3, double beta, OutputArray _dst, int flags, Stream& stream)
{
#ifndef HAVE_CUBLAS
CV_UNUSED(_src1);
CV_UNUSED(_src2);
CV_UNUSED(alpha);
CV_UNUSED(_src3);
CV_UNUSED(beta);
CV_UNUSED(_dst);
CV_UNUSED(flags);
CV_UNUSED(stream);
CV_Error(Error::StsNotImplemented, "The library was build without CUBLAS");
#else
// CUBLAS works with column-major matrices
GpuMat src1 = getInputMat(_src1, stream);
GpuMat src2 = getInputMat(_src2, stream);
GpuMat src3 = getInputMat(_src3, stream);
CV_Assert( src1.type() == CV_32FC1 || src1.type() == CV_32FC2 || src1.type() == CV_64FC1 || src1.type() == CV_64FC2 );
CV_Assert( src2.type() == src1.type() && (src3.empty() || src3.type() == src1.type()) );
if (src1.depth() == CV_64F)
{
if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(cv::Error::StsUnsupportedFormat, "The device doesn't support double");
}
bool tr1 = (flags & GEMM_1_T) != 0;
bool tr2 = (flags & GEMM_2_T) != 0;
bool tr3 = (flags & GEMM_3_T) != 0;
if (src1.type() == CV_64FC2)
{
if (tr1 || tr2 || tr3)
CV_Error(cv::Error::StsNotImplemented, "transpose operation doesn't implemented for CV_64FC2 type");
}
Size src1Size = tr1 ? Size(src1.rows, src1.cols) : src1.size();
Size src2Size = tr2 ? Size(src2.rows, src2.cols) : src2.size();
Size src3Size = tr3 ? Size(src3.rows, src3.cols) : src3.size();
Size dstSize(src2Size.width, src1Size.height);
CV_Assert( src1Size.width == src2Size.height );
CV_Assert( src3.empty() || src3Size == dstSize );
GpuMat dst = getOutputMat(_dst, dstSize, src1.type(), stream);
if (beta != 0)
{
if (src3.empty())
{
dst.setTo(Scalar::all(0), stream);
}
else
{
if (tr3)
{
cuda::transpose(src3, dst, stream);
}
else
{
src3.copyTo(dst, stream);
}
}
}
cublasHandle_t handle;
cublasSafeCall( cublasCreate_v2(&handle) );
cublasSafeCall( cublasSetStream_v2(handle, StreamAccessor::getStream(stream)) );
cublasSafeCall( cublasSetPointerMode_v2(handle, CUBLAS_POINTER_MODE_HOST) );
const float alphaf = static_cast<float>(alpha);
const float betaf = static_cast<float>(beta);
const cuComplex alphacf = make_cuComplex(alphaf, 0);
const cuComplex betacf = make_cuComplex(betaf, 0);
const cuDoubleComplex alphac = make_cuDoubleComplex(alpha, 0);
const cuDoubleComplex betac = make_cuDoubleComplex(beta, 0);
cublasOperation_t transa = tr2 ? CUBLAS_OP_T : CUBLAS_OP_N;
cublasOperation_t transb = tr1 ? CUBLAS_OP_T : CUBLAS_OP_N;
switch (src1.type())
{
case CV_32FC1:
cublasSafeCall( cublasSgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alphaf,
src2.ptr<float>(), static_cast<int>(src2.step / sizeof(float)),
src1.ptr<float>(), static_cast<int>(src1.step / sizeof(float)),
&betaf,
dst.ptr<float>(), static_cast<int>(dst.step / sizeof(float))) );
break;
case CV_64FC1:
cublasSafeCall( cublasDgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alpha,
src2.ptr<double>(), static_cast<int>(src2.step / sizeof(double)),
src1.ptr<double>(), static_cast<int>(src1.step / sizeof(double)),
&beta,
dst.ptr<double>(), static_cast<int>(dst.step / sizeof(double))) );
break;
case CV_32FC2:
cublasSafeCall( cublasCgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alphacf,
src2.ptr<cuComplex>(), static_cast<int>(src2.step / sizeof(cuComplex)),
src1.ptr<cuComplex>(), static_cast<int>(src1.step / sizeof(cuComplex)),
&betacf,
dst.ptr<cuComplex>(), static_cast<int>(dst.step / sizeof(cuComplex))) );
break;
case CV_64FC2:
cublasSafeCall( cublasZgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alphac,
src2.ptr<cuDoubleComplex>(), static_cast<int>(src2.step / sizeof(cuDoubleComplex)),
src1.ptr<cuDoubleComplex>(), static_cast<int>(src1.step / sizeof(cuDoubleComplex)),
&betac,
dst.ptr<cuDoubleComplex>(), static_cast<int>(dst.step / sizeof(cuDoubleComplex))) );
break;
}
cublasSafeCall( cublasDestroy_v2(handle) );
syncOutput(dst, _dst, stream);
#endif
}
//////////////////////////////////////////////////////////////////////////////
// DFT function
void cv::cuda::dft(InputArray _src, OutputArray _dst, Size dft_size, int flags, Stream& stream)
{
if (getInputMat(_src, stream).channels() == 2)
flags |= DFT_COMPLEX_INPUT;
Ptr<DFT> dft = createDFT(dft_size, flags);
dft->compute(_src, _dst, stream);
}
//////////////////////////////////////////////////////////////////////////////
// DFT algorithm
#ifdef HAVE_CUFFT
namespace
{
class DFTImpl : public DFT
{
Size dft_size, dft_size_opt;
bool is_1d_input, is_row_dft, is_scaled_dft, is_inverse, is_complex_input, is_complex_output;
cufftType dft_type;
cufftHandle plan;
public:
DFTImpl(Size dft_size, int flags)
: dft_size(dft_size),
dft_size_opt(dft_size),
is_1d_input((dft_size.height == 1) || (dft_size.width == 1)),
is_row_dft((flags & DFT_ROWS) != 0),
is_scaled_dft((flags & DFT_SCALE) != 0),
is_inverse((flags & DFT_INVERSE) != 0),
is_complex_input((flags & DFT_COMPLEX_INPUT) != 0),
is_complex_output(!(flags & DFT_REAL_OUTPUT)),
dft_type(!is_complex_input ? CUFFT_R2C : (is_complex_output ? CUFFT_C2C : CUFFT_C2R))
{
// We don't support unpacked output (in the case of real input)
CV_Assert( !(flags & DFT_COMPLEX_OUTPUT) );
// We don't support real-to-real transform
CV_Assert( is_complex_input || is_complex_output );
if (is_1d_input && !is_row_dft)
{
// If the source matrix is single column handle it as single row
dft_size_opt.width = std::max(dft_size.width, dft_size.height);
dft_size_opt.height = std::min(dft_size.width, dft_size.height);
}
CV_Assert( dft_size_opt.width > 1 );
if (is_1d_input || is_row_dft)
cufftSafeCall( cufftPlan1d(&plan, dft_size_opt.width, dft_type, dft_size_opt.height) );
else
cufftSafeCall( cufftPlan2d(&plan, dft_size_opt.height, dft_size_opt.width, dft_type) );
}
~DFTImpl()
{
cufftSafeCall( cufftDestroy(plan) );
}
void compute(InputArray _src, OutputArray _dst, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.type() == CV_32FC1 || src.type() == CV_32FC2 );
CV_Assert( is_complex_input == (src.channels() == 2) );
// Make sure here we work with the continuous input,
// as CUFFT can't handle gaps
GpuMat src_cont;
if (src.isContinuous())
{
src_cont = src;
}
else
{
BufferPool pool(stream);
src_cont.allocator = pool.getAllocator();
createContinuous(src.rows, src.cols, src.type(), src_cont);
src.copyTo(src_cont, stream);
}
cufftSafeCall( cufftSetStream(plan, StreamAccessor::getStream(stream)) );
if (is_complex_input)
{
if (is_complex_output)
{
createContinuous(dft_size, CV_32FC2, _dst);
GpuMat dst = _dst.getGpuMat();
cufftSafeCall(cufftExecC2C(
plan, src_cont.ptr<cufftComplex>(), dst.ptr<cufftComplex>(),
is_inverse ? CUFFT_INVERSE : CUFFT_FORWARD));
}
else
{
createContinuous(dft_size, CV_32F, _dst);
GpuMat dst = _dst.getGpuMat();
cufftSafeCall(cufftExecC2R(
plan, src_cont.ptr<cufftComplex>(), dst.ptr<cufftReal>()));
}
}
else
{
// We could swap dft_size for efficiency. Here we must reflect it
if (dft_size == dft_size_opt)
createContinuous(Size(dft_size.width / 2 + 1, dft_size.height), CV_32FC2, _dst);
else
createContinuous(Size(dft_size.width, dft_size.height / 2 + 1), CV_32FC2, _dst);
GpuMat dst = _dst.getGpuMat();
cufftSafeCall(cufftExecR2C(
plan, src_cont.ptr<cufftReal>(), dst.ptr<cufftComplex>()));
}
if (is_scaled_dft)
cuda::multiply(_dst, Scalar::all(1. / dft_size.area()), _dst, 1, -1, stream);
}
};
}
#endif
Ptr<DFT> cv::cuda::createDFT(Size dft_size, int flags)
{
#ifndef HAVE_CUFFT
CV_UNUSED(dft_size);
CV_UNUSED(flags);
CV_Error(Error::StsNotImplemented, "The library was build without CUFFT");
return Ptr<DFT>();
#else
return makePtr<DFTImpl>(dft_size, flags);
#endif
}
//////////////////////////////////////////////////////////////////////////////
// Convolution
#ifdef HAVE_CUFFT
namespace
{
class ConvolutionImpl : public Convolution
{
public:
explicit ConvolutionImpl(Size user_block_size_) : user_block_size(user_block_size_) {}
void convolve(InputArray image, InputArray templ, OutputArray result, bool ccorr = false, Stream& stream = Stream::Null());
private:
void create(Size image_size, Size templ_size);
static Size estimateBlockSize(Size result_size);
Size result_size;
Size block_size;
Size user_block_size;
Size dft_size;
GpuMat image_spect, templ_spect, result_spect;
GpuMat image_block, templ_block, result_data;
};
void ConvolutionImpl::create(Size image_size, Size templ_size)
{
result_size = Size(image_size.width - templ_size.width + 1,
image_size.height - templ_size.height + 1);
block_size = user_block_size;
if (user_block_size.width == 0 || user_block_size.height == 0)
block_size = estimateBlockSize(result_size);
dft_size.width = 1 << int(ceil(std::log(block_size.width + templ_size.width - 1.) / std::log(2.)));
dft_size.height = 1 << int(ceil(std::log(block_size.height + templ_size.height - 1.) / std::log(2.)));
// CUFFT has hard-coded kernels for power-of-2 sizes (up to 8192),
// see CUDA Toolkit 4.1 CUFFT Library Programming Guide
if (dft_size.width > 8192)
dft_size.width = getOptimalDFTSize(block_size.width + templ_size.width - 1);
if (dft_size.height > 8192)
dft_size.height = getOptimalDFTSize(block_size.height + templ_size.height - 1);
// To avoid wasting time doing small DFTs
dft_size.width = std::max(dft_size.width, 512);
dft_size.height = std::max(dft_size.height, 512);
createContinuous(dft_size, CV_32F, image_block);
createContinuous(dft_size, CV_32F, templ_block);
createContinuous(dft_size, CV_32F, result_data);
int spect_len = dft_size.height * (dft_size.width / 2 + 1);
createContinuous(1, spect_len, CV_32FC2, image_spect);
createContinuous(1, spect_len, CV_32FC2, templ_spect);
createContinuous(1, spect_len, CV_32FC2, result_spect);
// Use maximum result matrix block size for the estimated DFT block size
block_size.width = std::min(dft_size.width - templ_size.width + 1, result_size.width);
block_size.height = std::min(dft_size.height - templ_size.height + 1, result_size.height);
}
Size ConvolutionImpl::estimateBlockSize(Size result_size)
{
int width = (result_size.width + 2) / 3;
int height = (result_size.height + 2) / 3;
width = std::min(width, result_size.width);
height = std::min(height, result_size.height);
return Size(width, height);
}
void ConvolutionImpl::convolve(InputArray _image, InputArray _templ, OutputArray _result, bool ccorr, Stream& _stream)
{
GpuMat image = getInputMat(_image, _stream);
GpuMat templ = getInputMat(_templ, _stream);
CV_Assert( image.type() == CV_32FC1 );
CV_Assert( templ.type() == CV_32FC1 );
create(image.size(), templ.size());
GpuMat result = getOutputMat(_result, result_size, CV_32FC1, _stream);
cudaStream_t stream = StreamAccessor::getStream(_stream);
cufftHandle planR2C, planC2R;
cufftSafeCall( cufftPlan2d(&planC2R, dft_size.height, dft_size.width, CUFFT_C2R) );
cufftSafeCall( cufftPlan2d(&planR2C, dft_size.height, dft_size.width, CUFFT_R2C) );
cufftSafeCall( cufftSetStream(planR2C, stream) );
cufftSafeCall( cufftSetStream(planC2R, stream) );
GpuMat templ_roi(templ.size(), CV_32FC1, templ.data, templ.step);
cuda::copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0,
templ_block.cols - templ_roi.cols, 0, Scalar(), _stream);
cufftSafeCall( cufftExecR2C(planR2C, templ_block.ptr<cufftReal>(), templ_spect.ptr<cufftComplex>()) );
// Process all blocks of the result matrix
for (int y = 0; y < result.rows; y += block_size.height)
{
for (int x = 0; x < result.cols; x += block_size.width)
{
Size image_roi_size(std::min(x + dft_size.width, image.cols) - x,
std::min(y + dft_size.height, image.rows) - y);
GpuMat image_roi(image_roi_size, CV_32F, (void*)(image.ptr<float>(y) + x),
image.step);
cuda::copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows,
0, image_block.cols - image_roi.cols, 0, Scalar(), _stream);
cufftSafeCall(cufftExecR2C(planR2C, image_block.ptr<cufftReal>(),
image_spect.ptr<cufftComplex>()));
cuda::mulAndScaleSpectrums(image_spect, templ_spect, result_spect, 0,
1.f / dft_size.area(), ccorr, _stream);
cufftSafeCall(cufftExecC2R(planC2R, result_spect.ptr<cufftComplex>(),
result_data.ptr<cufftReal>()));
Size result_roi_size(std::min(x + block_size.width, result.cols) - x,
std::min(y + block_size.height, result.rows) - y);
GpuMat result_roi(result_roi_size, result.type(),
(void*)(result.ptr<float>(y) + x), result.step);
GpuMat result_block(result_roi_size, result_data.type(),
result_data.ptr(), result_data.step);
result_block.copyTo(result_roi, _stream);
}
}
cufftSafeCall( cufftDestroy(planR2C) );
cufftSafeCall( cufftDestroy(planC2R) );
syncOutput(result, _result, _stream);
}
}
#endif
Ptr<Convolution> cv::cuda::createConvolution(Size user_block_size)
{
#ifndef HAVE_CUFFT
CV_UNUSED(user_block_size);
CV_Error(Error::StsNotImplemented, "The library was build without CUFFT");
return Ptr<Convolution>();
#else
return makePtr<ConvolutionImpl>(user_block_size);
#endif
}
#endif /* !defined (HAVE_CUDA) */

135
modules/cudaarithm/src/core.cpp
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@ -1,135 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::cuda::merge(const GpuMat*, size_t, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::merge(const std::vector<GpuMat>&, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::split(InputArray, GpuMat*, Stream&) { throw_no_cuda(); }
void cv::cuda::split(InputArray, std::vector<GpuMat>&, Stream&) { throw_no_cuda(); }
void cv::cuda::transpose(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::flip(InputArray, OutputArray, int, Stream&) { throw_no_cuda(); }
Ptr<LookUpTable> cv::cuda::createLookUpTable(InputArray) { throw_no_cuda(); return Ptr<LookUpTable>(); }
void cv::cuda::copyMakeBorder(InputArray, OutputArray, int, int, int, int, int, Scalar, Stream&) { throw_no_cuda(); }
#else /* !defined (HAVE_CUDA) */
////////////////////////////////////////////////////////////////////////
// flip
namespace
{
template<int DEPTH> struct NppTypeTraits;
template<> struct NppTypeTraits<CV_8U> { typedef Npp8u npp_t; };
template<> struct NppTypeTraits<CV_8S> { typedef Npp8s npp_t; };
template<> struct NppTypeTraits<CV_16U> { typedef Npp16u npp_t; };
template<> struct NppTypeTraits<CV_16S> { typedef Npp16s npp_t; };
template<> struct NppTypeTraits<CV_32S> { typedef Npp32s npp_t; };
template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; };
template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; };
template <int DEPTH> struct NppMirrorFunc
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef NppStatus (*func_t)(const npp_t* pSrc, int nSrcStep, npp_t* pDst, int nDstStep, NppiSize oROI, NppiAxis flip);
};
template <int DEPTH, typename NppMirrorFunc<DEPTH>::func_t func> struct NppMirror
{
typedef typename NppMirrorFunc<DEPTH>::npp_t npp_t;
static void call(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step),
dst.ptr<npp_t>(), static_cast<int>(dst.step), sz,
(flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
}
void cv::cuda::flip(InputArray _src, OutputArray _dst, int flipCode, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream);
static const func_t funcs[6][4] =
{
{NppMirror<CV_8U, nppiMirror_8u_C1R>::call, 0, NppMirror<CV_8U, nppiMirror_8u_C3R>::call, NppMirror<CV_8U, nppiMirror_8u_C4R>::call},
{0,0,0,0},
{NppMirror<CV_16U, nppiMirror_16u_C1R>::call, 0, NppMirror<CV_16U, nppiMirror_16u_C3R>::call, NppMirror<CV_16U, nppiMirror_16u_C4R>::call},
{0,0,0,0},
{NppMirror<CV_32S, nppiMirror_32s_C1R>::call, 0, NppMirror<CV_32S, nppiMirror_32s_C3R>::call, NppMirror<CV_32S, nppiMirror_32s_C4R>::call},
{NppMirror<CV_32F, nppiMirror_32f_C1R>::call, 0, NppMirror<CV_32F, nppiMirror_32f_C3R>::call, NppMirror<CV_32F, nppiMirror_32f_C4R>::call}
};
GpuMat src = getInputMat(_src, stream);
CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S || src.depth() == CV_32F);
CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);
_dst.create(src.size(), src.type());
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
funcs[src.depth()][src.channels() - 1](src, dst, flipCode, StreamAccessor::getStream(stream));
syncOutput(dst, _dst, stream);
}
#endif /* !defined (HAVE_CUDA) */

188
modules/cudaarithm/src/cuda/absdiff_mat.cu
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@ -1,188 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void absDiffMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int);
namespace
{
__device__ __forceinline__ int _abs(int a)
{
return ::abs(a);
}
__device__ __forceinline__ float _abs(float a)
{
return ::fabsf(a);
}
__device__ __forceinline__ double _abs(double a)
{
return ::fabs(a);
}
template <typename T> struct AbsDiffOp1 : binary_function<T, T, T>
{
__device__ __forceinline__ T operator ()(T a, T b) const
{
return saturate_cast<T>(_abs(a - b));
}
};
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <typename T>
void absDiffMat_v1(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
gridTransformBinary_< TransformPolicy<T> >(globPtr<T>(src1), globPtr<T>(src2), globPtr<T>(dst), AbsDiffOp1<T>(), stream);
}
struct AbsDiffOp2 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vabsdiff2(a, b);
}
};
void absDiffMat_v2(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 1;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, AbsDiffOp2(), stream);
}
struct AbsDiffOp4 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vabsdiff4(a, b);
}
};
void absDiffMat_v4(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 2;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, AbsDiffOp4(), stream);
}
}
void absDiffMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int)
{
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
static const func_t funcs[] =
{
absDiffMat_v1<uchar>,
absDiffMat_v1<schar>,
absDiffMat_v1<ushort>,
absDiffMat_v1<short>,
absDiffMat_v1<int>,
absDiffMat_v1<float>,
absDiffMat_v1<double>
};
const int depth = src1.depth();
CV_DbgAssert( depth <= CV_64F );
GpuMat src1_ = src1.reshape(1);
GpuMat src2_ = src2.reshape(1);
GpuMat dst_ = dst.reshape(1);
if (depth == CV_8U || depth == CV_16U)
{
const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
if (isAllAligned)
{
if (depth == CV_8U && (src1_.cols & 3) == 0)
{
absDiffMat_v4(src1_, src2_, dst_, stream);
return;
}
else if (depth == CV_16U && (src1_.cols & 1) == 0)
{
absDiffMat_v2(src1_, src2_, dst_, stream);
return;
}
}
}
const func_t func = funcs[depth];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src1_, src2_, dst_, stream);
}
#endif

133
modules/cudaarithm/src/cuda/absdiff_scalar.cu
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@ -1,133 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void absDiffScalar(const GpuMat& src, cv::Scalar val, bool, GpuMat& dst, const GpuMat&, double, Stream& stream, int);
namespace
{
template <typename SrcType, typename ScalarType, typename DstType> struct AbsDiffScalarOp : unary_function<SrcType, DstType>
{
ScalarType val;
__device__ __forceinline__ DstType operator ()(SrcType a) const
{
abs_func<ScalarType> f;
return saturate_cast<DstType>(f(saturate_cast<ScalarType>(a) - val));
}
};
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <typename SrcType, typename ScalarDepth>
void absDiffScalarImpl(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& stream)
{
typedef typename MakeVec<ScalarDepth, VecTraits<SrcType>::cn>::type ScalarType;
cv::Scalar_<ScalarDepth> value_ = value;
AbsDiffScalarOp<SrcType, ScalarType, SrcType> op;
op.val = VecTraits<ScalarType>::make(value_.val);
gridTransformUnary_< TransformPolicy<ScalarDepth> >(globPtr<SrcType>(src), globPtr<SrcType>(dst), op, stream);
}
}
void absDiffScalar(const GpuMat& src, cv::Scalar val, bool, GpuMat& dst, const GpuMat&, double, Stream& stream, int)
{
typedef void (*func_t)(const GpuMat& src, cv::Scalar val, GpuMat& dst, Stream& stream);
static const func_t funcs[7][4] =
{
{
absDiffScalarImpl<uchar, float>, absDiffScalarImpl<uchar2, float>, absDiffScalarImpl<uchar3, float>, absDiffScalarImpl<uchar4, float>
},
{
absDiffScalarImpl<schar, float>, absDiffScalarImpl<char2, float>, absDiffScalarImpl<char3, float>, absDiffScalarImpl<char4, float>
},
{
absDiffScalarImpl<ushort, float>, absDiffScalarImpl<ushort2, float>, absDiffScalarImpl<ushort3, float>, absDiffScalarImpl<ushort4, float>
},
{
absDiffScalarImpl<short, float>, absDiffScalarImpl<short2, float>, absDiffScalarImpl<short3, float>, absDiffScalarImpl<short4, float>
},
{
absDiffScalarImpl<int, float>, absDiffScalarImpl<int2, float>, absDiffScalarImpl<int3, float>, absDiffScalarImpl<int4, float>
},
{
absDiffScalarImpl<float, float>, absDiffScalarImpl<float2, float>, absDiffScalarImpl<float3, float>, absDiffScalarImpl<float4, float>
},
{
absDiffScalarImpl<double, double>, absDiffScalarImpl<double2, double>, absDiffScalarImpl<double3, double>, absDiffScalarImpl<double4, double>
}
};
const int sdepth = src.depth();
const int cn = src.channels();
CV_DbgAssert( sdepth <= CV_64F && cn <= 4 && src.type() == dst.type());
const func_t func = funcs[sdepth][cn - 1];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src, val, dst, stream);
}
#endif

225
modules/cudaarithm/src/cuda/add_mat.cu
View File

@ -1,225 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void addMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& _stream, int);
namespace
{
template <typename T, typename D> struct AddOp1 : binary_function<T, T, D>
{
__device__ __forceinline__ D operator ()(T a, T b) const
{
return saturate_cast<D>(a + b);
}
};
template <typename T, typename D>
void addMat_v1(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream)
{
if (mask.data)
gridTransformBinary(globPtr<T>(src1), globPtr<T>(src2), globPtr<D>(dst), AddOp1<T, D>(), globPtr<uchar>(mask), stream);
else
gridTransformBinary(globPtr<T>(src1), globPtr<T>(src2), globPtr<D>(dst), AddOp1<T, D>(), stream);
}
struct AddOp2 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vadd2(a, b);
}
};
void addMat_v2(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 1;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, AddOp2(), stream);
}
struct AddOp4 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vadd4(a, b);
}
};
void addMat_v4(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 2;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, AddOp4(), stream);
}
}
void addMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int)
{
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream);
static const func_t funcs[7][7] =
{
{
addMat_v1<uchar, uchar>,
addMat_v1<uchar, schar>,
addMat_v1<uchar, ushort>,
addMat_v1<uchar, short>,
addMat_v1<uchar, int>,
addMat_v1<uchar, float>,
addMat_v1<uchar, double>
},
{
addMat_v1<schar, uchar>,
addMat_v1<schar, schar>,
addMat_v1<schar, ushort>,
addMat_v1<schar, short>,
addMat_v1<schar, int>,
addMat_v1<schar, float>,
addMat_v1<schar, double>
},
{
0 /*addMat_v1<ushort, uchar>*/,
0 /*addMat_v1<ushort, schar>*/,
addMat_v1<ushort, ushort>,
addMat_v1<ushort, short>,
addMat_v1<ushort, int>,
addMat_v1<ushort, float>,
addMat_v1<ushort, double>
},
{
0 /*addMat_v1<short, uchar>*/,
0 /*addMat_v1<short, schar>*/,
addMat_v1<short, ushort>,
addMat_v1<short, short>,
addMat_v1<short, int>,
addMat_v1<short, float>,
addMat_v1<short, double>
},
{
0 /*addMat_v1<int, uchar>*/,
0 /*addMat_v1<int, schar>*/,
0 /*addMat_v1<int, ushort>*/,
0 /*addMat_v1<int, short>*/,
addMat_v1<int, int>,
addMat_v1<int, float>,
addMat_v1<int, double>
},
{
0 /*addMat_v1<float, uchar>*/,
0 /*addMat_v1<float, schar>*/,
0 /*addMat_v1<float, ushort>*/,
0 /*addMat_v1<float, short>*/,
0 /*addMat_v1<float, int>*/,
addMat_v1<float, float>,
addMat_v1<float, double>
},
{
0 /*addMat_v1<double, uchar>*/,
0 /*addMat_v1<double, schar>*/,
0 /*addMat_v1<double, ushort>*/,
0 /*addMat_v1<double, short>*/,
0 /*addMat_v1<double, int>*/,
0 /*addMat_v1<double, float>*/,
addMat_v1<double, double>
}
};
const int sdepth = src1.depth();
const int ddepth = dst.depth();
CV_DbgAssert( sdepth <= CV_64F && ddepth <= CV_64F );
GpuMat src1_ = src1.reshape(1);
GpuMat src2_ = src2.reshape(1);
GpuMat dst_ = dst.reshape(1);
if (mask.empty() && (sdepth == CV_8U || sdepth == CV_16U) && ddepth == sdepth)
{
const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
if (isAllAligned)
{
if (sdepth == CV_8U && (src1_.cols & 3) == 0)
{
addMat_v4(src1_, src2_, dst_, stream);
return;
}
else if (sdepth == CV_16U && (src1_.cols & 1) == 0)
{
addMat_v2(src1_, src2_, dst_, stream);
return;
}
}
}
const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src1_, src2_, dst_, mask, stream);
}
#endif

180
modules/cudaarithm/src/cuda/add_scalar.cu
View File

@ -1,180 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void addScalar(const GpuMat& src, cv::Scalar val, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int);
namespace
{
template <typename SrcType, typename ScalarType, typename DstType> struct AddScalarOp : unary_function<SrcType, DstType>
{
ScalarType val;
__device__ __forceinline__ DstType operator ()(SrcType a) const
{
return saturate_cast<DstType>(saturate_cast<ScalarType>(a) + val);
}
};
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <typename SrcType, typename ScalarDepth, typename DstType>
void addScalarImpl(const GpuMat& src, cv::Scalar value, GpuMat& dst, const GpuMat& mask, Stream& stream)
{
typedef typename MakeVec<ScalarDepth, VecTraits<SrcType>::cn>::type ScalarType;
cv::Scalar_<ScalarDepth> value_ = value;
AddScalarOp<SrcType, ScalarType, DstType> op;
op.val = VecTraits<ScalarType>::make(value_.val);
if (mask.data)
gridTransformUnary_< TransformPolicy<ScalarDepth> >(globPtr<SrcType>(src), globPtr<DstType>(dst), op, globPtr<uchar>(mask), stream);
else
gridTransformUnary_< TransformPolicy<ScalarDepth> >(globPtr<SrcType>(src), globPtr<DstType>(dst), op, stream);
}
}
void addScalar(const GpuMat& src, cv::Scalar val, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int)
{
typedef void (*func_t)(const GpuMat& src, cv::Scalar val, GpuMat& dst, const GpuMat& mask, Stream& stream);
static const func_t funcs[7][7][4] =
{
{
{addScalarImpl<uchar, float, uchar>, addScalarImpl<uchar2, float, uchar2>, addScalarImpl<uchar3, float, uchar3>, addScalarImpl<uchar4, float, uchar4>},
{addScalarImpl<uchar, float, schar>, addScalarImpl<uchar2, float, char2>, addScalarImpl<uchar3, float, char3>, addScalarImpl<uchar4, float, char4>},
{addScalarImpl<uchar, float, ushort>, addScalarImpl<uchar2, float, ushort2>, addScalarImpl<uchar3, float, ushort3>, addScalarImpl<uchar4, float, ushort4>},
{addScalarImpl<uchar, float, short>, addScalarImpl<uchar2, float, short2>, addScalarImpl<uchar3, float, short3>, addScalarImpl<uchar4, float, short4>},
{addScalarImpl<uchar, float, int>, addScalarImpl<uchar2, float, int2>, addScalarImpl<uchar3, float, int3>, addScalarImpl<uchar4, float, int4>},
{addScalarImpl<uchar, float, float>, addScalarImpl<uchar2, float, float2>, addScalarImpl<uchar3, float, float3>, addScalarImpl<uchar4, float, float4>},
{addScalarImpl<uchar, double, double>, addScalarImpl<uchar2, double, double2>, addScalarImpl<uchar3, double, double3>, addScalarImpl<uchar4, double, double4>}
},
{
{addScalarImpl<schar, float, uchar>, addScalarImpl<char2, float, uchar2>, addScalarImpl<char3, float, uchar3>, addScalarImpl<char4, float, uchar4>},
{addScalarImpl<schar, float, schar>, addScalarImpl<char2, float, char2>, addScalarImpl<char3, float, char3>, addScalarImpl<char4, float, char4>},
{addScalarImpl<schar, float, ushort>, addScalarImpl<char2, float, ushort2>, addScalarImpl<char3, float, ushort3>, addScalarImpl<char4, float, ushort4>},
{addScalarImpl<schar, float, short>, addScalarImpl<char2, float, short2>, addScalarImpl<char3, float, short3>, addScalarImpl<char4, float, short4>},
{addScalarImpl<schar, float, int>, addScalarImpl<char2, float, int2>, addScalarImpl<char3, float, int3>, addScalarImpl<char4, float, int4>},
{addScalarImpl<schar, float, float>, addScalarImpl<char2, float, float2>, addScalarImpl<char3, float, float3>, addScalarImpl<char4, float, float4>},
{addScalarImpl<schar, double, double>, addScalarImpl<char2, double, double2>, addScalarImpl<char3, double, double3>, addScalarImpl<char4, double, double4>}
},
{
{0 /*addScalarImpl<ushort, float, uchar>*/, 0 /*addScalarImpl<ushort2, float, uchar2>*/, 0 /*addScalarImpl<ushort3, float, uchar3>*/, 0 /*addScalarImpl<ushort4, float, uchar4>*/},
{0 /*addScalarImpl<ushort, float, schar>*/, 0 /*addScalarImpl<ushort2, float, char2>*/, 0 /*addScalarImpl<ushort3, float, char3>*/, 0 /*addScalarImpl<ushort4, float, char4>*/},
{addScalarImpl<ushort, float, ushort>, addScalarImpl<ushort2, float, ushort2>, addScalarImpl<ushort3, float, ushort3>, addScalarImpl<ushort4, float, ushort4>},
{addScalarImpl<ushort, float, short>, addScalarImpl<ushort2, float, short2>, addScalarImpl<ushort3, float, short3>, addScalarImpl<ushort4, float, short4>},
{addScalarImpl<ushort, float, int>, addScalarImpl<ushort2, float, int2>, addScalarImpl<ushort3, float, int3>, addScalarImpl<ushort4, float, int4>},
{addScalarImpl<ushort, float, float>, addScalarImpl<ushort2, float, float2>, addScalarImpl<ushort3, float, float3>, addScalarImpl<ushort4, float, float4>},
{addScalarImpl<ushort, double, double>, addScalarImpl<ushort2, double, double2>, addScalarImpl<ushort3, double, double3>, addScalarImpl<ushort4, double, double4>}
},
{
{0 /*addScalarImpl<short, float, uchar>*/, 0 /*addScalarImpl<short2, float, uchar2>*/, 0 /*addScalarImpl<short3, float, uchar3>*/, 0 /*addScalarImpl<short4, float, uchar4>*/},
{0 /*addScalarImpl<short, float, schar>*/, 0 /*addScalarImpl<short2, float, char2>*/, 0 /*addScalarImpl<short3, float, char3>*/, 0 /*addScalarImpl<short4, float, char4>*/},
{addScalarImpl<short, float, ushort>, addScalarImpl<short2, float, ushort2>, addScalarImpl<short3, float, ushort3>, addScalarImpl<short4, float, ushort4>},
{addScalarImpl<short, float, short>, addScalarImpl<short2, float, short2>, addScalarImpl<short3, float, short3>, addScalarImpl<short4, float, short4>},
{addScalarImpl<short, float, int>, addScalarImpl<short2, float, int2>, addScalarImpl<short3, float, int3>, addScalarImpl<short4, float, int4>},
{addScalarImpl<short, float, float>, addScalarImpl<short2, float, float2>, addScalarImpl<short3, float, float3>, addScalarImpl<short4, float, float4>},
{addScalarImpl<short, double, double>, addScalarImpl<short2, double, double2>, addScalarImpl<short3, double, double3>, addScalarImpl<short4, double, double4>}
},
{
{0 /*addScalarImpl<int, float, uchar>*/, 0 /*addScalarImpl<int2, float, uchar2>*/, 0 /*addScalarImpl<int3, float, uchar3>*/, 0 /*addScalarImpl<int4, float, uchar4>*/},
{0 /*addScalarImpl<int, float, schar>*/, 0 /*addScalarImpl<int2, float, char2>*/, 0 /*addScalarImpl<int3, float, char3>*/, 0 /*addScalarImpl<int4, float, char4>*/},
{0 /*addScalarImpl<int, float, ushort>*/, 0 /*addScalarImpl<int2, float, ushort2>*/, 0 /*addScalarImpl<int3, float, ushort3>*/, 0 /*addScalarImpl<int4, float, ushort4>*/},
{0 /*addScalarImpl<int, float, short>*/, 0 /*addScalarImpl<int2, float, short2>*/, 0 /*addScalarImpl<int3, float, short3>*/, 0 /*addScalarImpl<int4, float, short4>*/},
{addScalarImpl<int, float, int>, addScalarImpl<int2, float, int2>, addScalarImpl<int3, float, int3>, addScalarImpl<int4, float, int4>},
{addScalarImpl<int, float, float>, addScalarImpl<int2, float, float2>, addScalarImpl<int3, float, float3>, addScalarImpl<int4, float, float4>},
{addScalarImpl<int, double, double>, addScalarImpl<int2, double, double2>, addScalarImpl<int3, double, double3>, addScalarImpl<int4, double, double4>}
},
{
{0 /*addScalarImpl<float, float, uchar>*/, 0 /*addScalarImpl<float2, float, uchar2>*/, 0 /*addScalarImpl<float3, float, uchar3>*/, 0 /*addScalarImpl<float4, float, uchar4>*/},
{0 /*addScalarImpl<float, float, schar>*/, 0 /*addScalarImpl<float2, float, char2>*/, 0 /*addScalarImpl<float3, float, char3>*/, 0 /*addScalarImpl<float4, float, char4>*/},
{0 /*addScalarImpl<float, float, ushort>*/, 0 /*addScalarImpl<float2, float, ushort2>*/, 0 /*addScalarImpl<float3, float, ushort3>*/, 0 /*addScalarImpl<float4, float, ushort4>*/},
{0 /*addScalarImpl<float, float, short>*/, 0 /*addScalarImpl<float2, float, short2>*/, 0 /*addScalarImpl<float3, float, short3>*/, 0 /*addScalarImpl<float4, float, short4>*/},
{0 /*addScalarImpl<float, float, int>*/, 0 /*addScalarImpl<float2, float, int2>*/, 0 /*addScalarImpl<float3, float, int3>*/, 0 /*addScalarImpl<float4, float, int4>*/},
{addScalarImpl<float, float, float>, addScalarImpl<float2, float, float2>, addScalarImpl<float3, float, float3>, addScalarImpl<float4, float, float4>},
{addScalarImpl<float, double, double>, addScalarImpl<float2, double, double2>, addScalarImpl<float3, double, double3>, addScalarImpl<float4, double, double4>}
},
{
{0 /*addScalarImpl<double, double, uchar>*/, 0 /*addScalarImpl<double2, double, uchar2>*/, 0 /*addScalarImpl<double3, double, uchar3>*/, 0 /*addScalarImpl<double4, double, uchar4>*/},
{0 /*addScalarImpl<double, double, schar>*/, 0 /*addScalarImpl<double2, double, char2>*/, 0 /*addScalarImpl<double3, double, char3>*/, 0 /*addScalarImpl<double4, double, char4>*/},
{0 /*addScalarImpl<double, double, ushort>*/, 0 /*addScalarImpl<double2, double, ushort2>*/, 0 /*addScalarImpl<double3, double, ushort3>*/, 0 /*addScalarImpl<double4, double, ushort4>*/},
{0 /*addScalarImpl<double, double, short>*/, 0 /*addScalarImpl<double2, double, short2>*/, 0 /*addScalarImpl<double3, double, short3>*/, 0 /*addScalarImpl<double4, double, short4>*/},
{0 /*addScalarImpl<double, double, int>*/, 0 /*addScalarImpl<double2, double, int2>*/, 0 /*addScalarImpl<double3, double, int3>*/, 0 /*addScalarImpl<double4, double, int4>*/},
{0 /*addScalarImpl<double, double, float>*/, 0 /*addScalarImpl<double2, double, float2>*/, 0 /*addScalarImpl<double3, double, float3>*/, 0 /*addScalarImpl<double4, double, float4>*/},
{addScalarImpl<double, double, double>, addScalarImpl<double2, double, double2>, addScalarImpl<double3, double, double3>, addScalarImpl<double4, double, double4>}
}
};
const int sdepth = src.depth();
const int ddepth = dst.depth();
const int cn = src.channels();
CV_DbgAssert( sdepth <= CV_64F && ddepth <= CV_64F && cn <= 4 );
const func_t func = funcs[sdepth][ddepth][cn - 1];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src, val, dst, mask, stream);
}
#endif

596
modules/cudaarithm/src/cuda/add_weighted.cu
View File

@ -1,596 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
template <typename T1, typename T2, typename D, typename S> struct AddWeightedOp : binary_function<T1, T2, D>
{
S alpha;
S beta;
S gamma;
__device__ __forceinline__ D operator ()(T1 a, T2 b) const
{
return cudev::saturate_cast<D>(a * alpha + b * beta + gamma);
}
};
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <typename T1, typename T2, typename D>
void addWeightedImpl(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst, Stream& stream)
{
typedef typename LargerType<T1, T2>::type larger_type1;
typedef typename LargerType<larger_type1, D>::type larger_type2;
typedef typename LargerType<larger_type2, float>::type scalar_type;
AddWeightedOp<T1, T2, D, scalar_type> op;
op.alpha = static_cast<scalar_type>(alpha);
op.beta = static_cast<scalar_type>(beta);
op.gamma = static_cast<scalar_type>(gamma);
gridTransformBinary_< TransformPolicy<scalar_type> >(globPtr<T1>(src1), globPtr<T2>(src2), globPtr<D>(dst), op, stream);
}
}
void cv::cuda::addWeighted(InputArray _src1, double alpha, InputArray _src2, double beta, double gamma, OutputArray _dst, int ddepth, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst, Stream& stream);
static const func_t funcs[7][7][7] =
{
{
{
addWeightedImpl<uchar, uchar, uchar >,
addWeightedImpl<uchar, uchar, schar >,
addWeightedImpl<uchar, uchar, ushort>,
addWeightedImpl<uchar, uchar, short >,
addWeightedImpl<uchar, uchar, int >,
addWeightedImpl<uchar, uchar, float >,
addWeightedImpl<uchar, uchar, double>
},
{
addWeightedImpl<uchar, schar, uchar >,
addWeightedImpl<uchar, schar, schar >,
addWeightedImpl<uchar, schar, ushort>,
addWeightedImpl<uchar, schar, short >,
addWeightedImpl<uchar, schar, int >,
addWeightedImpl<uchar, schar, float >,
addWeightedImpl<uchar, schar, double>
},
{
addWeightedImpl<uchar, ushort, uchar >,
addWeightedImpl<uchar, ushort, schar >,
addWeightedImpl<uchar, ushort, ushort>,
addWeightedImpl<uchar, ushort, short >,
addWeightedImpl<uchar, ushort, int >,
addWeightedImpl<uchar, ushort, float >,
addWeightedImpl<uchar, ushort, double>
},
{
addWeightedImpl<uchar, short, uchar >,
addWeightedImpl<uchar, short, schar >,
addWeightedImpl<uchar, short, ushort>,
addWeightedImpl<uchar, short, short >,
addWeightedImpl<uchar, short, int >,
addWeightedImpl<uchar, short, float >,
addWeightedImpl<uchar, short, double>
},
{
addWeightedImpl<uchar, int, uchar >,
addWeightedImpl<uchar, int, schar >,
addWeightedImpl<uchar, int, ushort>,
addWeightedImpl<uchar, int, short >,
addWeightedImpl<uchar, int, int >,
addWeightedImpl<uchar, int, float >,
addWeightedImpl<uchar, int, double>
},
{
addWeightedImpl<uchar, float, uchar >,
addWeightedImpl<uchar, float, schar >,
addWeightedImpl<uchar, float, ushort>,
addWeightedImpl<uchar, float, short >,
addWeightedImpl<uchar, float, int >,
addWeightedImpl<uchar, float, float >,
addWeightedImpl<uchar, float, double>
},
{
addWeightedImpl<uchar, double, uchar >,
addWeightedImpl<uchar, double, schar >,
addWeightedImpl<uchar, double, ushort>,
addWeightedImpl<uchar, double, short >,
addWeightedImpl<uchar, double, int >,
addWeightedImpl<uchar, double, float >,
addWeightedImpl<uchar, double, double>
}
},
{
{
0/*addWeightedImpl<schar, uchar, uchar >*/,
0/*addWeightedImpl<schar, uchar, schar >*/,
0/*addWeightedImpl<schar, uchar, ushort>*/,
0/*addWeightedImpl<schar, uchar, short >*/,
0/*addWeightedImpl<schar, uchar, int >*/,
0/*addWeightedImpl<schar, uchar, float >*/,
0/*addWeightedImpl<schar, uchar, double>*/
},
{
addWeightedImpl<schar, schar, uchar >,
addWeightedImpl<schar, schar, schar >,
addWeightedImpl<schar, schar, ushort>,
addWeightedImpl<schar, schar, short >,
addWeightedImpl<schar, schar, int >,
addWeightedImpl<schar, schar, float >,
addWeightedImpl<schar, schar, double>
},
{
addWeightedImpl<schar, ushort, uchar >,
addWeightedImpl<schar, ushort, schar >,
addWeightedImpl<schar, ushort, ushort>,
addWeightedImpl<schar, ushort, short >,
addWeightedImpl<schar, ushort, int >,
addWeightedImpl<schar, ushort, float >,
addWeightedImpl<schar, ushort, double>
},
{
addWeightedImpl<schar, short, uchar >,
addWeightedImpl<schar, short, schar >,
addWeightedImpl<schar, short, ushort>,
addWeightedImpl<schar, short, short >,
addWeightedImpl<schar, short, int >,
addWeightedImpl<schar, short, float >,
addWeightedImpl<schar, short, double>
},
{
addWeightedImpl<schar, int, uchar >,
addWeightedImpl<schar, int, schar >,
addWeightedImpl<schar, int, ushort>,
addWeightedImpl<schar, int, short >,
addWeightedImpl<schar, int, int >,
addWeightedImpl<schar, int, float >,
addWeightedImpl<schar, int, double>
},
{
addWeightedImpl<schar, float, uchar >,
addWeightedImpl<schar, float, schar >,
addWeightedImpl<schar, float, ushort>,
addWeightedImpl<schar, float, short >,
addWeightedImpl<schar, float, int >,
addWeightedImpl<schar, float, float >,
addWeightedImpl<schar, float, double>
},
{
addWeightedImpl<schar, double, uchar >,
addWeightedImpl<schar, double, schar >,
addWeightedImpl<schar, double, ushort>,
addWeightedImpl<schar, double, short >,
addWeightedImpl<schar, double, int >,
addWeightedImpl<schar, double, float >,
addWeightedImpl<schar, double, double>
}
},
{
{
0/*addWeightedImpl<ushort, uchar, uchar >*/,
0/*addWeightedImpl<ushort, uchar, schar >*/,
0/*addWeightedImpl<ushort, uchar, ushort>*/,
0/*addWeightedImpl<ushort, uchar, short >*/,
0/*addWeightedImpl<ushort, uchar, int >*/,
0/*addWeightedImpl<ushort, uchar, float >*/,
0/*addWeightedImpl<ushort, uchar, double>*/
},
{
0/*addWeightedImpl<ushort, schar, uchar >*/,
0/*addWeightedImpl<ushort, schar, schar >*/,
0/*addWeightedImpl<ushort, schar, ushort>*/,
0/*addWeightedImpl<ushort, schar, short >*/,
0/*addWeightedImpl<ushort, schar, int >*/,
0/*addWeightedImpl<ushort, schar, float >*/,
0/*addWeightedImpl<ushort, schar, double>*/
},
{
addWeightedImpl<ushort, ushort, uchar >,
addWeightedImpl<ushort, ushort, schar >,
addWeightedImpl<ushort, ushort, ushort>,
addWeightedImpl<ushort, ushort, short >,
addWeightedImpl<ushort, ushort, int >,
addWeightedImpl<ushort, ushort, float >,
addWeightedImpl<ushort, ushort, double>
},
{
addWeightedImpl<ushort, short, uchar >,
addWeightedImpl<ushort, short, schar >,
addWeightedImpl<ushort, short, ushort>,
addWeightedImpl<ushort, short, short >,
addWeightedImpl<ushort, short, int >,
addWeightedImpl<ushort, short, float >,
addWeightedImpl<ushort, short, double>
},
{
addWeightedImpl<ushort, int, uchar >,
addWeightedImpl<ushort, int, schar >,
addWeightedImpl<ushort, int, ushort>,
addWeightedImpl<ushort, int, short >,
addWeightedImpl<ushort, int, int >,
addWeightedImpl<ushort, int, float >,
addWeightedImpl<ushort, int, double>
},
{
addWeightedImpl<ushort, float, uchar >,
addWeightedImpl<ushort, float, schar >,
addWeightedImpl<ushort, float, ushort>,
addWeightedImpl<ushort, float, short >,
addWeightedImpl<ushort, float, int >,
addWeightedImpl<ushort, float, float >,
addWeightedImpl<ushort, float, double>
},
{
addWeightedImpl<ushort, double, uchar >,
addWeightedImpl<ushort, double, schar >,
addWeightedImpl<ushort, double, ushort>,
addWeightedImpl<ushort, double, short >,
addWeightedImpl<ushort, double, int >,
addWeightedImpl<ushort, double, float >,
addWeightedImpl<ushort, double, double>
}
},
{
{
0/*addWeightedImpl<short, uchar, uchar >*/,
0/*addWeightedImpl<short, uchar, schar >*/,
0/*addWeightedImpl<short, uchar, ushort>*/,
0/*addWeightedImpl<short, uchar, short >*/,
0/*addWeightedImpl<short, uchar, int >*/,
0/*addWeightedImpl<short, uchar, float >*/,
0/*addWeightedImpl<short, uchar, double>*/
},
{
0/*addWeightedImpl<short, schar, uchar >*/,
0/*addWeightedImpl<short, schar, schar >*/,
0/*addWeightedImpl<short, schar, ushort>*/,
0/*addWeightedImpl<short, schar, short >*/,
0/*addWeightedImpl<short, schar, int >*/,
0/*addWeightedImpl<short, schar, float >*/,
0/*addWeightedImpl<short, schar, double>*/
},
{
0/*addWeightedImpl<short, ushort, uchar >*/,
0/*addWeightedImpl<short, ushort, schar >*/,
0/*addWeightedImpl<short, ushort, ushort>*/,
0/*addWeightedImpl<short, ushort, short >*/,
0/*addWeightedImpl<short, ushort, int >*/,
0/*addWeightedImpl<short, ushort, float >*/,
0/*addWeightedImpl<short, ushort, double>*/
},
{
addWeightedImpl<short, short, uchar >,
addWeightedImpl<short, short, schar >,
addWeightedImpl<short, short, ushort>,
addWeightedImpl<short, short, short >,
addWeightedImpl<short, short, int >,
addWeightedImpl<short, short, float >,
addWeightedImpl<short, short, double>
},
{
addWeightedImpl<short, int, uchar >,
addWeightedImpl<short, int, schar >,
addWeightedImpl<short, int, ushort>,
addWeightedImpl<short, int, short >,
addWeightedImpl<short, int, int >,
addWeightedImpl<short, int, float >,
addWeightedImpl<short, int, double>
},
{
addWeightedImpl<short, float, uchar >,
addWeightedImpl<short, float, schar >,
addWeightedImpl<short, float, ushort>,
addWeightedImpl<short, float, short >,
addWeightedImpl<short, float, int >,
addWeightedImpl<short, float, float >,
addWeightedImpl<short, float, double>
},
{
addWeightedImpl<short, double, uchar >,
addWeightedImpl<short, double, schar >,
addWeightedImpl<short, double, ushort>,
addWeightedImpl<short, double, short >,
addWeightedImpl<short, double, int >,
addWeightedImpl<short, double, float >,
addWeightedImpl<short, double, double>
}
},
{
{
0/*addWeightedImpl<int, uchar, uchar >*/,
0/*addWeightedImpl<int, uchar, schar >*/,
0/*addWeightedImpl<int, uchar, ushort>*/,
0/*addWeightedImpl<int, uchar, short >*/,
0/*addWeightedImpl<int, uchar, int >*/,
0/*addWeightedImpl<int, uchar, float >*/,
0/*addWeightedImpl<int, uchar, double>*/
},
{
0/*addWeightedImpl<int, schar, uchar >*/,
0/*addWeightedImpl<int, schar, schar >*/,
0/*addWeightedImpl<int, schar, ushort>*/,
0/*addWeightedImpl<int, schar, short >*/,
0/*addWeightedImpl<int, schar, int >*/,
0/*addWeightedImpl<int, schar, float >*/,
0/*addWeightedImpl<int, schar, double>*/
},
{
0/*addWeightedImpl<int, ushort, uchar >*/,
0/*addWeightedImpl<int, ushort, schar >*/,
0/*addWeightedImpl<int, ushort, ushort>*/,
0/*addWeightedImpl<int, ushort, short >*/,
0/*addWeightedImpl<int, ushort, int >*/,
0/*addWeightedImpl<int, ushort, float >*/,
0/*addWeightedImpl<int, ushort, double>*/
},
{
0/*addWeightedImpl<int, short, uchar >*/,
0/*addWeightedImpl<int, short, schar >*/,
0/*addWeightedImpl<int, short, ushort>*/,
0/*addWeightedImpl<int, short, short >*/,
0/*addWeightedImpl<int, short, int >*/,
0/*addWeightedImpl<int, short, float >*/,
0/*addWeightedImpl<int, short, double>*/
},
{
addWeightedImpl<int, int, uchar >,
addWeightedImpl<int, int, schar >,
addWeightedImpl<int, int, ushort>,
addWeightedImpl<int, int, short >,
addWeightedImpl<int, int, int >,
addWeightedImpl<int, int, float >,
addWeightedImpl<int, int, double>
},
{
addWeightedImpl<int, float, uchar >,
addWeightedImpl<int, float, schar >,
addWeightedImpl<int, float, ushort>,
addWeightedImpl<int, float, short >,
addWeightedImpl<int, float, int >,
addWeightedImpl<int, float, float >,
addWeightedImpl<int, float, double>
},
{
addWeightedImpl<int, double, uchar >,
addWeightedImpl<int, double, schar >,
addWeightedImpl<int, double, ushort>,
addWeightedImpl<int, double, short >,
addWeightedImpl<int, double, int >,
addWeightedImpl<int, double, float >,
addWeightedImpl<int, double, double>
}
},
{
{
0/*addWeightedImpl<float, uchar, uchar >*/,
0/*addWeightedImpl<float, uchar, schar >*/,
0/*addWeightedImpl<float, uchar, ushort>*/,
0/*addWeightedImpl<float, uchar, short >*/,
0/*addWeightedImpl<float, uchar, int >*/,
0/*addWeightedImpl<float, uchar, float >*/,
0/*addWeightedImpl<float, uchar, double>*/
},
{
0/*addWeightedImpl<float, schar, uchar >*/,
0/*addWeightedImpl<float, schar, schar >*/,
0/*addWeightedImpl<float, schar, ushort>*/,
0/*addWeightedImpl<float, schar, short >*/,
0/*addWeightedImpl<float, schar, int >*/,
0/*addWeightedImpl<float, schar, float >*/,
0/*addWeightedImpl<float, schar, double>*/
},
{
0/*addWeightedImpl<float, ushort, uchar >*/,
0/*addWeightedImpl<float, ushort, schar >*/,
0/*addWeightedImpl<float, ushort, ushort>*/,
0/*addWeightedImpl<float, ushort, short >*/,
0/*addWeightedImpl<float, ushort, int >*/,
0/*addWeightedImpl<float, ushort, float >*/,
0/*addWeightedImpl<float, ushort, double>*/
},
{
0/*addWeightedImpl<float, short, uchar >*/,
0/*addWeightedImpl<float, short, schar >*/,
0/*addWeightedImpl<float, short, ushort>*/,
0/*addWeightedImpl<float, short, short >*/,
0/*addWeightedImpl<float, short, int >*/,
0/*addWeightedImpl<float, short, float >*/,
0/*addWeightedImpl<float, short, double>*/
},
{
0/*addWeightedImpl<float, int, uchar >*/,
0/*addWeightedImpl<float, int, schar >*/,
0/*addWeightedImpl<float, int, ushort>*/,
0/*addWeightedImpl<float, int, short >*/,
0/*addWeightedImpl<float, int, int >*/,
0/*addWeightedImpl<float, int, float >*/,
0/*addWeightedImpl<float, int, double>*/
},
{
addWeightedImpl<float, float, uchar >,
addWeightedImpl<float, float, schar >,
addWeightedImpl<float, float, ushort>,
addWeightedImpl<float, float, short >,
addWeightedImpl<float, float, int >,
addWeightedImpl<float, float, float >,
addWeightedImpl<float, float, double>
},
{
addWeightedImpl<float, double, uchar >,
addWeightedImpl<float, double, schar >,
addWeightedImpl<float, double, ushort>,
addWeightedImpl<float, double, short >,
addWeightedImpl<float, double, int >,
addWeightedImpl<float, double, float >,
addWeightedImpl<float, double, double>
}
},
{
{
0/*addWeightedImpl<double, uchar, uchar >*/,
0/*addWeightedImpl<double, uchar, schar >*/,
0/*addWeightedImpl<double, uchar, ushort>*/,
0/*addWeightedImpl<double, uchar, short >*/,
0/*addWeightedImpl<double, uchar, int >*/,
0/*addWeightedImpl<double, uchar, float >*/,
0/*addWeightedImpl<double, uchar, double>*/
},
{
0/*addWeightedImpl<double, schar, uchar >*/,
0/*addWeightedImpl<double, schar, schar >*/,
0/*addWeightedImpl<double, schar, ushort>*/,
0/*addWeightedImpl<double, schar, short >*/,
0/*addWeightedImpl<double, schar, int >*/,
0/*addWeightedImpl<double, schar, float >*/,
0/*addWeightedImpl<double, schar, double>*/
},
{
0/*addWeightedImpl<double, ushort, uchar >*/,
0/*addWeightedImpl<double, ushort, schar >*/,
0/*addWeightedImpl<double, ushort, ushort>*/,
0/*addWeightedImpl<double, ushort, short >*/,
0/*addWeightedImpl<double, ushort, int >*/,
0/*addWeightedImpl<double, ushort, float >*/,
0/*addWeightedImpl<double, ushort, double>*/
},
{
0/*addWeightedImpl<double, short, uchar >*/,
0/*addWeightedImpl<double, short, schar >*/,
0/*addWeightedImpl<double, short, ushort>*/,
0/*addWeightedImpl<double, short, short >*/,
0/*addWeightedImpl<double, short, int >*/,
0/*addWeightedImpl<double, short, float >*/,
0/*addWeightedImpl<double, short, double>*/
},
{
0/*addWeightedImpl<double, int, uchar >*/,
0/*addWeightedImpl<double, int, schar >*/,
0/*addWeightedImpl<double, int, ushort>*/,
0/*addWeightedImpl<double, int, short >*/,
0/*addWeightedImpl<double, int, int >*/,
0/*addWeightedImpl<double, int, float >*/,
0/*addWeightedImpl<double, int, double>*/
},
{
0/*addWeightedImpl<double, float, uchar >*/,
0/*addWeightedImpl<double, float, schar >*/,
0/*addWeightedImpl<double, float, ushort>*/,
0/*addWeightedImpl<double, float, short >*/,
0/*addWeightedImpl<double, float, int >*/,
0/*addWeightedImpl<double, float, float >*/,
0/*addWeightedImpl<double, float, double>*/
},
{
addWeightedImpl<double, double, uchar >,
addWeightedImpl<double, double, schar >,
addWeightedImpl<double, double, ushort>,
addWeightedImpl<double, double, short >,
addWeightedImpl<double, double, int >,
addWeightedImpl<double, double, float >,
addWeightedImpl<double, double, double>
}
}
};
GpuMat src1 = getInputMat(_src1, stream);
GpuMat src2 = getInputMat(_src2, stream);
int sdepth1 = src1.depth();
int sdepth2 = src2.depth();
ddepth = ddepth >= 0 ? CV_MAT_DEPTH(ddepth) : std::max(sdepth1, sdepth2);
const int cn = src1.channels();
CV_Assert( src2.size() == src1.size() && src2.channels() == cn );
CV_Assert( sdepth1 <= CV_64F && sdepth2 <= CV_64F && ddepth <= CV_64F );
GpuMat dst = getOutputMat(_dst, src1.size(), CV_MAKE_TYPE(ddepth, cn), stream);
GpuMat src1_single = src1.reshape(1);
GpuMat src2_single = src2.reshape(1);
GpuMat dst_single = dst.reshape(1);
if (sdepth1 > sdepth2)
{
src1_single.swap(src2_single);
std::swap(alpha, beta);
std::swap(sdepth1, sdepth2);
}
const func_t func = funcs[sdepth1][sdepth2][ddepth];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src1_single, alpha, src2_single, beta, gamma, dst_single, stream);
syncOutput(dst, _dst, stream);
}
#endif

230
modules/cudaarithm/src/cuda/bitwise_mat.cu
View File

@ -1,230 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
void bitMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op);
//////////////////////////////////////////////////////////////////////////////
/// bitwise_not
void cv::cuda::bitwise_not(InputArray _src, OutputArray _dst, InputArray _mask, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
GpuMat mask = getInputMat(_mask, stream);
const int depth = src.depth();
CV_DbgAssert( depth <= CV_32F );
CV_DbgAssert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
if (mask.empty())
{
const int bcols = (int) (src.cols * src.elemSize());
if ((bcols & 3) == 0)
{
const int vcols = bcols >> 2;
GlobPtrSz<uint> vsrc = globPtr((uint*) src.data, src.step, src.rows, vcols);
GlobPtrSz<uint> vdst = globPtr((uint*) dst.data, dst.step, src.rows, vcols);
gridTransformUnary(vsrc, vdst, bit_not<uint>(), stream);
}
else if ((bcols & 1) == 0)
{
const int vcols = bcols >> 1;
GlobPtrSz<ushort> vsrc = globPtr((ushort*) src.data, src.step, src.rows, vcols);
GlobPtrSz<ushort> vdst = globPtr((ushort*) dst.data, dst.step, src.rows, vcols);
gridTransformUnary(vsrc, vdst, bit_not<ushort>(), stream);
}
else
{
GlobPtrSz<uchar> vsrc = globPtr((uchar*) src.data, src.step, src.rows, bcols);
GlobPtrSz<uchar> vdst = globPtr((uchar*) dst.data, dst.step, src.rows, bcols);
gridTransformUnary(vsrc, vdst, bit_not<uchar>(), stream);
}
}
else
{
if (depth == CV_32F || depth == CV_32S)
{
GlobPtrSz<uint> vsrc = globPtr((uint*) src.data, src.step, src.rows, src.cols * src.channels());
GlobPtrSz<uint> vdst = globPtr((uint*) dst.data, dst.step, src.rows, src.cols * src.channels());
gridTransformUnary(vsrc, vdst, bit_not<uint>(), singleMaskChannels(globPtr<uchar>(mask), src.channels()), stream);
}
else if (depth == CV_16S || depth == CV_16U)
{
GlobPtrSz<ushort> vsrc = globPtr((ushort*) src.data, src.step, src.rows, src.cols * src.channels());
GlobPtrSz<ushort> vdst = globPtr((ushort*) dst.data, dst.step, src.rows, src.cols * src.channels());
gridTransformUnary(vsrc, vdst, bit_not<ushort>(), singleMaskChannels(globPtr<uchar>(mask), src.channels()), stream);
}
else
{
GlobPtrSz<uchar> vsrc = globPtr((uchar*) src.data, src.step, src.rows, src.cols * src.channels());
GlobPtrSz<uchar> vdst = globPtr((uchar*) dst.data, dst.step, src.rows, src.cols * src.channels());
gridTransformUnary(vsrc, vdst, bit_not<uchar>(), singleMaskChannels(globPtr<uchar>(mask), src.channels()), stream);
}
}
syncOutput(dst, _dst, stream);
}
//////////////////////////////////////////////////////////////////////////////
/// Binary bitwise logical operations
namespace
{
template <template <typename> class Op, typename T>
void bitMatOp(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream)
{
GlobPtrSz<T> vsrc1 = globPtr((T*) src1.data, src1.step, src1.rows, src1.cols * src1.channels());
GlobPtrSz<T> vsrc2 = globPtr((T*) src2.data, src2.step, src1.rows, src1.cols * src1.channels());
GlobPtrSz<T> vdst = globPtr((T*) dst.data, dst.step, src1.rows, src1.cols * src1.channels());
if (mask.data)
gridTransformBinary(vsrc1, vsrc2, vdst, Op<T>(), singleMaskChannels(globPtr<uchar>(mask), src1.channels()), stream);
else
gridTransformBinary(vsrc1, vsrc2, vdst, Op<T>(), stream);
}
}
void bitMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op)
{
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream);
static const func_t funcs32[] =
{
bitMatOp<bit_and, uint>,
bitMatOp<bit_or, uint>,
bitMatOp<bit_xor, uint>
};
static const func_t funcs16[] =
{
bitMatOp<bit_and, ushort>,
bitMatOp<bit_or, ushort>,
bitMatOp<bit_xor, ushort>
};
static const func_t funcs8[] =
{
bitMatOp<bit_and, uchar>,
bitMatOp<bit_or, uchar>,
bitMatOp<bit_xor, uchar>
};
const int depth = src1.depth();
CV_DbgAssert( depth <= CV_32F );
CV_DbgAssert( op >= 0 && op < 3 );
if (mask.empty())
{
const int bcols = (int) (src1.cols * src1.elemSize());
if ((bcols & 3) == 0)
{
const int vcols = bcols >> 2;
GpuMat vsrc1(src1.rows, vcols, CV_32SC1, src1.data, src1.step);
GpuMat vsrc2(src1.rows, vcols, CV_32SC1, src2.data, src2.step);
GpuMat vdst(src1.rows, vcols, CV_32SC1, dst.data, dst.step);
funcs32[op](vsrc1, vsrc2, vdst, GpuMat(), stream);
}
else if ((bcols & 1) == 0)
{
const int vcols = bcols >> 1;
GpuMat vsrc1(src1.rows, vcols, CV_16UC1, src1.data, src1.step);
GpuMat vsrc2(src1.rows, vcols, CV_16UC1, src2.data, src2.step);
GpuMat vdst(src1.rows, vcols, CV_16UC1, dst.data, dst.step);
funcs16[op](vsrc1, vsrc2, vdst, GpuMat(), stream);
}
else
{
GpuMat vsrc1(src1.rows, bcols, CV_8UC1, src1.data, src1.step);
GpuMat vsrc2(src1.rows, bcols, CV_8UC1, src2.data, src2.step);
GpuMat vdst(src1.rows, bcols, CV_8UC1, dst.data, dst.step);
funcs8[op](vsrc1, vsrc2, vdst, GpuMat(), stream);
}
}
else
{
if (depth == CV_32F || depth == CV_32S)
{
funcs32[op](src1, src2, dst, mask, stream);
}
else if (depth == CV_16S || depth == CV_16U)
{
funcs16[op](src1, src2, dst, mask, stream);
}
else
{
funcs8[op](src1, src2, dst, mask, stream);
}
}
}
#endif

171
modules/cudaarithm/src/cuda/bitwise_scalar.cu
View File

@ -1,171 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv::cudev;
void bitScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op);
namespace
{
template <template <typename> class Op, typename T>
void bitScalarOp(const GpuMat& src, uint value, GpuMat& dst, Stream& stream)
{
gridTransformUnary(globPtr<T>(src), globPtr<T>(dst), bind2nd(Op<T>(), value), stream);
}
typedef void (*bit_scalar_func_t)(const GpuMat& src, uint value, GpuMat& dst, Stream& stream);
template <typename T, bit_scalar_func_t func> struct BitScalar
{
static void call(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& stream)
{
func(src, cv::saturate_cast<T>(value[0]), dst, stream);
}
};
template <bit_scalar_func_t func> struct BitScalar4
{
static void call(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& stream)
{
uint packedVal = 0;
packedVal |= cv::saturate_cast<uchar>(value[0]);
packedVal |= cv::saturate_cast<uchar>(value[1]) << 8;
packedVal |= cv::saturate_cast<uchar>(value[2]) << 16;
packedVal |= cv::saturate_cast<uchar>(value[3]) << 24;
func(src, packedVal, dst, stream);
}
};
template <int DEPTH, int cn> struct NppBitwiseCFunc
{
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
typedef NppStatus (*func_t)(const npp_type* pSrc1, int nSrc1Step, const npp_type* pConstants, npp_type* pDst, int nDstStep, NppiSize oSizeROI);
};
template <int DEPTH, int cn, typename NppBitwiseCFunc<DEPTH, cn>::func_t func> struct NppBitwiseC
{
typedef typename NppBitwiseCFunc<DEPTH, cn>::npp_type npp_type;
static void call(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& _stream)
{
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStreamHandler h(stream);
NppiSize oSizeROI;
oSizeROI.width = src.cols;
oSizeROI.height = src.rows;
const npp_type pConstants[] =
{
cv::saturate_cast<npp_type>(value[0]),
cv::saturate_cast<npp_type>(value[1]),
cv::saturate_cast<npp_type>(value[2]),
cv::saturate_cast<npp_type>(value[3])
};
nppSafeCall( func(src.ptr<npp_type>(), static_cast<int>(src.step), pConstants, dst.ptr<npp_type>(), static_cast<int>(dst.step), oSizeROI) );
if (stream == 0)
CV_CUDEV_SAFE_CALL( cudaDeviceSynchronize() );
}
};
}
void bitScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op)
{
CV_UNUSED(mask);
typedef void (*func_t)(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& stream);
static const func_t funcs[3][6][4] =
{
{
{BitScalar<uchar, bitScalarOp<bit_and, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiAndC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_and, uint> >::call},
{BitScalar<uchar, bitScalarOp<bit_and, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiAndC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_and, uint> >::call},
{BitScalar<ushort, bitScalarOp<bit_and, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiAndC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiAndC_16u_C4R>::call},
{BitScalar<ushort, bitScalarOp<bit_and, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiAndC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiAndC_16u_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_and, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiAndC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiAndC_32s_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_and, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiAndC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiAndC_32s_C4R>::call}
},
{
{BitScalar<uchar, bitScalarOp<bit_or, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiOrC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_or, uint> >::call},
{BitScalar<uchar, bitScalarOp<bit_or, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiOrC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_or, uint> >::call},
{BitScalar<ushort, bitScalarOp<bit_or, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiOrC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiOrC_16u_C4R>::call},
{BitScalar<ushort, bitScalarOp<bit_or, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiOrC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiOrC_16u_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_or, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiOrC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiOrC_32s_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_or, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiOrC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiOrC_32s_C4R>::call}
},
{
{BitScalar<uchar, bitScalarOp<bit_xor, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiXorC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_xor, uint> >::call},
{BitScalar<uchar, bitScalarOp<bit_xor, uchar> >::call , 0, NppBitwiseC<CV_8U , 3, nppiXorC_8u_C3R >::call, BitScalar4< bitScalarOp<bit_xor, uint> >::call},
{BitScalar<ushort, bitScalarOp<bit_xor, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiXorC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiXorC_16u_C4R>::call},
{BitScalar<ushort, bitScalarOp<bit_xor, ushort> >::call, 0, NppBitwiseC<CV_16U, 3, nppiXorC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiXorC_16u_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_xor, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiXorC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiXorC_32s_C4R>::call},
{BitScalar<uint, bitScalarOp<bit_xor, uint> >::call , 0, NppBitwiseC<CV_32S, 3, nppiXorC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiXorC_32s_C4R>::call}
}
};
const int depth = src.depth();
const int cn = src.channels();
CV_DbgAssert( depth <= CV_32F );
CV_DbgAssert( cn == 1 || cn == 3 || cn == 4 );
CV_DbgAssert( mask.empty() );
CV_DbgAssert( op >= 0 && op < 3 );
funcs[op][depth][cn - 1](src, value, dst, stream);
}
#endif

219
modules/cudaarithm/src/cuda/cmp_mat.cu
View File

@ -1,219 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void cmpMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int cmpop);
namespace
{
template <class Op, typename T> struct CmpOp : binary_function<T, T, uchar>
{
__device__ __forceinline__ uchar operator()(T a, T b) const
{
Op op;
return -op(a, b);
}
};
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <template <typename> class Op, typename T>
void cmpMat_v1(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
CmpOp<Op<T>, T> op;
gridTransformBinary_< TransformPolicy<T> >(globPtr<T>(src1), globPtr<T>(src2), globPtr<uchar>(dst), op, stream);
}
struct VCmpEq4 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vcmpeq4(a, b);
}
};
struct VCmpNe4 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vcmpne4(a, b);
}
};
struct VCmpLt4 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vcmplt4(a, b);
}
};
struct VCmpLe4 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vcmple4(a, b);
}
};
void cmpMatEq_v4(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 2;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, VCmpEq4(), stream);
}
void cmpMatNe_v4(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 2;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, VCmpNe4(), stream);
}
void cmpMatLt_v4(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 2;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, VCmpLt4(), stream);
}
void cmpMatLe_v4(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 2;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, VCmpLe4(), stream);
}
}
void cmpMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int cmpop)
{
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
static const func_t funcs[7][4] =
{
{cmpMat_v1<equal_to, uchar> , cmpMat_v1<not_equal_to, uchar> , cmpMat_v1<less, uchar> , cmpMat_v1<less_equal, uchar> },
{cmpMat_v1<equal_to, schar> , cmpMat_v1<not_equal_to, schar> , cmpMat_v1<less, schar> , cmpMat_v1<less_equal, schar> },
{cmpMat_v1<equal_to, ushort>, cmpMat_v1<not_equal_to, ushort>, cmpMat_v1<less, ushort>, cmpMat_v1<less_equal, ushort>},
{cmpMat_v1<equal_to, short> , cmpMat_v1<not_equal_to, short> , cmpMat_v1<less, short> , cmpMat_v1<less_equal, short> },
{cmpMat_v1<equal_to, int> , cmpMat_v1<not_equal_to, int> , cmpMat_v1<less, int> , cmpMat_v1<less_equal, int> },
{cmpMat_v1<equal_to, float> , cmpMat_v1<not_equal_to, float> , cmpMat_v1<less, float> , cmpMat_v1<less_equal, float> },
{cmpMat_v1<equal_to, double>, cmpMat_v1<not_equal_to, double>, cmpMat_v1<less, double>, cmpMat_v1<less_equal, double>}
};
typedef void (*func_v4_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
static const func_v4_t funcs_v4[] =
{
cmpMatEq_v4, cmpMatNe_v4, cmpMatLt_v4, cmpMatLe_v4
};
const int depth = src1.depth();
CV_DbgAssert( depth <= CV_64F );
static const int codes[] =
{
0, 2, 3, 2, 3, 1
};
const GpuMat* psrc1[] =
{
&src1, &src2, &src2, &src1, &src1, &src1
};
const GpuMat* psrc2[] =
{
&src2, &src1, &src1, &src2, &src2, &src2
};
const int code = codes[cmpop];
GpuMat src1_ = psrc1[cmpop]->reshape(1);
GpuMat src2_ = psrc2[cmpop]->reshape(1);
GpuMat dst_ = dst.reshape(1);
if (depth == CV_8U && (src1_.cols & 3) == 0)
{
const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
if (isAllAligned)
{
funcs_v4[code](src1_, src2_, dst_, stream);
return;
}
}
const func_t func = funcs[depth][code];
func(src1_, src2_, dst_, stream);
}
#endif

225
modules/cudaarithm/src/cuda/cmp_scalar.cu
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@ -1,225 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void cmpScalar(const GpuMat& src, cv::Scalar val, bool inv, GpuMat& dst, const GpuMat&, double, Stream& stream, int cmpop);
namespace
{
template <class Op, typename T> struct CmpOp : binary_function<T, T, uchar>
{
__device__ __forceinline__ uchar operator()(T a, T b) const
{
Op op;
return -op(a, b);
}
};
#define MAKE_VEC(_type, _cn) typename MakeVec<_type, _cn>::type
template <class Op, typename T, int cn> struct CmpScalarOp;
template <class Op, typename T>
struct CmpScalarOp<Op, T, 1> : unary_function<T, uchar>
{
T val;
__device__ __forceinline__ uchar operator()(T src) const
{
CmpOp<Op, T> op;
return op(src, val);
}
};
template <class Op, typename T>
struct CmpScalarOp<Op, T, 2> : unary_function<MAKE_VEC(T, 2), MAKE_VEC(uchar, 2)>
{
MAKE_VEC(T, 2) val;
__device__ __forceinline__ MAKE_VEC(uchar, 2) operator()(const MAKE_VEC(T, 2) & src) const
{
CmpOp<Op, T> op;
return VecTraits<MAKE_VEC(uchar, 2)>::make(op(src.x, val.x), op(src.y, val.y));
}
};
template <class Op, typename T>
struct CmpScalarOp<Op, T, 3> : unary_function<MAKE_VEC(T, 3), MAKE_VEC(uchar, 3)>
{
MAKE_VEC(T, 3) val;
__device__ __forceinline__ MAKE_VEC(uchar, 3) operator()(const MAKE_VEC(T, 3) & src) const
{
CmpOp<Op, T> op;
return VecTraits<MAKE_VEC(uchar, 3)>::make(op(src.x, val.x), op(src.y, val.y), op(src.z, val.z));
}
};
template <class Op, typename T>
struct CmpScalarOp<Op, T, 4> : unary_function<MAKE_VEC(T, 4), MAKE_VEC(uchar, 4)>
{
MAKE_VEC(T, 4) val;
__device__ __forceinline__ MAKE_VEC(uchar, 4) operator()(const MAKE_VEC(T, 4) & src) const
{
CmpOp<Op, T> op;
return VecTraits<MAKE_VEC(uchar, 4)>::make(op(src.x, val.x), op(src.y, val.y), op(src.z, val.z), op(src.w, val.w));
}
};
#undef TYPE_VEC
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <template <typename> class Op, typename T, int cn>
void cmpScalarImpl(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& stream)
{
typedef typename MakeVec<T, cn>::type src_type;
typedef typename MakeVec<uchar, cn>::type dst_type;
cv::Scalar_<T> value_ = value;
CmpScalarOp<Op<T>, T, cn> op;
op.val = VecTraits<src_type>::make(value_.val);
gridTransformUnary_< TransformPolicy<T> >(globPtr<src_type>(src), globPtr<dst_type>(dst), op, stream);
}
}
void cmpScalar(const GpuMat& src, cv::Scalar val, bool inv, GpuMat& dst, const GpuMat&, double, Stream& stream, int cmpop)
{
typedef void (*func_t)(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& stream);
static const func_t funcs[7][6][4] =
{
{
{cmpScalarImpl<equal_to, uchar, 1>, cmpScalarImpl<equal_to, uchar, 2>, cmpScalarImpl<equal_to, uchar, 3>, cmpScalarImpl<equal_to, uchar, 4>},
{cmpScalarImpl<greater, uchar, 1>, cmpScalarImpl<greater, uchar, 2>, cmpScalarImpl<greater, uchar, 3>, cmpScalarImpl<greater, uchar, 4>},
{cmpScalarImpl<greater_equal, uchar, 1>, cmpScalarImpl<greater_equal, uchar, 2>, cmpScalarImpl<greater_equal, uchar, 3>, cmpScalarImpl<greater_equal, uchar, 4>},
{cmpScalarImpl<less, uchar, 1>, cmpScalarImpl<less, uchar, 2>, cmpScalarImpl<less, uchar, 3>, cmpScalarImpl<less, uchar, 4>},
{cmpScalarImpl<less_equal, uchar, 1>, cmpScalarImpl<less_equal, uchar, 2>, cmpScalarImpl<less_equal, uchar, 3>, cmpScalarImpl<less_equal, uchar, 4>},
{cmpScalarImpl<not_equal_to, uchar, 1>, cmpScalarImpl<not_equal_to, uchar, 2>, cmpScalarImpl<not_equal_to, uchar, 3>, cmpScalarImpl<not_equal_to, uchar, 4>}
},
{
{cmpScalarImpl<equal_to, schar, 1>, cmpScalarImpl<equal_to, schar, 2>, cmpScalarImpl<equal_to, schar, 3>, cmpScalarImpl<equal_to, schar, 4>},
{cmpScalarImpl<greater, schar, 1>, cmpScalarImpl<greater, schar, 2>, cmpScalarImpl<greater, schar, 3>, cmpScalarImpl<greater, schar, 4>},
{cmpScalarImpl<greater_equal, schar, 1>, cmpScalarImpl<greater_equal, schar, 2>, cmpScalarImpl<greater_equal, schar, 3>, cmpScalarImpl<greater_equal, schar, 4>},
{cmpScalarImpl<less, schar, 1>, cmpScalarImpl<less, schar, 2>, cmpScalarImpl<less, schar, 3>, cmpScalarImpl<less, schar, 4>},
{cmpScalarImpl<less_equal, schar, 1>, cmpScalarImpl<less_equal, schar, 2>, cmpScalarImpl<less_equal, schar, 3>, cmpScalarImpl<less_equal, schar, 4>},
{cmpScalarImpl<not_equal_to, schar, 1>, cmpScalarImpl<not_equal_to, schar, 2>, cmpScalarImpl<not_equal_to, schar, 3>, cmpScalarImpl<not_equal_to, schar, 4>}
},
{
{cmpScalarImpl<equal_to, ushort, 1>, cmpScalarImpl<equal_to, ushort, 2>, cmpScalarImpl<equal_to, ushort, 3>, cmpScalarImpl<equal_to, ushort, 4>},
{cmpScalarImpl<greater, ushort, 1>, cmpScalarImpl<greater, ushort, 2>, cmpScalarImpl<greater, ushort, 3>, cmpScalarImpl<greater, ushort, 4>},
{cmpScalarImpl<greater_equal, ushort, 1>, cmpScalarImpl<greater_equal, ushort, 2>, cmpScalarImpl<greater_equal, ushort, 3>, cmpScalarImpl<greater_equal, ushort, 4>},
{cmpScalarImpl<less, ushort, 1>, cmpScalarImpl<less, ushort, 2>, cmpScalarImpl<less, ushort, 3>, cmpScalarImpl<less, ushort, 4>},
{cmpScalarImpl<less_equal, ushort, 1>, cmpScalarImpl<less_equal, ushort, 2>, cmpScalarImpl<less_equal, ushort, 3>, cmpScalarImpl<less_equal, ushort, 4>},
{cmpScalarImpl<not_equal_to, ushort, 1>, cmpScalarImpl<not_equal_to, ushort, 2>, cmpScalarImpl<not_equal_to, ushort, 3>, cmpScalarImpl<not_equal_to, ushort, 4>}
},
{
{cmpScalarImpl<equal_to, short, 1>, cmpScalarImpl<equal_to, short, 2>, cmpScalarImpl<equal_to, short, 3>, cmpScalarImpl<equal_to, short, 4>},
{cmpScalarImpl<greater, short, 1>, cmpScalarImpl<greater, short, 2>, cmpScalarImpl<greater, short, 3>, cmpScalarImpl<greater, short, 4>},
{cmpScalarImpl<greater_equal, short, 1>, cmpScalarImpl<greater_equal, short, 2>, cmpScalarImpl<greater_equal, short, 3>, cmpScalarImpl<greater_equal, short, 4>},
{cmpScalarImpl<less, short, 1>, cmpScalarImpl<less, short, 2>, cmpScalarImpl<less, short, 3>, cmpScalarImpl<less, short, 4>},
{cmpScalarImpl<less_equal, short, 1>, cmpScalarImpl<less_equal, short, 2>, cmpScalarImpl<less_equal, short, 3>, cmpScalarImpl<less_equal, short, 4>},
{cmpScalarImpl<not_equal_to, short, 1>, cmpScalarImpl<not_equal_to, short, 2>, cmpScalarImpl<not_equal_to, short, 3>, cmpScalarImpl<not_equal_to, short, 4>}
},
{
{cmpScalarImpl<equal_to, int, 1>, cmpScalarImpl<equal_to, int, 2>, cmpScalarImpl<equal_to, int, 3>, cmpScalarImpl<equal_to, int, 4>},
{cmpScalarImpl<greater, int, 1>, cmpScalarImpl<greater, int, 2>, cmpScalarImpl<greater, int, 3>, cmpScalarImpl<greater, int, 4>},
{cmpScalarImpl<greater_equal, int, 1>, cmpScalarImpl<greater_equal, int, 2>, cmpScalarImpl<greater_equal, int, 3>, cmpScalarImpl<greater_equal, int, 4>},
{cmpScalarImpl<less, int, 1>, cmpScalarImpl<less, int, 2>, cmpScalarImpl<less, int, 3>, cmpScalarImpl<less, int, 4>},
{cmpScalarImpl<less_equal, int, 1>, cmpScalarImpl<less_equal, int, 2>, cmpScalarImpl<less_equal, int, 3>, cmpScalarImpl<less_equal, int, 4>},
{cmpScalarImpl<not_equal_to, int, 1>, cmpScalarImpl<not_equal_to, int, 2>, cmpScalarImpl<not_equal_to, int, 3>, cmpScalarImpl<not_equal_to, int, 4>}
},
{
{cmpScalarImpl<equal_to, float, 1>, cmpScalarImpl<equal_to, float, 2>, cmpScalarImpl<equal_to, float, 3>, cmpScalarImpl<equal_to, float, 4>},
{cmpScalarImpl<greater, float, 1>, cmpScalarImpl<greater, float, 2>, cmpScalarImpl<greater, float, 3>, cmpScalarImpl<greater, float, 4>},
{cmpScalarImpl<greater_equal, float, 1>, cmpScalarImpl<greater_equal, float, 2>, cmpScalarImpl<greater_equal, float, 3>, cmpScalarImpl<greater_equal, float, 4>},
{cmpScalarImpl<less, float, 1>, cmpScalarImpl<less, float, 2>, cmpScalarImpl<less, float, 3>, cmpScalarImpl<less, float, 4>},
{cmpScalarImpl<less_equal, float, 1>, cmpScalarImpl<less_equal, float, 2>, cmpScalarImpl<less_equal, float, 3>, cmpScalarImpl<less_equal, float, 4>},
{cmpScalarImpl<not_equal_to, float, 1>, cmpScalarImpl<not_equal_to, float, 2>, cmpScalarImpl<not_equal_to, float, 3>, cmpScalarImpl<not_equal_to, float, 4>}
},
{
{cmpScalarImpl<equal_to, double, 1>, cmpScalarImpl<equal_to, double, 2>, cmpScalarImpl<equal_to, double, 3>, cmpScalarImpl<equal_to, double, 4>},
{cmpScalarImpl<greater, double, 1>, cmpScalarImpl<greater, double, 2>, cmpScalarImpl<greater, double, 3>, cmpScalarImpl<greater, double, 4>},
{cmpScalarImpl<greater_equal, double, 1>, cmpScalarImpl<greater_equal, double, 2>, cmpScalarImpl<greater_equal, double, 3>, cmpScalarImpl<greater_equal, double, 4>},
{cmpScalarImpl<less, double, 1>, cmpScalarImpl<less, double, 2>, cmpScalarImpl<less, double, 3>, cmpScalarImpl<less, double, 4>},
{cmpScalarImpl<less_equal, double, 1>, cmpScalarImpl<less_equal, double, 2>, cmpScalarImpl<less_equal, double, 3>, cmpScalarImpl<less_equal, double, 4>},
{cmpScalarImpl<not_equal_to, double, 1>, cmpScalarImpl<not_equal_to, double, 2>, cmpScalarImpl<not_equal_to, double, 3>, cmpScalarImpl<not_equal_to, double, 4>}
}
};
if (inv)
{
// src1 is a scalar; swap it with src2
cmpop = cmpop == cv::CMP_LT ? cv::CMP_GT : cmpop == cv::CMP_LE ? cv::CMP_GE :
cmpop == cv::CMP_GE ? cv::CMP_LE : cmpop == cv::CMP_GT ? cv::CMP_LT : cmpop;
}
const int depth = src.depth();
const int cn = src.channels();
CV_DbgAssert( depth <= CV_64F && cn <= 4 );
funcs[depth][cmpop][cn - 1](src, val, dst, stream);
}
#endif

159
modules/cudaarithm/src/cuda/copy_make_border.cu
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@ -1,159 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
struct ShiftMap
{
typedef int2 value_type;
typedef int index_type;
int top;
int left;
__device__ __forceinline__ int2 operator ()(int y, int x) const
{
return make_int2(x - left, y - top);
}
};
struct ShiftMapSz : ShiftMap
{
int rows, cols;
};
}
namespace cv { namespace cudev {
template <> struct PtrTraits<ShiftMapSz> : PtrTraitsBase<ShiftMapSz, ShiftMap>
{
};
}}
namespace
{
template <typename T, int cn>
void copyMakeBorderImpl(const GpuMat& src, GpuMat& dst, int top, int left, int borderMode, cv::Scalar borderValue, Stream& stream)
{
typedef typename MakeVec<T, cn>::type src_type;
cv::Scalar_<T> borderValue_ = borderValue;
const src_type brdVal = VecTraits<src_type>::make(borderValue_.val);
ShiftMapSz map;
map.top = top;
map.left = left;
map.rows = dst.rows;
map.cols = dst.cols;
switch (borderMode)
{
case cv::BORDER_CONSTANT:
gridCopy(remapPtr(brdConstant(globPtr<src_type>(src), brdVal), map), globPtr<src_type>(dst), stream);
break;
case cv::BORDER_REPLICATE:
gridCopy(remapPtr(brdReplicate(globPtr<src_type>(src)), map), globPtr<src_type>(dst), stream);
break;
case cv::BORDER_REFLECT:
gridCopy(remapPtr(brdReflect(globPtr<src_type>(src)), map), globPtr<src_type>(dst), stream);
break;
case cv::BORDER_WRAP:
gridCopy(remapPtr(brdWrap(globPtr<src_type>(src)), map), globPtr<src_type>(dst), stream);
break;
case cv::BORDER_REFLECT_101:
gridCopy(remapPtr(brdReflect101(globPtr<src_type>(src)), map), globPtr<src_type>(dst), stream);
break;
};
}
}
void cv::cuda::copyMakeBorder(InputArray _src, OutputArray _dst, int top, int bottom, int left, int right, int borderType, Scalar value, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int top, int left, int borderMode, cv::Scalar borderValue, Stream& stream);
static const func_t funcs[6][4] =
{
{ copyMakeBorderImpl<uchar , 1> , copyMakeBorderImpl<uchar , 2> , copyMakeBorderImpl<uchar , 3> , copyMakeBorderImpl<uchar , 4> },
{0 /*copyMakeBorderImpl<schar , 1>*/, 0 /*copyMakeBorderImpl<schar , 2>*/, 0 /*copyMakeBorderImpl<schar , 3>*/, 0 /*copyMakeBorderImpl<schar , 4>*/},
{ copyMakeBorderImpl<ushort, 1> , 0 /*copyMakeBorderImpl<ushort, 2>*/, copyMakeBorderImpl<ushort, 3> , copyMakeBorderImpl<ushort, 4> },
{ copyMakeBorderImpl<short , 1> , 0 /*copyMakeBorderImpl<short , 2>*/, copyMakeBorderImpl<short , 3> , copyMakeBorderImpl<short , 4> },
{0 /*copyMakeBorderImpl<int , 1>*/, 0 /*copyMakeBorderImpl<int , 2>*/, 0 /*copyMakeBorderImpl<int , 3>*/, 0 /*copyMakeBorderImpl<int , 4>*/},
{ copyMakeBorderImpl<float , 1> , 0 /*copyMakeBorderImpl<float , 2>*/, copyMakeBorderImpl<float , 3> , copyMakeBorderImpl<float ,4> }
};
GpuMat src = getInputMat(_src, stream);
const int depth = src.depth();
const int cn = src.channels();
CV_Assert( depth <= CV_32F && cn <= 4 );
CV_Assert( borderType == BORDER_REFLECT_101 || borderType == BORDER_REPLICATE || borderType == BORDER_CONSTANT || borderType == BORDER_REFLECT || borderType == BORDER_WRAP );
GpuMat dst = getOutputMat(_dst, src.rows + top + bottom, src.cols + left + right, src.type(), stream);
const func_t func = funcs[depth][cn - 1];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src, dst, top, left, borderType, value, stream);
syncOutput(dst, _dst, stream);
}
#endif

113
modules/cudaarithm/src/cuda/countnonzero.cu
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@ -1,113 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
template <typename T, typename D>
void countNonZeroImpl(const GpuMat& _src, GpuMat& _dst, Stream& stream)
{
const GpuMat_<T>& src = (const GpuMat_<T>&) _src;
GpuMat_<D>& dst = (GpuMat_<D>&) _dst;
gridCountNonZero(src, dst, stream);
}
}
void cv::cuda::countNonZero(InputArray _src, OutputArray _dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, Stream& stream);
static const func_t funcs[] =
{
countNonZeroImpl<uchar, int>,
countNonZeroImpl<schar, int>,
countNonZeroImpl<ushort, int>,
countNonZeroImpl<short, int>,
countNonZeroImpl<int, int>,
countNonZeroImpl<float, int>,
countNonZeroImpl<double, int>,
};
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.depth() <= CV_64F );
CV_Assert( src.channels() == 1 );
GpuMat dst = getOutputMat(_dst, 1, 1, CV_32SC1, stream);
const func_t func = funcs[src.depth()];
func(src, dst, stream);
syncOutput(dst, _dst, stream);
}
int cv::cuda::countNonZero(InputArray _src)
{
Stream& stream = Stream::Null();
BufferPool pool(stream);
GpuMat buf = pool.getBuffer(1, 1, CV_32SC1);
countNonZero(_src, buf, stream);
int data;
buf.download(Mat(1, 1, CV_32SC1, &data));
return data;
}
#endif

242
modules/cudaarithm/src/cuda/div_mat.cu
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@ -1,242 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void divMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double scale, Stream& stream, int);
void divMat_8uc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
void divMat_16sc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
namespace
{
template <typename T, typename D> struct DivOp : binary_function<T, T, D>
{
__device__ __forceinline__ D operator ()(T a, T b) const
{
return b != 0 ? saturate_cast<D>(a / b) : 0;
}
};
template <typename T> struct DivOp<T, float> : binary_function<T, T, float>
{
__device__ __forceinline__ float operator ()(T a, T b) const
{
return b != 0 ? static_cast<float>(a) / b : 0.0f;
}
};
template <typename T> struct DivOp<T, double> : binary_function<T, T, double>
{
__device__ __forceinline__ double operator ()(T a, T b) const
{
return b != 0 ? static_cast<double>(a) / b : 0.0;
}
};
template <typename T, typename S, typename D> struct DivScaleOp : binary_function<T, T, D>
{
S scale;
__device__ __forceinline__ D operator ()(T a, T b) const
{
return b != 0 ? saturate_cast<D>(scale * a / b) : 0;
}
};
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <typename T, typename S, typename D>
void divMatImpl(const GpuMat& src1, const GpuMat& src2, const GpuMat& dst, double scale, Stream& stream)
{
if (scale == 1)
{
DivOp<T, D> op;
gridTransformBinary_< TransformPolicy<S> >(globPtr<T>(src1), globPtr<T>(src2), globPtr<D>(dst), op, stream);
}
else
{
DivScaleOp<T, S, D> op;
op.scale = static_cast<S>(scale);
gridTransformBinary_< TransformPolicy<S> >(globPtr<T>(src1), globPtr<T>(src2), globPtr<D>(dst), op, stream);
}
}
}
void divMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double scale, Stream& stream, int)
{
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, const GpuMat& dst, double scale, Stream& stream);
static const func_t funcs[7][7] =
{
{
divMatImpl<uchar, float, uchar>,
divMatImpl<uchar, float, schar>,
divMatImpl<uchar, float, ushort>,
divMatImpl<uchar, float, short>,
divMatImpl<uchar, float, int>,
divMatImpl<uchar, float, float>,
divMatImpl<uchar, double, double>
},
{
divMatImpl<schar, float, uchar>,
divMatImpl<schar, float, schar>,
divMatImpl<schar, float, ushort>,
divMatImpl<schar, float, short>,
divMatImpl<schar, float, int>,
divMatImpl<schar, float, float>,
divMatImpl<schar, double, double>
},
{
0 /*divMatImpl<ushort, float, uchar>*/,
0 /*divMatImpl<ushort, float, schar>*/,
divMatImpl<ushort, float, ushort>,
divMatImpl<ushort, float, short>,
divMatImpl<ushort, float, int>,
divMatImpl<ushort, float, float>,
divMatImpl<ushort, double, double>
},
{
0 /*divMatImpl<short, float, uchar>*/,
0 /*divMatImpl<short, float, schar>*/,
divMatImpl<short, float, ushort>,
divMatImpl<short, float, short>,
divMatImpl<short, float, int>,
divMatImpl<short, float, float>,
divMatImpl<short, double, double>
},
{
0 /*divMatImpl<int, float, uchar>*/,
0 /*divMatImpl<int, float, schar>*/,
0 /*divMatImpl<int, float, ushort>*/,
0 /*divMatImpl<int, float, short>*/,
divMatImpl<int, float, int>,
divMatImpl<int, float, float>,
divMatImpl<int, double, double>
},
{
0 /*divMatImpl<float, float, uchar>*/,
0 /*divMatImpl<float, float, schar>*/,
0 /*divMatImpl<float, float, ushort>*/,
0 /*divMatImpl<float, float, short>*/,
0 /*divMatImpl<float, float, int>*/,
divMatImpl<float, float, float>,
divMatImpl<float, double, double>
},
{
0 /*divMatImpl<double, double, uchar>*/,
0 /*divMatImpl<double, double, schar>*/,
0 /*divMatImpl<double, double, ushort>*/,
0 /*divMatImpl<double, double, short>*/,
0 /*divMatImpl<double, double, int>*/,
0 /*divMatImpl<double, double, float>*/,
divMatImpl<double, double, double>
}
};
const int sdepth = src1.depth();
const int ddepth = dst.depth();
CV_DbgAssert( sdepth <= CV_64F && ddepth <= CV_64F );
GpuMat src1_ = src1.reshape(1);
GpuMat src2_ = src2.reshape(1);
GpuMat dst_ = dst.reshape(1);
const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src1_, src2_, dst_, scale, stream);
}
namespace
{
template <typename T>
struct DivOpSpecial : binary_function<T, float, T>
{
__device__ __forceinline__ T operator ()(const T& a, float b) const
{
typedef typename VecTraits<T>::elem_type elem_type;
T res = VecTraits<T>::all(0);
if (b != 0)
{
b = 1.0f / b;
res.x = saturate_cast<elem_type>(a.x * b);
res.y = saturate_cast<elem_type>(a.y * b);
res.z = saturate_cast<elem_type>(a.z * b);
res.w = saturate_cast<elem_type>(a.w * b);
}
return res;
}
};
}
void divMat_8uc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
gridTransformBinary(globPtr<uchar4>(src1), globPtr<float>(src2), globPtr<uchar4>(dst), DivOpSpecial<uchar4>(), stream);
}
void divMat_16sc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
gridTransformBinary(globPtr<short4>(src1), globPtr<float>(src2), globPtr<short4>(dst), DivOpSpecial<short4>(), stream);
}
#endif

260
modules/cudaarithm/src/cuda/div_scalar.cu
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@ -1,260 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void divScalar(const GpuMat& src, cv::Scalar val, bool inv, GpuMat& dst, const GpuMat& mask, double scale, Stream& stream, int);
namespace
{
template <typename T, int cn> struct SafeDiv;
template <typename T> struct SafeDiv<T, 1>
{
__device__ __forceinline__ static T op(T a, T b)
{
return b != 0 ? a / b : 0;
}
};
template <typename T> struct SafeDiv<T, 2>
{
__device__ __forceinline__ static T op(const T& a, const T& b)
{
T res;
res.x = b.x != 0 ? a.x / b.x : 0;
res.y = b.y != 0 ? a.y / b.y : 0;
return res;
}
};
template <typename T> struct SafeDiv<T, 3>
{
__device__ __forceinline__ static T op(const T& a, const T& b)
{
T res;
res.x = b.x != 0 ? a.x / b.x : 0;
res.y = b.y != 0 ? a.y / b.y : 0;
res.z = b.z != 0 ? a.z / b.z : 0;
return res;
}
};
template <typename T> struct SafeDiv<T, 4>
{
__device__ __forceinline__ static T op(const T& a, const T& b)
{
T res;
res.x = b.x != 0 ? a.x / b.x : 0;
res.y = b.y != 0 ? a.y / b.y : 0;
res.z = b.z != 0 ? a.z / b.z : 0;
res.w = b.w != 0 ? a.w / b.w : 0;
return res;
}
};
template <typename SrcType, typename ScalarType, typename DstType> struct DivScalarOp : unary_function<SrcType, DstType>
{
ScalarType val;
__device__ __forceinline__ DstType operator ()(SrcType a) const
{
return saturate_cast<DstType>(SafeDiv<ScalarType, VecTraits<ScalarType>::cn>::op(saturate_cast<ScalarType>(a), val));
}
};
template <typename SrcType, typename ScalarType, typename DstType> struct DivScalarOpInv : unary_function<SrcType, DstType>
{
ScalarType val;
__device__ __forceinline__ DstType operator ()(SrcType a) const
{
return saturate_cast<DstType>(SafeDiv<ScalarType, VecTraits<ScalarType>::cn>::op(val, saturate_cast<ScalarType>(a)));
}
};
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <typename SrcType, typename ScalarDepth, typename DstType>
void divScalarImpl(const GpuMat& src, cv::Scalar value, bool inv, GpuMat& dst, Stream& stream)
{
typedef typename MakeVec<ScalarDepth, VecTraits<SrcType>::cn>::type ScalarType;
cv::Scalar_<ScalarDepth> value_ = value;
if (inv)
{
DivScalarOpInv<SrcType, ScalarType, DstType> op;
op.val = VecTraits<ScalarType>::make(value_.val);
gridTransformUnary_< TransformPolicy<ScalarDepth> >(globPtr<SrcType>(src), globPtr<DstType>(dst), op, stream);
}
else
{
DivScalarOp<SrcType, ScalarType, DstType> op;
op.val = VecTraits<ScalarType>::make(value_.val);
gridTransformUnary_< TransformPolicy<ScalarDepth> >(globPtr<SrcType>(src), globPtr<DstType>(dst), op, stream);
}
}
}
void divScalar(const GpuMat& src, cv::Scalar val, bool inv, GpuMat& dst, const GpuMat&, double scale, Stream& stream, int)
{
typedef void (*func_t)(const GpuMat& src, cv::Scalar val, bool inv, GpuMat& dst, Stream& stream);
static const func_t funcs[7][7][4] =
{
{
{divScalarImpl<uchar, float, uchar>, divScalarImpl<uchar2, float, uchar2>, divScalarImpl<uchar3, float, uchar3>, divScalarImpl<uchar4, float, uchar4>},
{divScalarImpl<uchar, float, schar>, divScalarImpl<uchar2, float, char2>, divScalarImpl<uchar3, float, char3>, divScalarImpl<uchar4, float, char4>},
{divScalarImpl<uchar, float, ushort>, divScalarImpl<uchar2, float, ushort2>, divScalarImpl<uchar3, float, ushort3>, divScalarImpl<uchar4, float, ushort4>},
{divScalarImpl<uchar, float, short>, divScalarImpl<uchar2, float, short2>, divScalarImpl<uchar3, float, short3>, divScalarImpl<uchar4, float, short4>},
{divScalarImpl<uchar, float, int>, divScalarImpl<uchar2, float, int2>, divScalarImpl<uchar3, float, int3>, divScalarImpl<uchar4, float, int4>},
{divScalarImpl<uchar, float, float>, divScalarImpl<uchar2, float, float2>, divScalarImpl<uchar3, float, float3>, divScalarImpl<uchar4, float, float4>},
{divScalarImpl<uchar, double, double>, divScalarImpl<uchar2, double, double2>, divScalarImpl<uchar3, double, double3>, divScalarImpl<uchar4, double, double4>}
},
{
{divScalarImpl<schar, float, uchar>, divScalarImpl<char2, float, uchar2>, divScalarImpl<char3, float, uchar3>, divScalarImpl<char4, float, uchar4>},
{divScalarImpl<schar, float, schar>, divScalarImpl<char2, float, char2>, divScalarImpl<char3, float, char3>, divScalarImpl<char4, float, char4>},
{divScalarImpl<schar, float, ushort>, divScalarImpl<char2, float, ushort2>, divScalarImpl<char3, float, ushort3>, divScalarImpl<char4, float, ushort4>},
{divScalarImpl<schar, float, short>, divScalarImpl<char2, float, short2>, divScalarImpl<char3, float, short3>, divScalarImpl<char4, float, short4>},
{divScalarImpl<schar, float, int>, divScalarImpl<char2, float, int2>, divScalarImpl<char3, float, int3>, divScalarImpl<char4, float, int4>},
{divScalarImpl<schar, float, float>, divScalarImpl<char2, float, float2>, divScalarImpl<char3, float, float3>, divScalarImpl<char4, float, float4>},
{divScalarImpl<schar, double, double>, divScalarImpl<char2, double, double2>, divScalarImpl<char3, double, double3>, divScalarImpl<char4, double, double4>}
},
{
{0 /*divScalarImpl<ushort, float, uchar>*/, 0 /*divScalarImpl<ushort2, float, uchar2>*/, 0 /*divScalarImpl<ushort3, float, uchar3>*/, 0 /*divScalarImpl<ushort4, float, uchar4>*/},
{0 /*divScalarImpl<ushort, float, schar>*/, 0 /*divScalarImpl<ushort2, float, char2>*/, 0 /*divScalarImpl<ushort3, float, char3>*/, 0 /*divScalarImpl<ushort4, float, char4>*/},
{divScalarImpl<ushort, float, ushort>, divScalarImpl<ushort2, float, ushort2>, divScalarImpl<ushort3, float, ushort3>, divScalarImpl<ushort4, float, ushort4>},
{divScalarImpl<ushort, float, short>, divScalarImpl<ushort2, float, short2>, divScalarImpl<ushort3, float, short3>, divScalarImpl<ushort4, float, short4>},
{divScalarImpl<ushort, float, int>, divScalarImpl<ushort2, float, int2>, divScalarImpl<ushort3, float, int3>, divScalarImpl<ushort4, float, int4>},
{divScalarImpl<ushort, float, float>, divScalarImpl<ushort2, float, float2>, divScalarImpl<ushort3, float, float3>, divScalarImpl<ushort4, float, float4>},
{divScalarImpl<ushort, double, double>, divScalarImpl<ushort2, double, double2>, divScalarImpl<ushort3, double, double3>, divScalarImpl<ushort4, double, double4>}
},
{
{0 /*divScalarImpl<short, float, uchar>*/, 0 /*divScalarImpl<short2, float, uchar2>*/, 0 /*divScalarImpl<short3, float, uchar3>*/, 0 /*divScalarImpl<short4, float, uchar4>*/},
{0 /*divScalarImpl<short, float, schar>*/, 0 /*divScalarImpl<short2, float, char2>*/, 0 /*divScalarImpl<short3, float, char3>*/, 0 /*divScalarImpl<short4, float, char4>*/},
{divScalarImpl<short, float, ushort>, divScalarImpl<short2, float, ushort2>, divScalarImpl<short3, float, ushort3>, divScalarImpl<short4, float, ushort4>},
{divScalarImpl<short, float, short>, divScalarImpl<short2, float, short2>, divScalarImpl<short3, float, short3>, divScalarImpl<short4, float, short4>},
{divScalarImpl<short, float, int>, divScalarImpl<short2, float, int2>, divScalarImpl<short3, float, int3>, divScalarImpl<short4, float, int4>},
{divScalarImpl<short, float, float>, divScalarImpl<short2, float, float2>, divScalarImpl<short3, float, float3>, divScalarImpl<short4, float, float4>},
{divScalarImpl<short, double, double>, divScalarImpl<short2, double, double2>, divScalarImpl<short3, double, double3>, divScalarImpl<short4, double, double4>}
},
{
{0 /*divScalarImpl<int, float, uchar>*/, 0 /*divScalarImpl<int2, float, uchar2>*/, 0 /*divScalarImpl<int3, float, uchar3>*/, 0 /*divScalarImpl<int4, float, uchar4>*/},
{0 /*divScalarImpl<int, float, schar>*/, 0 /*divScalarImpl<int2, float, char2>*/, 0 /*divScalarImpl<int3, float, char3>*/, 0 /*divScalarImpl<int4, float, char4>*/},
{0 /*divScalarImpl<int, float, ushort>*/, 0 /*divScalarImpl<int2, float, ushort2>*/, 0 /*divScalarImpl<int3, float, ushort3>*/, 0 /*divScalarImpl<int4, float, ushort4>*/},
{0 /*divScalarImpl<int, float, short>*/, 0 /*divScalarImpl<int2, float, short2>*/, 0 /*divScalarImpl<int3, float, short3>*/, 0 /*divScalarImpl<int4, float, short4>*/},
{divScalarImpl<int, float, int>, divScalarImpl<int2, float, int2>, divScalarImpl<int3, float, int3>, divScalarImpl<int4, float, int4>},
{divScalarImpl<int, float, float>, divScalarImpl<int2, float, float2>, divScalarImpl<int3, float, float3>, divScalarImpl<int4, float, float4>},
{divScalarImpl<int, double, double>, divScalarImpl<int2, double, double2>, divScalarImpl<int3, double, double3>, divScalarImpl<int4, double, double4>}
},
{
{0 /*divScalarImpl<float, float, uchar>*/, 0 /*divScalarImpl<float2, float, uchar2>*/, 0 /*divScalarImpl<float3, float, uchar3>*/, 0 /*divScalarImpl<float4, float, uchar4>*/},
{0 /*divScalarImpl<float, float, schar>*/, 0 /*divScalarImpl<float2, float, char2>*/, 0 /*divScalarImpl<float3, float, char3>*/, 0 /*divScalarImpl<float4, float, char4>*/},
{0 /*divScalarImpl<float, float, ushort>*/, 0 /*divScalarImpl<float2, float, ushort2>*/, 0 /*divScalarImpl<float3, float, ushort3>*/, 0 /*divScalarImpl<float4, float, ushort4>*/},
{0 /*divScalarImpl<float, float, short>*/, 0 /*divScalarImpl<float2, float, short2>*/, 0 /*divScalarImpl<float3, float, short3>*/, 0 /*divScalarImpl<float4, float, short4>*/},
{0 /*divScalarImpl<float, float, int>*/, 0 /*divScalarImpl<float2, float, int2>*/, 0 /*divScalarImpl<float3, float, int3>*/, 0 /*divScalarImpl<float4, float, int4>*/},
{divScalarImpl<float, float, float>, divScalarImpl<float2, float, float2>, divScalarImpl<float3, float, float3>, divScalarImpl<float4, float, float4>},
{divScalarImpl<float, double, double>, divScalarImpl<float2, double, double2>, divScalarImpl<float3, double, double3>, divScalarImpl<float4, double, double4>}
},
{
{0 /*divScalarImpl<double, double, uchar>*/, 0 /*divScalarImpl<double2, double, uchar2>*/, 0 /*divScalarImpl<double3, double, uchar3>*/, 0 /*divScalarImpl<double4, double, uchar4>*/},
{0 /*divScalarImpl<double, double, schar>*/, 0 /*divScalarImpl<double2, double, char2>*/, 0 /*divScalarImpl<double3, double, char3>*/, 0 /*divScalarImpl<double4, double, char4>*/},
{0 /*divScalarImpl<double, double, ushort>*/, 0 /*divScalarImpl<double2, double, ushort2>*/, 0 /*divScalarImpl<double3, double, ushort3>*/, 0 /*divScalarImpl<double4, double, ushort4>*/},
{0 /*divScalarImpl<double, double, short>*/, 0 /*divScalarImpl<double2, double, short2>*/, 0 /*divScalarImpl<double3, double, short3>*/, 0 /*divScalarImpl<double4, double, short4>*/},
{0 /*divScalarImpl<double, double, int>*/, 0 /*divScalarImpl<double2, double, int2>*/, 0 /*divScalarImpl<double3, double, int3>*/, 0 /*divScalarImpl<double4, double, int4>*/},
{0 /*divScalarImpl<double, double, float>*/, 0 /*divScalarImpl<double2, double, float2>*/, 0 /*divScalarImpl<double3, double, float3>*/, 0 /*divScalarImpl<double4, double, float4>*/},
{divScalarImpl<double, double, double>, divScalarImpl<double2, double, double2>, divScalarImpl<double3, double, double3>, divScalarImpl<double4, double, double4>}
}
};
const int sdepth = src.depth();
const int ddepth = dst.depth();
const int cn = src.channels();
CV_DbgAssert( sdepth <= CV_64F && ddepth <= CV_64F && cn <= 4 );
if (inv)
{
val[0] *= scale;
val[1] *= scale;
val[2] *= scale;
val[3] *= scale;
}
else
{
val[0] /= scale;
val[1] /= scale;
val[2] /= scale;
val[3] /= scale;
}
const func_t func = funcs[sdepth][ddepth][cn - 1];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src, val, inv, dst, stream);
}
#endif

107
modules/cudaarithm/src/cuda/integral.cu
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@ -1,107 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
////////////////////////////////////////////////////////////////////////
// integral
void cv::cuda::integral(InputArray _src, OutputArray _dst, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.type() == CV_8UC1 );
BufferPool pool(stream);
GpuMat_<int> res(src.size(), pool.getAllocator());
gridIntegral(globPtr<uchar>(src), res, stream);
GpuMat dst = getOutputMat(_dst, src.rows + 1, src.cols + 1, CV_32SC1, stream);
dst.setTo(Scalar::all(0), stream);
GpuMat inner = dst(Rect(1, 1, src.cols, src.rows));
res.copyTo(inner, stream);
syncOutput(dst, _dst, stream);
}
//////////////////////////////////////////////////////////////////////////////
// sqrIntegral
void cv::cuda::sqrIntegral(InputArray _src, OutputArray _dst, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.type() == CV_8UC1 );
BufferPool pool(Stream::Null());
GpuMat_<double> res(pool.getBuffer(src.size(), CV_64FC1));
gridIntegral(sqr_(cvt_<int>(globPtr<uchar>(src))), res, stream);
GpuMat dst = getOutputMat(_dst, src.rows + 1, src.cols + 1, CV_64FC1, stream);
dst.setTo(Scalar::all(0), stream);
GpuMat inner = dst(Rect(1, 1, src.cols, src.rows));
res.copyTo(inner, stream);
syncOutput(dst, _dst, stream);
}
#endif

210
modules/cudaarithm/src/cuda/lut.cu
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@ -1,210 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
texture<uchar, cudaTextureType1D, cudaReadModeElementType> texLutTable;
class LookUpTableImpl : public LookUpTable
{
public:
LookUpTableImpl(InputArray lut);
~LookUpTableImpl();
void transform(InputArray src, OutputArray dst, Stream& stream = Stream::Null()) CV_OVERRIDE;
private:
GpuMat d_lut;
cudaTextureObject_t texLutTableObj;
bool cc30;
};
LookUpTableImpl::LookUpTableImpl(InputArray _lut)
{
if (_lut.kind() == _InputArray::CUDA_GPU_MAT)
{
d_lut = _lut.getGpuMat();
}
else
{
Mat h_lut = _lut.getMat();
d_lut.upload(Mat(1, 256, h_lut.type(), h_lut.data));
}
CV_Assert( d_lut.depth() == CV_8U );
CV_Assert( d_lut.rows == 1 && d_lut.cols == 256 );
cc30 = deviceSupports(FEATURE_SET_COMPUTE_30);
if (cc30)
{
// Use the texture object
cudaResourceDesc texRes;
std::memset(&texRes, 0, sizeof(texRes));
texRes.resType = cudaResourceTypeLinear;
texRes.res.linear.devPtr = d_lut.data;
texRes.res.linear.desc = cudaCreateChannelDesc<uchar>();
texRes.res.linear.sizeInBytes = 256 * d_lut.channels() * sizeof(uchar);
cudaTextureDesc texDescr;
std::memset(&texDescr, 0, sizeof(texDescr));
CV_CUDEV_SAFE_CALL( cudaCreateTextureObject(&texLutTableObj, &texRes, &texDescr, 0) );
}
else
{
// Use the texture reference
cudaChannelFormatDesc desc = cudaCreateChannelDesc<uchar>();
CV_CUDEV_SAFE_CALL( cudaBindTexture(0, &texLutTable, d_lut.data, &desc) );
}
}
LookUpTableImpl::~LookUpTableImpl()
{
if (cc30)
{
// Use the texture object
cudaDestroyTextureObject(texLutTableObj);
}
else
{
// Use the texture reference
cudaUnbindTexture(texLutTable);
}
}
struct LutTablePtrC1
{
typedef uchar value_type;
typedef uchar index_type;
cudaTextureObject_t texLutTableObj;
__device__ __forceinline__ uchar operator ()(uchar, uchar x) const
{
#if CV_CUDEV_ARCH < 300
// Use the texture reference
return tex1Dfetch(texLutTable, x);
#else
// Use the texture object
return tex1Dfetch<uchar>(texLutTableObj, x);
#endif
}
};
struct LutTablePtrC3
{
typedef uchar3 value_type;
typedef uchar3 index_type;
cudaTextureObject_t texLutTableObj;
__device__ __forceinline__ uchar3 operator ()(const uchar3&, const uchar3& x) const
{
#if CV_CUDEV_ARCH < 300
// Use the texture reference
return make_uchar3(tex1Dfetch(texLutTable, x.x * 3), tex1Dfetch(texLutTable, x.y * 3 + 1), tex1Dfetch(texLutTable, x.z * 3 + 2));
#else
// Use the texture object
return make_uchar3(tex1Dfetch<uchar>(texLutTableObj, x.x * 3), tex1Dfetch<uchar>(texLutTableObj, x.y * 3 + 1), tex1Dfetch<uchar>(texLutTableObj, x.z * 3 + 2));
#endif
}
};
void LookUpTableImpl::transform(InputArray _src, OutputArray _dst, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
const int cn = src.channels();
const int lut_cn = d_lut.channels();
CV_Assert( src.type() == CV_8UC1 || src.type() == CV_8UC3 );
CV_Assert( lut_cn == 1 || lut_cn == cn );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
if (lut_cn == 1)
{
GpuMat_<uchar> src1(src.reshape(1));
GpuMat_<uchar> dst1(dst.reshape(1));
LutTablePtrC1 tbl;
tbl.texLutTableObj = texLutTableObj;
dst1.assign(lut_(src1, tbl), stream);
}
else if (lut_cn == 3)
{
GpuMat_<uchar3>& src3 = (GpuMat_<uchar3>&) src;
GpuMat_<uchar3>& dst3 = (GpuMat_<uchar3>&) dst;
LutTablePtrC3 tbl;
tbl.texLutTableObj = texLutTableObj;
dst3.assign(lut_(src3, tbl), stream);
}
syncOutput(dst, _dst, stream);
}
}
Ptr<LookUpTable> cv::cuda::createLookUpTable(InputArray lut)
{
return makePtr<LookUpTableImpl>(lut);
}
#endif

341
modules/cudaarithm/src/cuda/math.cu
View File

@ -1,341 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
}
//////////////////////////////////////////////////////////////////////////////
/// abs
namespace
{
template <typename T>
void absMat(const GpuMat& src, const GpuMat& dst, Stream& stream)
{
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), abs_func<T>(), stream);
}
}
void cv::cuda::abs(InputArray _src, OutputArray _dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, const GpuMat& dst, Stream& stream);
static const func_t funcs[] =
{
absMat<uchar>,
absMat<schar>,
absMat<ushort>,
absMat<short>,
absMat<int>,
absMat<float>,
absMat<double>
};
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.depth() <= CV_64F );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
funcs[src.depth()](src.reshape(1), dst.reshape(1), stream);
syncOutput(dst, _dst, stream);
}
//////////////////////////////////////////////////////////////////////////////
/// sqr
namespace
{
template <typename T> struct SqrOp : unary_function<T, T>
{
__device__ __forceinline__ T operator ()(T x) const
{
return cudev::saturate_cast<T>(x * x);
}
};
template <typename T>
void sqrMat(const GpuMat& src, const GpuMat& dst, Stream& stream)
{
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), SqrOp<T>(), stream);
}
}
void cv::cuda::sqr(InputArray _src, OutputArray _dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, const GpuMat& dst, Stream& stream);
static const func_t funcs[] =
{
sqrMat<uchar>,
sqrMat<schar>,
sqrMat<ushort>,
sqrMat<short>,
sqrMat<int>,
sqrMat<float>,
sqrMat<double>
};
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.depth() <= CV_64F );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
funcs[src.depth()](src.reshape(1), dst.reshape(1), stream);
syncOutput(dst, _dst, stream);
}
//////////////////////////////////////////////////////////////////////////////
/// sqrt
namespace
{
template <typename T>
void sqrtMat(const GpuMat& src, const GpuMat& dst, Stream& stream)
{
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), sqrt_func<T>(), stream);
}
}
void cv::cuda::sqrt(InputArray _src, OutputArray _dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, const GpuMat& dst, Stream& stream);
static const func_t funcs[] =
{
sqrtMat<uchar>,
sqrtMat<schar>,
sqrtMat<ushort>,
sqrtMat<short>,
sqrtMat<int>,
sqrtMat<float>,
sqrtMat<double>
};
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.depth() <= CV_64F );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
funcs[src.depth()](src.reshape(1), dst.reshape(1), stream);
syncOutput(dst, _dst, stream);
}
////////////////////////////////////////////////////////////////////////
/// exp
namespace
{
template <typename T> struct ExpOp : unary_function<T, T>
{
__device__ __forceinline__ T operator ()(T x) const
{
exp_func<T> f;
return cudev::saturate_cast<T>(f(x));
}
};
template <typename T>
void expMat(const GpuMat& src, const GpuMat& dst, Stream& stream)
{
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), ExpOp<T>(), stream);
}
}
void cv::cuda::exp(InputArray _src, OutputArray _dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, const GpuMat& dst, Stream& stream);
static const func_t funcs[] =
{
expMat<uchar>,
expMat<schar>,
expMat<ushort>,
expMat<short>,
expMat<int>,
expMat<float>,
expMat<double>
};
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.depth() <= CV_64F );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
funcs[src.depth()](src.reshape(1), dst.reshape(1), stream);
syncOutput(dst, _dst, stream);
}
////////////////////////////////////////////////////////////////////////
// log
namespace
{
template <typename T>
void logMat(const GpuMat& src, const GpuMat& dst, Stream& stream)
{
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), log_func<T>(), stream);
}
}
void cv::cuda::log(InputArray _src, OutputArray _dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, const GpuMat& dst, Stream& stream);
static const func_t funcs[] =
{
logMat<uchar>,
logMat<schar>,
logMat<ushort>,
logMat<short>,
logMat<int>,
logMat<float>,
logMat<double>
};
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.depth() <= CV_64F );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
funcs[src.depth()](src.reshape(1), dst.reshape(1), stream);
syncOutput(dst, _dst, stream);
}
////////////////////////////////////////////////////////////////////////
// pow
namespace
{
template<typename T, bool Signed = numeric_limits<T>::is_signed> struct PowOp : unary_function<T, T>
{
typedef typename LargerType<T, float>::type LargerType;
LargerType power;
__device__ __forceinline__ T operator()(T e) const
{
T res = cudev::saturate_cast<T>(__powf(e < 0 ? -e : e, power));
if ((e < 0) && (1 & static_cast<int>(power)))
res *= -1;
return res;
}
};
template<typename T> struct PowOp<T, false> : unary_function<T, T>
{
typedef typename LargerType<T, float>::type LargerType;
LargerType power;
__device__ __forceinline__ T operator()(T e) const
{
return cudev::saturate_cast<T>(__powf(e, power));
}
};
template<typename T>
void powMat(const GpuMat& src, double power, const GpuMat& dst, Stream& stream)
{
PowOp<T> op;
op.power = static_cast<typename LargerType<T, float>::type>(power);
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), op, stream);
}
}
void cv::cuda::pow(InputArray _src, double power, OutputArray _dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, double power, const GpuMat& dst, Stream& stream);
static const func_t funcs[] =
{
powMat<uchar>,
powMat<schar>,
powMat<ushort>,
powMat<short>,
powMat<int>,
powMat<float>,
powMat<double>
};
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.depth() <= CV_64F );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
funcs[src.depth()](src.reshape(1), power, dst.reshape(1), stream);
syncOutput(dst, _dst, stream);
}
#endif

189
modules/cudaarithm/src/cuda/minmax.cu
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@ -1,189 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
template <typename T, typename R>
void minMaxImpl(const GpuMat& _src, const GpuMat& mask, GpuMat& _dst, Stream& stream)
{
const GpuMat_<T>& src = (const GpuMat_<T>&) _src;
GpuMat_<R>& dst = (GpuMat_<R>&) _dst;
if (mask.empty())
gridFindMinMaxVal(src, dst, stream);
else
gridFindMinMaxVal(src, dst, globPtr<uchar>(mask), stream);
}
template <typename T, typename R>
void minMaxImpl(const GpuMat& src, const GpuMat& mask, double* minVal, double* maxVal)
{
BufferPool pool(Stream::Null());
GpuMat buf(pool.getBuffer(1, 2, DataType<R>::type));
minMaxImpl<T, R>(src, mask, buf, Stream::Null());
R data[2];
buf.download(Mat(1, 2, buf.type(), data));
}
}
void cv::cuda::findMinMax(InputArray _src, OutputArray _dst, InputArray _mask, Stream& stream)
{
typedef void (*func_t)(const GpuMat& _src, const GpuMat& mask, GpuMat& _dst, Stream& stream);
static const func_t funcs[] =
{
minMaxImpl<uchar, int>,
minMaxImpl<schar, int>,
minMaxImpl<ushort, int>,
minMaxImpl<short, int>,
minMaxImpl<int, int>,
minMaxImpl<float, float>,
minMaxImpl<double, double>
};
const GpuMat src = getInputMat(_src, stream);
const GpuMat mask = getInputMat(_mask, stream);
CV_Assert( src.channels() == 1 );
CV_Assert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
const int src_depth = src.depth();
const int dst_depth = src_depth < CV_32F ? CV_32S : src_depth;
GpuMat dst = getOutputMat(_dst, 1, 2, dst_depth, stream);
const func_t func = funcs[src.depth()];
func(src, mask, dst, stream);
syncOutput(dst, _dst, stream);
}
void cv::cuda::minMax(InputArray _src, double* minVal, double* maxVal, InputArray _mask)
{
Stream& stream = Stream::Null();
HostMem dst;
findMinMax(_src, dst, _mask, stream);
stream.waitForCompletion();
double vals[2];
dst.createMatHeader().convertTo(Mat(1, 2, CV_64FC1, &vals[0]), CV_64F);
if (minVal)
*minVal = vals[0];
if (maxVal)
*maxVal = vals[1];
}
namespace cv { namespace cuda { namespace device {
void findMaxAbs(InputArray _src, OutputArray _dst, InputArray _mask, Stream& stream);
}}}
namespace
{
template <typename T, typename R>
void findMaxAbsImpl(const GpuMat& _src, const GpuMat& mask, GpuMat& _dst, Stream& stream)
{
const GpuMat_<T>& src = (const GpuMat_<T>&) _src;
GpuMat_<R>& dst = (GpuMat_<R>&) _dst;
if (mask.empty())
gridFindMaxVal(abs_(src), dst, stream);
else
gridFindMaxVal(abs_(src), dst, globPtr<uchar>(mask), stream);
}
}
void cv::cuda::device::findMaxAbs(InputArray _src, OutputArray _dst, InputArray _mask, Stream& stream)
{
typedef void (*func_t)(const GpuMat& _src, const GpuMat& mask, GpuMat& _dst, Stream& stream);
static const func_t funcs[] =
{
findMaxAbsImpl<uchar, int>,
findMaxAbsImpl<schar, int>,
findMaxAbsImpl<ushort, int>,
findMaxAbsImpl<short, int>,
findMaxAbsImpl<int, int>,
findMaxAbsImpl<float, float>,
findMaxAbsImpl<double, double>
};
const GpuMat src = getInputMat(_src, stream);
const GpuMat mask = getInputMat(_mask, stream);
CV_Assert( src.channels() == 1 );
CV_Assert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
const int src_depth = src.depth();
const int dst_depth = src_depth < CV_32F ? CV_32S : src_depth;
GpuMat dst = getOutputMat(_dst, 1, 1, dst_depth, stream);
const func_t func = funcs[src.depth()];
func(src, mask, dst, stream);
syncOutput(dst, _dst, stream);
}
#endif

243
modules/cudaarithm/src/cuda/minmax_mat.cu
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@ -1,243 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void minMaxMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int op);
void minMaxScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat&, double, Stream& stream, int op);
///////////////////////////////////////////////////////////////////////
/// minMaxMat
namespace
{
template <template <typename> class Op, typename T>
void minMaxMat_v1(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
gridTransformBinary(globPtr<T>(src1), globPtr<T>(src2), globPtr<T>(dst), Op<T>(), stream);
}
struct MinOp2 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vmin2(a, b);
}
};
struct MaxOp2 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vmax2(a, b);
}
};
template <class Op2>
void minMaxMat_v2(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 1;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, Op2(), stream);
}
struct MinOp4 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vmin4(a, b);
}
};
struct MaxOp4 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vmax4(a, b);
}
};
template <class Op4>
void minMaxMat_v4(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 2;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, Op4(), stream);
}
}
void minMaxMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int op)
{
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
static const func_t funcs_v1[2][7] =
{
{
minMaxMat_v1<minimum, uchar>,
minMaxMat_v1<minimum, schar>,
minMaxMat_v1<minimum, ushort>,
minMaxMat_v1<minimum, short>,
minMaxMat_v1<minimum, int>,
minMaxMat_v1<minimum, float>,
minMaxMat_v1<minimum, double>
},
{
minMaxMat_v1<maximum, uchar>,
minMaxMat_v1<maximum, schar>,
minMaxMat_v1<maximum, ushort>,
minMaxMat_v1<maximum, short>,
minMaxMat_v1<maximum, int>,
minMaxMat_v1<maximum, float>,
minMaxMat_v1<maximum, double>
}
};
static const func_t funcs_v2[2] =
{
minMaxMat_v2<MinOp2>, minMaxMat_v2<MaxOp2>
};
static const func_t funcs_v4[2] =
{
minMaxMat_v4<MinOp4>, minMaxMat_v4<MaxOp4>
};
const int depth = src1.depth();
CV_DbgAssert( depth <= CV_64F );
GpuMat src1_ = src1.reshape(1);
GpuMat src2_ = src2.reshape(1);
GpuMat dst_ = dst.reshape(1);
if (depth == CV_8U || depth == CV_16U)
{
const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
if (isAllAligned)
{
if (depth == CV_8U && (src1_.cols & 3) == 0)
{
funcs_v4[op](src1_, src2_, dst_, stream);
return;
}
else if (depth == CV_16U && (src1_.cols & 1) == 0)
{
funcs_v2[op](src1_, src2_, dst_, stream);
return;
}
}
}
const func_t func = funcs_v1[op][depth];
func(src1_, src2_, dst_, stream);
}
///////////////////////////////////////////////////////////////////////
/// minMaxScalar
namespace
{
template <template <typename> class Op, typename T>
void minMaxScalar(const GpuMat& src, double value, GpuMat& dst, Stream& stream)
{
gridTransformUnary(globPtr<T>(src), globPtr<T>(dst), bind2nd(Op<T>(), cv::saturate_cast<T>(value)), stream);
}
}
void minMaxScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat&, double, Stream& stream, int op)
{
typedef void (*func_t)(const GpuMat& src, double value, GpuMat& dst, Stream& stream);
static const func_t funcs[2][7] =
{
{
minMaxScalar<minimum, uchar>,
minMaxScalar<minimum, schar>,
minMaxScalar<minimum, ushort>,
minMaxScalar<minimum, short>,
minMaxScalar<minimum, int>,
minMaxScalar<minimum, float>,
minMaxScalar<minimum, double>
},
{
minMaxScalar<maximum, uchar>,
minMaxScalar<maximum, schar>,
minMaxScalar<maximum, ushort>,
minMaxScalar<maximum, short>,
minMaxScalar<maximum, int>,
minMaxScalar<maximum, float>,
minMaxScalar<maximum, double>
}
};
const int depth = src.depth();
CV_DbgAssert( depth <= CV_64F );
CV_DbgAssert( src.channels() == 1 );
funcs[op][depth](src, value[0], dst, stream);
}
#endif

159
modules/cudaarithm/src/cuda/minmaxloc.cu
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@ -1,159 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
template <typename T, typename R>
void minMaxLocImpl(const GpuMat& _src, const GpuMat& mask, GpuMat& _valBuf, GpuMat& _locBuf, Stream& stream)
{
const GpuMat_<T>& src = (const GpuMat_<T>&) _src;
GpuMat_<R>& valBuf = (GpuMat_<R>&) _valBuf;
GpuMat_<int>& locBuf = (GpuMat_<int>&) _locBuf;
if (mask.empty())
gridMinMaxLoc(src, valBuf, locBuf, stream);
else
gridMinMaxLoc(src, valBuf, locBuf, globPtr<uchar>(mask), stream);
}
}
void cv::cuda::findMinMaxLoc(InputArray _src, OutputArray _minMaxVals, OutputArray _loc, InputArray _mask, Stream& stream)
{
typedef void (*func_t)(const GpuMat& _src, const GpuMat& mask, GpuMat& _valBuf, GpuMat& _locBuf, Stream& stream);
static const func_t funcs[] =
{
minMaxLocImpl<uchar, int>,
minMaxLocImpl<schar, int>,
minMaxLocImpl<ushort, int>,
minMaxLocImpl<short, int>,
minMaxLocImpl<int, int>,
minMaxLocImpl<float, float>,
minMaxLocImpl<double, double>
};
const GpuMat src = getInputMat(_src, stream);
const GpuMat mask = getInputMat(_mask, stream);
CV_Assert( src.channels() == 1 );
CV_Assert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
const int src_depth = src.depth();
BufferPool pool(stream);
GpuMat valBuf(pool.getAllocator());
GpuMat locBuf(pool.getAllocator());
const func_t func = funcs[src_depth];
func(src, mask, valBuf, locBuf, stream);
GpuMat minMaxVals = valBuf.colRange(0, 1);
GpuMat loc = locBuf.colRange(0, 1);
if (_minMaxVals.kind() == _InputArray::CUDA_GPU_MAT)
{
minMaxVals.copyTo(_minMaxVals, stream);
}
else
{
minMaxVals.download(_minMaxVals, stream);
}
if (_loc.kind() == _InputArray::CUDA_GPU_MAT)
{
loc.copyTo(_loc, stream);
}
else
{
loc.download(_loc, stream);
}
}
void cv::cuda::minMaxLoc(InputArray _src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, InputArray _mask)
{
Stream& stream = Stream::Null();
HostMem minMaxVals, locVals;
findMinMaxLoc(_src, minMaxVals, locVals, _mask, stream);
stream.waitForCompletion();
double vals[2];
minMaxVals.createMatHeader().convertTo(Mat(minMaxVals.size(), CV_64FC1, &vals[0]), CV_64F);
int locs[2];
locVals.createMatHeader().copyTo(Mat(locVals.size(), CV_32SC1, &locs[0]));
Size size = _src.size();
cv::Point locs2D[] = {
cv::Point(locs[0] % size.width, locs[0] / size.width),
cv::Point(locs[1] % size.width, locs[1] / size.width),
};
if (minVal)
*minVal = vals[0];
if (maxVal)
*maxVal = vals[1];
if (minLoc)
*minLoc = locs2D[0];
if (maxLoc)
*maxLoc = locs2D[1];
}
#endif

224
modules/cudaarithm/src/cuda/mul_mat.cu
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@ -1,224 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void mulMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double scale, Stream& stream, int);
void mulMat_8uc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
void mulMat_16sc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
namespace
{
template <typename T, typename D> struct MulOp : binary_function<T, T, D>
{
__device__ __forceinline__ D operator ()(T a, T b) const
{
return saturate_cast<D>(a * b);
}
};
template <typename T, typename S, typename D> struct MulScaleOp : binary_function<T, T, D>
{
S scale;
__device__ __forceinline__ D operator ()(T a, T b) const
{
return saturate_cast<D>(scale * a * b);
}
};
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <typename T, typename S, typename D>
void mulMatImpl(const GpuMat& src1, const GpuMat& src2, const GpuMat& dst, double scale, Stream& stream)
{
if (scale == 1)
{
MulOp<T, D> op;
gridTransformBinary_< TransformPolicy<S> >(globPtr<T>(src1), globPtr<T>(src2), globPtr<D>(dst), op, stream);
}
else
{
MulScaleOp<T, S, D> op;
op.scale = static_cast<S>(scale);
gridTransformBinary_< TransformPolicy<S> >(globPtr<T>(src1), globPtr<T>(src2), globPtr<D>(dst), op, stream);
}
}
}
void mulMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double scale, Stream& stream, int)
{
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, const GpuMat& dst, double scale, Stream& stream);
static const func_t funcs[7][7] =
{
{
mulMatImpl<uchar, float, uchar>,
mulMatImpl<uchar, float, schar>,
mulMatImpl<uchar, float, ushort>,
mulMatImpl<uchar, float, short>,
mulMatImpl<uchar, float, int>,
mulMatImpl<uchar, float, float>,
mulMatImpl<uchar, double, double>
},
{
mulMatImpl<schar, float, uchar>,
mulMatImpl<schar, float, schar>,
mulMatImpl<schar, float, ushort>,
mulMatImpl<schar, float, short>,
mulMatImpl<schar, float, int>,
mulMatImpl<schar, float, float>,
mulMatImpl<schar, double, double>
},
{
0 /*mulMatImpl<ushort, float, uchar>*/,
0 /*mulMatImpl<ushort, float, schar>*/,
mulMatImpl<ushort, float, ushort>,
mulMatImpl<ushort, float, short>,
mulMatImpl<ushort, float, int>,
mulMatImpl<ushort, float, float>,
mulMatImpl<ushort, double, double>
},
{
0 /*mulMatImpl<short, float, uchar>*/,
0 /*mulMatImpl<short, float, schar>*/,
mulMatImpl<short, float, ushort>,
mulMatImpl<short, float, short>,
mulMatImpl<short, float, int>,
mulMatImpl<short, float, float>,
mulMatImpl<short, double, double>
},
{
0 /*mulMatImpl<int, float, uchar>*/,
0 /*mulMatImpl<int, float, schar>*/,
0 /*mulMatImpl<int, float, ushort>*/,
0 /*mulMatImpl<int, float, short>*/,
mulMatImpl<int, float, int>,
mulMatImpl<int, float, float>,
mulMatImpl<int, double, double>
},
{
0 /*mulMatImpl<float, float, uchar>*/,
0 /*mulMatImpl<float, float, schar>*/,
0 /*mulMatImpl<float, float, ushort>*/,
0 /*mulMatImpl<float, float, short>*/,
0 /*mulMatImpl<float, float, int>*/,
mulMatImpl<float, float, float>,
mulMatImpl<float, double, double>
},
{
0 /*mulMatImpl<double, double, uchar>*/,
0 /*mulMatImpl<double, double, schar>*/,
0 /*mulMatImpl<double, double, ushort>*/,
0 /*mulMatImpl<double, double, short>*/,
0 /*mulMatImpl<double, double, int>*/,
0 /*mulMatImpl<double, double, float>*/,
mulMatImpl<double, double, double>
}
};
const int sdepth = src1.depth();
const int ddepth = dst.depth();
CV_DbgAssert( sdepth <= CV_64F && ddepth <= CV_64F );
GpuMat src1_ = src1.reshape(1);
GpuMat src2_ = src2.reshape(1);
GpuMat dst_ = dst.reshape(1);
const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src1_, src2_, dst_, scale, stream);
}
namespace
{
template <typename T>
struct MulOpSpecial : binary_function<T, float, T>
{
__device__ __forceinline__ T operator ()(const T& a, float b) const
{
typedef typename VecTraits<T>::elem_type elem_type;
T res;
res.x = saturate_cast<elem_type>(a.x * b);
res.y = saturate_cast<elem_type>(a.y * b);
res.z = saturate_cast<elem_type>(a.z * b);
res.w = saturate_cast<elem_type>(a.w * b);
return res;
}
};
}
void mulMat_8uc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
gridTransformBinary(globPtr<uchar4>(src1), globPtr<float>(src2), globPtr<uchar4>(dst), MulOpSpecial<uchar4>(), stream);
}
void mulMat_16sc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
gridTransformBinary(globPtr<short4>(src1), globPtr<float>(src2), globPtr<short4>(dst), MulOpSpecial<short4>(), stream);
}
#endif

182
modules/cudaarithm/src/cuda/mul_scalar.cu
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@ -1,182 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void mulScalar(const GpuMat& src, cv::Scalar val, bool, GpuMat& dst, const GpuMat& mask, double scale, Stream& stream, int);
namespace
{
template <typename SrcType, typename ScalarType, typename DstType> struct MulScalarOp : unary_function<SrcType, DstType>
{
ScalarType val;
__device__ __forceinline__ DstType operator ()(SrcType a) const
{
return saturate_cast<DstType>(saturate_cast<ScalarType>(a) * val);
}
};
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <typename SrcType, typename ScalarDepth, typename DstType>
void mulScalarImpl(const GpuMat& src, cv::Scalar value, GpuMat& dst, Stream& stream)
{
typedef typename MakeVec<ScalarDepth, VecTraits<SrcType>::cn>::type ScalarType;
cv::Scalar_<ScalarDepth> value_ = value;
MulScalarOp<SrcType, ScalarType, DstType> op;
op.val = VecTraits<ScalarType>::make(value_.val);
gridTransformUnary_< TransformPolicy<ScalarDepth> >(globPtr<SrcType>(src), globPtr<DstType>(dst), op, stream);
}
}
void mulScalar(const GpuMat& src, cv::Scalar val, bool, GpuMat& dst, const GpuMat&, double scale, Stream& stream, int)
{
typedef void (*func_t)(const GpuMat& src, cv::Scalar val, GpuMat& dst, Stream& stream);
static const func_t funcs[7][7][4] =
{
{
{mulScalarImpl<uchar, float, uchar>, mulScalarImpl<uchar2, float, uchar2>, mulScalarImpl<uchar3, float, uchar3>, mulScalarImpl<uchar4, float, uchar4>},
{mulScalarImpl<uchar, float, schar>, mulScalarImpl<uchar2, float, char2>, mulScalarImpl<uchar3, float, char3>, mulScalarImpl<uchar4, float, char4>},
{mulScalarImpl<uchar, float, ushort>, mulScalarImpl<uchar2, float, ushort2>, mulScalarImpl<uchar3, float, ushort3>, mulScalarImpl<uchar4, float, ushort4>},
{mulScalarImpl<uchar, float, short>, mulScalarImpl<uchar2, float, short2>, mulScalarImpl<uchar3, float, short3>, mulScalarImpl<uchar4, float, short4>},
{mulScalarImpl<uchar, float, int>, mulScalarImpl<uchar2, float, int2>, mulScalarImpl<uchar3, float, int3>, mulScalarImpl<uchar4, float, int4>},
{mulScalarImpl<uchar, float, float>, mulScalarImpl<uchar2, float, float2>, mulScalarImpl<uchar3, float, float3>, mulScalarImpl<uchar4, float, float4>},
{mulScalarImpl<uchar, double, double>, mulScalarImpl<uchar2, double, double2>, mulScalarImpl<uchar3, double, double3>, mulScalarImpl<uchar4, double, double4>}
},
{
{mulScalarImpl<schar, float, uchar>, mulScalarImpl<char2, float, uchar2>, mulScalarImpl<char3, float, uchar3>, mulScalarImpl<char4, float, uchar4>},
{mulScalarImpl<schar, float, schar>, mulScalarImpl<char2, float, char2>, mulScalarImpl<char3, float, char3>, mulScalarImpl<char4, float, char4>},
{mulScalarImpl<schar, float, ushort>, mulScalarImpl<char2, float, ushort2>, mulScalarImpl<char3, float, ushort3>, mulScalarImpl<char4, float, ushort4>},
{mulScalarImpl<schar, float, short>, mulScalarImpl<char2, float, short2>, mulScalarImpl<char3, float, short3>, mulScalarImpl<char4, float, short4>},
{mulScalarImpl<schar, float, int>, mulScalarImpl<char2, float, int2>, mulScalarImpl<char3, float, int3>, mulScalarImpl<char4, float, int4>},
{mulScalarImpl<schar, float, float>, mulScalarImpl<char2, float, float2>, mulScalarImpl<char3, float, float3>, mulScalarImpl<char4, float, float4>},
{mulScalarImpl<schar, double, double>, mulScalarImpl<char2, double, double2>, mulScalarImpl<char3, double, double3>, mulScalarImpl<char4, double, double4>}
},
{
{0 /*mulScalarImpl<ushort, float, uchar>*/, 0 /*mulScalarImpl<ushort2, float, uchar2>*/, 0 /*mulScalarImpl<ushort3, float, uchar3>*/, 0 /*mulScalarImpl<ushort4, float, uchar4>*/},
{0 /*mulScalarImpl<ushort, float, schar>*/, 0 /*mulScalarImpl<ushort2, float, char2>*/, 0 /*mulScalarImpl<ushort3, float, char3>*/, 0 /*mulScalarImpl<ushort4, float, char4>*/},
{mulScalarImpl<ushort, float, ushort>, mulScalarImpl<ushort2, float, ushort2>, mulScalarImpl<ushort3, float, ushort3>, mulScalarImpl<ushort4, float, ushort4>},
{mulScalarImpl<ushort, float, short>, mulScalarImpl<ushort2, float, short2>, mulScalarImpl<ushort3, float, short3>, mulScalarImpl<ushort4, float, short4>},
{mulScalarImpl<ushort, float, int>, mulScalarImpl<ushort2, float, int2>, mulScalarImpl<ushort3, float, int3>, mulScalarImpl<ushort4, float, int4>},
{mulScalarImpl<ushort, float, float>, mulScalarImpl<ushort2, float, float2>, mulScalarImpl<ushort3, float, float3>, mulScalarImpl<ushort4, float, float4>},
{mulScalarImpl<ushort, double, double>, mulScalarImpl<ushort2, double, double2>, mulScalarImpl<ushort3, double, double3>, mulScalarImpl<ushort4, double, double4>}
},
{
{0 /*mulScalarImpl<short, float, uchar>*/, 0 /*mulScalarImpl<short2, float, uchar2>*/, 0 /*mulScalarImpl<short3, float, uchar3>*/, 0 /*mulScalarImpl<short4, float, uchar4>*/},
{0 /*mulScalarImpl<short, float, schar>*/, 0 /*mulScalarImpl<short2, float, char2>*/, 0 /*mulScalarImpl<short3, float, char3>*/, 0 /*mulScalarImpl<short4, float, char4>*/},
{mulScalarImpl<short, float, ushort>, mulScalarImpl<short2, float, ushort2>, mulScalarImpl<short3, float, ushort3>, mulScalarImpl<short4, float, ushort4>},
{mulScalarImpl<short, float, short>, mulScalarImpl<short2, float, short2>, mulScalarImpl<short3, float, short3>, mulScalarImpl<short4, float, short4>},
{mulScalarImpl<short, float, int>, mulScalarImpl<short2, float, int2>, mulScalarImpl<short3, float, int3>, mulScalarImpl<short4, float, int4>},
{mulScalarImpl<short, float, float>, mulScalarImpl<short2, float, float2>, mulScalarImpl<short3, float, float3>, mulScalarImpl<short4, float, float4>},
{mulScalarImpl<short, double, double>, mulScalarImpl<short2, double, double2>, mulScalarImpl<short3, double, double3>, mulScalarImpl<short4, double, double4>}
},
{
{0 /*mulScalarImpl<int, float, uchar>*/, 0 /*mulScalarImpl<int2, float, uchar2>*/, 0 /*mulScalarImpl<int3, float, uchar3>*/, 0 /*mulScalarImpl<int4, float, uchar4>*/},
{0 /*mulScalarImpl<int, float, schar>*/, 0 /*mulScalarImpl<int2, float, char2>*/, 0 /*mulScalarImpl<int3, float, char3>*/, 0 /*mulScalarImpl<int4, float, char4>*/},
{0 /*mulScalarImpl<int, float, ushort>*/, 0 /*mulScalarImpl<int2, float, ushort2>*/, 0 /*mulScalarImpl<int3, float, ushort3>*/, 0 /*mulScalarImpl<int4, float, ushort4>*/},
{0 /*mulScalarImpl<int, float, short>*/, 0 /*mulScalarImpl<int2, float, short2>*/, 0 /*mulScalarImpl<int3, float, short3>*/, 0 /*mulScalarImpl<int4, float, short4>*/},
{mulScalarImpl<int, float, int>, mulScalarImpl<int2, float, int2>, mulScalarImpl<int3, float, int3>, mulScalarImpl<int4, float, int4>},
{mulScalarImpl<int, float, float>, mulScalarImpl<int2, float, float2>, mulScalarImpl<int3, float, float3>, mulScalarImpl<int4, float, float4>},
{mulScalarImpl<int, double, double>, mulScalarImpl<int2, double, double2>, mulScalarImpl<int3, double, double3>, mulScalarImpl<int4, double, double4>}
},
{
{0 /*mulScalarImpl<float, float, uchar>*/, 0 /*mulScalarImpl<float2, float, uchar2>*/, 0 /*mulScalarImpl<float3, float, uchar3>*/, 0 /*mulScalarImpl<float4, float, uchar4>*/},
{0 /*mulScalarImpl<float, float, schar>*/, 0 /*mulScalarImpl<float2, float, char2>*/, 0 /*mulScalarImpl<float3, float, char3>*/, 0 /*mulScalarImpl<float4, float, char4>*/},
{0 /*mulScalarImpl<float, float, ushort>*/, 0 /*mulScalarImpl<float2, float, ushort2>*/, 0 /*mulScalarImpl<float3, float, ushort3>*/, 0 /*mulScalarImpl<float4, float, ushort4>*/},
{0 /*mulScalarImpl<float, float, short>*/, 0 /*mulScalarImpl<float2, float, short2>*/, 0 /*mulScalarImpl<float3, float, short3>*/, 0 /*mulScalarImpl<float4, float, short4>*/},
{0 /*mulScalarImpl<float, float, int>*/, 0 /*mulScalarImpl<float2, float, int2>*/, 0 /*mulScalarImpl<float3, float, int3>*/, 0 /*mulScalarImpl<float4, float, int4>*/},
{mulScalarImpl<float, float, float>, mulScalarImpl<float2, float, float2>, mulScalarImpl<float3, float, float3>, mulScalarImpl<float4, float, float4>},
{mulScalarImpl<float, double, double>, mulScalarImpl<float2, double, double2>, mulScalarImpl<float3, double, double3>, mulScalarImpl<float4, double, double4>}
},
{
{0 /*mulScalarImpl<double, double, uchar>*/, 0 /*mulScalarImpl<double2, double, uchar2>*/, 0 /*mulScalarImpl<double3, double, uchar3>*/, 0 /*mulScalarImpl<double4, double, uchar4>*/},
{0 /*mulScalarImpl<double, double, schar>*/, 0 /*mulScalarImpl<double2, double, char2>*/, 0 /*mulScalarImpl<double3, double, char3>*/, 0 /*mulScalarImpl<double4, double, char4>*/},
{0 /*mulScalarImpl<double, double, ushort>*/, 0 /*mulScalarImpl<double2, double, ushort2>*/, 0 /*mulScalarImpl<double3, double, ushort3>*/, 0 /*mulScalarImpl<double4, double, ushort4>*/},
{0 /*mulScalarImpl<double, double, short>*/, 0 /*mulScalarImpl<double2, double, short2>*/, 0 /*mulScalarImpl<double3, double, short3>*/, 0 /*mulScalarImpl<double4, double, short4>*/},
{0 /*mulScalarImpl<double, double, int>*/, 0 /*mulScalarImpl<double2, double, int2>*/, 0 /*mulScalarImpl<double3, double, int3>*/, 0 /*mulScalarImpl<double4, double, int4>*/},
{0 /*mulScalarImpl<double, double, float>*/, 0 /*mulScalarImpl<double2, double, float2>*/, 0 /*mulScalarImpl<double3, double, float3>*/, 0 /*mulScalarImpl<double4, double, float4>*/},
{mulScalarImpl<double, double, double>, mulScalarImpl<double2, double, double2>, mulScalarImpl<double3, double, double3>, mulScalarImpl<double4, double, double4>}
}
};
const int sdepth = src.depth();
const int ddepth = dst.depth();
const int cn = src.channels();
CV_DbgAssert( sdepth <= CV_64F && ddepth <= CV_64F && cn <= 4 );
val[0] *= scale;
val[1] *= scale;
val[2] *= scale;
val[3] *= scale;
const func_t func = funcs[sdepth][ddepth][cn - 1];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src, val, dst, stream);
}
#endif

170
modules/cudaarithm/src/cuda/mul_spectrums.cu
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@ -1,170 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
//////////////////////////////////////////////////////////////////////////////
// mulSpectrums
namespace
{
__device__ __forceinline__ float real(const float2& val)
{
return val.x;
}
__device__ __forceinline__ float imag(const float2& val)
{
return val.y;
}
__device__ __forceinline__ float2 cmul(const float2& a, const float2& b)
{
return make_float2((real(a) * real(b)) - (imag(a) * imag(b)),
(real(a) * imag(b)) + (imag(a) * real(b)));
}
__device__ __forceinline__ float2 conj(const float2& a)
{
return make_float2(real(a), -imag(a));
}
struct comlex_mul : binary_function<float2, float2, float2>
{
__device__ __forceinline__ float2 operator ()(const float2& a, const float2& b) const
{
return cmul(a, b);
}
};
struct comlex_mul_conj : binary_function<float2, float2, float2>
{
__device__ __forceinline__ float2 operator ()(const float2& a, const float2& b) const
{
return cmul(a, conj(b));
}
};
struct comlex_mul_scale : binary_function<float2, float2, float2>
{
float scale;
__device__ __forceinline__ float2 operator ()(const float2& a, const float2& b) const
{
return scale * cmul(a, b);
}
};
struct comlex_mul_conj_scale : binary_function<float2, float2, float2>
{
float scale;
__device__ __forceinline__ float2 operator ()(const float2& a, const float2& b) const
{
return scale * cmul(a, conj(b));
}
};
}
void cv::cuda::mulSpectrums(InputArray _src1, InputArray _src2, OutputArray _dst, int flags, bool conjB, Stream& stream)
{
CV_UNUSED(flags);
GpuMat src1 = getInputMat(_src1, stream);
GpuMat src2 = getInputMat(_src2, stream);
CV_Assert( src1.type() == src2.type() && src1.type() == CV_32FC2 );
CV_Assert( src1.size() == src2.size() );
GpuMat dst = getOutputMat(_dst, src1.size(), CV_32FC2, stream);
if (conjB)
gridTransformBinary(globPtr<float2>(src1), globPtr<float2>(src2), globPtr<float2>(dst), comlex_mul_conj(), stream);
else
gridTransformBinary(globPtr<float2>(src1), globPtr<float2>(src2), globPtr<float2>(dst), comlex_mul(), stream);
syncOutput(dst, _dst, stream);
}
void cv::cuda::mulAndScaleSpectrums(InputArray _src1, InputArray _src2, OutputArray _dst, int flags, float scale, bool conjB, Stream& stream)
{
CV_UNUSED(flags);
GpuMat src1 = getInputMat(_src1, stream);
GpuMat src2 = getInputMat(_src2, stream);
CV_Assert( src1.type() == src2.type() && src1.type() == CV_32FC2);
CV_Assert( src1.size() == src2.size() );
GpuMat dst = getOutputMat(_dst, src1.size(), CV_32FC2, stream);
if (conjB)
{
comlex_mul_conj_scale op;
op.scale = scale;
gridTransformBinary(globPtr<float2>(src1), globPtr<float2>(src2), globPtr<float2>(dst), op, stream);
}
else
{
comlex_mul_scale op;
op.scale = scale;
gridTransformBinary(globPtr<float2>(src1), globPtr<float2>(src2), globPtr<float2>(dst), op, stream);
}
syncOutput(dst, _dst, stream);
}
#endif

189
modules/cudaarithm/src/cuda/norm.cu
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@ -1,189 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
void normDiffInf(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _dst, Stream& stream)
{
const GpuMat_<uchar>& src1 = (const GpuMat_<uchar>&) _src1;
const GpuMat_<uchar>& src2 = (const GpuMat_<uchar>&) _src2;
GpuMat_<int>& dst = (GpuMat_<int>&) _dst;
gridFindMaxVal(abs_(cvt_<int>(src1) - cvt_<int>(src2)), dst, stream);
}
void normDiffL1(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _dst, Stream& stream)
{
const GpuMat_<uchar>& src1 = (const GpuMat_<uchar>&) _src1;
const GpuMat_<uchar>& src2 = (const GpuMat_<uchar>&) _src2;
GpuMat_<int>& dst = (GpuMat_<int>&) _dst;
gridCalcSum(abs_(cvt_<int>(src1) - cvt_<int>(src2)), dst, stream);
}
void normDiffL2(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _dst, Stream& stream)
{
const GpuMat_<uchar>& src1 = (const GpuMat_<uchar>&) _src1;
const GpuMat_<uchar>& src2 = (const GpuMat_<uchar>&) _src2;
GpuMat_<double>& dst = (GpuMat_<double>&) _dst;
BufferPool pool(stream);
GpuMat_<double> buf(1, 1, pool.getAllocator());
gridCalcSum(sqr_(cvt_<double>(src1) - cvt_<double>(src2)), buf, stream);
gridTransformUnary(buf, dst, sqrt_func<double>(), stream);
}
}
void cv::cuda::calcNormDiff(InputArray _src1, InputArray _src2, OutputArray _dst, int normType, Stream& stream)
{
typedef void (*func_t)(const GpuMat& _src1, const GpuMat& _src2, GpuMat& _dst, Stream& stream);
static const func_t funcs[] =
{
0, normDiffInf, normDiffL1, 0, normDiffL2
};
GpuMat src1 = getInputMat(_src1, stream);
GpuMat src2 = getInputMat(_src2, stream);
CV_Assert( src1.type() == CV_8UC1 );
CV_Assert( src1.size() == src2.size() && src1.type() == src2.type() );
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
GpuMat dst = getOutputMat(_dst, 1, 1, normType == NORM_L2 ? CV_64FC1 : CV_32SC1, stream);
const func_t func = funcs[normType];
func(src1, src2, dst, stream);
syncOutput(dst, _dst, stream);
}
double cv::cuda::norm(InputArray _src1, InputArray _src2, int normType)
{
Stream& stream = Stream::Null();
HostMem dst;
calcNormDiff(_src1, _src2, dst, normType, stream);
stream.waitForCompletion();
double val;
dst.createMatHeader().convertTo(Mat(1, 1, CV_64FC1, &val), CV_64F);
return val;
}
namespace cv { namespace cuda { namespace device {
void normL2(cv::InputArray _src, cv::OutputArray _dst, cv::InputArray _mask, Stream& stream);
}}}
namespace
{
template <typename T, typename R>
void normL2Impl(const GpuMat& _src, const GpuMat& mask, GpuMat& _dst, Stream& stream)
{
const GpuMat_<T>& src = (const GpuMat_<T>&) _src;
GpuMat_<R>& dst = (GpuMat_<R>&) _dst;
BufferPool pool(stream);
GpuMat_<double> buf(1, 1, pool.getAllocator());
if (mask.empty())
{
gridCalcSum(sqr_(cvt_<double>(src)), buf, stream);
}
else
{
gridCalcSum(sqr_(cvt_<double>(src)), buf, globPtr<uchar>(mask), stream);
}
gridTransformUnary(buf, dst, sqrt_func<double>(), stream);
}
}
void cv::cuda::device::normL2(InputArray _src, OutputArray _dst, InputArray _mask, Stream& stream)
{
typedef void (*func_t)(const GpuMat& _src, const GpuMat& mask, GpuMat& _dst, Stream& stream);
static const func_t funcs[] =
{
normL2Impl<uchar, double>,
normL2Impl<schar, double>,
normL2Impl<ushort, double>,
normL2Impl<short, double>,
normL2Impl<int, double>,
normL2Impl<float, double>,
normL2Impl<double, double>
};
const GpuMat src = getInputMat(_src, stream);
const GpuMat mask = getInputMat(_mask, stream);
CV_Assert( src.channels() == 1 );
CV_Assert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
GpuMat dst = getOutputMat(_dst, 1, 1, CV_64FC1, stream);
const func_t func = funcs[src.depth()];
func(src, mask, dst, stream);
syncOutput(dst, _dst, stream);
}
#endif

294
modules/cudaarithm/src/cuda/normalize.cu
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@ -1,294 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace {
template <typename T, typename R, typename I>
struct ConvertorMinMax : unary_function<T, R>
{
typedef typename LargerType<T, R>::type larger_type1;
typedef typename LargerType<larger_type1, I>::type larger_type2;
typedef typename LargerType<larger_type2, float>::type scalar_type;
scalar_type dmin, dmax;
const I* minMaxVals;
__device__ R operator ()(typename TypeTraits<T>::parameter_type src) const
{
const scalar_type smin = minMaxVals[0];
const scalar_type smax = minMaxVals[1];
const scalar_type scale = (dmax - dmin) * (smax - smin > numeric_limits<scalar_type>::epsilon() ? 1.0 / (smax - smin) : 0.0);
const scalar_type shift = dmin - smin * scale;
return cudev::saturate_cast<R>(scale * src + shift);
}
};
template <typename T, typename R, typename I>
void normalizeMinMax(const GpuMat& _src, GpuMat& _dst, double a, double b, const GpuMat& mask, Stream& stream)
{
const GpuMat_<T>& src = (const GpuMat_<T>&)_src;
GpuMat_<R>& dst = (GpuMat_<R>&)_dst;
BufferPool pool(stream);
GpuMat_<I> minMaxVals(1, 2, pool.getAllocator());
if (mask.empty())
{
gridFindMinMaxVal(src, minMaxVals, stream);
}
else
{
gridFindMinMaxVal(src, minMaxVals, globPtr<uchar>(mask), stream);
}
ConvertorMinMax<T, R, I> cvt;
cvt.dmin = std::min(a, b);
cvt.dmax = std::max(a, b);
cvt.minMaxVals = minMaxVals[0];
if (mask.empty())
{
gridTransformUnary(src, dst, cvt, stream);
}
else
{
dst.setTo(Scalar::all(0), stream);
gridTransformUnary(src, dst, cvt, globPtr<uchar>(mask), stream);
}
}
template <typename T, typename R, typename I, bool normL2>
struct ConvertorNorm : unary_function<T, R>
{
typedef typename LargerType<T, R>::type larger_type1;
typedef typename LargerType<larger_type1, I>::type larger_type2;
typedef typename LargerType<larger_type2, float>::type scalar_type;
scalar_type a;
const I* normVal;
__device__ R operator ()(typename TypeTraits<T>::parameter_type src) const
{
sqrt_func<scalar_type> sqrt;
scalar_type scale = normL2 ? sqrt(*normVal) : *normVal;
scale = scale > numeric_limits<scalar_type>::epsilon() ? a / scale : 0.0;
return cudev::saturate_cast<R>(scale * src);
}
};
template <typename T, typename R, typename I>
void normalizeNorm(const GpuMat& _src, GpuMat& _dst, double a, int normType, const GpuMat& mask, Stream& stream)
{
const GpuMat_<T>& src = (const GpuMat_<T>&)_src;
GpuMat_<R>& dst = (GpuMat_<R>&)_dst;
BufferPool pool(stream);
GpuMat_<I> normVal(1, 1, pool.getAllocator());
if (normType == NORM_L1)
{
if (mask.empty())
{
gridCalcSum(abs_(cvt_<I>(src)), normVal, stream);
}
else
{
gridCalcSum(abs_(cvt_<I>(src)), normVal, globPtr<uchar>(mask), stream);
}
}
else if (normType == NORM_L2)
{
if (mask.empty())
{
gridCalcSum(sqr_(cvt_<I>(src)), normVal, stream);
}
else
{
gridCalcSum(sqr_(cvt_<I>(src)), normVal, globPtr<uchar>(mask), stream);
}
}
else // NORM_INF
{
if (mask.empty())
{
gridFindMaxVal(abs_(cvt_<I>(src)), normVal, stream);
}
else
{
gridFindMaxVal(abs_(cvt_<I>(src)), normVal, globPtr<uchar>(mask), stream);
}
}
if (normType == NORM_L2)
{
ConvertorNorm<T, R, I, true> cvt;
cvt.a = a;
cvt.normVal = normVal[0];
if (mask.empty())
{
gridTransformUnary(src, dst, cvt, stream);
}
else
{
dst.setTo(Scalar::all(0), stream);
gridTransformUnary(src, dst, cvt, globPtr<uchar>(mask), stream);
}
}
else
{
ConvertorNorm<T, R, I, false> cvt;
cvt.a = a;
cvt.normVal = normVal[0];
if (mask.empty())
{
gridTransformUnary(src, dst, cvt, stream);
}
else
{
dst.setTo(Scalar::all(0), stream);
gridTransformUnary(src, dst, cvt, globPtr<uchar>(mask), stream);
}
}
}
} // namespace
void cv::cuda::normalize(InputArray _src, OutputArray _dst, double a, double b, int normType, int dtype, InputArray _mask, Stream& stream)
{
typedef void (*func_minmax_t)(const GpuMat& _src, GpuMat& _dst, double a, double b, const GpuMat& mask, Stream& stream);
typedef void (*func_norm_t)(const GpuMat& _src, GpuMat& _dst, double a, int normType, const GpuMat& mask, Stream& stream);
static const func_minmax_t funcs_minmax[] =
{
normalizeMinMax<uchar, float, float>,
normalizeMinMax<schar, float, float>,
normalizeMinMax<ushort, float, float>,
normalizeMinMax<short, float, float>,
normalizeMinMax<int, float, float>,
normalizeMinMax<float, float, float>,
normalizeMinMax<double, double, double>
};
static const func_norm_t funcs_norm[] =
{
normalizeNorm<uchar, float, float>,
normalizeNorm<schar, float, float>,
normalizeNorm<ushort, float, float>,
normalizeNorm<short, float, float>,
normalizeNorm<int, float, float>,
normalizeNorm<float, float, float>,
normalizeNorm<double, double, double>
};
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 || normType == NORM_MINMAX );
const GpuMat src = getInputMat(_src, stream);
const GpuMat mask = getInputMat(_mask, stream);
CV_Assert( src.channels() == 1 );
CV_Assert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
if (dtype < 0)
{
dtype = _dst.fixedType() ? _dst.type() : src.type();
}
dtype = CV_MAT_DEPTH(dtype);
const int src_depth = src.depth();
const int tmp_depth = src_depth <= CV_32F ? CV_32F : src_depth;
GpuMat dst;
if (dtype == tmp_depth)
{
_dst.create(src.size(), tmp_depth);
dst = getOutputMat(_dst, src.size(), tmp_depth, stream);
}
else
{
BufferPool pool(stream);
dst = pool.getBuffer(src.size(), tmp_depth);
}
if (normType == NORM_MINMAX)
{
const func_minmax_t func = funcs_minmax[src_depth];
func(src, dst, a, b, mask, stream);
}
else
{
const func_norm_t func = funcs_norm[src_depth];
func(src, dst, a, normType, mask, stream);
}
if (dtype == tmp_depth)
{
syncOutput(dst, _dst, stream);
}
else
{
dst.convertTo(_dst, dtype, stream);
}
}
#endif

217
modules/cudaarithm/src/cuda/polar_cart.cu
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@ -1,217 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
void cv::cuda::magnitude(InputArray _x, InputArray _y, OutputArray _dst, Stream& stream)
{
GpuMat x = getInputMat(_x, stream);
GpuMat y = getInputMat(_y, stream);
CV_Assert( x.depth() == CV_32F );
CV_Assert( y.type() == x.type() && y.size() == x.size() );
GpuMat dst = getOutputMat(_dst, x.size(), CV_32FC1, stream);
GpuMat_<float> xc(x.reshape(1));
GpuMat_<float> yc(y.reshape(1));
GpuMat_<float> magc(dst.reshape(1));
gridTransformBinary(xc, yc, magc, magnitude_func<float>(), stream);
syncOutput(dst, _dst, stream);
}
void cv::cuda::magnitudeSqr(InputArray _x, InputArray _y, OutputArray _dst, Stream& stream)
{
GpuMat x = getInputMat(_x, stream);
GpuMat y = getInputMat(_y, stream);
CV_Assert( x.depth() == CV_32F );
CV_Assert( y.type() == x.type() && y.size() == x.size() );
GpuMat dst = getOutputMat(_dst, x.size(), CV_32FC1, stream);
GpuMat_<float> xc(x.reshape(1));
GpuMat_<float> yc(y.reshape(1));
GpuMat_<float> magc(dst.reshape(1));
gridTransformBinary(xc, yc, magc, magnitude_sqr_func<float>(), stream);
syncOutput(dst, _dst, stream);
}
void cv::cuda::phase(InputArray _x, InputArray _y, OutputArray _dst, bool angleInDegrees, Stream& stream)
{
GpuMat x = getInputMat(_x, stream);
GpuMat y = getInputMat(_y, stream);
CV_Assert( x.depth() == CV_32F );
CV_Assert( y.type() == x.type() && y.size() == x.size() );
GpuMat dst = getOutputMat(_dst, x.size(), CV_32FC1, stream);
GpuMat_<float> xc(x.reshape(1));
GpuMat_<float> yc(y.reshape(1));
GpuMat_<float> anglec(dst.reshape(1));
if (angleInDegrees)
gridTransformBinary(xc, yc, anglec, direction_func<float, true>(), stream);
else
gridTransformBinary(xc, yc, anglec, direction_func<float, false>(), stream);
syncOutput(dst, _dst, stream);
}
void cv::cuda::cartToPolar(InputArray _x, InputArray _y, OutputArray _mag, OutputArray _angle, bool angleInDegrees, Stream& stream)
{
GpuMat x = getInputMat(_x, stream);
GpuMat y = getInputMat(_y, stream);
CV_Assert( x.depth() == CV_32F );
CV_Assert( y.type() == x.type() && y.size() == x.size() );
GpuMat mag = getOutputMat(_mag, x.size(), CV_32FC1, stream);
GpuMat angle = getOutputMat(_angle, x.size(), CV_32FC1, stream);
GpuMat_<float> xc(x.reshape(1));
GpuMat_<float> yc(y.reshape(1));
GpuMat_<float> magc(mag.reshape(1));
GpuMat_<float> anglec(angle.reshape(1));
if (angleInDegrees)
{
gridTransformTuple(zipPtr(xc, yc),
tie(magc, anglec),
make_tuple(
binaryTupleAdapter<0, 1>(magnitude_func<float>()),
binaryTupleAdapter<0, 1>(direction_func<float, true>())),
stream);
}
else
{
gridTransformTuple(zipPtr(xc, yc),
tie(magc, anglec),
make_tuple(
binaryTupleAdapter<0, 1>(magnitude_func<float>()),
binaryTupleAdapter<0, 1>(direction_func<float, false>())),
stream);
}
syncOutput(mag, _mag, stream);
syncOutput(angle, _angle, stream);
}
namespace
{
template <bool useMag>
__global__ void polarToCartImpl(const GlobPtr<float> mag, const GlobPtr<float> angle, GlobPtr<float> xmat, GlobPtr<float> ymat, const float scale, const int rows, const int cols)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x >= cols || y >= rows)
return;
const float mag_val = useMag ? mag(y, x) : 1.0f;
const float angle_val = angle(y, x);
float sin_a, cos_a;
::sincosf(scale * angle_val, &sin_a, &cos_a);
xmat(y, x) = mag_val * cos_a;
ymat(y, x) = mag_val * sin_a;
}
}
void cv::cuda::polarToCart(InputArray _mag, InputArray _angle, OutputArray _x, OutputArray _y, bool angleInDegrees, Stream& _stream)
{
GpuMat mag = getInputMat(_mag, _stream);
GpuMat angle = getInputMat(_angle, _stream);
CV_Assert( angle.depth() == CV_32F );
CV_Assert( mag.empty() || (mag.type() == angle.type() && mag.size() == angle.size()) );
GpuMat x = getOutputMat(_x, angle.size(), CV_32FC1, _stream);
GpuMat y = getOutputMat(_y, angle.size(), CV_32FC1, _stream);
GpuMat_<float> xc(x.reshape(1));
GpuMat_<float> yc(y.reshape(1));
GpuMat_<float> magc(mag.reshape(1));
GpuMat_<float> anglec(angle.reshape(1));
const dim3 block(32, 8);
const dim3 grid(divUp(anglec.cols, block.x), divUp(anglec.rows, block.y));
const float scale = angleInDegrees ? (CV_PI_F / 180.0f) : 1.0f;
cudaStream_t stream = StreamAccessor::getStream(_stream);
if (magc.empty())
polarToCartImpl<false><<<grid, block, 0, stream>>>(shrinkPtr(magc), shrinkPtr(anglec), shrinkPtr(xc), shrinkPtr(yc), scale, anglec.rows, anglec.cols);
else
polarToCartImpl<true><<<grid, block, 0, stream>>>(shrinkPtr(magc), shrinkPtr(anglec), shrinkPtr(xc), shrinkPtr(yc), scale, anglec.rows, anglec.cols);
CV_CUDEV_SAFE_CALL( cudaGetLastError() );
syncOutput(x, _x, _stream);
syncOutput(y, _y, _stream);
if (stream == 0)
CV_CUDEV_SAFE_CALL( cudaDeviceSynchronize() );
}
#endif

301
modules/cudaarithm/src/cuda/reduce.cu
View File

@ -1,301 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
template <typename T, typename S, typename D>
void reduceToRowImpl(const GpuMat& _src, GpuMat& _dst, int reduceOp, Stream& stream)
{
const GpuMat_<T>& src = (const GpuMat_<T>&) _src;
GpuMat_<D>& dst = (GpuMat_<D>&) _dst;
switch (reduceOp)
{
case cv::REDUCE_SUM:
gridReduceToRow< Sum<S> >(src, dst, stream);
break;
case cv::REDUCE_AVG:
gridReduceToRow< Avg<S> >(src, dst, stream);
break;
case cv::REDUCE_MIN:
gridReduceToRow< Min<S> >(src, dst, stream);
break;
case cv::REDUCE_MAX:
gridReduceToRow< Max<S> >(src, dst, stream);
break;
};
}
template <typename T, typename S, typename D>
void reduceToColumnImpl_(const GpuMat& _src, GpuMat& _dst, int reduceOp, Stream& stream)
{
const GpuMat_<T>& src = (const GpuMat_<T>&) _src;
GpuMat_<D>& dst = (GpuMat_<D>&) _dst;
switch (reduceOp)
{
case cv::REDUCE_SUM:
gridReduceToColumn< Sum<S> >(src, dst, stream);
break;
case cv::REDUCE_AVG:
gridReduceToColumn< Avg<S> >(src, dst, stream);
break;
case cv::REDUCE_MIN:
gridReduceToColumn< Min<S> >(src, dst, stream);
break;
case cv::REDUCE_MAX:
gridReduceToColumn< Max<S> >(src, dst, stream);
break;
};
}
template <typename T, typename S, typename D>
void reduceToColumnImpl(const GpuMat& src, GpuMat& dst, int reduceOp, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int reduceOp, Stream& stream);
static const func_t funcs[4] =
{
reduceToColumnImpl_<T, S, D>,
reduceToColumnImpl_<typename MakeVec<T, 2>::type, typename MakeVec<S, 2>::type, typename MakeVec<D, 2>::type>,
reduceToColumnImpl_<typename MakeVec<T, 3>::type, typename MakeVec<S, 3>::type, typename MakeVec<D, 3>::type>,
reduceToColumnImpl_<typename MakeVec<T, 4>::type, typename MakeVec<S, 4>::type, typename MakeVec<D, 4>::type>
};
funcs[src.channels() - 1](src, dst, reduceOp, stream);
}
}
void cv::cuda::reduce(InputArray _src, OutputArray _dst, int dim, int reduceOp, int dtype, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.channels() <= 4 );
CV_Assert( dim == 0 || dim == 1 );
CV_Assert( reduceOp == REDUCE_SUM || reduceOp == REDUCE_AVG || reduceOp == REDUCE_MAX || reduceOp == REDUCE_MIN );
if (dtype < 0)
dtype = src.depth();
GpuMat dst = getOutputMat(_dst, dim == 0 ? 1 : src.rows, dim == 0 ? src.cols : 1, CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()), stream);
if (dim == 0)
{
typedef void (*func_t)(const GpuMat& _src, GpuMat& _dst, int reduceOp, Stream& stream);
static const func_t funcs[7][7] =
{
{
reduceToRowImpl<uchar, int, uchar>,
0 /*reduceToRowImpl<uchar, int, schar>*/,
0 /*reduceToRowImpl<uchar, int, ushort>*/,
0 /*reduceToRowImpl<uchar, int, short>*/,
reduceToRowImpl<uchar, int, int>,
reduceToRowImpl<uchar, float, float>,
reduceToRowImpl<uchar, double, double>
},
{
0 /*reduceToRowImpl<schar, int, uchar>*/,
0 /*reduceToRowImpl<schar, int, schar>*/,
0 /*reduceToRowImpl<schar, int, ushort>*/,
0 /*reduceToRowImpl<schar, int, short>*/,
0 /*reduceToRowImpl<schar, int, int>*/,
0 /*reduceToRowImpl<schar, float, float>*/,
0 /*reduceToRowImpl<schar, double, double>*/
},
{
0 /*reduceToRowImpl<ushort, int, uchar>*/,
0 /*reduceToRowImpl<ushort, int, schar>*/,
reduceToRowImpl<ushort, int, ushort>,
0 /*reduceToRowImpl<ushort, int, short>*/,
reduceToRowImpl<ushort, int, int>,
reduceToRowImpl<ushort, float, float>,
reduceToRowImpl<ushort, double, double>
},
{
0 /*reduceToRowImpl<short, int, uchar>*/,
0 /*reduceToRowImpl<short, int, schar>*/,
0 /*reduceToRowImpl<short, int, ushort>*/,
reduceToRowImpl<short, int, short>,
reduceToRowImpl<short, int, int>,
reduceToRowImpl<short, float, float>,
reduceToRowImpl<short, double, double>
},
{
0 /*reduceToRowImpl<int, int, uchar>*/,
0 /*reduceToRowImpl<int, int, schar>*/,
0 /*reduceToRowImpl<int, int, ushort>*/,
0 /*reduceToRowImpl<int, int, short>*/,
reduceToRowImpl<int, int, int>,
reduceToRowImpl<int, float, float>,
reduceToRowImpl<int, double, double>
},
{
0 /*reduceToRowImpl<float, float, uchar>*/,
0 /*reduceToRowImpl<float, float, schar>*/,
0 /*reduceToRowImpl<float, float, ushort>*/,
0 /*reduceToRowImpl<float, float, short>*/,
0 /*reduceToRowImpl<float, float, int>*/,
reduceToRowImpl<float, float, float>,
reduceToRowImpl<float, double, double>
},
{
0 /*reduceToRowImpl<double, double, uchar>*/,
0 /*reduceToRowImpl<double, double, schar>*/,
0 /*reduceToRowImpl<double, double, ushort>*/,
0 /*reduceToRowImpl<double, double, short>*/,
0 /*reduceToRowImpl<double, double, int>*/,
0 /*reduceToRowImpl<double, double, float>*/,
reduceToRowImpl<double, double, double>
}
};
const func_t func = funcs[src.depth()][dst.depth()];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of input and output array formats");
GpuMat dst_cont = dst.reshape(1);
func(src.reshape(1), dst_cont, reduceOp, stream);
}
else
{
typedef void (*func_t)(const GpuMat& _src, GpuMat& _dst, int reduceOp, Stream& stream);
static const func_t funcs[7][7] =
{
{
reduceToColumnImpl<uchar, int, uchar>,
0 /*reduceToColumnImpl<uchar, int, schar>*/,
0 /*reduceToColumnImpl<uchar, int, ushort>*/,
0 /*reduceToColumnImpl<uchar, int, short>*/,
reduceToColumnImpl<uchar, int, int>,
reduceToColumnImpl<uchar, float, float>,
reduceToColumnImpl<uchar, double, double>
},
{
0 /*reduceToColumnImpl<schar, int, uchar>*/,
0 /*reduceToColumnImpl<schar, int, schar>*/,
0 /*reduceToColumnImpl<schar, int, ushort>*/,
0 /*reduceToColumnImpl<schar, int, short>*/,
0 /*reduceToColumnImpl<schar, int, int>*/,
0 /*reduceToColumnImpl<schar, float, float>*/,
0 /*reduceToColumnImpl<schar, double, double>*/
},
{
0 /*reduceToColumnImpl<ushort, int, uchar>*/,
0 /*reduceToColumnImpl<ushort, int, schar>*/,
reduceToColumnImpl<ushort, int, ushort>,
0 /*reduceToColumnImpl<ushort, int, short>*/,
reduceToColumnImpl<ushort, int, int>,
reduceToColumnImpl<ushort, float, float>,
reduceToColumnImpl<ushort, double, double>
},
{
0 /*reduceToColumnImpl<short, int, uchar>*/,
0 /*reduceToColumnImpl<short, int, schar>*/,
0 /*reduceToColumnImpl<short, int, ushort>*/,
reduceToColumnImpl<short, int, short>,
reduceToColumnImpl<short, int, int>,
reduceToColumnImpl<short, float, float>,
reduceToColumnImpl<short, double, double>
},
{
0 /*reduceToColumnImpl<int, int, uchar>*/,
0 /*reduceToColumnImpl<int, int, schar>*/,
0 /*reduceToColumnImpl<int, int, ushort>*/,
0 /*reduceToColumnImpl<int, int, short>*/,
reduceToColumnImpl<int, int, int>,
reduceToColumnImpl<int, float, float>,
reduceToColumnImpl<int, double, double>
},
{
0 /*reduceToColumnImpl<float, float, uchar>*/,
0 /*reduceToColumnImpl<float, float, schar>*/,
0 /*reduceToColumnImpl<float, float, ushort>*/,
0 /*reduceToColumnImpl<float, float, short>*/,
0 /*reduceToColumnImpl<float, float, int>*/,
reduceToColumnImpl<float, float, float>,
reduceToColumnImpl<float, double, double>
},
{
0 /*reduceToColumnImpl<double, double, uchar>*/,
0 /*reduceToColumnImpl<double, double, schar>*/,
0 /*reduceToColumnImpl<double, double, ushort>*/,
0 /*reduceToColumnImpl<double, double, short>*/,
0 /*reduceToColumnImpl<double, double, int>*/,
0 /*reduceToColumnImpl<double, double, float>*/,
reduceToColumnImpl<double, double, double>
}
};
const func_t func = funcs[src.depth()][dst.depth()];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of input and output array formats");
func(src, dst, reduceOp, stream);
}
syncOutput(dst, _dst, stream);
}
#endif

250
modules/cudaarithm/src/cuda/split_merge.cu
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@ -1,250 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
////////////////////////////////////////////////////////////////////////
/// merge
namespace
{
template <int cn, typename T> struct MergeFunc;
template <typename T> struct MergeFunc<2, T>
{
static void call(const GpuMat* src, GpuMat& dst, Stream& stream)
{
gridMerge(zipPtr(globPtr<T>(src[0]), globPtr<T>(src[1])),
globPtr<typename MakeVec<T, 2>::type>(dst),
stream);
}
};
template <typename T> struct MergeFunc<3, T>
{
static void call(const GpuMat* src, GpuMat& dst, Stream& stream)
{
gridMerge(zipPtr(globPtr<T>(src[0]), globPtr<T>(src[1]), globPtr<T>(src[2])),
globPtr<typename MakeVec<T, 3>::type>(dst),
stream);
}
};
template <typename T> struct MergeFunc<4, T>
{
static void call(const GpuMat* src, GpuMat& dst, Stream& stream)
{
gridMerge(zipPtr(globPtr<T>(src[0]), globPtr<T>(src[1]), globPtr<T>(src[2]), globPtr<T>(src[3])),
globPtr<typename MakeVec<T, 4>::type>(dst),
stream);
}
};
void mergeImpl(const GpuMat* src, size_t n, cv::OutputArray _dst, Stream& stream)
{
CV_Assert( src != 0 );
CV_Assert( n > 0 && n <= 4 );
const int depth = src[0].depth();
const cv::Size size = src[0].size();
for (size_t i = 0; i < n; ++i)
{
CV_Assert( src[i].size() == size );
CV_Assert( src[i].depth() == depth );
CV_Assert( src[i].channels() == 1 );
}
if (n == 1)
{
src[0].copyTo(_dst, stream);
}
else
{
typedef void (*func_t)(const GpuMat* src, GpuMat& dst, Stream& stream);
static const func_t funcs[3][5] =
{
{MergeFunc<2, uchar>::call, MergeFunc<2, ushort>::call, MergeFunc<2, int>::call, 0, MergeFunc<2, double>::call},
{MergeFunc<3, uchar>::call, MergeFunc<3, ushort>::call, MergeFunc<3, int>::call, 0, MergeFunc<3, double>::call},
{MergeFunc<4, uchar>::call, MergeFunc<4, ushort>::call, MergeFunc<4, int>::call, 0, MergeFunc<4, double>::call}
};
const int channels = static_cast<int>(n);
GpuMat dst = getOutputMat(_dst, size, CV_MAKE_TYPE(depth, channels), stream);
const func_t func = funcs[channels - 2][CV_ELEM_SIZE(depth) / 2];
if (func == 0)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported channel count or data type");
func(src, dst, stream);
syncOutput(dst, _dst, stream);
}
}
}
void cv::cuda::merge(const GpuMat* src, size_t n, OutputArray dst, Stream& stream)
{
mergeImpl(src, n, dst, stream);
}
void cv::cuda::merge(const std::vector<GpuMat>& src, OutputArray dst, Stream& stream)
{
mergeImpl(&src[0], src.size(), dst, stream);
}
////////////////////////////////////////////////////////////////////////
/// split
namespace
{
template <int cn, typename T> struct SplitFunc;
template <typename T> struct SplitFunc<2, T>
{
static void call(const GpuMat& src, GpuMat* dst, Stream& stream)
{
GlobPtrSz<T> dstarr[2] =
{
globPtr<T>(dst[0]), globPtr<T>(dst[1])
};
gridSplit(globPtr<typename MakeVec<T, 2>::type>(src), dstarr, stream);
}
};
template <typename T> struct SplitFunc<3, T>
{
static void call(const GpuMat& src, GpuMat* dst, Stream& stream)
{
GlobPtrSz<T> dstarr[3] =
{
globPtr<T>(dst[0]), globPtr<T>(dst[1]), globPtr<T>(dst[2])
};
gridSplit(globPtr<typename MakeVec<T, 3>::type>(src), dstarr, stream);
}
};
template <typename T> struct SplitFunc<4, T>
{
static void call(const GpuMat& src, GpuMat* dst, Stream& stream)
{
GlobPtrSz<T> dstarr[4] =
{
globPtr<T>(dst[0]), globPtr<T>(dst[1]), globPtr<T>(dst[2]), globPtr<T>(dst[3])
};
gridSplit(globPtr<typename MakeVec<T, 4>::type>(src), dstarr, stream);
}
};
void splitImpl(const GpuMat& src, GpuMat* dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat* dst, Stream& stream);
static const func_t funcs[3][5] =
{
{SplitFunc<2, uchar>::call, SplitFunc<2, ushort>::call, SplitFunc<2, int>::call, 0, SplitFunc<2, double>::call},
{SplitFunc<3, uchar>::call, SplitFunc<3, ushort>::call, SplitFunc<3, int>::call, 0, SplitFunc<3, double>::call},
{SplitFunc<4, uchar>::call, SplitFunc<4, ushort>::call, SplitFunc<4, int>::call, 0, SplitFunc<4, double>::call}
};
CV_Assert( dst != 0 );
const int depth = src.depth();
const int channels = src.channels();
CV_Assert( channels <= 4 );
if (channels == 0)
return;
if (channels == 1)
{
src.copyTo(dst[0], stream);
return;
}
for (int i = 0; i < channels; ++i)
dst[i].create(src.size(), depth);
const func_t func = funcs[channels - 2][CV_ELEM_SIZE(depth) / 2];
if (func == 0)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported channel count or data type");
func(src, dst, stream);
}
}
void cv::cuda::split(InputArray _src, GpuMat* dst, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
splitImpl(src, dst, stream);
}
void cv::cuda::split(InputArray _src, std::vector<GpuMat>& dst, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
dst.resize(src.channels());
if (src.channels() > 0)
splitImpl(src, &dst[0], stream);
}
#endif

225
modules/cudaarithm/src/cuda/sub_mat.cu
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@ -1,225 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void subMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& _stream, int);
namespace
{
template <typename T, typename D> struct SubOp1 : binary_function<T, T, D>
{
__device__ __forceinline__ D operator ()(T a, T b) const
{
return saturate_cast<D>(a - b);
}
};
template <typename T, typename D>
void subMat_v1(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream)
{
if (mask.data)
gridTransformBinary(globPtr<T>(src1), globPtr<T>(src2), globPtr<D>(dst), SubOp1<T, D>(), globPtr<uchar>(mask), stream);
else
gridTransformBinary(globPtr<T>(src1), globPtr<T>(src2), globPtr<D>(dst), SubOp1<T, D>(), stream);
}
struct SubOp2 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vsub2(a, b);
}
};
void subMat_v2(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 1;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, SubOp2(), stream);
}
struct SubOp4 : binary_function<uint, uint, uint>
{
__device__ __forceinline__ uint operator ()(uint a, uint b) const
{
return vsub4(a, b);
}
};
void subMat_v4(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
const int vcols = src1.cols >> 2;
GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
gridTransformBinary(src1_, src2_, dst_, SubOp4(), stream);
}
}
void subMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int)
{
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& stream);
static const func_t funcs[7][7] =
{
{
subMat_v1<uchar, uchar>,
subMat_v1<uchar, schar>,
subMat_v1<uchar, ushort>,
subMat_v1<uchar, short>,
subMat_v1<uchar, int>,
subMat_v1<uchar, float>,
subMat_v1<uchar, double>
},
{
subMat_v1<schar, uchar>,
subMat_v1<schar, schar>,
subMat_v1<schar, ushort>,
subMat_v1<schar, short>,
subMat_v1<schar, int>,
subMat_v1<schar, float>,
subMat_v1<schar, double>
},
{
0 /*subMat_v1<ushort, uchar>*/,
0 /*subMat_v1<ushort, schar>*/,
subMat_v1<ushort, ushort>,
subMat_v1<ushort, short>,
subMat_v1<ushort, int>,
subMat_v1<ushort, float>,
subMat_v1<ushort, double>
},
{
0 /*subMat_v1<short, uchar>*/,
0 /*subMat_v1<short, schar>*/,
subMat_v1<short, ushort>,
subMat_v1<short, short>,
subMat_v1<short, int>,
subMat_v1<short, float>,
subMat_v1<short, double>
},
{
0 /*subMat_v1<int, uchar>*/,
0 /*subMat_v1<int, schar>*/,
0 /*subMat_v1<int, ushort>*/,
0 /*subMat_v1<int, short>*/,
subMat_v1<int, int>,
subMat_v1<int, float>,
subMat_v1<int, double>
},
{
0 /*subMat_v1<float, uchar>*/,
0 /*subMat_v1<float, schar>*/,
0 /*subMat_v1<float, ushort>*/,
0 /*subMat_v1<float, short>*/,
0 /*subMat_v1<float, int>*/,
subMat_v1<float, float>,
subMat_v1<float, double>
},
{
0 /*subMat_v1<double, uchar>*/,
0 /*subMat_v1<double, schar>*/,
0 /*subMat_v1<double, ushort>*/,
0 /*subMat_v1<double, short>*/,
0 /*subMat_v1<double, int>*/,
0 /*subMat_v1<double, float>*/,
subMat_v1<double, double>
}
};
const int sdepth = src1.depth();
const int ddepth = dst.depth();
CV_DbgAssert( sdepth <= CV_64F && ddepth <= CV_64F );
GpuMat src1_ = src1.reshape(1);
GpuMat src2_ = src2.reshape(1);
GpuMat dst_ = dst.reshape(1);
if (mask.empty() && (sdepth == CV_8U || sdepth == CV_16U) && ddepth == sdepth)
{
const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
if (isAllAligned)
{
if (sdepth == CV_8U && (src1_.cols & 3) == 0)
{
subMat_v4(src1_, src2_, dst_, stream);
return;
}
else if (sdepth == CV_16U && (src1_.cols & 1) == 0)
{
subMat_v2(src1_, src2_, dst_, stream);
return;
}
}
}
const func_t func = funcs[sdepth][ddepth];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src1_, src2_, dst_, mask, stream);
}
#endif

203
modules/cudaarithm/src/cuda/sub_scalar.cu
View File

@ -1,203 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev.hpp"
using namespace cv::cudev;
void subScalar(const GpuMat& src, cv::Scalar val, bool inv, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int);
namespace
{
template <typename SrcType, typename ScalarType, typename DstType> struct SubScalarOp : unary_function<SrcType, DstType>
{
ScalarType val;
__device__ __forceinline__ DstType operator ()(SrcType a) const
{
return saturate_cast<DstType>(saturate_cast<ScalarType>(a) - val);
}
};
template <typename SrcType, typename ScalarType, typename DstType> struct SubScalarOpInv : unary_function<SrcType, DstType>
{
ScalarType val;
__device__ __forceinline__ DstType operator ()(SrcType a) const
{
return saturate_cast<DstType>(val - saturate_cast<ScalarType>(a));
}
};
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <typename SrcType, typename ScalarDepth, typename DstType>
void subScalarImpl(const GpuMat& src, cv::Scalar value, bool inv, GpuMat& dst, const GpuMat& mask, Stream& stream)
{
typedef typename MakeVec<ScalarDepth, VecTraits<SrcType>::cn>::type ScalarType;
cv::Scalar_<ScalarDepth> value_ = value;
if (inv)
{
SubScalarOpInv<SrcType, ScalarType, DstType> op;
op.val = VecTraits<ScalarType>::make(value_.val);
if (mask.data)
gridTransformUnary_< TransformPolicy<ScalarDepth> >(globPtr<SrcType>(src), globPtr<DstType>(dst), op, globPtr<uchar>(mask), stream);
else
gridTransformUnary_< TransformPolicy<ScalarDepth> >(globPtr<SrcType>(src), globPtr<DstType>(dst), op, stream);
}
else
{
SubScalarOp<SrcType, ScalarType, DstType> op;
op.val = VecTraits<ScalarType>::make(value_.val);
if (mask.data)
gridTransformUnary_< TransformPolicy<ScalarDepth> >(globPtr<SrcType>(src), globPtr<DstType>(dst), op, globPtr<uchar>(mask), stream);
else
gridTransformUnary_< TransformPolicy<ScalarDepth> >(globPtr<SrcType>(src), globPtr<DstType>(dst), op, stream);
}
}
}
void subScalar(const GpuMat& src, cv::Scalar val, bool inv, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int)
{
typedef void (*func_t)(const GpuMat& src, cv::Scalar val, bool inv, GpuMat& dst, const GpuMat& mask, Stream& stream);
static const func_t funcs[7][7][4] =
{
{
{subScalarImpl<uchar, float, uchar>, subScalarImpl<uchar2, float, uchar2>, subScalarImpl<uchar3, float, uchar3>, subScalarImpl<uchar4, float, uchar4>},
{subScalarImpl<uchar, float, schar>, subScalarImpl<uchar2, float, char2>, subScalarImpl<uchar3, float, char3>, subScalarImpl<uchar4, float, char4>},
{subScalarImpl<uchar, float, ushort>, subScalarImpl<uchar2, float, ushort2>, subScalarImpl<uchar3, float, ushort3>, subScalarImpl<uchar4, float, ushort4>},
{subScalarImpl<uchar, float, short>, subScalarImpl<uchar2, float, short2>, subScalarImpl<uchar3, float, short3>, subScalarImpl<uchar4, float, short4>},
{subScalarImpl<uchar, float, int>, subScalarImpl<uchar2, float, int2>, subScalarImpl<uchar3, float, int3>, subScalarImpl<uchar4, float, int4>},
{subScalarImpl<uchar, float, float>, subScalarImpl<uchar2, float, float2>, subScalarImpl<uchar3, float, float3>, subScalarImpl<uchar4, float, float4>},
{subScalarImpl<uchar, double, double>, subScalarImpl<uchar2, double, double2>, subScalarImpl<uchar3, double, double3>, subScalarImpl<uchar4, double, double4>}
},
{
{subScalarImpl<schar, float, uchar>, subScalarImpl<char2, float, uchar2>, subScalarImpl<char3, float, uchar3>, subScalarImpl<char4, float, uchar4>},
{subScalarImpl<schar, float, schar>, subScalarImpl<char2, float, char2>, subScalarImpl<char3, float, char3>, subScalarImpl<char4, float, char4>},
{subScalarImpl<schar, float, ushort>, subScalarImpl<char2, float, ushort2>, subScalarImpl<char3, float, ushort3>, subScalarImpl<char4, float, ushort4>},
{subScalarImpl<schar, float, short>, subScalarImpl<char2, float, short2>, subScalarImpl<char3, float, short3>, subScalarImpl<char4, float, short4>},
{subScalarImpl<schar, float, int>, subScalarImpl<char2, float, int2>, subScalarImpl<char3, float, int3>, subScalarImpl<char4, float, int4>},
{subScalarImpl<schar, float, float>, subScalarImpl<char2, float, float2>, subScalarImpl<char3, float, float3>, subScalarImpl<char4, float, float4>},
{subScalarImpl<schar, double, double>, subScalarImpl<char2, double, double2>, subScalarImpl<char3, double, double3>, subScalarImpl<char4, double, double4>}
},
{
{0 /*subScalarImpl<ushort, float, uchar>*/, 0 /*subScalarImpl<ushort2, float, uchar2>*/, 0 /*subScalarImpl<ushort3, float, uchar3>*/, 0 /*subScalarImpl<ushort4, float, uchar4>*/},
{0 /*subScalarImpl<ushort, float, schar>*/, 0 /*subScalarImpl<ushort2, float, char2>*/, 0 /*subScalarImpl<ushort3, float, char3>*/, 0 /*subScalarImpl<ushort4, float, char4>*/},
{subScalarImpl<ushort, float, ushort>, subScalarImpl<ushort2, float, ushort2>, subScalarImpl<ushort3, float, ushort3>, subScalarImpl<ushort4, float, ushort4>},
{subScalarImpl<ushort, float, short>, subScalarImpl<ushort2, float, short2>, subScalarImpl<ushort3, float, short3>, subScalarImpl<ushort4, float, short4>},
{subScalarImpl<ushort, float, int>, subScalarImpl<ushort2, float, int2>, subScalarImpl<ushort3, float, int3>, subScalarImpl<ushort4, float, int4>},
{subScalarImpl<ushort, float, float>, subScalarImpl<ushort2, float, float2>, subScalarImpl<ushort3, float, float3>, subScalarImpl<ushort4, float, float4>},
{subScalarImpl<ushort, double, double>, subScalarImpl<ushort2, double, double2>, subScalarImpl<ushort3, double, double3>, subScalarImpl<ushort4, double, double4>}
},
{
{0 /*subScalarImpl<short, float, uchar>*/, 0 /*subScalarImpl<short2, float, uchar2>*/, 0 /*subScalarImpl<short3, float, uchar3>*/, 0 /*subScalarImpl<short4, float, uchar4>*/},
{0 /*subScalarImpl<short, float, schar>*/, 0 /*subScalarImpl<short2, float, char2>*/, 0 /*subScalarImpl<short3, float, char3>*/, 0 /*subScalarImpl<short4, float, char4>*/},
{subScalarImpl<short, float, ushort>, subScalarImpl<short2, float, ushort2>, subScalarImpl<short3, float, ushort3>, subScalarImpl<short4, float, ushort4>},
{subScalarImpl<short, float, short>, subScalarImpl<short2, float, short2>, subScalarImpl<short3, float, short3>, subScalarImpl<short4, float, short4>},
{subScalarImpl<short, float, int>, subScalarImpl<short2, float, int2>, subScalarImpl<short3, float, int3>, subScalarImpl<short4, float, int4>},
{subScalarImpl<short, float, float>, subScalarImpl<short2, float, float2>, subScalarImpl<short3, float, float3>, subScalarImpl<short4, float, float4>},
{subScalarImpl<short, double, double>, subScalarImpl<short2, double, double2>, subScalarImpl<short3, double, double3>, subScalarImpl<short4, double, double4>}
},
{
{0 /*subScalarImpl<int, float, uchar>*/, 0 /*subScalarImpl<int2, float, uchar2>*/, 0 /*subScalarImpl<int3, float, uchar3>*/, 0 /*subScalarImpl<int4, float, uchar4>*/},
{0 /*subScalarImpl<int, float, schar>*/, 0 /*subScalarImpl<int2, float, char2>*/, 0 /*subScalarImpl<int3, float, char3>*/, 0 /*subScalarImpl<int4, float, char4>*/},
{0 /*subScalarImpl<int, float, ushort>*/, 0 /*subScalarImpl<int2, float, ushort2>*/, 0 /*subScalarImpl<int3, float, ushort3>*/, 0 /*subScalarImpl<int4, float, ushort4>*/},
{0 /*subScalarImpl<int, float, short>*/, 0 /*subScalarImpl<int2, float, short2>*/, 0 /*subScalarImpl<int3, float, short3>*/, 0 /*subScalarImpl<int4, float, short4>*/},
{subScalarImpl<int, float, int>, subScalarImpl<int2, float, int2>, subScalarImpl<int3, float, int3>, subScalarImpl<int4, float, int4>},
{subScalarImpl<int, float, float>, subScalarImpl<int2, float, float2>, subScalarImpl<int3, float, float3>, subScalarImpl<int4, float, float4>},
{subScalarImpl<int, double, double>, subScalarImpl<int2, double, double2>, subScalarImpl<int3, double, double3>, subScalarImpl<int4, double, double4>}
},
{
{0 /*subScalarImpl<float, float, uchar>*/, 0 /*subScalarImpl<float2, float, uchar2>*/, 0 /*subScalarImpl<float3, float, uchar3>*/, 0 /*subScalarImpl<float4, float, uchar4>*/},
{0 /*subScalarImpl<float, float, schar>*/, 0 /*subScalarImpl<float2, float, char2>*/, 0 /*subScalarImpl<float3, float, char3>*/, 0 /*subScalarImpl<float4, float, char4>*/},
{0 /*subScalarImpl<float, float, ushort>*/, 0 /*subScalarImpl<float2, float, ushort2>*/, 0 /*subScalarImpl<float3, float, ushort3>*/, 0 /*subScalarImpl<float4, float, ushort4>*/},
{0 /*subScalarImpl<float, float, short>*/, 0 /*subScalarImpl<float2, float, short2>*/, 0 /*subScalarImpl<float3, float, short3>*/, 0 /*subScalarImpl<float4, float, short4>*/},
{0 /*subScalarImpl<float, float, int>*/, 0 /*subScalarImpl<float2, float, int2>*/, 0 /*subScalarImpl<float3, float, int3>*/, 0 /*subScalarImpl<float4, float, int4>*/},
{subScalarImpl<float, float, float>, subScalarImpl<float2, float, float2>, subScalarImpl<float3, float, float3>, subScalarImpl<float4, float, float4>},
{subScalarImpl<float, double, double>, subScalarImpl<float2, double, double2>, subScalarImpl<float3, double, double3>, subScalarImpl<float4, double, double4>}
},
{
{0 /*subScalarImpl<double, double, uchar>*/, 0 /*subScalarImpl<double2, double, uchar2>*/, 0 /*subScalarImpl<double3, double, uchar3>*/, 0 /*subScalarImpl<double4, double, uchar4>*/},
{0 /*subScalarImpl<double, double, schar>*/, 0 /*subScalarImpl<double2, double, char2>*/, 0 /*subScalarImpl<double3, double, char3>*/, 0 /*subScalarImpl<double4, double, char4>*/},
{0 /*subScalarImpl<double, double, ushort>*/, 0 /*subScalarImpl<double2, double, ushort2>*/, 0 /*subScalarImpl<double3, double, ushort3>*/, 0 /*subScalarImpl<double4, double, ushort4>*/},
{0 /*subScalarImpl<double, double, short>*/, 0 /*subScalarImpl<double2, double, short2>*/, 0 /*subScalarImpl<double3, double, short3>*/, 0 /*subScalarImpl<double4, double, short4>*/},
{0 /*subScalarImpl<double, double, int>*/, 0 /*subScalarImpl<double2, double, int2>*/, 0 /*subScalarImpl<double3, double, int3>*/, 0 /*subScalarImpl<double4, double, int4>*/},
{0 /*subScalarImpl<double, double, float>*/, 0 /*subScalarImpl<double2, double, float2>*/, 0 /*subScalarImpl<double3, double, float3>*/, 0 /*subScalarImpl<double4, double, float4>*/},
{subScalarImpl<double, double, double>, subScalarImpl<double2, double, double2>, subScalarImpl<double3, double, double3>, subScalarImpl<double4, double, double4>}
}
};
const int sdepth = src.depth();
const int ddepth = dst.depth();
const int cn = src.channels();
CV_DbgAssert( sdepth <= CV_64F && ddepth <= CV_64F && cn <= 4 );
const func_t func = funcs[sdepth][ddepth][cn - 1];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(src, val, inv, dst, mask, stream);
}
#endif

242
modules/cudaarithm/src/cuda/sum.cu
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@ -1,242 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
template <typename T, typename R, int cn>
void sumImpl(const GpuMat& _src, GpuMat& _dst, const GpuMat& mask, Stream& stream)
{
typedef typename MakeVec<T, cn>::type src_type;
typedef typename MakeVec<R, cn>::type res_type;
const GpuMat_<src_type>& src = (const GpuMat_<src_type>&) _src;
GpuMat_<res_type>& dst = (GpuMat_<res_type>&) _dst;
if (mask.empty())
gridCalcSum(src, dst, stream);
else
gridCalcSum(src, dst, globPtr<uchar>(mask), stream);
}
template <typename T, typename R, int cn>
void sumAbsImpl(const GpuMat& _src, GpuMat& _dst, const GpuMat& mask, Stream& stream)
{
typedef typename MakeVec<T, cn>::type src_type;
typedef typename MakeVec<R, cn>::type res_type;
const GpuMat_<src_type>& src = (const GpuMat_<src_type>&) _src;
GpuMat_<res_type>& dst = (GpuMat_<res_type>&) _dst;
if (mask.empty())
gridCalcSum(abs_(cvt_<res_type>(src)), dst, stream);
else
gridCalcSum(abs_(cvt_<res_type>(src)), dst, globPtr<uchar>(mask), stream);
}
template <typename T, typename R, int cn>
void sumSqrImpl(const GpuMat& _src, GpuMat& _dst, const GpuMat& mask, Stream& stream)
{
typedef typename MakeVec<T, cn>::type src_type;
typedef typename MakeVec<R, cn>::type res_type;
const GpuMat_<src_type>& src = (const GpuMat_<src_type>&) _src;
GpuMat_<res_type>& dst = (GpuMat_<res_type>&) _dst;
if (mask.empty())
gridCalcSum(sqr_(cvt_<res_type>(src)), dst, stream);
else
gridCalcSum(sqr_(cvt_<res_type>(src)), dst, globPtr<uchar>(mask), stream);
}
}
void cv::cuda::calcSum(InputArray _src, OutputArray _dst, InputArray _mask, Stream& stream)
{
typedef void (*func_t)(const GpuMat& _src, GpuMat& _dst, const GpuMat& mask, Stream& stream);
static const func_t funcs[7][4] =
{
{sumImpl<uchar , double, 1>, sumImpl<uchar , double, 2>, sumImpl<uchar , double, 3>, sumImpl<uchar , double, 4>},
{sumImpl<schar , double, 1>, sumImpl<schar , double, 2>, sumImpl<schar , double, 3>, sumImpl<schar , double, 4>},
{sumImpl<ushort, double, 1>, sumImpl<ushort, double, 2>, sumImpl<ushort, double, 3>, sumImpl<ushort, double, 4>},
{sumImpl<short , double, 1>, sumImpl<short , double, 2>, sumImpl<short , double, 3>, sumImpl<short , double, 4>},
{sumImpl<int , double, 1>, sumImpl<int , double, 2>, sumImpl<int , double, 3>, sumImpl<int , double, 4>},
{sumImpl<float , double, 1>, sumImpl<float , double, 2>, sumImpl<float , double, 3>, sumImpl<float , double, 4>},
{sumImpl<double, double, 1>, sumImpl<double, double, 2>, sumImpl<double, double, 3>, sumImpl<double, double, 4>}
};
const GpuMat src = getInputMat(_src, stream);
const GpuMat mask = getInputMat(_mask, stream);
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
const int src_depth = src.depth();
const int channels = src.channels();
GpuMat dst = getOutputMat(_dst, 1, 1, CV_64FC(channels), stream);
const func_t func = funcs[src_depth][channels - 1];
func(src, dst, mask, stream);
syncOutput(dst, _dst, stream);
}
cv::Scalar cv::cuda::sum(InputArray _src, InputArray _mask)
{
Stream& stream = Stream::Null();
HostMem dst;
calcSum(_src, dst, _mask, stream);
stream.waitForCompletion();
cv::Scalar val;
dst.createMatHeader().convertTo(cv::Mat(dst.size(), CV_64FC(dst.channels()), val.val), CV_64F);
return val;
}
void cv::cuda::calcAbsSum(InputArray _src, OutputArray _dst, InputArray _mask, Stream& stream)
{
typedef void (*func_t)(const GpuMat& _src, GpuMat& _dst, const GpuMat& mask, Stream& stream);
static const func_t funcs[7][4] =
{
{sumAbsImpl<uchar , double, 1>, sumAbsImpl<uchar , double, 2>, sumAbsImpl<uchar , double, 3>, sumAbsImpl<uchar , double, 4>},
{sumAbsImpl<schar , double, 1>, sumAbsImpl<schar , double, 2>, sumAbsImpl<schar , double, 3>, sumAbsImpl<schar , double, 4>},
{sumAbsImpl<ushort, double, 1>, sumAbsImpl<ushort, double, 2>, sumAbsImpl<ushort, double, 3>, sumAbsImpl<ushort, double, 4>},
{sumAbsImpl<short , double, 1>, sumAbsImpl<short , double, 2>, sumAbsImpl<short , double, 3>, sumAbsImpl<short , double, 4>},
{sumAbsImpl<int , double, 1>, sumAbsImpl<int , double, 2>, sumAbsImpl<int , double, 3>, sumAbsImpl<int , double, 4>},
{sumAbsImpl<float , double, 1>, sumAbsImpl<float , double, 2>, sumAbsImpl<float , double, 3>, sumAbsImpl<float , double, 4>},
{sumAbsImpl<double, double, 1>, sumAbsImpl<double, double, 2>, sumAbsImpl<double, double, 3>, sumAbsImpl<double, double, 4>}
};
const GpuMat src = getInputMat(_src, stream);
const GpuMat mask = getInputMat(_mask, stream);
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
const int src_depth = src.depth();
const int channels = src.channels();
GpuMat dst = getOutputMat(_dst, 1, 1, CV_64FC(channels), stream);
const func_t func = funcs[src_depth][channels - 1];
func(src, dst, mask, stream);
syncOutput(dst, _dst, stream);
}
cv::Scalar cv::cuda::absSum(InputArray _src, InputArray _mask)
{
Stream& stream = Stream::Null();
HostMem dst;
calcAbsSum(_src, dst, _mask, stream);
stream.waitForCompletion();
cv::Scalar val;
dst.createMatHeader().convertTo(cv::Mat(dst.size(), CV_64FC(dst.channels()), val.val), CV_64F);
return val;
}
void cv::cuda::calcSqrSum(InputArray _src, OutputArray _dst, InputArray _mask, Stream& stream)
{
typedef void (*func_t)(const GpuMat& _src, GpuMat& _dst, const GpuMat& mask, Stream& stream);
static const func_t funcs[7][4] =
{
{sumSqrImpl<uchar , double, 1>, sumSqrImpl<uchar , double, 2>, sumSqrImpl<uchar , double, 3>, sumSqrImpl<uchar , double, 4>},
{sumSqrImpl<schar , double, 1>, sumSqrImpl<schar , double, 2>, sumSqrImpl<schar , double, 3>, sumSqrImpl<schar , double, 4>},
{sumSqrImpl<ushort, double, 1>, sumSqrImpl<ushort, double, 2>, sumSqrImpl<ushort, double, 3>, sumSqrImpl<ushort, double, 4>},
{sumSqrImpl<short , double, 1>, sumSqrImpl<short , double, 2>, sumSqrImpl<short , double, 3>, sumSqrImpl<short , double, 4>},
{sumSqrImpl<int , double, 1>, sumSqrImpl<int , double, 2>, sumSqrImpl<int , double, 3>, sumSqrImpl<int , double, 4>},
{sumSqrImpl<float , double, 1>, sumSqrImpl<float , double, 2>, sumSqrImpl<float , double, 3>, sumSqrImpl<float , double, 4>},
{sumSqrImpl<double, double, 1>, sumSqrImpl<double, double, 2>, sumSqrImpl<double, double, 3>, sumSqrImpl<double, double, 4>}
};
const GpuMat src = getInputMat(_src, stream);
const GpuMat mask = getInputMat(_mask, stream);
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
const int src_depth = src.depth();
const int channels = src.channels();
GpuMat dst = getOutputMat(_dst, 1, 1, CV_64FC(channels), stream);
const func_t func = funcs[src_depth][channels - 1];
func(src, dst, mask, stream);
syncOutput(dst, _dst, stream);
}
cv::Scalar cv::cuda::sqrSum(InputArray _src, InputArray _mask)
{
Stream& stream = Stream::Null();
HostMem dst;
calcSqrSum(_src, dst, _mask, stream);
stream.waitForCompletion();
cv::Scalar val;
dst.createMatHeader().convertTo(cv::Mat(dst.size(), CV_64FC(dst.channels()), val.val), CV_64F);
return val;
}
#endif

153
modules/cudaarithm/src/cuda/threshold.cu
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@ -1,153 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
namespace
{
template <typename ScalarDepth> struct TransformPolicy : DefaultTransformPolicy
{
};
template <> struct TransformPolicy<double> : DefaultTransformPolicy
{
enum {
shift = 1
};
};
template <typename T>
void thresholdImpl(const GpuMat& src, GpuMat& dst, double thresh, double maxVal, int type, Stream& stream)
{
const T thresh_ = static_cast<T>(thresh);
const T maxVal_ = static_cast<T>(maxVal);
switch (type)
{
case 0:
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), thresh_binary_func(thresh_, maxVal_), stream);
break;
case 1:
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), thresh_binary_inv_func(thresh_, maxVal_), stream);
break;
case 2:
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), thresh_trunc_func(thresh_), stream);
break;
case 3:
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), thresh_to_zero_func(thresh_), stream);
break;
case 4:
gridTransformUnary_< TransformPolicy<T> >(globPtr<T>(src), globPtr<T>(dst), thresh_to_zero_inv_func(thresh_), stream);
break;
};
}
}
double cv::cuda::threshold(InputArray _src, OutputArray _dst, double thresh, double maxVal, int type, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
const int depth = src.depth();
CV_Assert( depth <= CV_64F );
CV_Assert( type <= 4 /*THRESH_TOZERO_INV*/ );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
src = src.reshape(1);
dst = dst.reshape(1);
if (depth == CV_32F && type == 2 /*THRESH_TRUNC*/)
{
NppStreamHandler h(StreamAccessor::getStream(stream));
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiThreshold_32f_C1R(src.ptr<Npp32f>(), static_cast<int>(src.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, static_cast<Npp32f>(thresh), NPP_CMP_GREATER) );
if (!stream)
CV_CUDEV_SAFE_CALL( cudaDeviceSynchronize() );
}
else
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, double thresh, double maxVal, int type, Stream& stream);
static const func_t funcs[] =
{
thresholdImpl<uchar>,
thresholdImpl<schar>,
thresholdImpl<ushort>,
thresholdImpl<short>,
thresholdImpl<int>,
thresholdImpl<float>,
thresholdImpl<double>
};
if (depth != CV_32F && depth != CV_64F)
{
thresh = cvFloor(thresh);
maxVal = cvRound(maxVal);
}
funcs[depth](src, dst, thresh, maxVal, type, stream);
}
syncOutput(dst, _dst, stream);
return thresh;
}
#endif

95
modules/cudaarithm/src/cuda/transpose.cu
View File

@ -1,95 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudaarithm.hpp"
#include "opencv2/cudev.hpp"
#include "opencv2/core/private.cuda.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudev;
void cv::cuda::transpose(InputArray _src, OutputArray _dst, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
const size_t elemSize = src.elemSize();
CV_Assert( elemSize == 1 || elemSize == 4 || elemSize == 8 );
GpuMat dst = getOutputMat(_dst, src.cols, src.rows, src.type(), stream);
if (elemSize == 1)
{
NppStreamHandler h(StreamAccessor::getStream(stream));
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiTranspose_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) );
if (!stream)
CV_CUDEV_SAFE_CALL( cudaDeviceSynchronize() );
}
else if (elemSize == 4)
{
gridTranspose(globPtr<int>(src), globPtr<int>(dst), stream);
}
else // if (elemSize == 8)
{
gridTranspose(globPtr<double>(src), globPtr<double>(dst), stream);
}
syncOutput(dst, _dst, stream);
}
#endif

505
modules/cudaarithm/src/element_operations.cpp
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@ -1,505 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::cuda::add(InputArray, InputArray, OutputArray, InputArray, int, Stream&) { throw_no_cuda(); }
void cv::cuda::subtract(InputArray, InputArray, OutputArray, InputArray, int, Stream&) { throw_no_cuda(); }
void cv::cuda::multiply(InputArray, InputArray, OutputArray, double, int, Stream&) { throw_no_cuda(); }
void cv::cuda::divide(InputArray, InputArray, OutputArray, double, int, Stream&) { throw_no_cuda(); }
void cv::cuda::absdiff(InputArray, InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::abs(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::sqr(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::sqrt(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::exp(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::log(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::pow(InputArray, double, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::compare(InputArray, InputArray, OutputArray, int, Stream&) { throw_no_cuda(); }
void cv::cuda::bitwise_not(InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::bitwise_or(InputArray, InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::bitwise_and(InputArray, InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::bitwise_xor(InputArray, InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::rshift(InputArray, Scalar_<int>, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::lshift(InputArray, Scalar_<int>, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::min(InputArray, InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::max(InputArray, InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::addWeighted(InputArray, double, InputArray, double, double, OutputArray, int, Stream&) { throw_no_cuda(); }
double cv::cuda::threshold(InputArray, OutputArray, double, double, int, Stream&) {throw_no_cuda(); return 0.0;}
void cv::cuda::magnitude(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::magnitude(InputArray, InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::magnitudeSqr(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::magnitudeSqr(InputArray, InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::phase(InputArray, InputArray, OutputArray, bool, Stream&) { throw_no_cuda(); }
void cv::cuda::cartToPolar(InputArray, InputArray, OutputArray, OutputArray, bool, Stream&) { throw_no_cuda(); }
void cv::cuda::polarToCart(InputArray, InputArray, OutputArray, OutputArray, bool, Stream&) { throw_no_cuda(); }
#else
////////////////////////////////////////////////////////////////////////
// arithm_op
namespace
{
typedef void (*mat_mat_func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double scale, Stream& stream, int op);
typedef void (*mat_scalar_func_t)(const GpuMat& src, Scalar val, bool inv, GpuMat& dst, const GpuMat& mask, double scale, Stream& stream, int op);
void arithm_op(InputArray _src1, InputArray _src2, OutputArray _dst, InputArray _mask, double scale, int dtype, Stream& stream,
mat_mat_func_t mat_mat_func, mat_scalar_func_t mat_scalar_func, int op = 0)
{
const int kind1 = _src1.kind();
const int kind2 = _src2.kind();
const bool isScalar1 = (kind1 == _InputArray::MATX);
const bool isScalar2 = (kind2 == _InputArray::MATX);
CV_Assert( !isScalar1 || !isScalar2 );
GpuMat src1;
if (!isScalar1)
src1 = getInputMat(_src1, stream);
GpuMat src2;
if (!isScalar2)
src2 = getInputMat(_src2, stream);
Mat scalar;
if (isScalar1)
scalar = _src1.getMat();
else if (isScalar2)
scalar = _src2.getMat();
Scalar val;
if (!scalar.empty())
{
CV_Assert( scalar.total() <= 4 );
scalar.convertTo(Mat_<double>(scalar.rows, scalar.cols, &val[0]), CV_64F);
}
GpuMat mask = getInputMat(_mask, stream);
const int sdepth = src1.empty() ? src2.depth() : src1.depth();
const int cn = src1.empty() ? src2.channels() : src1.channels();
const Size size = src1.empty() ? src2.size() : src1.size();
if (dtype < 0)
dtype = sdepth;
const int ddepth = CV_MAT_DEPTH(dtype);
CV_Assert( sdepth <= CV_64F && ddepth <= CV_64F );
CV_Assert( !scalar.empty() || (src2.type() == src1.type() && src2.size() == src1.size()) );
CV_Assert( mask.empty() || (cn == 1 && mask.size() == size && mask.type() == CV_8UC1) );
if (sdepth == CV_64F || ddepth == CV_64F)
{
if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(Error::StsUnsupportedFormat, "The device doesn't support double");
}
GpuMat dst = getOutputMat(_dst, size, CV_MAKE_TYPE(ddepth, cn), stream);
if (isScalar1)
mat_scalar_func(src2, val, true, dst, mask, scale, stream, op);
else if (isScalar2)
mat_scalar_func(src1, val, false, dst, mask, scale, stream, op);
else
mat_mat_func(src1, src2, dst, mask, scale, stream, op);
syncOutput(dst, _dst, stream);
}
}
////////////////////////////////////////////////////////////////////////
// add
void addMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& _stream, int);
void addScalar(const GpuMat& src, Scalar val, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int);
void cv::cuda::add(InputArray src1, InputArray src2, OutputArray dst, InputArray mask, int dtype, Stream& stream)
{
arithm_op(src1, src2, dst, mask, 1.0, dtype, stream, addMat, addScalar);
}
////////////////////////////////////////////////////////////////////////
// subtract
void subMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& _stream, int);
void subScalar(const GpuMat& src, Scalar val, bool inv, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int);
void cv::cuda::subtract(InputArray src1, InputArray src2, OutputArray dst, InputArray mask, int dtype, Stream& stream)
{
arithm_op(src1, src2, dst, mask, 1.0, dtype, stream, subMat, subScalar);
}
////////////////////////////////////////////////////////////////////////
// multiply
void mulMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double scale, Stream& stream, int);
void mulMat_8uc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
void mulMat_16sc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
void mulScalar(const GpuMat& src, cv::Scalar val, bool, GpuMat& dst, const GpuMat& mask, double scale, Stream& stream, int);
void cv::cuda::multiply(InputArray _src1, InputArray _src2, OutputArray _dst, double scale, int dtype, Stream& stream)
{
if (_src1.type() == CV_8UC4 && _src2.type() == CV_32FC1)
{
GpuMat src1 = getInputMat(_src1, stream);
GpuMat src2 = getInputMat(_src2, stream);
CV_Assert( src1.size() == src2.size() );
GpuMat dst = getOutputMat(_dst, src1.size(), src1.type(), stream);
mulMat_8uc4_32f(src1, src2, dst, stream);
syncOutput(dst, _dst, stream);
}
else if (_src1.type() == CV_16SC4 && _src2.type() == CV_32FC1)
{
GpuMat src1 = getInputMat(_src1, stream);
GpuMat src2 = getInputMat(_src2, stream);
CV_Assert( src1.size() == src2.size() );
GpuMat dst = getOutputMat(_dst, src1.size(), src1.type(), stream);
mulMat_16sc4_32f(src1, src2, dst, stream);
syncOutput(dst, _dst, stream);
}
else
{
arithm_op(_src1, _src2, _dst, GpuMat(), scale, dtype, stream, mulMat, mulScalar);
}
}
////////////////////////////////////////////////////////////////////////
// divide
void divMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double scale, Stream& stream, int);
void divMat_8uc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
void divMat_16sc4_32f(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
void divScalar(const GpuMat& src, cv::Scalar val, bool inv, GpuMat& dst, const GpuMat& mask, double scale, Stream& stream, int);
void cv::cuda::divide(InputArray _src1, InputArray _src2, OutputArray _dst, double scale, int dtype, Stream& stream)
{
if (_src1.type() == CV_8UC4 && _src2.type() == CV_32FC1)
{
GpuMat src1 = getInputMat(_src1, stream);
GpuMat src2 = getInputMat(_src2, stream);
CV_Assert( src1.size() == src2.size() );
GpuMat dst = getOutputMat(_dst, src1.size(), src1.type(), stream);
divMat_8uc4_32f(src1, src2, dst, stream);
syncOutput(dst, _dst, stream);
}
else if (_src1.type() == CV_16SC4 && _src2.type() == CV_32FC1)
{
GpuMat src1 = getInputMat(_src1, stream);
GpuMat src2 = getInputMat(_src2, stream);
CV_Assert( src1.size() == src2.size() );
GpuMat dst = getOutputMat(_dst, src1.size(), src1.type(), stream);
divMat_16sc4_32f(src1, src2, dst, stream);
syncOutput(dst, _dst, stream);
}
else
{
arithm_op(_src1, _src2, _dst, GpuMat(), scale, dtype, stream, divMat, divScalar);
}
}
//////////////////////////////////////////////////////////////////////////////
// absdiff
void absDiffMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int);
void absDiffScalar(const GpuMat& src, cv::Scalar val, bool, GpuMat& dst, const GpuMat&, double, Stream& stream, int);
void cv::cuda::absdiff(InputArray src1, InputArray src2, OutputArray dst, Stream& stream)
{
arithm_op(src1, src2, dst, noArray(), 1.0, -1, stream, absDiffMat, absDiffScalar);
}
//////////////////////////////////////////////////////////////////////////////
// compare
void cmpMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int cmpop);
void cmpScalar(const GpuMat& src, Scalar val, bool inv, GpuMat& dst, const GpuMat&, double, Stream& stream, int cmpop);
void cv::cuda::compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop, Stream& stream)
{
arithm_op(src1, src2, dst, noArray(), 1.0, CV_8U, stream, cmpMat, cmpScalar, cmpop);
}
//////////////////////////////////////////////////////////////////////////////
// Binary bitwise logical operations
namespace
{
enum
{
BIT_OP_AND,
BIT_OP_OR,
BIT_OP_XOR
};
}
void bitMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op);
void bitScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op);
void cv::cuda::bitwise_or(InputArray src1, InputArray src2, OutputArray dst, InputArray mask, Stream& stream)
{
arithm_op(src1, src2, dst, mask, 1.0, -1, stream, bitMat, bitScalar, BIT_OP_OR);
}
void cv::cuda::bitwise_and(InputArray src1, InputArray src2, OutputArray dst, InputArray mask, Stream& stream)
{
arithm_op(src1, src2, dst, mask, 1.0, -1, stream, bitMat, bitScalar, BIT_OP_AND);
}
void cv::cuda::bitwise_xor(InputArray src1, InputArray src2, OutputArray dst, InputArray mask, Stream& stream)
{
arithm_op(src1, src2, dst, mask, 1.0, -1, stream, bitMat, bitScalar, BIT_OP_XOR);
}
//////////////////////////////////////////////////////////////////////////////
// shift
namespace
{
template <int DEPTH, int cn> struct NppShiftFunc
{
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
typedef NppStatus (*func_t)(const npp_type* pSrc1, int nSrc1Step, const Npp32u* pConstants, npp_type* pDst, int nDstStep, NppiSize oSizeROI);
};
template <int DEPTH> struct NppShiftFunc<DEPTH, 1>
{
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
typedef NppStatus (*func_t)(const npp_type* pSrc1, int nSrc1Step, const Npp32u pConstants, npp_type* pDst, int nDstStep, NppiSize oSizeROI);
};
template <int DEPTH, int cn, typename NppShiftFunc<DEPTH, cn>::func_t func> struct NppShift
{
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
static void call(const GpuMat& src, Scalar_<Npp32u> sc, GpuMat& dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize oSizeROI;
oSizeROI.width = src.cols;
oSizeROI.height = src.rows;
nppSafeCall( func(src.ptr<npp_type>(), static_cast<int>(src.step), sc.val, dst.ptr<npp_type>(), static_cast<int>(dst.step), oSizeROI) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template <int DEPTH, typename NppShiftFunc<DEPTH, 1>::func_t func> struct NppShift<DEPTH, 1, func>
{
typedef typename NPPTypeTraits<DEPTH>::npp_type npp_type;
static void call(const GpuMat& src, Scalar_<Npp32u> sc, GpuMat& dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
NppiSize oSizeROI;
oSizeROI.width = src.cols;
oSizeROI.height = src.rows;
nppSafeCall( func(src.ptr<npp_type>(), static_cast<int>(src.step), sc.val[0], dst.ptr<npp_type>(), static_cast<int>(dst.step), oSizeROI) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
}
void cv::cuda::rshift(InputArray _src, Scalar_<int> val, OutputArray _dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, Scalar_<Npp32u> sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
{NppShift<CV_8U , 1, nppiRShiftC_8u_C1R >::call, 0, NppShift<CV_8U , 3, nppiRShiftC_8u_C3R >::call, NppShift<CV_8U , 4, nppiRShiftC_8u_C4R>::call },
{NppShift<CV_8S , 1, nppiRShiftC_8s_C1R >::call, 0, NppShift<CV_8S , 3, nppiRShiftC_8s_C3R >::call, NppShift<CV_8S , 4, nppiRShiftC_8s_C4R>::call },
{NppShift<CV_16U, 1, nppiRShiftC_16u_C1R>::call, 0, NppShift<CV_16U, 3, nppiRShiftC_16u_C3R>::call, NppShift<CV_16U, 4, nppiRShiftC_16u_C4R>::call},
{NppShift<CV_16S, 1, nppiRShiftC_16s_C1R>::call, 0, NppShift<CV_16S, 3, nppiRShiftC_16s_C3R>::call, NppShift<CV_16S, 4, nppiRShiftC_16s_C4R>::call},
{NppShift<CV_32S, 1, nppiRShiftC_32s_C1R>::call, 0, NppShift<CV_32S, 3, nppiRShiftC_32s_C3R>::call, NppShift<CV_32S, 4, nppiRShiftC_32s_C4R>::call},
};
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.depth() < CV_32F );
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
funcs[src.depth()][src.channels() - 1](src, val, dst, StreamAccessor::getStream(stream));
syncOutput(dst, _dst, stream);
}
void cv::cuda::lshift(InputArray _src, Scalar_<int> val, OutputArray _dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, Scalar_<Npp32u> sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
{NppShift<CV_8U , 1, nppiLShiftC_8u_C1R>::call , 0, NppShift<CV_8U , 3, nppiLShiftC_8u_C3R>::call , NppShift<CV_8U , 4, nppiLShiftC_8u_C4R>::call },
{0 , 0, 0 , 0 },
{NppShift<CV_16U, 1, nppiLShiftC_16u_C1R>::call, 0, NppShift<CV_16U, 3, nppiLShiftC_16u_C3R>::call, NppShift<CV_16U, 4, nppiLShiftC_16u_C4R>::call},
{0 , 0, 0 , 0 },
{NppShift<CV_32S, 1, nppiLShiftC_32s_C1R>::call, 0, NppShift<CV_32S, 3, nppiLShiftC_32s_C3R>::call, NppShift<CV_32S, 4, nppiLShiftC_32s_C4R>::call},
};
GpuMat src = getInputMat(_src, stream);
CV_Assert( src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S );
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 );
GpuMat dst = getOutputMat(_dst, src.size(), src.type(), stream);
funcs[src.depth()][src.channels() - 1](src, val, dst, StreamAccessor::getStream(stream));
syncOutput(dst, _dst, stream);
}
//////////////////////////////////////////////////////////////////////////////
// Minimum and maximum operations
namespace
{
enum
{
MIN_OP,
MAX_OP
};
}
void minMaxMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int op);
void minMaxScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat&, double, Stream& stream, int op);
void cv::cuda::min(InputArray src1, InputArray src2, OutputArray dst, Stream& stream)
{
arithm_op(src1, src2, dst, noArray(), 1.0, -1, stream, minMaxMat, minMaxScalar, MIN_OP);
}
void cv::cuda::max(InputArray src1, InputArray src2, OutputArray dst, Stream& stream)
{
arithm_op(src1, src2, dst, noArray(), 1.0, -1, stream, minMaxMat, minMaxScalar, MAX_OP);
}
////////////////////////////////////////////////////////////////////////
// NPP magnitide
namespace
{
typedef NppStatus (*nppMagnitude_t)(const Npp32fc* pSrc, int nSrcStep, Npp32f* pDst, int nDstStep, NppiSize oSizeROI);
void npp_magnitude(const GpuMat& src, GpuMat& dst, nppMagnitude_t func, cudaStream_t stream)
{
CV_Assert(src.type() == CV_32FC2);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
NppStreamHandler h(stream);
nppSafeCall( func(src.ptr<Npp32fc>(), static_cast<int>(src.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
}
void cv::cuda::magnitude(InputArray _src, OutputArray _dst, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
GpuMat dst = getOutputMat(_dst, src.size(), CV_32FC1, stream);
npp_magnitude(src, dst, nppiMagnitude_32fc32f_C1R, StreamAccessor::getStream(stream));
syncOutput(dst, _dst, stream);
}
void cv::cuda::magnitudeSqr(InputArray _src, OutputArray _dst, Stream& stream)
{
GpuMat src = getInputMat(_src, stream);
GpuMat dst = getOutputMat(_dst, src.size(), CV_32FC1, stream);
npp_magnitude(src, dst, nppiMagnitudeSqr_32fc32f_C1R, StreamAccessor::getStream(stream));
syncOutput(dst, _dst, stream);
}
#endif

63
modules/cudaarithm/src/precomp.hpp
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@ -1,63 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#include <limits>
#include "cvconfig.h"
#include "opencv2/cudaarithm.hpp"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/private.cuda.hpp"
#ifdef HAVE_CUBLAS
# include <cublas.h>
#endif
#ifdef HAVE_CUFFT
# include <cufft.h>
#endif
#endif /* __OPENCV_PRECOMP_H__ */

219
modules/cudaarithm/src/reductions.cpp
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@ -1,219 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
double cv::cuda::norm(InputArray, int, InputArray) { throw_no_cuda(); return 0.0; }
void cv::cuda::calcNorm(InputArray, OutputArray, int, InputArray, Stream&) { throw_no_cuda(); }
double cv::cuda::norm(InputArray, InputArray, int) { throw_no_cuda(); return 0.0; }
void cv::cuda::calcNormDiff(InputArray, InputArray, OutputArray, int, Stream&) { throw_no_cuda(); }
Scalar cv::cuda::sum(InputArray, InputArray) { throw_no_cuda(); return Scalar(); }
void cv::cuda::calcSum(InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
Scalar cv::cuda::absSum(InputArray, InputArray) { throw_no_cuda(); return Scalar(); }
void cv::cuda::calcAbsSum(InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
Scalar cv::cuda::sqrSum(InputArray, InputArray) { throw_no_cuda(); return Scalar(); }
void cv::cuda::calcSqrSum(InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::minMax(InputArray, double*, double*, InputArray) { throw_no_cuda(); }
void cv::cuda::findMinMax(InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::minMaxLoc(InputArray, double*, double*, Point*, Point*, InputArray) { throw_no_cuda(); }
void cv::cuda::findMinMaxLoc(InputArray, OutputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
int cv::cuda::countNonZero(InputArray) { throw_no_cuda(); return 0; }
void cv::cuda::countNonZero(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::reduce(InputArray, OutputArray, int, int, int, Stream&) { throw_no_cuda(); }
void cv::cuda::meanStdDev(InputArray, Scalar&, Scalar&) { throw_no_cuda(); }
void cv::cuda::meanStdDev(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::rectStdDev(InputArray, InputArray, OutputArray, Rect, Stream&) { throw_no_cuda(); }
void cv::cuda::normalize(InputArray, OutputArray, double, double, int, int, InputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::integral(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
void cv::cuda::sqrIntegral(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
#else
////////////////////////////////////////////////////////////////////////
// norm
namespace cv { namespace cuda { namespace device {
void normL2(cv::InputArray _src, cv::OutputArray _dst, cv::InputArray _mask, Stream& stream);
void findMaxAbs(cv::InputArray _src, cv::OutputArray _dst, cv::InputArray _mask, Stream& stream);
}}}
void cv::cuda::calcNorm(InputArray _src, OutputArray dst, int normType, InputArray mask, Stream& stream)
{
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
GpuMat src = getInputMat(_src, stream);
GpuMat src_single_channel = src.reshape(1);
if (normType == NORM_L1)
{
calcAbsSum(src_single_channel, dst, mask, stream);
}
else if (normType == NORM_L2)
{
cv::cuda::device::normL2(src_single_channel, dst, mask, stream);
}
else // NORM_INF
{
cv::cuda::device::findMaxAbs(src_single_channel, dst, mask, stream);
}
}
double cv::cuda::norm(InputArray _src, int normType, InputArray _mask)
{
Stream& stream = Stream::Null();
HostMem dst;
calcNorm(_src, dst, normType, _mask, stream);
stream.waitForCompletion();
double val;
dst.createMatHeader().convertTo(Mat(1, 1, CV_64FC1, &val), CV_64F);
return val;
}
////////////////////////////////////////////////////////////////////////
// meanStdDev
void cv::cuda::meanStdDev(InputArray _src, OutputArray _dst, Stream& stream)
{
if (!deviceSupports(FEATURE_SET_COMPUTE_13))
CV_Error(cv::Error::StsNotImplemented, "Not sufficient compute capebility");
const GpuMat src = getInputMat(_src, stream);
CV_Assert( src.type() == CV_8UC1 );
GpuMat dst = getOutputMat(_dst, 1, 2, CV_64FC1, stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
int bufSize;
#if (CUDA_VERSION <= 4020)
nppSafeCall( nppiMeanStdDev8uC1RGetBufferHostSize(sz, &bufSize) );
#else
nppSafeCall( nppiMeanStdDevGetBufferHostSize_8u_C1R(sz, &bufSize) );
#endif
BufferPool pool(stream);
GpuMat buf = pool.getBuffer(1, bufSize, CV_8UC1);
// detail: https://github.com/opencv/opencv/issues/11063
//NppStreamHandler h(StreamAccessor::getStream(stream));
nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), sz, buf.ptr<Npp8u>(), dst.ptr<Npp64f>(), dst.ptr<Npp64f>() + 1) );
syncOutput(dst, _dst, stream);
}
void cv::cuda::meanStdDev(InputArray _src, Scalar& mean, Scalar& stddev)
{
Stream& stream = Stream::Null();
HostMem dst;
meanStdDev(_src, dst, stream);
stream.waitForCompletion();
double vals[2];
dst.createMatHeader().copyTo(Mat(1, 2, CV_64FC1, &vals[0]));
mean = Scalar(vals[0]);
stddev = Scalar(vals[1]);
}
//////////////////////////////////////////////////////////////////////////////
// rectStdDev
void cv::cuda::rectStdDev(InputArray _src, InputArray _sqr, OutputArray _dst, Rect rect, Stream& _stream)
{
GpuMat src = getInputMat(_src, _stream);
GpuMat sqr = getInputMat(_sqr, _stream);
CV_Assert( src.type() == CV_32SC1 && sqr.type() == CV_64FC1 );
GpuMat dst = getOutputMat(_dst, src.size(), CV_32FC1, _stream);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
NppiRect nppRect;
nppRect.height = rect.height;
nppRect.width = rect.width;
nppRect.x = rect.x;
nppRect.y = rect.y;
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStreamHandler h(stream);
nppSafeCall( nppiRectStdDev_32s32f_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), sqr.ptr<Npp64f>(), static_cast<int>(sqr.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, nppRect) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
syncOutput(dst, _dst, _stream);
}
#endif

433
modules/cudaarithm/test/test_arithm.cpp
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@ -1,433 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace opencv_test { namespace {
//////////////////////////////////////////////////////////////////////////////
// GEMM
#ifdef HAVE_CUBLAS
CV_FLAGS(GemmFlags, 0, cv::GEMM_1_T, cv::GEMM_2_T, cv::GEMM_3_T);
#define ALL_GEMM_FLAGS testing::Values(GemmFlags(0), GemmFlags(cv::GEMM_1_T), GemmFlags(cv::GEMM_2_T), GemmFlags(cv::GEMM_3_T), GemmFlags(cv::GEMM_1_T | cv::GEMM_2_T), GemmFlags(cv::GEMM_1_T | cv::GEMM_3_T), GemmFlags(cv::GEMM_1_T | cv::GEMM_2_T | cv::GEMM_3_T))
PARAM_TEST_CASE(GEMM, cv::cuda::DeviceInfo, cv::Size, MatType, GemmFlags, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int type;
int flags;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
flags = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(GEMM, Accuracy)
{
cv::Mat src1 = randomMat(size, type, -10.0, 10.0);
cv::Mat src2 = randomMat(size, type, -10.0, 10.0);
cv::Mat src3 = randomMat(size, type, -10.0, 10.0);
double alpha = randomDouble(-10.0, 10.0);
double beta = randomDouble(-10.0, 10.0);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
{
try
{
cv::cuda::GpuMat dst;
cv::cuda::gemm(loadMat(src1), loadMat(src2), alpha, loadMat(src3), beta, dst, flags);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else if (type == CV_64FC2 && flags != 0)
{
try
{
cv::cuda::GpuMat dst;
cv::cuda::gemm(loadMat(src1), loadMat(src2), alpha, loadMat(src3), beta, dst, flags);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsNotImplemented, e.code);
}
}
else
{
cv::cuda::GpuMat dst = createMat(size, type, useRoi);
cv::cuda::gemm(loadMat(src1, useRoi), loadMat(src2, useRoi), alpha, loadMat(src3, useRoi), beta, dst, flags);
cv::Mat dst_gold;
cv::gemm(src1, src2, alpha, src3, beta, dst_gold, flags);
EXPECT_MAT_NEAR(dst_gold, dst, CV_MAT_DEPTH(type) == CV_32F ? 1e-1 : 1e-10);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, GEMM, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_32FC1), MatType(CV_32FC2), MatType(CV_64FC1), MatType(CV_64FC2)),
ALL_GEMM_FLAGS,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////
// MulSpectrums
CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT)
PARAM_TEST_CASE(MulSpectrums, cv::cuda::DeviceInfo, cv::Size, DftFlags)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int flag;
cv::Mat a, b;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
flag = GET_PARAM(2);
cv::cuda::setDevice(devInfo.deviceID());
a = randomMat(size, CV_32FC2);
b = randomMat(size, CV_32FC2);
}
};
CUDA_TEST_P(MulSpectrums, Simple)
{
cv::cuda::GpuMat c;
cv::cuda::mulSpectrums(loadMat(a), loadMat(b), c, flag, false);
cv::Mat c_gold;
cv::mulSpectrums(a, b, c_gold, flag, false);
EXPECT_MAT_NEAR(c_gold, c, 1e-2);
}
CUDA_TEST_P(MulSpectrums, Scaled)
{
float scale = 1.f / size.area();
cv::cuda::GpuMat c;
cv::cuda::mulAndScaleSpectrums(loadMat(a), loadMat(b), c, flag, scale, false);
cv::Mat c_gold;
cv::mulSpectrums(a, b, c_gold, flag, false);
c_gold.convertTo(c_gold, c_gold.type(), scale);
EXPECT_MAT_NEAR(c_gold, c, 1e-2);
}
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, MulSpectrums, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(DftFlags(0), DftFlags(cv::DFT_ROWS))));
////////////////////////////////////////////////////////////////////////////
// Dft
struct Dft : testing::TestWithParam<cv::cuda::DeviceInfo>
{
cv::cuda::DeviceInfo devInfo;
virtual void SetUp()
{
devInfo = GetParam();
cv::cuda::setDevice(devInfo.deviceID());
}
};
namespace
{
void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace)
{
SCOPED_TRACE(hint);
cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC2, 0.0, 10.0);
cv::Mat b_gold;
cv::dft(a, b_gold, flags);
cv::cuda::GpuMat d_b;
cv::cuda::GpuMat d_b_data;
if (inplace)
{
d_b_data.create(1, a.size().area(), CV_32FC2);
d_b = cv::cuda::GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize());
}
cv::cuda::dft(loadMat(a), d_b, cv::Size(cols, rows), flags);
EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr());
ASSERT_EQ(CV_32F, d_b.depth());
ASSERT_EQ(2, d_b.channels());
EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), rows * cols * 1e-4);
}
}
CUDA_TEST_P(Dft, C2C)
{
int cols = randomInt(2, 100);
int rows = randomInt(2, 100);
for (int i = 0; i < 2; ++i)
{
bool inplace = i != 0;
testC2C("no flags", cols, rows, 0, inplace);
testC2C("no flags 0 1", cols, rows + 1, 0, inplace);
testC2C("no flags 1 0", cols, rows + 1, 0, inplace);
testC2C("no flags 1 1", cols + 1, rows, 0, inplace);
testC2C("DFT_INVERSE", cols, rows, cv::DFT_INVERSE, inplace);
testC2C("DFT_ROWS", cols, rows, cv::DFT_ROWS, inplace);
testC2C("single col", 1, rows, 0, inplace);
testC2C("single row", cols, 1, 0, inplace);
testC2C("single col inversed", 1, rows, cv::DFT_INVERSE, inplace);
testC2C("single row inversed", cols, 1, cv::DFT_INVERSE, inplace);
testC2C("single row DFT_ROWS", cols, 1, cv::DFT_ROWS, inplace);
testC2C("size 1 2", 1, 2, 0, inplace);
testC2C("size 2 1", 2, 1, 0, inplace);
}
}
CUDA_TEST_P(Dft, Algorithm)
{
int cols = randomInt(2, 100);
int rows = randomInt(2, 100);
int flags = 0 | DFT_COMPLEX_INPUT;
cv::Ptr<cv::cuda::DFT> dft = cv::cuda::createDFT(cv::Size(cols, rows), flags);
for (int i = 0; i < 5; ++i)
{
SCOPED_TRACE("dft algorithm");
cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC2, 0.0, 10.0);
cv::cuda::GpuMat d_b;
cv::cuda::GpuMat d_b_data;
dft->compute(loadMat(a), d_b);
cv::Mat b_gold;
cv::dft(a, b_gold, flags);
ASSERT_EQ(CV_32F, d_b.depth());
ASSERT_EQ(2, d_b.channels());
EXPECT_MAT_NEAR(b_gold, cv::Mat(d_b), rows * cols * 1e-4);
}
}
namespace
{
void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace)
{
SCOPED_TRACE(hint);
cv::Mat a = randomMat(cv::Size(cols, rows), CV_32FC1, 0.0, 10.0);
cv::cuda::GpuMat d_b, d_c;
cv::cuda::GpuMat d_b_data, d_c_data;
if (inplace)
{
if (a.cols == 1)
{
d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2);
d_b = cv::cuda::GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize());
}
else
{
d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2);
d_b = cv::cuda::GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize());
}
d_c_data.create(1, a.size().area(), CV_32F);
d_c = cv::cuda::GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize());
}
cv::cuda::dft(loadMat(a), d_b, cv::Size(cols, rows), 0);
cv::cuda::dft(d_b, d_c, cv::Size(cols, rows), cv::DFT_REAL_OUTPUT | cv::DFT_SCALE);
EXPECT_TRUE(!inplace || d_b.ptr() == d_b_data.ptr());
EXPECT_TRUE(!inplace || d_c.ptr() == d_c_data.ptr());
ASSERT_EQ(CV_32F, d_c.depth());
ASSERT_EQ(1, d_c.channels());
cv::Mat c(d_c);
EXPECT_MAT_NEAR(a, c, rows * cols * 1e-5);
}
}
CUDA_TEST_P(Dft, R2CThenC2R)
{
int cols = randomInt(2, 100);
int rows = randomInt(2, 100);
testR2CThenC2R("sanity", cols, rows, false);
testR2CThenC2R("sanity 0 1", cols, rows + 1, false);
testR2CThenC2R("sanity 1 0", cols + 1, rows, false);
testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, false);
testR2CThenC2R("single col", 1, rows, false);
testR2CThenC2R("single col 1", 1, rows + 1, false);
testR2CThenC2R("single row", cols, 1, false);
testR2CThenC2R("single row 1", cols + 1, 1, false);
testR2CThenC2R("sanity", cols, rows, true);
testR2CThenC2R("sanity 0 1", cols, rows + 1, true);
testR2CThenC2R("sanity 1 0", cols + 1, rows, true);
testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true);
testR2CThenC2R("single row", cols, 1, true);
testR2CThenC2R("single row 1", cols + 1, 1, true);
}
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Dft, ALL_DEVICES);
////////////////////////////////////////////////////////
// Convolve
namespace
{
void convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false)
{
// reallocate the output array if needed
C.create(std::abs(A.rows - B.rows) + 1, std::abs(A.cols - B.cols) + 1, A.type());
cv::Size dftSize;
// compute the size of DFT transform
dftSize.width = cv::getOptimalDFTSize(A.cols + B.cols - 1);
dftSize.height = cv::getOptimalDFTSize(A.rows + B.rows - 1);
// allocate temporary buffers and initialize them with 0s
cv::Mat tempA(dftSize, A.type(), cv::Scalar::all(0));
cv::Mat tempB(dftSize, B.type(), cv::Scalar::all(0));
// copy A and B to the top-left corners of tempA and tempB, respectively
cv::Mat roiA(tempA, cv::Rect(0, 0, A.cols, A.rows));
A.copyTo(roiA);
cv::Mat roiB(tempB, cv::Rect(0, 0, B.cols, B.rows));
B.copyTo(roiB);
// now transform the padded A & B in-place;
// use "nonzeroRows" hint for faster processing
cv::dft(tempA, tempA, 0, A.rows);
cv::dft(tempB, tempB, 0, B.rows);
// multiply the spectrums;
// the function handles packed spectrum representations well
cv::mulSpectrums(tempA, tempB, tempA, 0, ccorr);
// transform the product back from the frequency domain.
// Even though all the result rows will be non-zero,
// you need only the first C.rows of them, and thus you
// pass nonzeroRows == C.rows
cv::dft(tempA, tempA, cv::DFT_INVERSE + cv::DFT_SCALE, C.rows);
// now copy the result back to C.
tempA(cv::Rect(0, 0, C.cols, C.rows)).copyTo(C);
}
IMPLEMENT_PARAM_CLASS(KSize, int)
IMPLEMENT_PARAM_CLASS(Ccorr, bool)
}
PARAM_TEST_CASE(Convolve, cv::cuda::DeviceInfo, cv::Size, KSize, Ccorr)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int ksize;
bool ccorr;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
ksize = GET_PARAM(2);
ccorr = GET_PARAM(3);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(Convolve, Accuracy)
{
cv::Mat src = randomMat(size, CV_32FC1, 0.0, 100.0);
cv::Mat kernel = randomMat(cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0);
cv::Ptr<cv::cuda::Convolution> conv = cv::cuda::createConvolution();
cv::cuda::GpuMat dst;
conv->convolve(loadMat(src), loadMat(kernel), dst, ccorr);
cv::Mat dst_gold;
convolveDFT(src, kernel, dst_gold, ccorr);
EXPECT_MAT_NEAR(dst, dst_gold, 1e-1);
}
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Convolve, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(KSize(3), KSize(7), KSize(11), KSize(17), KSize(19), KSize(23), KSize(45)),
testing::Values(Ccorr(false), Ccorr(true))));
#endif // HAVE_CUBLAS
}} // namespace
#endif // HAVE_CUDA

120
modules/cudaarithm/test/test_buffer_pool.cpp
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@ -1,120 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
#include "opencv2/core/cuda.hpp"
#include "opencv2/core/private.cuda.hpp"
#include "opencv2/ts/cuda_test.hpp"
namespace opencv_test { namespace {
struct BufferPoolTest : TestWithParam<DeviceInfo>
{
void RunSimpleTest(Stream& stream, HostMem& dst_1, HostMem& dst_2)
{
BufferPool pool(stream);
{
GpuMat buf0 = pool.getBuffer(Size(640, 480), CV_8UC1);
EXPECT_FALSE( buf0.empty() );
buf0.setTo(Scalar::all(0), stream);
GpuMat buf1 = pool.getBuffer(Size(640, 480), CV_8UC1);
EXPECT_FALSE( buf1.empty() );
buf0.convertTo(buf1, buf1.type(), 1.0, 1.0, stream);
buf1.download(dst_1, stream);
}
{
GpuMat buf2 = pool.getBuffer(Size(1280, 1024), CV_32SC1);
EXPECT_FALSE( buf2.empty() );
buf2.setTo(Scalar::all(2), stream);
buf2.download(dst_2, stream);
}
}
void CheckSimpleTest(HostMem& dst_1, HostMem& dst_2)
{
EXPECT_MAT_NEAR(Mat(Size(640, 480), CV_8UC1, Scalar::all(1)), dst_1, 0.0);
EXPECT_MAT_NEAR(Mat(Size(1280, 1024), CV_32SC1, Scalar::all(2)), dst_2, 0.0);
}
};
CUDA_TEST_P(BufferPoolTest, FromNullStream)
{
HostMem dst_1, dst_2;
RunSimpleTest(Stream::Null(), dst_1, dst_2);
cudaSafeCall(cudaDeviceSynchronize());
CheckSimpleTest(dst_1, dst_2);
}
CUDA_TEST_P(BufferPoolTest, From2Streams)
{
HostMem dst1_1, dst1_2;
HostMem dst2_1, dst2_2;
Stream stream1, stream2;
RunSimpleTest(stream1, dst1_1, dst1_2);
RunSimpleTest(stream2, dst2_1, dst2_2);
stream1.waitForCompletion();
stream2.waitForCompletion();
CheckSimpleTest(dst1_1, dst1_2);
CheckSimpleTest(dst2_1, dst2_2);
}
INSTANTIATE_TEST_CASE_P(CUDA_Stream, BufferPoolTest, ALL_DEVICES);
}} // namespace
#endif // HAVE_CUDA

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modules/cudaarithm/test/test_core.cpp
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@ -1,421 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace opencv_test { namespace {
////////////////////////////////////////////////////////////////////////////////
// Merge
PARAM_TEST_CASE(Merge, cv::cuda::DeviceInfo, cv::Size, MatDepth, Channels, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int depth;
int channels;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth = GET_PARAM(2);
channels = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(Merge, Accuracy)
{
std::vector<cv::Mat> src;
src.reserve(channels);
for (int i = 0; i < channels; ++i)
src.push_back(cv::Mat(size, depth, cv::Scalar::all(i)));
std::vector<cv::cuda::GpuMat> d_src;
for (int i = 0; i < channels; ++i)
d_src.push_back(loadMat(src[i], useRoi));
if (depth == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
{
try
{
cv::cuda::GpuMat dst;
cv::cuda::merge(d_src, dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::cuda::GpuMat dst;
cv::cuda::merge(d_src, dst);
cv::Mat dst_gold;
cv::merge(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Merge, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_DEPTH,
testing::Values(1, 2, 3, 4),
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Split
PARAM_TEST_CASE(Split, cv::cuda::DeviceInfo, cv::Size, MatDepth, Channels, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int depth;
int channels;
bool useRoi;
int type;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth = GET_PARAM(2);
channels = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::cuda::setDevice(devInfo.deviceID());
type = CV_MAKE_TYPE(depth, channels);
}
};
CUDA_TEST_P(Split, Accuracy)
{
cv::Mat src = randomMat(size, type);
if (depth == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
{
try
{
std::vector<cv::cuda::GpuMat> dst;
cv::cuda::split(loadMat(src), dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
std::vector<cv::cuda::GpuMat> dst;
cv::cuda::split(loadMat(src, useRoi), dst);
std::vector<cv::Mat> dst_gold;
cv::split(src, dst_gold);
ASSERT_EQ(dst_gold.size(), dst.size());
for (size_t i = 0; i < dst_gold.size(); ++i)
{
EXPECT_MAT_NEAR(dst_gold[i], dst[i], 0.0);
}
}
}
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Split, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_DEPTH,
testing::Values(1, 2, 3, 4),
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Transpose
PARAM_TEST_CASE(Transpose, cv::cuda::DeviceInfo, cv::Size, MatType, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(Transpose, Accuracy)
{
cv::Mat src = randomMat(size, type);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
{
try
{
cv::cuda::GpuMat dst;
cv::cuda::transpose(loadMat(src), dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::cuda::GpuMat dst = createMat(cv::Size(size.height, size.width), type, useRoi);
cv::cuda::transpose(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::transpose(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Transpose, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1),
MatType(CV_8UC4),
MatType(CV_16UC2),
MatType(CV_16SC2),
MatType(CV_32SC1),
MatType(CV_32SC2),
MatType(CV_64FC1)),
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// Flip
enum {FLIP_BOTH = 0, FLIP_X = 1, FLIP_Y = -1};
CV_ENUM(FlipCode, FLIP_BOTH, FLIP_X, FLIP_Y)
#define ALL_FLIP_CODES testing::Values(FlipCode(FLIP_BOTH), FlipCode(FLIP_X), FlipCode(FLIP_Y))
PARAM_TEST_CASE(Flip, cv::cuda::DeviceInfo, cv::Size, MatType, FlipCode, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int type;
int flip_code;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
flip_code = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(Flip, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::cuda::GpuMat dst = createMat(size, type, useRoi);
cv::cuda::flip(loadMat(src, useRoi), dst, flip_code);
cv::Mat dst_gold;
cv::flip(src, dst_gold, flip_code);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, Flip, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1),
MatType(CV_8UC3),
MatType(CV_8UC4),
MatType(CV_16UC1),
MatType(CV_16UC3),
MatType(CV_16UC4),
MatType(CV_32SC1),
MatType(CV_32SC3),
MatType(CV_32SC4),
MatType(CV_32FC1),
MatType(CV_32FC3),
MatType(CV_32FC4)),
ALL_FLIP_CODES,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// LUT
PARAM_TEST_CASE(LUT, cv::cuda::DeviceInfo, cv::Size, MatType, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(LUT, OneChannel)
{
cv::Mat src = randomMat(size, type);
cv::Mat lut = randomMat(cv::Size(256, 1), CV_8UC1);
cv::Ptr<cv::cuda::LookUpTable> lutAlg = cv::cuda::createLookUpTable(lut);
cv::cuda::GpuMat dst = createMat(size, CV_MAKE_TYPE(lut.depth(), src.channels()));
lutAlg->transform(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::LUT(src, lut, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
CUDA_TEST_P(LUT, MultiChannel)
{
cv::Mat src = randomMat(size, type);
cv::Mat lut = randomMat(cv::Size(256, 1), CV_MAKE_TYPE(CV_8U, src.channels()));
cv::Ptr<cv::cuda::LookUpTable> lutAlg = cv::cuda::createLookUpTable(lut);
cv::cuda::GpuMat dst = createMat(size, CV_MAKE_TYPE(lut.depth(), src.channels()), useRoi);
lutAlg->transform(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::LUT(src, lut, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, LUT, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3)),
WHOLE_SUBMAT));
//////////////////////////////////////////////////////////////////////////////
// CopyMakeBorder
namespace
{
IMPLEMENT_PARAM_CLASS(Border, int)
}
PARAM_TEST_CASE(CopyMakeBorder, cv::cuda::DeviceInfo, cv::Size, MatType, Border, BorderType, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int type;
int border;
int borderType;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
border = GET_PARAM(3);
borderType = GET_PARAM(4);
useRoi = GET_PARAM(5);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(CopyMakeBorder, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::Scalar val = randomScalar(0, 255);
cv::cuda::GpuMat dst = createMat(cv::Size(size.width + 2 * border, size.height + 2 * border), type, useRoi);
cv::cuda::copyMakeBorder(loadMat(src, useRoi), dst, border, border, border, border, borderType, val);
cv::Mat dst_gold;
cv::copyMakeBorder(src, dst_gold, border, border, border, border, borderType, val);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_Arithm, CopyMakeBorder, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_8UC1),
MatType(CV_8UC3),
MatType(CV_8UC4),
MatType(CV_16UC1),
MatType(CV_16UC3),
MatType(CV_16UC4),
MatType(CV_32FC1),
MatType(CV_32FC3),
MatType(CV_32FC4)),
testing::Values(Border(1), Border(10), Border(50)),
ALL_BORDER_TYPES,
WHOLE_SUBMAT));
}} // namespace
#endif // HAVE_CUDA

2799
modules/cudaarithm/test/test_element_operations.cpp
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412
modules/cudaarithm/test/test_gpumat.cpp
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@ -1,412 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
#include "opencv2/core/cuda.hpp"
#include "opencv2/ts/cuda_test.hpp"
namespace opencv_test { namespace {
////////////////////////////////////////////////////////////////////////////////
// SetTo
PARAM_TEST_CASE(GpuMat_SetTo, cv::cuda::DeviceInfo, cv::Size, MatType, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(GpuMat_SetTo, Zero)
{
cv::Scalar zero = cv::Scalar::all(0);
cv::cuda::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(zero);
EXPECT_MAT_NEAR(cv::Mat::zeros(size, type), mat, 0.0);
}
CUDA_TEST_P(GpuMat_SetTo, SameVal)
{
cv::Scalar val = cv::Scalar::all(randomDouble(0.0, 255.0));
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
{
try
{
cv::cuda::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::cuda::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
EXPECT_MAT_NEAR(cv::Mat(size, type, val), mat, 0.0);
}
}
CUDA_TEST_P(GpuMat_SetTo, DifferentVal)
{
cv::Scalar val = randomScalar(0.0, 255.0);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
{
try
{
cv::cuda::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::cuda::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val);
EXPECT_MAT_NEAR(cv::Mat(size, type, val), mat, 0.0);
}
}
CUDA_TEST_P(GpuMat_SetTo, Masked)
{
cv::Scalar val = randomScalar(0.0, 255.0);
cv::Mat mat_gold = randomMat(size, type);
cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
{
try
{
cv::cuda::GpuMat mat = createMat(size, type, useRoi);
mat.setTo(val, loadMat(mask));
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::cuda::GpuMat mat = loadMat(mat_gold, useRoi);
mat.setTo(val, loadMat(mask, useRoi));
mat_gold.setTo(val, mask);
EXPECT_MAT_NEAR(mat_gold, mat, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(CUDA, GpuMat_SetTo, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_TYPES,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// CopyTo
PARAM_TEST_CASE(GpuMat_CopyTo, cv::cuda::DeviceInfo, cv::Size, MatType, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int type;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
type = GET_PARAM(2);
useRoi = GET_PARAM(3);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(GpuMat_CopyTo, WithOutMask)
{
cv::Mat src = randomMat(size, type);
cv::cuda::GpuMat d_src = loadMat(src, useRoi);
cv::cuda::GpuMat dst = createMat(size, type, useRoi);
d_src.copyTo(dst);
EXPECT_MAT_NEAR(src, dst, 0.0);
}
CUDA_TEST_P(GpuMat_CopyTo, Masked)
{
cv::Mat src = randomMat(size, type);
cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
{
try
{
cv::cuda::GpuMat d_src = loadMat(src);
cv::cuda::GpuMat dst;
d_src.copyTo(dst, loadMat(mask, useRoi));
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::cuda::GpuMat d_src = loadMat(src, useRoi);
cv::cuda::GpuMat dst = loadMat(cv::Mat::zeros(size, type), useRoi);
d_src.copyTo(dst, loadMat(mask, useRoi));
cv::Mat dst_gold = cv::Mat::zeros(size, type);
src.copyTo(dst_gold, mask);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(CUDA, GpuMat_CopyTo, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_TYPES,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// ConvertTo
PARAM_TEST_CASE(GpuMat_ConvertTo, cv::cuda::DeviceInfo, cv::Size, MatDepth, MatDepth, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
cv::Size size;
int depth1;
int depth2;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
size = GET_PARAM(1);
depth1 = GET_PARAM(2);
depth2 = GET_PARAM(3);
useRoi = GET_PARAM(4);
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(GpuMat_ConvertTo, WithOutScaling)
{
cv::Mat src = randomMat(size, depth1);
if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
{
try
{
cv::cuda::GpuMat d_src = loadMat(src);
cv::cuda::GpuMat dst;
d_src.convertTo(dst, depth2);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::cuda::GpuMat d_src = loadMat(src, useRoi);
cv::cuda::GpuMat dst = createMat(size, depth2, useRoi);
d_src.convertTo(dst, depth2);
cv::Mat dst_gold;
src.convertTo(dst_gold, depth2);
EXPECT_MAT_NEAR(dst_gold, dst, depth2 < CV_32F ? 1.0 : 1e-4);
}
}
CUDA_TEST_P(GpuMat_ConvertTo, WithScaling)
{
cv::Mat src = randomMat(size, depth1);
double a = randomDouble(0.0, 1.0);
double b = randomDouble(-10.0, 10.0);
if ((depth1 == CV_64F || depth2 == CV_64F) && !supportFeature(devInfo, cv::cuda::NATIVE_DOUBLE))
{
try
{
cv::cuda::GpuMat d_src = loadMat(src);
cv::cuda::GpuMat dst;
d_src.convertTo(dst, depth2, a, b);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(cv::Error::StsUnsupportedFormat, e.code);
}
}
else
{
cv::cuda::GpuMat d_src = loadMat(src, useRoi);
cv::cuda::GpuMat dst = createMat(size, depth2, useRoi);
d_src.convertTo(dst, depth2, a, b);
cv::Mat dst_gold;
src.convertTo(dst_gold, depth2, a, b);
EXPECT_MAT_NEAR(dst_gold, dst, depth2 < CV_32F ? 1.0 : 1e-4);
}
}
INSTANTIATE_TEST_CASE_P(CUDA, GpuMat_ConvertTo, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
ALL_DEPTH,
ALL_DEPTH,
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
// ensureSizeIsEnough
struct EnsureSizeIsEnough : testing::TestWithParam<cv::cuda::DeviceInfo>
{
virtual void SetUp()
{
cv::cuda::DeviceInfo devInfo = GetParam();
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(EnsureSizeIsEnough, BufferReuse)
{
cv::cuda::GpuMat buffer(100, 100, CV_8U);
cv::cuda::GpuMat old = buffer;
// don't reallocate memory
cv::cuda::ensureSizeIsEnough(10, 20, CV_8U, buffer);
EXPECT_EQ(10, buffer.rows);
EXPECT_EQ(20, buffer.cols);
EXPECT_EQ(CV_8UC1, buffer.type());
EXPECT_EQ(reinterpret_cast<intptr_t>(old.data), reinterpret_cast<intptr_t>(buffer.data));
// don't reallocate memory
cv::cuda::ensureSizeIsEnough(20, 30, CV_8U, buffer);
EXPECT_EQ(20, buffer.rows);
EXPECT_EQ(30, buffer.cols);
EXPECT_EQ(CV_8UC1, buffer.type());
EXPECT_EQ(reinterpret_cast<intptr_t>(old.data), reinterpret_cast<intptr_t>(buffer.data));
}
INSTANTIATE_TEST_CASE_P(CUDA, EnsureSizeIsEnough, ALL_DEVICES);
////////////////////////////////////////////////////////////////////////////////
// createContinuous
struct CreateContinuous : testing::TestWithParam<cv::cuda::DeviceInfo>
{
virtual void SetUp()
{
cv::cuda::DeviceInfo devInfo = GetParam();
cv::cuda::setDevice(devInfo.deviceID());
}
};
CUDA_TEST_P(CreateContinuous, BufferReuse)
{
cv::cuda::GpuMat buffer;
cv::cuda::createContinuous(100, 100, CV_8UC1, buffer);
EXPECT_EQ(100, buffer.rows);
EXPECT_EQ(100, buffer.cols);
EXPECT_EQ(CV_8UC1, buffer.type());
EXPECT_TRUE(buffer.isContinuous());
EXPECT_EQ(buffer.cols * sizeof(uchar), buffer.step);
cv::cuda::createContinuous(10, 1000, CV_8UC1, buffer);
EXPECT_EQ(10, buffer.rows);
EXPECT_EQ(1000, buffer.cols);
EXPECT_EQ(CV_8UC1, buffer.type());
EXPECT_TRUE(buffer.isContinuous());
EXPECT_EQ(buffer.cols * sizeof(uchar), buffer.step);
cv::cuda::createContinuous(10, 10, CV_8UC1, buffer);
EXPECT_EQ(10, buffer.rows);
EXPECT_EQ(10, buffer.cols);
EXPECT_EQ(CV_8UC1, buffer.type());
EXPECT_TRUE(buffer.isContinuous());
EXPECT_EQ(buffer.cols * sizeof(uchar), buffer.step);
cv::cuda::createContinuous(100, 100, CV_8UC1, buffer);
EXPECT_EQ(100, buffer.rows);
EXPECT_EQ(100, buffer.cols);
EXPECT_EQ(CV_8UC1, buffer.type());
EXPECT_TRUE(buffer.isContinuous());
EXPECT_EQ(buffer.cols * sizeof(uchar), buffer.step);
}
INSTANTIATE_TEST_CASE_P(CUDA, CreateContinuous, ALL_DEVICES);
}} // namespace
#endif // HAVE_CUDA

45
modules/cudaarithm/test/test_main.cpp
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@ -1,45 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
CV_CUDA_TEST_MAIN("gpu")

457
modules/cudaarithm/test/test_opengl.cpp
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@ -1,457 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#if defined(HAVE_CUDA) && defined(HAVE_OPENGL)
#include "opencv2/core/cuda.hpp"
#include "opencv2/core/opengl.hpp"
#include "opencv2/ts/cuda_test.hpp"
namespace opencv_test { namespace {
/////////////////////////////////////////////
// Buffer
PARAM_TEST_CASE(Buffer, cv::Size, MatType)
{
static void SetUpTestCase()
{
cv::namedWindow("test", cv::WINDOW_OPENGL);
}
static void TearDownTestCase()
{
cv::destroyAllWindows();
}
cv::Size size;
int type;
virtual void SetUp()
{
size = GET_PARAM(0);
type = GET_PARAM(1);
}
};
CUDA_TEST_P(Buffer, Constructor1)
{
cv::ogl::Buffer buf(size.height, size.width, type, cv::ogl::Buffer::ARRAY_BUFFER, true);
EXPECT_EQ(size.height, buf.rows());
EXPECT_EQ(size.width, buf.cols());
EXPECT_EQ(type, buf.type());
}
CUDA_TEST_P(Buffer, Constructor2)
{
cv::ogl::Buffer buf(size, type, cv::ogl::Buffer::ARRAY_BUFFER, true);
EXPECT_EQ(size.height, buf.rows());
EXPECT_EQ(size.width, buf.cols());
EXPECT_EQ(type, buf.type());
}
CUDA_TEST_P(Buffer, ConstructorFromMat)
{
cv::Mat gold = randomMat(size, type);
cv::ogl::Buffer buf(gold, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::Mat bufData;
buf.copyTo(bufData);
EXPECT_MAT_NEAR(gold, bufData, 0);
}
CUDA_TEST_P(Buffer, ConstructorFromGpuMat)
{
cv::Mat gold = randomMat(size, type);
cv::cuda::GpuMat d_gold(gold);
cv::ogl::Buffer buf(d_gold, cv::ogl::Buffer::ARRAY_BUFFER);
cv::Mat bufData;
buf.copyTo(bufData);
EXPECT_MAT_NEAR(gold, bufData, 0);
}
CUDA_TEST_P(Buffer, ConstructorFromBuffer)
{
cv::ogl::Buffer buf_gold(size, type, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::ogl::Buffer buf(buf_gold);
EXPECT_EQ(buf_gold.bufId(), buf.bufId());
EXPECT_EQ(buf_gold.rows(), buf.rows());
EXPECT_EQ(buf_gold.cols(), buf.cols());
EXPECT_EQ(buf_gold.type(), buf.type());
}
CUDA_TEST_P(Buffer, Create)
{
cv::ogl::Buffer buf;
buf.create(size.height, size.width, type, cv::ogl::Buffer::ARRAY_BUFFER, true);
EXPECT_EQ(size.height, buf.rows());
EXPECT_EQ(size.width, buf.cols());
EXPECT_EQ(type, buf.type());
}
CUDA_TEST_P(Buffer, CopyFromMat)
{
cv::Mat gold = randomMat(size, type);
cv::ogl::Buffer buf;
buf.copyFrom(gold, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::Mat bufData;
buf.copyTo(bufData);
EXPECT_MAT_NEAR(gold, bufData, 0);
}
CUDA_TEST_P(Buffer, CopyFromGpuMat)
{
cv::Mat gold = randomMat(size, type);
cv::cuda::GpuMat d_gold(gold);
cv::ogl::Buffer buf;
buf.copyFrom(d_gold, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::Mat bufData;
buf.copyTo(bufData);
EXPECT_MAT_NEAR(gold, bufData, 0);
}
CUDA_TEST_P(Buffer, CopyFromBuffer)
{
cv::Mat gold = randomMat(size, type);
cv::ogl::Buffer buf_gold(gold, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::ogl::Buffer buf;
buf.copyFrom(buf_gold, cv::ogl::Buffer::ARRAY_BUFFER, true);
EXPECT_NE(buf_gold.bufId(), buf.bufId());
cv::Mat bufData;
buf.copyTo(bufData);
EXPECT_MAT_NEAR(gold, bufData, 0);
}
CUDA_TEST_P(Buffer, CopyToGpuMat)
{
cv::Mat gold = randomMat(size, type);
cv::ogl::Buffer buf(gold, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::cuda::GpuMat dst;
buf.copyTo(dst);
EXPECT_MAT_NEAR(gold, dst, 0);
}
CUDA_TEST_P(Buffer, CopyToBuffer)
{
cv::Mat gold = randomMat(size, type);
cv::ogl::Buffer buf(gold, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::ogl::Buffer dst;
buf.copyTo(dst);
dst.setAutoRelease(true);
EXPECT_NE(buf.bufId(), dst.bufId());
cv::Mat bufData;
dst.copyTo(bufData);
EXPECT_MAT_NEAR(gold, bufData, 0);
}
CUDA_TEST_P(Buffer, Clone)
{
cv::Mat gold = randomMat(size, type);
cv::ogl::Buffer buf(gold, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::ogl::Buffer dst = buf.clone(cv::ogl::Buffer::ARRAY_BUFFER, true);
EXPECT_NE(buf.bufId(), dst.bufId());
cv::Mat bufData;
dst.copyTo(bufData);
EXPECT_MAT_NEAR(gold, bufData, 0);
}
CUDA_TEST_P(Buffer, MapHostRead)
{
cv::Mat gold = randomMat(size, type);
cv::ogl::Buffer buf(gold, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::Mat dst = buf.mapHost(cv::ogl::Buffer::READ_ONLY);
EXPECT_MAT_NEAR(gold, dst, 0);
buf.unmapHost();
}
CUDA_TEST_P(Buffer, MapHostWrite)
{
cv::Mat gold = randomMat(size, type);
cv::ogl::Buffer buf(size, type, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::Mat dst = buf.mapHost(cv::ogl::Buffer::WRITE_ONLY);
gold.copyTo(dst);
buf.unmapHost();
dst.release();
cv::Mat bufData;
buf.copyTo(bufData);
EXPECT_MAT_NEAR(gold, bufData, 0);
}
CUDA_TEST_P(Buffer, MapDevice)
{
cv::Mat gold = randomMat(size, type);
cv::ogl::Buffer buf(gold, cv::ogl::Buffer::ARRAY_BUFFER, true);
cv::cuda::GpuMat dst = buf.mapDevice();
EXPECT_MAT_NEAR(gold, dst, 0);
buf.unmapDevice();
}
INSTANTIATE_TEST_CASE_P(OpenGL, Buffer, testing::Combine(DIFFERENT_SIZES, ALL_TYPES));
/////////////////////////////////////////////
// Texture2D
PARAM_TEST_CASE(Texture2D, cv::Size, MatType)
{
static void SetUpTestCase()
{
cv::namedWindow("test", cv::WINDOW_OPENGL);
}
static void TearDownTestCase()
{
cv::destroyAllWindows();
}
cv::Size size;
int type;
int depth;
int cn;
cv::ogl::Texture2D::Format format;
virtual void SetUp()
{
size = GET_PARAM(0);
type = GET_PARAM(1);
depth = CV_MAT_DEPTH(type);
cn = CV_MAT_CN(type);
format = cn == 1 ? cv::ogl::Texture2D::DEPTH_COMPONENT : cn == 3 ? cv::ogl::Texture2D::RGB : cn == 4 ? cv::ogl::Texture2D::RGBA : cv::ogl::Texture2D::NONE;
}
};
CUDA_TEST_P(Texture2D, Constructor1)
{
cv::ogl::Texture2D tex(size.height, size.width, format, true);
EXPECT_EQ(size.height, tex.rows());
EXPECT_EQ(size.width, tex.cols());
EXPECT_EQ(format, tex.format());
}
CUDA_TEST_P(Texture2D, Constructor2)
{
cv::ogl::Texture2D tex(size, format, true);
EXPECT_EQ(size.height, tex.rows());
EXPECT_EQ(size.width, tex.cols());
EXPECT_EQ(format, tex.format());
}
CUDA_TEST_P(Texture2D, ConstructorFromMat)
{
cv::Mat gold = randomMat(size, type, 0, depth == CV_8U ? 255 : 1);
cv::ogl::Texture2D tex(gold, true);
cv::Mat texData;
tex.copyTo(texData, depth);
EXPECT_MAT_NEAR(gold, texData, 1e-2);
}
CUDA_TEST_P(Texture2D, ConstructorFromGpuMat)
{
cv::Mat gold = randomMat(size, type, 0, depth == CV_8U ? 255 : 1);
cv::cuda::GpuMat d_gold(gold);
cv::ogl::Texture2D tex(d_gold, true);
cv::Mat texData;
tex.copyTo(texData, depth);
EXPECT_MAT_NEAR(gold, texData, 1e-2);
}
CUDA_TEST_P(Texture2D, ConstructorFromBuffer)
{
cv::Mat gold = randomMat(size, type, 0, depth == CV_8U ? 255 : 1);
cv::ogl::Buffer buf_gold(gold, cv::ogl::Buffer::PIXEL_UNPACK_BUFFER, true);
cv::ogl::Texture2D tex(buf_gold, true);
cv::Mat texData;
tex.copyTo(texData, depth);
EXPECT_MAT_NEAR(gold, texData, 1e-2);
}
CUDA_TEST_P(Texture2D, ConstructorFromTexture2D)
{
cv::ogl::Texture2D tex_gold(size, format, true);
cv::ogl::Texture2D tex(tex_gold);
EXPECT_EQ(tex_gold.texId(), tex.texId());
EXPECT_EQ(tex_gold.rows(), tex.rows());
EXPECT_EQ(tex_gold.cols(), tex.cols());
EXPECT_EQ(tex_gold.format(), tex.format());
}
CUDA_TEST_P(Texture2D, Create)
{
cv::ogl::Texture2D tex;
tex.create(size.height, size.width, format, true);
EXPECT_EQ(size.height, tex.rows());
EXPECT_EQ(size.width, tex.cols());
EXPECT_EQ(format, tex.format());
}
CUDA_TEST_P(Texture2D, CopyFromMat)
{
cv::Mat gold = randomMat(size, type, 0, depth == CV_8U ? 255 : 1);
cv::ogl::Texture2D tex;
tex.copyFrom(gold, true);
cv::Mat texData;
tex.copyTo(texData, depth);
EXPECT_MAT_NEAR(gold, texData, 1e-2);
}
CUDA_TEST_P(Texture2D, CopyFromGpuMat)
{
cv::Mat gold = randomMat(size, type, 0, depth == CV_8U ? 255 : 1);
cv::cuda::GpuMat d_gold(gold);
cv::ogl::Texture2D tex;
tex.copyFrom(d_gold, true);
cv::Mat texData;
tex.copyTo(texData, depth);
EXPECT_MAT_NEAR(gold, texData, 1e-2);
}
CUDA_TEST_P(Texture2D, CopyFromBuffer)
{
cv::Mat gold = randomMat(size, type, 0, depth == CV_8U ? 255 : 1);
cv::ogl::Buffer buf_gold(gold, cv::ogl::Buffer::PIXEL_UNPACK_BUFFER, true);
cv::ogl::Texture2D tex;
tex.copyFrom(buf_gold, true);
cv::Mat texData;
tex.copyTo(texData, depth);
EXPECT_MAT_NEAR(gold, texData, 1e-2);
}
CUDA_TEST_P(Texture2D, CopyToGpuMat)
{
cv::Mat gold = randomMat(size, type, 0, depth == CV_8U ? 255 : 1);
cv::ogl::Texture2D tex(gold, true);
cv::cuda::GpuMat dst;
tex.copyTo(dst, depth);
EXPECT_MAT_NEAR(gold, dst, 1e-2);
}
CUDA_TEST_P(Texture2D, CopyToBuffer)
{
cv::Mat gold = randomMat(size, type, 0, depth == CV_8U ? 255 : 1);
cv::ogl::Texture2D tex(gold, true);
cv::ogl::Buffer dst;
tex.copyTo(dst, depth, true);
cv::Mat bufData;
dst.copyTo(bufData);
EXPECT_MAT_NEAR(gold, bufData, 1e-2);
}
INSTANTIATE_TEST_CASE_P(OpenGL, Texture2D, testing::Combine(DIFFERENT_SIZES, testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1, CV_32FC3, CV_32FC4)));
}} // namespace
#endif

56
modules/cudaarithm/test/test_precomp.hpp
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@ -1,56 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_TEST_PRECOMP_HPP__
#define __OPENCV_TEST_PRECOMP_HPP__
#include "opencv2/ts.hpp"
#include "opencv2/ts/cuda_test.hpp"
#include "opencv2/cudaarithm.hpp"
#include "cvconfig.h"
namespace opencv_test {
using namespace cv::cuda;
}
#endif

1121
modules/cudaarithm/test/test_reductions.cpp
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File diff suppressed because it is too large Load Diff

176
modules/cudaarithm/test/test_stream.cpp
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@ -1,176 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
#include <cuda_runtime.h>
#include "opencv2/core/cuda.hpp"
#include "opencv2/core/cuda_stream_accessor.hpp"
#include "opencv2/ts/cuda_test.hpp"
namespace opencv_test { namespace {
struct Async : testing::TestWithParam<cv::cuda::DeviceInfo>
{
cv::cuda::HostMem src;
cv::cuda::GpuMat d_src;
cv::cuda::HostMem dst;
cv::cuda::GpuMat d_dst;
virtual void SetUp()
{
cv::cuda::DeviceInfo devInfo = GetParam();
cv::cuda::setDevice(devInfo.deviceID());
src = cv::cuda::HostMem(cv::cuda::HostMem::PAGE_LOCKED);
cv::Mat m = randomMat(cv::Size(128, 128), CV_8UC1);
m.copyTo(src);
}
};
void checkMemSet(int status, void* userData)
{
ASSERT_EQ(cudaSuccess, status);
Async* test = reinterpret_cast<Async*>(userData);
cv::cuda::HostMem src = test->src;
cv::cuda::HostMem dst = test->dst;
cv::Mat dst_gold = cv::Mat::zeros(src.size(), src.type());
ASSERT_MAT_NEAR(dst_gold, dst, 0);
}
CUDA_TEST_P(Async, MemSet)
{
cv::cuda::Stream stream;
d_dst.upload(src);
d_dst.setTo(cv::Scalar::all(0), stream);
d_dst.download(dst, stream);
Async* test = this;
stream.enqueueHostCallback(checkMemSet, test);
stream.waitForCompletion();
}
void checkConvert(int status, void* userData)
{
ASSERT_EQ(cudaSuccess, status);
Async* test = reinterpret_cast<Async*>(userData);
cv::cuda::HostMem src = test->src;
cv::cuda::HostMem dst = test->dst;
cv::Mat dst_gold;
src.createMatHeader().convertTo(dst_gold, CV_32S);
ASSERT_MAT_NEAR(dst_gold, dst, 0);
}
CUDA_TEST_P(Async, Convert)
{
cv::cuda::Stream stream;
d_src.upload(src, stream);
d_src.convertTo(d_dst, CV_32S, stream);
d_dst.download(dst, stream);
Async* test = this;
stream.enqueueHostCallback(checkConvert, test);
stream.waitForCompletion();
}
CUDA_TEST_P(Async, WrapStream)
{
cudaStream_t cuda_stream = NULL;
ASSERT_EQ(cudaSuccess, cudaStreamCreate(&cuda_stream));
{
cv::cuda::Stream stream = cv::cuda::StreamAccessor::wrapStream(cuda_stream);
d_src.upload(src, stream);
d_src.convertTo(d_dst, CV_32S, stream);
d_dst.download(dst, stream);
Async* test = this;
stream.enqueueHostCallback(checkConvert, test);
stream.waitForCompletion();
}
ASSERT_EQ(cudaSuccess, cudaStreamDestroy(cuda_stream));
}
CUDA_TEST_P(Async, HostMemAllocator)
{
cv::cuda::Stream stream;
cv::Mat h_dst;
h_dst.allocator = cv::cuda::HostMem::getAllocator();
d_src.upload(src, stream);
d_src.convertTo(d_dst, CV_32S, stream);
d_dst.download(h_dst, stream);
stream.waitForCompletion();
cv::Mat dst_gold;
src.createMatHeader().convertTo(dst_gold, CV_32S);
ASSERT_MAT_NEAR(dst_gold, h_dst, 0);
}
INSTANTIATE_TEST_CASE_P(CUDA_Stream, Async, ALL_DEVICES);
}} // namespace
#endif // HAVE_CUDA

9
modules/cudabgsegm/CMakeLists.txt
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@ -1,9 +0,0 @@
if(IOS OR (NOT HAVE_CUDA AND NOT BUILD_CUDA_STUBS))
ocv_module_disable(cudabgsegm)
endif()
set(the_description "CUDA-accelerated Background Segmentation")
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4127 /wd4324 /wd4512 -Wundef -Wmissing-declarations -Wshadow)
ocv_define_module(cudabgsegm opencv_video WRAP python)

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CUDABGSEGM_HPP
#define OPENCV_CUDABGSEGM_HPP
#ifndef __cplusplus
# error cudabgsegm.hpp header must be compiled as C++
#endif
#include "opencv2/core/cuda.hpp"
#include "opencv2/video/background_segm.hpp"
/**
@addtogroup cuda
@{
@defgroup cudabgsegm Background Segmentation
@}
*/
namespace cv { namespace cuda {
//! @addtogroup cudabgsegm
//! @{
////////////////////////////////////////////////////
// MOG
/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
The class discriminates between foreground and background pixels by building and maintaining a model
of the background. Any pixel which does not fit this model is then deemed to be foreground. The
class implements algorithm described in @cite MOG2001 .
@sa BackgroundSubtractorMOG
@note
- An example on gaussian mixture based background/foreground segmantation can be found at
opencv_source_code/samples/gpu/bgfg_segm.cpp
*/
class CV_EXPORTS_W BackgroundSubtractorMOG : public cv::BackgroundSubtractor
{
public:
using cv::BackgroundSubtractor::apply;
CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) = 0;
using cv::BackgroundSubtractor::getBackgroundImage;
CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage, Stream& stream) const = 0;
CV_WRAP virtual int getHistory() const = 0;
CV_WRAP virtual void setHistory(int nframes) = 0;
CV_WRAP virtual int getNMixtures() const = 0;
CV_WRAP virtual void setNMixtures(int nmix) = 0;
CV_WRAP virtual double getBackgroundRatio() const = 0;
CV_WRAP virtual void setBackgroundRatio(double backgroundRatio) = 0;
CV_WRAP virtual double getNoiseSigma() const = 0;
CV_WRAP virtual void setNoiseSigma(double noiseSigma) = 0;
};
/** @brief Creates mixture-of-gaussian background subtractor
@param history Length of the history.
@param nmixtures Number of Gaussian mixtures.
@param backgroundRatio Background ratio.
@param noiseSigma Noise strength (standard deviation of the brightness or each color channel). 0
means some automatic value.
*/
CV_EXPORTS_W Ptr<cuda::BackgroundSubtractorMOG>
createBackgroundSubtractorMOG(int history = 200, int nmixtures = 5,
double backgroundRatio = 0.7, double noiseSigma = 0);
////////////////////////////////////////////////////
// MOG2
/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
The class discriminates between foreground and background pixels by building and maintaining a model
of the background. Any pixel which does not fit this model is then deemed to be foreground. The
class implements algorithm described in @cite Zivkovic2004 .
@sa BackgroundSubtractorMOG2
*/
class CV_EXPORTS_W BackgroundSubtractorMOG2 : public cv::BackgroundSubtractorMOG2
{
public:
using cv::BackgroundSubtractorMOG2::apply;
using cv::BackgroundSubtractorMOG2::getBackgroundImage;
CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) = 0;
CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage, Stream& stream) const = 0;
};
/** @brief Creates MOG2 Background Subtractor
@param history Length of the history.
@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
to decide whether a pixel is well described by the background model. This parameter does not
affect the background update.
@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
speed a bit, so if you do not need this feature, set the parameter to false.
*/
CV_EXPORTS_W Ptr<cuda::BackgroundSubtractorMOG2>
createBackgroundSubtractorMOG2(int history = 500, double varThreshold = 16,
bool detectShadows = true);
//! @}
}} // namespace cv { namespace cuda {
#endif /* OPENCV_CUDABGSEGM_HPP */

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modules/cudabgsegm/perf/perf_bgsegm.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
namespace opencv_test { namespace {
//////////////////////////////////////////////////////
// MOG
#ifdef HAVE_VIDEO_INPUT
DEF_PARAM_TEST(Video_Cn_LearningRate, string, MatCn, double);
PERF_TEST_P(Video_Cn_LearningRate, MOG,
Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"),
CUDA_CHANNELS_1_3_4,
Values(0.0, 0.01)))
{
const int numIters = 10;
const string inputFile = perf::TestBase::getDataPath(GET_PARAM(0));
const int cn = GET_PARAM(1);
const float learningRate = static_cast<float>(GET_PARAM(2));
cv::VideoCapture cap(inputFile);
ASSERT_TRUE(cap.isOpened());
cv::Mat frame;
cap >> frame;
ASSERT_FALSE(frame.empty());
if (cn != 3)
{
cv::Mat temp;
if (cn == 1)
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
else
cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
cv::swap(temp, frame);
}
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::BackgroundSubtractor> d_mog = cv::cuda::createBackgroundSubtractorMOG();
cv::cuda::GpuMat d_frame(frame);
cv::cuda::GpuMat foreground;
d_mog->apply(d_frame, foreground, learningRate);
int i = 0;
// collect performance data
for (; i < numIters; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
if (cn != 3)
{
cv::Mat temp;
if (cn == 1)
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
else
cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
cv::swap(temp, frame);
}
d_frame.upload(frame);
startTimer();
if(!next())
break;
d_mog->apply(d_frame, foreground, learningRate);
stopTimer();
}
// process last frame in sequence to get data for sanity test
for (; i < numIters; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
if (cn != 3)
{
cv::Mat temp;
if (cn == 1)
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
else
cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
cv::swap(temp, frame);
}
d_frame.upload(frame);
d_mog->apply(d_frame, foreground, learningRate);
}
CUDA_SANITY_CHECK(foreground);
}
else
{
FAIL_NO_CPU();
}
}
#endif
//////////////////////////////////////////////////////
// MOG2
#ifdef HAVE_VIDEO_INPUT
DEF_PARAM_TEST(Video_Cn, string, int);
PERF_TEST_P(Video_Cn, DISABLED_MOG2,
Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"),
CUDA_CHANNELS_1_3_4))
{
const int numIters = 10;
const string inputFile = perf::TestBase::getDataPath(GET_PARAM(0));
const int cn = GET_PARAM(1);
cv::VideoCapture cap(inputFile);
ASSERT_TRUE(cap.isOpened());
cv::Mat frame;
cap >> frame;
ASSERT_FALSE(frame.empty());
if (cn != 3)
{
cv::Mat temp;
if (cn == 1)
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
else
cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
cv::swap(temp, frame);
}
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::BackgroundSubtractorMOG2> d_mog2 = cv::cuda::createBackgroundSubtractorMOG2();
d_mog2->setDetectShadows(false);
cv::cuda::GpuMat d_frame(frame);
cv::cuda::GpuMat foreground;
d_mog2->apply(d_frame, foreground);
int i = 0;
// collect performance data
for (; i < numIters; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
if (cn != 3)
{
cv::Mat temp;
if (cn == 1)
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
else
cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
cv::swap(temp, frame);
}
d_frame.upload(frame);
startTimer();
if(!next())
break;
d_mog2->apply(d_frame, foreground);
stopTimer();
}
// process last frame in sequence to get data for sanity test
for (; i < numIters; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
if (cn != 3)
{
cv::Mat temp;
if (cn == 1)
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
else
cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
cv::swap(temp, frame);
}
d_frame.upload(frame);
d_mog2->apply(d_frame, foreground);
}
CUDA_SANITY_CHECK(foreground);
}
else
{
cv::Ptr<cv::BackgroundSubtractorMOG2> mog2 = cv::createBackgroundSubtractorMOG2();
mog2->setDetectShadows(false);
cv::Mat foreground;
mog2->apply(frame, foreground);
int i = 0;
// collect performance data
for (; i < numIters; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
if (cn != 3)
{
cv::Mat temp;
if (cn == 1)
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
else
cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
cv::swap(temp, frame);
}
startTimer();
if(!next())
break;
mog2->apply(frame, foreground);
stopTimer();
}
// process last frame in sequence to get data for sanity test
for (; i < numIters; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
if (cn != 3)
{
cv::Mat temp;
if (cn == 1)
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
else
cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
cv::swap(temp, frame);
}
mog2->apply(frame, foreground);
}
CPU_SANITY_CHECK(foreground);
}
}
#endif
//////////////////////////////////////////////////////
// MOG2GetBackgroundImage
#ifdef HAVE_VIDEO_INPUT
PERF_TEST_P(Video_Cn, MOG2GetBackgroundImage,
Combine(Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"),
CUDA_CHANNELS_1_3_4))
{
const string inputFile = perf::TestBase::getDataPath(GET_PARAM(0));
const int cn = GET_PARAM(1);
cv::VideoCapture cap(inputFile);
ASSERT_TRUE(cap.isOpened());
cv::Mat frame;
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::BackgroundSubtractor> d_mog2 = cv::cuda::createBackgroundSubtractorMOG2();
cv::cuda::GpuMat d_frame;
cv::cuda::GpuMat d_foreground;
for (int i = 0; i < 10; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
if (cn != 3)
{
cv::Mat temp;
if (cn == 1)
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
else
cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
cv::swap(temp, frame);
}
d_frame.upload(frame);
d_mog2->apply(d_frame, d_foreground);
}
cv::cuda::GpuMat background;
TEST_CYCLE() d_mog2->getBackgroundImage(background);
CUDA_SANITY_CHECK(background, 1);
}
else
{
cv::Ptr<cv::BackgroundSubtractor> mog2 = cv::createBackgroundSubtractorMOG2();
cv::Mat foreground;
for (int i = 0; i < 10; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
if (cn != 3)
{
cv::Mat temp;
if (cn == 1)
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
else
cv::cvtColor(frame, temp, cv::COLOR_BGR2BGRA);
cv::swap(temp, frame);
}
mog2->apply(frame, foreground);
}
cv::Mat background;
TEST_CYCLE() mog2->getBackgroundImage(background);
CPU_SANITY_CHECK(background);
}
}
#endif
}} // namespace

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
using namespace perf;
CV_PERF_TEST_CUDA_MAIN(cudabgsegm)

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modules/cudabgsegm/perf/perf_precomp.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_PERF_PRECOMP_HPP
#define OPENCV_PERF_PRECOMP_HPP
#include "opencv2/ts.hpp"
#include "opencv2/ts/cuda_perf.hpp"
#include "opencv2/cudabgsegm.hpp"
#include "opencv2/video.hpp"
namespace opencv_test {
using namespace perf;
}
#endif

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modules/cudabgsegm/src/cuda/mog.cu
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@ -1,425 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#if !defined CUDA_DISABLER
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/vec_traits.hpp"
#include "opencv2/core/cuda/vec_math.hpp"
#include "opencv2/core/cuda/limits.hpp"
namespace cv { namespace cuda { namespace device
{
namespace mog
{
///////////////////////////////////////////////////////////////
// Utility
__device__ __forceinline__ float cvt(uchar val)
{
return val;
}
__device__ __forceinline__ float3 cvt(const uchar3& val)
{
return make_float3(val.x, val.y, val.z);
}
__device__ __forceinline__ float4 cvt(const uchar4& val)
{
return make_float4(val.x, val.y, val.z, val.w);
}
__device__ __forceinline__ float sqr(float val)
{
return val * val;
}
__device__ __forceinline__ float sqr(const float3& val)
{
return val.x * val.x + val.y * val.y + val.z * val.z;
}
__device__ __forceinline__ float sqr(const float4& val)
{
return val.x * val.x + val.y * val.y + val.z * val.z;
}
__device__ __forceinline__ float sum(float val)
{
return val;
}
__device__ __forceinline__ float sum(const float3& val)
{
return val.x + val.y + val.z;
}
__device__ __forceinline__ float sum(const float4& val)
{
return val.x + val.y + val.z;
}
__device__ __forceinline__ float clamp(float var, float learningRate, float diff, float minVar)
{
return ::fmaxf(var + learningRate * (diff * diff - var), minVar);
}
__device__ __forceinline__ float3 clamp(const float3& var, float learningRate, const float3& diff, float minVar)
{
return make_float3(::fmaxf(var.x + learningRate * (diff.x * diff.x - var.x), minVar),
::fmaxf(var.y + learningRate * (diff.y * diff.y - var.y), minVar),
::fmaxf(var.z + learningRate * (diff.z * diff.z - var.z), minVar));
}
__device__ __forceinline__ float4 clamp(const float4& var, float learningRate, const float4& diff, float minVar)
{
return make_float4(::fmaxf(var.x + learningRate * (diff.x * diff.x - var.x), minVar),
::fmaxf(var.y + learningRate * (diff.y * diff.y - var.y), minVar),
::fmaxf(var.z + learningRate * (diff.z * diff.z - var.z), minVar),
0.0f);
}
///////////////////////////////////////////////////////////////
// MOG without learning
template <typename SrcT, typename WorkT>
__global__ void mog_withoutLearning(const PtrStepSz<SrcT> frame, PtrStepb fgmask,
const PtrStepf gmm_weight, const PtrStep<WorkT> gmm_mean, const PtrStep<WorkT> gmm_var,
const int nmixtures, const float varThreshold, const float backgroundRatio)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= frame.cols || y >= frame.rows)
return;
WorkT pix = cvt(frame(y, x));
int kHit = -1;
int kForeground = -1;
for (int k = 0; k < nmixtures; ++k)
{
if (gmm_weight(k * frame.rows + y, x) < numeric_limits<float>::epsilon())
break;
WorkT mu = gmm_mean(k * frame.rows + y, x);
WorkT var = gmm_var(k * frame.rows + y, x);
WorkT diff = pix - mu;
if (sqr(diff) < varThreshold * sum(var))
{
kHit = k;
break;
}
}
if (kHit >= 0)
{
float wsum = 0.0f;
for (int k = 0; k < nmixtures; ++k)
{
wsum += gmm_weight(k * frame.rows + y, x);
if (wsum > backgroundRatio)
{
kForeground = k + 1;
break;
}
}
}
fgmask(y, x) = (uchar) (-(kHit < 0 || kHit >= kForeground));
}
template <typename SrcT, typename WorkT>
void mog_withoutLearning_caller(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb var,
int nmixtures, float varThreshold, float backgroundRatio, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(frame.cols, block.x), divUp(frame.rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(mog_withoutLearning<SrcT, WorkT>, cudaFuncCachePreferL1) );
mog_withoutLearning<SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask,
weight, (PtrStepSz<WorkT>) mean, (PtrStepSz<WorkT>) var,
nmixtures, varThreshold, backgroundRatio);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
///////////////////////////////////////////////////////////////
// MOG with learning
template <typename SrcT, typename WorkT>
__global__ void mog_withLearning(const PtrStepSz<SrcT> frame, PtrStepb fgmask,
PtrStepf gmm_weight, PtrStepf gmm_sortKey, PtrStep<WorkT> gmm_mean, PtrStep<WorkT> gmm_var,
const int nmixtures, const float varThreshold, const float backgroundRatio, const float learningRate, const float minVar)
{
const float w0 = 0.05f;
const float sk0 = w0 / (30.0f * 0.5f * 2.0f);
const float var0 = 30.0f * 0.5f * 30.0f * 0.5f * 4.0f;
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= frame.cols || y >= frame.rows)
return;
WorkT pix = cvt(frame(y, x));
float wsum = 0.0f;
int kHit = -1;
int kForeground = -1;
int k = 0;
for (; k < nmixtures; ++k)
{
float w = gmm_weight(k * frame.rows + y, x);
wsum += w;
if (w < numeric_limits<float>::epsilon())
break;
WorkT mu = gmm_mean(k * frame.rows + y, x);
WorkT var = gmm_var(k * frame.rows + y, x);
WorkT diff = pix - mu;
if (sqr(diff) < varThreshold * sum(var))
{
wsum -= w;
float dw = learningRate * (1.0f - w);
var = clamp(var, learningRate, diff, minVar);
float sortKey_prev = w / ::sqrtf(sum(var));
gmm_sortKey(k * frame.rows + y, x) = sortKey_prev;
float weight_prev = w + dw;
gmm_weight(k * frame.rows + y, x) = weight_prev;
WorkT mean_prev = mu + learningRate * diff;
gmm_mean(k * frame.rows + y, x) = mean_prev;
WorkT var_prev = var;
gmm_var(k * frame.rows + y, x) = var_prev;
int k1 = k - 1;
if (k1 >= 0)
{
float sortKey_next = gmm_sortKey(k1 * frame.rows + y, x);
float weight_next = gmm_weight(k1 * frame.rows + y, x);
WorkT mean_next = gmm_mean(k1 * frame.rows + y, x);
WorkT var_next = gmm_var(k1 * frame.rows + y, x);
for (; sortKey_next < sortKey_prev && k1 >= 0; --k1)
{
gmm_sortKey(k1 * frame.rows + y, x) = sortKey_prev;
gmm_sortKey((k1 + 1) * frame.rows + y, x) = sortKey_next;
gmm_weight(k1 * frame.rows + y, x) = weight_prev;
gmm_weight((k1 + 1) * frame.rows + y, x) = weight_next;
gmm_mean(k1 * frame.rows + y, x) = mean_prev;
gmm_mean((k1 + 1) * frame.rows + y, x) = mean_next;
gmm_var(k1 * frame.rows + y, x) = var_prev;
gmm_var((k1 + 1) * frame.rows + y, x) = var_next;
sortKey_prev = sortKey_next;
sortKey_next = k1 > 0 ? gmm_sortKey((k1 - 1) * frame.rows + y, x) : 0.0f;
weight_prev = weight_next;
weight_next = k1 > 0 ? gmm_weight((k1 - 1) * frame.rows + y, x) : 0.0f;
mean_prev = mean_next;
mean_next = k1 > 0 ? gmm_mean((k1 - 1) * frame.rows + y, x) : VecTraits<WorkT>::all(0.0f);
var_prev = var_next;
var_next = k1 > 0 ? gmm_var((k1 - 1) * frame.rows + y, x) : VecTraits<WorkT>::all(0.0f);
}
}
kHit = k1 + 1;
break;
}
}
if (kHit < 0)
{
// no appropriate gaussian mixture found at all, remove the weakest mixture and create a new one
kHit = k = ::min(k, nmixtures - 1);
wsum += w0 - gmm_weight(k * frame.rows + y, x);
gmm_weight(k * frame.rows + y, x) = w0;
gmm_mean(k * frame.rows + y, x) = pix;
gmm_var(k * frame.rows + y, x) = VecTraits<WorkT>::all(var0);
gmm_sortKey(k * frame.rows + y, x) = sk0;
}
else
{
for( ; k < nmixtures; k++)
wsum += gmm_weight(k * frame.rows + y, x);
}
float wscale = 1.0f / wsum;
wsum = 0;
for (k = 0; k < nmixtures; ++k)
{
float w = gmm_weight(k * frame.rows + y, x);
wsum += w *= wscale;
gmm_weight(k * frame.rows + y, x) = w;
gmm_sortKey(k * frame.rows + y, x) *= wscale;
if (wsum > backgroundRatio && kForeground < 0)
kForeground = k + 1;
}
fgmask(y, x) = (uchar)(-(kHit >= kForeground));
}
template <typename SrcT, typename WorkT>
void mog_withLearning_caller(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzf weight, PtrStepSzf sortKey, PtrStepSzb mean, PtrStepSzb var,
int nmixtures, float varThreshold, float backgroundRatio, float learningRate, float minVar,
cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(frame.cols, block.x), divUp(frame.rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(mog_withLearning<SrcT, WorkT>, cudaFuncCachePreferL1) );
mog_withLearning<SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask,
weight, sortKey, (PtrStepSz<WorkT>) mean, (PtrStepSz<WorkT>) var,
nmixtures, varThreshold, backgroundRatio, learningRate, minVar);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
///////////////////////////////////////////////////////////////
// MOG
void mog_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzf weight, PtrStepSzf sortKey, PtrStepSzb mean, PtrStepSzb var, int nmixtures, float varThreshold, float learningRate, float backgroundRatio, float noiseSigma, cudaStream_t stream)
{
typedef void (*withoutLearning_t)(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb var, int nmixtures, float varThreshold, float backgroundRatio, cudaStream_t stream);
typedef void (*withLearning_t)(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzf weight, PtrStepSzf sortKey, PtrStepSzb mean, PtrStepSzb var, int nmixtures, float varThreshold, float backgroundRatio, float learningRate, float minVar, cudaStream_t stream);
static const withoutLearning_t withoutLearning[] =
{
0, mog_withoutLearning_caller<uchar, float>, 0, mog_withoutLearning_caller<uchar3, float3>, mog_withoutLearning_caller<uchar4, float4>
};
static const withLearning_t withLearning[] =
{
0, mog_withLearning_caller<uchar, float>, 0, mog_withLearning_caller<uchar3, float3>, mog_withLearning_caller<uchar4, float4>
};
const float minVar = noiseSigma * noiseSigma;
if (learningRate > 0.0f)
withLearning[cn](frame, fgmask, weight, sortKey, mean, var, nmixtures, varThreshold, backgroundRatio, learningRate, minVar, stream);
else
withoutLearning[cn](frame, fgmask, weight, mean, var, nmixtures, varThreshold, backgroundRatio, stream);
}
template <typename WorkT, typename OutT>
__global__ void getBackgroundImage(const PtrStepf gmm_weight, const PtrStep<WorkT> gmm_mean, PtrStepSz<OutT> dst, const int nmixtures, const float backgroundRatio)
{
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;
WorkT meanVal = VecTraits<WorkT>::all(0.0f);
float totalWeight = 0.0f;
for (int mode = 0; mode < nmixtures; ++mode)
{
float weight = gmm_weight(mode * dst.rows + y, x);
WorkT mean = gmm_mean(mode * dst.rows + y, x);
meanVal = meanVal + weight * mean;
totalWeight += weight;
if(totalWeight > backgroundRatio)
break;
}
meanVal = meanVal * (1.f / totalWeight);
dst(y, x) = saturate_cast<OutT>(meanVal);
}
template <typename WorkT, typename OutT>
void getBackgroundImage_caller(PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, int nmixtures, float backgroundRatio, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(getBackgroundImage<WorkT, OutT>, cudaFuncCachePreferL1) );
getBackgroundImage<WorkT, OutT><<<grid, block, 0, stream>>>(weight, (PtrStepSz<WorkT>) mean, (PtrStepSz<OutT>) dst, nmixtures, backgroundRatio);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void getBackgroundImage_gpu(int cn, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, int nmixtures, float backgroundRatio, cudaStream_t stream)
{
typedef void (*func_t)(PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, int nmixtures, float backgroundRatio, cudaStream_t stream);
static const func_t funcs[] =
{
0, getBackgroundImage_caller<float, uchar>, 0, getBackgroundImage_caller<float3, uchar3>, getBackgroundImage_caller<float4, uchar4>
};
funcs[cn](weight, mean, dst, nmixtures, backgroundRatio, stream);
}
}
}}}
#endif /* CUDA_DISABLER */

439
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@ -1,439 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#if !defined CUDA_DISABLER
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/vec_traits.hpp"
#include "opencv2/core/cuda/vec_math.hpp"
#include "opencv2/core/cuda/limits.hpp"
namespace cv { namespace cuda { namespace device
{
namespace mog2
{
///////////////////////////////////////////////////////////////
// Utility
__device__ __forceinline__ float cvt(uchar val)
{
return val;
}
__device__ __forceinline__ float3 cvt(const uchar3& val)
{
return make_float3(val.x, val.y, val.z);
}
__device__ __forceinline__ float4 cvt(const uchar4& val)
{
return make_float4(val.x, val.y, val.z, val.w);
}
__device__ __forceinline__ float sqr(float val)
{
return val * val;
}
__device__ __forceinline__ float sqr(const float3& val)
{
return val.x * val.x + val.y * val.y + val.z * val.z;
}
__device__ __forceinline__ float sqr(const float4& val)
{
return val.x * val.x + val.y * val.y + val.z * val.z;
}
__device__ __forceinline__ float sum(float val)
{
return val;
}
__device__ __forceinline__ float sum(const float3& val)
{
return val.x + val.y + val.z;
}
__device__ __forceinline__ float sum(const float4& val)
{
return val.x + val.y + val.z;
}
template <class Ptr2D>
__device__ __forceinline__ void swap(Ptr2D& ptr, int x, int y, int k, int rows)
{
typename Ptr2D::elem_type val = ptr(k * rows + y, x);
ptr(k * rows + y, x) = ptr((k + 1) * rows + y, x);
ptr((k + 1) * rows + y, x) = val;
}
///////////////////////////////////////////////////////////////
// MOG2
__constant__ int c_nmixtures;
__constant__ float c_Tb;
__constant__ float c_TB;
__constant__ float c_Tg;
__constant__ float c_varInit;
__constant__ float c_varMin;
__constant__ float c_varMax;
__constant__ float c_tau;
__constant__ unsigned char c_shadowVal;
void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal)
{
varMin = ::fminf(varMin, varMax);
varMax = ::fmaxf(varMin, varMax);
cudaSafeCall( cudaMemcpyToSymbol(c_nmixtures, &nmixtures, sizeof(int)) );
cudaSafeCall( cudaMemcpyToSymbol(c_Tb, &Tb, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_TB, &TB, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_Tg, &Tg, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_varInit, &varInit, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_varMin, &varMin, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_varMax, &varMax, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_tau, &tau, sizeof(float)) );
cudaSafeCall( cudaMemcpyToSymbol(c_shadowVal, &shadowVal, sizeof(unsigned char)) );
}
template <bool detectShadows, typename SrcT, typename WorkT>
__global__ void mog2(const PtrStepSz<SrcT> frame, PtrStepb fgmask, PtrStepb modesUsed,
PtrStepf gmm_weight, PtrStepf gmm_variance, PtrStep<WorkT> gmm_mean,
const float alphaT, const float alpha1, const float prune)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= frame.cols || y >= frame.rows)
return;
WorkT pix = cvt(frame(y, x));
//calculate distances to the modes (+ sort)
//here we need to go in descending order!!!
bool background = false; // true - the pixel classified as background
//internal:
bool fitsPDF = false; //if it remains zero a new GMM mode will be added
int nmodes = modesUsed(y, x);
int nNewModes = nmodes; //current number of modes in GMM
float totalWeight = 0.0f;
//go through all modes
for (int mode = 0; mode < nmodes; ++mode)
{
//need only weight if fit is found
float weight = alpha1 * gmm_weight(mode * frame.rows + y, x) + prune;
int swap_count = 0;
//fit not found yet
if (!fitsPDF)
{
//check if it belongs to some of the remaining modes
float var = gmm_variance(mode * frame.rows + y, x);
WorkT mean = gmm_mean(mode * frame.rows + y, x);
//calculate difference and distance
WorkT diff = mean - pix;
float dist2 = sqr(diff);
//background? - Tb - usually larger than Tg
if (totalWeight < c_TB && dist2 < c_Tb * var)
background = true;
//check fit
if (dist2 < c_Tg * var)
{
//belongs to the mode
fitsPDF = true;
//update distribution
//update weight
weight += alphaT;
float k = alphaT / weight;
//update mean
gmm_mean(mode * frame.rows + y, x) = mean - k * diff;
//update variance
float varnew = var + k * (dist2 - var);
//limit the variance
varnew = ::fmaxf(varnew, c_varMin);
varnew = ::fminf(varnew, c_varMax);
gmm_variance(mode * frame.rows + y, x) = varnew;
//sort
//all other weights are at the same place and
//only the matched (iModes) is higher -> just find the new place for it
for (int i = mode; i > 0; --i)
{
//check one up
if (weight < gmm_weight((i - 1) * frame.rows + y, x))
break;
swap_count++;
//swap one up
swap(gmm_weight, x, y, i - 1, frame.rows);
swap(gmm_variance, x, y, i - 1, frame.rows);
swap(gmm_mean, x, y, i - 1, frame.rows);
}
//belongs to the mode - bFitsPDF becomes 1
}
} // !fitsPDF
//check prune
if (weight < -prune)
{
weight = 0.0f;
nmodes--;
}
gmm_weight((mode - swap_count) * frame.rows + y, x) = weight; //update weight by the calculated value
totalWeight += weight;
}
//renormalize weights
totalWeight = 1.f / totalWeight;
for (int mode = 0; mode < nmodes; ++mode)
gmm_weight(mode * frame.rows + y, x) *= totalWeight;
nmodes = nNewModes;
//make new mode if needed and exit
if (!fitsPDF)
{
// replace the weakest or add a new one
int mode = nmodes == c_nmixtures ? c_nmixtures - 1 : nmodes++;
if (nmodes == 1)
gmm_weight(mode * frame.rows + y, x) = 1.f;
else
{
gmm_weight(mode * frame.rows + y, x) = alphaT;
// renormalize all other weights
for (int i = 0; i < nmodes - 1; ++i)
gmm_weight(i * frame.rows + y, x) *= alpha1;
}
// init
gmm_mean(mode * frame.rows + y, x) = pix;
gmm_variance(mode * frame.rows + y, x) = c_varInit;
//sort
//find the new place for it
for (int i = nmodes - 1; i > 0; --i)
{
// check one up
if (alphaT < gmm_weight((i - 1) * frame.rows + y, x))
break;
//swap one up
swap(gmm_weight, x, y, i - 1, frame.rows);
swap(gmm_variance, x, y, i - 1, frame.rows);
swap(gmm_mean, x, y, i - 1, frame.rows);
}
}
//set the number of modes
modesUsed(y, x) = nmodes;
bool isShadow = false;
if (detectShadows && !background)
{
float tWeight = 0.0f;
// check all the components marked as background:
for (int mode = 0; mode < nmodes; ++mode)
{
WorkT mean = gmm_mean(mode * frame.rows + y, x);
WorkT pix_mean = pix * mean;
float numerator = sum(pix_mean);
float denominator = sqr(mean);
// no division by zero allowed
if (denominator == 0)
break;
// if tau < a < 1 then also check the color distortion
if (numerator <= denominator && numerator >= c_tau * denominator)
{
float a = numerator / denominator;
WorkT dD = a * mean - pix;
if (sqr(dD) < c_Tb * gmm_variance(mode * frame.rows + y, x) * a * a)
{
isShadow = true;
break;
}
};
tWeight += gmm_weight(mode * frame.rows + y, x);
if (tWeight > c_TB)
break;
}
}
fgmask(y, x) = background ? 0 : isShadow ? c_shadowVal : 255;
}
template <typename SrcT, typename WorkT>
void mog2_caller(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean,
float alphaT, float prune, bool detectShadows, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(frame.cols, block.x), divUp(frame.rows, block.y));
const float alpha1 = 1.0f - alphaT;
if (detectShadows)
{
cudaSafeCall( cudaFuncSetCacheConfig(mog2<true, SrcT, WorkT>, cudaFuncCachePreferL1) );
mog2<true, SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, modesUsed,
weight, variance, (PtrStepSz<WorkT>) mean,
alphaT, alpha1, prune);
}
else
{
cudaSafeCall( cudaFuncSetCacheConfig(mog2<false, SrcT, WorkT>, cudaFuncCachePreferL1) );
mog2<false, SrcT, WorkT><<<grid, block, 0, stream>>>((PtrStepSz<SrcT>) frame, fgmask, modesUsed,
weight, variance, (PtrStepSz<WorkT>) mean,
alphaT, alpha1, prune);
}
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void mog2_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean,
float alphaT, float prune, bool detectShadows, cudaStream_t stream)
{
typedef void (*func_t)(PtrStepSzb frame, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
static const func_t funcs[] =
{
0, mog2_caller<uchar, float>, 0, mog2_caller<uchar3, float3>, mog2_caller<uchar4, float4>
};
funcs[cn](frame, fgmask, modesUsed, weight, variance, mean, alphaT, prune, detectShadows, stream);
}
template <typename WorkT, typename OutT>
__global__ void getBackgroundImage2(const PtrStepSzb modesUsed, const PtrStepf gmm_weight, const PtrStep<WorkT> gmm_mean, PtrStep<OutT> dst)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= modesUsed.cols || y >= modesUsed.rows)
return;
int nmodes = modesUsed(y, x);
WorkT meanVal = VecTraits<WorkT>::all(0.0f);
float totalWeight = 0.0f;
for (int mode = 0; mode < nmodes; ++mode)
{
float weight = gmm_weight(mode * modesUsed.rows + y, x);
WorkT mean = gmm_mean(mode * modesUsed.rows + y, x);
meanVal = meanVal + weight * mean;
totalWeight += weight;
if(totalWeight > c_TB)
break;
}
meanVal = meanVal * (1.f / totalWeight);
dst(y, x) = saturate_cast<OutT>(meanVal);
}
template <typename WorkT, typename OutT>
void getBackgroundImage2_caller(PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream)
{
dim3 block(32, 8);
dim3 grid(divUp(modesUsed.cols, block.x), divUp(modesUsed.rows, block.y));
cudaSafeCall( cudaFuncSetCacheConfig(getBackgroundImage2<WorkT, OutT>, cudaFuncCachePreferL1) );
getBackgroundImage2<WorkT, OutT><<<grid, block, 0, stream>>>(modesUsed, weight, (PtrStepSz<WorkT>) mean, (PtrStepSz<OutT>) dst);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream)
{
typedef void (*func_t)(PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[] =
{
0, getBackgroundImage2_caller<float, uchar>, 0, getBackgroundImage2_caller<float3, uchar3>, getBackgroundImage2_caller<float4, uchar4>
};
funcs[cn](modesUsed, weight, mean, dst, stream);
}
}
}}}
#endif /* CUDA_DISABLER */

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@ -1,209 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::cuda;
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
Ptr<cuda::BackgroundSubtractorMOG> cv::cuda::createBackgroundSubtractorMOG(int, int, double, double) { throw_no_cuda(); return Ptr<cuda::BackgroundSubtractorMOG>(); }
#else
namespace cv { namespace cuda { namespace device
{
namespace mog
{
void mog_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzf weight, PtrStepSzf sortKey, PtrStepSzb mean, PtrStepSzb var,
int nmixtures, float varThreshold, float learningRate, float backgroundRatio, float noiseSigma,
cudaStream_t stream);
void getBackgroundImage_gpu(int cn, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, int nmixtures, float backgroundRatio, cudaStream_t stream);
}
}}}
namespace
{
const int defaultNMixtures = 5;
const int defaultHistory = 200;
const float defaultBackgroundRatio = 0.7f;
const float defaultVarThreshold = 2.5f * 2.5f;
const float defaultNoiseSigma = 30.0f * 0.5f;
const float defaultInitialWeight = 0.05f;
class MOGImpl CV_FINAL : public cuda::BackgroundSubtractorMOG
{
public:
MOGImpl(int history, int nmixtures, double backgroundRatio, double noiseSigma);
void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE;
void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) CV_OVERRIDE;
void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE;
void getBackgroundImage(OutputArray backgroundImage, Stream& stream) const CV_OVERRIDE;
int getHistory() const CV_OVERRIDE { return history_; }
void setHistory(int nframes) CV_OVERRIDE { history_ = nframes; }
int getNMixtures() const CV_OVERRIDE { return nmixtures_; }
void setNMixtures(int nmix) CV_OVERRIDE { nmixtures_ = nmix; }
double getBackgroundRatio() const CV_OVERRIDE { return backgroundRatio_; }
void setBackgroundRatio(double backgroundRatio) CV_OVERRIDE { backgroundRatio_ = (float) backgroundRatio; }
double getNoiseSigma() const CV_OVERRIDE { return noiseSigma_; }
void setNoiseSigma(double noiseSigma) CV_OVERRIDE { noiseSigma_ = (float) noiseSigma; }
private:
//! re-initiaization method
void initialize(Size frameSize, int frameType);
int history_;
int nmixtures_;
float backgroundRatio_;
float noiseSigma_;
float varThreshold_;
Size frameSize_;
int frameType_;
int nframes_;
GpuMat weight_;
GpuMat sortKey_;
GpuMat mean_;
GpuMat var_;
};
MOGImpl::MOGImpl(int history, int nmixtures, double backgroundRatio, double noiseSigma) :
frameSize_(0, 0), frameType_(0), nframes_(0)
{
history_ = history > 0 ? history : defaultHistory;
nmixtures_ = std::min(nmixtures > 0 ? nmixtures : defaultNMixtures, 8);
backgroundRatio_ = backgroundRatio > 0 ? (float) backgroundRatio : defaultBackgroundRatio;
noiseSigma_ = noiseSigma > 0 ? (float) noiseSigma : defaultNoiseSigma;
varThreshold_ = defaultVarThreshold;
}
void MOGImpl::apply(InputArray image, OutputArray fgmask, double learningRate)
{
apply(image, fgmask, learningRate, Stream::Null());
}
void MOGImpl::apply(InputArray _frame, OutputArray _fgmask, double learningRate, Stream& stream)
{
using namespace cv::cuda::device::mog;
GpuMat frame = _frame.getGpuMat();
CV_Assert( frame.depth() == CV_8U );
int ch = frame.channels();
int work_ch = ch;
if (nframes_ == 0 || learningRate >= 1.0 || frame.size() != frameSize_ || work_ch != mean_.channels())
initialize(frame.size(), frame.type());
_fgmask.create(frameSize_, CV_8UC1);
GpuMat fgmask = _fgmask.getGpuMat();
++nframes_;
learningRate = learningRate >= 0 && nframes_ > 1 ? learningRate : 1.0 / std::min(nframes_, history_);
CV_Assert( learningRate >= 0 );
mog_gpu(frame, ch, fgmask, weight_, sortKey_, mean_, var_, nmixtures_,
varThreshold_, (float) learningRate, backgroundRatio_, noiseSigma_,
StreamAccessor::getStream(stream));
}
void MOGImpl::getBackgroundImage(OutputArray backgroundImage) const
{
getBackgroundImage(backgroundImage, Stream::Null());
}
void MOGImpl::getBackgroundImage(OutputArray _backgroundImage, Stream& stream) const
{
using namespace cv::cuda::device::mog;
_backgroundImage.create(frameSize_, frameType_);
GpuMat backgroundImage = _backgroundImage.getGpuMat();
getBackgroundImage_gpu(backgroundImage.channels(), weight_, mean_, backgroundImage, nmixtures_, backgroundRatio_, StreamAccessor::getStream(stream));
}
void MOGImpl::initialize(Size frameSize, int frameType)
{
CV_Assert( frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4 );
frameSize_ = frameSize;
frameType_ = frameType;
int ch = CV_MAT_CN(frameType);
int work_ch = ch;
// for each gaussian mixture of each pixel bg model we store
// the mixture sort key (w/sum_of_variances), the mixture weight (w),
// the mean (nchannels values) and
// the diagonal covariance matrix (another nchannels values)
weight_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
sortKey_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
mean_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));
var_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));
weight_.setTo(cv::Scalar::all(0));
sortKey_.setTo(cv::Scalar::all(0));
mean_.setTo(cv::Scalar::all(0));
var_.setTo(cv::Scalar::all(0));
nframes_ = 0;
}
}
Ptr<cuda::BackgroundSubtractorMOG> cv::cuda::createBackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma)
{
return makePtr<MOGImpl>(history, nmixtures, backgroundRatio, noiseSigma);
}
#endif

253
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@ -1,253 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::cuda;
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
Ptr<cuda::BackgroundSubtractorMOG2> cv::cuda::createBackgroundSubtractorMOG2(int, double, bool) { throw_no_cuda(); return Ptr<cuda::BackgroundSubtractorMOG2>(); }
#else
namespace cv { namespace cuda { namespace device
{
namespace mog2
{
void loadConstants(int nmixtures, float Tb, float TB, float Tg, float varInit, float varMin, float varMax, float tau, unsigned char shadowVal);
void mog2_gpu(PtrStepSzb frame, int cn, PtrStepSzb fgmask, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzf variance, PtrStepSzb mean, float alphaT, float prune, bool detectShadows, cudaStream_t stream);
void getBackgroundImage2_gpu(int cn, PtrStepSzb modesUsed, PtrStepSzf weight, PtrStepSzb mean, PtrStepSzb dst, cudaStream_t stream);
}
}}}
namespace
{
// default parameters of gaussian background detection algorithm
const int defaultHistory = 500; // Learning rate; alpha = 1/defaultHistory2
const float defaultVarThreshold = 4.0f * 4.0f;
const int defaultNMixtures = 5; // maximal number of Gaussians in mixture
const float defaultBackgroundRatio = 0.9f; // threshold sum of weights for background test
const float defaultVarThresholdGen = 3.0f * 3.0f;
const float defaultVarInit = 15.0f; // initial variance for new components
const float defaultVarMax = 5.0f * defaultVarInit;
const float defaultVarMin = 4.0f;
// additional parameters
const float defaultCT = 0.05f; // complexity reduction prior constant 0 - no reduction of number of components
const unsigned char defaultShadowValue = 127; // value to use in the segmentation mask for shadows, set 0 not to do shadow detection
const float defaultShadowThreshold = 0.5f; // Tau - shadow threshold, see the paper for explanation
class MOG2Impl CV_FINAL : public cuda::BackgroundSubtractorMOG2
{
public:
MOG2Impl(int history, double varThreshold, bool detectShadows);
void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE;
void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) CV_OVERRIDE;
void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE;
void getBackgroundImage(OutputArray backgroundImage, Stream& stream) const CV_OVERRIDE;
int getHistory() const CV_OVERRIDE { return history_; }
void setHistory(int history) CV_OVERRIDE { history_ = history; }
int getNMixtures() const CV_OVERRIDE { return nmixtures_; }
void setNMixtures(int nmixtures) CV_OVERRIDE { nmixtures_ = nmixtures; }
double getBackgroundRatio() const CV_OVERRIDE { return backgroundRatio_; }
void setBackgroundRatio(double ratio) CV_OVERRIDE { backgroundRatio_ = (float) ratio; }
double getVarThreshold() const CV_OVERRIDE { return varThreshold_; }
void setVarThreshold(double varThreshold) CV_OVERRIDE { varThreshold_ = (float) varThreshold; }
double getVarThresholdGen() const CV_OVERRIDE { return varThresholdGen_; }
void setVarThresholdGen(double varThresholdGen) CV_OVERRIDE { varThresholdGen_ = (float) varThresholdGen; }
double getVarInit() const CV_OVERRIDE { return varInit_; }
void setVarInit(double varInit) CV_OVERRIDE { varInit_ = (float) varInit; }
double getVarMin() const CV_OVERRIDE { return varMin_; }
void setVarMin(double varMin) CV_OVERRIDE { varMin_ = (float) varMin; }
double getVarMax() const CV_OVERRIDE { return varMax_; }
void setVarMax(double varMax) CV_OVERRIDE { varMax_ = (float) varMax; }
double getComplexityReductionThreshold() const CV_OVERRIDE { return ct_; }
void setComplexityReductionThreshold(double ct) CV_OVERRIDE { ct_ = (float) ct; }
bool getDetectShadows() const CV_OVERRIDE { return detectShadows_; }
void setDetectShadows(bool detectShadows) CV_OVERRIDE { detectShadows_ = detectShadows; }
int getShadowValue() const CV_OVERRIDE { return shadowValue_; }
void setShadowValue(int value) CV_OVERRIDE { shadowValue_ = (uchar) value; }
double getShadowThreshold() const CV_OVERRIDE { return shadowThreshold_; }
void setShadowThreshold(double threshold) CV_OVERRIDE { shadowThreshold_ = (float) threshold; }
private:
void initialize(Size frameSize, int frameType);
int history_;
int nmixtures_;
float backgroundRatio_;
float varThreshold_;
float varThresholdGen_;
float varInit_;
float varMin_;
float varMax_;
float ct_;
bool detectShadows_;
uchar shadowValue_;
float shadowThreshold_;
Size frameSize_;
int frameType_;
int nframes_;
GpuMat weight_;
GpuMat variance_;
GpuMat mean_;
//keep track of number of modes per pixel
GpuMat bgmodelUsedModes_;
};
MOG2Impl::MOG2Impl(int history, double varThreshold, bool detectShadows) :
frameSize_(0, 0), frameType_(0), nframes_(0)
{
history_ = history > 0 ? history : defaultHistory;
varThreshold_ = varThreshold > 0 ? (float) varThreshold : defaultVarThreshold;
detectShadows_ = detectShadows;
nmixtures_ = defaultNMixtures;
backgroundRatio_ = defaultBackgroundRatio;
varInit_ = defaultVarInit;
varMax_ = defaultVarMax;
varMin_ = defaultVarMin;
varThresholdGen_ = defaultVarThresholdGen;
ct_ = defaultCT;
shadowValue_ = defaultShadowValue;
shadowThreshold_ = defaultShadowThreshold;
}
void MOG2Impl::apply(InputArray image, OutputArray fgmask, double learningRate)
{
apply(image, fgmask, learningRate, Stream::Null());
}
void MOG2Impl::apply(InputArray _frame, OutputArray _fgmask, double learningRate, Stream& stream)
{
using namespace cv::cuda::device::mog2;
GpuMat frame = _frame.getGpuMat();
int ch = frame.channels();
int work_ch = ch;
if (nframes_ == 0 || learningRate >= 1.0 || frame.size() != frameSize_ || work_ch != mean_.channels())
initialize(frame.size(), frame.type());
_fgmask.create(frameSize_, CV_8UC1);
GpuMat fgmask = _fgmask.getGpuMat();
fgmask.setTo(Scalar::all(0), stream);
++nframes_;
learningRate = learningRate >= 0 && nframes_ > 1 ? learningRate : 1.0 / std::min(2 * nframes_, history_);
CV_Assert( learningRate >= 0 );
mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_,
(float) learningRate, static_cast<float>(-learningRate * ct_), detectShadows_, StreamAccessor::getStream(stream));
}
void MOG2Impl::getBackgroundImage(OutputArray backgroundImage) const
{
getBackgroundImage(backgroundImage, Stream::Null());
}
void MOG2Impl::getBackgroundImage(OutputArray _backgroundImage, Stream& stream) const
{
using namespace cv::cuda::device::mog2;
_backgroundImage.create(frameSize_, frameType_);
GpuMat backgroundImage = _backgroundImage.getGpuMat();
getBackgroundImage2_gpu(backgroundImage.channels(), bgmodelUsedModes_, weight_, mean_, backgroundImage, StreamAccessor::getStream(stream));
}
void MOG2Impl::initialize(cv::Size frameSize, int frameType)
{
using namespace cv::cuda::device::mog2;
CV_Assert( frameType == CV_8UC1 || frameType == CV_8UC3 || frameType == CV_8UC4 );
frameSize_ = frameSize;
frameType_ = frameType;
nframes_ = 0;
int ch = CV_MAT_CN(frameType);
int work_ch = ch;
// for each gaussian mixture of each pixel bg model we store ...
// the mixture weight (w),
// the mean (nchannels values) and
// the covariance
weight_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
variance_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC1);
mean_.create(frameSize.height * nmixtures_, frameSize_.width, CV_32FC(work_ch));
//make the array for keeping track of the used modes per pixel - all zeros at start
bgmodelUsedModes_.create(frameSize_, CV_8UC1);
bgmodelUsedModes_.setTo(Scalar::all(0));
loadConstants(nmixtures_, varThreshold_, backgroundRatio_, varThresholdGen_, varInit_, varMin_, varMax_, shadowThreshold_, shadowValue_);
}
}
Ptr<cuda::BackgroundSubtractorMOG2> cv::cuda::createBackgroundSubtractorMOG2(int history, double varThreshold, bool detectShadows)
{
return makePtr<MOG2Impl>(history, varThreshold, detectShadows);
}
#endif

54
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_PRECOMP_H
#define OPENCV_PRECOMP_H
#include <limits>
#include "opencv2/cudabgsegm.hpp"
#include "opencv2/core/private.cuda.hpp"
#include "opencv2/opencv_modules.hpp"
#endif /* OPENCV_PRECOMP_H */

171
modules/cudabgsegm/test/test_bgsegm.cpp
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@ -1,171 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace opencv_test { namespace {
//////////////////////////////////////////////////////
// MOG2
#ifdef HAVE_VIDEO_INPUT
namespace
{
IMPLEMENT_PARAM_CLASS(UseGray, bool)
IMPLEMENT_PARAM_CLASS(DetectShadow, bool)
}
PARAM_TEST_CASE(MOG2, cv::cuda::DeviceInfo, std::string, UseGray, DetectShadow, UseRoi)
{
cv::cuda::DeviceInfo devInfo;
std::string inputFile;
bool useGray;
bool detectShadow;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);
useGray = GET_PARAM(2);
detectShadow = GET_PARAM(3);
useRoi = GET_PARAM(4);
}
};
CUDA_TEST_P(MOG2, Update)
{
cv::VideoCapture cap(inputFile);
ASSERT_TRUE(cap.isOpened());
cv::Mat frame;
cap >> frame;
ASSERT_FALSE(frame.empty());
cv::Ptr<cv::BackgroundSubtractorMOG2> mog2 = cv::cuda::createBackgroundSubtractorMOG2();
mog2->setDetectShadows(detectShadow);
cv::cuda::GpuMat foreground = createMat(frame.size(), CV_8UC1, useRoi);
cv::Ptr<cv::BackgroundSubtractorMOG2> mog2_gold = cv::createBackgroundSubtractorMOG2();
mog2_gold->setDetectShadows(detectShadow);
cv::Mat foreground_gold;
for (int i = 0; i < 10; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
if (useGray)
{
cv::Mat temp;
cv::cvtColor(frame, temp, cv::COLOR_BGR2GRAY);
cv::swap(temp, frame);
}
mog2->apply(loadMat(frame, useRoi), foreground);
mog2_gold->apply(frame, foreground_gold);
if (detectShadow)
{
ASSERT_MAT_SIMILAR(foreground_gold, foreground, 1e-2);
}
else
{
ASSERT_MAT_NEAR(foreground_gold, foreground, 0);
}
}
}
CUDA_TEST_P(MOG2, getBackgroundImage)
{
if (useGray)
return;
cv::VideoCapture cap(inputFile);
ASSERT_TRUE(cap.isOpened());
cv::Mat frame;
cv::Ptr<cv::BackgroundSubtractorMOG2> mog2 = cv::cuda::createBackgroundSubtractorMOG2();
mog2->setDetectShadows(detectShadow);
cv::cuda::GpuMat foreground;
cv::Ptr<cv::BackgroundSubtractorMOG2> mog2_gold = cv::createBackgroundSubtractorMOG2();
mog2_gold->setDetectShadows(detectShadow);
cv::Mat foreground_gold;
for (int i = 0; i < 10; ++i)
{
cap >> frame;
ASSERT_FALSE(frame.empty());
mog2->apply(loadMat(frame, useRoi), foreground);
mog2_gold->apply(frame, foreground_gold);
}
cv::cuda::GpuMat background = createMat(frame.size(), frame.type(), useRoi);
mog2->getBackgroundImage(background);
cv::Mat background_gold;
mog2_gold->getBackgroundImage(background_gold);
ASSERT_MAT_NEAR(background_gold, background, 1);
}
INSTANTIATE_TEST_CASE_P(CUDA_BgSegm, MOG2, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("768x576.avi")),
testing::Values(UseGray(true), UseGray(false)),
testing::Values(DetectShadow(true), DetectShadow(false)),
WHOLE_SUBMAT));
#endif
}} // namespace
#endif // HAVE_CUDA

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modules/cudabgsegm/test/test_main.cpp
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@ -1,45 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
CV_CUDA_TEST_MAIN("gpu")

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modules/cudabgsegm/test/test_precomp.hpp
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@ -1,54 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_TEST_PRECOMP_HPP
#define OPENCV_TEST_PRECOMP_HPP
#include "opencv2/ts.hpp"
#include "opencv2/ts/cuda_test.hpp"
#include "opencv2/cudabgsegm.hpp"
#include "opencv2/video.hpp"
#include "opencv2/opencv_modules.hpp"
#include "cvconfig.h"
#endif

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if(IOS OR APPLE OR WINRT OR (NOT HAVE_CUDA AND NOT BUILD_CUDA_STUBS))
ocv_module_disable(cudacodec)
endif()
set(the_description "CUDA-accelerated Video Encoding/Decoding")
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4127 /wd4324 /wd4512 -Wundef -Wshadow)
ocv_add_module(cudacodec opencv_core opencv_videoio OPTIONAL opencv_cudev WRAP python)
ocv_module_include_directories()
ocv_glob_module_sources()
set(extra_libs "")
if(HAVE_NVCUVID)
list(APPEND extra_libs ${CUDA_CUDA_LIBRARY} ${CUDA_nvcuvid_LIBRARY})
endif()
if(HAVE_NVCUVENC)
if(WIN32)
list(APPEND extra_libs ${CUDA_nvcuvenc_LIBRARY})
endif()
endif()
ocv_create_module(${extra_libs})
ocv_add_accuracy_tests()
ocv_add_perf_tests()

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CUDACODEC_HPP
#define OPENCV_CUDACODEC_HPP
#ifndef __cplusplus
# error cudacodec.hpp header must be compiled as C++
#endif
#include "opencv2/core/cuda.hpp"
/**
@addtogroup cuda
@{
@defgroup cudacodec Video Encoding/Decoding
@}
*/
namespace cv { namespace cudacodec {
//! @addtogroup cudacodec
//! @{
////////////////////////////////// Video Encoding //////////////////////////////////
// Works only under Windows.
// Supports only H264 video codec and AVI files.
enum SurfaceFormat
{
SF_UYVY = 0,
SF_YUY2,
SF_YV12,
SF_NV12,
SF_IYUV,
SF_BGR,
SF_GRAY = SF_BGR
};
/** @brief Different parameters for CUDA video encoder.
*/
struct CV_EXPORTS_W EncoderParams
{
int P_Interval; //!< NVVE_P_INTERVAL,
int IDR_Period; //!< NVVE_IDR_PERIOD,
int DynamicGOP; //!< NVVE_DYNAMIC_GOP,
int RCType; //!< NVVE_RC_TYPE,
int AvgBitrate; //!< NVVE_AVG_BITRATE,
int PeakBitrate; //!< NVVE_PEAK_BITRATE,
int QP_Level_Intra; //!< NVVE_QP_LEVEL_INTRA,
int QP_Level_InterP; //!< NVVE_QP_LEVEL_INTER_P,
int QP_Level_InterB; //!< NVVE_QP_LEVEL_INTER_B,
int DeblockMode; //!< NVVE_DEBLOCK_MODE,
int ProfileLevel; //!< NVVE_PROFILE_LEVEL,
int ForceIntra; //!< NVVE_FORCE_INTRA,
int ForceIDR; //!< NVVE_FORCE_IDR,
int ClearStat; //!< NVVE_CLEAR_STAT,
int DIMode; //!< NVVE_SET_DEINTERLACE,
int Presets; //!< NVVE_PRESETS,
int DisableCabac; //!< NVVE_DISABLE_CABAC,
int NaluFramingType; //!< NVVE_CONFIGURE_NALU_FRAMING_TYPE
int DisableSPSPPS; //!< NVVE_DISABLE_SPS_PPS
EncoderParams();
/** @brief Constructors.
@param configFile Config file name.
Creates default parameters or reads parameters from config file.
*/
explicit EncoderParams(const String& configFile);
/** @brief Reads parameters from config file.
@param configFile Config file name.
*/
void load(const String& configFile);
/** @brief Saves parameters to config file.
@param configFile Config file name.
*/
void save(const String& configFile) const;
};
/** @brief Callbacks for CUDA video encoder.
*/
class CV_EXPORTS_W EncoderCallBack
{
public:
enum PicType
{
IFRAME = 1,
PFRAME = 2,
BFRAME = 3
};
virtual ~EncoderCallBack() {}
/** @brief Callback function to signal the start of bitstream that is to be encoded.
Callback must allocate buffer for CUDA encoder and return pointer to it and it's size.
*/
virtual uchar* acquireBitStream(int* bufferSize) = 0;
/** @brief Callback function to signal that the encoded bitstream is ready to be written to file.
*/
virtual void releaseBitStream(unsigned char* data, int size) = 0;
/** @brief Callback function to signal that the encoding operation on the frame has started.
@param frameNumber
@param picType Specify frame type (I-Frame, P-Frame or B-Frame).
*/
CV_WRAP virtual void onBeginFrame(int frameNumber, EncoderCallBack::PicType picType) = 0;
/** @brief Callback function signals that the encoding operation on the frame has finished.
@param frameNumber
@param picType Specify frame type (I-Frame, P-Frame or B-Frame).
*/
CV_WRAP virtual void onEndFrame(int frameNumber, EncoderCallBack::PicType picType) = 0;
};
/** @brief Video writer interface.
The implementation uses H264 video codec.
@note Currently only Windows platform is supported.
@note
- An example on how to use the videoWriter class can be found at
opencv_source_code/samples/gpu/video_writer.cpp
*/
class CV_EXPORTS_W VideoWriter
{
public:
virtual ~VideoWriter() {}
/** @brief Writes the next video frame.
@param frame The written frame.
@param lastFrame Indicates that it is end of stream. The parameter can be ignored.
The method write the specified image to video file. The image must have the same size and the same
surface format as has been specified when opening the video writer.
*/
CV_WRAP virtual void write(InputArray frame, bool lastFrame = false) = 0;
CV_WRAP virtual EncoderParams getEncoderParams() const = 0;
};
/** @brief Creates video writer.
@param fileName Name of the output video file. Only AVI file format is supported.
@param frameSize Size of the input video frames.
@param fps Framerate of the created video stream.
@param format Surface format of input frames ( SF_UYVY , SF_YUY2 , SF_YV12 , SF_NV12 ,
SF_IYUV , SF_BGR or SF_GRAY). BGR or gray frames will be converted to YV12 format before
encoding, frames with other formats will be used as is.
The constructors initialize video writer. FFMPEG is used to write videos. User can implement own
multiplexing with cudacodec::EncoderCallBack .
*/
CV_EXPORTS_W Ptr<cudacodec::VideoWriter> createVideoWriter(const String& fileName, Size frameSize, double fps, SurfaceFormat format = SF_BGR);
/** @overload
@param fileName Name of the output video file. Only AVI file format is supported.
@param frameSize Size of the input video frames.
@param fps Framerate of the created video stream.
@param params Encoder parameters. See cudacodec::EncoderParams .
@param format Surface format of input frames ( SF_UYVY , SF_YUY2 , SF_YV12 , SF_NV12 ,
SF_IYUV , SF_BGR or SF_GRAY). BGR or gray frames will be converted to YV12 format before
encoding, frames with other formats will be used as is.
*/
CV_EXPORTS_W Ptr<cudacodec::VideoWriter> createVideoWriter(const String& fileName, Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
/** @overload
@param encoderCallback Callbacks for video encoder. See cudacodec::EncoderCallBack . Use it if you
want to work with raw video stream.
@param frameSize Size of the input video frames.
@param fps Framerate of the created video stream.
@param format Surface format of input frames ( SF_UYVY , SF_YUY2 , SF_YV12 , SF_NV12 ,
SF_IYUV , SF_BGR or SF_GRAY). BGR or gray frames will be converted to YV12 format before
encoding, frames with other formats will be used as is.
*/
CV_EXPORTS_W Ptr<cudacodec::VideoWriter> createVideoWriter(const Ptr<EncoderCallBack>& encoderCallback, Size frameSize, double fps, SurfaceFormat format = SF_BGR);
/** @overload
@param encoderCallback Callbacks for video encoder. See cudacodec::EncoderCallBack . Use it if you
want to work with raw video stream.
@param frameSize Size of the input video frames.
@param fps Framerate of the created video stream.
@param params Encoder parameters. See cudacodec::EncoderParams .
@param format Surface format of input frames ( SF_UYVY , SF_YUY2 , SF_YV12 , SF_NV12 ,
SF_IYUV , SF_BGR or SF_GRAY). BGR or gray frames will be converted to YV12 format before
encoding, frames with other formats will be used as is.
*/
CV_EXPORTS_W Ptr<cudacodec::VideoWriter> createVideoWriter(const Ptr<EncoderCallBack>& encoderCallback, Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format = SF_BGR);
////////////////////////////////// Video Decoding //////////////////////////////////////////
/** @brief Video codecs supported by cudacodec::VideoReader .
*/
enum Codec
{
MPEG1 = 0,
MPEG2,
MPEG4,
VC1,
H264,
JPEG,
H264_SVC,
H264_MVC,
Uncompressed_YUV420 = (('I'<<24)|('Y'<<16)|('U'<<8)|('V')), //!< Y,U,V (4:2:0)
Uncompressed_YV12 = (('Y'<<24)|('V'<<16)|('1'<<8)|('2')), //!< Y,V,U (4:2:0)
Uncompressed_NV12 = (('N'<<24)|('V'<<16)|('1'<<8)|('2')), //!< Y,UV (4:2:0)
Uncompressed_YUYV = (('Y'<<24)|('U'<<16)|('Y'<<8)|('V')), //!< YUYV/YUY2 (4:2:2)
Uncompressed_UYVY = (('U'<<24)|('Y'<<16)|('V'<<8)|('Y')) //!< UYVY (4:2:2)
};
/** @brief Chroma formats supported by cudacodec::VideoReader .
*/
enum ChromaFormat
{
Monochrome = 0,
YUV420,
YUV422,
YUV444
};
/** @brief Struct providing information about video file format. :
*/
struct FormatInfo
{
Codec codec;
ChromaFormat chromaFormat;
int width;
int height;
};
/** @brief Video reader interface.
@note
- An example on how to use the videoReader class can be found at
opencv_source_code/samples/gpu/video_reader.cpp
*/
class CV_EXPORTS_W VideoReader
{
public:
virtual ~VideoReader() {}
/** @brief Grabs, decodes and returns the next video frame.
If no frames has been grabbed (there are no more frames in video file), the methods return false .
The method throws Exception if error occurs.
*/
CV_WRAP virtual bool nextFrame(OutputArray frame) = 0;
/** @brief Returns information about video file format.
*/
virtual FormatInfo format() const = 0;
};
/** @brief Interface for video demultiplexing. :
User can implement own demultiplexing by implementing this interface.
*/
class CV_EXPORTS_W RawVideoSource
{
public:
virtual ~RawVideoSource() {}
/** @brief Returns next packet with RAW video frame.
@param data Pointer to frame data.
@param size Size in bytes of current frame.
@param endOfFile Indicates that it is end of stream.
*/
virtual bool getNextPacket(unsigned char** data, int* size, bool* endOfFile) = 0;
/** @brief Returns information about video file format.
*/
virtual FormatInfo format() const = 0;
};
/** @brief Creates video reader.
@param filename Name of the input video file.
FFMPEG is used to read videos. User can implement own demultiplexing with cudacodec::RawVideoSource
*/
CV_EXPORTS_W Ptr<VideoReader> createVideoReader(const String& filename);
/** @overload
@param source RAW video source implemented by user.
*/
CV_EXPORTS_W Ptr<VideoReader> createVideoReader(const Ptr<RawVideoSource>& source);
//! @}
}} // namespace cv { namespace cudacodec {
#endif /* OPENCV_CUDACODEC_HPP */

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#ifdef HAVE_OPENCV_CUDACODEC
#include "opencv2/cudacodec.hpp"
typedef cudacodec::EncoderCallBack::PicType EncoderCallBack_PicType;
CV_PY_TO_CLASS(cudacodec::EncoderParams);
CV_PY_TO_ENUM(cudacodec::EncoderCallBack::PicType);
CV_PY_TO_ENUM(cudacodec::SurfaceFormat);
CV_PY_FROM_CLASS(cudacodec::EncoderParams);
#endif

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
using namespace perf;
CV_PERF_TEST_CUDA_MAIN(cudacodec)

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_PERF_PRECOMP_HPP
#define OPENCV_PERF_PRECOMP_HPP
#include "opencv2/ts.hpp"
#include "opencv2/ts/cuda_perf.hpp"
#include "opencv2/cudacodec.hpp"
namespace opencv_test {
using namespace perf;
}
#endif

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
#include "opencv2/highgui/highgui_c.h"
namespace opencv_test { namespace {
DEF_PARAM_TEST_1(FileName, string);
//////////////////////////////////////////////////////
// VideoReader
#if defined(HAVE_NVCUVID) && defined(HAVE_VIDEO_INPUT)
PERF_TEST_P(FileName, VideoReader, Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"))
{
declare.time(20);
const string inputFile = perf::TestBase::getDataPath(GetParam());
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cudacodec::VideoReader> d_reader = cv::cudacodec::createVideoReader(inputFile);
cv::cuda::GpuMat frame;
TEST_CYCLE_N(10) d_reader->nextFrame(frame);
CUDA_SANITY_CHECK(frame);
}
else
{
cv::VideoCapture reader(inputFile);
ASSERT_TRUE( reader.isOpened() );
cv::Mat frame;
TEST_CYCLE_N(10) reader >> frame;
CPU_SANITY_CHECK(frame);
}
}
#endif
//////////////////////////////////////////////////////
// VideoWriter
#if defined(HAVE_NVCUVID) && defined(_WIN32)
PERF_TEST_P(FileName, VideoWriter, Values("gpu/video/768x576.avi", "gpu/video/1920x1080.avi"))
{
declare.time(30);
const string inputFile = perf::TestBase::getDataPath(GetParam());
const string outputFile = cv::tempfile(".avi");
const double FPS = 25.0;
cv::VideoCapture reader(inputFile);
ASSERT_TRUE( reader.isOpened() );
cv::Mat frame;
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cudacodec::VideoWriter> d_writer;
cv::cuda::GpuMat d_frame;
for (int i = 0; i < 10; ++i)
{
reader >> frame;
ASSERT_FALSE(frame.empty());
d_frame.upload(frame);
if (d_writer.empty())
d_writer = cv::cudacodec::createVideoWriter(outputFile, frame.size(), FPS);
startTimer(); next();
d_writer->write(d_frame);
stopTimer();
}
}
else
{
cv::VideoWriter writer;
for (int i = 0; i < 10; ++i)
{
reader >> frame;
ASSERT_FALSE(frame.empty());
if (!writer.isOpened())
writer.open(outputFile, CV_FOURCC('X', 'V', 'I', 'D'), FPS, frame.size());
startTimer(); next();
writer.write(frame);
stopTimer();
}
}
SANITY_CHECK(frame);
}
#endif
}} // namespace

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* NV12ToARGB color space conversion CUDA kernel
*
* This sample uses CUDA to perform a simple NV12 (YUV 4:2:0 planar)
* source and converts to output in ARGB format
*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev/common.hpp"
using namespace cv;
using namespace cv::cudev;
void videoDecPostProcessFrame(const GpuMat& decodedFrame, OutputArray _outFrame, int width, int height);
namespace
{
__constant__ float constHueColorSpaceMat[9] = {1.1644f, 0.0f, 1.596f, 1.1644f, -0.3918f, -0.813f, 1.1644f, 2.0172f, 0.0f};
__device__ static void YUV2RGB(const uint* yuvi, float* red, float* green, float* blue)
{
float luma, chromaCb, chromaCr;
// Prepare for hue adjustment
luma = (float)yuvi[0];
chromaCb = (float)((int)yuvi[1] - 512.0f);
chromaCr = (float)((int)yuvi[2] - 512.0f);
// Convert YUV To RGB with hue adjustment
*red = (luma * constHueColorSpaceMat[0]) +
(chromaCb * constHueColorSpaceMat[1]) +
(chromaCr * constHueColorSpaceMat[2]);
*green = (luma * constHueColorSpaceMat[3]) +
(chromaCb * constHueColorSpaceMat[4]) +
(chromaCr * constHueColorSpaceMat[5]);
*blue = (luma * constHueColorSpaceMat[6]) +
(chromaCb * constHueColorSpaceMat[7]) +
(chromaCr * constHueColorSpaceMat[8]);
}
__device__ static uint RGBA_pack_10bit(float red, float green, float blue, uint alpha)
{
uint ARGBpixel = 0;
// Clamp final 10 bit results
red = ::fmin(::fmax(red, 0.0f), 1023.f);
green = ::fmin(::fmax(green, 0.0f), 1023.f);
blue = ::fmin(::fmax(blue, 0.0f), 1023.f);
// Convert to 8 bit unsigned integers per color component
ARGBpixel = (((uint)blue >> 2) |
(((uint)green >> 2) << 8) |
(((uint)red >> 2) << 16) |
(uint)alpha);
return ARGBpixel;
}
// CUDA kernel for outputting the final ARGB output from NV12
#define COLOR_COMPONENT_BIT_SIZE 10
#define COLOR_COMPONENT_MASK 0x3FF
__global__ void NV12_to_RGB(const uchar* srcImage, size_t nSourcePitch,
uint* dstImage, size_t nDestPitch,
uint width, uint height)
{
// Pad borders with duplicate pixels, and we multiply by 2 because we process 2 pixels per thread
const int x = blockIdx.x * (blockDim.x << 1) + (threadIdx.x << 1);
const int y = blockIdx.y * blockDim.y + threadIdx.y;
if (x >= width || y >= height)
return;
// Read 2 Luma components at a time, so we don't waste processing since CbCr are decimated this way.
// if we move to texture we could read 4 luminance values
uint yuv101010Pel[2];
yuv101010Pel[0] = (srcImage[y * nSourcePitch + x ]) << 2;
yuv101010Pel[1] = (srcImage[y * nSourcePitch + x + 1]) << 2;
const size_t chromaOffset = nSourcePitch * height;
const int y_chroma = y >> 1;
if (y & 1) // odd scanline ?
{
uint chromaCb = srcImage[chromaOffset + y_chroma * nSourcePitch + x ];
uint chromaCr = srcImage[chromaOffset + y_chroma * nSourcePitch + x + 1];
if (y_chroma < ((height >> 1) - 1)) // interpolate chroma vertically
{
chromaCb = (chromaCb + srcImage[chromaOffset + (y_chroma + 1) * nSourcePitch + x ] + 1) >> 1;
chromaCr = (chromaCr + srcImage[chromaOffset + (y_chroma + 1) * nSourcePitch + x + 1] + 1) >> 1;
}
yuv101010Pel[0] |= (chromaCb << ( COLOR_COMPONENT_BIT_SIZE + 2));
yuv101010Pel[0] |= (chromaCr << ((COLOR_COMPONENT_BIT_SIZE << 1) + 2));
yuv101010Pel[1] |= (chromaCb << ( COLOR_COMPONENT_BIT_SIZE + 2));
yuv101010Pel[1] |= (chromaCr << ((COLOR_COMPONENT_BIT_SIZE << 1) + 2));
}
else
{
yuv101010Pel[0] |= ((uint)srcImage[chromaOffset + y_chroma * nSourcePitch + x ] << ( COLOR_COMPONENT_BIT_SIZE + 2));
yuv101010Pel[0] |= ((uint)srcImage[chromaOffset + y_chroma * nSourcePitch + x + 1] << ((COLOR_COMPONENT_BIT_SIZE << 1) + 2));
yuv101010Pel[1] |= ((uint)srcImage[chromaOffset + y_chroma * nSourcePitch + x ] << ( COLOR_COMPONENT_BIT_SIZE + 2));
yuv101010Pel[1] |= ((uint)srcImage[chromaOffset + y_chroma * nSourcePitch + x + 1] << ((COLOR_COMPONENT_BIT_SIZE << 1) + 2));
}
// this steps performs the color conversion
uint yuvi[6];
float red[2], green[2], blue[2];
yuvi[0] = (yuv101010Pel[0] & COLOR_COMPONENT_MASK );
yuvi[1] = ((yuv101010Pel[0] >> COLOR_COMPONENT_BIT_SIZE) & COLOR_COMPONENT_MASK);
yuvi[2] = ((yuv101010Pel[0] >> (COLOR_COMPONENT_BIT_SIZE << 1)) & COLOR_COMPONENT_MASK);
yuvi[3] = (yuv101010Pel[1] & COLOR_COMPONENT_MASK );
yuvi[4] = ((yuv101010Pel[1] >> COLOR_COMPONENT_BIT_SIZE) & COLOR_COMPONENT_MASK);
yuvi[5] = ((yuv101010Pel[1] >> (COLOR_COMPONENT_BIT_SIZE << 1)) & COLOR_COMPONENT_MASK);
// YUV to RGB Transformation conversion
YUV2RGB(&yuvi[0], &red[0], &green[0], &blue[0]);
YUV2RGB(&yuvi[3], &red[1], &green[1], &blue[1]);
// Clamp the results to RGBA
const size_t dstImagePitch = nDestPitch >> 2;
dstImage[y * dstImagePitch + x ] = RGBA_pack_10bit(red[0], green[0], blue[0], ((uint)0xff << 24));
dstImage[y * dstImagePitch + x + 1 ] = RGBA_pack_10bit(red[1], green[1], blue[1], ((uint)0xff << 24));
}
}
void videoDecPostProcessFrame(const GpuMat& decodedFrame, OutputArray _outFrame, int width, int height)
{
// Final Stage: NV12toARGB color space conversion
_outFrame.create(height, width, CV_8UC4);
GpuMat outFrame = _outFrame.getGpuMat();
dim3 block(32, 8);
dim3 grid(divUp(width, 2 * block.x), divUp(height, block.y));
NV12_to_RGB<<<grid, block>>>(decodedFrame.ptr<uchar>(), decodedFrame.step,
outFrame.ptr<uint>(), outFrame.step,
width, height);
CV_CUDEV_SAFE_CALL( cudaGetLastError() );
CV_CUDEV_SAFE_CALL( cudaDeviceSynchronize() );
}
#endif

167
modules/cudacodec/src/cuda/rgb_to_yv12.cu
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/opencv_modules.hpp"
#ifndef HAVE_OPENCV_CUDEV
#error "opencv_cudev is required"
#else
#include "opencv2/cudev/ptr2d/glob.hpp"
using namespace cv::cudev;
void RGB_to_YV12(const GpuMat& src, GpuMat& dst);
namespace
{
__device__ __forceinline__ void rgb_to_y(const uchar b, const uchar g, const uchar r, uchar& y)
{
y = static_cast<uchar>(((int)(30 * r) + (int)(59 * g) + (int)(11 * b)) / 100);
}
__device__ __forceinline__ void rgb_to_yuv(const uchar b, const uchar g, const uchar r, uchar& y, uchar& u, uchar& v)
{
rgb_to_y(b, g, r, y);
u = static_cast<uchar>(((int)(-17 * r) - (int)(33 * g) + (int)(50 * b) + 12800) / 100);
v = static_cast<uchar>(((int)(50 * r) - (int)(42 * g) - (int)(8 * b) + 12800) / 100);
}
__global__ void Gray_to_YV12(const GlobPtrSz<uchar> src, GlobPtr<uchar> dst)
{
const int x = (blockIdx.x * blockDim.x + threadIdx.x) * 2;
const int y = (blockIdx.y * blockDim.y + threadIdx.y) * 2;
if (x + 1 >= src.cols || y + 1 >= src.rows)
return;
// get pointers to the data
const size_t planeSize = src.rows * dst.step;
GlobPtr<uchar> y_plane = globPtr(dst.data, dst.step);
GlobPtr<uchar> u_plane = globPtr(y_plane.data + planeSize, dst.step / 2);
GlobPtr<uchar> v_plane = globPtr(u_plane.data + (planeSize / 4), dst.step / 2);
uchar pix;
uchar y_val, u_val, v_val;
pix = src(y, x);
rgb_to_y(pix, pix, pix, y_val);
y_plane(y, x) = y_val;
pix = src(y, x + 1);
rgb_to_y(pix, pix, pix, y_val);
y_plane(y, x + 1) = y_val;
pix = src(y + 1, x);
rgb_to_y(pix, pix, pix, y_val);
y_plane(y + 1, x) = y_val;
pix = src(y + 1, x + 1);
rgb_to_yuv(pix, pix, pix, y_val, u_val, v_val);
y_plane(y + 1, x + 1) = y_val;
u_plane(y / 2, x / 2) = u_val;
v_plane(y / 2, x / 2) = v_val;
}
template <typename T>
__global__ void RGB_to_YV12(const GlobPtrSz<T> src, GlobPtr<uchar> dst)
{
const int x = (blockIdx.x * blockDim.x + threadIdx.x) * 2;
const int y = (blockIdx.y * blockDim.y + threadIdx.y) * 2;
if (x + 1 >= src.cols || y + 1 >= src.rows)
return;
// get pointers to the data
const size_t planeSize = src.rows * dst.step;
GlobPtr<uchar> y_plane = globPtr(dst.data, dst.step);
GlobPtr<uchar> u_plane = globPtr(y_plane.data + planeSize, dst.step / 2);
GlobPtr<uchar> v_plane = globPtr(u_plane.data + (planeSize / 4), dst.step / 2);
T pix;
uchar y_val, u_val, v_val;
pix = src(y, x);
rgb_to_y(pix.z, pix.y, pix.x, y_val);
y_plane(y, x) = y_val;
pix = src(y, x + 1);
rgb_to_y(pix.z, pix.y, pix.x, y_val);
y_plane(y, x + 1) = y_val;
pix = src(y + 1, x);
rgb_to_y(pix.z, pix.y, pix.x, y_val);
y_plane(y + 1, x) = y_val;
pix = src(y + 1, x + 1);
rgb_to_yuv(pix.z, pix.y, pix.x, y_val, u_val, v_val);
y_plane(y + 1, x + 1) = y_val;
u_plane(y / 2, x / 2) = u_val;
v_plane(y / 2, x / 2) = v_val;
}
}
void RGB_to_YV12(const GpuMat& src, GpuMat& dst)
{
const dim3 block(32, 8);
const dim3 grid(divUp(src.cols, block.x * 2), divUp(src.rows, block.y * 2));
switch (src.channels())
{
case 1:
Gray_to_YV12<<<grid, block>>>(globPtr<uchar>(src), globPtr<uchar>(dst));
break;
case 3:
RGB_to_YV12<<<grid, block>>>(globPtr<uchar3>(src), globPtr<uchar>(dst));
break;
case 4:
RGB_to_YV12<<<grid, block>>>(globPtr<uchar4>(src), globPtr<uchar>(dst));
break;
}
CV_CUDEV_SAFE_CALL( cudaGetLastError() );
CV_CUDEV_SAFE_CALL( cudaDeviceSynchronize() );
}
#endif

114
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#ifdef HAVE_NVCUVID
using namespace cv;
using namespace cv::cudacodec;
using namespace cv::cudacodec::detail;
cv::cudacodec::detail::CuvidVideoSource::CuvidVideoSource(const String& fname)
{
CUVIDSOURCEPARAMS params;
std::memset(&params, 0, sizeof(CUVIDSOURCEPARAMS));
// Fill parameter struct
params.pUserData = this; // will be passed to data handlers
params.pfnVideoDataHandler = HandleVideoData; // our local video-handler callback
params.pfnAudioDataHandler = 0;
// now create the actual source
CUresult cuRes = cuvidCreateVideoSource(&videoSource_, fname.c_str(), &params);
if (cuRes == CUDA_ERROR_INVALID_SOURCE)
throw std::runtime_error("");
cuSafeCall( cuRes );
CUVIDEOFORMAT vidfmt;
cuSafeCall( cuvidGetSourceVideoFormat(videoSource_, &vidfmt, 0) );
format_.codec = static_cast<Codec>(vidfmt.codec);
format_.chromaFormat = static_cast<ChromaFormat>(vidfmt.chroma_format);
format_.width = vidfmt.coded_width;
format_.height = vidfmt.coded_height;
}
cv::cudacodec::detail::CuvidVideoSource::~CuvidVideoSource()
{
cuvidDestroyVideoSource(videoSource_);
}
FormatInfo cv::cudacodec::detail::CuvidVideoSource::format() const
{
return format_;
}
void cv::cudacodec::detail::CuvidVideoSource::start()
{
cuSafeCall( cuvidSetVideoSourceState(videoSource_, cudaVideoState_Started) );
}
void cv::cudacodec::detail::CuvidVideoSource::stop()
{
cuSafeCall( cuvidSetVideoSourceState(videoSource_, cudaVideoState_Stopped) );
}
bool cv::cudacodec::detail::CuvidVideoSource::isStarted() const
{
return (cuvidGetVideoSourceState(videoSource_) == cudaVideoState_Started);
}
bool cv::cudacodec::detail::CuvidVideoSource::hasError() const
{
return (cuvidGetVideoSourceState(videoSource_) == cudaVideoState_Error);
}
int CUDAAPI cv::cudacodec::detail::CuvidVideoSource::HandleVideoData(void* userData, CUVIDSOURCEDATAPACKET* packet)
{
CuvidVideoSource* thiz = static_cast<CuvidVideoSource*>(userData);
return thiz->parseVideoData(packet->payload, packet->payload_size, (packet->flags & CUVID_PKT_ENDOFSTREAM) != 0);
}
#endif // HAVE_NVCUVID

90
modules/cudacodec/src/cuvid_video_source.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __CUVID_VIDEO_SOURCE_HPP__
#define __CUVID_VIDEO_SOURCE_HPP__
#if CUDA_VERSION >= 9000
#include <dynlink_nvcuvid.h>
#else
#include <nvcuvid.h>
#endif
#include "opencv2/core/private.cuda.hpp"
#include "opencv2/cudacodec.hpp"
#include "video_source.hpp"
namespace cv { namespace cudacodec { namespace detail
{
class CuvidVideoSource : public VideoSource
{
public:
explicit CuvidVideoSource(const String& fname);
~CuvidVideoSource();
FormatInfo format() const CV_OVERRIDE;
void start() CV_OVERRIDE;
void stop() CV_OVERRIDE;
bool isStarted() const CV_OVERRIDE;
bool hasError() const CV_OVERRIDE;
private:
// Callback for handling packages of demuxed video data.
//
// Parameters:
// pUserData - Pointer to user data. We must pass a pointer to a
// VideoSourceData struct here, that contains a valid CUvideoparser
// and FrameQueue.
// pPacket - video-source data packet.
//
// NOTE: called from a different thread that doesn't not have a cuda context
//
static int CUDAAPI HandleVideoData(void* pUserData, CUVIDSOURCEDATAPACKET* pPacket);
CUvideosource videoSource_;
FormatInfo format_;
};
}}}
#endif // __CUVID_VIDEO_SOURCE_HPP__

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modules/cudacodec/src/ffmpeg_video_source.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#ifdef HAVE_NVCUVID
using namespace cv;
using namespace cv::cudacodec;
using namespace cv::cudacodec::detail;
namespace
{
Create_InputMediaStream_FFMPEG_Plugin create_InputMediaStream_FFMPEG_p = 0;
Release_InputMediaStream_FFMPEG_Plugin release_InputMediaStream_FFMPEG_p = 0;
Read_InputMediaStream_FFMPEG_Plugin read_InputMediaStream_FFMPEG_p = 0;
bool init_MediaStream_FFMPEG()
{
static bool initialized = 0;
if (!initialized)
{
#if defined _WIN32
const char* module_name = "opencv_ffmpeg"
CVAUX_STR(CV_VERSION_MAJOR) CVAUX_STR(CV_VERSION_MINOR) CVAUX_STR(CV_VERSION_REVISION)
#if (defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__)
"_64"
#endif
".dll";
static HMODULE cvFFOpenCV = LoadLibrary(module_name);
if (cvFFOpenCV)
{
create_InputMediaStream_FFMPEG_p =
(Create_InputMediaStream_FFMPEG_Plugin)GetProcAddress(cvFFOpenCV, "create_InputMediaStream_FFMPEG");
release_InputMediaStream_FFMPEG_p =
(Release_InputMediaStream_FFMPEG_Plugin)GetProcAddress(cvFFOpenCV, "release_InputMediaStream_FFMPEG");
read_InputMediaStream_FFMPEG_p =
(Read_InputMediaStream_FFMPEG_Plugin)GetProcAddress(cvFFOpenCV, "read_InputMediaStream_FFMPEG");
initialized = create_InputMediaStream_FFMPEG_p != 0 && release_InputMediaStream_FFMPEG_p != 0 && read_InputMediaStream_FFMPEG_p != 0;
}
#elif defined HAVE_FFMPEG
create_InputMediaStream_FFMPEG_p = create_InputMediaStream_FFMPEG;
release_InputMediaStream_FFMPEG_p = release_InputMediaStream_FFMPEG;
read_InputMediaStream_FFMPEG_p = read_InputMediaStream_FFMPEG;
initialized = true;
#endif
}
return initialized;
}
}
cv::cudacodec::detail::FFmpegVideoSource::FFmpegVideoSource(const String& fname) :
stream_(0)
{
CV_Assert( init_MediaStream_FFMPEG() );
int codec;
int chroma_format;
int width;
int height;
stream_ = create_InputMediaStream_FFMPEG_p(fname.c_str(), &codec, &chroma_format, &width, &height);
if (!stream_)
CV_Error(Error::StsUnsupportedFormat, "Unsupported video source");
format_.codec = static_cast<Codec>(codec);
format_.chromaFormat = static_cast<ChromaFormat>(chroma_format);
format_.width = width;
format_.height = height;
}
cv::cudacodec::detail::FFmpegVideoSource::~FFmpegVideoSource()
{
if (stream_)
release_InputMediaStream_FFMPEG_p(stream_);
}
FormatInfo cv::cudacodec::detail::FFmpegVideoSource::format() const
{
return format_;
}
bool cv::cudacodec::detail::FFmpegVideoSource::getNextPacket(unsigned char** data, int* size, bool* bEndOfFile)
{
int endOfFile;
int res = read_InputMediaStream_FFMPEG_p(stream_, data, size, &endOfFile);
*bEndOfFile = (endOfFile != 0);
return res != 0;
}
#endif // HAVE_CUDA

71
modules/cudacodec/src/ffmpeg_video_source.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __FFMPEG_VIDEO_SOURCE_HPP__
#define __FFMPEG_VIDEO_SOURCE_HPP__
#include "opencv2/cudacodec.hpp"
struct InputMediaStream_FFMPEG;
namespace cv { namespace cudacodec { namespace detail {
class FFmpegVideoSource : public RawVideoSource
{
public:
FFmpegVideoSource(const String& fname);
~FFmpegVideoSource();
bool getNextPacket(unsigned char** data, int* size, bool* endOfFile) CV_OVERRIDE;
FormatInfo format() const CV_OVERRIDE;
private:
FormatInfo format_;
InputMediaStream_FFMPEG* stream_;
};
}}}
#endif // __FFMPEG_VIDEO_SOURCE_HPP__

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#ifdef HAVE_NVCUVID
cv::cudacodec::detail::FrameQueue::FrameQueue() :
endOfDecode_(0),
framesInQueue_(0),
readPosition_(0)
{
std::memset(displayQueue_, 0, sizeof(displayQueue_));
std::memset((void*) isFrameInUse_, 0, sizeof(isFrameInUse_));
}
bool cv::cudacodec::detail::FrameQueue::waitUntilFrameAvailable(int pictureIndex)
{
while (isInUse(pictureIndex))
{
// Decoder is getting too far ahead from display
Thread::sleep(1);
if (isEndOfDecode())
return false;
}
return true;
}
void cv::cudacodec::detail::FrameQueue::enqueue(const CUVIDPARSERDISPINFO* picParams)
{
// Mark the frame as 'in-use' so we don't re-use it for decoding until it is no longer needed
// for display
isFrameInUse_[picParams->picture_index] = true;
// Wait until we have a free entry in the display queue (should never block if we have enough entries)
do
{
bool isFramePlaced = false;
{
AutoLock autoLock(mtx_);
if (framesInQueue_ < MaximumSize)
{
int writePosition = (readPosition_ + framesInQueue_) % MaximumSize;
displayQueue_[writePosition] = *picParams;
framesInQueue_++;
isFramePlaced = true;
}
}
if (isFramePlaced) // Done
break;
// Wait a bit
Thread::sleep(1);
} while (!isEndOfDecode());
}
bool cv::cudacodec::detail::FrameQueue::dequeue(CUVIDPARSERDISPINFO& displayInfo)
{
AutoLock autoLock(mtx_);
if (framesInQueue_ > 0)
{
int entry = readPosition_;
displayInfo = displayQueue_[entry];
readPosition_ = (entry + 1) % MaximumSize;
framesInQueue_--;
return true;
}
return false;
}
#endif // HAVE_NVCUVID

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __FRAME_QUEUE_HPP__
#define __FRAME_QUEUE_HPP__
#include "opencv2/core/utility.hpp"
#include "opencv2/core/private.cuda.hpp"
#if CUDA_VERSION >= 9000
#include <dynlink_nvcuvid.h>
#else
#include <nvcuvid.h>
#endif
namespace cv { namespace cudacodec { namespace detail
{
class FrameQueue
{
public:
static const int MaximumSize = 20; // MAX_FRM_CNT;
FrameQueue();
void endDecode() { endOfDecode_ = true; }
bool isEndOfDecode() const { return endOfDecode_ != 0;}
// Spins until frame becomes available or decoding gets canceled.
// If the requested frame is available the method returns true.
// If decoding was interrupted before the requested frame becomes
// available, the method returns false.
bool waitUntilFrameAvailable(int pictureIndex);
void enqueue(const CUVIDPARSERDISPINFO* picParams);
// Deque the next frame.
// Parameters:
// displayInfo - New frame info gets placed into this object.
// Returns:
// true, if a new frame was returned,
// false, if the queue was empty and no new frame could be returned.
bool dequeue(CUVIDPARSERDISPINFO& displayInfo);
void releaseFrame(const CUVIDPARSERDISPINFO& picParams) { isFrameInUse_[picParams.picture_index] = false; }
private:
bool isInUse(int pictureIndex) const { return isFrameInUse_[pictureIndex] != 0; }
Mutex mtx_;
volatile int isFrameInUse_[MaximumSize];
volatile int endOfDecode_;
int framesInQueue_;
int readPosition_;
CUVIDPARSERDISPINFO displayQueue_[MaximumSize];
};
}}}
#endif // __FRAME_QUEUE_HPP__

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_PRECOMP_H
#define OPENCV_PRECOMP_H
#include <cstdlib>
#include <cstring>
#include <deque>
#include <utility>
#include <stdexcept>
#include <iostream>
#include "opencv2/cudacodec.hpp"
#include "opencv2/core/private.cuda.hpp"
#ifdef HAVE_NVCUVID
#if CUDA_VERSION >= 9000
#include <dynlink_nvcuvid.h>
#else
#include <nvcuvid.h>
#endif
#ifdef _WIN32
#define NOMINMAX
#include <windows.h>
#ifdef HAVE_NVCUVENC
#include <NVEncoderAPI.h>
#endif
#else
#include <pthread.h>
#include <unistd.h>
#endif
#include "thread.hpp"
#include "video_source.hpp"
#include "ffmpeg_video_source.hpp"
#include "cuvid_video_source.hpp"
#include "frame_queue.hpp"
#include "video_decoder.hpp"
#include "video_parser.hpp"
#include "../src/cap_ffmpeg_api.hpp"
#endif
#endif /* OPENCV_PRECOMP_H */

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#ifdef HAVE_NVCUVID
using namespace cv::cudacodec::detail;
#ifdef _WIN32
namespace
{
struct UserData
{
Thread::Func func;
void* param;
};
DWORD WINAPI WinThreadFunction(LPVOID lpParam)
{
UserData* userData = static_cast<UserData*>(lpParam);
userData->func(userData->param);
return 0;
}
}
class cv::cudacodec::detail::Thread::Impl
{
public:
Impl(Thread::Func func, void* userData)
{
userData_.func = func;
userData_.param = userData;
thread_ = CreateThread(
NULL, // default security attributes
0, // use default stack size
WinThreadFunction, // thread function name
&userData_, // argument to thread function
0, // use default creation flags
&threadId_); // returns the thread identifier
}
~Impl()
{
CloseHandle(thread_);
}
void wait()
{
WaitForSingleObject(thread_, INFINITE);
}
private:
UserData userData_;
HANDLE thread_;
DWORD threadId_;
};
#else
namespace
{
struct UserData
{
Thread::Func func;
void* param;
};
void* PThreadFunction(void* lpParam)
{
UserData* userData = static_cast<UserData*>(lpParam);
userData->func(userData->param);
return 0;
}
}
class cv::cudacodec::detail::Thread::Impl
{
public:
Impl(Thread::Func func, void* userData)
{
userData_.func = func;
userData_.param = userData;
pthread_create(&thread_, NULL, PThreadFunction, &userData_);
}
~Impl()
{
pthread_detach(thread_);
}
void wait()
{
pthread_join(thread_, NULL);
}
private:
pthread_t thread_;
UserData userData_;
};
#endif
cv::cudacodec::detail::Thread::Thread(Func func, void* userData) :
impl_(new Impl(func, userData))
{
}
void cv::cudacodec::detail::Thread::wait()
{
impl_->wait();
}
void cv::cudacodec::detail::Thread::sleep(int ms)
{
#ifdef _WIN32
::Sleep(ms);
#else
::usleep(ms * 1000);
#endif
}
#endif // HAVE_NVCUVID

70
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __THREAD_WRAPPERS_HPP__
#define __THREAD_WRAPPERS_HPP__
#include "opencv2/core.hpp"
namespace cv { namespace cudacodec { namespace detail {
class Thread
{
public:
typedef void (*Func)(void* userData);
explicit Thread(Func func, void* userData = 0);
void wait();
static void sleep(int ms);
class Impl;
private:
cv::Ptr<Impl> impl_;
};
}}}
#endif // __THREAD_WRAPPERS_HPP__

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#ifdef HAVE_NVCUVID
void cv::cudacodec::detail::VideoDecoder::create(const FormatInfo& videoFormat)
{
release();
cudaVideoCodec _codec = static_cast<cudaVideoCodec>(videoFormat.codec);
cudaVideoChromaFormat _chromaFormat = static_cast<cudaVideoChromaFormat>(videoFormat.chromaFormat);
cudaVideoCreateFlags videoCreateFlags = (_codec == cudaVideoCodec_JPEG || _codec == cudaVideoCodec_MPEG2) ?
cudaVideoCreate_PreferCUDA :
cudaVideoCreate_PreferCUVID;
// Validate video format. These are the currently supported formats via NVCUVID
CV_Assert(cudaVideoCodec_MPEG1 == _codec ||
cudaVideoCodec_MPEG2 == _codec ||
cudaVideoCodec_MPEG4 == _codec ||
cudaVideoCodec_VC1 == _codec ||
cudaVideoCodec_H264 == _codec ||
cudaVideoCodec_JPEG == _codec ||
cudaVideoCodec_YUV420== _codec ||
cudaVideoCodec_YV12 == _codec ||
cudaVideoCodec_NV12 == _codec ||
cudaVideoCodec_YUYV == _codec ||
cudaVideoCodec_UYVY == _codec );
CV_Assert(cudaVideoChromaFormat_Monochrome == _chromaFormat ||
cudaVideoChromaFormat_420 == _chromaFormat ||
cudaVideoChromaFormat_422 == _chromaFormat ||
cudaVideoChromaFormat_444 == _chromaFormat);
// Fill the decoder-create-info struct from the given video-format struct.
std::memset(&createInfo_, 0, sizeof(CUVIDDECODECREATEINFO));
// Create video decoder
createInfo_.CodecType = _codec;
createInfo_.ulWidth = videoFormat.width;
createInfo_.ulHeight = videoFormat.height;
createInfo_.ulNumDecodeSurfaces = FrameQueue::MaximumSize;
// Limit decode memory to 24MB (16M pixels at 4:2:0 = 24M bytes)
while (createInfo_.ulNumDecodeSurfaces * videoFormat.width * videoFormat.height > 16 * 1024 * 1024)
createInfo_.ulNumDecodeSurfaces--;
createInfo_.ChromaFormat = _chromaFormat;
createInfo_.OutputFormat = cudaVideoSurfaceFormat_NV12;
createInfo_.DeinterlaceMode = cudaVideoDeinterlaceMode_Adaptive;
// No scaling
static const int MAX_FRAME_COUNT = 2;
createInfo_.ulTargetWidth = createInfo_.ulWidth;
createInfo_.ulTargetHeight = createInfo_.ulHeight;
createInfo_.ulNumOutputSurfaces = MAX_FRAME_COUNT; // We won't simultaneously map more than 8 surfaces
createInfo_.ulCreationFlags = videoCreateFlags;
createInfo_.vidLock = lock_;
// create the decoder
cuSafeCall( cuvidCreateDecoder(&decoder_, &createInfo_) );
}
void cv::cudacodec::detail::VideoDecoder::release()
{
if (decoder_)
{
cuvidDestroyDecoder(decoder_);
decoder_ = 0;
}
}
#endif // HAVE_NVCUVID

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __VIDEO_DECODER_HPP__
#define __VIDEO_DECODER_HPP__
#if CUDA_VERSION >= 9000
#include <dynlink_nvcuvid.h>
#else
#include <nvcuvid.h>
#endif
#include "opencv2/core/private.cuda.hpp"
#include "opencv2/cudacodec.hpp"
namespace cv { namespace cudacodec { namespace detail
{
class VideoDecoder
{
public:
VideoDecoder(const FormatInfo& videoFormat, CUvideoctxlock lock) : lock_(lock), decoder_(0)
{
create(videoFormat);
}
~VideoDecoder()
{
release();
}
void create(const FormatInfo& videoFormat);
void release();
// Get the code-type currently used.
cudaVideoCodec codec() const { return createInfo_.CodecType; }
unsigned long maxDecodeSurfaces() const { return createInfo_.ulNumDecodeSurfaces; }
unsigned long frameWidth() const { return createInfo_.ulWidth; }
unsigned long frameHeight() const { return createInfo_.ulHeight; }
unsigned long targetWidth() const { return createInfo_.ulTargetWidth; }
unsigned long targetHeight() const { return createInfo_.ulTargetHeight; }
cudaVideoChromaFormat chromaFormat() const { return createInfo_.ChromaFormat; }
bool decodePicture(CUVIDPICPARAMS* picParams)
{
return cuvidDecodePicture(decoder_, picParams) == CUDA_SUCCESS;
}
cuda::GpuMat mapFrame(int picIdx, CUVIDPROCPARAMS& videoProcParams)
{
CUdeviceptr ptr;
unsigned int pitch;
cuSafeCall( cuvidMapVideoFrame(decoder_, picIdx, &ptr, &pitch, &videoProcParams) );
return cuda::GpuMat(targetHeight() * 3 / 2, targetWidth(), CV_8UC1, (void*) ptr, pitch);
}
void unmapFrame(cuda::GpuMat& frame)
{
cuSafeCall( cuvidUnmapVideoFrame(decoder_, (CUdeviceptr) frame.data) );
frame.release();
}
private:
CUvideoctxlock lock_;
CUVIDDECODECREATEINFO createInfo_;
CUvideodecoder decoder_;
};
}}}
#endif // __VIDEO_DECODER_HPP__

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#ifdef HAVE_NVCUVID
cv::cudacodec::detail::VideoParser::VideoParser(VideoDecoder* videoDecoder, FrameQueue* frameQueue) :
videoDecoder_(videoDecoder), frameQueue_(frameQueue), unparsedPackets_(0), hasError_(false)
{
CUVIDPARSERPARAMS params;
std::memset(&params, 0, sizeof(CUVIDPARSERPARAMS));
params.CodecType = videoDecoder->codec();
params.ulMaxNumDecodeSurfaces = videoDecoder->maxDecodeSurfaces();
params.ulMaxDisplayDelay = 1; // this flag is needed so the parser will push frames out to the decoder as quickly as it can
params.pUserData = this;
params.pfnSequenceCallback = HandleVideoSequence; // Called before decoding frames and/or whenever there is a format change
params.pfnDecodePicture = HandlePictureDecode; // Called when a picture is ready to be decoded (decode order)
params.pfnDisplayPicture = HandlePictureDisplay; // Called whenever a picture is ready to be displayed (display order)
cuSafeCall( cuvidCreateVideoParser(&parser_, &params) );
}
bool cv::cudacodec::detail::VideoParser::parseVideoData(const unsigned char* data, size_t size, bool endOfStream)
{
CUVIDSOURCEDATAPACKET packet;
std::memset(&packet, 0, sizeof(CUVIDSOURCEDATAPACKET));
if (endOfStream)
packet.flags |= CUVID_PKT_ENDOFSTREAM;
packet.payload_size = static_cast<unsigned long>(size);
packet.payload = data;
if (cuvidParseVideoData(parser_, &packet) != CUDA_SUCCESS)
{
hasError_ = true;
frameQueue_->endDecode();
return false;
}
const int maxUnparsedPackets = 15;
++unparsedPackets_;
if (unparsedPackets_ > maxUnparsedPackets)
{
hasError_ = true;
frameQueue_->endDecode();
return false;
}
if (endOfStream)
frameQueue_->endDecode();
return !frameQueue_->isEndOfDecode();
}
int CUDAAPI cv::cudacodec::detail::VideoParser::HandleVideoSequence(void* userData, CUVIDEOFORMAT* format)
{
VideoParser* thiz = static_cast<VideoParser*>(userData);
thiz->unparsedPackets_ = 0;
if (format->codec != thiz->videoDecoder_->codec() ||
format->coded_width != thiz->videoDecoder_->frameWidth() ||
format->coded_height != thiz->videoDecoder_->frameHeight() ||
format->chroma_format != thiz->videoDecoder_->chromaFormat())
{
FormatInfo newFormat;
newFormat.codec = static_cast<Codec>(format->codec);
newFormat.chromaFormat = static_cast<ChromaFormat>(format->chroma_format);
newFormat.width = format->coded_width;
newFormat.height = format->coded_height;
try
{
thiz->videoDecoder_->create(newFormat);
}
catch (const cv::Exception&)
{
thiz->hasError_ = true;
return false;
}
}
return true;
}
int CUDAAPI cv::cudacodec::detail::VideoParser::HandlePictureDecode(void* userData, CUVIDPICPARAMS* picParams)
{
VideoParser* thiz = static_cast<VideoParser*>(userData);
thiz->unparsedPackets_ = 0;
bool isFrameAvailable = thiz->frameQueue_->waitUntilFrameAvailable(picParams->CurrPicIdx);
if (!isFrameAvailable)
return false;
if (!thiz->videoDecoder_->decodePicture(picParams))
{
thiz->hasError_ = true;
return false;
}
return true;
}
int CUDAAPI cv::cudacodec::detail::VideoParser::HandlePictureDisplay(void* userData, CUVIDPARSERDISPINFO* picParams)
{
VideoParser* thiz = static_cast<VideoParser*>(userData);
thiz->unparsedPackets_ = 0;
thiz->frameQueue_->enqueue(picParams);
return true;
}
#endif // HAVE_NVCUVID

99
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@ -1,99 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __VIDEO_PARSER_HPP__
#define __VIDEO_PARSER_HPP__
#if CUDA_VERSION >= 9000
#include <dynlink_nvcuvid.h>
#else
#include <nvcuvid.h>
#endif
#include "opencv2/core/private.cuda.hpp"
#include "opencv2/cudacodec.hpp"
#include "frame_queue.hpp"
#include "video_decoder.hpp"
namespace cv { namespace cudacodec { namespace detail
{
class VideoParser
{
public:
VideoParser(VideoDecoder* videoDecoder, FrameQueue* frameQueue);
~VideoParser()
{
cuvidDestroyVideoParser(parser_);
}
bool parseVideoData(const unsigned char* data, size_t size, bool endOfStream);
bool hasError() const { return hasError_; }
private:
VideoDecoder* videoDecoder_;
FrameQueue* frameQueue_;
CUvideoparser parser_;
int unparsedPackets_;
volatile bool hasError_;
// Called when the decoder encounters a video format change (or initial sequence header)
// This particular implementation of the callback returns 0 in case the video format changes
// to something different than the original format. Returning 0 causes a stop of the app.
static int CUDAAPI HandleVideoSequence(void* pUserData, CUVIDEOFORMAT* pFormat);
// Called by the video parser to decode a single picture
// Since the parser will deliver data as fast as it can, we need to make sure that the picture
// index we're attempting to use for decode is no longer used for display
static int CUDAAPI HandlePictureDecode(void* pUserData, CUVIDPICPARAMS* pPicParams);
// Called by the video parser to display a video frame (in the case of field pictures, there may be
// 2 decode calls per 1 display call, since two fields make up one frame)
static int CUDAAPI HandlePictureDisplay(void* pUserData, CUVIDPARSERDISPINFO* pPicParams);
};
}}}
#endif // __VIDEO_PARSER_HPP__

223
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@ -1,223 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudacodec;
#ifndef HAVE_NVCUVID
Ptr<VideoReader> cv::cudacodec::createVideoReader(const String&) { throw_no_cuda(); return Ptr<VideoReader>(); }
Ptr<VideoReader> cv::cudacodec::createVideoReader(const Ptr<RawVideoSource>&) { throw_no_cuda(); return Ptr<VideoReader>(); }
#else // HAVE_NVCUVID
void videoDecPostProcessFrame(const GpuMat& decodedFrame, OutputArray _outFrame, int width, int height);
using namespace cv::cudacodec::detail;
namespace
{
class VideoReaderImpl : public VideoReader
{
public:
explicit VideoReaderImpl(const Ptr<VideoSource>& source);
~VideoReaderImpl();
bool nextFrame(OutputArray frame) CV_OVERRIDE;
FormatInfo format() const CV_OVERRIDE;
private:
Ptr<VideoSource> videoSource_;
Ptr<FrameQueue> frameQueue_;
Ptr<VideoDecoder> videoDecoder_;
Ptr<VideoParser> videoParser_;
CUvideoctxlock lock_;
std::deque< std::pair<CUVIDPARSERDISPINFO, CUVIDPROCPARAMS> > frames_;
};
FormatInfo VideoReaderImpl::format() const
{
return videoSource_->format();
}
VideoReaderImpl::VideoReaderImpl(const Ptr<VideoSource>& source) :
videoSource_(source),
lock_(0)
{
// init context
GpuMat temp(1, 1, CV_8UC1);
temp.release();
CUcontext ctx;
cuSafeCall( cuCtxGetCurrent(&ctx) );
cuSafeCall( cuvidCtxLockCreate(&lock_, ctx) );
frameQueue_.reset(new FrameQueue);
videoDecoder_.reset(new VideoDecoder(videoSource_->format(), lock_));
videoParser_.reset(new VideoParser(videoDecoder_, frameQueue_));
videoSource_->setVideoParser(videoParser_);
videoSource_->start();
}
VideoReaderImpl::~VideoReaderImpl()
{
frameQueue_->endDecode();
videoSource_->stop();
}
class VideoCtxAutoLock
{
public:
VideoCtxAutoLock(CUvideoctxlock lock) : m_lock(lock) { cuSafeCall( cuvidCtxLock(m_lock, 0) ); }
~VideoCtxAutoLock() { cuvidCtxUnlock(m_lock, 0); }
private:
CUvideoctxlock m_lock;
};
bool VideoReaderImpl::nextFrame(OutputArray frame)
{
if (videoSource_->hasError() || videoParser_->hasError())
CV_Error(Error::StsUnsupportedFormat, "Unsupported video source");
if (!videoSource_->isStarted() || frameQueue_->isEndOfDecode())
return false;
if (frames_.empty())
{
CUVIDPARSERDISPINFO displayInfo;
for (;;)
{
if (frameQueue_->dequeue(displayInfo))
break;
if (videoSource_->hasError() || videoParser_->hasError())
CV_Error(Error::StsUnsupportedFormat, "Unsupported video source");
if (frameQueue_->isEndOfDecode())
return false;
// Wait a bit
Thread::sleep(1);
}
bool isProgressive = displayInfo.progressive_frame != 0;
const int num_fields = isProgressive ? 1 : 2 + displayInfo.repeat_first_field;
for (int active_field = 0; active_field < num_fields; ++active_field)
{
CUVIDPROCPARAMS videoProcParams;
std::memset(&videoProcParams, 0, sizeof(CUVIDPROCPARAMS));
videoProcParams.progressive_frame = displayInfo.progressive_frame;
videoProcParams.second_field = active_field;
videoProcParams.top_field_first = displayInfo.top_field_first;
videoProcParams.unpaired_field = (num_fields == 1);
frames_.push_back(std::make_pair(displayInfo, videoProcParams));
}
}
if (frames_.empty())
return false;
std::pair<CUVIDPARSERDISPINFO, CUVIDPROCPARAMS> frameInfo = frames_.front();
frames_.pop_front();
{
VideoCtxAutoLock autoLock(lock_);
// map decoded video frame to CUDA surface
GpuMat decodedFrame = videoDecoder_->mapFrame(frameInfo.first.picture_index, frameInfo.second);
// perform post processing on the CUDA surface (performs colors space conversion and post processing)
// comment this out if we include the line of code seen above
videoDecPostProcessFrame(decodedFrame, frame, videoDecoder_->targetWidth(), videoDecoder_->targetHeight());
// unmap video frame
// unmapFrame() synchronizes with the VideoDecode API (ensures the frame has finished decoding)
videoDecoder_->unmapFrame(decodedFrame);
}
// release the frame, so it can be re-used in decoder
if (frames_.empty())
frameQueue_->releaseFrame(frameInfo.first);
return true;
}
}
Ptr<VideoReader> cv::cudacodec::createVideoReader(const String& filename)
{
CV_Assert( !filename.empty() );
Ptr<VideoSource> videoSource;
try
{
videoSource.reset(new CuvidVideoSource(filename));
}
catch (...)
{
Ptr<RawVideoSource> source(new FFmpegVideoSource(filename));
videoSource.reset(new RawVideoSourceWrapper(source));
}
return makePtr<VideoReaderImpl>(videoSource);
}
Ptr<VideoReader> cv::cudacodec::createVideoReader(const Ptr<RawVideoSource>& source)
{
Ptr<VideoSource> videoSource(new RawVideoSourceWrapper(source));
return makePtr<VideoReaderImpl>(videoSource);
}
#endif // HAVE_NVCUVID

121
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
#ifdef HAVE_NVCUVID
using namespace cv;
using namespace cv::cudacodec;
using namespace cv::cudacodec::detail;
bool cv::cudacodec::detail::VideoSource::parseVideoData(const unsigned char* data, size_t size, bool endOfStream)
{
return videoParser_->parseVideoData(data, size, endOfStream);
}
cv::cudacodec::detail::RawVideoSourceWrapper::RawVideoSourceWrapper(const Ptr<RawVideoSource>& source) :
source_(source)
{
CV_Assert( !source_.empty() );
}
cv::cudacodec::FormatInfo cv::cudacodec::detail::RawVideoSourceWrapper::format() const
{
return source_->format();
}
void cv::cudacodec::detail::RawVideoSourceWrapper::start()
{
stop_ = false;
hasError_ = false;
thread_.reset(new Thread(readLoop, this));
}
void cv::cudacodec::detail::RawVideoSourceWrapper::stop()
{
stop_ = true;
thread_->wait();
thread_.release();
}
bool cv::cudacodec::detail::RawVideoSourceWrapper::isStarted() const
{
return !stop_;
}
bool cv::cudacodec::detail::RawVideoSourceWrapper::hasError() const
{
return hasError_;
}
void cv::cudacodec::detail::RawVideoSourceWrapper::readLoop(void* userData)
{
RawVideoSourceWrapper* thiz = static_cast<RawVideoSourceWrapper*>(userData);
for (;;)
{
unsigned char* data;
int size;
bool endOfFile;
if (!thiz->source_->getNextPacket(&data, &size, &endOfFile))
{
thiz->hasError_ = !endOfFile;
break;
}
if (!thiz->parseVideoData(data, size))
{
thiz->hasError_ = true;
break;
}
if (thiz->stop_)
break;
}
thiz->parseVideoData(0, 0, true);
}
#endif // HAVE_NVCUVID

99
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __CUDACODEC_VIDEO_SOURCE_H__
#define __CUDACODEC_VIDEO_SOURCE_H__
#include "opencv2/core/private.cuda.hpp"
#include "opencv2/cudacodec.hpp"
#include "thread.hpp"
namespace cv { namespace cudacodec { namespace detail
{
class VideoParser;
class VideoSource
{
public:
virtual ~VideoSource() {}
virtual FormatInfo format() const = 0;
virtual void start() = 0;
virtual void stop() = 0;
virtual bool isStarted() const = 0;
virtual bool hasError() const = 0;
void setVideoParser(detail::VideoParser* videoParser) { videoParser_ = videoParser; }
protected:
bool parseVideoData(const uchar* data, size_t size, bool endOfStream = false);
private:
detail::VideoParser* videoParser_;
};
class RawVideoSourceWrapper : public VideoSource
{
public:
RawVideoSourceWrapper(const Ptr<RawVideoSource>& source);
FormatInfo format() const CV_OVERRIDE;
void start() CV_OVERRIDE;
void stop() CV_OVERRIDE;
bool isStarted() const CV_OVERRIDE;
bool hasError() const CV_OVERRIDE;
private:
Ptr<RawVideoSource> source_;
Ptr<Thread> thread_;
volatile bool stop_;
volatile bool hasError_;
static void readLoop(void* userData);
};
}}}
#endif // __CUDACODEC_VIDEO_SOURCE_H__

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modules/cudacodec/src/video_writer.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::cuda;
using namespace cv::cudacodec;
#if !defined(HAVE_NVCUVENC) || !defined(_WIN32)
cv::cudacodec::EncoderParams::EncoderParams() { throw_no_cuda(); }
cv::cudacodec::EncoderParams::EncoderParams(const String&) { throw_no_cuda(); }
void cv::cudacodec::EncoderParams::load(const String&) { throw_no_cuda(); }
void cv::cudacodec::EncoderParams::save(const String&) const { throw_no_cuda(); }
Ptr<VideoWriter> cv::cudacodec::createVideoWriter(const String&, Size, double, SurfaceFormat) { throw_no_cuda(); return Ptr<VideoWriter>(); }
Ptr<VideoWriter> cv::cudacodec::createVideoWriter(const String&, Size, double, const EncoderParams&, SurfaceFormat) { throw_no_cuda(); return Ptr<VideoWriter>(); }
Ptr<VideoWriter> cv::cudacodec::createVideoWriter(const Ptr<EncoderCallBack>&, Size, double, SurfaceFormat) { throw_no_cuda(); return Ptr<VideoWriter>(); }
Ptr<VideoWriter> cv::cudacodec::createVideoWriter(const Ptr<EncoderCallBack>&, Size, double, const EncoderParams&, SurfaceFormat) { throw_no_cuda(); return Ptr<VideoWriter>(); }
#else // !defined HAVE_NVCUVENC || !defined _WIN32
void RGB_to_YV12(const GpuMat& src, GpuMat& dst);
///////////////////////////////////////////////////////////////////////////
// VideoWriterImpl
namespace
{
class NVEncoderWrapper
{
public:
NVEncoderWrapper() : encoder_(0)
{
int err;
err = NVGetHWEncodeCaps();
if (err)
CV_Error(Error::GpuNotSupported, "No CUDA capability present");
// Create the Encoder API Interface
err = NVCreateEncoder(&encoder_);
CV_Assert( err == 0 );
}
~NVEncoderWrapper()
{
if (encoder_)
NVDestroyEncoder(encoder_);
}
operator NVEncoder() const
{
return encoder_;
}
private:
NVEncoder encoder_;
};
enum CodecType
{
MPEG1, // not supported yet
MPEG2, // not supported yet
MPEG4, // not supported yet
H264
};
class VideoWriterImpl : public VideoWriter
{
public:
VideoWriterImpl(const Ptr<EncoderCallBack>& callback, Size frameSize, double fps, SurfaceFormat format, CodecType codec = H264);
VideoWriterImpl(const Ptr<EncoderCallBack>& callback, Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format, CodecType codec = H264);
void write(InputArray frame, bool lastFrame = false);
EncoderParams getEncoderParams() const;
private:
void initEncoder(double fps);
void setEncodeParams(const EncoderParams& params);
void initGpuMemory();
void initCallBacks();
void createHWEncoder();
Ptr<EncoderCallBack> callback_;
Size frameSize_;
CodecType codec_;
SurfaceFormat inputFormat_;
NVVE_SurfaceFormat surfaceFormat_;
NVEncoderWrapper encoder_;
GpuMat videoFrame_;
CUvideoctxlock cuCtxLock_;
// CallBacks
static unsigned char* NVENCAPI HandleAcquireBitStream(int* pBufferSize, void* pUserdata);
static void NVENCAPI HandleReleaseBitStream(int nBytesInBuffer, unsigned char* cb, void* pUserdata);
static void NVENCAPI HandleOnBeginFrame(const NVVE_BeginFrameInfo* pbfi, void* pUserdata);
static void NVENCAPI HandleOnEndFrame(const NVVE_EndFrameInfo* pefi, void* pUserdata);
};
VideoWriterImpl::VideoWriterImpl(const Ptr<EncoderCallBack>& callback, Size frameSize, double fps, SurfaceFormat format, CodecType codec) :
callback_(callback),
frameSize_(frameSize),
codec_(codec),
inputFormat_(format),
cuCtxLock_(0)
{
surfaceFormat_ = (inputFormat_ == SF_BGR ? YV12 : static_cast<NVVE_SurfaceFormat>(inputFormat_));
initEncoder(fps);
initGpuMemory();
initCallBacks();
createHWEncoder();
}
VideoWriterImpl::VideoWriterImpl(const Ptr<EncoderCallBack>& callback, Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format, CodecType codec) :
callback_(callback),
frameSize_(frameSize),
codec_(codec),
inputFormat_(format),
cuCtxLock_(0)
{
surfaceFormat_ = (inputFormat_ == SF_BGR ? YV12 : static_cast<NVVE_SurfaceFormat>(inputFormat_));
initEncoder(fps);
setEncodeParams(params);
initGpuMemory();
initCallBacks();
createHWEncoder();
}
void VideoWriterImpl::initEncoder(double fps)
{
int err;
// Set codec
static const unsigned long codecs_id[] =
{
NV_CODEC_TYPE_MPEG1, NV_CODEC_TYPE_MPEG2, NV_CODEC_TYPE_MPEG4, NV_CODEC_TYPE_H264, NV_CODEC_TYPE_VC1
};
err = NVSetCodec(encoder_, codecs_id[codec_]);
if (err)
CV_Error(Error::StsNotImplemented, "Codec format is not supported");
// Set default params
err = NVSetDefaultParam(encoder_);
CV_Assert( err == 0 );
// Set some common params
int inputSize[] = { frameSize_.width, frameSize_.height };
err = NVSetParamValue(encoder_, NVVE_IN_SIZE, &inputSize);
CV_Assert( err == 0 );
err = NVSetParamValue(encoder_, NVVE_OUT_SIZE, &inputSize);
CV_Assert( err == 0 );
int aspectRatio[] = { frameSize_.width, frameSize_.height, ASPECT_RATIO_DAR };
err = NVSetParamValue(encoder_, NVVE_ASPECT_RATIO, &aspectRatio);
CV_Assert( err == 0 );
// FPS
int frame_rate = static_cast<int>(fps + 0.5);
int frame_rate_base = 1;
while (fabs(static_cast<double>(frame_rate) / frame_rate_base) - fps > 0.001)
{
frame_rate_base *= 10;
frame_rate = static_cast<int>(fps*frame_rate_base + 0.5);
}
int FrameRate[] = { frame_rate, frame_rate_base };
err = NVSetParamValue(encoder_, NVVE_FRAME_RATE, &FrameRate);
CV_Assert( err == 0 );
// Select device for encoding
int gpuID = getDevice();
err = NVSetParamValue(encoder_, NVVE_FORCE_GPU_SELECTION, &gpuID);
CV_Assert( err == 0 );
}
void VideoWriterImpl::setEncodeParams(const EncoderParams& params)
{
int err;
int P_Interval = params.P_Interval;
err = NVSetParamValue(encoder_, NVVE_P_INTERVAL, &P_Interval);
CV_Assert( err == 0 );
int IDR_Period = params.IDR_Period;
err = NVSetParamValue(encoder_, NVVE_IDR_PERIOD, &IDR_Period);
CV_Assert( err == 0 );
int DynamicGOP = params.DynamicGOP;
err = NVSetParamValue(encoder_, NVVE_DYNAMIC_GOP, &DynamicGOP);
CV_Assert( err == 0 );
NVVE_RateCtrlType RCType = static_cast<NVVE_RateCtrlType>(params.RCType);
err = NVSetParamValue(encoder_, NVVE_RC_TYPE, &RCType);
CV_Assert( err == 0 );
int AvgBitrate = params.AvgBitrate;
err = NVSetParamValue(encoder_, NVVE_AVG_BITRATE, &AvgBitrate);
CV_Assert( err == 0 );
int PeakBitrate = params.PeakBitrate;
err = NVSetParamValue(encoder_, NVVE_PEAK_BITRATE, &PeakBitrate);
CV_Assert( err == 0 );
int QP_Level_Intra = params.QP_Level_Intra;
err = NVSetParamValue(encoder_, NVVE_QP_LEVEL_INTRA, &QP_Level_Intra);
CV_Assert( err == 0 );
int QP_Level_InterP = params.QP_Level_InterP;
err = NVSetParamValue(encoder_, NVVE_QP_LEVEL_INTER_P, &QP_Level_InterP);
CV_Assert( err == 0 );
int QP_Level_InterB = params.QP_Level_InterB;
err = NVSetParamValue(encoder_, NVVE_QP_LEVEL_INTER_B, &QP_Level_InterB);
CV_Assert( err == 0 );
int DeblockMode = params.DeblockMode;
err = NVSetParamValue(encoder_, NVVE_DEBLOCK_MODE, &DeblockMode);
CV_Assert( err == 0 );
int ProfileLevel = params.ProfileLevel;
err = NVSetParamValue(encoder_, NVVE_PROFILE_LEVEL, &ProfileLevel);
CV_Assert( err == 0 );
int ForceIntra = params.ForceIntra;
err = NVSetParamValue(encoder_, NVVE_FORCE_INTRA, &ForceIntra);
CV_Assert( err == 0 );
int ForceIDR = params.ForceIDR;
err = NVSetParamValue(encoder_, NVVE_FORCE_IDR, &ForceIDR);
CV_Assert( err == 0 );
int ClearStat = params.ClearStat;
err = NVSetParamValue(encoder_, NVVE_CLEAR_STAT, &ClearStat);
CV_Assert( err == 0 );
NVVE_DI_MODE DIMode = static_cast<NVVE_DI_MODE>(params.DIMode);
err = NVSetParamValue(encoder_, NVVE_SET_DEINTERLACE, &DIMode);
CV_Assert( err == 0 );
if (params.Presets != -1)
{
NVVE_PRESETS_TARGET Presets = static_cast<NVVE_PRESETS_TARGET>(params.Presets);
err = NVSetParamValue(encoder_, NVVE_PRESETS, &Presets);
CV_Assert( err == 0 );
}
int DisableCabac = params.DisableCabac;
err = NVSetParamValue(encoder_, NVVE_DISABLE_CABAC, &DisableCabac);
CV_Assert( err == 0 );
int NaluFramingType = params.NaluFramingType;
err = NVSetParamValue(encoder_, NVVE_CONFIGURE_NALU_FRAMING_TYPE, &NaluFramingType);
CV_Assert( err == 0 );
int DisableSPSPPS = params.DisableSPSPPS;
err = NVSetParamValue(encoder_, NVVE_DISABLE_SPS_PPS, &DisableSPSPPS);
CV_Assert( err == 0 );
}
EncoderParams VideoWriterImpl::getEncoderParams() const
{
int err;
EncoderParams params;
int P_Interval;
err = NVGetParamValue(encoder_, NVVE_P_INTERVAL, &P_Interval);
CV_Assert( err == 0 );
params.P_Interval = P_Interval;
int IDR_Period;
err = NVGetParamValue(encoder_, NVVE_IDR_PERIOD, &IDR_Period);
CV_Assert( err == 0 );
params.IDR_Period = IDR_Period;
int DynamicGOP;
err = NVGetParamValue(encoder_, NVVE_DYNAMIC_GOP, &DynamicGOP);
CV_Assert( err == 0 );
params.DynamicGOP = DynamicGOP;
NVVE_RateCtrlType RCType;
err = NVGetParamValue(encoder_, NVVE_RC_TYPE, &RCType);
CV_Assert( err == 0 );
params.RCType = RCType;
int AvgBitrate;
err = NVGetParamValue(encoder_, NVVE_AVG_BITRATE, &AvgBitrate);
CV_Assert( err == 0 );
params.AvgBitrate = AvgBitrate;
int PeakBitrate;
err = NVGetParamValue(encoder_, NVVE_PEAK_BITRATE, &PeakBitrate);
CV_Assert( err == 0 );
params.PeakBitrate = PeakBitrate;
int QP_Level_Intra;
err = NVGetParamValue(encoder_, NVVE_QP_LEVEL_INTRA, &QP_Level_Intra);
CV_Assert( err == 0 );
params.QP_Level_Intra = QP_Level_Intra;
int QP_Level_InterP;
err = NVGetParamValue(encoder_, NVVE_QP_LEVEL_INTER_P, &QP_Level_InterP);
CV_Assert( err == 0 );
params.QP_Level_InterP = QP_Level_InterP;
int QP_Level_InterB;
err = NVGetParamValue(encoder_, NVVE_QP_LEVEL_INTER_B, &QP_Level_InterB);
CV_Assert( err == 0 );
params.QP_Level_InterB = QP_Level_InterB;
int DeblockMode;
err = NVGetParamValue(encoder_, NVVE_DEBLOCK_MODE, &DeblockMode);
CV_Assert( err == 0 );
params.DeblockMode = DeblockMode;
int ProfileLevel;
err = NVGetParamValue(encoder_, NVVE_PROFILE_LEVEL, &ProfileLevel);
CV_Assert( err == 0 );
params.ProfileLevel = ProfileLevel;
int ForceIntra;
err = NVGetParamValue(encoder_, NVVE_FORCE_INTRA, &ForceIntra);
CV_Assert( err == 0 );
params.ForceIntra = ForceIntra;
int ForceIDR;
err = NVGetParamValue(encoder_, NVVE_FORCE_IDR, &ForceIDR);
CV_Assert( err == 0 );
params.ForceIDR = ForceIDR;
int ClearStat;
err = NVGetParamValue(encoder_, NVVE_CLEAR_STAT, &ClearStat);
CV_Assert( err == 0 );
params.ClearStat = ClearStat;
NVVE_DI_MODE DIMode;
err = NVGetParamValue(encoder_, NVVE_SET_DEINTERLACE, &DIMode);
CV_Assert( err == 0 );
params.DIMode = DIMode;
params.Presets = -1;
int DisableCabac;
err = NVGetParamValue(encoder_, NVVE_DISABLE_CABAC, &DisableCabac);
CV_Assert( err == 0 );
params.DisableCabac = DisableCabac;
int NaluFramingType;
err = NVGetParamValue(encoder_, NVVE_CONFIGURE_NALU_FRAMING_TYPE, &NaluFramingType);
CV_Assert( err == 0 );
params.NaluFramingType = NaluFramingType;
int DisableSPSPPS;
err = NVGetParamValue(encoder_, NVVE_DISABLE_SPS_PPS, &DisableSPSPPS);
CV_Assert( err == 0 );
params.DisableSPSPPS = DisableSPSPPS;
return params;
}
void VideoWriterImpl::initGpuMemory()
{
int err;
// initialize context
GpuMat temp(1, 1, CV_8U);
temp.release();
static const int bpp[] =
{
16, // UYVY, 4:2:2
16, // YUY2, 4:2:2
12, // YV12, 4:2:0
12, // NV12, 4:2:0
12, // IYUV, 4:2:0
};
CUcontext cuContext;
cuSafeCall( cuCtxGetCurrent(&cuContext) );
// Allocate the CUDA memory Pitched Surface
if (surfaceFormat_ == UYVY || surfaceFormat_ == YUY2)
videoFrame_.create(frameSize_.height, (frameSize_.width * bpp[surfaceFormat_]) / 8, CV_8UC1);
else
videoFrame_.create((frameSize_.height * bpp[surfaceFormat_]) / 8, frameSize_.width, CV_8UC1);
// Create the Video Context Lock (used for synchronization)
cuSafeCall( cuvidCtxLockCreate(&cuCtxLock_, cuContext) );
// If we are using GPU Device Memory with NVCUVENC, it is necessary to create a
// CUDA Context with a Context Lock cuvidCtxLock. The Context Lock needs to be passed to NVCUVENC
int iUseDeviceMem = 1;
err = NVSetParamValue(encoder_, NVVE_DEVICE_MEMORY_INPUT, &iUseDeviceMem);
CV_Assert( err == 0 );
err = NVSetParamValue(encoder_, NVVE_DEVICE_CTX_LOCK, &cuCtxLock_);
CV_Assert( err == 0 );
}
void VideoWriterImpl::initCallBacks()
{
NVVE_CallbackParams cb;
memset(&cb, 0, sizeof(NVVE_CallbackParams));
cb.pfnacquirebitstream = HandleAcquireBitStream;
cb.pfnonbeginframe = HandleOnBeginFrame;
cb.pfnonendframe = HandleOnEndFrame;
cb.pfnreleasebitstream = HandleReleaseBitStream;
NVRegisterCB(encoder_, cb, this);
}
void VideoWriterImpl::createHWEncoder()
{
int err;
// Create the NVIDIA HW resources for Encoding on NVIDIA hardware
err = NVCreateHWEncoder(encoder_);
CV_Assert( err == 0 );
}
// UYVY/YUY2 are both 4:2:2 formats (16bpc)
// Luma, U, V are interleaved, chroma is subsampled (w/2,h)
void copyUYVYorYUY2Frame(Size frameSize, const GpuMat& src, GpuMat& dst)
{
// Source is YUVY/YUY2 4:2:2, the YUV data in a packed and interleaved
// YUV Copy setup
CUDA_MEMCPY2D stCopyYUV422;
memset(&stCopyYUV422, 0, sizeof(CUDA_MEMCPY2D));
stCopyYUV422.srcXInBytes = 0;
stCopyYUV422.srcY = 0;
stCopyYUV422.srcMemoryType = CU_MEMORYTYPE_DEVICE;
stCopyYUV422.srcHost = 0;
stCopyYUV422.srcDevice = (CUdeviceptr) src.data;
stCopyYUV422.srcArray = 0;
stCopyYUV422.srcPitch = src.step;
stCopyYUV422.dstXInBytes = 0;
stCopyYUV422.dstY = 0;
stCopyYUV422.dstMemoryType = CU_MEMORYTYPE_DEVICE;
stCopyYUV422.dstHost = 0;
stCopyYUV422.dstDevice = (CUdeviceptr) dst.data;
stCopyYUV422.dstArray = 0;
stCopyYUV422.dstPitch = dst.step;
stCopyYUV422.WidthInBytes = frameSize.width * 2;
stCopyYUV422.Height = frameSize.height;
// DMA Luma/Chroma
cuSafeCall( cuMemcpy2D(&stCopyYUV422) );
}
// YV12/IYUV are both 4:2:0 planar formats (12bpc)
// Luma, U, V chroma planar (12bpc), chroma is subsampled (w/2,h/2)
void copyYV12orIYUVFrame(Size frameSize, const GpuMat& src, GpuMat& dst)
{
// Source is YV12/IYUV, this native format is converted to NV12 format by the video encoder
// (1) luma copy setup
CUDA_MEMCPY2D stCopyLuma;
memset(&stCopyLuma, 0, sizeof(CUDA_MEMCPY2D));
stCopyLuma.srcXInBytes = 0;
stCopyLuma.srcY = 0;
stCopyLuma.srcMemoryType = CU_MEMORYTYPE_DEVICE;
stCopyLuma.srcHost = 0;
stCopyLuma.srcDevice = (CUdeviceptr) src.data;
stCopyLuma.srcArray = 0;
stCopyLuma.srcPitch = src.step;
stCopyLuma.dstXInBytes = 0;
stCopyLuma.dstY = 0;
stCopyLuma.dstMemoryType = CU_MEMORYTYPE_DEVICE;
stCopyLuma.dstHost = 0;
stCopyLuma.dstDevice = (CUdeviceptr) dst.data;
stCopyLuma.dstArray = 0;
stCopyLuma.dstPitch = dst.step;
stCopyLuma.WidthInBytes = frameSize.width;
stCopyLuma.Height = frameSize.height;
// (2) chroma copy setup, U/V can be done together
CUDA_MEMCPY2D stCopyChroma;
memset(&stCopyChroma, 0, sizeof(CUDA_MEMCPY2D));
stCopyChroma.srcXInBytes = 0;
stCopyChroma.srcY = frameSize.height << 1; // U/V chroma offset
stCopyChroma.srcMemoryType = CU_MEMORYTYPE_DEVICE;
stCopyChroma.srcHost = 0;
stCopyChroma.srcDevice = (CUdeviceptr) src.data;
stCopyChroma.srcArray = 0;
stCopyChroma.srcPitch = src.step >> 1; // chroma is subsampled by 2 (but it has U/V are next to each other)
stCopyChroma.dstXInBytes = 0;
stCopyChroma.dstY = frameSize.height << 1; // chroma offset (srcY*srcPitch now points to the chroma planes)
stCopyChroma.dstMemoryType = CU_MEMORYTYPE_DEVICE;
stCopyChroma.dstHost = 0;
stCopyChroma.dstDevice = (CUdeviceptr) dst.data;
stCopyChroma.dstArray = 0;
stCopyChroma.dstPitch = dst.step >> 1;
stCopyChroma.WidthInBytes = frameSize.width >> 1;
stCopyChroma.Height = frameSize.height; // U/V are sent together
// DMA Luma
cuSafeCall( cuMemcpy2D(&stCopyLuma) );
// DMA Chroma channels (UV side by side)
cuSafeCall( cuMemcpy2D(&stCopyChroma) );
}
// NV12 is 4:2:0 format (12bpc)
// Luma followed by U/V chroma interleaved (12bpc), chroma is subsampled (w/2,h/2)
void copyNV12Frame(Size frameSize, const GpuMat& src, GpuMat& dst)
{
// Source is NV12 in pitch linear memory
// Because we are assume input is NV12 (if we take input in the native format), the encoder handles NV12 as a native format in pitch linear memory
// Luma/Chroma can be done in a single transfer
CUDA_MEMCPY2D stCopyNV12;
memset(&stCopyNV12, 0, sizeof(CUDA_MEMCPY2D));
stCopyNV12.srcXInBytes = 0;
stCopyNV12.srcY = 0;
stCopyNV12.srcMemoryType = CU_MEMORYTYPE_DEVICE;
stCopyNV12.srcHost = 0;
stCopyNV12.srcDevice = (CUdeviceptr) src.data;
stCopyNV12.srcArray = 0;
stCopyNV12.srcPitch = src.step;
stCopyNV12.dstXInBytes = 0;
stCopyNV12.dstY = 0;
stCopyNV12.dstMemoryType = CU_MEMORYTYPE_DEVICE;
stCopyNV12.dstHost = 0;
stCopyNV12.dstDevice = (CUdeviceptr) dst.data;
stCopyNV12.dstArray = 0;
stCopyNV12.dstPitch = dst.step;
stCopyNV12.WidthInBytes = frameSize.width;
stCopyNV12.Height = (frameSize.height * 3) >> 1;
// DMA Luma/Chroma
cuSafeCall( cuMemcpy2D(&stCopyNV12) );
}
void VideoWriterImpl::write(InputArray _frame, bool lastFrame)
{
GpuMat frame = _frame.getGpuMat();
if (inputFormat_ == SF_BGR)
{
CV_Assert( frame.size() == frameSize_ );
CV_Assert( frame.type() == CV_8UC1 || frame.type() == CV_8UC3 || frame.type() == CV_8UC4 );
}
else
{
CV_Assert( frame.size() == videoFrame_.size() );
CV_Assert( frame.type() == videoFrame_.type() );
}
NVVE_EncodeFrameParams efparams;
efparams.Width = frameSize_.width;
efparams.Height = frameSize_.height;
efparams.Pitch = static_cast<int>(videoFrame_.step);
efparams.SurfFmt = surfaceFormat_;
efparams.PictureStruc = FRAME_PICTURE;
efparams.topfieldfirst = 0;
efparams.repeatFirstField = 0;
efparams.progressiveFrame = (surfaceFormat_ == NV12) ? 1 : 0;
efparams.bLast = lastFrame;
efparams.picBuf = 0; // Must be set to NULL in order to support device memory input
// Don't forget we need to lock/unlock between memcopies
cuSafeCall( cuvidCtxLock(cuCtxLock_, 0) );
if (inputFormat_ == SF_BGR)
{
RGB_to_YV12(frame, videoFrame_);
}
else
{
switch (surfaceFormat_)
{
case UYVY: // UYVY (4:2:2)
case YUY2: // YUY2 (4:2:2)
copyUYVYorYUY2Frame(frameSize_, frame, videoFrame_);
break;
case YV12: // YV12 (4:2:0), Y V U
case IYUV: // IYUV (4:2:0), Y U V
copyYV12orIYUVFrame(frameSize_, frame, videoFrame_);
break;
case NV12: // NV12 (4:2:0)
copyNV12Frame(frameSize_, frame, videoFrame_);
break;
}
}
cuSafeCall( cuvidCtxUnlock(cuCtxLock_, 0) );
int err = NVEncodeFrame(encoder_, &efparams, 0, videoFrame_.data);
CV_Assert( err == 0 );
}
unsigned char* NVENCAPI VideoWriterImpl::HandleAcquireBitStream(int* pBufferSize, void* pUserdata)
{
VideoWriterImpl* thiz = static_cast<VideoWriterImpl*>(pUserdata);
return thiz->callback_->acquireBitStream(pBufferSize);
}
void NVENCAPI VideoWriterImpl::HandleReleaseBitStream(int nBytesInBuffer, unsigned char* cb, void* pUserdata)
{
VideoWriterImpl* thiz = static_cast<VideoWriterImpl*>(pUserdata);
thiz->callback_->releaseBitStream(cb, nBytesInBuffer);
}
void NVENCAPI VideoWriterImpl::HandleOnBeginFrame(const NVVE_BeginFrameInfo* pbfi, void* pUserdata)
{
VideoWriterImpl* thiz = static_cast<VideoWriterImpl*>(pUserdata);
thiz->callback_->onBeginFrame(pbfi->nFrameNumber, static_cast<EncoderCallBack::PicType>(pbfi->nPicType));
}
void NVENCAPI VideoWriterImpl::HandleOnEndFrame(const NVVE_EndFrameInfo* pefi, void* pUserdata)
{
VideoWriterImpl* thiz = static_cast<VideoWriterImpl*>(pUserdata);
thiz->callback_->onEndFrame(pefi->nFrameNumber, static_cast<EncoderCallBack::PicType>(pefi->nPicType));
}
///////////////////////////////////////////////////////////////////////////
// FFMPEG
class EncoderCallBackFFMPEG : public EncoderCallBack
{
public:
EncoderCallBackFFMPEG(const String& fileName, Size frameSize, double fps);
~EncoderCallBackFFMPEG();
unsigned char* acquireBitStream(int* bufferSize);
void releaseBitStream(unsigned char* data, int size);
void onBeginFrame(int frameNumber, PicType picType);
void onEndFrame(int frameNumber, PicType picType);
private:
static bool init_MediaStream_FFMPEG();
struct OutputMediaStream_FFMPEG* stream_;
std::vector<uchar> buf_;
bool isKeyFrame_;
static Create_OutputMediaStream_FFMPEG_Plugin create_OutputMediaStream_FFMPEG_p;
static Release_OutputMediaStream_FFMPEG_Plugin release_OutputMediaStream_FFMPEG_p;
static Write_OutputMediaStream_FFMPEG_Plugin write_OutputMediaStream_FFMPEG_p;
};
Create_OutputMediaStream_FFMPEG_Plugin EncoderCallBackFFMPEG::create_OutputMediaStream_FFMPEG_p = 0;
Release_OutputMediaStream_FFMPEG_Plugin EncoderCallBackFFMPEG::release_OutputMediaStream_FFMPEG_p = 0;
Write_OutputMediaStream_FFMPEG_Plugin EncoderCallBackFFMPEG::write_OutputMediaStream_FFMPEG_p = 0;
bool EncoderCallBackFFMPEG::init_MediaStream_FFMPEG()
{
static bool initialized = false;
if (!initialized)
{
#if defined(_WIN32)
const char* module_name = "opencv_ffmpeg"
CVAUX_STR(CV_VERSION_MAJOR) CVAUX_STR(CV_VERSION_MINOR) CVAUX_STR(CV_VERSION_REVISION)
#if (defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__)
"_64"
#endif
".dll";
static HMODULE cvFFOpenCV = LoadLibrary(module_name);
if (cvFFOpenCV)
{
create_OutputMediaStream_FFMPEG_p =
(Create_OutputMediaStream_FFMPEG_Plugin)GetProcAddress(cvFFOpenCV, "create_OutputMediaStream_FFMPEG");
release_OutputMediaStream_FFMPEG_p =
(Release_OutputMediaStream_FFMPEG_Plugin)GetProcAddress(cvFFOpenCV, "release_OutputMediaStream_FFMPEG");
write_OutputMediaStream_FFMPEG_p =
(Write_OutputMediaStream_FFMPEG_Plugin)GetProcAddress(cvFFOpenCV, "write_OutputMediaStream_FFMPEG");
initialized = create_OutputMediaStream_FFMPEG_p != 0 && release_OutputMediaStream_FFMPEG_p != 0 && write_OutputMediaStream_FFMPEG_p != 0;
}
#elif defined(HAVE_FFMPEG)
create_OutputMediaStream_FFMPEG_p = create_OutputMediaStream_FFMPEG;
release_OutputMediaStream_FFMPEG_p = release_OutputMediaStream_FFMPEG;
write_OutputMediaStream_FFMPEG_p = write_OutputMediaStream_FFMPEG;
initialized = true;
#endif
}
return initialized;
}
EncoderCallBackFFMPEG::EncoderCallBackFFMPEG(const String& fileName, Size frameSize, double fps) :
stream_(0), isKeyFrame_(false)
{
int buf_size = std::max(frameSize.area() * 4, 1024 * 1024);
buf_.resize(buf_size);
CV_Assert( init_MediaStream_FFMPEG() );
stream_ = create_OutputMediaStream_FFMPEG_p(fileName.c_str(), frameSize.width, frameSize.height, fps);
CV_Assert( stream_ != 0 );
}
EncoderCallBackFFMPEG::~EncoderCallBackFFMPEG()
{
release_OutputMediaStream_FFMPEG_p(stream_);
}
unsigned char* EncoderCallBackFFMPEG::acquireBitStream(int* bufferSize)
{
*bufferSize = static_cast<int>(buf_.size());
return &buf_[0];
}
void EncoderCallBackFFMPEG::releaseBitStream(unsigned char* data, int size)
{
write_OutputMediaStream_FFMPEG_p(stream_, data, size, isKeyFrame_);
}
void EncoderCallBackFFMPEG::onBeginFrame(int frameNumber, PicType picType)
{
CV_UNUSED(frameNumber);
isKeyFrame_ = (picType == IFRAME);
}
void EncoderCallBackFFMPEG::onEndFrame(int frameNumber, PicType picType)
{
CV_UNUSED(frameNumber);
CV_UNUSED(picType);
}
}
///////////////////////////////////////////////////////////////////////////
// EncoderParams
cv::cudacodec::EncoderParams::EncoderParams()
{
P_Interval = 3;
IDR_Period = 15;
DynamicGOP = 0;
RCType = 1;
AvgBitrate = 4000000;
PeakBitrate = 10000000;
QP_Level_Intra = 25;
QP_Level_InterP = 28;
QP_Level_InterB = 31;
DeblockMode = 1;
ProfileLevel = 65357;
ForceIntra = 0;
ForceIDR = 0;
ClearStat = 0;
DIMode = 1;
Presets = 2;
DisableCabac = 0;
NaluFramingType = 0;
DisableSPSPPS = 0;
}
cv::cudacodec::EncoderParams::EncoderParams(const String& configFile)
{
load(configFile);
}
void cv::cudacodec::EncoderParams::load(const String& configFile)
{
FileStorage fs(configFile, FileStorage::READ);
CV_Assert( fs.isOpened() );
read(fs["P_Interval" ], P_Interval, 3);
read(fs["IDR_Period" ], IDR_Period, 15);
read(fs["DynamicGOP" ], DynamicGOP, 0);
read(fs["RCType" ], RCType, 1);
read(fs["AvgBitrate" ], AvgBitrate, 4000000);
read(fs["PeakBitrate" ], PeakBitrate, 10000000);
read(fs["QP_Level_Intra" ], QP_Level_Intra, 25);
read(fs["QP_Level_InterP"], QP_Level_InterP, 28);
read(fs["QP_Level_InterB"], QP_Level_InterB, 31);
read(fs["DeblockMode" ], DeblockMode, 1);
read(fs["ProfileLevel" ], ProfileLevel, 65357);
read(fs["ForceIntra" ], ForceIntra, 0);
read(fs["ForceIDR" ], ForceIDR, 0);
read(fs["ClearStat" ], ClearStat, 0);
read(fs["DIMode" ], DIMode, 1);
read(fs["Presets" ], Presets, 2);
read(fs["DisableCabac" ], DisableCabac, 0);
read(fs["NaluFramingType"], NaluFramingType, 0);
read(fs["DisableSPSPPS" ], DisableSPSPPS, 0);
}
void cv::cudacodec::EncoderParams::save(const String& configFile) const
{
FileStorage fs(configFile, FileStorage::WRITE);
CV_Assert( fs.isOpened() );
write(fs, "P_Interval" , P_Interval);
write(fs, "IDR_Period" , IDR_Period);
write(fs, "DynamicGOP" , DynamicGOP);
write(fs, "RCType" , RCType);
write(fs, "AvgBitrate" , AvgBitrate);
write(fs, "PeakBitrate" , PeakBitrate);
write(fs, "QP_Level_Intra" , QP_Level_Intra);
write(fs, "QP_Level_InterP", QP_Level_InterP);
write(fs, "QP_Level_InterB", QP_Level_InterB);
write(fs, "DeblockMode" , DeblockMode);
write(fs, "ProfileLevel" , ProfileLevel);
write(fs, "ForceIntra" , ForceIntra);
write(fs, "ForceIDR" , ForceIDR);
write(fs, "ClearStat" , ClearStat);
write(fs, "DIMode" , DIMode);
write(fs, "Presets" , Presets);
write(fs, "DisableCabac" , DisableCabac);
write(fs, "NaluFramingType", NaluFramingType);
write(fs, "DisableSPSPPS" , DisableSPSPPS);
}
///////////////////////////////////////////////////////////////////////////
// createVideoWriter
Ptr<VideoWriter> cv::cudacodec::createVideoWriter(const String& fileName, Size frameSize, double fps, SurfaceFormat format)
{
Ptr<EncoderCallBack> encoderCallback(new EncoderCallBackFFMPEG(fileName, frameSize, fps));
return createVideoWriter(encoderCallback, frameSize, fps, format);
}
Ptr<VideoWriter> cv::cudacodec::createVideoWriter(const String& fileName, Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format)
{
Ptr<EncoderCallBack> encoderCallback(new EncoderCallBackFFMPEG(fileName, frameSize, fps));
return createVideoWriter(encoderCallback, frameSize, fps, params, format);
}
Ptr<VideoWriter> cv::cudacodec::createVideoWriter(const Ptr<EncoderCallBack>& encoderCallback, Size frameSize, double fps, SurfaceFormat format)
{
return makePtr<VideoWriterImpl>(encoderCallback, frameSize, fps, format);
}
Ptr<VideoWriter> cv::cudacodec::createVideoWriter(const Ptr<EncoderCallBack>& encoderCallback, Size frameSize, double fps, const EncoderParams& params, SurfaceFormat format)
{
return makePtr<VideoWriterImpl>(encoderCallback, frameSize, fps, params, format);
}
#endif // !defined HAVE_NVCUVENC || !defined _WIN32

45
modules/cudacodec/test/test_main.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
CV_CUDA_TEST_MAIN("gpu")

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@ -1,52 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_TEST_PRECOMP_HPP
#define OPENCV_TEST_PRECOMP_HPP
#include "opencv2/ts.hpp"
#include "opencv2/ts/cuda_test.hpp"
#include "opencv2/cudacodec.hpp"
#include "cvconfig.h"
#endif

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modules/cudacodec/test/test_video.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
#ifdef HAVE_NVCUVID
PARAM_TEST_CASE(Video, cv::cuda::DeviceInfo, std::string)
{
};
//////////////////////////////////////////////////////
// VideoReader
CUDA_TEST_P(Video, Reader)
{
cv::cuda::setDevice(GET_PARAM(0).deviceID());
const std::string inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);
cv::Ptr<cv::cudacodec::VideoReader> reader = cv::cudacodec::createVideoReader(inputFile);
cv::cuda::GpuMat frame;
for (int i = 0; i < 10; ++i)
{
ASSERT_TRUE(reader->nextFrame(frame));
ASSERT_FALSE(frame.empty());
}
}
//////////////////////////////////////////////////////
// VideoWriter
#ifdef _WIN32
CUDA_TEST_P(Video, Writer)
{
cv::cuda::setDevice(GET_PARAM(0).deviceID());
const std::string inputFile = std::string(cvtest::TS::ptr()->get_data_path()) + "video/" + GET_PARAM(1);
std::string outputFile = cv::tempfile(".avi");
const double FPS = 25.0;
cv::VideoCapture reader(inputFile);
ASSERT_TRUE(reader.isOpened());
cv::Ptr<cv::cudacodec::VideoWriter> d_writer;
cv::Mat frame;
cv::cuda::GpuMat d_frame;
for (int i = 0; i < 10; ++i)
{
reader >> frame;
ASSERT_FALSE(frame.empty());
d_frame.upload(frame);
if (d_writer.empty())
d_writer = cv::cudacodec::createVideoWriter(outputFile, frame.size(), FPS);
d_writer->write(d_frame);
}
reader.release();
d_writer.release();
reader.open(outputFile);
ASSERT_TRUE(reader.isOpened());
for (int i = 0; i < 5; ++i)
{
reader >> frame;
ASSERT_FALSE(frame.empty());
}
}
#endif // _WIN32
INSTANTIATE_TEST_CASE_P(CUDA_Codec, Video, testing::Combine(
ALL_DEVICES,
testing::Values(std::string("768x576.avi"), std::string("1920x1080.avi"))));
#endif // HAVE_NVCUVID
}} // namespace

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modules/cudafeatures2d/CMakeLists.txt
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if(IOS OR WINRT OR (NOT HAVE_CUDA AND NOT BUILD_CUDA_STUBS))
ocv_module_disable(cudafeatures2d)
endif()
set(the_description "CUDA-accelerated Feature Detection and Description")
ocv_warnings_disable(CMAKE_CXX_FLAGS /wd4127 /wd4100 /wd4324 /wd4512 /wd4515 -Wundef -Wmissing-declarations -Wshadow -Wunused-parameter -Wshadow)
ocv_define_module(cudafeatures2d opencv_features2d opencv_cudafilters opencv_cudawarping WRAP python)

490
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef OPENCV_CUDAFEATURES2D_HPP
#define OPENCV_CUDAFEATURES2D_HPP
#ifndef __cplusplus
# error cudafeatures2d.hpp header must be compiled as C++
#endif
#include "opencv2/core/cuda.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/cudafilters.hpp"
/**
@addtogroup cuda
@{
@defgroup cudafeatures2d Feature Detection and Description
@}
*/
namespace cv { namespace cuda {
//! @addtogroup cudafeatures2d
//! @{
//
// DescriptorMatcher
//
/** @brief Abstract base class for matching keypoint descriptors.
It has two groups of match methods: for matching descriptors of an image with another image or with
an image set.
*/
class CV_EXPORTS_W DescriptorMatcher : public cv::Algorithm
{
public:
//
// Factories
//
/** @brief Brute-force descriptor matcher.
For each descriptor in the first set, this matcher finds the closest descriptor in the second set
by trying each one. This descriptor matcher supports masking permissible matches of descriptor
sets.
@param normType One of NORM_L1, NORM_L2, NORM_HAMMING. L1 and L2 norms are
preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
BRIEF).
*/
CV_WRAP static Ptr<cuda::DescriptorMatcher> createBFMatcher(int normType = cv::NORM_L2);
//
// Utility
//
/** @brief Returns true if the descriptor matcher supports masking permissible matches.
*/
CV_WRAP virtual bool isMaskSupported() const = 0;
//
// Descriptor collection
//
/** @brief Adds descriptors to train a descriptor collection.
If the collection is not empty, the new descriptors are added to existing train descriptors.
@param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same
train image.
*/
CV_WRAP virtual void add(const std::vector<GpuMat>& descriptors) = 0;
/** @brief Returns a constant link to the train descriptor collection.
*/
CV_WRAP virtual const std::vector<GpuMat>& getTrainDescriptors() const = 0;
/** @brief Clears the train descriptor collection.
*/
CV_WRAP virtual void clear() = 0;
/** @brief Returns true if there are no train descriptors in the collection.
*/
CV_WRAP virtual bool empty() const = 0;
/** @brief Trains a descriptor matcher.
Trains a descriptor matcher (for example, the flann index). In all methods to match, the method
train() is run every time before matching.
*/
CV_WRAP virtual void train() = 0;
//
// 1 to 1 match
//
/** @brief Finds the best match for each descriptor from a query set (blocking version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
descriptor. So, matches size may be smaller than the query descriptors count.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.
*/
CV_WRAP virtual void match(InputArray queryDescriptors, InputArray trainDescriptors,
CV_OUT std::vector<DMatch>& matches,
InputArray mask = noArray()) = 0;
/** @overload
*/
CV_WRAP virtual void match(InputArray queryDescriptors,
CV_OUT std::vector<DMatch>& matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>()) = 0;
/** @brief Finds the best match for each descriptor from a query set (asynchronous version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches array stored in GPU memory. Internal representation is not defined.
Use DescriptorMatcher::matchConvert method to retrieve results in standard representation.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param stream CUDA stream.
In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.
*/
CV_WRAP virtual void matchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
InputArray mask = noArray(),
Stream& stream = Stream::Null()) = 0;
/** @overload
*/
CV_WRAP virtual void matchAsync(InputArray queryDescriptors,
OutputArray matches,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null()) = 0;
/** @brief Converts matches array from internal representation to standard matches vector.
The method is supposed to be used with DescriptorMatcher::matchAsync to get final result.
Call this method only after DescriptorMatcher::matchAsync is completed (ie. after synchronization).
@param gpu_matches Matches, returned from DescriptorMatcher::matchAsync.
@param matches Vector of DMatch objects.
*/
CV_WRAP virtual void matchConvert(InputArray gpu_matches,
CV_OUT std::vector<DMatch>& matches) = 0;
//
// knn match
//
/** @brief Finds the k best matches for each descriptor from a query set (blocking version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
These extended variants of DescriptorMatcher::match methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
for the details about query and train descriptors.
*/
CV_WRAP virtual void knnMatch(InputArray queryDescriptors, InputArray trainDescriptors,
CV_OUT std::vector<std::vector<DMatch> >& matches,
int k,
InputArray mask = noArray(),
bool compactResult = false) = 0;
/** @overload
*/
CV_WRAP virtual void knnMatch(InputArray queryDescriptors,
CV_OUT std::vector<std::vector<DMatch> >& matches,
int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
bool compactResult = false) = 0;
/** @brief Finds the k best matches for each descriptor from a query set (asynchronous version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches array stored in GPU memory. Internal representation is not defined.
Use DescriptorMatcher::knnMatchConvert method to retrieve results in standard representation.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param stream CUDA stream.
These extended variants of DescriptorMatcher::matchAsync methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::matchAsync
for the details about query and train descriptors.
*/
CV_WRAP virtual void knnMatchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
int k,
InputArray mask = noArray(),
Stream& stream = Stream::Null()) = 0;
/** @overload
*/
CV_WRAP virtual void knnMatchAsync(InputArray queryDescriptors,
OutputArray matches,
int k,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null()) = 0;
/** @brief Converts matches array from internal representation to standard matches vector.
The method is supposed to be used with DescriptorMatcher::knnMatchAsync to get final result.
Call this method only after DescriptorMatcher::knnMatchAsync is completed (ie. after synchronization).
@param gpu_matches Matches, returned from DescriptorMatcher::knnMatchAsync.
@param matches Vector of DMatch objects.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
*/
CV_WRAP virtual void knnMatchConvert(InputArray gpu_matches,
std::vector< std::vector<DMatch> >& matches,
bool compactResult = false) = 0;
//
// radius match
//
/** @brief For each query descriptor, finds the training descriptors not farther than the specified distance (blocking version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Found matches.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.
*/
CV_WRAP virtual void radiusMatch(InputArray queryDescriptors, InputArray trainDescriptors,
CV_OUT std::vector<std::vector<DMatch> >& matches,
float maxDistance,
InputArray mask = noArray(),
bool compactResult = false) = 0;
/** @overload
*/
CV_WRAP virtual void radiusMatch(InputArray queryDescriptors,
CV_OUT std::vector<std::vector<DMatch> >& matches,
float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
bool compactResult = false) = 0;
/** @brief For each query descriptor, finds the training descriptors not farther than the specified distance (asynchronous version).
@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches array stored in GPU memory. Internal representation is not defined.
Use DescriptorMatcher::radiusMatchConvert method to retrieve results in standard representation.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param stream CUDA stream.
For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.
*/
CV_WRAP virtual void radiusMatchAsync(InputArray queryDescriptors, InputArray trainDescriptors,
OutputArray matches,
float maxDistance,
InputArray mask = noArray(),
Stream& stream = Stream::Null()) = 0;
/** @overload
*/
CV_WRAP virtual void radiusMatchAsync(InputArray queryDescriptors,
OutputArray matches,
float maxDistance,
const std::vector<GpuMat>& masks = std::vector<GpuMat>(),
Stream& stream = Stream::Null()) = 0;
/** @brief Converts matches array from internal representation to standard matches vector.
The method is supposed to be used with DescriptorMatcher::radiusMatchAsync to get final result.
Call this method only after DescriptorMatcher::radiusMatchAsync is completed (ie. after synchronization).
@param gpu_matches Matches, returned from DescriptorMatcher::radiusMatchAsync.
@param matches Vector of DMatch objects.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
*/
CV_WRAP virtual void radiusMatchConvert(InputArray gpu_matches,
std::vector< std::vector<DMatch> >& matches,
bool compactResult = false) = 0;
};
//
// Feature2DAsync
//
/** @brief Abstract base class for CUDA asynchronous 2D image feature detectors and descriptor extractors.
*/
class CV_EXPORTS_W Feature2DAsync : public cv::Feature2D
{
public:
CV_WRAP virtual ~Feature2DAsync();
/** @brief Detects keypoints in an image.
@param image Image.
@param keypoints The detected keypoints.
@param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
matrix with non-zero values in the region of interest.
@param stream CUDA stream.
*/
CV_WRAP virtual void detectAsync(InputArray image,
OutputArray keypoints,
InputArray mask = noArray(),
Stream& stream = Stream::Null());
/** @brief Computes the descriptors for a set of keypoints detected in an image.
@param image Image.
@param keypoints Input collection of keypoints.
@param descriptors Computed descriptors. Row j is the descriptor for j-th keypoint.
@param stream CUDA stream.
*/
CV_WRAP virtual void computeAsync(InputArray image,
OutputArray keypoints,
OutputArray descriptors,
Stream& stream = Stream::Null());
/** Detects keypoints and computes the descriptors. */
CV_WRAP virtual void detectAndComputeAsync(InputArray image,
InputArray mask,
OutputArray keypoints,
OutputArray descriptors,
bool useProvidedKeypoints = false,
Stream& stream = Stream::Null());
/** Converts keypoints array from internal representation to standard vector. */
CV_WRAP virtual void convert(InputArray gpu_keypoints,
std::vector<KeyPoint>& keypoints) = 0;
};
//
// FastFeatureDetector
//
/** @brief Wrapping class for feature detection using the FAST method.
*/
class CV_EXPORTS_W FastFeatureDetector : public Feature2DAsync
{
public:
enum
{
LOCATION_ROW = 0,
RESPONSE_ROW,
ROWS_COUNT,
FEATURE_SIZE = 7
};
CV_WRAP static Ptr<cuda::FastFeatureDetector> create(int threshold=10,
bool nonmaxSuppression=true,
int type=cv::FastFeatureDetector::TYPE_9_16,
int max_npoints = 5000);
CV_WRAP virtual void setThreshold(int threshold) = 0;
CV_WRAP virtual void setMaxNumPoints(int max_npoints) = 0;
CV_WRAP virtual int getMaxNumPoints() const = 0;
};
//
// ORB
//
/** @brief Class implementing the ORB (*oriented BRIEF*) keypoint detector and descriptor extractor
*
* @sa cv::ORB
*/
class CV_EXPORTS_W ORB : public Feature2DAsync
{
public:
enum
{
X_ROW = 0,
Y_ROW,
RESPONSE_ROW,
ANGLE_ROW,
OCTAVE_ROW,
SIZE_ROW,
ROWS_COUNT
};
CV_WRAP static Ptr<cuda::ORB> create(int nfeatures=500,
float scaleFactor=1.2f,
int nlevels=8,
int edgeThreshold=31,
int firstLevel=0,
int WTA_K=2,
int scoreType=cv::ORB::HARRIS_SCORE,
int patchSize=31,
int fastThreshold=20,
bool blurForDescriptor=false);
//! if true, image will be blurred before descriptors calculation
CV_WRAP virtual void setBlurForDescriptor(bool blurForDescriptor) = 0;
CV_WRAP virtual bool getBlurForDescriptor() const = 0;
};
//! @}
}} // namespace cv { namespace cuda {
#endif /* OPENCV_CUDAFEATURES2D_HPP */

312
modules/cudafeatures2d/perf/perf_features2d.cpp
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@ -1,312 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
namespace opencv_test { namespace {
//////////////////////////////////////////////////////////////////////
// FAST
DEF_PARAM_TEST(Image_Threshold_NonMaxSuppression, string, int, bool);
PERF_TEST_P(Image_Threshold_NonMaxSuppression, FAST,
Combine(Values<string>("gpu/perf/aloe.png"),
Values(20),
Bool()))
{
const cv::Mat img = readImage(GET_PARAM(0), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
const int threshold = GET_PARAM(1);
const bool nonMaxSuppersion = GET_PARAM(2);
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cuda::FastFeatureDetector> d_fast =
cv::cuda::FastFeatureDetector::create(threshold, nonMaxSuppersion,
cv::FastFeatureDetector::TYPE_9_16,
0.5 * img.size().area());
const cv::cuda::GpuMat d_img(img);
cv::cuda::GpuMat d_keypoints;
TEST_CYCLE() d_fast->detectAsync(d_img, d_keypoints);
std::vector<cv::KeyPoint> gpu_keypoints;
d_fast->convert(d_keypoints, gpu_keypoints);
sortKeyPoints(gpu_keypoints);
SANITY_CHECK_KEYPOINTS(gpu_keypoints);
}
else
{
std::vector<cv::KeyPoint> cpu_keypoints;
TEST_CYCLE() cv::FAST(img, cpu_keypoints, threshold, nonMaxSuppersion);
SANITY_CHECK_KEYPOINTS(cpu_keypoints);
}
}
//////////////////////////////////////////////////////////////////////
// ORB
DEF_PARAM_TEST(Image_NFeatures, string, int);
PERF_TEST_P(Image_NFeatures, ORB,
Combine(Values<string>("gpu/perf/aloe.png"),
Values(4000)))
{
declare.time(300.0);
const cv::Mat img = readImage(GET_PARAM(0), cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(img.empty());
const int nFeatures = GET_PARAM(1);
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cuda::ORB> d_orb = cv::cuda::ORB::create(nFeatures);
const cv::cuda::GpuMat d_img(img);
cv::cuda::GpuMat d_keypoints, d_descriptors;
TEST_CYCLE() d_orb->detectAndComputeAsync(d_img, cv::noArray(), d_keypoints, d_descriptors);
std::vector<cv::KeyPoint> gpu_keypoints;
d_orb->convert(d_keypoints, gpu_keypoints);
cv::Mat gpu_descriptors(d_descriptors);
gpu_keypoints.resize(10);
gpu_descriptors = gpu_descriptors.rowRange(0, 10);
sortKeyPoints(gpu_keypoints, gpu_descriptors);
SANITY_CHECK_KEYPOINTS(gpu_keypoints, 1e-4);
SANITY_CHECK(gpu_descriptors);
}
else
{
cv::Ptr<cv::ORB> orb = cv::ORB::create(nFeatures);
std::vector<cv::KeyPoint> cpu_keypoints;
cv::Mat cpu_descriptors;
TEST_CYCLE() orb->detectAndCompute(img, cv::noArray(), cpu_keypoints, cpu_descriptors);
SANITY_CHECK_KEYPOINTS(cpu_keypoints);
SANITY_CHECK(cpu_descriptors);
}
}
//////////////////////////////////////////////////////////////////////
// BFMatch
DEF_PARAM_TEST(DescSize_Norm, int, NormType);
PERF_TEST_P(DescSize_Norm, BFMatch,
Combine(Values(64, 128, 256),
Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2), NormType(cv::NORM_HAMMING))))
{
declare.time(20.0);
const int desc_size = GET_PARAM(0);
const int normType = GET_PARAM(1);
const int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F;
cv::Mat query(3000, desc_size, type);
declare.in(query, WARMUP_RNG);
cv::Mat train(3000, desc_size, type);
declare.in(train, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
const cv::cuda::GpuMat d_query(query);
const cv::cuda::GpuMat d_train(train);
cv::cuda::GpuMat d_matches;
TEST_CYCLE() d_matcher->matchAsync(d_query, d_train, d_matches);
std::vector<cv::DMatch> gpu_matches;
d_matcher->matchConvert(d_matches, gpu_matches);
SANITY_CHECK_MATCHES(gpu_matches);
}
else
{
cv::BFMatcher matcher(normType);
std::vector<cv::DMatch> cpu_matches;
TEST_CYCLE() matcher.match(query, train, cpu_matches);
SANITY_CHECK_MATCHES(cpu_matches);
}
}
//////////////////////////////////////////////////////////////////////
// BFKnnMatch
static void toOneRowMatches(const std::vector< std::vector<cv::DMatch> >& src, std::vector<cv::DMatch>& dst)
{
dst.clear();
for (size_t i = 0; i < src.size(); ++i)
for (size_t j = 0; j < src[i].size(); ++j)
dst.push_back(src[i][j]);
}
DEF_PARAM_TEST(DescSize_K_Norm, int, int, NormType);
PERF_TEST_P(DescSize_K_Norm, BFKnnMatch,
Combine(Values(64, 128, 256),
Values(2, 3),
Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2))))
{
declare.time(30.0);
const int desc_size = GET_PARAM(0);
const int k = GET_PARAM(1);
const int normType = GET_PARAM(2);
const int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F;
cv::Mat query(3000, desc_size, type);
declare.in(query, WARMUP_RNG);
cv::Mat train(3000, desc_size, type);
declare.in(train, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
const cv::cuda::GpuMat d_query(query);
const cv::cuda::GpuMat d_train(train);
cv::cuda::GpuMat d_matches;
TEST_CYCLE() d_matcher->knnMatchAsync(d_query, d_train, d_matches, k);
std::vector< std::vector<cv::DMatch> > matchesTbl;
d_matcher->knnMatchConvert(d_matches, matchesTbl);
std::vector<cv::DMatch> gpu_matches;
toOneRowMatches(matchesTbl, gpu_matches);
SANITY_CHECK_MATCHES(gpu_matches);
}
else
{
cv::BFMatcher matcher(normType);
std::vector< std::vector<cv::DMatch> > matchesTbl;
TEST_CYCLE() matcher.knnMatch(query, train, matchesTbl, k);
std::vector<cv::DMatch> cpu_matches;
toOneRowMatches(matchesTbl, cpu_matches);
SANITY_CHECK_MATCHES(cpu_matches);
}
}
//////////////////////////////////////////////////////////////////////
// BFRadiusMatch
PERF_TEST_P(DescSize_Norm, BFRadiusMatch,
Combine(Values(64, 128, 256),
Values(NormType(cv::NORM_L1), NormType(cv::NORM_L2))))
{
declare.time(30.0);
const int desc_size = GET_PARAM(0);
const int normType = GET_PARAM(1);
const int type = normType == cv::NORM_HAMMING ? CV_8U : CV_32F;
const float maxDistance = 10000;
cv::Mat query(3000, desc_size, type);
declare.in(query, WARMUP_RNG);
cv::Mat train(3000, desc_size, type);
declare.in(train, WARMUP_RNG);
if (PERF_RUN_CUDA())
{
cv::Ptr<cv::cuda::DescriptorMatcher> d_matcher = cv::cuda::DescriptorMatcher::createBFMatcher(normType);
const cv::cuda::GpuMat d_query(query);
const cv::cuda::GpuMat d_train(train);
cv::cuda::GpuMat d_matches;
TEST_CYCLE() d_matcher->radiusMatchAsync(d_query, d_train, d_matches, maxDistance);
std::vector< std::vector<cv::DMatch> > matchesTbl;
d_matcher->radiusMatchConvert(d_matches, matchesTbl);
std::vector<cv::DMatch> gpu_matches;
toOneRowMatches(matchesTbl, gpu_matches);
SANITY_CHECK_MATCHES(gpu_matches);
}
else
{
cv::BFMatcher matcher(normType);
std::vector< std::vector<cv::DMatch> > matchesTbl;
TEST_CYCLE() matcher.radiusMatch(query, train, matchesTbl, maxDistance);
std::vector<cv::DMatch> cpu_matches;
toOneRowMatches(matchesTbl, cpu_matches);
SANITY_CHECK_MATCHES(cpu_matches);
}
}
}} // namespace

47
modules/cudafeatures2d/perf/perf_main.cpp
View File

@ -1,47 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "perf_precomp.hpp"
using namespace perf;
CV_PERF_TEST_CUDA_MAIN(cudafeatures2d)

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