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face_num_recongation/src/3rdParty/tensorflow/r1.8/libs/Linux/cuda9/libtensorflow_cc.so
face_num_recongation/src/3rdParty/tensorflow/r1.8/libs/Linux/cpu/libtensorflow_cc.so
face_num_recongation/src/3rdParty/ubuntu-cuda10/libcudnn.so.7
face_num_recongation/src/3rdParty/ubuntu-cuda10/libnvinfer.so.5
face_num_recongation/src/3rdParty/ubuntu-cuda10/libcublas.so.10.0
face_num_recongation/src/det_face_id/libs/linux/gpu/libcudnn.so.7
face_num_recongation/src/det_face_id/libs/linux/gpu/libnvinfer.so.5
face_num_recongation/src/det_face_id/libs/linux/gpu/libcublas.so.10.0

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.vscode/

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face_num_recongation/README.md Normal file
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<<<<<<< HEAD
# face_num_recongation
=======
# face_num_recognition
## 根据CUDA类型进行调整模型
```bash
bin/bash src/det_face_id/init_model.sh
```
## 编译cpp项目
```bash
cd src/
cmake CMakeLists.txt
make -j4
```
## 运行程序
```
./bin/GodeyeVideoCrow
```
>>>>>>> origin/renlianshuliang

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# Install script for directory: /home/mayang8/open_source/face_recognition/src
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if(NOT DEFINED CMAKE_INSTALL_PREFIX)
set(CMAKE_INSTALL_PREFIX "/home/mayang8/open_source/face_recognition/src/package")
endif()
string(REGEX REPLACE "/$" "" CMAKE_INSTALL_PREFIX "${CMAKE_INSTALL_PREFIX}")
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if(BUILD_TYPE)
string(REGEX REPLACE "^[^A-Za-z0-9_]+" ""
CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}")
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set(CMAKE_INSTALL_CONFIG_NAME "")
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message(STATUS "Install configuration: \"${CMAKE_INSTALL_CONFIG_NAME}\"")
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if(COMPONENT)
message(STATUS "Install component: \"${COMPONENT}\"")
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set(CMAKE_INSTALL_COMPONENT)
endif()
endif()
# Install shared libraries without execute permission?
if(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE)
set(CMAKE_INSTALL_SO_NO_EXE "1")
endif()
# Is this installation the result of a crosscompile?
if(NOT DEFINED CMAKE_CROSSCOMPILING)
set(CMAKE_CROSSCOMPILING "FALSE")
endif()
# Set default install directory permissions.
if(NOT DEFINED CMAKE_OBJDUMP)
set(CMAKE_OBJDUMP "/usr/bin/objdump")
endif()
if("x${CMAKE_INSTALL_COMPONENT}x" STREQUAL "xUnspecifiedx" OR NOT CMAKE_INSTALL_COMPONENT)
file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE DIRECTORY FILES "/home/mayang8/open_source/face_recognition/src/bin//")
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file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE DIRECTORY FILES "/home/mayang8/open_source/face_recognition/src/lib//")
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file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE DIRECTORY FILES "/home/mayang8/open_source/face_recognition/src/3rdParty/tensorflow/r1.8/libs/Linux/cpu")
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file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/bin" TYPE DIRECTORY FILES "/home/mayang8/open_source/face_recognition/src/ai_model")
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file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/." TYPE DIRECTORY FILES "/home/mayang8/open_source/face_recognition/src/det_face_id")
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file(INSTALL DESTINATION "${CMAKE_INSTALL_PREFIX}/lib" TYPE DIRECTORY FILES "/home/mayang8/open_source/face_recognition/src/3rdParty/ubuntu-cuda10")
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:../det_face_id/libs/linux/gpu/
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-10.0/lib64

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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*/
#ifdef __OPENCV_BUILD
#error this is a compatibility header which should not be used inside the OpenCV library
#endif
#include "opencv2/calib3d.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_CALIB3D_C_H
#define OPENCV_CALIB3D_C_H
#include "opencv2/core/types_c.h"
#ifdef __cplusplus
extern "C" {
#endif
/* Calculates fundamental matrix given a set of corresponding points */
#define CV_FM_7POINT 1
#define CV_FM_8POINT 2
#define CV_LMEDS 4
#define CV_RANSAC 8
#define CV_FM_LMEDS_ONLY CV_LMEDS
#define CV_FM_RANSAC_ONLY CV_RANSAC
#define CV_FM_LMEDS CV_LMEDS
#define CV_FM_RANSAC CV_RANSAC
enum
{
CV_ITERATIVE = 0,
CV_EPNP = 1, // F.Moreno-Noguer, V.Lepetit and P.Fua "EPnP: Efficient Perspective-n-Point Camera Pose Estimation"
CV_P3P = 2, // X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang; "Complete Solution Classification for the Perspective-Three-Point Problem"
CV_DLS = 3 // Joel A. Hesch and Stergios I. Roumeliotis. "A Direct Least-Squares (DLS) Method for PnP"
};
#define CV_CALIB_CB_ADAPTIVE_THRESH 1
#define CV_CALIB_CB_NORMALIZE_IMAGE 2
#define CV_CALIB_CB_FILTER_QUADS 4
#define CV_CALIB_CB_FAST_CHECK 8
#define CV_CALIB_USE_INTRINSIC_GUESS 1
#define CV_CALIB_FIX_ASPECT_RATIO 2
#define CV_CALIB_FIX_PRINCIPAL_POINT 4
#define CV_CALIB_ZERO_TANGENT_DIST 8
#define CV_CALIB_FIX_FOCAL_LENGTH 16
#define CV_CALIB_FIX_K1 32
#define CV_CALIB_FIX_K2 64
#define CV_CALIB_FIX_K3 128
#define CV_CALIB_FIX_K4 2048
#define CV_CALIB_FIX_K5 4096
#define CV_CALIB_FIX_K6 8192
#define CV_CALIB_RATIONAL_MODEL 16384
#define CV_CALIB_THIN_PRISM_MODEL 32768
#define CV_CALIB_FIX_S1_S2_S3_S4 65536
#define CV_CALIB_TILTED_MODEL 262144
#define CV_CALIB_FIX_TAUX_TAUY 524288
#define CV_CALIB_FIX_TANGENT_DIST 2097152
#define CV_CALIB_NINTRINSIC 18
#define CV_CALIB_FIX_INTRINSIC 256
#define CV_CALIB_SAME_FOCAL_LENGTH 512
#define CV_CALIB_ZERO_DISPARITY 1024
/* stereo correspondence parameters and functions */
#define CV_STEREO_BM_NORMALIZED_RESPONSE 0
#define CV_STEREO_BM_XSOBEL 1
#ifdef __cplusplus
} // extern "C"
//////////////////////////////////////////////////////////////////////////////////////////
class CV_EXPORTS CvLevMarq
{
public:
CvLevMarq();
CvLevMarq( int nparams, int nerrs, CvTermCriteria criteria=
cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON),
bool completeSymmFlag=false );
~CvLevMarq();
void init( int nparams, int nerrs, CvTermCriteria criteria=
cvTermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,30,DBL_EPSILON),
bool completeSymmFlag=false );
bool update( const CvMat*& param, CvMat*& J, CvMat*& err );
bool updateAlt( const CvMat*& param, CvMat*& JtJ, CvMat*& JtErr, double*& errNorm );
void clear();
void step();
enum { DONE=0, STARTED=1, CALC_J=2, CHECK_ERR=3 };
cv::Ptr<CvMat> mask;
cv::Ptr<CvMat> prevParam;
cv::Ptr<CvMat> param;
cv::Ptr<CvMat> J;
cv::Ptr<CvMat> err;
cv::Ptr<CvMat> JtJ;
cv::Ptr<CvMat> JtJN;
cv::Ptr<CvMat> JtErr;
cv::Ptr<CvMat> JtJV;
cv::Ptr<CvMat> JtJW;
double prevErrNorm, errNorm;
int lambdaLg10;
CvTermCriteria criteria;
int state;
int iters;
bool completeSymmFlag;
int solveMethod;
};
#endif
#endif /* OPENCV_CALIB3D_C_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*/
#ifndef OPENCV_CORE_AFFINE3_HPP
#define OPENCV_CORE_AFFINE3_HPP
#ifdef __cplusplus
#include <opencv2/core.hpp>
namespace cv
{
//! @addtogroup core
//! @{
/** @brief Affine transform
*
* It represents a 4x4 homogeneous transformation matrix \f$T\f$
*
* \f[T =
* \begin{bmatrix}
* R & t\\
* 0 & 1\\
* \end{bmatrix}
* \f]
*
* where \f$R\f$ is a 3x3 rotation matrix and \f$t\f$ is a 3x1 translation vector.
*
* You can specify \f$R\f$ either by a 3x3 rotation matrix or by a 3x1 rotation vector,
* which is converted to a 3x3 rotation matrix by the Rodrigues formula.
*
* To construct a matrix \f$T\f$ representing first rotation around the axis \f$r\f$ with rotation
* angle \f$|r|\f$ in radian (right hand rule) and then translation by the vector \f$t\f$, you can use
*
* @code
* cv::Vec3f r, t;
* cv::Affine3f T(r, t);
* @endcode
*
* If you already have the rotation matrix \f$R\f$, then you can use
*
* @code
* cv::Matx33f R;
* cv::Affine3f T(R, t);
* @endcode
*
* To extract the rotation matrix \f$R\f$ from \f$T\f$, use
*
* @code
* cv::Matx33f R = T.rotation();
* @endcode
*
* To extract the translation vector \f$t\f$ from \f$T\f$, use
*
* @code
* cv::Vec3f t = T.translation();
* @endcode
*
* To extract the rotation vector \f$r\f$ from \f$T\f$, use
*
* @code
* cv::Vec3f r = T.rvec();
* @endcode
*
* Note that since the mapping from rotation vectors to rotation matrices
* is many to one. The returned rotation vector is not necessarily the one
* you used before to set the matrix.
*
* If you have two transformations \f$T = T_1 * T_2\f$, use
*
* @code
* cv::Affine3f T, T1, T2;
* T = T2.concatenate(T1);
* @endcode
*
* To get the inverse transform of \f$T\f$, use
*
* @code
* cv::Affine3f T, T_inv;
* T_inv = T.inv();
* @endcode
*
*/
template<typename T>
class Affine3
{
public:
typedef T float_type;
typedef Matx<float_type, 3, 3> Mat3;
typedef Matx<float_type, 4, 4> Mat4;
typedef Vec<float_type, 3> Vec3;
//! Default constructor. It represents a 4x4 identity matrix.
Affine3();
//! Augmented affine matrix
Affine3(const Mat4& affine);
/**
* The resulting 4x4 matrix is
*
* \f[
* \begin{bmatrix}
* R & t\\
* 0 & 1\\
* \end{bmatrix}
* \f]
*
* @param R 3x3 rotation matrix.
* @param t 3x1 translation vector.
*/
Affine3(const Mat3& R, const Vec3& t = Vec3::all(0));
/**
* Rodrigues vector.
*
* The last row of the current matrix is set to [0,0,0,1].
*
* @param rvec 3x1 rotation vector. Its direction indicates the rotation axis and its length
* indicates the rotation angle in radian (using right hand rule).
* @param t 3x1 translation vector.
*/
Affine3(const Vec3& rvec, const Vec3& t = Vec3::all(0));
/**
* Combines all constructors above. Supports 4x4, 3x4, 3x3, 1x3, 3x1 sizes of data matrix.
*
* The last row of the current matrix is set to [0,0,0,1] when data is not 4x4.
*
* @param data 1-channel matrix.
* when it is 4x4, it is copied to the current matrix and t is not used.
* When it is 3x4, it is copied to the upper part 3x4 of the current matrix and t is not used.
* When it is 3x3, it is copied to the upper left 3x3 part of the current matrix.
* When it is 3x1 or 1x3, it is treated as a rotation vector and the Rodrigues formula is used
* to compute a 3x3 rotation matrix.
* @param t 3x1 translation vector. It is used only when data is neither 4x4 nor 3x4.
*/
explicit Affine3(const Mat& data, const Vec3& t = Vec3::all(0));
//! From 16-element array
explicit Affine3(const float_type* vals);
//! Create an 4x4 identity transform
static Affine3 Identity();
/**
* Rotation matrix.
*
* Copy the rotation matrix to the upper left 3x3 part of the current matrix.
* The remaining elements of the current matrix are not changed.
*
* @param R 3x3 rotation matrix.
*
*/
void rotation(const Mat3& R);
/**
* Rodrigues vector.
*
* It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
*
* @param rvec 3x1 rotation vector. The direction indicates the rotation axis and
* its length indicates the rotation angle in radian (using the right thumb convention).
*/
void rotation(const Vec3& rvec);
/**
* Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix.
*
* It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
*
* @param data 1-channel matrix.
* When it is a 3x3 matrix, it sets the upper left 3x3 part of the current matrix.
* When it is a 1x3 or 3x1 matrix, it is used as a rotation vector. The Rodrigues formula
* is used to compute the rotation matrix and sets the upper left 3x3 part of the current matrix.
*/
void rotation(const Mat& data);
/**
* Copy the 3x3 matrix L to the upper left part of the current matrix
*
* It sets the upper left 3x3 part of the matrix. The remaining part is unaffected.
*
* @param L 3x3 matrix.
*/
void linear(const Mat3& L);
/**
* Copy t to the first three elements of the last column of the current matrix
*
* It sets the upper right 3x1 part of the matrix. The remaining part is unaffected.
*
* @param t 3x1 translation vector.
*/
void translation(const Vec3& t);
//! @return the upper left 3x3 part
Mat3 rotation() const;
//! @return the upper left 3x3 part
Mat3 linear() const;
//! @return the upper right 3x1 part
Vec3 translation() const;
//! Rodrigues vector.
//! @return a vector representing the upper left 3x3 rotation matrix of the current matrix.
//! @warning Since the mapping between rotation vectors and rotation matrices is many to one,
//! this function returns only one rotation vector that represents the current rotation matrix,
//! which is not necessarily the same one set by `rotation(const Vec3& rvec)`.
Vec3 rvec() const;
//! @return the inverse of the current matrix.
Affine3 inv(int method = cv::DECOMP_SVD) const;
//! a.rotate(R) is equivalent to Affine(R, 0) * a;
Affine3 rotate(const Mat3& R) const;
//! a.rotate(rvec) is equivalent to Affine(rvec, 0) * a;
Affine3 rotate(const Vec3& rvec) const;
//! a.translate(t) is equivalent to Affine(E, t) * a, where E is an identity matrix
Affine3 translate(const Vec3& t) const;
//! a.concatenate(affine) is equivalent to affine * a;
Affine3 concatenate(const Affine3& affine) const;
template <typename Y> operator Affine3<Y>() const;
template <typename Y> Affine3<Y> cast() const;
Mat4 matrix;
#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H
Affine3(const Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>& affine);
Affine3(const Eigen::Transform<T, 3, Eigen::Affine>& affine);
operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>() const;
operator Eigen::Transform<T, 3, Eigen::Affine>() const;
#endif
};
template<typename T> static
Affine3<T> operator*(const Affine3<T>& affine1, const Affine3<T>& affine2);
//! V is a 3-element vector with member fields x, y and z
template<typename T, typename V> static
V operator*(const Affine3<T>& affine, const V& vector);
typedef Affine3<float> Affine3f;
typedef Affine3<double> Affine3d;
static Vec3f operator*(const Affine3f& affine, const Vec3f& vector);
static Vec3d operator*(const Affine3d& affine, const Vec3d& vector);
template<typename _Tp> class DataType< Affine3<_Tp> >
{
public:
typedef Affine3<_Tp> value_type;
typedef Affine3<typename DataType<_Tp>::work_type> work_type;
typedef _Tp channel_type;
enum { generic_type = 0,
channels = 16,
fmt = traits::SafeFmt<channel_type>::fmt + ((channels - 1) << 8)
#ifdef OPENCV_TRAITS_ENABLE_DEPRECATED
,depth = DataType<channel_type>::depth
,type = CV_MAKETYPE(depth, channels)
#endif
};
typedef Vec<channel_type, channels> vec_type;
};
namespace traits {
template<typename _Tp>
struct Depth< Affine3<_Tp> > { enum { value = Depth<_Tp>::value }; };
template<typename _Tp>
struct Type< Affine3<_Tp> > { enum { value = CV_MAKETYPE(Depth<_Tp>::value, 16) }; };
} // namespace
//! @} core
}
//! @cond IGNORED
///////////////////////////////////////////////////////////////////////////////////
// Implementation
template<typename T> inline
cv::Affine3<T>::Affine3()
: matrix(Mat4::eye())
{}
template<typename T> inline
cv::Affine3<T>::Affine3(const Mat4& affine)
: matrix(affine)
{}
template<typename T> inline
cv::Affine3<T>::Affine3(const Mat3& R, const Vec3& t)
{
rotation(R);
translation(t);
matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
matrix.val[15] = 1;
}
template<typename T> inline
cv::Affine3<T>::Affine3(const Vec3& _rvec, const Vec3& t)
{
rotation(_rvec);
translation(t);
matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
matrix.val[15] = 1;
}
template<typename T> inline
cv::Affine3<T>::Affine3(const cv::Mat& data, const Vec3& t)
{
CV_Assert(data.type() == cv::traits::Type<T>::value);
CV_Assert(data.channels() == 1);
if (data.cols == 4 && data.rows == 4)
{
data.copyTo(matrix);
return;
}
else if (data.cols == 4 && data.rows == 3)
{
rotation(data(Rect(0, 0, 3, 3)));
translation(data(Rect(3, 0, 1, 3)));
}
else
{
rotation(data);
translation(t);
}
matrix.val[12] = matrix.val[13] = matrix.val[14] = 0;
matrix.val[15] = 1;
}
template<typename T> inline
cv::Affine3<T>::Affine3(const float_type* vals) : matrix(vals)
{}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::Identity()
{
return Affine3<T>(cv::Affine3<T>::Mat4::eye());
}
template<typename T> inline
void cv::Affine3<T>::rotation(const Mat3& R)
{
linear(R);
}
template<typename T> inline
void cv::Affine3<T>::rotation(const Vec3& _rvec)
{
double theta = norm(_rvec);
if (theta < DBL_EPSILON)
rotation(Mat3::eye());
else
{
double c = std::cos(theta);
double s = std::sin(theta);
double c1 = 1. - c;
double itheta = (theta != 0) ? 1./theta : 0.;
Point3_<T> r = _rvec*itheta;
Mat3 rrt( r.x*r.x, r.x*r.y, r.x*r.z, r.x*r.y, r.y*r.y, r.y*r.z, r.x*r.z, r.y*r.z, r.z*r.z );
Mat3 r_x( 0, -r.z, r.y, r.z, 0, -r.x, -r.y, r.x, 0 );
// R = cos(theta)*I + (1 - cos(theta))*r*rT + sin(theta)*[r_x]
// where [r_x] is [0 -rz ry; rz 0 -rx; -ry rx 0]
Mat3 R = c*Mat3::eye() + c1*rrt + s*r_x;
rotation(R);
}
}
//Combines rotation methods above. Supports 3x3, 1x3, 3x1 sizes of data matrix;
template<typename T> inline
void cv::Affine3<T>::rotation(const cv::Mat& data)
{
CV_Assert(data.type() == cv::traits::Type<T>::value);
CV_Assert(data.channels() == 1);
if (data.cols == 3 && data.rows == 3)
{
Mat3 R;
data.copyTo(R);
rotation(R);
}
else if ((data.cols == 3 && data.rows == 1) || (data.cols == 1 && data.rows == 3))
{
Vec3 _rvec;
data.reshape(1, 3).copyTo(_rvec);
rotation(_rvec);
}
else
CV_Error(Error::StsError, "Input matrix can only be 3x3, 1x3 or 3x1");
}
template<typename T> inline
void cv::Affine3<T>::linear(const Mat3& L)
{
matrix.val[0] = L.val[0]; matrix.val[1] = L.val[1]; matrix.val[ 2] = L.val[2];
matrix.val[4] = L.val[3]; matrix.val[5] = L.val[4]; matrix.val[ 6] = L.val[5];
matrix.val[8] = L.val[6]; matrix.val[9] = L.val[7]; matrix.val[10] = L.val[8];
}
template<typename T> inline
void cv::Affine3<T>::translation(const Vec3& t)
{
matrix.val[3] = t[0]; matrix.val[7] = t[1]; matrix.val[11] = t[2];
}
template<typename T> inline
typename cv::Affine3<T>::Mat3 cv::Affine3<T>::rotation() const
{
return linear();
}
template<typename T> inline
typename cv::Affine3<T>::Mat3 cv::Affine3<T>::linear() const
{
typename cv::Affine3<T>::Mat3 R;
R.val[0] = matrix.val[0]; R.val[1] = matrix.val[1]; R.val[2] = matrix.val[ 2];
R.val[3] = matrix.val[4]; R.val[4] = matrix.val[5]; R.val[5] = matrix.val[ 6];
R.val[6] = matrix.val[8]; R.val[7] = matrix.val[9]; R.val[8] = matrix.val[10];
return R;
}
template<typename T> inline
typename cv::Affine3<T>::Vec3 cv::Affine3<T>::translation() const
{
return Vec3(matrix.val[3], matrix.val[7], matrix.val[11]);
}
template<typename T> inline
typename cv::Affine3<T>::Vec3 cv::Affine3<T>::rvec() const
{
cv::Vec3d w;
cv::Matx33d u, vt, R = rotation();
cv::SVD::compute(R, w, u, vt, cv::SVD::FULL_UV + cv::SVD::MODIFY_A);
R = u * vt;
double rx = R.val[7] - R.val[5];
double ry = R.val[2] - R.val[6];
double rz = R.val[3] - R.val[1];
double s = std::sqrt((rx*rx + ry*ry + rz*rz)*0.25);
double c = (R.val[0] + R.val[4] + R.val[8] - 1) * 0.5;
c = c > 1.0 ? 1.0 : c < -1.0 ? -1.0 : c;
double theta = acos(c);
if( s < 1e-5 )
{
if( c > 0 )
rx = ry = rz = 0;
else
{
double t;
t = (R.val[0] + 1) * 0.5;
rx = std::sqrt(std::max(t, 0.0));
t = (R.val[4] + 1) * 0.5;
ry = std::sqrt(std::max(t, 0.0)) * (R.val[1] < 0 ? -1.0 : 1.0);
t = (R.val[8] + 1) * 0.5;
rz = std::sqrt(std::max(t, 0.0)) * (R.val[2] < 0 ? -1.0 : 1.0);
if( fabs(rx) < fabs(ry) && fabs(rx) < fabs(rz) && (R.val[5] > 0) != (ry*rz > 0) )
rz = -rz;
theta /= std::sqrt(rx*rx + ry*ry + rz*rz);
rx *= theta;
ry *= theta;
rz *= theta;
}
}
else
{
double vth = 1/(2*s);
vth *= theta;
rx *= vth; ry *= vth; rz *= vth;
}
return cv::Vec3d(rx, ry, rz);
}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::inv(int method) const
{
return matrix.inv(method);
}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::rotate(const Mat3& R) const
{
Mat3 Lc = linear();
Vec3 tc = translation();
Mat4 result;
result.val[12] = result.val[13] = result.val[14] = 0;
result.val[15] = 1;
for(int j = 0; j < 3; ++j)
{
for(int i = 0; i < 3; ++i)
{
float_type value = 0;
for(int k = 0; k < 3; ++k)
value += R(j, k) * Lc(k, i);
result(j, i) = value;
}
result(j, 3) = R.row(j).dot(tc.t());
}
return result;
}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::rotate(const Vec3& _rvec) const
{
return rotate(Affine3f(_rvec).rotation());
}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::translate(const Vec3& t) const
{
Mat4 m = matrix;
m.val[ 3] += t[0];
m.val[ 7] += t[1];
m.val[11] += t[2];
return m;
}
template<typename T> inline
cv::Affine3<T> cv::Affine3<T>::concatenate(const Affine3<T>& affine) const
{
return (*this).rotate(affine.rotation()).translate(affine.translation());
}
template<typename T> template <typename Y> inline
cv::Affine3<T>::operator Affine3<Y>() const
{
return Affine3<Y>(matrix);
}
template<typename T> template <typename Y> inline
cv::Affine3<Y> cv::Affine3<T>::cast() const
{
return Affine3<Y>(matrix);
}
template<typename T> inline
cv::Affine3<T> cv::operator*(const cv::Affine3<T>& affine1, const cv::Affine3<T>& affine2)
{
return affine2.concatenate(affine1);
}
template<typename T, typename V> inline
V cv::operator*(const cv::Affine3<T>& affine, const V& v)
{
const typename Affine3<T>::Mat4& m = affine.matrix;
V r;
r.x = m.val[0] * v.x + m.val[1] * v.y + m.val[ 2] * v.z + m.val[ 3];
r.y = m.val[4] * v.x + m.val[5] * v.y + m.val[ 6] * v.z + m.val[ 7];
r.z = m.val[8] * v.x + m.val[9] * v.y + m.val[10] * v.z + m.val[11];
return r;
}
static inline
cv::Vec3f cv::operator*(const cv::Affine3f& affine, const cv::Vec3f& v)
{
const cv::Matx44f& m = affine.matrix;
cv::Vec3f r;
r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3];
r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7];
r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11];
return r;
}
static inline
cv::Vec3d cv::operator*(const cv::Affine3d& affine, const cv::Vec3d& v)
{
const cv::Matx44d& m = affine.matrix;
cv::Vec3d r;
r.val[0] = m.val[0] * v[0] + m.val[1] * v[1] + m.val[ 2] * v[2] + m.val[ 3];
r.val[1] = m.val[4] * v[0] + m.val[5] * v[1] + m.val[ 6] * v[2] + m.val[ 7];
r.val[2] = m.val[8] * v[0] + m.val[9] * v[1] + m.val[10] * v[2] + m.val[11];
return r;
}
#if defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H
template<typename T> inline
cv::Affine3<T>::Affine3(const Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>& affine)
{
cv::Mat(4, 4, cv::traits::Type<T>::value, affine.matrix().data()).copyTo(matrix);
}
template<typename T> inline
cv::Affine3<T>::Affine3(const Eigen::Transform<T, 3, Eigen::Affine>& affine)
{
Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)> a = affine;
cv::Mat(4, 4, cv::traits::Type<T>::value, a.matrix().data()).copyTo(matrix);
}
template<typename T> inline
cv::Affine3<T>::operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>() const
{
Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)> r;
cv::Mat hdr(4, 4, cv::traits::Type<T>::value, r.matrix().data());
cv::Mat(matrix, false).copyTo(hdr);
return r;
}
template<typename T> inline
cv::Affine3<T>::operator Eigen::Transform<T, 3, Eigen::Affine>() const
{
return this->operator Eigen::Transform<T, 3, Eigen::Affine, (Eigen::RowMajor)>();
}
#endif /* defined EIGEN_WORLD_VERSION && defined EIGEN_GEOMETRY_MODULE_H */
//! @endcond
#endif /* __cplusplus */
#endif /* OPENCV_CORE_AFFINE3_HPP */

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_ASYNC_HPP
#define OPENCV_CORE_ASYNC_HPP
#include <opencv2/core/mat.hpp>
#ifdef CV_CXX11
//#include <future>
#include <chrono>
#endif
namespace cv {
/** @addtogroup core_async
@{
*/
/** @brief Returns result of asynchronous operations
Object has attached asynchronous state.
Assignment operator doesn't clone asynchronous state (it is shared between all instances).
Result can be fetched via get() method only once.
*/
class CV_EXPORTS_W AsyncArray
{
public:
~AsyncArray() CV_NOEXCEPT;
CV_WRAP AsyncArray() CV_NOEXCEPT;
AsyncArray(const AsyncArray& o) CV_NOEXCEPT;
AsyncArray& operator=(const AsyncArray& o) CV_NOEXCEPT;
CV_WRAP void release() CV_NOEXCEPT;
/** Fetch the result.
@param[out] dst destination array
Waits for result until container has valid result.
Throws exception if exception was stored as a result.
Throws exception on invalid container state.
@note Result or stored exception can be fetched only once.
*/
CV_WRAP void get(OutputArray dst) const;
/** Retrieving the result with timeout
@param[out] dst destination array
@param[in] timeoutNs timeout in nanoseconds, -1 for infinite wait
@returns true if result is ready, false if the timeout has expired
@note Result or stored exception can be fetched only once.
*/
bool get(OutputArray dst, int64 timeoutNs) const;
CV_WRAP inline
bool get(OutputArray dst, double timeoutNs) const { return get(dst, (int64)timeoutNs); }
bool wait_for(int64 timeoutNs) const;
CV_WRAP inline
bool wait_for(double timeoutNs) const { return wait_for((int64)timeoutNs); }
CV_WRAP bool valid() const CV_NOEXCEPT;
#ifdef CV_CXX11
inline AsyncArray(AsyncArray&& o) { p = o.p; o.p = NULL; }
inline AsyncArray& operator=(AsyncArray&& o) CV_NOEXCEPT { std::swap(p, o.p); return *this; }
template<typename _Rep, typename _Period>
inline bool get(OutputArray dst, const std::chrono::duration<_Rep, _Period>& timeout)
{
return get(dst, (int64)(std::chrono::nanoseconds(timeout).count()));
}
template<typename _Rep, typename _Period>
inline bool wait_for(const std::chrono::duration<_Rep, _Period>& timeout)
{
return wait_for((int64)(std::chrono::nanoseconds(timeout).count()));
}
#if 0
std::future<Mat> getFutureMat() const;
std::future<UMat> getFutureUMat() const;
#endif
#endif
// PImpl
struct Impl; friend struct Impl;
inline void* _getImpl() const CV_NOEXCEPT { return p; }
protected:
Impl* p;
};
//! @}
} // namespace
#endif // OPENCV_CORE_ASYNC_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.
// Copyright (C) 2014, Itseez 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_CORE_BASE_HPP
#define OPENCV_CORE_BASE_HPP
#ifndef __cplusplus
# error base.hpp header must be compiled as C++
#endif
#include "opencv2/opencv_modules.hpp"
#include <climits>
#include <algorithm>
#include "opencv2/core/cvdef.h"
#include "opencv2/core/cvstd.hpp"
namespace cv
{
//! @addtogroup core_utils
//! @{
namespace Error {
//! error codes
enum Code {
StsOk= 0, //!< everything is ok
StsBackTrace= -1, //!< pseudo error for back trace
StsError= -2, //!< unknown /unspecified error
StsInternal= -3, //!< internal error (bad state)
StsNoMem= -4, //!< insufficient memory
StsBadArg= -5, //!< function arg/param is bad
StsBadFunc= -6, //!< unsupported function
StsNoConv= -7, //!< iteration didn't converge
StsAutoTrace= -8, //!< tracing
HeaderIsNull= -9, //!< image header is NULL
BadImageSize= -10, //!< image size is invalid
BadOffset= -11, //!< offset is invalid
BadDataPtr= -12, //!<
BadStep= -13, //!< image step is wrong, this may happen for a non-continuous matrix.
BadModelOrChSeq= -14, //!<
BadNumChannels= -15, //!< bad number of channels, for example, some functions accept only single channel matrices.
BadNumChannel1U= -16, //!<
BadDepth= -17, //!< input image depth is not supported by the function
BadAlphaChannel= -18, //!<
BadOrder= -19, //!< number of dimensions is out of range
BadOrigin= -20, //!< incorrect input origin
BadAlign= -21, //!< incorrect input align
BadCallBack= -22, //!<
BadTileSize= -23, //!<
BadCOI= -24, //!< input COI is not supported
BadROISize= -25, //!< incorrect input roi
MaskIsTiled= -26, //!<
StsNullPtr= -27, //!< null pointer
StsVecLengthErr= -28, //!< incorrect vector length
StsFilterStructContentErr= -29, //!< incorrect filter structure content
StsKernelStructContentErr= -30, //!< incorrect transform kernel content
StsFilterOffsetErr= -31, //!< incorrect filter offset value
StsBadSize= -201, //!< the input/output structure size is incorrect
StsDivByZero= -202, //!< division by zero
StsInplaceNotSupported= -203, //!< in-place operation is not supported
StsObjectNotFound= -204, //!< request can't be completed
StsUnmatchedFormats= -205, //!< formats of input/output arrays differ
StsBadFlag= -206, //!< flag is wrong or not supported
StsBadPoint= -207, //!< bad CvPoint
StsBadMask= -208, //!< bad format of mask (neither 8uC1 nor 8sC1)
StsUnmatchedSizes= -209, //!< sizes of input/output structures do not match
StsUnsupportedFormat= -210, //!< the data format/type is not supported by the function
StsOutOfRange= -211, //!< some of parameters are out of range
StsParseError= -212, //!< invalid syntax/structure of the parsed file
StsNotImplemented= -213, //!< the requested function/feature is not implemented
StsBadMemBlock= -214, //!< an allocated block has been corrupted
StsAssert= -215, //!< assertion failed
GpuNotSupported= -216, //!< no CUDA support
GpuApiCallError= -217, //!< GPU API call error
OpenGlNotSupported= -218, //!< no OpenGL support
OpenGlApiCallError= -219, //!< OpenGL API call error
OpenCLApiCallError= -220, //!< OpenCL API call error
OpenCLDoubleNotSupported= -221,
OpenCLInitError= -222, //!< OpenCL initialization error
OpenCLNoAMDBlasFft= -223
};
} //Error
//! @} core_utils
//! @addtogroup core_array
//! @{
//! matrix decomposition types
enum DecompTypes {
/** Gaussian elimination with the optimal pivot element chosen. */
DECOMP_LU = 0,
/** singular value decomposition (SVD) method; the system can be over-defined and/or the matrix
src1 can be singular */
DECOMP_SVD = 1,
/** eigenvalue decomposition; the matrix src1 must be symmetrical */
DECOMP_EIG = 2,
/** Cholesky \f$LL^T\f$ factorization; the matrix src1 must be symmetrical and positively
defined */
DECOMP_CHOLESKY = 3,
/** QR factorization; the system can be over-defined and/or the matrix src1 can be singular */
DECOMP_QR = 4,
/** while all the previous flags are mutually exclusive, this flag can be used together with
any of the previous; it means that the normal equations
\f$\texttt{src1}^T\cdot\texttt{src1}\cdot\texttt{dst}=\texttt{src1}^T\texttt{src2}\f$ are
solved instead of the original system
\f$\texttt{src1}\cdot\texttt{dst}=\texttt{src2}\f$ */
DECOMP_NORMAL = 16
};
/** norm types
src1 and src2 denote input arrays.
*/
enum NormTypes {
/**
\f[
norm = \forkthree
{\|\texttt{src1}\|_{L_{\infty}} = \max _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) }
{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} = \max _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_INF}\) }
{\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} }{\|\texttt{src2}\|_{L_{\infty}} }}{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_INF}\) }
\f]
*/
NORM_INF = 1,
/**
\f[
norm = \forkthree
{\| \texttt{src1} \| _{L_1} = \sum _I | \texttt{src1} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\)}
{ \| \texttt{src1} - \texttt{src2} \| _{L_1} = \sum _I | \texttt{src1} (I) - \texttt{src2} (I)|}{if \(\texttt{normType} = \texttt{NORM_L1}\) }
{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L1}\) }
\f]*/
NORM_L1 = 2,
/**
\f[
norm = \forkthree
{ \| \texttt{src1} \| _{L_2} = \sqrt{\sum_I \texttt{src1}(I)^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }
{ \| \texttt{src1} - \texttt{src2} \| _{L_2} = \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if \(\texttt{normType} = \texttt{NORM_L2}\) }
{ \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2}\) }
\f]
*/
NORM_L2 = 4,
/**
\f[
norm = \forkthree
{ \| \texttt{src1} \| _{L_2} ^{2} = \sum_I \texttt{src1}(I)^2} {if \(\texttt{normType} = \texttt{NORM_L2SQR}\)}
{ \| \texttt{src1} - \texttt{src2} \| _{L_2} ^{2} = \sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2 }{if \(\texttt{normType} = \texttt{NORM_L2SQR}\) }
{ \left(\frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}}\right)^2 }{if \(\texttt{normType} = \texttt{NORM_RELATIVE | NORM_L2SQR}\) }
\f]
*/
NORM_L2SQR = 5,
/**
In the case of one input array, calculates the Hamming distance of the array from zero,
In the case of two input arrays, calculates the Hamming distance between the arrays.
*/
NORM_HAMMING = 6,
/**
Similar to NORM_HAMMING, but in the calculation, each two bits of the input sequence will
be added and treated as a single bit to be used in the same calculation as NORM_HAMMING.
*/
NORM_HAMMING2 = 7,
NORM_TYPE_MASK = 7, //!< bit-mask which can be used to separate norm type from norm flags
NORM_RELATIVE = 8, //!< flag
NORM_MINMAX = 32 //!< flag
};
//! comparison types
enum CmpTypes { CMP_EQ = 0, //!< src1 is equal to src2.
CMP_GT = 1, //!< src1 is greater than src2.
CMP_GE = 2, //!< src1 is greater than or equal to src2.
CMP_LT = 3, //!< src1 is less than src2.
CMP_LE = 4, //!< src1 is less than or equal to src2.
CMP_NE = 5 //!< src1 is unequal to src2.
};
//! generalized matrix multiplication flags
enum GemmFlags { GEMM_1_T = 1, //!< transposes src1
GEMM_2_T = 2, //!< transposes src2
GEMM_3_T = 4 //!< transposes src3
};
enum DftFlags {
/** performs an inverse 1D or 2D transform instead of the default forward
transform. */
DFT_INVERSE = 1,
/** scales the result: divide it by the number of array elements. Normally, it is
combined with DFT_INVERSE. */
DFT_SCALE = 2,
/** performs a forward or inverse transform of every individual row of the input
matrix; this flag enables you to transform multiple vectors simultaneously and can be used to
decrease the overhead (which is sometimes several times larger than the processing itself) to
perform 3D and higher-dimensional transformations and so forth.*/
DFT_ROWS = 4,
/** performs a forward transformation of 1D or 2D real array; the result,
though being a complex array, has complex-conjugate symmetry (*CCS*, see the function
description below for details), and such an array can be packed into a real array of the same
size as input, which is the fastest option and which is what the function does by default;
however, you may wish to get a full complex array (for simpler spectrum analysis, and so on) -
pass the flag to enable the function to produce a full-size complex output array. */
DFT_COMPLEX_OUTPUT = 16,
/** performs an inverse transformation of a 1D or 2D complex array; the
result is normally a complex array of the same size, however, if the input array has
conjugate-complex symmetry (for example, it is a result of forward transformation with
DFT_COMPLEX_OUTPUT flag), the output is a real array; while the function itself does not
check whether the input is symmetrical or not, you can pass the flag and then the function
will assume the symmetry and produce the real output array (note that when the input is packed
into a real array and inverse transformation is executed, the function treats the input as a
packed complex-conjugate symmetrical array, and the output will also be a real array). */
DFT_REAL_OUTPUT = 32,
/** specifies that input is complex input. If this flag is set, the input must have 2 channels.
On the other hand, for backwards compatibility reason, if input has 2 channels, input is
already considered complex. */
DFT_COMPLEX_INPUT = 64,
/** performs an inverse 1D or 2D transform instead of the default forward transform. */
DCT_INVERSE = DFT_INVERSE,
/** performs a forward or inverse transform of every individual row of the input
matrix. This flag enables you to transform multiple vectors simultaneously and can be used to
decrease the overhead (which is sometimes several times larger than the processing itself) to
perform 3D and higher-dimensional transforms and so forth.*/
DCT_ROWS = DFT_ROWS
};
//! Various border types, image boundaries are denoted with `|`
//! @see borderInterpolate, copyMakeBorder
enum BorderTypes {
BORDER_CONSTANT = 0, //!< `iiiiii|abcdefgh|iiiiiii` with some specified `i`
BORDER_REPLICATE = 1, //!< `aaaaaa|abcdefgh|hhhhhhh`
BORDER_REFLECT = 2, //!< `fedcba|abcdefgh|hgfedcb`
BORDER_WRAP = 3, //!< `cdefgh|abcdefgh|abcdefg`
BORDER_REFLECT_101 = 4, //!< `gfedcb|abcdefgh|gfedcba`
BORDER_TRANSPARENT = 5, //!< `uvwxyz|abcdefgh|ijklmno`
BORDER_REFLECT101 = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101
BORDER_DEFAULT = BORDER_REFLECT_101, //!< same as BORDER_REFLECT_101
BORDER_ISOLATED = 16 //!< do not look outside of ROI
};
//! @} core_array
//! @addtogroup core_utils
//! @{
/*! @brief Signals an error and raises the exception.
By default the function prints information about the error to stderr,
then it either stops if setBreakOnError() had been called before or raises the exception.
It is possible to alternate error processing by using redirectError().
@param _code - error code (Error::Code)
@param _err - error description
@param _func - function name. Available only when the compiler supports getting it
@param _file - source file name where the error has occurred
@param _line - line number in the source file where the error has occurred
@see CV_Error, CV_Error_, CV_Assert, CV_DbgAssert
*/
CV_EXPORTS CV_NORETURN void error(int _code, const String& _err, const char* _func, const char* _file, int _line);
#ifdef CV_STATIC_ANALYSIS
// In practice, some macro are not processed correctly (noreturn is not detected).
// We need to use simplified definition for them.
#define CV_Error(code, msg) do { (void)(code); (void)(msg); abort(); } while (0)
#define CV_Error_(code, args) do { (void)(code); (void)(cv::format args); abort(); } while (0)
#define CV_Assert( expr ) do { if (!(expr)) abort(); } while (0)
#else // CV_STATIC_ANALYSIS
/** @brief Call the error handler.
Currently, the error handler prints the error code and the error message to the standard
error stream `stderr`. In the Debug configuration, it then provokes memory access violation, so that
the execution stack and all the parameters can be analyzed by the debugger. In the Release
configuration, the exception is thrown.
@param code one of Error::Code
@param msg error message
*/
#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ )
/** @brief Call the error handler.
This macro can be used to construct an error message on-fly to include some dynamic information,
for example:
@code
// note the extra parentheses around the formatted text message
CV_Error_(Error::StsOutOfRange,
("the value at (%d, %d)=%g is out of range", badPt.x, badPt.y, badValue));
@endcode
@param code one of Error::Code
@param args printf-like formatted error message in parentheses
*/
#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ )
/** @brief Checks a condition at runtime and throws exception if it fails
The macros CV_Assert (and CV_DbgAssert(expr)) evaluate the specified expression. If it is 0, the macros
raise an error (see cv::error). The macro CV_Assert checks the condition in both Debug and Release
configurations while CV_DbgAssert is only retained in the Debug configuration.
*/
#define CV_Assert( expr ) do { if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ ); } while(0)
#endif // CV_STATIC_ANALYSIS
//! @cond IGNORED
#if !defined(__OPENCV_BUILD) // TODO: backward compatibility only
#ifndef CV_ErrorNoReturn
#define CV_ErrorNoReturn CV_Error
#endif
#ifndef CV_ErrorNoReturn_
#define CV_ErrorNoReturn_ CV_Error_
#endif
#endif
#define CV_Assert_1 CV_Assert
#define CV_Assert_2( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_1( __VA_ARGS__ ))
#define CV_Assert_3( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_2( __VA_ARGS__ ))
#define CV_Assert_4( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_3( __VA_ARGS__ ))
#define CV_Assert_5( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_4( __VA_ARGS__ ))
#define CV_Assert_6( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_5( __VA_ARGS__ ))
#define CV_Assert_7( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_6( __VA_ARGS__ ))
#define CV_Assert_8( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_7( __VA_ARGS__ ))
#define CV_Assert_9( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_8( __VA_ARGS__ ))
#define CV_Assert_10( expr, ... ) CV_Assert_1(expr); __CV_EXPAND(CV_Assert_9( __VA_ARGS__ ))
#define CV_Assert_N(...) do { __CV_EXPAND(__CV_CAT(CV_Assert_, __CV_VA_NUM_ARGS(__VA_ARGS__)) (__VA_ARGS__)); } while(0)
//! @endcond
#if defined _DEBUG || defined CV_STATIC_ANALYSIS
# define CV_DbgAssert(expr) CV_Assert(expr)
#else
/** replaced with CV_Assert(expr) in Debug configuration */
# define CV_DbgAssert(expr)
#endif
/*
* Hamming distance functor - counts the bit differences between two strings - useful for the Brief descriptor
* bit count of A exclusive XOR'ed with B
*/
struct CV_EXPORTS Hamming
{
static const NormTypes normType = NORM_HAMMING;
typedef unsigned char ValueType;
typedef int ResultType;
/** this will count the bits in a ^ b
*/
ResultType operator()( const unsigned char* a, const unsigned char* b, int size ) const;
};
typedef Hamming HammingLUT;
/////////////////////////////////// inline norms ////////////////////////////////////
template<typename _Tp> inline _Tp cv_abs(_Tp x) { return std::abs(x); }
inline int cv_abs(uchar x) { return x; }
inline int cv_abs(schar x) { return std::abs(x); }
inline int cv_abs(ushort x) { return x; }
inline int cv_abs(short x) { return std::abs(x); }
template<typename _Tp, typename _AccTp> static inline
_AccTp normL2Sqr(const _Tp* a, int n)
{
_AccTp s = 0;
int i=0;
#if CV_ENABLE_UNROLLED
for( ; i <= n - 4; i += 4 )
{
_AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3];
s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
}
#endif
for( ; i < n; i++ )
{
_AccTp v = a[i];
s += v*v;
}
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normL1(const _Tp* a, int n)
{
_AccTp s = 0;
int i = 0;
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
s += (_AccTp)cv_abs(a[i]) + (_AccTp)cv_abs(a[i+1]) +
(_AccTp)cv_abs(a[i+2]) + (_AccTp)cv_abs(a[i+3]);
}
#endif
for( ; i < n; i++ )
s += cv_abs(a[i]);
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normInf(const _Tp* a, int n)
{
_AccTp s = 0;
for( int i = 0; i < n; i++ )
s = std::max(s, (_AccTp)cv_abs(a[i]));
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n)
{
_AccTp s = 0;
int i= 0;
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
_AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
}
#endif
for( ; i < n; i++ )
{
_AccTp v = _AccTp(a[i] - b[i]);
s += v*v;
}
return s;
}
static inline float normL2Sqr(const float* a, const float* b, int n)
{
float s = 0.f;
for( int i = 0; i < n; i++ )
{
float v = a[i] - b[i];
s += v*v;
}
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normL1(const _Tp* a, const _Tp* b, int n)
{
_AccTp s = 0;
int i= 0;
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
_AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3);
}
#endif
for( ; i < n; i++ )
{
_AccTp v = _AccTp(a[i] - b[i]);
s += std::abs(v);
}
return s;
}
inline float normL1(const float* a, const float* b, int n)
{
float s = 0.f;
for( int i = 0; i < n; i++ )
{
s += std::abs(a[i] - b[i]);
}
return s;
}
inline int normL1(const uchar* a, const uchar* b, int n)
{
int s = 0;
for( int i = 0; i < n; i++ )
{
s += std::abs(a[i] - b[i]);
}
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normInf(const _Tp* a, const _Tp* b, int n)
{
_AccTp s = 0;
for( int i = 0; i < n; i++ )
{
_AccTp v0 = a[i] - b[i];
s = std::max(s, std::abs(v0));
}
return s;
}
/** @brief Computes the cube root of an argument.
The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly.
NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for
single-precision data.
@param val A function argument.
*/
CV_EXPORTS_W float cubeRoot(float val);
/** @brief Calculates the angle of a 2D vector in degrees.
The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured
in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees.
@param x x-coordinate of the vector.
@param y y-coordinate of the vector.
*/
CV_EXPORTS_W float fastAtan2(float y, float x);
/** proxy for hal::LU */
CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n);
/** proxy for hal::LU */
CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n);
/** proxy for hal::Cholesky */
CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n);
/** proxy for hal::Cholesky */
CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n);
////////////////// forward declarations for important OpenCV types //////////////////
//! @cond IGNORED
template<typename _Tp, int cn> class Vec;
template<typename _Tp, int m, int n> class Matx;
template<typename _Tp> class Complex;
template<typename _Tp> class Point_;
template<typename _Tp> class Point3_;
template<typename _Tp> class Size_;
template<typename _Tp> class Rect_;
template<typename _Tp> class Scalar_;
class CV_EXPORTS RotatedRect;
class CV_EXPORTS Range;
class CV_EXPORTS TermCriteria;
class CV_EXPORTS KeyPoint;
class CV_EXPORTS DMatch;
class CV_EXPORTS RNG;
class CV_EXPORTS Mat;
class CV_EXPORTS MatExpr;
class CV_EXPORTS UMat;
class CV_EXPORTS SparseMat;
typedef Mat MatND;
template<typename _Tp> class Mat_;
template<typename _Tp> class SparseMat_;
class CV_EXPORTS MatConstIterator;
class CV_EXPORTS SparseMatIterator;
class CV_EXPORTS SparseMatConstIterator;
template<typename _Tp> class MatIterator_;
template<typename _Tp> class MatConstIterator_;
template<typename _Tp> class SparseMatIterator_;
template<typename _Tp> class SparseMatConstIterator_;
namespace ogl
{
class CV_EXPORTS Buffer;
class CV_EXPORTS Texture2D;
class CV_EXPORTS Arrays;
}
namespace cuda
{
class CV_EXPORTS GpuMat;
class CV_EXPORTS HostMem;
class CV_EXPORTS Stream;
class CV_EXPORTS Event;
}
namespace cudev
{
template <typename _Tp> class GpuMat_;
}
namespace ipp
{
CV_EXPORTS unsigned long long getIppFeatures();
CV_EXPORTS void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL,
int line = 0);
CV_EXPORTS int getIppStatus();
CV_EXPORTS String getIppErrorLocation();
CV_EXPORTS_W bool useIPP();
CV_EXPORTS_W void setUseIPP(bool flag);
CV_EXPORTS_W String getIppVersion();
// IPP Not-Exact mode. This function may force use of IPP then both IPP and OpenCV provide proper results
// but have internal accuracy differences which have too much direct or indirect impact on accuracy tests.
CV_EXPORTS_W bool useIPP_NotExact();
CV_EXPORTS_W void setUseIPP_NotExact(bool flag);
#ifndef DISABLE_OPENCV_3_COMPATIBILITY
static inline bool useIPP_NE() { return useIPP_NotExact(); }
static inline void setUseIPP_NE(bool flag) { setUseIPP_NotExact(flag); }
#endif
} // ipp
//! @endcond
//! @} core_utils
} // cv
#include "opencv2/core/neon_utils.hpp"
#include "opencv2/core/vsx_utils.hpp"
#include "opencv2/core/check.hpp"
#endif //OPENCV_CORE_BASE_HPP

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_BINDINGS_UTILS_HPP
#define OPENCV_CORE_BINDINGS_UTILS_HPP
#include <opencv2/core/async.hpp>
#include <opencv2/core/detail/async_promise.hpp>
namespace cv { namespace utils {
//! @addtogroup core_utils
//! @{
CV_EXPORTS_W String dumpInputArray(InputArray argument);
CV_EXPORTS_W String dumpInputArrayOfArrays(InputArrayOfArrays argument);
CV_EXPORTS_W String dumpInputOutputArray(InputOutputArray argument);
CV_EXPORTS_W String dumpInputOutputArrayOfArrays(InputOutputArrayOfArrays argument);
CV_WRAP static inline
String dumpBool(bool argument)
{
return (argument) ? String("Bool: True") : String("Bool: False");
}
CV_WRAP static inline
String dumpInt(int argument)
{
return cv::format("Int: %d", argument);
}
CV_WRAP static inline
String dumpSizeT(size_t argument)
{
std::ostringstream oss("size_t: ", std::ios::ate);
oss << argument;
return oss.str();
}
CV_WRAP static inline
String dumpFloat(float argument)
{
return cv::format("Float: %.2f", argument);
}
CV_WRAP static inline
String dumpDouble(double argument)
{
return cv::format("Double: %.2f", argument);
}
CV_WRAP static inline
String dumpCString(const char* argument)
{
return cv::format("String: %s", argument);
}
CV_WRAP static inline
AsyncArray testAsyncArray(InputArray argument)
{
AsyncPromise p;
p.setValue(argument);
return p.getArrayResult();
}
CV_WRAP static inline
AsyncArray testAsyncException()
{
AsyncPromise p;
try
{
CV_Error(Error::StsOk, "Test: Generated async error");
}
catch (const cv::Exception& e)
{
p.setException(e);
}
return p.getArrayResult();
}
//! @}
}} // namespace
#endif // OPENCV_CORE_BINDINGS_UTILS_HPP

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2014, Advanced Micro Devices, Inc., all rights reserved.
#ifndef OPENCV_CORE_BUFFER_POOL_HPP
#define OPENCV_CORE_BUFFER_POOL_HPP
#ifdef _MSC_VER
#pragma warning(push)
#pragma warning(disable: 4265)
#endif
namespace cv
{
//! @addtogroup core
//! @{
class BufferPoolController
{
protected:
~BufferPoolController() { }
public:
virtual size_t getReservedSize() const = 0;
virtual size_t getMaxReservedSize() const = 0;
virtual void setMaxReservedSize(size_t size) = 0;
virtual void freeAllReservedBuffers() = 0;
};
//! @}
}
#ifdef _MSC_VER
#pragma warning(pop)
#endif
#endif // OPENCV_CORE_BUFFER_POOL_HPP

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_CHECK_HPP
#define OPENCV_CORE_CHECK_HPP
#include <opencv2/core/base.hpp>
namespace cv {
/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or "<invalid depth>" */
CV_EXPORTS const char* depthToString(int depth);
/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or "<invalid type>" */
CV_EXPORTS const String typeToString(int type);
//! @cond IGNORED
namespace detail {
/** Returns string of cv::Mat depth value: CV_8U -> "CV_8U" or NULL */
CV_EXPORTS const char* depthToString_(int depth);
/** Returns string of cv::Mat depth value: CV_8UC3 -> "CV_8UC3" or cv::String() */
CV_EXPORTS const cv::String typeToString_(int type);
enum TestOp {
TEST_CUSTOM = 0,
TEST_EQ = 1,
TEST_NE = 2,
TEST_LE = 3,
TEST_LT = 4,
TEST_GE = 5,
TEST_GT = 6,
CV__LAST_TEST_OP
};
struct CheckContext {
const char* func;
const char* file;
int line;
enum TestOp testOp;
const char* message;
const char* p1_str;
const char* p2_str;
};
#ifndef CV__CHECK_FILENAME
# define CV__CHECK_FILENAME __FILE__
#endif
#ifndef CV__CHECK_FUNCTION
# if defined _MSC_VER
# define CV__CHECK_FUNCTION __FUNCSIG__
# elif defined __GNUC__
# define CV__CHECK_FUNCTION __PRETTY_FUNCTION__
# else
# define CV__CHECK_FUNCTION "<unknown>"
# endif
#endif
#define CV__CHECK_LOCATION_VARNAME(id) CVAUX_CONCAT(CVAUX_CONCAT(__cv_check_, id), __LINE__)
#define CV__DEFINE_CHECK_CONTEXT(id, message, testOp, p1_str, p2_str) \
static const cv::detail::CheckContext CV__CHECK_LOCATION_VARNAME(id) = \
{ CV__CHECK_FUNCTION, CV__CHECK_FILENAME, __LINE__, testOp, message, p1_str, p2_str }
CV_EXPORTS void CV_NORETURN check_failed_auto(const int v1, const int v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v1, const size_t v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const float v1, const float v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const double v1, const double v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const Size_<int> v1, const Size_<int> v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v1, const int v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v1, const int v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v1, const int v2, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const int v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const size_t v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const float v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const double v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_auto(const Size_<int> v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatDepth(const int v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatType(const int v, const CheckContext& ctx);
CV_EXPORTS void CV_NORETURN check_failed_MatChannels(const int v, const CheckContext& ctx);
#define CV__TEST_EQ(v1, v2) ((v1) == (v2))
#define CV__TEST_NE(v1, v2) ((v1) != (v2))
#define CV__TEST_LE(v1, v2) ((v1) <= (v2))
#define CV__TEST_LT(v1, v2) ((v1) < (v2))
#define CV__TEST_GE(v1, v2) ((v1) >= (v2))
#define CV__TEST_GT(v1, v2) ((v1) > (v2))
#define CV__CHECK(id, op, type, v1, v2, v1_str, v2_str, msg_str) do { \
if(CV__TEST_##op((v1), (v2))) ; else { \
CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_ ## op, v1_str, v2_str); \
cv::detail::check_failed_ ## type((v1), (v2), CV__CHECK_LOCATION_VARNAME(id)); \
} \
} while (0)
#define CV__CHECK_CUSTOM_TEST(id, type, v, test_expr, v_str, test_expr_str, msg_str) do { \
if(!!(test_expr)) ; else { \
CV__DEFINE_CHECK_CONTEXT(id, msg_str, cv::detail::TEST_CUSTOM, v_str, test_expr_str); \
cv::detail::check_failed_ ## type((v), CV__CHECK_LOCATION_VARNAME(id)); \
} \
} while (0)
} // namespace
//! @endcond
/// Supported values of these types: int, float, double
#define CV_CheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg)
#define CV_CheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg)
#define CV_CheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg)
#define CV_CheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg)
#define CV_CheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg)
#define CV_CheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg)
/// Check with additional "decoding" of type values in error message
#define CV_CheckTypeEQ(t1, t2, msg) CV__CHECK(_, EQ, MatType, t1, t2, #t1, #t2, msg)
/// Check with additional "decoding" of depth values in error message
#define CV_CheckDepthEQ(d1, d2, msg) CV__CHECK(_, EQ, MatDepth, d1, d2, #d1, #d2, msg)
#define CV_CheckChannelsEQ(c1, c2, msg) CV__CHECK(_, EQ, MatChannels, c1, c2, #c1, #c2, msg)
/// Example: type == CV_8UC1 || type == CV_8UC3
#define CV_CheckType(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatType, t, (test_expr), #t, #test_expr, msg)
/// Example: depth == CV_32F || depth == CV_64F
#define CV_CheckDepth(t, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, MatDepth, t, (test_expr), #t, #test_expr, msg)
/// Example: v == A || v == B
#define CV_Check(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg)
/// Some complex conditions: CV_Check(src2, src2.empty() || (src2.type() == src1.type() && src2.size() == src1.size()), "src2 should have same size/type as src1")
// TODO define pretty-printers
#ifndef NDEBUG
#define CV_DbgCheck(v, test_expr, msg) CV__CHECK_CUSTOM_TEST(_, auto, v, (test_expr), #v, #test_expr, msg)
#define CV_DbgCheckEQ(v1, v2, msg) CV__CHECK(_, EQ, auto, v1, v2, #v1, #v2, msg)
#define CV_DbgCheckNE(v1, v2, msg) CV__CHECK(_, NE, auto, v1, v2, #v1, #v2, msg)
#define CV_DbgCheckLE(v1, v2, msg) CV__CHECK(_, LE, auto, v1, v2, #v1, #v2, msg)
#define CV_DbgCheckLT(v1, v2, msg) CV__CHECK(_, LT, auto, v1, v2, #v1, #v2, msg)
#define CV_DbgCheckGE(v1, v2, msg) CV__CHECK(_, GE, auto, v1, v2, #v1, #v2, msg)
#define CV_DbgCheckGT(v1, v2, msg) CV__CHECK(_, GT, auto, v1, v2, #v1, #v2, msg)
#else
#define CV_DbgCheck(v, test_expr, msg) do { } while (0)
#define CV_DbgCheckEQ(v1, v2, msg) do { } while (0)
#define CV_DbgCheckNE(v1, v2, msg) do { } while (0)
#define CV_DbgCheckLE(v1, v2, msg) do { } while (0)
#define CV_DbgCheckLT(v1, v2, msg) do { } while (0)
#define CV_DbgCheckGE(v1, v2, msg) do { } while (0)
#define CV_DbgCheckGT(v1, v2, msg) do { } while (0)
#endif
} // namespace
#endif // OPENCV_CORE_CHECK_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*/
#ifdef __OPENCV_BUILD
#error this is a compatibility header which should not be used inside the OpenCV library
#endif
#include "opencv2/core.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_CORE_CUDAINL_HPP
#define OPENCV_CORE_CUDAINL_HPP
#include "opencv2/core/cuda.hpp"
//! @cond IGNORED
namespace cv { namespace cuda {
//===================================================================================
// GpuMat
//===================================================================================
inline
GpuMat::GpuMat(Allocator* allocator_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
{}
inline
GpuMat::GpuMat(int rows_, int cols_, int type_, Allocator* allocator_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
{
if (rows_ > 0 && cols_ > 0)
create(rows_, cols_, type_);
}
inline
GpuMat::GpuMat(Size size_, int type_, Allocator* allocator_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
{
if (size_.height > 0 && size_.width > 0)
create(size_.height, size_.width, type_);
}
inline
GpuMat::GpuMat(int rows_, int cols_, int type_, Scalar s_, Allocator* allocator_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
{
if (rows_ > 0 && cols_ > 0)
{
create(rows_, cols_, type_);
setTo(s_);
}
}
inline
GpuMat::GpuMat(Size size_, int type_, Scalar s_, Allocator* allocator_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
{
if (size_.height > 0 && size_.width > 0)
{
create(size_.height, size_.width, type_);
setTo(s_);
}
}
inline
GpuMat::GpuMat(const GpuMat& m)
: flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), allocator(m.allocator)
{
if (refcount)
CV_XADD(refcount, 1);
}
inline
GpuMat::GpuMat(InputArray arr, Allocator* allocator_) :
flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), allocator(allocator_)
{
upload(arr);
}
inline
GpuMat::~GpuMat()
{
release();
}
inline
GpuMat& GpuMat::operator =(const GpuMat& m)
{
if (this != &m)
{
GpuMat temp(m);
swap(temp);
}
return *this;
}
inline
void GpuMat::create(Size size_, int type_)
{
create(size_.height, size_.width, type_);
}
inline
void GpuMat::swap(GpuMat& b)
{
std::swap(flags, b.flags);
std::swap(rows, b.rows);
std::swap(cols, b.cols);
std::swap(step, b.step);
std::swap(data, b.data);
std::swap(datastart, b.datastart);
std::swap(dataend, b.dataend);
std::swap(refcount, b.refcount);
std::swap(allocator, b.allocator);
}
inline
GpuMat GpuMat::clone() const
{
GpuMat m;
copyTo(m);
return m;
}
inline
void GpuMat::copyTo(OutputArray dst, InputArray mask) const
{
copyTo(dst, mask, Stream::Null());
}
inline
GpuMat& GpuMat::setTo(Scalar s)
{
return setTo(s, Stream::Null());
}
inline
GpuMat& GpuMat::setTo(Scalar s, InputArray mask)
{
return setTo(s, mask, Stream::Null());
}
inline
void GpuMat::convertTo(OutputArray dst, int rtype) const
{
convertTo(dst, rtype, Stream::Null());
}
inline
void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, double beta) const
{
convertTo(dst, rtype, alpha, beta, Stream::Null());
}
inline
void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const
{
convertTo(dst, rtype, alpha, 0.0, stream);
}
inline
void GpuMat::assignTo(GpuMat& m, int _type) const
{
if (_type < 0)
m = *this;
else
convertTo(m, _type);
}
inline
uchar* GpuMat::ptr(int y)
{
CV_DbgAssert( (unsigned)y < (unsigned)rows );
return data + step * y;
}
inline
const uchar* GpuMat::ptr(int y) const
{
CV_DbgAssert( (unsigned)y < (unsigned)rows );
return data + step * y;
}
template<typename _Tp> inline
_Tp* GpuMat::ptr(int y)
{
return (_Tp*)ptr(y);
}
template<typename _Tp> inline
const _Tp* GpuMat::ptr(int y) const
{
return (const _Tp*)ptr(y);
}
template <class T> inline
GpuMat::operator PtrStepSz<T>() const
{
return PtrStepSz<T>(rows, cols, (T*)data, step);
}
template <class T> inline
GpuMat::operator PtrStep<T>() const
{
return PtrStep<T>((T*)data, step);
}
inline
GpuMat GpuMat::row(int y) const
{
return GpuMat(*this, Range(y, y+1), Range::all());
}
inline
GpuMat GpuMat::col(int x) const
{
return GpuMat(*this, Range::all(), Range(x, x+1));
}
inline
GpuMat GpuMat::rowRange(int startrow, int endrow) const
{
return GpuMat(*this, Range(startrow, endrow), Range::all());
}
inline
GpuMat GpuMat::rowRange(Range r) const
{
return GpuMat(*this, r, Range::all());
}
inline
GpuMat GpuMat::colRange(int startcol, int endcol) const
{
return GpuMat(*this, Range::all(), Range(startcol, endcol));
}
inline
GpuMat GpuMat::colRange(Range r) const
{
return GpuMat(*this, Range::all(), r);
}
inline
GpuMat GpuMat::operator ()(Range rowRange_, Range colRange_) const
{
return GpuMat(*this, rowRange_, colRange_);
}
inline
GpuMat GpuMat::operator ()(Rect roi) const
{
return GpuMat(*this, roi);
}
inline
bool GpuMat::isContinuous() const
{
return (flags & Mat::CONTINUOUS_FLAG) != 0;
}
inline
size_t GpuMat::elemSize() const
{
return CV_ELEM_SIZE(flags);
}
inline
size_t GpuMat::elemSize1() const
{
return CV_ELEM_SIZE1(flags);
}
inline
int GpuMat::type() const
{
return CV_MAT_TYPE(flags);
}
inline
int GpuMat::depth() const
{
return CV_MAT_DEPTH(flags);
}
inline
int GpuMat::channels() const
{
return CV_MAT_CN(flags);
}
inline
size_t GpuMat::step1() const
{
return step / elemSize1();
}
inline
Size GpuMat::size() const
{
return Size(cols, rows);
}
inline
bool GpuMat::empty() const
{
return data == 0;
}
static inline
GpuMat createContinuous(int rows, int cols, int type)
{
GpuMat m;
createContinuous(rows, cols, type, m);
return m;
}
static inline
void createContinuous(Size size, int type, OutputArray arr)
{
createContinuous(size.height, size.width, type, arr);
}
static inline
GpuMat createContinuous(Size size, int type)
{
GpuMat m;
createContinuous(size, type, m);
return m;
}
static inline
void ensureSizeIsEnough(Size size, int type, OutputArray arr)
{
ensureSizeIsEnough(size.height, size.width, type, arr);
}
static inline
void swap(GpuMat& a, GpuMat& b)
{
a.swap(b);
}
//===================================================================================
// HostMem
//===================================================================================
inline
HostMem::HostMem(AllocType alloc_type_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
{
}
inline
HostMem::HostMem(const HostMem& m)
: flags(m.flags), rows(m.rows), cols(m.cols), step(m.step), data(m.data), refcount(m.refcount), datastart(m.datastart), dataend(m.dataend), alloc_type(m.alloc_type)
{
if( refcount )
CV_XADD(refcount, 1);
}
inline
HostMem::HostMem(int rows_, int cols_, int type_, AllocType alloc_type_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
{
if (rows_ > 0 && cols_ > 0)
create(rows_, cols_, type_);
}
inline
HostMem::HostMem(Size size_, int type_, AllocType alloc_type_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
{
if (size_.height > 0 && size_.width > 0)
create(size_.height, size_.width, type_);
}
inline
HostMem::HostMem(InputArray arr, AllocType alloc_type_)
: flags(0), rows(0), cols(0), step(0), data(0), refcount(0), datastart(0), dataend(0), alloc_type(alloc_type_)
{
arr.getMat().copyTo(*this);
}
inline
HostMem::~HostMem()
{
release();
}
inline
HostMem& HostMem::operator =(const HostMem& m)
{
if (this != &m)
{
HostMem temp(m);
swap(temp);
}
return *this;
}
inline
void HostMem::swap(HostMem& b)
{
std::swap(flags, b.flags);
std::swap(rows, b.rows);
std::swap(cols, b.cols);
std::swap(step, b.step);
std::swap(data, b.data);
std::swap(datastart, b.datastart);
std::swap(dataend, b.dataend);
std::swap(refcount, b.refcount);
std::swap(alloc_type, b.alloc_type);
}
inline
HostMem HostMem::clone() const
{
HostMem m(size(), type(), alloc_type);
createMatHeader().copyTo(m);
return m;
}
inline
void HostMem::create(Size size_, int type_)
{
create(size_.height, size_.width, type_);
}
inline
Mat HostMem::createMatHeader() const
{
return Mat(size(), type(), data, step);
}
inline
bool HostMem::isContinuous() const
{
return (flags & Mat::CONTINUOUS_FLAG) != 0;
}
inline
size_t HostMem::elemSize() const
{
return CV_ELEM_SIZE(flags);
}
inline
size_t HostMem::elemSize1() const
{
return CV_ELEM_SIZE1(flags);
}
inline
int HostMem::type() const
{
return CV_MAT_TYPE(flags);
}
inline
int HostMem::depth() const
{
return CV_MAT_DEPTH(flags);
}
inline
int HostMem::channels() const
{
return CV_MAT_CN(flags);
}
inline
size_t HostMem::step1() const
{
return step / elemSize1();
}
inline
Size HostMem::size() const
{
return Size(cols, rows);
}
inline
bool HostMem::empty() const
{
return data == 0;
}
static inline
void swap(HostMem& a, HostMem& b)
{
a.swap(b);
}
//===================================================================================
// Stream
//===================================================================================
inline
Stream::Stream(const Ptr<Impl>& impl)
: impl_(impl)
{
}
//===================================================================================
// Event
//===================================================================================
inline
Event::Event(const Ptr<Impl>& impl)
: impl_(impl)
{
}
//===================================================================================
// Initialization & Info
//===================================================================================
inline
bool TargetArchs::has(int major, int minor)
{
return hasPtx(major, minor) || hasBin(major, minor);
}
inline
bool TargetArchs::hasEqualOrGreater(int major, int minor)
{
return hasEqualOrGreaterPtx(major, minor) || hasEqualOrGreaterBin(major, minor);
}
inline
DeviceInfo::DeviceInfo()
{
device_id_ = getDevice();
}
inline
DeviceInfo::DeviceInfo(int device_id)
{
CV_Assert( device_id >= 0 && device_id < getCudaEnabledDeviceCount() );
device_id_ = device_id;
}
inline
int DeviceInfo::deviceID() const
{
return device_id_;
}
inline
size_t DeviceInfo::freeMemory() const
{
size_t _totalMemory = 0, _freeMemory = 0;
queryMemory(_totalMemory, _freeMemory);
return _freeMemory;
}
inline
size_t DeviceInfo::totalMemory() const
{
size_t _totalMemory = 0, _freeMemory = 0;
queryMemory(_totalMemory, _freeMemory);
return _totalMemory;
}
inline
bool DeviceInfo::supports(FeatureSet feature_set) const
{
int version = majorVersion() * 10 + minorVersion();
return version >= feature_set;
}
}} // namespace cv { namespace cuda {
//===================================================================================
// Mat
//===================================================================================
namespace cv {
inline
Mat::Mat(const cuda::GpuMat& m)
: flags(0), dims(0), rows(0), cols(0), data(0), datastart(0), dataend(0), datalimit(0), allocator(0), u(0), size(&rows)
{
m.download(*this);
}
}
//! @endcond
#endif // OPENCV_CORE_CUDAINL_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_CUDA_DEVICE_BLOCK_HPP
#define OPENCV_CUDA_DEVICE_BLOCK_HPP
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
struct Block
{
static __device__ __forceinline__ unsigned int id()
{
return blockIdx.x;
}
static __device__ __forceinline__ unsigned int stride()
{
return blockDim.x * blockDim.y * blockDim.z;
}
static __device__ __forceinline__ void sync()
{
__syncthreads();
}
static __device__ __forceinline__ int flattenedThreadId()
{
return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
}
template<typename It, typename T>
static __device__ __forceinline__ void fill(It beg, It end, const T& value)
{
int STRIDE = stride();
It t = beg + flattenedThreadId();
for(; t < end; t += STRIDE)
*t = value;
}
template<typename OutIt, typename T>
static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
{
int STRIDE = stride();
int tid = flattenedThreadId();
value += tid;
for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE)
*t = value;
}
template<typename InIt, typename OutIt>
static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out)
{
int STRIDE = stride();
InIt t = beg + flattenedThreadId();
OutIt o = out + (t - beg);
for(; t < end; t += STRIDE, o += STRIDE)
*o = *t;
}
template<typename InIt, typename OutIt, class UnOp>
static __device__ __forceinline__ void transform(InIt beg, InIt end, OutIt out, UnOp op)
{
int STRIDE = stride();
InIt t = beg + flattenedThreadId();
OutIt o = out + (t - beg);
for(; t < end; t += STRIDE, o += STRIDE)
*o = op(*t);
}
template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
static __device__ __forceinline__ void transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
{
int STRIDE = stride();
InIt1 t1 = beg1 + flattenedThreadId();
InIt2 t2 = beg2 + flattenedThreadId();
OutIt o = out + (t1 - beg1);
for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE)
*o = op(*t1, *t2);
}
template<int CTA_SIZE, typename T, class BinOp>
static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op)
{
int tid = flattenedThreadId();
T val = buffer[tid];
if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); }
if (tid < 32)
{
if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); }
if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); }
if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); }
if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); }
if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); }
if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); }
}
}
template<int CTA_SIZE, typename T, class BinOp>
static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op)
{
int tid = flattenedThreadId();
T val = buffer[tid] = init;
__syncthreads();
if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); }
if (tid < 32)
{
if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); }
if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); }
if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); }
if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); }
if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); }
if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); }
}
__syncthreads();
return buffer[0];
}
template <typename T, class BinOp>
static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op)
{
int ftid = flattenedThreadId();
int sft = stride();
if (sft < n)
{
for (unsigned int i = sft + ftid; i < n; i += sft)
data[ftid] = op(data[ftid], data[i]);
__syncthreads();
n = sft;
}
while (n > 1)
{
unsigned int half = n/2;
if (ftid < half)
data[ftid] = op(data[ftid], data[n - ftid - 1]);
__syncthreads();
n = n - half;
}
}
};
}}}
//! @endcond
#endif /* OPENCV_CUDA_DEVICE_BLOCK_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_CUDA_BORDER_INTERPOLATE_HPP
#define OPENCV_CUDA_BORDER_INTERPOLATE_HPP
#include "saturate_cast.hpp"
#include "vec_traits.hpp"
#include "vec_math.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
//////////////////////////////////////////////////////////////
// BrdConstant
template <typename D> struct BrdRowConstant
{
typedef D result_type;
explicit __host__ __device__ __forceinline__ BrdRowConstant(int width_, const D& val_ = VecTraits<D>::all(0)) : width(width_), val(val_) {}
template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
{
return x >= 0 ? saturate_cast<D>(data[x]) : val;
}
template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
{
return x < width ? saturate_cast<D>(data[x]) : val;
}
template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
{
return (x >= 0 && x < width) ? saturate_cast<D>(data[x]) : val;
}
int width;
D val;
};
template <typename D> struct BrdColConstant
{
typedef D result_type;
explicit __host__ __device__ __forceinline__ BrdColConstant(int height_, const D& val_ = VecTraits<D>::all(0)) : height(height_), val(val_) {}
template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
{
return y >= 0 ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
}
template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
{
return y < height ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
}
template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
{
return (y >= 0 && y < height) ? saturate_cast<D>(*(const T*)((const char*)data + y * step)) : val;
}
int height;
D val;
};
template <typename D> struct BrdConstant
{
typedef D result_type;
__host__ __device__ __forceinline__ BrdConstant(int height_, int width_, const D& val_ = VecTraits<D>::all(0)) : height(height_), width(width_), val(val_)
{
}
template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
{
return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(((const T*)((const uchar*)data + y * step))[x]) : val;
}
template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
{
return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(src(y, x)) : val;
}
int height;
int width;
D val;
};
//////////////////////////////////////////////////////////////
// BrdReplicate
template <typename D> struct BrdRowReplicate
{
typedef D result_type;
explicit __host__ __device__ __forceinline__ BrdRowReplicate(int width) : last_col(width - 1) {}
template <typename U> __host__ __device__ __forceinline__ BrdRowReplicate(int width, U) : last_col(width - 1) {}
__device__ __forceinline__ int idx_col_low(int x) const
{
return ::max(x, 0);
}
__device__ __forceinline__ int idx_col_high(int x) const
{
return ::min(x, last_col);
}
__device__ __forceinline__ int idx_col(int x) const
{
return idx_col_low(idx_col_high(x));
}
template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col_low(x)]);
}
template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col_high(x)]);
}
template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col(x)]);
}
int last_col;
};
template <typename D> struct BrdColReplicate
{
typedef D result_type;
explicit __host__ __device__ __forceinline__ BrdColReplicate(int height) : last_row(height - 1) {}
template <typename U> __host__ __device__ __forceinline__ BrdColReplicate(int height, U) : last_row(height - 1) {}
__device__ __forceinline__ int idx_row_low(int y) const
{
return ::max(y, 0);
}
__device__ __forceinline__ int idx_row_high(int y) const
{
return ::min(y, last_row);
}
__device__ __forceinline__ int idx_row(int y) const
{
return idx_row_low(idx_row_high(y));
}
template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const T*)((const char*)data + idx_row_low(y) * step));
}
template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const T*)((const char*)data + idx_row_high(y) * step));
}
template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const T*)((const char*)data + idx_row(y) * step));
}
int last_row;
};
template <typename D> struct BrdReplicate
{
typedef D result_type;
__host__ __device__ __forceinline__ BrdReplicate(int height, int width) : last_row(height - 1), last_col(width - 1) {}
template <typename U> __host__ __device__ __forceinline__ BrdReplicate(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
__device__ __forceinline__ int idx_row_low(int y) const
{
return ::max(y, 0);
}
__device__ __forceinline__ int idx_row_high(int y) const
{
return ::min(y, last_row);
}
__device__ __forceinline__ int idx_row(int y) const
{
return idx_row_low(idx_row_high(y));
}
__device__ __forceinline__ int idx_col_low(int x) const
{
return ::max(x, 0);
}
__device__ __forceinline__ int idx_col_high(int x) const
{
return ::min(x, last_col);
}
__device__ __forceinline__ int idx_col(int x) const
{
return idx_col_low(idx_col_high(x));
}
template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
{
return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
}
template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
{
return saturate_cast<D>(src(idx_row(y), idx_col(x)));
}
int last_row;
int last_col;
};
//////////////////////////////////////////////////////////////
// BrdReflect101
template <typename D> struct BrdRowReflect101
{
typedef D result_type;
explicit __host__ __device__ __forceinline__ BrdRowReflect101(int width) : last_col(width - 1) {}
template <typename U> __host__ __device__ __forceinline__ BrdRowReflect101(int width, U) : last_col(width - 1) {}
__device__ __forceinline__ int idx_col_low(int x) const
{
return ::abs(x) % (last_col + 1);
}
__device__ __forceinline__ int idx_col_high(int x) const
{
return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1);
}
__device__ __forceinline__ int idx_col(int x) const
{
return idx_col_low(idx_col_high(x));
}
template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col_low(x)]);
}
template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col_high(x)]);
}
template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col(x)]);
}
int last_col;
};
template <typename D> struct BrdColReflect101
{
typedef D result_type;
explicit __host__ __device__ __forceinline__ BrdColReflect101(int height) : last_row(height - 1) {}
template <typename U> __host__ __device__ __forceinline__ BrdColReflect101(int height, U) : last_row(height - 1) {}
__device__ __forceinline__ int idx_row_low(int y) const
{
return ::abs(y) % (last_row + 1);
}
__device__ __forceinline__ int idx_row_high(int y) const
{
return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1);
}
__device__ __forceinline__ int idx_row(int y) const
{
return idx_row_low(idx_row_high(y));
}
template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
}
template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
}
template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
}
int last_row;
};
template <typename D> struct BrdReflect101
{
typedef D result_type;
__host__ __device__ __forceinline__ BrdReflect101(int height, int width) : last_row(height - 1), last_col(width - 1) {}
template <typename U> __host__ __device__ __forceinline__ BrdReflect101(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
__device__ __forceinline__ int idx_row_low(int y) const
{
return ::abs(y) % (last_row + 1);
}
__device__ __forceinline__ int idx_row_high(int y) const
{
return ::abs(last_row - ::abs(last_row - y)) % (last_row + 1);
}
__device__ __forceinline__ int idx_row(int y) const
{
return idx_row_low(idx_row_high(y));
}
__device__ __forceinline__ int idx_col_low(int x) const
{
return ::abs(x) % (last_col + 1);
}
__device__ __forceinline__ int idx_col_high(int x) const
{
return ::abs(last_col - ::abs(last_col - x)) % (last_col + 1);
}
__device__ __forceinline__ int idx_col(int x) const
{
return idx_col_low(idx_col_high(x));
}
template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
{
return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
}
template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
{
return saturate_cast<D>(src(idx_row(y), idx_col(x)));
}
int last_row;
int last_col;
};
//////////////////////////////////////////////////////////////
// BrdReflect
template <typename D> struct BrdRowReflect
{
typedef D result_type;
explicit __host__ __device__ __forceinline__ BrdRowReflect(int width) : last_col(width - 1) {}
template <typename U> __host__ __device__ __forceinline__ BrdRowReflect(int width, U) : last_col(width - 1) {}
__device__ __forceinline__ int idx_col_low(int x) const
{
return (::abs(x) - (x < 0)) % (last_col + 1);
}
__device__ __forceinline__ int idx_col_high(int x) const
{
return ::abs(last_col - ::abs(last_col - x) + (x > last_col)) % (last_col + 1);
}
__device__ __forceinline__ int idx_col(int x) const
{
return idx_col_high(::abs(x) - (x < 0));
}
template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col_low(x)]);
}
template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col_high(x)]);
}
template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col(x)]);
}
int last_col;
};
template <typename D> struct BrdColReflect
{
typedef D result_type;
explicit __host__ __device__ __forceinline__ BrdColReflect(int height) : last_row(height - 1) {}
template <typename U> __host__ __device__ __forceinline__ BrdColReflect(int height, U) : last_row(height - 1) {}
__device__ __forceinline__ int idx_row_low(int y) const
{
return (::abs(y) - (y < 0)) % (last_row + 1);
}
__device__ __forceinline__ int idx_row_high(int y) const
{
return ::abs(last_row - ::abs(last_row - y) + (y > last_row)) % (last_row + 1);
}
__device__ __forceinline__ int idx_row(int y) const
{
return idx_row_high(::abs(y) - (y < 0));
}
template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
}
template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
}
template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
}
int last_row;
};
template <typename D> struct BrdReflect
{
typedef D result_type;
__host__ __device__ __forceinline__ BrdReflect(int height, int width) : last_row(height - 1), last_col(width - 1) {}
template <typename U> __host__ __device__ __forceinline__ BrdReflect(int height, int width, U) : last_row(height - 1), last_col(width - 1) {}
__device__ __forceinline__ int idx_row_low(int y) const
{
return (::abs(y) - (y < 0)) % (last_row + 1);
}
__device__ __forceinline__ int idx_row_high(int y) const
{
return /*::abs*/(last_row - ::abs(last_row - y) + (y > last_row)) /*% (last_row + 1)*/;
}
__device__ __forceinline__ int idx_row(int y) const
{
return idx_row_low(idx_row_high(y));
}
__device__ __forceinline__ int idx_col_low(int x) const
{
return (::abs(x) - (x < 0)) % (last_col + 1);
}
__device__ __forceinline__ int idx_col_high(int x) const
{
return (last_col - ::abs(last_col - x) + (x > last_col));
}
__device__ __forceinline__ int idx_col(int x) const
{
return idx_col_low(idx_col_high(x));
}
template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
{
return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
}
template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
{
return saturate_cast<D>(src(idx_row(y), idx_col(x)));
}
int last_row;
int last_col;
};
//////////////////////////////////////////////////////////////
// BrdWrap
template <typename D> struct BrdRowWrap
{
typedef D result_type;
explicit __host__ __device__ __forceinline__ BrdRowWrap(int width_) : width(width_) {}
template <typename U> __host__ __device__ __forceinline__ BrdRowWrap(int width_, U) : width(width_) {}
__device__ __forceinline__ int idx_col_low(int x) const
{
return (x >= 0) * x + (x < 0) * (x - ((x - width + 1) / width) * width);
}
__device__ __forceinline__ int idx_col_high(int x) const
{
return (x < width) * x + (x >= width) * (x % width);
}
__device__ __forceinline__ int idx_col(int x) const
{
return idx_col_high(idx_col_low(x));
}
template <typename T> __device__ __forceinline__ D at_low(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col_low(x)]);
}
template <typename T> __device__ __forceinline__ D at_high(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col_high(x)]);
}
template <typename T> __device__ __forceinline__ D at(int x, const T* data) const
{
return saturate_cast<D>(data[idx_col(x)]);
}
int width;
};
template <typename D> struct BrdColWrap
{
typedef D result_type;
explicit __host__ __device__ __forceinline__ BrdColWrap(int height_) : height(height_) {}
template <typename U> __host__ __device__ __forceinline__ BrdColWrap(int height_, U) : height(height_) {}
__device__ __forceinline__ int idx_row_low(int y) const
{
return (y >= 0) * y + (y < 0) * (y - ((y - height + 1) / height) * height);
}
__device__ __forceinline__ int idx_row_high(int y) const
{
return (y < height) * y + (y >= height) * (y % height);
}
__device__ __forceinline__ int idx_row(int y) const
{
return idx_row_high(idx_row_low(y));
}
template <typename T> __device__ __forceinline__ D at_low(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const D*)((const char*)data + idx_row_low(y) * step));
}
template <typename T> __device__ __forceinline__ D at_high(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const D*)((const char*)data + idx_row_high(y) * step));
}
template <typename T> __device__ __forceinline__ D at(int y, const T* data, size_t step) const
{
return saturate_cast<D>(*(const D*)((const char*)data + idx_row(y) * step));
}
int height;
};
template <typename D> struct BrdWrap
{
typedef D result_type;
__host__ __device__ __forceinline__ BrdWrap(int height_, int width_) :
height(height_), width(width_)
{
}
template <typename U>
__host__ __device__ __forceinline__ BrdWrap(int height_, int width_, U) :
height(height_), width(width_)
{
}
__device__ __forceinline__ int idx_row_low(int y) const
{
return (y >= 0) ? y : (y - ((y - height + 1) / height) * height);
}
__device__ __forceinline__ int idx_row_high(int y) const
{
return (y < height) ? y : (y % height);
}
__device__ __forceinline__ int idx_row(int y) const
{
return idx_row_high(idx_row_low(y));
}
__device__ __forceinline__ int idx_col_low(int x) const
{
return (x >= 0) ? x : (x - ((x - width + 1) / width) * width);
}
__device__ __forceinline__ int idx_col_high(int x) const
{
return (x < width) ? x : (x % width);
}
__device__ __forceinline__ int idx_col(int x) const
{
return idx_col_high(idx_col_low(x));
}
template <typename T> __device__ __forceinline__ D at(int y, int x, const T* data, size_t step) const
{
return saturate_cast<D>(((const T*)((const char*)data + idx_row(y) * step))[idx_col(x)]);
}
template <typename Ptr2D> __device__ __forceinline__ D at(typename Ptr2D::index_type y, typename Ptr2D::index_type x, const Ptr2D& src) const
{
return saturate_cast<D>(src(idx_row(y), idx_col(x)));
}
int height;
int width;
};
//////////////////////////////////////////////////////////////
// BorderReader
template <typename Ptr2D, typename B> struct BorderReader
{
typedef typename B::result_type elem_type;
typedef typename Ptr2D::index_type index_type;
__host__ __device__ __forceinline__ BorderReader(const Ptr2D& ptr_, const B& b_) : ptr(ptr_), b(b_) {}
__device__ __forceinline__ elem_type operator ()(index_type y, index_type x) const
{
return b.at(y, x, ptr);
}
Ptr2D ptr;
B b;
};
// under win32 there is some bug with templated types that passed as kernel parameters
// with this specialization all works fine
template <typename Ptr2D, typename D> struct BorderReader< Ptr2D, BrdConstant<D> >
{
typedef typename BrdConstant<D>::result_type elem_type;
typedef typename Ptr2D::index_type index_type;
__host__ __device__ __forceinline__ BorderReader(const Ptr2D& src_, const BrdConstant<D>& b) :
src(src_), height(b.height), width(b.width), val(b.val)
{
}
__device__ __forceinline__ D operator ()(index_type y, index_type x) const
{
return (x >= 0 && x < width && y >= 0 && y < height) ? saturate_cast<D>(src(y, x)) : val;
}
Ptr2D src;
int height;
int width;
D val;
};
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_BORDER_INTERPOLATE_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_CUDA_COLOR_HPP
#define OPENCV_CUDA_COLOR_HPP
#include "detail/color_detail.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
// All OPENCV_CUDA_IMPLEMENT_*_TRAITS(ColorSpace1_to_ColorSpace2, ...) macros implements
// template <typename T> class ColorSpace1_to_ColorSpace2_traits
// {
// typedef ... functor_type;
// static __host__ __device__ functor_type create_functor();
// };
OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgb, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_bgra, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgr_to_rgba, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_bgr, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgb, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS(bgra_to_rgba, 4, 4, 2)
#undef OPENCV_CUDA_IMPLEMENT_RGB2RGB_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr555, 3, 0, 5)
OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgr_to_bgr565, 3, 0, 6)
OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr555, 3, 2, 5)
OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgb_to_bgr565, 3, 2, 6)
OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr555, 4, 0, 5)
OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(bgra_to_bgr565, 4, 0, 6)
OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr555, 4, 2, 5)
OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS(rgba_to_bgr565, 4, 2, 6)
#undef OPENCV_CUDA_IMPLEMENT_RGB2RGB5x5_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgb, 3, 2, 5)
OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgb, 3, 2, 6)
OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgr, 3, 0, 5)
OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgr, 3, 0, 6)
OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_rgba, 4, 2, 5)
OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_rgba, 4, 2, 6)
OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr555_to_bgra, 4, 0, 5)
OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS(bgr565_to_bgra, 4, 0, 6)
#undef OPENCV_CUDA_IMPLEMENT_RGB5x52RGB_TRAITS
OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgr, 3)
OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS(gray_to_bgra, 4)
#undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB_TRAITS
OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr555, 5)
OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS(gray_to_bgr565, 6)
#undef OPENCV_CUDA_IMPLEMENT_GRAY2RGB5x5_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr555_to_gray, 5)
OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS(bgr565_to_gray, 6)
#undef OPENCV_CUDA_IMPLEMENT_RGB5x52GRAY_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgb_to_gray, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgr_to_gray, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(rgba_to_gray, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS(bgra_to_gray, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_RGB2GRAY_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgb_to_yuv4, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(rgba_to_yuv4, 4, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv, 3, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgr_to_yuv4, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS(bgra_to_yuv4, 4, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_RGB2YUV_TRAITS
OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgb, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_rgba, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgb, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_rgba, 4, 4, 2)
OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgr, 3, 3, 0)
OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv_to_bgra, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgr, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS(yuv4_to_bgra, 4, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_YUV2RGB_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgb_to_YCrCb4, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(rgba_to_YCrCb4, 4, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb, 3, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgr_to_YCrCb4, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS(bgra_to_YCrCb4, 4, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_RGB2YCrCb_TRAITS
OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgb, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_rgba, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgb, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_rgba, 4, 4, 2)
OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgr, 3, 3, 0)
OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb_to_bgra, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgr, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS(YCrCb4_to_bgra, 4, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_YCrCb2RGB_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgb_to_xyz4, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(rgba_to_xyz4, 4, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz, 3, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgr_to_xyz4, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS(bgra_to_xyz4, 4, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_RGB2XYZ_TRAITS
OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgb, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgb, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_rgba, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_rgba, 4, 4, 2)
OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgr, 3, 3, 0)
OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgr, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz_to_bgra, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS(xyz4_to_bgra, 4, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_XYZ2RGB_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgb_to_hsv4, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(rgba_to_hsv4, 4, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv, 3, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgr_to_hsv4, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS(bgra_to_hsv4, 4, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_RGB2HSV_TRAITS
OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgb, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_rgba, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgb, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_rgba, 4, 4, 2)
OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgr, 3, 3, 0)
OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv_to_bgra, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgr, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS(hsv4_to_bgra, 4, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_HSV2RGB_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgb_to_hls4, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(rgba_to_hls4, 4, 4, 2)
OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls, 3, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgr_to_hls4, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS(bgra_to_hls4, 4, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_RGB2HLS_TRAITS
OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgb, 3, 3, 2)
OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_rgba, 3, 4, 2)
OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgb, 4, 3, 2)
OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_rgba, 4, 4, 2)
OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgr, 3, 3, 0)
OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls_to_bgra, 3, 4, 0)
OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgr, 4, 3, 0)
OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS(hls4_to_bgra, 4, 4, 0)
#undef OPENCV_CUDA_IMPLEMENT_HLS2RGB_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab, 3, 3, true, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab, 4, 3, true, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgb_to_lab4, 3, 4, true, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(rgba_to_lab4, 4, 4, true, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab, 3, 3, true, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab, 4, 3, true, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgr_to_lab4, 3, 4, true, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(bgra_to_lab4, 4, 4, true, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab, 3, 3, false, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab, 4, 3, false, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgb_to_lab4, 3, 4, false, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lrgba_to_lab4, 4, 4, false, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab, 3, 3, false, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab, 4, 3, false, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgr_to_lab4, 3, 4, false, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS(lbgra_to_lab4, 4, 4, false, 0)
#undef OPENCV_CUDA_IMPLEMENT_RGB2Lab_TRAITS
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgb, 3, 3, true, 2)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgb, 4, 3, true, 2)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_rgba, 3, 4, true, 2)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_rgba, 4, 4, true, 2)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgr, 3, 3, true, 0)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgr, 4, 3, true, 0)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_bgra, 3, 4, true, 0)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_bgra, 4, 4, true, 0)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgb, 3, 3, false, 2)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgb, 4, 3, false, 2)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lrgba, 3, 4, false, 2)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lrgba, 4, 4, false, 2)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgr, 3, 3, false, 0)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgr, 4, 3, false, 0)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab_to_lbgra, 3, 4, false, 0)
OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS(lab4_to_lbgra, 4, 4, false, 0)
#undef OPENCV_CUDA_IMPLEMENT_Lab2RGB_TRAITS
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv, 3, 3, true, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv, 4, 3, true, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgb_to_luv4, 3, 4, true, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(rgba_to_luv4, 4, 4, true, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv, 3, 3, true, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv, 4, 3, true, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgr_to_luv4, 3, 4, true, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(bgra_to_luv4, 4, 4, true, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv, 3, 3, false, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv, 4, 3, false, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgb_to_luv4, 3, 4, false, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lrgba_to_luv4, 4, 4, false, 2)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv, 3, 3, false, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv, 4, 3, false, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgr_to_luv4, 3, 4, false, 0)
OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS(lbgra_to_luv4, 4, 4, false, 0)
#undef OPENCV_CUDA_IMPLEMENT_RGB2Luv_TRAITS
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgb, 3, 3, true, 2)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgb, 4, 3, true, 2)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_rgba, 3, 4, true, 2)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_rgba, 4, 4, true, 2)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgr, 3, 3, true, 0)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgr, 4, 3, true, 0)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_bgra, 3, 4, true, 0)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_bgra, 4, 4, true, 0)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgb, 3, 3, false, 2)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgb, 4, 3, false, 2)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lrgba, 3, 4, false, 2)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lrgba, 4, 4, false, 2)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgr, 3, 3, false, 0)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgr, 4, 3, false, 0)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv_to_lbgra, 3, 4, false, 0)
OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS(luv4_to_lbgra, 4, 4, false, 0)
#undef OPENCV_CUDA_IMPLEMENT_Luv2RGB_TRAITS
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_COLOR_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_CUDA_COMMON_HPP
#define OPENCV_CUDA_COMMON_HPP
#include <cuda_runtime.h>
#include "opencv2/core/cuda_types.hpp"
#include "opencv2/core/cvdef.h"
#include "opencv2/core/base.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
#ifndef CV_PI_F
#ifndef CV_PI
#define CV_PI_F 3.14159265f
#else
#define CV_PI_F ((float)CV_PI)
#endif
#endif
namespace cv { namespace cuda {
static inline void checkCudaError(cudaError_t err, const char* file, const int line, const char* func)
{
if (cudaSuccess != err)
cv::error(cv::Error::GpuApiCallError, cudaGetErrorString(err), func, file, line);
}
}}
#ifndef cudaSafeCall
#define cudaSafeCall(expr) cv::cuda::checkCudaError(expr, __FILE__, __LINE__, CV_Func)
#endif
namespace cv { namespace cuda
{
template <typename T> static inline bool isAligned(const T* ptr, size_t size)
{
return reinterpret_cast<size_t>(ptr) % size == 0;
}
static inline bool isAligned(size_t step, size_t size)
{
return step % size == 0;
}
}}
namespace cv { namespace cuda
{
namespace device
{
__host__ __device__ __forceinline__ int divUp(int total, int grain)
{
return (total + grain - 1) / grain;
}
template<class T> inline void bindTexture(const textureReference* tex, const PtrStepSz<T>& img)
{
cudaChannelFormatDesc desc = cudaCreateChannelDesc<T>();
cudaSafeCall( cudaBindTexture2D(0, tex, img.ptr(), &desc, img.cols, img.rows, img.step) );
}
}
}}
//! @endcond
#endif // OPENCV_CUDA_COMMON_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_CUDA_DATAMOV_UTILS_HPP
#define OPENCV_CUDA_DATAMOV_UTILS_HPP
#include "common.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 200
// for Fermi memory space is detected automatically
template <typename T> struct ForceGlob
{
__device__ __forceinline__ static void Load(const T* ptr, int offset, T& val) { val = ptr[offset]; }
};
#else // __CUDA_ARCH__ >= 200
#if defined(_WIN64) || defined(__LP64__)
// 64-bit register modifier for inlined asm
#define OPENCV_CUDA_ASM_PTR "l"
#else
// 32-bit register modifier for inlined asm
#define OPENCV_CUDA_ASM_PTR "r"
#endif
template<class T> struct ForceGlob;
#define OPENCV_CUDA_DEFINE_FORCE_GLOB(base_type, ptx_type, reg_mod) \
template <> struct ForceGlob<base_type> \
{ \
__device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \
{ \
asm("ld.global."#ptx_type" %0, [%1];" : "="#reg_mod(val) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \
} \
};
#define OPENCV_CUDA_DEFINE_FORCE_GLOB_B(base_type, ptx_type) \
template <> struct ForceGlob<base_type> \
{ \
__device__ __forceinline__ static void Load(const base_type* ptr, int offset, base_type& val) \
{ \
asm("ld.global."#ptx_type" %0, [%1];" : "=r"(*reinterpret_cast<uint*>(&val)) : OPENCV_CUDA_ASM_PTR(ptr + offset)); \
} \
};
OPENCV_CUDA_DEFINE_FORCE_GLOB_B(uchar, u8)
OPENCV_CUDA_DEFINE_FORCE_GLOB_B(schar, s8)
OPENCV_CUDA_DEFINE_FORCE_GLOB_B(char, b8)
OPENCV_CUDA_DEFINE_FORCE_GLOB (ushort, u16, h)
OPENCV_CUDA_DEFINE_FORCE_GLOB (short, s16, h)
OPENCV_CUDA_DEFINE_FORCE_GLOB (uint, u32, r)
OPENCV_CUDA_DEFINE_FORCE_GLOB (int, s32, r)
OPENCV_CUDA_DEFINE_FORCE_GLOB (float, f32, f)
OPENCV_CUDA_DEFINE_FORCE_GLOB (double, f64, d)
#undef OPENCV_CUDA_DEFINE_FORCE_GLOB
#undef OPENCV_CUDA_DEFINE_FORCE_GLOB_B
#undef OPENCV_CUDA_ASM_PTR
#endif // __CUDA_ARCH__ >= 200
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_DATAMOV_UTILS_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_CUDA_REDUCE_DETAIL_HPP
#define OPENCV_CUDA_REDUCE_DETAIL_HPP
#include <thrust/tuple.h>
#include "../warp.hpp"
#include "../warp_shuffle.hpp"
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
namespace reduce_detail
{
template <typename T> struct GetType;
template <typename T> struct GetType<T*>
{
typedef T type;
};
template <typename T> struct GetType<volatile T*>
{
typedef T type;
};
template <typename T> struct GetType<T&>
{
typedef T type;
};
template <unsigned int I, unsigned int N>
struct For
{
template <class PointerTuple, class ValTuple>
static __device__ void loadToSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid)
{
thrust::get<I>(smem)[tid] = thrust::get<I>(val);
For<I + 1, N>::loadToSmem(smem, val, tid);
}
template <class PointerTuple, class ValTuple>
static __device__ void loadFromSmem(const PointerTuple& smem, const ValTuple& val, unsigned int tid)
{
thrust::get<I>(val) = thrust::get<I>(smem)[tid];
For<I + 1, N>::loadFromSmem(smem, val, tid);
}
template <class PointerTuple, class ValTuple, class OpTuple>
static __device__ void merge(const PointerTuple& smem, const ValTuple& val, unsigned int tid, unsigned int delta, const OpTuple& op)
{
typename GetType<typename thrust::tuple_element<I, PointerTuple>::type>::type reg = thrust::get<I>(smem)[tid + delta];
thrust::get<I>(smem)[tid] = thrust::get<I>(val) = thrust::get<I>(op)(thrust::get<I>(val), reg);
For<I + 1, N>::merge(smem, val, tid, delta, op);
}
template <class ValTuple, class OpTuple>
static __device__ void mergeShfl(const ValTuple& val, unsigned int delta, unsigned int width, const OpTuple& op)
{
typename GetType<typename thrust::tuple_element<I, ValTuple>::type>::type reg = shfl_down(thrust::get<I>(val), delta, width);
thrust::get<I>(val) = thrust::get<I>(op)(thrust::get<I>(val), reg);
For<I + 1, N>::mergeShfl(val, delta, width, op);
}
};
template <unsigned int N>
struct For<N, N>
{
template <class PointerTuple, class ValTuple>
static __device__ void loadToSmem(const PointerTuple&, const ValTuple&, unsigned int)
{
}
template <class PointerTuple, class ValTuple>
static __device__ void loadFromSmem(const PointerTuple&, const ValTuple&, unsigned int)
{
}
template <class PointerTuple, class ValTuple, class OpTuple>
static __device__ void merge(const PointerTuple&, const ValTuple&, unsigned int, unsigned int, const OpTuple&)
{
}
template <class ValTuple, class OpTuple>
static __device__ void mergeShfl(const ValTuple&, unsigned int, unsigned int, const OpTuple&)
{
}
};
template <typename T>
__device__ __forceinline__ void loadToSmem(volatile T* smem, T& val, unsigned int tid)
{
smem[tid] = val;
}
template <typename T>
__device__ __forceinline__ void loadFromSmem(volatile T* smem, T& val, unsigned int tid)
{
val = smem[tid];
}
template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9>
__device__ __forceinline__ void loadToSmem(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
unsigned int tid)
{
For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::loadToSmem(smem, val, tid);
}
template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9>
__device__ __forceinline__ void loadFromSmem(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
unsigned int tid)
{
For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::loadFromSmem(smem, val, tid);
}
template <typename T, class Op>
__device__ __forceinline__ void merge(volatile T* smem, T& val, unsigned int tid, unsigned int delta, const Op& op)
{
T reg = smem[tid + delta];
smem[tid] = val = op(val, reg);
}
template <typename T, class Op>
__device__ __forceinline__ void mergeShfl(T& val, unsigned int delta, unsigned int width, const Op& op)
{
T reg = shfl_down(val, delta, width);
val = op(val, reg);
}
template <typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
__device__ __forceinline__ void merge(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
unsigned int tid,
unsigned int delta,
const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
{
For<0, thrust::tuple_size<thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9> >::value>::merge(smem, val, tid, delta, op);
}
template <typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
__device__ __forceinline__ void mergeShfl(const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
unsigned int delta,
unsigned int width,
const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
{
For<0, thrust::tuple_size<thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9> >::value>::mergeShfl(val, delta, width, op);
}
template <unsigned int N> struct Generic
{
template <typename Pointer, typename Reference, class Op>
static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
{
loadToSmem(smem, val, tid);
if (N >= 32)
__syncthreads();
if (N >= 2048)
{
if (tid < 1024)
merge(smem, val, tid, 1024, op);
__syncthreads();
}
if (N >= 1024)
{
if (tid < 512)
merge(smem, val, tid, 512, op);
__syncthreads();
}
if (N >= 512)
{
if (tid < 256)
merge(smem, val, tid, 256, op);
__syncthreads();
}
if (N >= 256)
{
if (tid < 128)
merge(smem, val, tid, 128, op);
__syncthreads();
}
if (N >= 128)
{
if (tid < 64)
merge(smem, val, tid, 64, op);
__syncthreads();
}
if (N >= 64)
{
if (tid < 32)
merge(smem, val, tid, 32, op);
}
if (tid < 16)
{
merge(smem, val, tid, 16, op);
merge(smem, val, tid, 8, op);
merge(smem, val, tid, 4, op);
merge(smem, val, tid, 2, op);
merge(smem, val, tid, 1, op);
}
}
};
template <unsigned int I, typename Pointer, typename Reference, class Op>
struct Unroll
{
static __device__ void loopShfl(Reference val, Op op, unsigned int N)
{
mergeShfl(val, I, N, op);
Unroll<I / 2, Pointer, Reference, Op>::loopShfl(val, op, N);
}
static __device__ void loop(Pointer smem, Reference val, unsigned int tid, Op op)
{
merge(smem, val, tid, I, op);
Unroll<I / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
}
};
template <typename Pointer, typename Reference, class Op>
struct Unroll<0, Pointer, Reference, Op>
{
static __device__ void loopShfl(Reference, Op, unsigned int)
{
}
static __device__ void loop(Pointer, Reference, unsigned int, Op)
{
}
};
template <unsigned int N> struct WarpOptimized
{
template <typename Pointer, typename Reference, class Op>
static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
CV_UNUSED(smem);
CV_UNUSED(tid);
Unroll<N / 2, Pointer, Reference, Op>::loopShfl(val, op, N);
#else
loadToSmem(smem, val, tid);
if (tid < N / 2)
Unroll<N / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
#endif
}
};
template <unsigned int N> struct GenericOptimized32
{
enum { M = N / 32 };
template <typename Pointer, typename Reference, class Op>
static __device__ void reduce(Pointer smem, Reference val, unsigned int tid, Op op)
{
const unsigned int laneId = Warp::laneId();
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
Unroll<16, Pointer, Reference, Op>::loopShfl(val, op, warpSize);
if (laneId == 0)
loadToSmem(smem, val, tid / 32);
#else
loadToSmem(smem, val, tid);
if (laneId < 16)
Unroll<16, Pointer, Reference, Op>::loop(smem, val, tid, op);
__syncthreads();
if (laneId == 0)
loadToSmem(smem, val, tid / 32);
#endif
__syncthreads();
loadFromSmem(smem, val, tid);
if (tid < 32)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
Unroll<M / 2, Pointer, Reference, Op>::loopShfl(val, op, M);
#else
Unroll<M / 2, Pointer, Reference, Op>::loop(smem, val, tid, op);
#endif
}
}
};
template <bool val, class T1, class T2> struct StaticIf;
template <class T1, class T2> struct StaticIf<true, T1, T2>
{
typedef T1 type;
};
template <class T1, class T2> struct StaticIf<false, T1, T2>
{
typedef T2 type;
};
template <unsigned int N> struct IsPowerOf2
{
enum { value = ((N != 0) && !(N & (N - 1))) };
};
template <unsigned int N> struct Dispatcher
{
typedef typename StaticIf<
(N <= 32) && IsPowerOf2<N>::value,
WarpOptimized<N>,
typename StaticIf<
(N <= 1024) && IsPowerOf2<N>::value,
GenericOptimized32<N>,
Generic<N>
>::type
>::type reductor;
};
}
}}}
//! @endcond
#endif // OPENCV_CUDA_REDUCE_DETAIL_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_CUDA_PRED_VAL_REDUCE_DETAIL_HPP
#define OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_HPP
#include <thrust/tuple.h>
#include "../warp.hpp"
#include "../warp_shuffle.hpp"
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
namespace reduce_key_val_detail
{
template <typename T> struct GetType;
template <typename T> struct GetType<T*>
{
typedef T type;
};
template <typename T> struct GetType<volatile T*>
{
typedef T type;
};
template <typename T> struct GetType<T&>
{
typedef T type;
};
template <unsigned int I, unsigned int N>
struct For
{
template <class PointerTuple, class ReferenceTuple>
static __device__ void loadToSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid)
{
thrust::get<I>(smem)[tid] = thrust::get<I>(data);
For<I + 1, N>::loadToSmem(smem, data, tid);
}
template <class PointerTuple, class ReferenceTuple>
static __device__ void loadFromSmem(const PointerTuple& smem, const ReferenceTuple& data, unsigned int tid)
{
thrust::get<I>(data) = thrust::get<I>(smem)[tid];
For<I + 1, N>::loadFromSmem(smem, data, tid);
}
template <class ReferenceTuple>
static __device__ void copyShfl(const ReferenceTuple& val, unsigned int delta, int width)
{
thrust::get<I>(val) = shfl_down(thrust::get<I>(val), delta, width);
For<I + 1, N>::copyShfl(val, delta, width);
}
template <class PointerTuple, class ReferenceTuple>
static __device__ void copy(const PointerTuple& svals, const ReferenceTuple& val, unsigned int tid, unsigned int delta)
{
thrust::get<I>(svals)[tid] = thrust::get<I>(val) = thrust::get<I>(svals)[tid + delta];
For<I + 1, N>::copy(svals, val, tid, delta);
}
template <class KeyReferenceTuple, class ValReferenceTuple, class CmpTuple>
static __device__ void mergeShfl(const KeyReferenceTuple& key, const ValReferenceTuple& val, const CmpTuple& cmp, unsigned int delta, int width)
{
typename GetType<typename thrust::tuple_element<I, KeyReferenceTuple>::type>::type reg = shfl_down(thrust::get<I>(key), delta, width);
if (thrust::get<I>(cmp)(reg, thrust::get<I>(key)))
{
thrust::get<I>(key) = reg;
thrust::get<I>(val) = shfl_down(thrust::get<I>(val), delta, width);
}
For<I + 1, N>::mergeShfl(key, val, cmp, delta, width);
}
template <class KeyPointerTuple, class KeyReferenceTuple, class ValPointerTuple, class ValReferenceTuple, class CmpTuple>
static __device__ void merge(const KeyPointerTuple& skeys, const KeyReferenceTuple& key,
const ValPointerTuple& svals, const ValReferenceTuple& val,
const CmpTuple& cmp,
unsigned int tid, unsigned int delta)
{
typename GetType<typename thrust::tuple_element<I, KeyPointerTuple>::type>::type reg = thrust::get<I>(skeys)[tid + delta];
if (thrust::get<I>(cmp)(reg, thrust::get<I>(key)))
{
thrust::get<I>(skeys)[tid] = thrust::get<I>(key) = reg;
thrust::get<I>(svals)[tid] = thrust::get<I>(val) = thrust::get<I>(svals)[tid + delta];
}
For<I + 1, N>::merge(skeys, key, svals, val, cmp, tid, delta);
}
};
template <unsigned int N>
struct For<N, N>
{
template <class PointerTuple, class ReferenceTuple>
static __device__ void loadToSmem(const PointerTuple&, const ReferenceTuple&, unsigned int)
{
}
template <class PointerTuple, class ReferenceTuple>
static __device__ void loadFromSmem(const PointerTuple&, const ReferenceTuple&, unsigned int)
{
}
template <class ReferenceTuple>
static __device__ void copyShfl(const ReferenceTuple&, unsigned int, int)
{
}
template <class PointerTuple, class ReferenceTuple>
static __device__ void copy(const PointerTuple&, const ReferenceTuple&, unsigned int, unsigned int)
{
}
template <class KeyReferenceTuple, class ValReferenceTuple, class CmpTuple>
static __device__ void mergeShfl(const KeyReferenceTuple&, const ValReferenceTuple&, const CmpTuple&, unsigned int, int)
{
}
template <class KeyPointerTuple, class KeyReferenceTuple, class ValPointerTuple, class ValReferenceTuple, class CmpTuple>
static __device__ void merge(const KeyPointerTuple&, const KeyReferenceTuple&,
const ValPointerTuple&, const ValReferenceTuple&,
const CmpTuple&,
unsigned int, unsigned int)
{
}
};
//////////////////////////////////////////////////////
// loadToSmem
template <typename T>
__device__ __forceinline__ void loadToSmem(volatile T* smem, T& data, unsigned int tid)
{
smem[tid] = data;
}
template <typename T>
__device__ __forceinline__ void loadFromSmem(volatile T* smem, T& data, unsigned int tid)
{
data = smem[tid];
}
template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
__device__ __forceinline__ void loadToSmem(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& smem,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& data,
unsigned int tid)
{
For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::loadToSmem(smem, data, tid);
}
template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
__device__ __forceinline__ void loadFromSmem(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& smem,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& data,
unsigned int tid)
{
For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::loadFromSmem(smem, data, tid);
}
//////////////////////////////////////////////////////
// copyVals
template <typename V>
__device__ __forceinline__ void copyValsShfl(V& val, unsigned int delta, int width)
{
val = shfl_down(val, delta, width);
}
template <typename V>
__device__ __forceinline__ void copyVals(volatile V* svals, V& val, unsigned int tid, unsigned int delta)
{
svals[tid] = val = svals[tid + delta];
}
template <typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
__device__ __forceinline__ void copyValsShfl(const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
unsigned int delta,
int width)
{
For<0, thrust::tuple_size<thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9> >::value>::copyShfl(val, delta, width);
}
template <typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9>
__device__ __forceinline__ void copyVals(const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
unsigned int tid, unsigned int delta)
{
For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::copy(svals, val, tid, delta);
}
//////////////////////////////////////////////////////
// merge
template <typename K, typename V, class Cmp>
__device__ __forceinline__ void mergeShfl(K& key, V& val, const Cmp& cmp, unsigned int delta, int width)
{
K reg = shfl_down(key, delta, width);
if (cmp(reg, key))
{
key = reg;
copyValsShfl(val, delta, width);
}
}
template <typename K, typename V, class Cmp>
__device__ __forceinline__ void merge(volatile K* skeys, K& key, volatile V* svals, V& val, const Cmp& cmp, unsigned int tid, unsigned int delta)
{
K reg = skeys[tid + delta];
if (cmp(reg, key))
{
skeys[tid] = key = reg;
copyVals(svals, val, tid, delta);
}
}
template <typename K,
typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
class Cmp>
__device__ __forceinline__ void mergeShfl(K& key,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
const Cmp& cmp,
unsigned int delta, int width)
{
K reg = shfl_down(key, delta, width);
if (cmp(reg, key))
{
key = reg;
copyValsShfl(val, delta, width);
}
}
template <typename K,
typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
class Cmp>
__device__ __forceinline__ void merge(volatile K* skeys, K& key,
const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
const Cmp& cmp, unsigned int tid, unsigned int delta)
{
K reg = skeys[tid + delta];
if (cmp(reg, key))
{
skeys[tid] = key = reg;
copyVals(svals, val, tid, delta);
}
}
template <typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
__device__ __forceinline__ void mergeShfl(const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp,
unsigned int delta, int width)
{
For<0, thrust::tuple_size<thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9> >::value>::mergeShfl(key, val, cmp, delta, width);
}
template <typename KP0, typename KP1, typename KP2, typename KP3, typename KP4, typename KP5, typename KP6, typename KP7, typename KP8, typename KP9,
typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
__device__ __forceinline__ void merge(const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>& skeys,
const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp,
unsigned int tid, unsigned int delta)
{
For<0, thrust::tuple_size<thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9> >::value>::merge(skeys, key, svals, val, cmp, tid, delta);
}
//////////////////////////////////////////////////////
// Generic
template <unsigned int N> struct Generic
{
template <class KP, class KR, class VP, class VR, class Cmp>
static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
{
loadToSmem(skeys, key, tid);
loadValsToSmem(svals, val, tid);
if (N >= 32)
__syncthreads();
if (N >= 2048)
{
if (tid < 1024)
merge(skeys, key, svals, val, cmp, tid, 1024);
__syncthreads();
}
if (N >= 1024)
{
if (tid < 512)
merge(skeys, key, svals, val, cmp, tid, 512);
__syncthreads();
}
if (N >= 512)
{
if (tid < 256)
merge(skeys, key, svals, val, cmp, tid, 256);
__syncthreads();
}
if (N >= 256)
{
if (tid < 128)
merge(skeys, key, svals, val, cmp, tid, 128);
__syncthreads();
}
if (N >= 128)
{
if (tid < 64)
merge(skeys, key, svals, val, cmp, tid, 64);
__syncthreads();
}
if (N >= 64)
{
if (tid < 32)
merge(skeys, key, svals, val, cmp, tid, 32);
}
if (tid < 16)
{
merge(skeys, key, svals, val, cmp, tid, 16);
merge(skeys, key, svals, val, cmp, tid, 8);
merge(skeys, key, svals, val, cmp, tid, 4);
merge(skeys, key, svals, val, cmp, tid, 2);
merge(skeys, key, svals, val, cmp, tid, 1);
}
}
};
template <unsigned int I, class KP, class KR, class VP, class VR, class Cmp>
struct Unroll
{
static __device__ void loopShfl(KR key, VR val, Cmp cmp, unsigned int N)
{
mergeShfl(key, val, cmp, I, N);
Unroll<I / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, N);
}
static __device__ void loop(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
{
merge(skeys, key, svals, val, cmp, tid, I);
Unroll<I / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
}
};
template <class KP, class KR, class VP, class VR, class Cmp>
struct Unroll<0, KP, KR, VP, VR, Cmp>
{
static __device__ void loopShfl(KR, VR, Cmp, unsigned int)
{
}
static __device__ void loop(KP, KR, VP, VR, unsigned int, Cmp)
{
}
};
template <unsigned int N> struct WarpOptimized
{
template <class KP, class KR, class VP, class VR, class Cmp>
static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
{
#if 0 // __CUDA_ARCH__ >= 300
CV_UNUSED(skeys);
CV_UNUSED(svals);
CV_UNUSED(tid);
Unroll<N / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, N);
#else
loadToSmem(skeys, key, tid);
loadToSmem(svals, val, tid);
if (tid < N / 2)
Unroll<N / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
#endif
}
};
template <unsigned int N> struct GenericOptimized32
{
enum { M = N / 32 };
template <class KP, class KR, class VP, class VR, class Cmp>
static __device__ void reduce(KP skeys, KR key, VP svals, VR val, unsigned int tid, Cmp cmp)
{
const unsigned int laneId = Warp::laneId();
#if 0 // __CUDA_ARCH__ >= 300
Unroll<16, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, warpSize);
if (laneId == 0)
{
loadToSmem(skeys, key, tid / 32);
loadToSmem(svals, val, tid / 32);
}
#else
loadToSmem(skeys, key, tid);
loadToSmem(svals, val, tid);
if (laneId < 16)
Unroll<16, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
__syncthreads();
if (laneId == 0)
{
loadToSmem(skeys, key, tid / 32);
loadToSmem(svals, val, tid / 32);
}
#endif
__syncthreads();
loadFromSmem(skeys, key, tid);
if (tid < 32)
{
#if 0 // __CUDA_ARCH__ >= 300
loadFromSmem(svals, val, tid);
Unroll<M / 2, KP, KR, VP, VR, Cmp>::loopShfl(key, val, cmp, M);
#else
Unroll<M / 2, KP, KR, VP, VR, Cmp>::loop(skeys, key, svals, val, tid, cmp);
#endif
}
}
};
template <bool val, class T1, class T2> struct StaticIf;
template <class T1, class T2> struct StaticIf<true, T1, T2>
{
typedef T1 type;
};
template <class T1, class T2> struct StaticIf<false, T1, T2>
{
typedef T2 type;
};
template <unsigned int N> struct IsPowerOf2
{
enum { value = ((N != 0) && !(N & (N - 1))) };
};
template <unsigned int N> struct Dispatcher
{
typedef typename StaticIf<
(N <= 32) && IsPowerOf2<N>::value,
WarpOptimized<N>,
typename StaticIf<
(N <= 1024) && IsPowerOf2<N>::value,
GenericOptimized32<N>,
Generic<N>
>::type
>::type reductor;
};
}
}}}
//! @endcond
#endif // OPENCV_CUDA_PRED_VAL_REDUCE_DETAIL_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_CUDA_TRANSFORM_DETAIL_HPP
#define OPENCV_CUDA_TRANSFORM_DETAIL_HPP
#include "../common.hpp"
#include "../vec_traits.hpp"
#include "../functional.hpp"
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
namespace transform_detail
{
//! Read Write Traits
template <typename T, typename D, int shift> struct UnaryReadWriteTraits
{
typedef typename TypeVec<T, shift>::vec_type read_type;
typedef typename TypeVec<D, shift>::vec_type write_type;
};
template <typename T1, typename T2, typename D, int shift> struct BinaryReadWriteTraits
{
typedef typename TypeVec<T1, shift>::vec_type read_type1;
typedef typename TypeVec<T2, shift>::vec_type read_type2;
typedef typename TypeVec<D, shift>::vec_type write_type;
};
//! Transform kernels
template <int shift> struct OpUnroller;
template <> struct OpUnroller<1>
{
template <typename T, typename D, typename UnOp, typename Mask>
static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
{
if (mask(y, x_shifted))
dst.x = op(src.x);
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
{
if (mask(y, x_shifted))
dst.x = op(src1.x, src2.x);
}
};
template <> struct OpUnroller<2>
{
template <typename T, typename D, typename UnOp, typename Mask>
static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, UnOp& op, int x_shifted, int y)
{
if (mask(y, x_shifted))
dst.x = op(src.x);
if (mask(y, x_shifted + 1))
dst.y = op(src.y);
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, BinOp& op, int x_shifted, int y)
{
if (mask(y, x_shifted))
dst.x = op(src1.x, src2.x);
if (mask(y, x_shifted + 1))
dst.y = op(src1.y, src2.y);
}
};
template <> struct OpUnroller<3>
{
template <typename T, typename D, typename UnOp, typename Mask>
static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
{
if (mask(y, x_shifted))
dst.x = op(src.x);
if (mask(y, x_shifted + 1))
dst.y = op(src.y);
if (mask(y, x_shifted + 2))
dst.z = op(src.z);
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
{
if (mask(y, x_shifted))
dst.x = op(src1.x, src2.x);
if (mask(y, x_shifted + 1))
dst.y = op(src1.y, src2.y);
if (mask(y, x_shifted + 2))
dst.z = op(src1.z, src2.z);
}
};
template <> struct OpUnroller<4>
{
template <typename T, typename D, typename UnOp, typename Mask>
static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
{
if (mask(y, x_shifted))
dst.x = op(src.x);
if (mask(y, x_shifted + 1))
dst.y = op(src.y);
if (mask(y, x_shifted + 2))
dst.z = op(src.z);
if (mask(y, x_shifted + 3))
dst.w = op(src.w);
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
{
if (mask(y, x_shifted))
dst.x = op(src1.x, src2.x);
if (mask(y, x_shifted + 1))
dst.y = op(src1.y, src2.y);
if (mask(y, x_shifted + 2))
dst.z = op(src1.z, src2.z);
if (mask(y, x_shifted + 3))
dst.w = op(src1.w, src2.w);
}
};
template <> struct OpUnroller<8>
{
template <typename T, typename D, typename UnOp, typename Mask>
static __device__ __forceinline__ void unroll(const T& src, D& dst, const Mask& mask, const UnOp& op, int x_shifted, int y)
{
if (mask(y, x_shifted))
dst.a0 = op(src.a0);
if (mask(y, x_shifted + 1))
dst.a1 = op(src.a1);
if (mask(y, x_shifted + 2))
dst.a2 = op(src.a2);
if (mask(y, x_shifted + 3))
dst.a3 = op(src.a3);
if (mask(y, x_shifted + 4))
dst.a4 = op(src.a4);
if (mask(y, x_shifted + 5))
dst.a5 = op(src.a5);
if (mask(y, x_shifted + 6))
dst.a6 = op(src.a6);
if (mask(y, x_shifted + 7))
dst.a7 = op(src.a7);
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __device__ __forceinline__ void unroll(const T1& src1, const T2& src2, D& dst, const Mask& mask, const BinOp& op, int x_shifted, int y)
{
if (mask(y, x_shifted))
dst.a0 = op(src1.a0, src2.a0);
if (mask(y, x_shifted + 1))
dst.a1 = op(src1.a1, src2.a1);
if (mask(y, x_shifted + 2))
dst.a2 = op(src1.a2, src2.a2);
if (mask(y, x_shifted + 3))
dst.a3 = op(src1.a3, src2.a3);
if (mask(y, x_shifted + 4))
dst.a4 = op(src1.a4, src2.a4);
if (mask(y, x_shifted + 5))
dst.a5 = op(src1.a5, src2.a5);
if (mask(y, x_shifted + 6))
dst.a6 = op(src1.a6, src2.a6);
if (mask(y, x_shifted + 7))
dst.a7 = op(src1.a7, src2.a7);
}
};
template <typename T, typename D, typename UnOp, typename Mask>
static __global__ void transformSmart(const PtrStepSz<T> src_, PtrStep<D> dst_, const Mask mask, const UnOp op)
{
typedef TransformFunctorTraits<UnOp> ft;
typedef typename UnaryReadWriteTraits<T, D, ft::smart_shift>::read_type read_type;
typedef typename UnaryReadWriteTraits<T, D, ft::smart_shift>::write_type write_type;
const int x = threadIdx.x + blockIdx.x * blockDim.x;
const int y = threadIdx.y + blockIdx.y * blockDim.y;
const int x_shifted = x * ft::smart_shift;
if (y < src_.rows)
{
const T* src = src_.ptr(y);
D* dst = dst_.ptr(y);
if (x_shifted + ft::smart_shift - 1 < src_.cols)
{
const read_type src_n_el = ((const read_type*)src)[x];
OpUnroller<ft::smart_shift>::unroll(src_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y);
}
else
{
for (int real_x = x_shifted; real_x < src_.cols; ++real_x)
{
if (mask(y, real_x))
dst[real_x] = op(src[real_x]);
}
}
}
}
template <typename T, typename D, typename UnOp, typename Mask>
__global__ static void transformSimple(const PtrStepSz<T> src, PtrStep<D> dst, const Mask mask, const UnOp op)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < src.cols && y < src.rows && mask(y, x))
{
dst.ptr(y)[x] = op(src.ptr(y)[x]);
}
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __global__ void transformSmart(const PtrStepSz<T1> src1_, const PtrStep<T2> src2_, PtrStep<D> dst_,
const Mask mask, const BinOp op)
{
typedef TransformFunctorTraits<BinOp> ft;
typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::read_type1 read_type1;
typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::read_type2 read_type2;
typedef typename BinaryReadWriteTraits<T1, T2, D, ft::smart_shift>::write_type write_type;
const int x = threadIdx.x + blockIdx.x * blockDim.x;
const int y = threadIdx.y + blockIdx.y * blockDim.y;
const int x_shifted = x * ft::smart_shift;
if (y < src1_.rows)
{
const T1* src1 = src1_.ptr(y);
const T2* src2 = src2_.ptr(y);
D* dst = dst_.ptr(y);
if (x_shifted + ft::smart_shift - 1 < src1_.cols)
{
const read_type1 src1_n_el = ((const read_type1*)src1)[x];
const read_type2 src2_n_el = ((const read_type2*)src2)[x];
OpUnroller<ft::smart_shift>::unroll(src1_n_el, src2_n_el, ((write_type*)dst)[x], mask, op, x_shifted, y);
}
else
{
for (int real_x = x_shifted; real_x < src1_.cols; ++real_x)
{
if (mask(y, real_x))
dst[real_x] = op(src1[real_x], src2[real_x]);
}
}
}
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static __global__ void transformSimple(const PtrStepSz<T1> src1, const PtrStep<T2> src2, PtrStep<D> dst,
const Mask mask, const BinOp op)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < src1.cols && y < src1.rows && mask(y, x))
{
const T1 src1_data = src1.ptr(y)[x];
const T2 src2_data = src2.ptr(y)[x];
dst.ptr(y)[x] = op(src1_data, src2_data);
}
}
template <bool UseSmart> struct TransformDispatcher;
template<> struct TransformDispatcher<false>
{
template <typename T, typename D, typename UnOp, typename Mask>
static void call(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, Mask mask, cudaStream_t stream)
{
typedef TransformFunctorTraits<UnOp> ft;
const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1);
const dim3 grid(divUp(src.cols, threads.x), divUp(src.rows, threads.y), 1);
transformSimple<T, D><<<grid, threads, 0, stream>>>(src, dst, mask, op);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static void call(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, Mask mask, cudaStream_t stream)
{
typedef TransformFunctorTraits<BinOp> ft;
const dim3 threads(ft::simple_block_dim_x, ft::simple_block_dim_y, 1);
const dim3 grid(divUp(src1.cols, threads.x), divUp(src1.rows, threads.y), 1);
transformSimple<T1, T2, D><<<grid, threads, 0, stream>>>(src1, src2, dst, mask, op);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
template<> struct TransformDispatcher<true>
{
template <typename T, typename D, typename UnOp, typename Mask>
static void call(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, Mask mask, cudaStream_t stream)
{
typedef TransformFunctorTraits<UnOp> ft;
CV_StaticAssert(ft::smart_shift != 1, "");
if (!isAligned(src.data, ft::smart_shift * sizeof(T)) || !isAligned(src.step, ft::smart_shift * sizeof(T)) ||
!isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D)))
{
TransformDispatcher<false>::call(src, dst, op, mask, stream);
return;
}
const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1);
const dim3 grid(divUp(src.cols, threads.x * ft::smart_shift), divUp(src.rows, threads.y), 1);
transformSmart<T, D><<<grid, threads, 0, stream>>>(src, dst, mask, op);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static void call(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, Mask mask, cudaStream_t stream)
{
typedef TransformFunctorTraits<BinOp> ft;
CV_StaticAssert(ft::smart_shift != 1, "");
if (!isAligned(src1.data, ft::smart_shift * sizeof(T1)) || !isAligned(src1.step, ft::smart_shift * sizeof(T1)) ||
!isAligned(src2.data, ft::smart_shift * sizeof(T2)) || !isAligned(src2.step, ft::smart_shift * sizeof(T2)) ||
!isAligned(dst.data, ft::smart_shift * sizeof(D)) || !isAligned(dst.step, ft::smart_shift * sizeof(D)))
{
TransformDispatcher<false>::call(src1, src2, dst, op, mask, stream);
return;
}
const dim3 threads(ft::smart_block_dim_x, ft::smart_block_dim_y, 1);
const dim3 grid(divUp(src1.cols, threads.x * ft::smart_shift), divUp(src1.rows, threads.y), 1);
transformSmart<T1, T2, D><<<grid, threads, 0, stream>>>(src1, src2, dst, mask, op);
cudaSafeCall( cudaGetLastError() );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
} // namespace transform_detail
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_TRANSFORM_DETAIL_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_CUDA_TYPE_TRAITS_DETAIL_HPP
#define OPENCV_CUDA_TYPE_TRAITS_DETAIL_HPP
#include "../common.hpp"
#include "../vec_traits.hpp"
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
namespace type_traits_detail
{
template <bool, typename T1, typename T2> struct Select { typedef T1 type; };
template <typename T1, typename T2> struct Select<false, T1, T2> { typedef T2 type; };
template <typename T> struct IsSignedIntergral { enum {value = 0}; };
template <> struct IsSignedIntergral<schar> { enum {value = 1}; };
template <> struct IsSignedIntergral<char1> { enum {value = 1}; };
template <> struct IsSignedIntergral<short> { enum {value = 1}; };
template <> struct IsSignedIntergral<short1> { enum {value = 1}; };
template <> struct IsSignedIntergral<int> { enum {value = 1}; };
template <> struct IsSignedIntergral<int1> { enum {value = 1}; };
template <typename T> struct IsUnsignedIntegral { enum {value = 0}; };
template <> struct IsUnsignedIntegral<uchar> { enum {value = 1}; };
template <> struct IsUnsignedIntegral<uchar1> { enum {value = 1}; };
template <> struct IsUnsignedIntegral<ushort> { enum {value = 1}; };
template <> struct IsUnsignedIntegral<ushort1> { enum {value = 1}; };
template <> struct IsUnsignedIntegral<uint> { enum {value = 1}; };
template <> struct IsUnsignedIntegral<uint1> { enum {value = 1}; };
template <typename T> struct IsIntegral { enum {value = IsSignedIntergral<T>::value || IsUnsignedIntegral<T>::value}; };
template <> struct IsIntegral<char> { enum {value = 1}; };
template <> struct IsIntegral<bool> { enum {value = 1}; };
template <typename T> struct IsFloat { enum {value = 0}; };
template <> struct IsFloat<float> { enum {value = 1}; };
template <> struct IsFloat<double> { enum {value = 1}; };
template <typename T> struct IsVec { enum {value = 0}; };
template <> struct IsVec<uchar1> { enum {value = 1}; };
template <> struct IsVec<uchar2> { enum {value = 1}; };
template <> struct IsVec<uchar3> { enum {value = 1}; };
template <> struct IsVec<uchar4> { enum {value = 1}; };
template <> struct IsVec<uchar8> { enum {value = 1}; };
template <> struct IsVec<char1> { enum {value = 1}; };
template <> struct IsVec<char2> { enum {value = 1}; };
template <> struct IsVec<char3> { enum {value = 1}; };
template <> struct IsVec<char4> { enum {value = 1}; };
template <> struct IsVec<char8> { enum {value = 1}; };
template <> struct IsVec<ushort1> { enum {value = 1}; };
template <> struct IsVec<ushort2> { enum {value = 1}; };
template <> struct IsVec<ushort3> { enum {value = 1}; };
template <> struct IsVec<ushort4> { enum {value = 1}; };
template <> struct IsVec<ushort8> { enum {value = 1}; };
template <> struct IsVec<short1> { enum {value = 1}; };
template <> struct IsVec<short2> { enum {value = 1}; };
template <> struct IsVec<short3> { enum {value = 1}; };
template <> struct IsVec<short4> { enum {value = 1}; };
template <> struct IsVec<short8> { enum {value = 1}; };
template <> struct IsVec<uint1> { enum {value = 1}; };
template <> struct IsVec<uint2> { enum {value = 1}; };
template <> struct IsVec<uint3> { enum {value = 1}; };
template <> struct IsVec<uint4> { enum {value = 1}; };
template <> struct IsVec<uint8> { enum {value = 1}; };
template <> struct IsVec<int1> { enum {value = 1}; };
template <> struct IsVec<int2> { enum {value = 1}; };
template <> struct IsVec<int3> { enum {value = 1}; };
template <> struct IsVec<int4> { enum {value = 1}; };
template <> struct IsVec<int8> { enum {value = 1}; };
template <> struct IsVec<float1> { enum {value = 1}; };
template <> struct IsVec<float2> { enum {value = 1}; };
template <> struct IsVec<float3> { enum {value = 1}; };
template <> struct IsVec<float4> { enum {value = 1}; };
template <> struct IsVec<float8> { enum {value = 1}; };
template <> struct IsVec<double1> { enum {value = 1}; };
template <> struct IsVec<double2> { enum {value = 1}; };
template <> struct IsVec<double3> { enum {value = 1}; };
template <> struct IsVec<double4> { enum {value = 1}; };
template <> struct IsVec<double8> { enum {value = 1}; };
template <class U> struct AddParameterType { typedef const U& type; };
template <class U> struct AddParameterType<U&> { typedef U& type; };
template <> struct AddParameterType<void> { typedef void type; };
template <class U> struct ReferenceTraits
{
enum { value = false };
typedef U type;
};
template <class U> struct ReferenceTraits<U&>
{
enum { value = true };
typedef U type;
};
template <class U> struct PointerTraits
{
enum { value = false };
typedef void type;
};
template <class U> struct PointerTraits<U*>
{
enum { value = true };
typedef U type;
};
template <class U> struct PointerTraits<U*&>
{
enum { value = true };
typedef U type;
};
template <class U> struct UnConst
{
typedef U type;
enum { value = 0 };
};
template <class U> struct UnConst<const U>
{
typedef U type;
enum { value = 1 };
};
template <class U> struct UnConst<const U&>
{
typedef U& type;
enum { value = 1 };
};
template <class U> struct UnVolatile
{
typedef U type;
enum { value = 0 };
};
template <class U> struct UnVolatile<volatile U>
{
typedef U type;
enum { value = 1 };
};
template <class U> struct UnVolatile<volatile U&>
{
typedef U& type;
enum { value = 1 };
};
} // namespace type_traits_detail
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_TYPE_TRAITS_DETAIL_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_CUDA_VEC_DISTANCE_DETAIL_HPP
#define OPENCV_CUDA_VEC_DISTANCE_DETAIL_HPP
#include "../datamov_utils.hpp"
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
namespace vec_distance_detail
{
template <int THREAD_DIM, int N> struct UnrollVecDiffCached
{
template <typename Dist, typename T1, typename T2>
static __device__ void calcCheck(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int ind)
{
if (ind < len)
{
T1 val1 = *vecCached++;
T2 val2;
ForceGlob<T2>::Load(vecGlob, ind, val2);
dist.reduceIter(val1, val2);
UnrollVecDiffCached<THREAD_DIM, N - 1>::calcCheck(vecCached, vecGlob, len, dist, ind + THREAD_DIM);
}
}
template <typename Dist, typename T1, typename T2>
static __device__ void calcWithoutCheck(const T1* vecCached, const T2* vecGlob, Dist& dist)
{
T1 val1 = *vecCached++;
T2 val2;
ForceGlob<T2>::Load(vecGlob, 0, val2);
vecGlob += THREAD_DIM;
dist.reduceIter(val1, val2);
UnrollVecDiffCached<THREAD_DIM, N - 1>::calcWithoutCheck(vecCached, vecGlob, dist);
}
};
template <int THREAD_DIM> struct UnrollVecDiffCached<THREAD_DIM, 0>
{
template <typename Dist, typename T1, typename T2>
static __device__ __forceinline__ void calcCheck(const T1*, const T2*, int, Dist&, int)
{
}
template <typename Dist, typename T1, typename T2>
static __device__ __forceinline__ void calcWithoutCheck(const T1*, const T2*, Dist&)
{
}
};
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN> struct VecDiffCachedCalculator;
template <int THREAD_DIM, int MAX_LEN> struct VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, false>
{
template <typename Dist, typename T1, typename T2>
static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid)
{
UnrollVecDiffCached<THREAD_DIM, MAX_LEN / THREAD_DIM>::calcCheck(vecCached, vecGlob, len, dist, tid);
}
};
template <int THREAD_DIM, int MAX_LEN> struct VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, true>
{
template <typename Dist, typename T1, typename T2>
static __device__ __forceinline__ void calc(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, int tid)
{
UnrollVecDiffCached<THREAD_DIM, MAX_LEN / THREAD_DIM>::calcWithoutCheck(vecCached, vecGlob + tid, dist);
}
};
} // namespace vec_distance_detail
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_VEC_DISTANCE_DETAIL_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_CUDA_DYNAMIC_SMEM_HPP
#define OPENCV_CUDA_DYNAMIC_SMEM_HPP
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template<class T> struct DynamicSharedMem
{
__device__ __forceinline__ operator T*()
{
extern __shared__ int __smem[];
return (T*)__smem;
}
__device__ __forceinline__ operator const T*() const
{
extern __shared__ int __smem[];
return (T*)__smem;
}
};
// specialize for double to avoid unaligned memory access compile errors
template<> struct DynamicSharedMem<double>
{
__device__ __forceinline__ operator double*()
{
extern __shared__ double __smem_d[];
return (double*)__smem_d;
}
__device__ __forceinline__ operator const double*() const
{
extern __shared__ double __smem_d[];
return (double*)__smem_d;
}
};
}}}
//! @endcond
#endif // OPENCV_CUDA_DYNAMIC_SMEM_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_CUDA_EMULATION_HPP_
#define OPENCV_CUDA_EMULATION_HPP_
#include "common.hpp"
#include "warp_reduce.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
struct Emulation
{
static __device__ __forceinline__ int syncthreadsOr(int pred)
{
#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 200)
// just campilation stab
return 0;
#else
return __syncthreads_or(pred);
#endif
}
template<int CTA_SIZE>
static __forceinline__ __device__ int Ballot(int predicate)
{
#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ >= 200)
return __ballot(predicate);
#else
__shared__ volatile int cta_buffer[CTA_SIZE];
int tid = threadIdx.x;
cta_buffer[tid] = predicate ? (1 << (tid & 31)) : 0;
return warp_reduce(cta_buffer);
#endif
}
struct smem
{
enum { TAG_MASK = (1U << ( (sizeof(unsigned int) << 3) - 5U)) - 1U };
template<typename T>
static __device__ __forceinline__ T atomicInc(T* address, T val)
{
#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
T count;
unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U);
do
{
count = *address & TAG_MASK;
count = tag | (count + 1);
*address = count;
} while (*address != count);
return (count & TAG_MASK) - 1;
#else
return ::atomicInc(address, val);
#endif
}
template<typename T>
static __device__ __forceinline__ T atomicAdd(T* address, T val)
{
#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
T count;
unsigned int tag = threadIdx.x << ( (sizeof(unsigned int) << 3) - 5U);
do
{
count = *address & TAG_MASK;
count = tag | (count + val);
*address = count;
} while (*address != count);
return (count & TAG_MASK) - val;
#else
return ::atomicAdd(address, val);
#endif
}
template<typename T>
static __device__ __forceinline__ T atomicMin(T* address, T val)
{
#if defined (__CUDA_ARCH__) && (__CUDA_ARCH__ < 120)
T count = ::min(*address, val);
do
{
*address = count;
} while (*address > count);
return count;
#else
return ::atomicMin(address, val);
#endif
}
}; // struct cmem
struct glob
{
static __device__ __forceinline__ int atomicAdd(int* address, int val)
{
return ::atomicAdd(address, val);
}
static __device__ __forceinline__ unsigned int atomicAdd(unsigned int* address, unsigned int val)
{
return ::atomicAdd(address, val);
}
static __device__ __forceinline__ float atomicAdd(float* address, float val)
{
#if __CUDA_ARCH__ >= 200
return ::atomicAdd(address, val);
#else
int* address_as_i = (int*) address;
int old = *address_as_i, assumed;
do {
assumed = old;
old = ::atomicCAS(address_as_i, assumed,
__float_as_int(val + __int_as_float(assumed)));
} while (assumed != old);
return __int_as_float(old);
#endif
}
static __device__ __forceinline__ double atomicAdd(double* address, double val)
{
#if __CUDA_ARCH__ >= 130
unsigned long long int* address_as_ull = (unsigned long long int*) address;
unsigned long long int old = *address_as_ull, assumed;
do {
assumed = old;
old = ::atomicCAS(address_as_ull, assumed,
__double_as_longlong(val + __longlong_as_double(assumed)));
} while (assumed != old);
return __longlong_as_double(old);
#else
CV_UNUSED(address);
CV_UNUSED(val);
return 0.0;
#endif
}
static __device__ __forceinline__ int atomicMin(int* address, int val)
{
return ::atomicMin(address, val);
}
static __device__ __forceinline__ float atomicMin(float* address, float val)
{
#if __CUDA_ARCH__ >= 120
int* address_as_i = (int*) address;
int old = *address_as_i, assumed;
do {
assumed = old;
old = ::atomicCAS(address_as_i, assumed,
__float_as_int(::fminf(val, __int_as_float(assumed))));
} while (assumed != old);
return __int_as_float(old);
#else
CV_UNUSED(address);
CV_UNUSED(val);
return 0.0f;
#endif
}
static __device__ __forceinline__ double atomicMin(double* address, double val)
{
#if __CUDA_ARCH__ >= 130
unsigned long long int* address_as_ull = (unsigned long long int*) address;
unsigned long long int old = *address_as_ull, assumed;
do {
assumed = old;
old = ::atomicCAS(address_as_ull, assumed,
__double_as_longlong(::fmin(val, __longlong_as_double(assumed))));
} while (assumed != old);
return __longlong_as_double(old);
#else
CV_UNUSED(address);
CV_UNUSED(val);
return 0.0;
#endif
}
static __device__ __forceinline__ int atomicMax(int* address, int val)
{
return ::atomicMax(address, val);
}
static __device__ __forceinline__ float atomicMax(float* address, float val)
{
#if __CUDA_ARCH__ >= 120
int* address_as_i = (int*) address;
int old = *address_as_i, assumed;
do {
assumed = old;
old = ::atomicCAS(address_as_i, assumed,
__float_as_int(::fmaxf(val, __int_as_float(assumed))));
} while (assumed != old);
return __int_as_float(old);
#else
CV_UNUSED(address);
CV_UNUSED(val);
return 0.0f;
#endif
}
static __device__ __forceinline__ double atomicMax(double* address, double val)
{
#if __CUDA_ARCH__ >= 130
unsigned long long int* address_as_ull = (unsigned long long int*) address;
unsigned long long int old = *address_as_ull, assumed;
do {
assumed = old;
old = ::atomicCAS(address_as_ull, assumed,
__double_as_longlong(::fmax(val, __longlong_as_double(assumed))));
} while (assumed != old);
return __longlong_as_double(old);
#else
CV_UNUSED(address);
CV_UNUSED(val);
return 0.0;
#endif
}
};
}; //struct Emulation
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif /* OPENCV_CUDA_EMULATION_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_CUDA_FILTERS_HPP
#define OPENCV_CUDA_FILTERS_HPP
#include "saturate_cast.hpp"
#include "vec_traits.hpp"
#include "vec_math.hpp"
#include "type_traits.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template <typename Ptr2D> struct PointFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ PointFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
: src(src_)
{
CV_UNUSED(fx);
CV_UNUSED(fy);
}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
return src(__float2int_rz(y), __float2int_rz(x));
}
Ptr2D src;
};
template <typename Ptr2D> struct LinearFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ LinearFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
: src(src_)
{
CV_UNUSED(fx);
CV_UNUSED(fy);
}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
work_type out = VecTraits<work_type>::all(0);
const int x1 = __float2int_rd(x);
const int y1 = __float2int_rd(y);
const int x2 = x1 + 1;
const int y2 = y1 + 1;
elem_type src_reg = src(y1, x1);
out = out + src_reg * ((x2 - x) * (y2 - y));
src_reg = src(y1, x2);
out = out + src_reg * ((x - x1) * (y2 - y));
src_reg = src(y2, x1);
out = out + src_reg * ((x2 - x) * (y - y1));
src_reg = src(y2, x2);
out = out + src_reg * ((x - x1) * (y - y1));
return saturate_cast<elem_type>(out);
}
Ptr2D src;
};
template <typename Ptr2D> struct CubicFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
explicit __host__ __device__ __forceinline__ CubicFilter(const Ptr2D& src_, float fx = 0.f, float fy = 0.f)
: src(src_)
{
CV_UNUSED(fx);
CV_UNUSED(fy);
}
static __device__ __forceinline__ float bicubicCoeff(float x_)
{
float x = fabsf(x_);
if (x <= 1.0f)
{
return x * x * (1.5f * x - 2.5f) + 1.0f;
}
else if (x < 2.0f)
{
return x * (x * (-0.5f * x + 2.5f) - 4.0f) + 2.0f;
}
else
{
return 0.0f;
}
}
__device__ elem_type operator ()(float y, float x) const
{
const float xmin = ::ceilf(x - 2.0f);
const float xmax = ::floorf(x + 2.0f);
const float ymin = ::ceilf(y - 2.0f);
const float ymax = ::floorf(y + 2.0f);
work_type sum = VecTraits<work_type>::all(0);
float wsum = 0.0f;
for (float cy = ymin; cy <= ymax; cy += 1.0f)
{
for (float cx = xmin; cx <= xmax; cx += 1.0f)
{
const float w = bicubicCoeff(x - cx) * bicubicCoeff(y - cy);
sum = sum + w * src(__float2int_rd(cy), __float2int_rd(cx));
wsum += w;
}
}
work_type res = (!wsum)? VecTraits<work_type>::all(0) : sum / wsum;
return saturate_cast<elem_type>(res);
}
Ptr2D src;
};
// for integer scaling
template <typename Ptr2D> struct IntegerAreaFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ IntegerAreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_)
: src(src_), scale_x(scale_x_), scale_y(scale_y_), scale(1.f / (scale_x * scale_y)) {}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
float fsx1 = x * scale_x;
float fsx2 = fsx1 + scale_x;
int sx1 = __float2int_ru(fsx1);
int sx2 = __float2int_rd(fsx2);
float fsy1 = y * scale_y;
float fsy2 = fsy1 + scale_y;
int sy1 = __float2int_ru(fsy1);
int sy2 = __float2int_rd(fsy2);
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
work_type out = VecTraits<work_type>::all(0.f);
for(int dy = sy1; dy < sy2; ++dy)
for(int dx = sx1; dx < sx2; ++dx)
{
out = out + src(dy, dx) * scale;
}
return saturate_cast<elem_type>(out);
}
Ptr2D src;
float scale_x, scale_y ,scale;
};
template <typename Ptr2D> struct AreaFilter
{
typedef typename Ptr2D::elem_type elem_type;
typedef float index_type;
explicit __host__ __device__ __forceinline__ AreaFilter(const Ptr2D& src_, float scale_x_, float scale_y_)
: src(src_), scale_x(scale_x_), scale_y(scale_y_){}
__device__ __forceinline__ elem_type operator ()(float y, float x) const
{
float fsx1 = x * scale_x;
float fsx2 = fsx1 + scale_x;
int sx1 = __float2int_ru(fsx1);
int sx2 = __float2int_rd(fsx2);
float fsy1 = y * scale_y;
float fsy2 = fsy1 + scale_y;
int sy1 = __float2int_ru(fsy1);
int sy2 = __float2int_rd(fsy2);
float scale = 1.f / (fminf(scale_x, src.width - fsx1) * fminf(scale_y, src.height - fsy1));
typedef typename TypeVec<float, VecTraits<elem_type>::cn>::vec_type work_type;
work_type out = VecTraits<work_type>::all(0.f);
for (int dy = sy1; dy < sy2; ++dy)
{
for (int dx = sx1; dx < sx2; ++dx)
out = out + src(dy, dx) * scale;
if (sx1 > fsx1)
out = out + src(dy, (sx1 -1) ) * ((sx1 - fsx1) * scale);
if (sx2 < fsx2)
out = out + src(dy, sx2) * ((fsx2 -sx2) * scale);
}
if (sy1 > fsy1)
for (int dx = sx1; dx < sx2; ++dx)
out = out + src( (sy1 - 1) , dx) * ((sy1 -fsy1) * scale);
if (sy2 < fsy2)
for (int dx = sx1; dx < sx2; ++dx)
out = out + src(sy2, dx) * ((fsy2 -sy2) * scale);
if ((sy1 > fsy1) && (sx1 > fsx1))
out = out + src( (sy1 - 1) , (sx1 - 1)) * ((sy1 -fsy1) * (sx1 -fsx1) * scale);
if ((sy1 > fsy1) && (sx2 < fsx2))
out = out + src( (sy1 - 1) , sx2) * ((sy1 -fsy1) * (fsx2 -sx2) * scale);
if ((sy2 < fsy2) && (sx2 < fsx2))
out = out + src(sy2, sx2) * ((fsy2 -sy2) * (fsx2 -sx2) * scale);
if ((sy2 < fsy2) && (sx1 > fsx1))
out = out + src(sy2, (sx1 - 1)) * ((fsy2 -sy2) * (sx1 -fsx1) * scale);
return saturate_cast<elem_type>(out);
}
Ptr2D src;
float scale_x, scale_y;
int width, haight;
};
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_FILTERS_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_CUDA_DEVICE_FUNCATTRIB_HPP
#define OPENCV_CUDA_DEVICE_FUNCATTRIB_HPP
#include <cstdio>
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template<class Func>
void printFuncAttrib(Func& func)
{
cudaFuncAttributes attrs;
cudaFuncGetAttributes(&attrs, func);
printf("=== Function stats ===\n");
printf("Name: \n");
printf("sharedSizeBytes = %d\n", attrs.sharedSizeBytes);
printf("constSizeBytes = %d\n", attrs.constSizeBytes);
printf("localSizeBytes = %d\n", attrs.localSizeBytes);
printf("maxThreadsPerBlock = %d\n", attrs.maxThreadsPerBlock);
printf("numRegs = %d\n", attrs.numRegs);
printf("ptxVersion = %d\n", attrs.ptxVersion);
printf("binaryVersion = %d\n", attrs.binaryVersion);
printf("\n");
fflush(stdout);
}
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif /* OPENCV_CUDA_DEVICE_FUNCATTRIB_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_CUDA_FUNCTIONAL_HPP
#define OPENCV_CUDA_FUNCTIONAL_HPP
#include <functional>
#include "saturate_cast.hpp"
#include "vec_traits.hpp"
#include "type_traits.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
// Function Objects
template<typename Argument, typename Result> struct unary_function
{
typedef Argument argument_type;
typedef Result result_type;
};
template<typename Argument1, typename Argument2, typename Result> struct binary_function
{
typedef Argument1 first_argument_type;
typedef Argument2 second_argument_type;
typedef Result result_type;
};
// Arithmetic Operations
template <typename T> struct plus : binary_function<T, T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a + b;
}
__host__ __device__ __forceinline__ plus() {}
__host__ __device__ __forceinline__ plus(const plus&) {}
};
template <typename T> struct minus : binary_function<T, T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a - b;
}
__host__ __device__ __forceinline__ minus() {}
__host__ __device__ __forceinline__ minus(const minus&) {}
};
template <typename T> struct multiplies : binary_function<T, T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a * b;
}
__host__ __device__ __forceinline__ multiplies() {}
__host__ __device__ __forceinline__ multiplies(const multiplies&) {}
};
template <typename T> struct divides : binary_function<T, T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a / b;
}
__host__ __device__ __forceinline__ divides() {}
__host__ __device__ __forceinline__ divides(const divides&) {}
};
template <typename T> struct modulus : binary_function<T, T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a % b;
}
__host__ __device__ __forceinline__ modulus() {}
__host__ __device__ __forceinline__ modulus(const modulus&) {}
};
template <typename T> struct negate : unary_function<T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a) const
{
return -a;
}
__host__ __device__ __forceinline__ negate() {}
__host__ __device__ __forceinline__ negate(const negate&) {}
};
// Comparison Operations
template <typename T> struct equal_to : binary_function<T, T, bool>
{
__device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a == b;
}
__host__ __device__ __forceinline__ equal_to() {}
__host__ __device__ __forceinline__ equal_to(const equal_to&) {}
};
template <typename T> struct not_equal_to : binary_function<T, T, bool>
{
__device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a != b;
}
__host__ __device__ __forceinline__ not_equal_to() {}
__host__ __device__ __forceinline__ not_equal_to(const not_equal_to&) {}
};
template <typename T> struct greater : binary_function<T, T, bool>
{
__device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a > b;
}
__host__ __device__ __forceinline__ greater() {}
__host__ __device__ __forceinline__ greater(const greater&) {}
};
template <typename T> struct less : binary_function<T, T, bool>
{
__device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a < b;
}
__host__ __device__ __forceinline__ less() {}
__host__ __device__ __forceinline__ less(const less&) {}
};
template <typename T> struct greater_equal : binary_function<T, T, bool>
{
__device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a >= b;
}
__host__ __device__ __forceinline__ greater_equal() {}
__host__ __device__ __forceinline__ greater_equal(const greater_equal&) {}
};
template <typename T> struct less_equal : binary_function<T, T, bool>
{
__device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a <= b;
}
__host__ __device__ __forceinline__ less_equal() {}
__host__ __device__ __forceinline__ less_equal(const less_equal&) {}
};
// Logical Operations
template <typename T> struct logical_and : binary_function<T, T, bool>
{
__device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a && b;
}
__host__ __device__ __forceinline__ logical_and() {}
__host__ __device__ __forceinline__ logical_and(const logical_and&) {}
};
template <typename T> struct logical_or : binary_function<T, T, bool>
{
__device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a || b;
}
__host__ __device__ __forceinline__ logical_or() {}
__host__ __device__ __forceinline__ logical_or(const logical_or&) {}
};
template <typename T> struct logical_not : unary_function<T, bool>
{
__device__ __forceinline__ bool operator ()(typename TypeTraits<T>::ParameterType a) const
{
return !a;
}
__host__ __device__ __forceinline__ logical_not() {}
__host__ __device__ __forceinline__ logical_not(const logical_not&) {}
};
// Bitwise Operations
template <typename T> struct bit_and : binary_function<T, T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a & b;
}
__host__ __device__ __forceinline__ bit_and() {}
__host__ __device__ __forceinline__ bit_and(const bit_and&) {}
};
template <typename T> struct bit_or : binary_function<T, T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a | b;
}
__host__ __device__ __forceinline__ bit_or() {}
__host__ __device__ __forceinline__ bit_or(const bit_or&) {}
};
template <typename T> struct bit_xor : binary_function<T, T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType a,
typename TypeTraits<T>::ParameterType b) const
{
return a ^ b;
}
__host__ __device__ __forceinline__ bit_xor() {}
__host__ __device__ __forceinline__ bit_xor(const bit_xor&) {}
};
template <typename T> struct bit_not : unary_function<T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType v) const
{
return ~v;
}
__host__ __device__ __forceinline__ bit_not() {}
__host__ __device__ __forceinline__ bit_not(const bit_not&) {}
};
// Generalized Identity Operations
template <typename T> struct identity : unary_function<T, T>
{
__device__ __forceinline__ typename TypeTraits<T>::ParameterType operator()(typename TypeTraits<T>::ParameterType x) const
{
return x;
}
__host__ __device__ __forceinline__ identity() {}
__host__ __device__ __forceinline__ identity(const identity&) {}
};
template <typename T1, typename T2> struct project1st : binary_function<T1, T2, T1>
{
__device__ __forceinline__ typename TypeTraits<T1>::ParameterType operator()(typename TypeTraits<T1>::ParameterType lhs, typename TypeTraits<T2>::ParameterType rhs) const
{
return lhs;
}
__host__ __device__ __forceinline__ project1st() {}
__host__ __device__ __forceinline__ project1st(const project1st&) {}
};
template <typename T1, typename T2> struct project2nd : binary_function<T1, T2, T2>
{
__device__ __forceinline__ typename TypeTraits<T2>::ParameterType operator()(typename TypeTraits<T1>::ParameterType lhs, typename TypeTraits<T2>::ParameterType rhs) const
{
return rhs;
}
__host__ __device__ __forceinline__ project2nd() {}
__host__ __device__ __forceinline__ project2nd(const project2nd&) {}
};
// Min/Max Operations
#define OPENCV_CUDA_IMPLEMENT_MINMAX(name, type, op) \
template <> struct name<type> : binary_function<type, type, type> \
{ \
__device__ __forceinline__ type operator()(type lhs, type rhs) const {return op(lhs, rhs);} \
__host__ __device__ __forceinline__ name() {}\
__host__ __device__ __forceinline__ name(const name&) {}\
};
template <typename T> struct maximum : binary_function<T, T, T>
{
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType lhs, typename TypeTraits<T>::ParameterType rhs) const
{
return max(lhs, rhs);
}
__host__ __device__ __forceinline__ maximum() {}
__host__ __device__ __forceinline__ maximum(const maximum&) {}
};
OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uchar, ::max)
OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, schar, ::max)
OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, char, ::max)
OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, ushort, ::max)
OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, short, ::max)
OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, int, ::max)
OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, uint, ::max)
OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, float, ::fmax)
OPENCV_CUDA_IMPLEMENT_MINMAX(maximum, double, ::fmax)
template <typename T> struct minimum : binary_function<T, T, T>
{
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType lhs, typename TypeTraits<T>::ParameterType rhs) const
{
return min(lhs, rhs);
}
__host__ __device__ __forceinline__ minimum() {}
__host__ __device__ __forceinline__ minimum(const minimum&) {}
};
OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uchar, ::min)
OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, schar, ::min)
OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, char, ::min)
OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, ushort, ::min)
OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, short, ::min)
OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, int, ::min)
OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, uint, ::min)
OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, float, ::fmin)
OPENCV_CUDA_IMPLEMENT_MINMAX(minimum, double, ::fmin)
#undef OPENCV_CUDA_IMPLEMENT_MINMAX
// Math functions
template <typename T> struct abs_func : unary_function<T, T>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType x) const
{
return abs(x);
}
__host__ __device__ __forceinline__ abs_func() {}
__host__ __device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<unsigned char> : unary_function<unsigned char, unsigned char>
{
__device__ __forceinline__ unsigned char operator ()(unsigned char x) const
{
return x;
}
__host__ __device__ __forceinline__ abs_func() {}
__host__ __device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<signed char> : unary_function<signed char, signed char>
{
__device__ __forceinline__ signed char operator ()(signed char x) const
{
return ::abs((int)x);
}
__host__ __device__ __forceinline__ abs_func() {}
__host__ __device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<char> : unary_function<char, char>
{
__device__ __forceinline__ char operator ()(char x) const
{
return ::abs((int)x);
}
__host__ __device__ __forceinline__ abs_func() {}
__host__ __device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<unsigned short> : unary_function<unsigned short, unsigned short>
{
__device__ __forceinline__ unsigned short operator ()(unsigned short x) const
{
return x;
}
__host__ __device__ __forceinline__ abs_func() {}
__host__ __device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<short> : unary_function<short, short>
{
__device__ __forceinline__ short operator ()(short x) const
{
return ::abs((int)x);
}
__host__ __device__ __forceinline__ abs_func() {}
__host__ __device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<unsigned int> : unary_function<unsigned int, unsigned int>
{
__device__ __forceinline__ unsigned int operator ()(unsigned int x) const
{
return x;
}
__host__ __device__ __forceinline__ abs_func() {}
__host__ __device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<int> : unary_function<int, int>
{
__device__ __forceinline__ int operator ()(int x) const
{
return ::abs(x);
}
__host__ __device__ __forceinline__ abs_func() {}
__host__ __device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<float> : unary_function<float, float>
{
__device__ __forceinline__ float operator ()(float x) const
{
return ::fabsf(x);
}
__host__ __device__ __forceinline__ abs_func() {}
__host__ __device__ __forceinline__ abs_func(const abs_func&) {}
};
template <> struct abs_func<double> : unary_function<double, double>
{
__device__ __forceinline__ double operator ()(double x) const
{
return ::fabs(x);
}
__host__ __device__ __forceinline__ abs_func() {}
__host__ __device__ __forceinline__ abs_func(const abs_func&) {}
};
#define OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(name, func) \
template <typename T> struct name ## _func : unary_function<T, float> \
{ \
__device__ __forceinline__ float operator ()(typename TypeTraits<T>::ParameterType v) const \
{ \
return func ## f(v); \
} \
__host__ __device__ __forceinline__ name ## _func() {} \
__host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
}; \
template <> struct name ## _func<double> : unary_function<double, double> \
{ \
__device__ __forceinline__ double operator ()(double v) const \
{ \
return func(v); \
} \
__host__ __device__ __forceinline__ name ## _func() {} \
__host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
};
#define OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(name, func) \
template <typename T> struct name ## _func : binary_function<T, T, float> \
{ \
__device__ __forceinline__ float operator ()(typename TypeTraits<T>::ParameterType v1, typename TypeTraits<T>::ParameterType v2) const \
{ \
return func ## f(v1, v2); \
} \
__host__ __device__ __forceinline__ name ## _func() {} \
__host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
}; \
template <> struct name ## _func<double> : binary_function<double, double, double> \
{ \
__device__ __forceinline__ double operator ()(double v1, double v2) const \
{ \
return func(v1, v2); \
} \
__host__ __device__ __forceinline__ name ## _func() {} \
__host__ __device__ __forceinline__ name ## _func(const name ## _func&) {} \
};
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sqrt, ::sqrt)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp, ::exp)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp2, ::exp2)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(exp10, ::exp10)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log, ::log)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log2, ::log2)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(log10, ::log10)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sin, ::sin)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cos, ::cos)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tan, ::tan)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asin, ::asin)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acos, ::acos)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atan, ::atan)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(sinh, ::sinh)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(cosh, ::cosh)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(tanh, ::tanh)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(asinh, ::asinh)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(acosh, ::acosh)
OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR(atanh, ::atanh)
OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(hypot, ::hypot)
OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(atan2, ::atan2)
OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR(pow, ::pow)
#undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR
#undef OPENCV_CUDA_IMPLEMENT_UN_FUNCTOR_NO_DOUBLE
#undef OPENCV_CUDA_IMPLEMENT_BIN_FUNCTOR
template<typename T> struct hypot_sqr_func : binary_function<T, T, float>
{
__device__ __forceinline__ T operator ()(typename TypeTraits<T>::ParameterType src1, typename TypeTraits<T>::ParameterType src2) const
{
return src1 * src1 + src2 * src2;
}
__host__ __device__ __forceinline__ hypot_sqr_func() {}
__host__ __device__ __forceinline__ hypot_sqr_func(const hypot_sqr_func&) {}
};
// Saturate Cast Functor
template <typename T, typename D> struct saturate_cast_func : unary_function<T, D>
{
__device__ __forceinline__ D operator ()(typename TypeTraits<T>::ParameterType v) const
{
return saturate_cast<D>(v);
}
__host__ __device__ __forceinline__ saturate_cast_func() {}
__host__ __device__ __forceinline__ saturate_cast_func(const saturate_cast_func&) {}
};
// Threshold Functors
template <typename T> struct thresh_binary_func : unary_function<T, T>
{
__host__ __device__ __forceinline__ thresh_binary_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {}
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
{
return (src > thresh) * maxVal;
}
__host__ __device__ __forceinline__ thresh_binary_func() {}
__host__ __device__ __forceinline__ thresh_binary_func(const thresh_binary_func& other)
: thresh(other.thresh), maxVal(other.maxVal) {}
T thresh;
T maxVal;
};
template <typename T> struct thresh_binary_inv_func : unary_function<T, T>
{
__host__ __device__ __forceinline__ thresh_binary_inv_func(T thresh_, T maxVal_) : thresh(thresh_), maxVal(maxVal_) {}
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
{
return (src <= thresh) * maxVal;
}
__host__ __device__ __forceinline__ thresh_binary_inv_func() {}
__host__ __device__ __forceinline__ thresh_binary_inv_func(const thresh_binary_inv_func& other)
: thresh(other.thresh), maxVal(other.maxVal) {}
T thresh;
T maxVal;
};
template <typename T> struct thresh_trunc_func : unary_function<T, T>
{
explicit __host__ __device__ __forceinline__ thresh_trunc_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);}
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
{
return minimum<T>()(src, thresh);
}
__host__ __device__ __forceinline__ thresh_trunc_func() {}
__host__ __device__ __forceinline__ thresh_trunc_func(const thresh_trunc_func& other)
: thresh(other.thresh) {}
T thresh;
};
template <typename T> struct thresh_to_zero_func : unary_function<T, T>
{
explicit __host__ __device__ __forceinline__ thresh_to_zero_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);}
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
{
return (src > thresh) * src;
}
__host__ __device__ __forceinline__ thresh_to_zero_func() {}
__host__ __device__ __forceinline__ thresh_to_zero_func(const thresh_to_zero_func& other)
: thresh(other.thresh) {}
T thresh;
};
template <typename T> struct thresh_to_zero_inv_func : unary_function<T, T>
{
explicit __host__ __device__ __forceinline__ thresh_to_zero_inv_func(T thresh_, T maxVal_ = 0) : thresh(thresh_) {CV_UNUSED(maxVal_);}
__device__ __forceinline__ T operator()(typename TypeTraits<T>::ParameterType src) const
{
return (src <= thresh) * src;
}
__host__ __device__ __forceinline__ thresh_to_zero_inv_func() {}
__host__ __device__ __forceinline__ thresh_to_zero_inv_func(const thresh_to_zero_inv_func& other)
: thresh(other.thresh) {}
T thresh;
};
// Function Object Adaptors
template <typename Predicate> struct unary_negate : unary_function<typename Predicate::argument_type, bool>
{
explicit __host__ __device__ __forceinline__ unary_negate(const Predicate& p) : pred(p) {}
__device__ __forceinline__ bool operator()(typename TypeTraits<typename Predicate::argument_type>::ParameterType x) const
{
return !pred(x);
}
__host__ __device__ __forceinline__ unary_negate() {}
__host__ __device__ __forceinline__ unary_negate(const unary_negate& other) : pred(other.pred) {}
Predicate pred;
};
template <typename Predicate> __host__ __device__ __forceinline__ unary_negate<Predicate> not1(const Predicate& pred)
{
return unary_negate<Predicate>(pred);
}
template <typename Predicate> struct binary_negate : binary_function<typename Predicate::first_argument_type, typename Predicate::second_argument_type, bool>
{
explicit __host__ __device__ __forceinline__ binary_negate(const Predicate& p) : pred(p) {}
__device__ __forceinline__ bool operator()(typename TypeTraits<typename Predicate::first_argument_type>::ParameterType x,
typename TypeTraits<typename Predicate::second_argument_type>::ParameterType y) const
{
return !pred(x,y);
}
__host__ __device__ __forceinline__ binary_negate() {}
__host__ __device__ __forceinline__ binary_negate(const binary_negate& other) : pred(other.pred) {}
Predicate pred;
};
template <typename BinaryPredicate> __host__ __device__ __forceinline__ binary_negate<BinaryPredicate> not2(const BinaryPredicate& pred)
{
return binary_negate<BinaryPredicate>(pred);
}
template <typename Op> struct binder1st : unary_function<typename Op::second_argument_type, typename Op::result_type>
{
__host__ __device__ __forceinline__ binder1st(const Op& op_, const typename Op::first_argument_type& arg1_) : op(op_), arg1(arg1_) {}
__device__ __forceinline__ typename Op::result_type operator ()(typename TypeTraits<typename Op::second_argument_type>::ParameterType a) const
{
return op(arg1, a);
}
__host__ __device__ __forceinline__ binder1st() {}
__host__ __device__ __forceinline__ binder1st(const binder1st& other) : op(other.op), arg1(other.arg1) {}
Op op;
typename Op::first_argument_type arg1;
};
template <typename Op, typename T> __host__ __device__ __forceinline__ binder1st<Op> bind1st(const Op& op, const T& x)
{
return binder1st<Op>(op, typename Op::first_argument_type(x));
}
template <typename Op> struct binder2nd : unary_function<typename Op::first_argument_type, typename Op::result_type>
{
__host__ __device__ __forceinline__ binder2nd(const Op& op_, const typename Op::second_argument_type& arg2_) : op(op_), arg2(arg2_) {}
__forceinline__ __device__ typename Op::result_type operator ()(typename TypeTraits<typename Op::first_argument_type>::ParameterType a) const
{
return op(a, arg2);
}
__host__ __device__ __forceinline__ binder2nd() {}
__host__ __device__ __forceinline__ binder2nd(const binder2nd& other) : op(other.op), arg2(other.arg2) {}
Op op;
typename Op::second_argument_type arg2;
};
template <typename Op, typename T> __host__ __device__ __forceinline__ binder2nd<Op> bind2nd(const Op& op, const T& x)
{
return binder2nd<Op>(op, typename Op::second_argument_type(x));
}
// Functor Traits
template <typename F> struct IsUnaryFunction
{
typedef char Yes;
struct No {Yes a[2];};
template <typename T, typename D> static Yes check(unary_function<T, D>);
static No check(...);
static F makeF();
enum { value = (sizeof(check(makeF())) == sizeof(Yes)) };
};
template <typename F> struct IsBinaryFunction
{
typedef char Yes;
struct No {Yes a[2];};
template <typename T1, typename T2, typename D> static Yes check(binary_function<T1, T2, D>);
static No check(...);
static F makeF();
enum { value = (sizeof(check(makeF())) == sizeof(Yes)) };
};
namespace functional_detail
{
template <size_t src_elem_size, size_t dst_elem_size> struct UnOpShift { enum { shift = 1 }; };
template <size_t src_elem_size> struct UnOpShift<src_elem_size, 1> { enum { shift = 4 }; };
template <size_t src_elem_size> struct UnOpShift<src_elem_size, 2> { enum { shift = 2 }; };
template <typename T, typename D> struct DefaultUnaryShift
{
enum { shift = UnOpShift<sizeof(T), sizeof(D)>::shift };
};
template <size_t src_elem_size1, size_t src_elem_size2, size_t dst_elem_size> struct BinOpShift { enum { shift = 1 }; };
template <size_t src_elem_size1, size_t src_elem_size2> struct BinOpShift<src_elem_size1, src_elem_size2, 1> { enum { shift = 4 }; };
template <size_t src_elem_size1, size_t src_elem_size2> struct BinOpShift<src_elem_size1, src_elem_size2, 2> { enum { shift = 2 }; };
template <typename T1, typename T2, typename D> struct DefaultBinaryShift
{
enum { shift = BinOpShift<sizeof(T1), sizeof(T2), sizeof(D)>::shift };
};
template <typename Func, bool unary = IsUnaryFunction<Func>::value> struct ShiftDispatcher;
template <typename Func> struct ShiftDispatcher<Func, true>
{
enum { shift = DefaultUnaryShift<typename Func::argument_type, typename Func::result_type>::shift };
};
template <typename Func> struct ShiftDispatcher<Func, false>
{
enum { shift = DefaultBinaryShift<typename Func::first_argument_type, typename Func::second_argument_type, typename Func::result_type>::shift };
};
}
template <typename Func> struct DefaultTransformShift
{
enum { shift = functional_detail::ShiftDispatcher<Func>::shift };
};
template <typename Func> struct DefaultTransformFunctorTraits
{
enum { simple_block_dim_x = 16 };
enum { simple_block_dim_y = 16 };
enum { smart_block_dim_x = 16 };
enum { smart_block_dim_y = 16 };
enum { smart_shift = DefaultTransformShift<Func>::shift };
};
template <typename Func> struct TransformFunctorTraits : DefaultTransformFunctorTraits<Func> {};
#define OPENCV_CUDA_TRANSFORM_FUNCTOR_TRAITS(type) \
template <> struct TransformFunctorTraits< type > : DefaultTransformFunctorTraits< type >
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_FUNCTIONAL_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_CUDA_LIMITS_HPP
#define OPENCV_CUDA_LIMITS_HPP
#include <limits.h>
#include <float.h>
#include "common.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template <class T> struct numeric_limits;
template <> struct numeric_limits<bool>
{
__device__ __forceinline__ static bool min() { return false; }
__device__ __forceinline__ static bool max() { return true; }
static const bool is_signed = false;
};
template <> struct numeric_limits<signed char>
{
__device__ __forceinline__ static signed char min() { return SCHAR_MIN; }
__device__ __forceinline__ static signed char max() { return SCHAR_MAX; }
static const bool is_signed = true;
};
template <> struct numeric_limits<unsigned char>
{
__device__ __forceinline__ static unsigned char min() { return 0; }
__device__ __forceinline__ static unsigned char max() { return UCHAR_MAX; }
static const bool is_signed = false;
};
template <> struct numeric_limits<short>
{
__device__ __forceinline__ static short min() { return SHRT_MIN; }
__device__ __forceinline__ static short max() { return SHRT_MAX; }
static const bool is_signed = true;
};
template <> struct numeric_limits<unsigned short>
{
__device__ __forceinline__ static unsigned short min() { return 0; }
__device__ __forceinline__ static unsigned short max() { return USHRT_MAX; }
static const bool is_signed = false;
};
template <> struct numeric_limits<int>
{
__device__ __forceinline__ static int min() { return INT_MIN; }
__device__ __forceinline__ static int max() { return INT_MAX; }
static const bool is_signed = true;
};
template <> struct numeric_limits<unsigned int>
{
__device__ __forceinline__ static unsigned int min() { return 0; }
__device__ __forceinline__ static unsigned int max() { return UINT_MAX; }
static const bool is_signed = false;
};
template <> struct numeric_limits<float>
{
__device__ __forceinline__ static float min() { return FLT_MIN; }
__device__ __forceinline__ static float max() { return FLT_MAX; }
__device__ __forceinline__ static float epsilon() { return FLT_EPSILON; }
static const bool is_signed = true;
};
template <> struct numeric_limits<double>
{
__device__ __forceinline__ static double min() { return DBL_MIN; }
__device__ __forceinline__ static double max() { return DBL_MAX; }
__device__ __forceinline__ static double epsilon() { return DBL_EPSILON; }
static const bool is_signed = true;
};
}}} // namespace cv { namespace cuda { namespace cudev {
//! @endcond
#endif // OPENCV_CUDA_LIMITS_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_CUDA_REDUCE_HPP
#define OPENCV_CUDA_REDUCE_HPP
#ifndef THRUST_DEBUG // eliminate -Wundef warning
#define THRUST_DEBUG 0
#endif
#include <thrust/tuple.h>
#include "detail/reduce.hpp"
#include "detail/reduce_key_val.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template <int N, typename T, class Op>
__device__ __forceinline__ void reduce(volatile T* smem, T& val, unsigned int tid, const Op& op)
{
reduce_detail::Dispatcher<N>::reductor::template reduce<volatile T*, T&, const Op&>(smem, val, tid, op);
}
template <int N,
typename P0, typename P1, typename P2, typename P3, typename P4, typename P5, typename P6, typename P7, typename P8, typename P9,
typename R0, typename R1, typename R2, typename R3, typename R4, typename R5, typename R6, typename R7, typename R8, typename R9,
class Op0, class Op1, class Op2, class Op3, class Op4, class Op5, class Op6, class Op7, class Op8, class Op9>
__device__ __forceinline__ void reduce(const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>& smem,
const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>& val,
unsigned int tid,
const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>& op)
{
reduce_detail::Dispatcher<N>::reductor::template reduce<
const thrust::tuple<P0, P1, P2, P3, P4, P5, P6, P7, P8, P9>&,
const thrust::tuple<R0, R1, R2, R3, R4, R5, R6, R7, R8, R9>&,
const thrust::tuple<Op0, Op1, Op2, Op3, Op4, Op5, Op6, Op7, Op8, Op9>&>(smem, val, tid, op);
}
template <unsigned int N, typename K, typename V, class Cmp>
__device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key, volatile V* svals, V& val, unsigned int tid, const Cmp& cmp)
{
reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<volatile K*, K&, volatile V*, V&, const Cmp&>(skeys, key, svals, val, tid, cmp);
}
template <unsigned int N,
typename K,
typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
class Cmp>
__device__ __forceinline__ void reduceKeyVal(volatile K* skeys, K& key,
const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
unsigned int tid, const Cmp& cmp)
{
reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<volatile K*, K&,
const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>&,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>&,
const Cmp&>(skeys, key, svals, val, tid, cmp);
}
template <unsigned int N,
typename KP0, typename KP1, typename KP2, typename KP3, typename KP4, typename KP5, typename KP6, typename KP7, typename KP8, typename KP9,
typename KR0, typename KR1, typename KR2, typename KR3, typename KR4, typename KR5, typename KR6, typename KR7, typename KR8, typename KR9,
typename VP0, typename VP1, typename VP2, typename VP3, typename VP4, typename VP5, typename VP6, typename VP7, typename VP8, typename VP9,
typename VR0, typename VR1, typename VR2, typename VR3, typename VR4, typename VR5, typename VR6, typename VR7, typename VR8, typename VR9,
class Cmp0, class Cmp1, class Cmp2, class Cmp3, class Cmp4, class Cmp5, class Cmp6, class Cmp7, class Cmp8, class Cmp9>
__device__ __forceinline__ void reduceKeyVal(const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>& skeys,
const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>& key,
const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>& svals,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>& val,
unsigned int tid,
const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>& cmp)
{
reduce_key_val_detail::Dispatcher<N>::reductor::template reduce<
const thrust::tuple<KP0, KP1, KP2, KP3, KP4, KP5, KP6, KP7, KP8, KP9>&,
const thrust::tuple<KR0, KR1, KR2, KR3, KR4, KR5, KR6, KR7, KR8, KR9>&,
const thrust::tuple<VP0, VP1, VP2, VP3, VP4, VP5, VP6, VP7, VP8, VP9>&,
const thrust::tuple<VR0, VR1, VR2, VR3, VR4, VR5, VR6, VR7, VR8, VR9>&,
const thrust::tuple<Cmp0, Cmp1, Cmp2, Cmp3, Cmp4, Cmp5, Cmp6, Cmp7, Cmp8, Cmp9>&
>(skeys, key, svals, val, tid, cmp);
}
// smem_tuple
template <typename T0>
__device__ __forceinline__
thrust::tuple<volatile T0*>
smem_tuple(T0* t0)
{
return thrust::make_tuple((volatile T0*) t0);
}
template <typename T0, typename T1>
__device__ __forceinline__
thrust::tuple<volatile T0*, volatile T1*>
smem_tuple(T0* t0, T1* t1)
{
return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1);
}
template <typename T0, typename T1, typename T2>
__device__ __forceinline__
thrust::tuple<volatile T0*, volatile T1*, volatile T2*>
smem_tuple(T0* t0, T1* t1, T2* t2)
{
return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2);
}
template <typename T0, typename T1, typename T2, typename T3>
__device__ __forceinline__
thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*>
smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3)
{
return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3);
}
template <typename T0, typename T1, typename T2, typename T3, typename T4>
__device__ __forceinline__
thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*>
smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4)
{
return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4);
}
template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5>
__device__ __forceinline__
thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*>
smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5)
{
return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5);
}
template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6>
__device__ __forceinline__
thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*>
smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6)
{
return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6);
}
template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7>
__device__ __forceinline__
thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*>
smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7)
{
return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7);
}
template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8>
__device__ __forceinline__
thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*, volatile T8*>
smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8)
{
return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8);
}
template <typename T0, typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, typename T7, typename T8, typename T9>
__device__ __forceinline__
thrust::tuple<volatile T0*, volatile T1*, volatile T2*, volatile T3*, volatile T4*, volatile T5*, volatile T6*, volatile T7*, volatile T8*, volatile T9*>
smem_tuple(T0* t0, T1* t1, T2* t2, T3* t3, T4* t4, T5* t5, T6* t6, T7* t7, T8* t8, T9* t9)
{
return thrust::make_tuple((volatile T0*) t0, (volatile T1*) t1, (volatile T2*) t2, (volatile T3*) t3, (volatile T4*) t4, (volatile T5*) t5, (volatile T6*) t6, (volatile T7*) t7, (volatile T8*) t8, (volatile T9*) t9);
}
}}}
//! @endcond
#endif // OPENCV_CUDA_REDUCE_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_CUDA_SATURATE_CAST_HPP
#define OPENCV_CUDA_SATURATE_CAST_HPP
#include "common.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(uchar v) { return _Tp(v); }
template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(schar v) { return _Tp(v); }
template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(ushort v) { return _Tp(v); }
template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(short v) { return _Tp(v); }
template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(uint v) { return _Tp(v); }
template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(int v) { return _Tp(v); }
template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(float v) { return _Tp(v); }
template<typename _Tp> __device__ __forceinline__ _Tp saturate_cast(double v) { return _Tp(v); }
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(schar v)
{
uint res = 0;
int vi = v;
asm("cvt.sat.u8.s8 %0, %1;" : "=r"(res) : "r"(vi));
return res;
}
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(short v)
{
uint res = 0;
asm("cvt.sat.u8.s16 %0, %1;" : "=r"(res) : "h"(v));
return res;
}
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(ushort v)
{
uint res = 0;
asm("cvt.sat.u8.u16 %0, %1;" : "=r"(res) : "h"(v));
return res;
}
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(int v)
{
uint res = 0;
asm("cvt.sat.u8.s32 %0, %1;" : "=r"(res) : "r"(v));
return res;
}
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(uint v)
{
uint res = 0;
asm("cvt.sat.u8.u32 %0, %1;" : "=r"(res) : "r"(v));
return res;
}
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(float v)
{
uint res = 0;
asm("cvt.rni.sat.u8.f32 %0, %1;" : "=r"(res) : "f"(v));
return res;
}
template<> __device__ __forceinline__ uchar saturate_cast<uchar>(double v)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
uint res = 0;
asm("cvt.rni.sat.u8.f64 %0, %1;" : "=r"(res) : "d"(v));
return res;
#else
return saturate_cast<uchar>((float)v);
#endif
}
template<> __device__ __forceinline__ schar saturate_cast<schar>(uchar v)
{
uint res = 0;
uint vi = v;
asm("cvt.sat.s8.u8 %0, %1;" : "=r"(res) : "r"(vi));
return res;
}
template<> __device__ __forceinline__ schar saturate_cast<schar>(short v)
{
uint res = 0;
asm("cvt.sat.s8.s16 %0, %1;" : "=r"(res) : "h"(v));
return res;
}
template<> __device__ __forceinline__ schar saturate_cast<schar>(ushort v)
{
uint res = 0;
asm("cvt.sat.s8.u16 %0, %1;" : "=r"(res) : "h"(v));
return res;
}
template<> __device__ __forceinline__ schar saturate_cast<schar>(int v)
{
uint res = 0;
asm("cvt.sat.s8.s32 %0, %1;" : "=r"(res) : "r"(v));
return res;
}
template<> __device__ __forceinline__ schar saturate_cast<schar>(uint v)
{
uint res = 0;
asm("cvt.sat.s8.u32 %0, %1;" : "=r"(res) : "r"(v));
return res;
}
template<> __device__ __forceinline__ schar saturate_cast<schar>(float v)
{
uint res = 0;
asm("cvt.rni.sat.s8.f32 %0, %1;" : "=r"(res) : "f"(v));
return res;
}
template<> __device__ __forceinline__ schar saturate_cast<schar>(double v)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
uint res = 0;
asm("cvt.rni.sat.s8.f64 %0, %1;" : "=r"(res) : "d"(v));
return res;
#else
return saturate_cast<schar>((float)v);
#endif
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(schar v)
{
ushort res = 0;
int vi = v;
asm("cvt.sat.u16.s8 %0, %1;" : "=h"(res) : "r"(vi));
return res;
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(short v)
{
ushort res = 0;
asm("cvt.sat.u16.s16 %0, %1;" : "=h"(res) : "h"(v));
return res;
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(int v)
{
ushort res = 0;
asm("cvt.sat.u16.s32 %0, %1;" : "=h"(res) : "r"(v));
return res;
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(uint v)
{
ushort res = 0;
asm("cvt.sat.u16.u32 %0, %1;" : "=h"(res) : "r"(v));
return res;
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(float v)
{
ushort res = 0;
asm("cvt.rni.sat.u16.f32 %0, %1;" : "=h"(res) : "f"(v));
return res;
}
template<> __device__ __forceinline__ ushort saturate_cast<ushort>(double v)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
ushort res = 0;
asm("cvt.rni.sat.u16.f64 %0, %1;" : "=h"(res) : "d"(v));
return res;
#else
return saturate_cast<ushort>((float)v);
#endif
}
template<> __device__ __forceinline__ short saturate_cast<short>(ushort v)
{
short res = 0;
asm("cvt.sat.s16.u16 %0, %1;" : "=h"(res) : "h"(v));
return res;
}
template<> __device__ __forceinline__ short saturate_cast<short>(int v)
{
short res = 0;
asm("cvt.sat.s16.s32 %0, %1;" : "=h"(res) : "r"(v));
return res;
}
template<> __device__ __forceinline__ short saturate_cast<short>(uint v)
{
short res = 0;
asm("cvt.sat.s16.u32 %0, %1;" : "=h"(res) : "r"(v));
return res;
}
template<> __device__ __forceinline__ short saturate_cast<short>(float v)
{
short res = 0;
asm("cvt.rni.sat.s16.f32 %0, %1;" : "=h"(res) : "f"(v));
return res;
}
template<> __device__ __forceinline__ short saturate_cast<short>(double v)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
short res = 0;
asm("cvt.rni.sat.s16.f64 %0, %1;" : "=h"(res) : "d"(v));
return res;
#else
return saturate_cast<short>((float)v);
#endif
}
template<> __device__ __forceinline__ int saturate_cast<int>(uint v)
{
int res = 0;
asm("cvt.sat.s32.u32 %0, %1;" : "=r"(res) : "r"(v));
return res;
}
template<> __device__ __forceinline__ int saturate_cast<int>(float v)
{
return __float2int_rn(v);
}
template<> __device__ __forceinline__ int saturate_cast<int>(double v)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
return __double2int_rn(v);
#else
return saturate_cast<int>((float)v);
#endif
}
template<> __device__ __forceinline__ uint saturate_cast<uint>(schar v)
{
uint res = 0;
int vi = v;
asm("cvt.sat.u32.s8 %0, %1;" : "=r"(res) : "r"(vi));
return res;
}
template<> __device__ __forceinline__ uint saturate_cast<uint>(short v)
{
uint res = 0;
asm("cvt.sat.u32.s16 %0, %1;" : "=r"(res) : "h"(v));
return res;
}
template<> __device__ __forceinline__ uint saturate_cast<uint>(int v)
{
uint res = 0;
asm("cvt.sat.u32.s32 %0, %1;" : "=r"(res) : "r"(v));
return res;
}
template<> __device__ __forceinline__ uint saturate_cast<uint>(float v)
{
return __float2uint_rn(v);
}
template<> __device__ __forceinline__ uint saturate_cast<uint>(double v)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 130
return __double2uint_rn(v);
#else
return saturate_cast<uint>((float)v);
#endif
}
}}}
//! @endcond
#endif /* OPENCV_CUDA_SATURATE_CAST_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_CUDA_SCAN_HPP
#define OPENCV_CUDA_SCAN_HPP
#include "opencv2/core/cuda/common.hpp"
#include "opencv2/core/cuda/utility.hpp"
#include "opencv2/core/cuda/warp.hpp"
#include "opencv2/core/cuda/warp_shuffle.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
enum ScanKind { EXCLUSIVE = 0, INCLUSIVE = 1 };
template <ScanKind Kind, typename T, typename F> struct WarpScan
{
__device__ __forceinline__ WarpScan() {}
__device__ __forceinline__ WarpScan(const WarpScan& other) { CV_UNUSED(other); }
__device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
{
const unsigned int lane = idx & 31;
F op;
if ( lane >= 1) ptr [idx ] = op(ptr [idx - 1], ptr [idx]);
if ( lane >= 2) ptr [idx ] = op(ptr [idx - 2], ptr [idx]);
if ( lane >= 4) ptr [idx ] = op(ptr [idx - 4], ptr [idx]);
if ( lane >= 8) ptr [idx ] = op(ptr [idx - 8], ptr [idx]);
if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
if( Kind == INCLUSIVE )
return ptr [idx];
else
return (lane > 0) ? ptr [idx - 1] : 0;
}
__device__ __forceinline__ unsigned int index(const unsigned int tid)
{
return tid;
}
__device__ __forceinline__ void init(volatile T *ptr){}
static const int warp_offset = 0;
typedef WarpScan<INCLUSIVE, T, F> merge;
};
template <ScanKind Kind , typename T, typename F> struct WarpScanNoComp
{
__device__ __forceinline__ WarpScanNoComp() {}
__device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { CV_UNUSED(other); }
__device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
{
const unsigned int lane = threadIdx.x & 31;
F op;
ptr [idx ] = op(ptr [idx - 1], ptr [idx]);
ptr [idx ] = op(ptr [idx - 2], ptr [idx]);
ptr [idx ] = op(ptr [idx - 4], ptr [idx]);
ptr [idx ] = op(ptr [idx - 8], ptr [idx]);
ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
if( Kind == INCLUSIVE )
return ptr [idx];
else
return (lane > 0) ? ptr [idx - 1] : 0;
}
__device__ __forceinline__ unsigned int index(const unsigned int tid)
{
return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask);
}
__device__ __forceinline__ void init(volatile T *ptr)
{
ptr[threadIdx.x] = 0;
}
static const int warp_smem_stride = 32 + 16 + 1;
static const int warp_offset = 16;
static const int warp_log = 5;
static const int warp_mask = 31;
typedef WarpScanNoComp<INCLUSIVE, T, F> merge;
};
template <ScanKind Kind , typename T, typename Sc, typename F> struct BlockScan
{
__device__ __forceinline__ BlockScan() {}
__device__ __forceinline__ BlockScan(const BlockScan& other) { CV_UNUSED(other); }
__device__ __forceinline__ T operator()(volatile T *ptr)
{
const unsigned int tid = threadIdx.x;
const unsigned int lane = tid & warp_mask;
const unsigned int warp = tid >> warp_log;
Sc scan;
typename Sc::merge merge_scan;
const unsigned int idx = scan.index(tid);
T val = scan(ptr, idx);
__syncthreads ();
if( warp == 0)
scan.init(ptr);
__syncthreads ();
if( lane == 31 )
ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx];
__syncthreads ();
if( warp == 0 )
merge_scan(ptr, idx);
__syncthreads();
if ( warp > 0)
val = ptr [scan.warp_offset + warp - 1] + val;
__syncthreads ();
ptr[idx] = val;
__syncthreads ();
return val ;
}
static const int warp_log = 5;
static const int warp_mask = 31;
};
template <typename T>
__device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid)
{
#if __CUDA_ARCH__ >= 300
const unsigned int laneId = cv::cuda::device::Warp::laneId();
// scan on shuffl functions
#pragma unroll
for (int i = 1; i <= (OPENCV_CUDA_WARP_SIZE / 2); i *= 2)
{
const T n = cv::cuda::device::shfl_up(idata, i);
if (laneId >= i)
idata += n;
}
return idata;
#else
unsigned int pos = 2 * tid - (tid & (OPENCV_CUDA_WARP_SIZE - 1));
s_Data[pos] = 0;
pos += OPENCV_CUDA_WARP_SIZE;
s_Data[pos] = idata;
s_Data[pos] += s_Data[pos - 1];
s_Data[pos] += s_Data[pos - 2];
s_Data[pos] += s_Data[pos - 4];
s_Data[pos] += s_Data[pos - 8];
s_Data[pos] += s_Data[pos - 16];
return s_Data[pos];
#endif
}
template <typename T>
__device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid)
{
return warpScanInclusive(idata, s_Data, tid) - idata;
}
template <int tiNumScanThreads, typename T>
__device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid)
{
if (tiNumScanThreads > OPENCV_CUDA_WARP_SIZE)
{
//Bottom-level inclusive warp scan
T warpResult = warpScanInclusive(idata, s_Data, tid);
//Save top elements of each warp for exclusive warp scan
//sync to wait for warp scans to complete (because s_Data is being overwritten)
__syncthreads();
if ((tid & (OPENCV_CUDA_WARP_SIZE - 1)) == (OPENCV_CUDA_WARP_SIZE - 1))
{
s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE] = warpResult;
}
//wait for warp scans to complete
__syncthreads();
if (tid < (tiNumScanThreads / OPENCV_CUDA_WARP_SIZE) )
{
//grab top warp elements
T val = s_Data[tid];
//calculate exclusive scan and write back to shared memory
s_Data[tid] = warpScanExclusive(val, s_Data, tid);
}
//return updated warp scans with exclusive scan results
__syncthreads();
return warpResult + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE];
}
else
{
return warpScanInclusive(idata, s_Data, tid);
}
}
}}}
//! @endcond
#endif // OPENCV_CUDA_SCAN_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*/
/*
* Copyright (c) 2013 NVIDIA Corporation. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* Redistributions 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.
*
* Neither the name of NVIDIA Corporation nor the names of its contributors
* may 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 COPYRIGHT HOLDER 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.
*/
#ifndef OPENCV_CUDA_SIMD_FUNCTIONS_HPP
#define OPENCV_CUDA_SIMD_FUNCTIONS_HPP
#include "common.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
// 2
static __device__ __forceinline__ unsigned int vadd2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vadd2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#elif __CUDA_ARCH__ >= 200
asm("vadd.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vadd.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int s;
s = a ^ b; // sum bits
r = a + b; // actual sum
s = s ^ r; // determine carry-ins for each bit position
s = s & 0x00010000; // carry-in to high word (= carry-out from low word)
r = r - s; // subtract out carry-out from low word
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsub2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vsub2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#elif __CUDA_ARCH__ >= 200
asm("vsub.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vsub.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int s;
s = a ^ b; // sum bits
r = a - b; // actual sum
s = s ^ r; // determine carry-ins for each bit position
s = s & 0x00010000; // borrow to high word
r = r + s; // compensate for borrow from low word
#endif
return r;
}
static __device__ __forceinline__ unsigned int vabsdiff2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vabsdiff2.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#elif __CUDA_ARCH__ >= 200
asm("vabsdiff.u32.u32.u32.sat %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vabsdiff.u32.u32.u32.sat %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int s, t, u, v;
s = a & 0x0000ffff; // extract low halfword
r = b & 0x0000ffff; // extract low halfword
u = ::max(r, s); // maximum of low halfwords
v = ::min(r, s); // minimum of low halfwords
s = a & 0xffff0000; // extract high halfword
r = b & 0xffff0000; // extract high halfword
t = ::max(r, s); // maximum of high halfwords
s = ::min(r, s); // minimum of high halfwords
r = u | t; // maximum of both halfwords
s = v | s; // minimum of both halfwords
r = r - s; // |a - b| = max(a,b) - min(a,b);
#endif
return r;
}
static __device__ __forceinline__ unsigned int vavg2(unsigned int a, unsigned int b)
{
unsigned int r, s;
// HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==>
// (a + b) / 2 = (a & b) + ((a ^ b) >> 1)
s = a ^ b;
r = a & b;
s = s & 0xfffefffe; // ensure shift doesn't cross halfword boundaries
s = s >> 1;
s = r + s;
return s;
}
static __device__ __forceinline__ unsigned int vavrg2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vavrg2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
// HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==>
// (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1)
unsigned int s;
s = a ^ b;
r = a | b;
s = s & 0xfffefffe; // ensure shift doesn't cross half-word boundaries
s = s >> 1;
r = r - s;
#endif
return r;
}
static __device__ __forceinline__ unsigned int vseteq2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset2.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
// inspired by Alan Mycroft's null-byte detection algorithm:
// null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
unsigned int c;
r = a ^ b; // 0x0000 if a == b
c = r | 0x80008000; // set msbs, to catch carry out
r = r ^ c; // extract msbs, msb = 1 if r < 0x8000
c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
c = r & ~c; // msb = 1, if r was 0x0000
r = c >> 15; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmpeq2(unsigned int a, unsigned int b)
{
unsigned int r, c;
#if __CUDA_ARCH__ >= 300
r = vseteq2(a, b);
c = r << 16; // convert bool
r = c - r; // into mask
#else
// inspired by Alan Mycroft's null-byte detection algorithm:
// null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
r = a ^ b; // 0x0000 if a == b
c = r | 0x80008000; // set msbs, to catch carry out
r = r ^ c; // extract msbs, msb = 1 if r < 0x8000
c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
c = r & ~c; // msb = 1, if r was 0x0000
r = c >> 15; // convert
r = c - r; // msbs to
r = c | r; // mask
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsetge2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset2.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int c;
asm("not.b32 %0, %0;" : "+r"(b));
c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2
c = c & 0x80008000; // msb = carry-outs
r = c >> 15; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmpge2(unsigned int a, unsigned int b)
{
unsigned int r, c;
#if __CUDA_ARCH__ >= 300
r = vsetge2(a, b);
c = r << 16; // convert bool
r = c - r; // into mask
#else
asm("not.b32 %0, %0;" : "+r"(b));
c = vavrg2(a, b); // (a + ~b + 1) / 2 = (a - b) / 2
c = c & 0x80008000; // msb = carry-outs
r = c >> 15; // convert
r = c - r; // msbs to
r = c | r; // mask
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsetgt2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset2.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int c;
asm("not.b32 %0, %0;" : "+r"(b));
c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down]
c = c & 0x80008000; // msbs = carry-outs
r = c >> 15; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmpgt2(unsigned int a, unsigned int b)
{
unsigned int r, c;
#if __CUDA_ARCH__ >= 300
r = vsetgt2(a, b);
c = r << 16; // convert bool
r = c - r; // into mask
#else
asm("not.b32 %0, %0;" : "+r"(b));
c = vavg2(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down]
c = c & 0x80008000; // msbs = carry-outs
r = c >> 15; // convert
r = c - r; // msbs to
r = c | r; // mask
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsetle2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset2.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int c;
asm("not.b32 %0, %0;" : "+r"(a));
c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2
c = c & 0x80008000; // msb = carry-outs
r = c >> 15; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmple2(unsigned int a, unsigned int b)
{
unsigned int r, c;
#if __CUDA_ARCH__ >= 300
r = vsetle2(a, b);
c = r << 16; // convert bool
r = c - r; // into mask
#else
asm("not.b32 %0, %0;" : "+r"(a));
c = vavrg2(a, b); // (b + ~a + 1) / 2 = (b - a) / 2
c = c & 0x80008000; // msb = carry-outs
r = c >> 15; // convert
r = c - r; // msbs to
r = c | r; // mask
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsetlt2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset2.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int c;
asm("not.b32 %0, %0;" : "+r"(a));
c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down]
c = c & 0x80008000; // msb = carry-outs
r = c >> 15; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmplt2(unsigned int a, unsigned int b)
{
unsigned int r, c;
#if __CUDA_ARCH__ >= 300
r = vsetlt2(a, b);
c = r << 16; // convert bool
r = c - r; // into mask
#else
asm("not.b32 %0, %0;" : "+r"(a));
c = vavg2(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down]
c = c & 0x80008000; // msb = carry-outs
r = c >> 15; // convert
r = c - r; // msbs to
r = c | r; // mask
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsetne2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm ("vset2.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
// inspired by Alan Mycroft's null-byte detection algorithm:
// null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
unsigned int c;
r = a ^ b; // 0x0000 if a == b
c = r | 0x80008000; // set msbs, to catch carry out
c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
c = r | c; // msb = 1, if r was not 0x0000
c = c & 0x80008000; // extract msbs
r = c >> 15; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmpne2(unsigned int a, unsigned int b)
{
unsigned int r, c;
#if __CUDA_ARCH__ >= 300
r = vsetne2(a, b);
c = r << 16; // convert bool
r = c - r; // into mask
#else
// inspired by Alan Mycroft's null-byte detection algorithm:
// null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
r = a ^ b; // 0x0000 if a == b
c = r | 0x80008000; // set msbs, to catch carry out
c = c - 0x00010001; // msb = 0, if r was 0x0000 or 0x8000
c = r | c; // msb = 1, if r was not 0x0000
c = c & 0x80008000; // extract msbs
r = c >> 15; // convert
r = c - r; // msbs to
r = c | r; // mask
#endif
return r;
}
static __device__ __forceinline__ unsigned int vmax2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vmax2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#elif __CUDA_ARCH__ >= 200
asm("vmax.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vmax.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int s, t, u;
r = a & 0x0000ffff; // extract low halfword
s = b & 0x0000ffff; // extract low halfword
t = ::max(r, s); // maximum of low halfwords
r = a & 0xffff0000; // extract high halfword
s = b & 0xffff0000; // extract high halfword
u = ::max(r, s); // maximum of high halfwords
r = t | u; // combine halfword maximums
#endif
return r;
}
static __device__ __forceinline__ unsigned int vmin2(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vmin2.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#elif __CUDA_ARCH__ >= 200
asm("vmin.u32.u32.u32 %0.h0, %1.h0, %2.h0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vmin.u32.u32.u32 %0.h1, %1.h1, %2.h1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int s, t, u;
r = a & 0x0000ffff; // extract low halfword
s = b & 0x0000ffff; // extract low halfword
t = ::min(r, s); // minimum of low halfwords
r = a & 0xffff0000; // extract high halfword
s = b & 0xffff0000; // extract high halfword
u = ::min(r, s); // minimum of high halfwords
r = t | u; // combine halfword minimums
#endif
return r;
}
// 4
static __device__ __forceinline__ unsigned int vadd4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vadd4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#elif __CUDA_ARCH__ >= 200
asm("vadd.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vadd.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vadd.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vadd.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int s, t;
s = a ^ b; // sum bits
r = a & 0x7f7f7f7f; // clear msbs
t = b & 0x7f7f7f7f; // clear msbs
s = s & 0x80808080; // msb sum bits
r = r + t; // add without msbs, record carry-out in msbs
r = r ^ s; // sum of msb sum and carry-in bits, w/o carry-out
#endif /* __CUDA_ARCH__ >= 300 */
return r;
}
static __device__ __forceinline__ unsigned int vsub4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vsub4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#elif __CUDA_ARCH__ >= 200
asm("vsub.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vsub.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vsub.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vsub.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int s, t;
s = a ^ ~b; // inverted sum bits
r = a | 0x80808080; // set msbs
t = b & 0x7f7f7f7f; // clear msbs
s = s & 0x80808080; // inverted msb sum bits
r = r - t; // subtract w/o msbs, record inverted borrows in msb
r = r ^ s; // combine inverted msb sum bits and borrows
#endif
return r;
}
static __device__ __forceinline__ unsigned int vavg4(unsigned int a, unsigned int b)
{
unsigned int r, s;
// HAKMEM #23: a + b = 2 * (a & b) + (a ^ b) ==>
// (a + b) / 2 = (a & b) + ((a ^ b) >> 1)
s = a ^ b;
r = a & b;
s = s & 0xfefefefe; // ensure following shift doesn't cross byte boundaries
s = s >> 1;
s = r + s;
return s;
}
static __device__ __forceinline__ unsigned int vavrg4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vavrg4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
// HAKMEM #23: a + b = 2 * (a | b) - (a ^ b) ==>
// (a + b + 1) / 2 = (a | b) - ((a ^ b) >> 1)
unsigned int c;
c = a ^ b;
r = a | b;
c = c & 0xfefefefe; // ensure following shift doesn't cross byte boundaries
c = c >> 1;
r = r - c;
#endif
return r;
}
static __device__ __forceinline__ unsigned int vseteq4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset4.u32.u32.eq %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
// inspired by Alan Mycroft's null-byte detection algorithm:
// null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
unsigned int c;
r = a ^ b; // 0x00 if a == b
c = r | 0x80808080; // set msbs, to catch carry out
r = r ^ c; // extract msbs, msb = 1 if r < 0x80
c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
c = r & ~c; // msb = 1, if r was 0x00
r = c >> 7; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmpeq4(unsigned int a, unsigned int b)
{
unsigned int r, t;
#if __CUDA_ARCH__ >= 300
r = vseteq4(a, b);
t = r << 8; // convert bool
r = t - r; // to mask
#else
// inspired by Alan Mycroft's null-byte detection algorithm:
// null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
t = a ^ b; // 0x00 if a == b
r = t | 0x80808080; // set msbs, to catch carry out
t = t ^ r; // extract msbs, msb = 1 if t < 0x80
r = r - 0x01010101; // msb = 0, if t was 0x00 or 0x80
r = t & ~r; // msb = 1, if t was 0x00
t = r >> 7; // build mask
t = r - t; // from
r = t | r; // msbs
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsetle4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset4.u32.u32.le %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int c;
asm("not.b32 %0, %0;" : "+r"(a));
c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2
c = c & 0x80808080; // msb = carry-outs
r = c >> 7; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmple4(unsigned int a, unsigned int b)
{
unsigned int r, c;
#if __CUDA_ARCH__ >= 300
r = vsetle4(a, b);
c = r << 8; // convert bool
r = c - r; // to mask
#else
asm("not.b32 %0, %0;" : "+r"(a));
c = vavrg4(a, b); // (b + ~a + 1) / 2 = (b - a) / 2
c = c & 0x80808080; // msbs = carry-outs
r = c >> 7; // convert
r = c - r; // msbs to
r = c | r; // mask
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsetlt4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset4.u32.u32.lt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int c;
asm("not.b32 %0, %0;" : "+r"(a));
c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down]
c = c & 0x80808080; // msb = carry-outs
r = c >> 7; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmplt4(unsigned int a, unsigned int b)
{
unsigned int r, c;
#if __CUDA_ARCH__ >= 300
r = vsetlt4(a, b);
c = r << 8; // convert bool
r = c - r; // to mask
#else
asm("not.b32 %0, %0;" : "+r"(a));
c = vavg4(a, b); // (b + ~a) / 2 = (b - a) / 2 [rounded down]
c = c & 0x80808080; // msbs = carry-outs
r = c >> 7; // convert
r = c - r; // msbs to
r = c | r; // mask
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsetge4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset4.u32.u32.ge %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int c;
asm("not.b32 %0, %0;" : "+r"(b));
c = vavrg4(a, b); // (a + ~b + 1) / 2 = (a - b) / 2
c = c & 0x80808080; // msb = carry-outs
r = c >> 7; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmpge4(unsigned int a, unsigned int b)
{
unsigned int r, s;
#if __CUDA_ARCH__ >= 300
r = vsetge4(a, b);
s = r << 8; // convert bool
r = s - r; // to mask
#else
asm ("not.b32 %0,%0;" : "+r"(b));
r = vavrg4 (a, b); // (a + ~b + 1) / 2 = (a - b) / 2
r = r & 0x80808080; // msb = carry-outs
s = r >> 7; // build mask
s = r - s; // from
r = s | r; // msbs
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsetgt4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset4.u32.u32.gt %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int c;
asm("not.b32 %0, %0;" : "+r"(b));
c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down]
c = c & 0x80808080; // msb = carry-outs
r = c >> 7; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmpgt4(unsigned int a, unsigned int b)
{
unsigned int r, c;
#if __CUDA_ARCH__ >= 300
r = vsetgt4(a, b);
c = r << 8; // convert bool
r = c - r; // to mask
#else
asm("not.b32 %0, %0;" : "+r"(b));
c = vavg4(a, b); // (a + ~b) / 2 = (a - b) / 2 [rounded down]
c = c & 0x80808080; // msb = carry-outs
r = c >> 7; // convert
r = c - r; // msbs to
r = c | r; // mask
#endif
return r;
}
static __device__ __forceinline__ unsigned int vsetne4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vset4.u32.u32.ne %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
// inspired by Alan Mycroft's null-byte detection algorithm:
// null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
unsigned int c;
r = a ^ b; // 0x00 if a == b
c = r | 0x80808080; // set msbs, to catch carry out
c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
c = r | c; // msb = 1, if r was not 0x00
c = c & 0x80808080; // extract msbs
r = c >> 7; // convert to bool
#endif
return r;
}
static __device__ __forceinline__ unsigned int vcmpne4(unsigned int a, unsigned int b)
{
unsigned int r, c;
#if __CUDA_ARCH__ >= 300
r = vsetne4(a, b);
c = r << 8; // convert bool
r = c - r; // to mask
#else
// inspired by Alan Mycroft's null-byte detection algorithm:
// null_byte(x) = ((x - 0x01010101) & (~x & 0x80808080))
r = a ^ b; // 0x00 if a == b
c = r | 0x80808080; // set msbs, to catch carry out
c = c - 0x01010101; // msb = 0, if r was 0x00 or 0x80
c = r | c; // msb = 1, if r was not 0x00
c = c & 0x80808080; // extract msbs
r = c >> 7; // convert
r = c - r; // msbs to
r = c | r; // mask
#endif
return r;
}
static __device__ __forceinline__ unsigned int vabsdiff4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vabsdiff4.u32.u32.u32.sat %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#elif __CUDA_ARCH__ >= 200
asm("vabsdiff.u32.u32.u32.sat %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vabsdiff.u32.u32.u32.sat %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vabsdiff.u32.u32.u32.sat %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vabsdiff.u32.u32.u32.sat %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int s;
s = vcmpge4(a, b); // mask = 0xff if a >= b
r = a ^ b; //
s = (r & s) ^ b; // select a when a >= b, else select b => max(a,b)
r = s ^ r; // select a when b >= a, else select b => min(a,b)
r = s - r; // |a - b| = max(a,b) - min(a,b);
#endif
return r;
}
static __device__ __forceinline__ unsigned int vmax4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vmax4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#elif __CUDA_ARCH__ >= 200
asm("vmax.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vmax.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vmax.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vmax.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int s;
s = vcmpge4(a, b); // mask = 0xff if a >= b
r = a & s; // select a when b >= a
s = b & ~s; // select b when b < a
r = r | s; // combine byte selections
#endif
return r; // byte-wise unsigned maximum
}
static __device__ __forceinline__ unsigned int vmin4(unsigned int a, unsigned int b)
{
unsigned int r = 0;
#if __CUDA_ARCH__ >= 300
asm("vmin4.u32.u32.u32 %0, %1, %2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#elif __CUDA_ARCH__ >= 200
asm("vmin.u32.u32.u32 %0.b0, %1.b0, %2.b0, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vmin.u32.u32.u32 %0.b1, %1.b1, %2.b1, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vmin.u32.u32.u32 %0.b2, %1.b2, %2.b2, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
asm("vmin.u32.u32.u32 %0.b3, %1.b3, %2.b3, %3;" : "=r"(r) : "r"(a), "r"(b), "r"(r));
#else
unsigned int s;
s = vcmpge4(b, a); // mask = 0xff if a >= b
r = a & s; // select a when b >= a
s = b & ~s; // select b when b < a
r = r | s; // combine byte selections
#endif
return r;
}
}}}
//! @endcond
#endif // OPENCV_CUDA_SIMD_FUNCTIONS_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_CUDA_TRANSFORM_HPP
#define OPENCV_CUDA_TRANSFORM_HPP
#include "common.hpp"
#include "utility.hpp"
#include "detail/transform_detail.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template <typename T, typename D, typename UnOp, typename Mask>
static inline void transform(PtrStepSz<T> src, PtrStepSz<D> dst, UnOp op, const Mask& mask, cudaStream_t stream)
{
typedef TransformFunctorTraits<UnOp> ft;
transform_detail::TransformDispatcher<VecTraits<T>::cn == 1 && VecTraits<D>::cn == 1 && ft::smart_shift != 1>::call(src, dst, op, mask, stream);
}
template <typename T1, typename T2, typename D, typename BinOp, typename Mask>
static inline void transform(PtrStepSz<T1> src1, PtrStepSz<T2> src2, PtrStepSz<D> dst, BinOp op, const Mask& mask, cudaStream_t stream)
{
typedef TransformFunctorTraits<BinOp> ft;
transform_detail::TransformDispatcher<VecTraits<T1>::cn == 1 && VecTraits<T2>::cn == 1 && VecTraits<D>::cn == 1 && ft::smart_shift != 1>::call(src1, src2, dst, op, mask, stream);
}
}}}
//! @endcond
#endif // OPENCV_CUDA_TRANSFORM_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_CUDA_TYPE_TRAITS_HPP
#define OPENCV_CUDA_TYPE_TRAITS_HPP
#include "detail/type_traits_detail.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template <typename T> struct IsSimpleParameter
{
enum {value = type_traits_detail::IsIntegral<T>::value || type_traits_detail::IsFloat<T>::value ||
type_traits_detail::PointerTraits<typename type_traits_detail::ReferenceTraits<T>::type>::value};
};
template <typename T> struct TypeTraits
{
typedef typename type_traits_detail::UnConst<T>::type NonConstType;
typedef typename type_traits_detail::UnVolatile<T>::type NonVolatileType;
typedef typename type_traits_detail::UnVolatile<typename type_traits_detail::UnConst<T>::type>::type UnqualifiedType;
typedef typename type_traits_detail::PointerTraits<UnqualifiedType>::type PointeeType;
typedef typename type_traits_detail::ReferenceTraits<T>::type ReferredType;
enum { isConst = type_traits_detail::UnConst<T>::value };
enum { isVolatile = type_traits_detail::UnVolatile<T>::value };
enum { isReference = type_traits_detail::ReferenceTraits<UnqualifiedType>::value };
enum { isPointer = type_traits_detail::PointerTraits<typename type_traits_detail::ReferenceTraits<UnqualifiedType>::type>::value };
enum { isUnsignedInt = type_traits_detail::IsUnsignedIntegral<UnqualifiedType>::value };
enum { isSignedInt = type_traits_detail::IsSignedIntergral<UnqualifiedType>::value };
enum { isIntegral = type_traits_detail::IsIntegral<UnqualifiedType>::value };
enum { isFloat = type_traits_detail::IsFloat<UnqualifiedType>::value };
enum { isArith = isIntegral || isFloat };
enum { isVec = type_traits_detail::IsVec<UnqualifiedType>::value };
typedef typename type_traits_detail::Select<IsSimpleParameter<UnqualifiedType>::value,
T, typename type_traits_detail::AddParameterType<T>::type>::type ParameterType;
};
}}}
//! @endcond
#endif // OPENCV_CUDA_TYPE_TRAITS_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_CUDA_UTILITY_HPP
#define OPENCV_CUDA_UTILITY_HPP
#include "saturate_cast.hpp"
#include "datamov_utils.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
struct CV_EXPORTS ThrustAllocator
{
typedef uchar value_type;
virtual ~ThrustAllocator();
virtual __device__ __host__ uchar* allocate(size_t numBytes) = 0;
virtual __device__ __host__ void deallocate(uchar* ptr, size_t numBytes) = 0;
static ThrustAllocator& getAllocator();
static void setAllocator(ThrustAllocator* allocator);
};
#define OPENCV_CUDA_LOG_WARP_SIZE (5)
#define OPENCV_CUDA_WARP_SIZE (1 << OPENCV_CUDA_LOG_WARP_SIZE)
#define OPENCV_CUDA_LOG_MEM_BANKS ((__CUDA_ARCH__ >= 200) ? 5 : 4) // 32 banks on fermi, 16 on tesla
#define OPENCV_CUDA_MEM_BANKS (1 << OPENCV_CUDA_LOG_MEM_BANKS)
///////////////////////////////////////////////////////////////////////////////
// swap
template <typename T> void __device__ __host__ __forceinline__ swap(T& a, T& b)
{
const T temp = a;
a = b;
b = temp;
}
///////////////////////////////////////////////////////////////////////////////
// Mask Reader
struct SingleMask
{
explicit __host__ __device__ __forceinline__ SingleMask(PtrStepb mask_) : mask(mask_) {}
__host__ __device__ __forceinline__ SingleMask(const SingleMask& mask_): mask(mask_.mask){}
__device__ __forceinline__ bool operator()(int y, int x) const
{
return mask.ptr(y)[x] != 0;
}
PtrStepb mask;
};
struct SingleMaskChannels
{
__host__ __device__ __forceinline__ SingleMaskChannels(PtrStepb mask_, int channels_)
: mask(mask_), channels(channels_) {}
__host__ __device__ __forceinline__ SingleMaskChannels(const SingleMaskChannels& mask_)
:mask(mask_.mask), channels(mask_.channels){}
__device__ __forceinline__ bool operator()(int y, int x) const
{
return mask.ptr(y)[x / channels] != 0;
}
PtrStepb mask;
int channels;
};
struct MaskCollection
{
explicit __host__ __device__ __forceinline__ MaskCollection(PtrStepb* maskCollection_)
: maskCollection(maskCollection_) {}
__device__ __forceinline__ MaskCollection(const MaskCollection& masks_)
: maskCollection(masks_.maskCollection), curMask(masks_.curMask){}
__device__ __forceinline__ void next()
{
curMask = *maskCollection++;
}
__device__ __forceinline__ void setMask(int z)
{
curMask = maskCollection[z];
}
__device__ __forceinline__ bool operator()(int y, int x) const
{
uchar val;
return curMask.data == 0 || (ForceGlob<uchar>::Load(curMask.ptr(y), x, val), (val != 0));
}
const PtrStepb* maskCollection;
PtrStepb curMask;
};
struct WithOutMask
{
__host__ __device__ __forceinline__ WithOutMask(){}
__host__ __device__ __forceinline__ WithOutMask(const WithOutMask&){}
__device__ __forceinline__ void next() const
{
}
__device__ __forceinline__ void setMask(int) const
{
}
__device__ __forceinline__ bool operator()(int, int) const
{
return true;
}
__device__ __forceinline__ bool operator()(int, int, int) const
{
return true;
}
static __device__ __forceinline__ bool check(int, int)
{
return true;
}
static __device__ __forceinline__ bool check(int, int, int)
{
return true;
}
};
///////////////////////////////////////////////////////////////////////////////
// Solve linear system
// solve 2x2 linear system Ax=b
template <typename T> __device__ __forceinline__ bool solve2x2(const T A[2][2], const T b[2], T x[2])
{
T det = A[0][0] * A[1][1] - A[1][0] * A[0][1];
if (det != 0)
{
double invdet = 1.0 / det;
x[0] = saturate_cast<T>(invdet * (b[0] * A[1][1] - b[1] * A[0][1]));
x[1] = saturate_cast<T>(invdet * (A[0][0] * b[1] - A[1][0] * b[0]));
return true;
}
return false;
}
// solve 3x3 linear system Ax=b
template <typename T> __device__ __forceinline__ bool solve3x3(const T A[3][3], const T b[3], T x[3])
{
T det = A[0][0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1])
- A[0][1] * (A[1][0] * A[2][2] - A[1][2] * A[2][0])
+ A[0][2] * (A[1][0] * A[2][1] - A[1][1] * A[2][0]);
if (det != 0)
{
double invdet = 1.0 / det;
x[0] = saturate_cast<T>(invdet *
(b[0] * (A[1][1] * A[2][2] - A[1][2] * A[2][1]) -
A[0][1] * (b[1] * A[2][2] - A[1][2] * b[2] ) +
A[0][2] * (b[1] * A[2][1] - A[1][1] * b[2] )));
x[1] = saturate_cast<T>(invdet *
(A[0][0] * (b[1] * A[2][2] - A[1][2] * b[2] ) -
b[0] * (A[1][0] * A[2][2] - A[1][2] * A[2][0]) +
A[0][2] * (A[1][0] * b[2] - b[1] * A[2][0])));
x[2] = saturate_cast<T>(invdet *
(A[0][0] * (A[1][1] * b[2] - b[1] * A[2][1]) -
A[0][1] * (A[1][0] * b[2] - b[1] * A[2][0]) +
b[0] * (A[1][0] * A[2][1] - A[1][1] * A[2][0])));
return true;
}
return false;
}
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_UTILITY_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_CUDA_VEC_DISTANCE_HPP
#define OPENCV_CUDA_VEC_DISTANCE_HPP
#include "reduce.hpp"
#include "functional.hpp"
#include "detail/vec_distance_detail.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template <typename T> struct L1Dist
{
typedef int value_type;
typedef int result_type;
__device__ __forceinline__ L1Dist() : mySum(0) {}
__device__ __forceinline__ void reduceIter(int val1, int val2)
{
mySum = __sad(val1, val2, mySum);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
}
__device__ __forceinline__ operator int() const
{
return mySum;
}
int mySum;
};
template <> struct L1Dist<float>
{
typedef float value_type;
typedef float result_type;
__device__ __forceinline__ L1Dist() : mySum(0.0f) {}
__device__ __forceinline__ void reduceIter(float val1, float val2)
{
mySum += ::fabs(val1 - val2);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
}
__device__ __forceinline__ operator float() const
{
return mySum;
}
float mySum;
};
struct L2Dist
{
typedef float value_type;
typedef float result_type;
__device__ __forceinline__ L2Dist() : mySum(0.0f) {}
__device__ __forceinline__ void reduceIter(float val1, float val2)
{
float reg = val1 - val2;
mySum += reg * reg;
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(float* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<float>());
}
__device__ __forceinline__ operator float() const
{
return sqrtf(mySum);
}
float mySum;
};
struct HammingDist
{
typedef int value_type;
typedef int result_type;
__device__ __forceinline__ HammingDist() : mySum(0) {}
__device__ __forceinline__ void reduceIter(int val1, int val2)
{
mySum += __popc(val1 ^ val2);
}
template <int THREAD_DIM> __device__ __forceinline__ void reduceAll(int* smem, int tid)
{
reduce<THREAD_DIM>(smem, mySum, tid, plus<int>());
}
__device__ __forceinline__ operator int() const
{
return mySum;
}
int mySum;
};
// calc distance between two vectors in global memory
template <int THREAD_DIM, typename Dist, typename T1, typename T2>
__device__ void calcVecDiffGlobal(const T1* vec1, const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid)
{
for (int i = tid; i < len; i += THREAD_DIM)
{
T1 val1;
ForceGlob<T1>::Load(vec1, i, val1);
T2 val2;
ForceGlob<T2>::Load(vec2, i, val2);
dist.reduceIter(val1, val2);
}
dist.reduceAll<THREAD_DIM>(smem, tid);
}
// calc distance between two vectors, first vector is cached in register or shared memory, second vector is in global memory
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename Dist, typename T1, typename T2>
__device__ __forceinline__ void calcVecDiffCached(const T1* vecCached, const T2* vecGlob, int len, Dist& dist, typename Dist::result_type* smem, int tid)
{
vec_distance_detail::VecDiffCachedCalculator<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>::calc(vecCached, vecGlob, len, dist, tid);
dist.reduceAll<THREAD_DIM>(smem, tid);
}
// calc distance between two vectors in global memory
template <int THREAD_DIM, typename T1> struct VecDiffGlobal
{
explicit __device__ __forceinline__ VecDiffGlobal(const T1* vec1_, int = 0, void* = 0, int = 0, int = 0)
{
vec1 = vec1_;
}
template <typename T2, typename Dist>
__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
{
calcVecDiffGlobal<THREAD_DIM>(vec1, vec2, len, dist, smem, tid);
}
const T1* vec1;
};
// calc distance between two vectors, first vector is cached in register memory, second vector is in global memory
template <int THREAD_DIM, int MAX_LEN, bool LEN_EQ_MAX_LEN, typename U> struct VecDiffCachedRegister
{
template <typename T1> __device__ __forceinline__ VecDiffCachedRegister(const T1* vec1, int len, U* smem, int glob_tid, int tid)
{
if (glob_tid < len)
smem[glob_tid] = vec1[glob_tid];
__syncthreads();
U* vec1ValsPtr = vec1Vals;
#pragma unroll
for (int i = tid; i < MAX_LEN; i += THREAD_DIM)
*vec1ValsPtr++ = smem[i];
__syncthreads();
}
template <typename T2, typename Dist>
__device__ __forceinline__ void calc(const T2* vec2, int len, Dist& dist, typename Dist::result_type* smem, int tid) const
{
calcVecDiffCached<THREAD_DIM, MAX_LEN, LEN_EQ_MAX_LEN>(vec1Vals, vec2, len, dist, smem, tid);
}
U vec1Vals[MAX_LEN / THREAD_DIM];
};
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_VEC_DISTANCE_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_CUDA_VECMATH_HPP
#define OPENCV_CUDA_VECMATH_HPP
#include "vec_traits.hpp"
#include "saturate_cast.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
// saturate_cast
namespace vec_math_detail
{
template <int cn, typename VecD> struct SatCastHelper;
template <typename VecD> struct SatCastHelper<1, VecD>
{
template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
{
typedef typename VecTraits<VecD>::elem_type D;
return VecTraits<VecD>::make(saturate_cast<D>(v.x));
}
};
template <typename VecD> struct SatCastHelper<2, VecD>
{
template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
{
typedef typename VecTraits<VecD>::elem_type D;
return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y));
}
};
template <typename VecD> struct SatCastHelper<3, VecD>
{
template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
{
typedef typename VecTraits<VecD>::elem_type D;
return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y), saturate_cast<D>(v.z));
}
};
template <typename VecD> struct SatCastHelper<4, VecD>
{
template <typename VecS> static __device__ __forceinline__ VecD cast(const VecS& v)
{
typedef typename VecTraits<VecD>::elem_type D;
return VecTraits<VecD>::make(saturate_cast<D>(v.x), saturate_cast<D>(v.y), saturate_cast<D>(v.z), saturate_cast<D>(v.w));
}
};
template <typename VecD, typename VecS> static __device__ __forceinline__ VecD saturate_cast_helper(const VecS& v)
{
return SatCastHelper<VecTraits<VecD>::cn, VecD>::cast(v);
}
}
template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const char1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const short1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const uint1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const int1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const float1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const double1& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const char2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const short2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const uint2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const int2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const float2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const double2& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const char3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const short3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const uint3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const int3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const float3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const double3& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const uchar4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const char4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const ushort4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const short4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const uint4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const int4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const float4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
template<typename T> static __device__ __forceinline__ T saturate_cast(const double4& v) {return vec_math_detail::saturate_cast_helper<T>(v);}
// unary operators
#define CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(op, input_type, output_type) \
__device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a) \
{ \
return VecTraits<output_type ## 1>::make(op (a.x)); \
} \
__device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a) \
{ \
return VecTraits<output_type ## 2>::make(op (a.x), op (a.y)); \
} \
__device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a) \
{ \
return VecTraits<output_type ## 3>::make(op (a.x), op (a.y), op (a.z)); \
} \
__device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a) \
{ \
return VecTraits<output_type ## 4>::make(op (a.x), op (a.y), op (a.z), op (a.w)); \
}
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, char, char)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, short, short)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, int, int)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(-, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, char, uchar)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, ushort, uchar)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, short, uchar)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, int, uchar)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, uint, uchar)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, float, uchar)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(!, double, uchar)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, char, char)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, ushort, ushort)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, short, short)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, int, int)
CV_CUDEV_IMPLEMENT_VEC_UNARY_OP(~, uint, uint)
#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_OP
// unary functions
#define CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(func_name, func, input_type, output_type) \
__device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a) \
{ \
return VecTraits<output_type ## 1>::make(func (a.x)); \
} \
__device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a) \
{ \
return VecTraits<output_type ## 2>::make(func (a.x), func (a.y)); \
} \
__device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a) \
{ \
return VecTraits<output_type ## 3>::make(func (a.x), func (a.y), func (a.z)); \
} \
__device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a) \
{ \
return VecTraits<output_type ## 4>::make(func (a.x), func (a.y), func (a.z), func (a.w)); \
}
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(abs, ::fabsf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrtf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sqrt, ::sqrt, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::expf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp, ::exp, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2f, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp2, ::exp2, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10f, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(exp10, ::exp10, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::logf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log, ::log, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2f, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log2, ::log2, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10f, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(log10, ::log10, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sinf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sin, ::sin, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cosf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cos, ::cos, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tanf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tan, ::tan, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asinf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asin, ::asin, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acosf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acos, ::acos, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atanf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atan, ::atan, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinhf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(sinh, ::sinh, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::coshf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(cosh, ::cosh, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanhf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(tanh, ::tanh, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinhf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(asinh, ::asinh, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acoshf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(acosh, ::acosh, double, double)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, char, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, short, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, int, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanhf, float, float)
CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC(atanh, ::atanh, double, double)
#undef CV_CUDEV_IMPLEMENT_VEC_UNARY_FUNC
// binary operators (vec & vec)
#define CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(op, input_type, output_type) \
__device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, const input_type ## 1 & b) \
{ \
return VecTraits<output_type ## 1>::make(a.x op b.x); \
} \
__device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, const input_type ## 2 & b) \
{ \
return VecTraits<output_type ## 2>::make(a.x op b.x, a.y op b.y); \
} \
__device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, const input_type ## 3 & b) \
{ \
return VecTraits<output_type ## 3>::make(a.x op b.x, a.y op b.y, a.z op b.z); \
} \
__device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, const input_type ## 4 & b) \
{ \
return VecTraits<output_type ## 4>::make(a.x op b.x, a.y op b.y, a.z op b.z, a.w op b.w); \
}
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uchar, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, char, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, ushort, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, short, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, int, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, uint, uint)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, float, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(+, double, double)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uchar, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, char, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, ushort, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, short, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, int, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, uint, uint)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, float, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(-, double, double)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uchar, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, char, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, ushort, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, short, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, int, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, uint, uint)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, float, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(*, double, double)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uchar, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, char, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, ushort, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, short, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, int, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, uint, uint)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, float, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(/, double, double)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, char, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, ushort, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, short, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, int, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, uint, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, float, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(==, double, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, char, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, ushort, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, short, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, int, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, uint, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, float, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(!=, double, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, char, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, ushort, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, short, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, int, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, uint, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, float, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>, double, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, char, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, ushort, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, short, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, int, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, uint, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, float, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<, double, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, char, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, ushort, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, short, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, int, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, uint, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, float, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(>=, double, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, char, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, ushort, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, short, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, int, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, uint, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, float, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(<=, double, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, char, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, ushort, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, short, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, int, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, uint, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, float, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&&, double, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, char, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, ushort, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, short, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, int, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, uint, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, float, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(||, double, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, char, char)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, ushort, ushort)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, short, short)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, int, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(&, uint, uint)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, char, char)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, ushort, ushort)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, short, short)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, int, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(|, uint, uint)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, char, char)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, ushort, ushort)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, short, short)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, int, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_OP(^, uint, uint)
#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_OP
// binary operators (vec & scalar)
#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(op, input_type, scalar_type, output_type) \
__device__ __forceinline__ output_type ## 1 operator op(const input_type ## 1 & a, scalar_type s) \
{ \
return VecTraits<output_type ## 1>::make(a.x op s); \
} \
__device__ __forceinline__ output_type ## 1 operator op(scalar_type s, const input_type ## 1 & b) \
{ \
return VecTraits<output_type ## 1>::make(s op b.x); \
} \
__device__ __forceinline__ output_type ## 2 operator op(const input_type ## 2 & a, scalar_type s) \
{ \
return VecTraits<output_type ## 2>::make(a.x op s, a.y op s); \
} \
__device__ __forceinline__ output_type ## 2 operator op(scalar_type s, const input_type ## 2 & b) \
{ \
return VecTraits<output_type ## 2>::make(s op b.x, s op b.y); \
} \
__device__ __forceinline__ output_type ## 3 operator op(const input_type ## 3 & a, scalar_type s) \
{ \
return VecTraits<output_type ## 3>::make(a.x op s, a.y op s, a.z op s); \
} \
__device__ __forceinline__ output_type ## 3 operator op(scalar_type s, const input_type ## 3 & b) \
{ \
return VecTraits<output_type ## 3>::make(s op b.x, s op b.y, s op b.z); \
} \
__device__ __forceinline__ output_type ## 4 operator op(const input_type ## 4 & a, scalar_type s) \
{ \
return VecTraits<output_type ## 4>::make(a.x op s, a.y op s, a.z op s, a.w op s); \
} \
__device__ __forceinline__ output_type ## 4 operator op(scalar_type s, const input_type ## 4 & b) \
{ \
return VecTraits<output_type ## 4>::make(s op b.x, s op b.y, s op b.z, s op b.w); \
}
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uchar, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, char, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, ushort, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, short, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, int, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, uint, uint)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, uint, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, float, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(+, double, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uchar, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, char, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, ushort, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, short, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, int, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, uint, uint)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, uint, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, float, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(-, double, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uchar, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, char, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, ushort, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, short, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, int, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, uint, uint)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, uint, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, float, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(*, double, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uchar, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, char, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, ushort, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, short, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, int, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, uint, uint)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, uint, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, float, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(/, double, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, char, char, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, ushort, ushort, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, short, short, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, int, int, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, uint, uint, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, float, float, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(==, double, double, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, char, char, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, ushort, ushort, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, short, short, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, int, int, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, uint, uint, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, float, float, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(!=, double, double, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, char, char, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, ushort, ushort, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, short, short, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, int, int, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, uint, uint, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, float, float, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>, double, double, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, char, char, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, ushort, ushort, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, short, short, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, int, int, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, uint, uint, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, float, float, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<, double, double, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, char, char, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, ushort, ushort, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, short, short, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, int, int, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, uint, uint, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, float, float, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(>=, double, double, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, char, char, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, ushort, ushort, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, short, short, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, int, int, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, uint, uint, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, float, float, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(<=, double, double, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, char, char, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, ushort, ushort, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, short, short, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, int, int, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, uint, uint, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, float, float, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&&, double, double, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, char, char, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, ushort, ushort, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, short, short, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, int, int, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, uint, uint, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, float, float, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(||, double, double, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, char, char, char)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, ushort, ushort, ushort)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, short, short, short)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, int, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(&, uint, uint, uint)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, char, char, char)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, ushort, ushort, ushort)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, short, short, short)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, int, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(|, uint, uint, uint)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, char, char, char)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, ushort, ushort, ushort)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, short, short, short)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, int, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP(^, uint, uint, uint)
#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_OP
// binary function (vec & vec)
#define CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(func_name, func, input_type, output_type) \
__device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, const input_type ## 1 & b) \
{ \
return VecTraits<output_type ## 1>::make(func (a.x, b.x)); \
} \
__device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, const input_type ## 2 & b) \
{ \
return VecTraits<output_type ## 2>::make(func (a.x, b.x), func (a.y, b.y)); \
} \
__device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, const input_type ## 3 & b) \
{ \
return VecTraits<output_type ## 3>::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z)); \
} \
__device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, const input_type ## 4 & b) \
{ \
return VecTraits<output_type ## 4>::make(func (a.x, b.x), func (a.y, b.y), func (a.z, b.z), func (a.w, b.w)); \
}
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, char, char)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, ushort, ushort)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, short, short)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, uint, uint)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::max, int, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmaxf, float, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(max, ::fmax, double, double)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uchar, uchar)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, char, char)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, ushort, ushort)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, short, short)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, uint, uint)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::min, int, int)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fminf, float, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(min, ::fmin, double, double)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, char, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, short, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, uint, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, int, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypotf, float, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(hypot, ::hypot, double, double)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uchar, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, char, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, ushort, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, short, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, uint, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, int, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2f, float, float)
CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC(atan2, ::atan2, double, double)
#undef CV_CUDEV_IMPLEMENT_VEC_BINARY_FUNC
// binary function (vec & scalar)
#define CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(func_name, func, input_type, scalar_type, output_type) \
__device__ __forceinline__ output_type ## 1 func_name(const input_type ## 1 & a, scalar_type s) \
{ \
return VecTraits<output_type ## 1>::make(func ((output_type) a.x, (output_type) s)); \
} \
__device__ __forceinline__ output_type ## 1 func_name(scalar_type s, const input_type ## 1 & b) \
{ \
return VecTraits<output_type ## 1>::make(func ((output_type) s, (output_type) b.x)); \
} \
__device__ __forceinline__ output_type ## 2 func_name(const input_type ## 2 & a, scalar_type s) \
{ \
return VecTraits<output_type ## 2>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s)); \
} \
__device__ __forceinline__ output_type ## 2 func_name(scalar_type s, const input_type ## 2 & b) \
{ \
return VecTraits<output_type ## 2>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y)); \
} \
__device__ __forceinline__ output_type ## 3 func_name(const input_type ## 3 & a, scalar_type s) \
{ \
return VecTraits<output_type ## 3>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s)); \
} \
__device__ __forceinline__ output_type ## 3 func_name(scalar_type s, const input_type ## 3 & b) \
{ \
return VecTraits<output_type ## 3>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z)); \
} \
__device__ __forceinline__ output_type ## 4 func_name(const input_type ## 4 & a, scalar_type s) \
{ \
return VecTraits<output_type ## 4>::make(func ((output_type) a.x, (output_type) s), func ((output_type) a.y, (output_type) s), func ((output_type) a.z, (output_type) s), func ((output_type) a.w, (output_type) s)); \
} \
__device__ __forceinline__ output_type ## 4 func_name(scalar_type s, const input_type ## 4 & b) \
{ \
return VecTraits<output_type ## 4>::make(func ((output_type) s, (output_type) b.x), func ((output_type) s, (output_type) b.y), func ((output_type) s, (output_type) b.z), func ((output_type) s, (output_type) b.w)); \
}
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uchar, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uchar, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, char, char, char)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, char, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, char, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, ushort, ushort, ushort)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, ushort, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, ushort, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, short, short, short)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, short, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, short, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, uint, uint, uint)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, uint, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, uint, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::max, int, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, int, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, int, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmaxf, float, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, float, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(max, ::fmax, double, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uchar, uchar, uchar)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uchar, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uchar, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, char, char, char)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, char, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, char, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, ushort, ushort, ushort)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, ushort, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, ushort, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, short, short, short)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, short, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, short, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, uint, uint, uint)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, uint, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, uint, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::min, int, int, int)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, int, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, int, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fminf, float, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, float, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(min, ::fmin, double, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uchar, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uchar, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, char, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, char, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, ushort, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, ushort, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, short, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, short, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, uint, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, uint, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, int, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, int, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypotf, float, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, float, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(hypot, ::hypot, double, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uchar, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uchar, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, char, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, char, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, ushort, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, ushort, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, short, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, short, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, uint, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, uint, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, int, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, int, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2f, float, float, float)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, float, double, double)
CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC(atan2, ::atan2, double, double, double)
#undef CV_CUDEV_IMPLEMENT_SCALAR_BINARY_FUNC
}}} // namespace cv { namespace cuda { namespace device
//! @endcond
#endif // OPENCV_CUDA_VECMATH_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_CUDA_VEC_TRAITS_HPP
#define OPENCV_CUDA_VEC_TRAITS_HPP
#include "common.hpp"
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template<typename T, int N> struct TypeVec;
struct __align__(8) uchar8
{
uchar a0, a1, a2, a3, a4, a5, a6, a7;
};
static __host__ __device__ __forceinline__ uchar8 make_uchar8(uchar a0, uchar a1, uchar a2, uchar a3, uchar a4, uchar a5, uchar a6, uchar a7)
{
uchar8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
return val;
}
struct __align__(8) char8
{
schar a0, a1, a2, a3, a4, a5, a6, a7;
};
static __host__ __device__ __forceinline__ char8 make_char8(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7)
{
char8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
return val;
}
struct __align__(16) ushort8
{
ushort a0, a1, a2, a3, a4, a5, a6, a7;
};
static __host__ __device__ __forceinline__ ushort8 make_ushort8(ushort a0, ushort a1, ushort a2, ushort a3, ushort a4, ushort a5, ushort a6, ushort a7)
{
ushort8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
return val;
}
struct __align__(16) short8
{
short a0, a1, a2, a3, a4, a5, a6, a7;
};
static __host__ __device__ __forceinline__ short8 make_short8(short a0, short a1, short a2, short a3, short a4, short a5, short a6, short a7)
{
short8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
return val;
}
struct __align__(32) uint8
{
uint a0, a1, a2, a3, a4, a5, a6, a7;
};
static __host__ __device__ __forceinline__ uint8 make_uint8(uint a0, uint a1, uint a2, uint a3, uint a4, uint a5, uint a6, uint a7)
{
uint8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
return val;
}
struct __align__(32) int8
{
int a0, a1, a2, a3, a4, a5, a6, a7;
};
static __host__ __device__ __forceinline__ int8 make_int8(int a0, int a1, int a2, int a3, int a4, int a5, int a6, int a7)
{
int8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
return val;
}
struct __align__(32) float8
{
float a0, a1, a2, a3, a4, a5, a6, a7;
};
static __host__ __device__ __forceinline__ float8 make_float8(float a0, float a1, float a2, float a3, float a4, float a5, float a6, float a7)
{
float8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
return val;
}
struct double8
{
double a0, a1, a2, a3, a4, a5, a6, a7;
};
static __host__ __device__ __forceinline__ double8 make_double8(double a0, double a1, double a2, double a3, double a4, double a5, double a6, double a7)
{
double8 val = {a0, a1, a2, a3, a4, a5, a6, a7};
return val;
}
#define OPENCV_CUDA_IMPLEMENT_TYPE_VEC(type) \
template<> struct TypeVec<type, 1> { typedef type vec_type; }; \
template<> struct TypeVec<type ## 1, 1> { typedef type ## 1 vec_type; }; \
template<> struct TypeVec<type, 2> { typedef type ## 2 vec_type; }; \
template<> struct TypeVec<type ## 2, 2> { typedef type ## 2 vec_type; }; \
template<> struct TypeVec<type, 3> { typedef type ## 3 vec_type; }; \
template<> struct TypeVec<type ## 3, 3> { typedef type ## 3 vec_type; }; \
template<> struct TypeVec<type, 4> { typedef type ## 4 vec_type; }; \
template<> struct TypeVec<type ## 4, 4> { typedef type ## 4 vec_type; }; \
template<> struct TypeVec<type, 8> { typedef type ## 8 vec_type; }; \
template<> struct TypeVec<type ## 8, 8> { typedef type ## 8 vec_type; };
OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uchar)
OPENCV_CUDA_IMPLEMENT_TYPE_VEC(char)
OPENCV_CUDA_IMPLEMENT_TYPE_VEC(ushort)
OPENCV_CUDA_IMPLEMENT_TYPE_VEC(short)
OPENCV_CUDA_IMPLEMENT_TYPE_VEC(int)
OPENCV_CUDA_IMPLEMENT_TYPE_VEC(uint)
OPENCV_CUDA_IMPLEMENT_TYPE_VEC(float)
OPENCV_CUDA_IMPLEMENT_TYPE_VEC(double)
#undef OPENCV_CUDA_IMPLEMENT_TYPE_VEC
template<> struct TypeVec<schar, 1> { typedef schar vec_type; };
template<> struct TypeVec<schar, 2> { typedef char2 vec_type; };
template<> struct TypeVec<schar, 3> { typedef char3 vec_type; };
template<> struct TypeVec<schar, 4> { typedef char4 vec_type; };
template<> struct TypeVec<schar, 8> { typedef char8 vec_type; };
template<> struct TypeVec<bool, 1> { typedef uchar vec_type; };
template<> struct TypeVec<bool, 2> { typedef uchar2 vec_type; };
template<> struct TypeVec<bool, 3> { typedef uchar3 vec_type; };
template<> struct TypeVec<bool, 4> { typedef uchar4 vec_type; };
template<> struct TypeVec<bool, 8> { typedef uchar8 vec_type; };
template<typename T> struct VecTraits;
#define OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(type) \
template<> struct VecTraits<type> \
{ \
typedef type elem_type; \
enum {cn=1}; \
static __device__ __host__ __forceinline__ type all(type v) {return v;} \
static __device__ __host__ __forceinline__ type make(type x) {return x;} \
static __device__ __host__ __forceinline__ type make(const type* v) {return *v;} \
}; \
template<> struct VecTraits<type ## 1> \
{ \
typedef type elem_type; \
enum {cn=1}; \
static __device__ __host__ __forceinline__ type ## 1 all(type v) {return make_ ## type ## 1(v);} \
static __device__ __host__ __forceinline__ type ## 1 make(type x) {return make_ ## type ## 1(x);} \
static __device__ __host__ __forceinline__ type ## 1 make(const type* v) {return make_ ## type ## 1(*v);} \
}; \
template<> struct VecTraits<type ## 2> \
{ \
typedef type elem_type; \
enum {cn=2}; \
static __device__ __host__ __forceinline__ type ## 2 all(type v) {return make_ ## type ## 2(v, v);} \
static __device__ __host__ __forceinline__ type ## 2 make(type x, type y) {return make_ ## type ## 2(x, y);} \
static __device__ __host__ __forceinline__ type ## 2 make(const type* v) {return make_ ## type ## 2(v[0], v[1]);} \
}; \
template<> struct VecTraits<type ## 3> \
{ \
typedef type elem_type; \
enum {cn=3}; \
static __device__ __host__ __forceinline__ type ## 3 all(type v) {return make_ ## type ## 3(v, v, v);} \
static __device__ __host__ __forceinline__ type ## 3 make(type x, type y, type z) {return make_ ## type ## 3(x, y, z);} \
static __device__ __host__ __forceinline__ type ## 3 make(const type* v) {return make_ ## type ## 3(v[0], v[1], v[2]);} \
}; \
template<> struct VecTraits<type ## 4> \
{ \
typedef type elem_type; \
enum {cn=4}; \
static __device__ __host__ __forceinline__ type ## 4 all(type v) {return make_ ## type ## 4(v, v, v, v);} \
static __device__ __host__ __forceinline__ type ## 4 make(type x, type y, type z, type w) {return make_ ## type ## 4(x, y, z, w);} \
static __device__ __host__ __forceinline__ type ## 4 make(const type* v) {return make_ ## type ## 4(v[0], v[1], v[2], v[3]);} \
}; \
template<> struct VecTraits<type ## 8> \
{ \
typedef type elem_type; \
enum {cn=8}; \
static __device__ __host__ __forceinline__ type ## 8 all(type v) {return make_ ## type ## 8(v, v, v, v, v, v, v, v);} \
static __device__ __host__ __forceinline__ type ## 8 make(type a0, type a1, type a2, type a3, type a4, type a5, type a6, type a7) {return make_ ## type ## 8(a0, a1, a2, a3, a4, a5, a6, a7);} \
static __device__ __host__ __forceinline__ type ## 8 make(const type* v) {return make_ ## type ## 8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);} \
};
OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uchar)
OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(ushort)
OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(short)
OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(int)
OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(uint)
OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(float)
OPENCV_CUDA_IMPLEMENT_VEC_TRAITS(double)
#undef OPENCV_CUDA_IMPLEMENT_VEC_TRAITS
template<> struct VecTraits<char>
{
typedef char elem_type;
enum {cn=1};
static __device__ __host__ __forceinline__ char all(char v) {return v;}
static __device__ __host__ __forceinline__ char make(char x) {return x;}
static __device__ __host__ __forceinline__ char make(const char* x) {return *x;}
};
template<> struct VecTraits<schar>
{
typedef schar elem_type;
enum {cn=1};
static __device__ __host__ __forceinline__ schar all(schar v) {return v;}
static __device__ __host__ __forceinline__ schar make(schar x) {return x;}
static __device__ __host__ __forceinline__ schar make(const schar* x) {return *x;}
};
template<> struct VecTraits<char1>
{
typedef schar elem_type;
enum {cn=1};
static __device__ __host__ __forceinline__ char1 all(schar v) {return make_char1(v);}
static __device__ __host__ __forceinline__ char1 make(schar x) {return make_char1(x);}
static __device__ __host__ __forceinline__ char1 make(const schar* v) {return make_char1(v[0]);}
};
template<> struct VecTraits<char2>
{
typedef schar elem_type;
enum {cn=2};
static __device__ __host__ __forceinline__ char2 all(schar v) {return make_char2(v, v);}
static __device__ __host__ __forceinline__ char2 make(schar x, schar y) {return make_char2(x, y);}
static __device__ __host__ __forceinline__ char2 make(const schar* v) {return make_char2(v[0], v[1]);}
};
template<> struct VecTraits<char3>
{
typedef schar elem_type;
enum {cn=3};
static __device__ __host__ __forceinline__ char3 all(schar v) {return make_char3(v, v, v);}
static __device__ __host__ __forceinline__ char3 make(schar x, schar y, schar z) {return make_char3(x, y, z);}
static __device__ __host__ __forceinline__ char3 make(const schar* v) {return make_char3(v[0], v[1], v[2]);}
};
template<> struct VecTraits<char4>
{
typedef schar elem_type;
enum {cn=4};
static __device__ __host__ __forceinline__ char4 all(schar v) {return make_char4(v, v, v, v);}
static __device__ __host__ __forceinline__ char4 make(schar x, schar y, schar z, schar w) {return make_char4(x, y, z, w);}
static __device__ __host__ __forceinline__ char4 make(const schar* v) {return make_char4(v[0], v[1], v[2], v[3]);}
};
template<> struct VecTraits<char8>
{
typedef schar elem_type;
enum {cn=8};
static __device__ __host__ __forceinline__ char8 all(schar v) {return make_char8(v, v, v, v, v, v, v, v);}
static __device__ __host__ __forceinline__ char8 make(schar a0, schar a1, schar a2, schar a3, schar a4, schar a5, schar a6, schar a7) {return make_char8(a0, a1, a2, a3, a4, a5, a6, a7);}
static __device__ __host__ __forceinline__ char8 make(const schar* v) {return make_char8(v[0], v[1], v[2], v[3], v[4], v[5], v[6], v[7]);}
};
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif // OPENCV_CUDA_VEC_TRAITS_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_CUDA_DEVICE_WARP_HPP
#define OPENCV_CUDA_DEVICE_WARP_HPP
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
struct Warp
{
enum
{
LOG_WARP_SIZE = 5,
WARP_SIZE = 1 << LOG_WARP_SIZE,
STRIDE = WARP_SIZE
};
/** \brief Returns the warp lane ID of the calling thread. */
static __device__ __forceinline__ unsigned int laneId()
{
unsigned int ret;
asm("mov.u32 %0, %%laneid;" : "=r"(ret) );
return ret;
}
template<typename It, typename T>
static __device__ __forceinline__ void fill(It beg, It end, const T& value)
{
for(It t = beg + laneId(); t < end; t += STRIDE)
*t = value;
}
template<typename InIt, typename OutIt>
static __device__ __forceinline__ OutIt copy(InIt beg, InIt end, OutIt out)
{
for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE)
*out = *t;
return out;
}
template<typename InIt, typename OutIt, class UnOp>
static __device__ __forceinline__ OutIt transform(InIt beg, InIt end, OutIt out, UnOp op)
{
for(InIt t = beg + laneId(); t < end; t += STRIDE, out += STRIDE)
*out = op(*t);
return out;
}
template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
static __device__ __forceinline__ OutIt transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
{
unsigned int lane = laneId();
InIt1 t1 = beg1 + lane;
InIt2 t2 = beg2 + lane;
for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, out += STRIDE)
*out = op(*t1, *t2);
return out;
}
template <class T, class BinOp>
static __device__ __forceinline__ T reduce(volatile T *ptr, BinOp op)
{
const unsigned int lane = laneId();
if (lane < 16)
{
T partial = ptr[lane];
ptr[lane] = partial = op(partial, ptr[lane + 16]);
ptr[lane] = partial = op(partial, ptr[lane + 8]);
ptr[lane] = partial = op(partial, ptr[lane + 4]);
ptr[lane] = partial = op(partial, ptr[lane + 2]);
ptr[lane] = partial = op(partial, ptr[lane + 1]);
}
return *ptr;
}
template<typename OutIt, typename T>
static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
{
unsigned int lane = laneId();
value += lane;
for(OutIt t = beg + lane; t < end; t += STRIDE, value += STRIDE)
*t = value;
}
};
}}} // namespace cv { namespace cuda { namespace cudev
//! @endcond
#endif /* OPENCV_CUDA_DEVICE_WARP_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_CUDA_WARP_REDUCE_HPP__
#define OPENCV_CUDA_WARP_REDUCE_HPP__
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
template <class T>
__device__ __forceinline__ T warp_reduce(volatile T *ptr , const unsigned int tid = threadIdx.x)
{
const unsigned int lane = tid & 31; // index of thread in warp (0..31)
if (lane < 16)
{
T partial = ptr[tid];
ptr[tid] = partial = partial + ptr[tid + 16];
ptr[tid] = partial = partial + ptr[tid + 8];
ptr[tid] = partial = partial + ptr[tid + 4];
ptr[tid] = partial = partial + ptr[tid + 2];
ptr[tid] = partial = partial + ptr[tid + 1];
}
return ptr[tid - lane];
}
}}} // namespace cv { namespace cuda { namespace cudev {
//! @endcond
#endif /* OPENCV_CUDA_WARP_REDUCE_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_CUDA_WARP_SHUFFLE_HPP
#define OPENCV_CUDA_WARP_SHUFFLE_HPP
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
namespace cv { namespace cuda { namespace device
{
#if __CUDACC_VER_MAJOR__ >= 9
# define __shfl(x, y, z) __shfl_sync(0xFFFFFFFFU, x, y, z)
# define __shfl_up(x, y, z) __shfl_up_sync(0xFFFFFFFFU, x, y, z)
# define __shfl_down(x, y, z) __shfl_down_sync(0xFFFFFFFFU, x, y, z)
#endif
template <typename T>
__device__ __forceinline__ T shfl(T val, int srcLane, int width = warpSize)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
return __shfl(val, srcLane, width);
#else
return T();
#endif
}
__device__ __forceinline__ unsigned int shfl(unsigned int val, int srcLane, int width = warpSize)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
return (unsigned int) __shfl((int) val, srcLane, width);
#else
return 0;
#endif
}
__device__ __forceinline__ double shfl(double val, int srcLane, int width = warpSize)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
int lo = __double2loint(val);
int hi = __double2hiint(val);
lo = __shfl(lo, srcLane, width);
hi = __shfl(hi, srcLane, width);
return __hiloint2double(hi, lo);
#else
return 0.0;
#endif
}
template <typename T>
__device__ __forceinline__ T shfl_down(T val, unsigned int delta, int width = warpSize)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
return __shfl_down(val, delta, width);
#else
return T();
#endif
}
__device__ __forceinline__ unsigned int shfl_down(unsigned int val, unsigned int delta, int width = warpSize)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
return (unsigned int) __shfl_down((int) val, delta, width);
#else
return 0;
#endif
}
__device__ __forceinline__ double shfl_down(double val, unsigned int delta, int width = warpSize)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
int lo = __double2loint(val);
int hi = __double2hiint(val);
lo = __shfl_down(lo, delta, width);
hi = __shfl_down(hi, delta, width);
return __hiloint2double(hi, lo);
#else
return 0.0;
#endif
}
template <typename T>
__device__ __forceinline__ T shfl_up(T val, unsigned int delta, int width = warpSize)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
return __shfl_up(val, delta, width);
#else
return T();
#endif
}
__device__ __forceinline__ unsigned int shfl_up(unsigned int val, unsigned int delta, int width = warpSize)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
return (unsigned int) __shfl_up((int) val, delta, width);
#else
return 0;
#endif
}
__device__ __forceinline__ double shfl_up(double val, unsigned int delta, int width = warpSize)
{
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 300
int lo = __double2loint(val);
int hi = __double2hiint(val);
lo = __shfl_up(lo, delta, width);
hi = __shfl_up(hi, delta, width);
return __hiloint2double(hi, lo);
#else
return 0.0;
#endif
}
}}}
# undef __shfl
# undef __shfl_up
# undef __shfl_down
//! @endcond
#endif // OPENCV_CUDA_WARP_SHUFFLE_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_CORE_CUDA_STREAM_ACCESSOR_HPP
#define OPENCV_CORE_CUDA_STREAM_ACCESSOR_HPP
#ifndef __cplusplus
# error cuda_stream_accessor.hpp header must be compiled as C++
#endif
/** @file cuda_stream_accessor.hpp
* This is only header file that depends on CUDA Runtime API. All other headers are independent.
*/
#include <cuda_runtime.h>
#include "opencv2/core/cuda.hpp"
namespace cv
{
namespace cuda
{
//! @addtogroup cudacore_struct
//! @{
/** @brief Class that enables getting cudaStream_t from cuda::Stream
*/
struct StreamAccessor
{
CV_EXPORTS static cudaStream_t getStream(const Stream& stream);
CV_EXPORTS static Stream wrapStream(cudaStream_t stream);
};
/** @brief Class that enables getting cudaEvent_t from cuda::Event
*/
struct EventAccessor
{
CV_EXPORTS static cudaEvent_t getEvent(const Event& event);
CV_EXPORTS static Event wrapEvent(cudaEvent_t event);
};
//! @}
}
}
#endif /* OPENCV_CORE_CUDA_STREAM_ACCESSOR_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_CORE_CUDA_TYPES_HPP
#define OPENCV_CORE_CUDA_TYPES_HPP
#ifndef __cplusplus
# error cuda_types.hpp header must be compiled as C++
#endif
#if defined(__OPENCV_BUILD) && defined(__clang__)
#pragma clang diagnostic ignored "-Winconsistent-missing-override"
#endif
#if defined(__OPENCV_BUILD) && defined(__GNUC__) && __GNUC__ >= 5
#pragma GCC diagnostic ignored "-Wsuggest-override"
#endif
/** @file
* @deprecated Use @ref cudev instead.
*/
//! @cond IGNORED
#ifdef __CUDACC__
#define __CV_CUDA_HOST_DEVICE__ __host__ __device__ __forceinline__
#else
#define __CV_CUDA_HOST_DEVICE__
#endif
namespace cv
{
namespace cuda
{
// Simple lightweight structures that encapsulates information about an image on device.
// It is intended to pass to nvcc-compiled code. GpuMat depends on headers that nvcc can't compile
template <typename T> struct DevPtr
{
typedef T elem_type;
typedef int index_type;
enum { elem_size = sizeof(elem_type) };
T* data;
__CV_CUDA_HOST_DEVICE__ DevPtr() : data(0) {}
__CV_CUDA_HOST_DEVICE__ DevPtr(T* data_) : data(data_) {}
__CV_CUDA_HOST_DEVICE__ size_t elemSize() const { return elem_size; }
__CV_CUDA_HOST_DEVICE__ operator T*() { return data; }
__CV_CUDA_HOST_DEVICE__ operator const T*() const { return data; }
};
template <typename T> struct PtrSz : public DevPtr<T>
{
__CV_CUDA_HOST_DEVICE__ PtrSz() : size(0) {}
__CV_CUDA_HOST_DEVICE__ PtrSz(T* data_, size_t size_) : DevPtr<T>(data_), size(size_) {}
size_t size;
};
template <typename T> struct PtrStep : public DevPtr<T>
{
__CV_CUDA_HOST_DEVICE__ PtrStep() : step(0) {}
__CV_CUDA_HOST_DEVICE__ PtrStep(T* data_, size_t step_) : DevPtr<T>(data_), step(step_) {}
size_t step;
__CV_CUDA_HOST_DEVICE__ T* ptr(int y = 0) { return ( T*)( ( char*)DevPtr<T>::data + y * step); }
__CV_CUDA_HOST_DEVICE__ const T* ptr(int y = 0) const { return (const T*)( (const char*)DevPtr<T>::data + y * step); }
__CV_CUDA_HOST_DEVICE__ T& operator ()(int y, int x) { return ptr(y)[x]; }
__CV_CUDA_HOST_DEVICE__ const T& operator ()(int y, int x) const { return ptr(y)[x]; }
};
template <typename T> struct PtrStepSz : public PtrStep<T>
{
__CV_CUDA_HOST_DEVICE__ PtrStepSz() : cols(0), rows(0) {}
__CV_CUDA_HOST_DEVICE__ PtrStepSz(int rows_, int cols_, T* data_, size_t step_)
: PtrStep<T>(data_, step_), cols(cols_), rows(rows_) {}
template <typename U>
explicit PtrStepSz(const PtrStepSz<U>& d) : PtrStep<T>((T*)d.data, d.step), cols(d.cols), rows(d.rows){}
int cols;
int rows;
};
typedef PtrStepSz<unsigned char> PtrStepSzb;
typedef PtrStepSz<unsigned short> PtrStepSzus;
typedef PtrStepSz<float> PtrStepSzf;
typedef PtrStepSz<int> PtrStepSzi;
typedef PtrStep<unsigned char> PtrStepb;
typedef PtrStep<unsigned short> PtrStepus;
typedef PtrStep<float> PtrStepf;
typedef PtrStep<int> PtrStepi;
}
}
//! @endcond
#endif /* OPENCV_CORE_CUDA_TYPES_HPP */

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#if defined __OPENCV_BUILD \
#include "cv_cpu_config.h"
#include "cv_cpu_helper.h"
#ifdef CV_CPU_DISPATCH_MODE
#define CV_CPU_OPTIMIZATION_NAMESPACE __CV_CAT(opt_, CV_CPU_DISPATCH_MODE)
#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace __CV_CAT(opt_, CV_CPU_DISPATCH_MODE) {
#define CV_CPU_OPTIMIZATION_NAMESPACE_END }
#else
#define CV_CPU_OPTIMIZATION_NAMESPACE cpu_baseline
#define CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN namespace cpu_baseline {
#define CV_CPU_OPTIMIZATION_NAMESPACE_END }
#define CV_CPU_BASELINE_MODE 1
#endif
#define __CV_CPU_DISPATCH_CHAIN_END(fn, args, mode, ...) /* done */
#define __CV_CPU_DISPATCH(fn, args, mode, ...) __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#define __CV_CPU_DISPATCH_EXPAND(fn, args, ...) __CV_EXPAND(__CV_CPU_DISPATCH(fn, args, __VA_ARGS__))
#define CV_CPU_DISPATCH(fn, args, ...) __CV_CPU_DISPATCH_EXPAND(fn, args, __VA_ARGS__, END) // expand macros
#if defined CV_ENABLE_INTRINSICS \
&& !defined CV_DISABLE_OPTIMIZATION \
&& !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */ \
#ifdef CV_CPU_COMPILE_SSE2
# include <emmintrin.h>
# define CV_MMX 1
# define CV_SSE 1
# define CV_SSE2 1
#endif
#ifdef CV_CPU_COMPILE_SSE3
# include <pmmintrin.h>
# define CV_SSE3 1
#endif
#ifdef CV_CPU_COMPILE_SSSE3
# include <tmmintrin.h>
# define CV_SSSE3 1
#endif
#ifdef CV_CPU_COMPILE_SSE4_1
# include <smmintrin.h>
# define CV_SSE4_1 1
#endif
#ifdef CV_CPU_COMPILE_SSE4_2
# include <nmmintrin.h>
# define CV_SSE4_2 1
#endif
#ifdef CV_CPU_COMPILE_POPCNT
# ifdef _MSC_VER
# include <nmmintrin.h>
# if defined(_M_X64)
# define CV_POPCNT_U64 _mm_popcnt_u64
# endif
# define CV_POPCNT_U32 _mm_popcnt_u32
# else
# include <popcntintrin.h>
# if defined(__x86_64__)
# define CV_POPCNT_U64 __builtin_popcountll
# endif
# define CV_POPCNT_U32 __builtin_popcount
# endif
# define CV_POPCNT 1
#endif
#ifdef CV_CPU_COMPILE_AVX
# include <immintrin.h>
# define CV_AVX 1
#endif
#ifdef CV_CPU_COMPILE_FP16
# if defined(__arm__) || defined(__aarch64__) || defined(_M_ARM) || defined(_M_ARM64)
# include <arm_neon.h>
# else
# include <immintrin.h>
# endif
# define CV_FP16 1
#endif
#ifdef CV_CPU_COMPILE_AVX2
# include <immintrin.h>
# define CV_AVX2 1
#endif
#ifdef CV_CPU_COMPILE_AVX_512F
# include <immintrin.h>
# define CV_AVX_512F 1
#endif
#ifdef CV_CPU_COMPILE_AVX512_COMMON
# define CV_AVX512_COMMON 1
# define CV_AVX_512CD 1
#endif
#ifdef CV_CPU_COMPILE_AVX512_KNL
# define CV_AVX512_KNL 1
# define CV_AVX_512ER 1
# define CV_AVX_512PF 1
#endif
#ifdef CV_CPU_COMPILE_AVX512_KNM
# define CV_AVX512_KNM 1
# define CV_AVX_5124FMAPS 1
# define CV_AVX_5124VNNIW 1
# define CV_AVX_512VPOPCNTDQ 1
#endif
#ifdef CV_CPU_COMPILE_AVX512_SKX
# define CV_AVX512_SKX 1
# define CV_AVX_512VL 1
# define CV_AVX_512BW 1
# define CV_AVX_512DQ 1
#endif
#ifdef CV_CPU_COMPILE_AVX512_CNL
# define CV_AVX512_CNL 1
# define CV_AVX_512IFMA 1
# define CV_AVX_512VBMI 1
#endif
#ifdef CV_CPU_COMPILE_AVX512_CLX
# define CV_AVX512_CLX 1
# define CV_AVX_512VNNI 1
#endif
#ifdef CV_CPU_COMPILE_AVX512_ICL
# define CV_AVX512_ICL 1
# undef CV_AVX_512IFMA
# define CV_AVX_512IFMA 1
# undef CV_AVX_512VBMI
# define CV_AVX_512VBMI 1
# undef CV_AVX_512VNNI
# define CV_AVX_512VNNI 1
# define CV_AVX_512VBMI2 1
# define CV_AVX_512BITALG 1
# define CV_AVX_512VPOPCNTDQ 1
#endif
#ifdef CV_CPU_COMPILE_FMA3
# define CV_FMA3 1
#endif
#if defined _WIN32 && (defined(_M_ARM) || defined(_M_ARM64)) && (defined(CV_CPU_COMPILE_NEON) || !defined(_MSC_VER))
# include <Intrin.h>
# include <arm_neon.h>
# define CV_NEON 1
#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__))
# include <arm_neon.h>
# define CV_NEON 1
#endif
#if defined(__ARM_NEON__) || defined(__aarch64__)
# include <arm_neon.h>
#endif
#ifdef CV_CPU_COMPILE_VSX
# include <altivec.h>
# undef vector
# undef pixel
# undef bool
# define CV_VSX 1
#endif
#ifdef CV_CPU_COMPILE_VSX3
# define CV_VSX3 1
#endif
#ifdef CV_CPU_COMPILE_MSA
# include "hal/msa_macros.h"
# define CV_MSA 1
#endif
#ifdef __EMSCRIPTEN__
# define CV_WASM_SIMD 1
# include <wasm_simd128.h>
#endif
#endif // CV_ENABLE_INTRINSICS && !CV_DISABLE_OPTIMIZATION && !__CUDACC__
#if defined CV_CPU_COMPILE_AVX && !defined CV_CPU_BASELINE_COMPILE_AVX
struct VZeroUpperGuard {
#ifdef __GNUC__
__attribute__((always_inline))
#endif
inline VZeroUpperGuard() { _mm256_zeroupper(); }
#ifdef __GNUC__
__attribute__((always_inline))
#endif
inline ~VZeroUpperGuard() { _mm256_zeroupper(); }
};
#define __CV_AVX_GUARD VZeroUpperGuard __vzeroupper_guard; CV_UNUSED(__vzeroupper_guard);
#endif
#ifdef __CV_AVX_GUARD
#define CV_AVX_GUARD __CV_AVX_GUARD
#else
#define CV_AVX_GUARD
#endif
#endif // __OPENCV_BUILD
#if !defined __OPENCV_BUILD /* Compatibility code */ \
&& !defined __CUDACC__ /* do not include SSE/AVX/NEON headers for NVCC compiler */
#if defined __SSE2__ || defined _M_X64 || (defined _M_IX86_FP && _M_IX86_FP >= 2)
# include <emmintrin.h>
# define CV_MMX 1
# define CV_SSE 1
# define CV_SSE2 1
#elif defined _WIN32 && (defined(_M_ARM) || defined(_M_ARM64)) && (defined(CV_CPU_COMPILE_NEON) || !defined(_MSC_VER))
# include <Intrin.h>
# include <arm_neon.h>
# define CV_NEON 1
#elif defined(__ARM_NEON__) || (defined (__ARM_NEON) && defined(__aarch64__))
# include <arm_neon.h>
# define CV_NEON 1
#elif defined(__VSX__) && defined(__PPC64__) && defined(__LITTLE_ENDIAN__)
# include <altivec.h>
# undef vector
# undef pixel
# undef bool
# define CV_VSX 1
#endif
#endif // !__OPENCV_BUILD && !__CUDACC (Compatibility code)
#ifndef CV_MMX
# define CV_MMX 0
#endif
#ifndef CV_SSE
# define CV_SSE 0
#endif
#ifndef CV_SSE2
# define CV_SSE2 0
#endif
#ifndef CV_SSE3
# define CV_SSE3 0
#endif
#ifndef CV_SSSE3
# define CV_SSSE3 0
#endif
#ifndef CV_SSE4_1
# define CV_SSE4_1 0
#endif
#ifndef CV_SSE4_2
# define CV_SSE4_2 0
#endif
#ifndef CV_POPCNT
# define CV_POPCNT 0
#endif
#ifndef CV_AVX
# define CV_AVX 0
#endif
#ifndef CV_FP16
# define CV_FP16 0
#endif
#ifndef CV_AVX2
# define CV_AVX2 0
#endif
#ifndef CV_FMA3
# define CV_FMA3 0
#endif
#ifndef CV_AVX_512F
# define CV_AVX_512F 0
#endif
#ifndef CV_AVX_512BW
# define CV_AVX_512BW 0
#endif
#ifndef CV_AVX_512CD
# define CV_AVX_512CD 0
#endif
#ifndef CV_AVX_512DQ
# define CV_AVX_512DQ 0
#endif
#ifndef CV_AVX_512ER
# define CV_AVX_512ER 0
#endif
#ifndef CV_AVX_512IFMA
# define CV_AVX_512IFMA 0
#endif
#define CV_AVX_512IFMA512 CV_AVX_512IFMA // deprecated
#ifndef CV_AVX_512PF
# define CV_AVX_512PF 0
#endif
#ifndef CV_AVX_512VBMI
# define CV_AVX_512VBMI 0
#endif
#ifndef CV_AVX_512VL
# define CV_AVX_512VL 0
#endif
#ifndef CV_AVX_5124FMAPS
# define CV_AVX_5124FMAPS 0
#endif
#ifndef CV_AVX_5124VNNIW
# define CV_AVX_5124VNNIW 0
#endif
#ifndef CV_AVX_512VPOPCNTDQ
# define CV_AVX_512VPOPCNTDQ 0
#endif
#ifndef CV_AVX_512VNNI
# define CV_AVX_512VNNI 0
#endif
#ifndef CV_AVX_512VBMI2
# define CV_AVX_512VBMI2 0
#endif
#ifndef CV_AVX_512BITALG
# define CV_AVX_512BITALG 0
#endif
#ifndef CV_AVX512_COMMON
# define CV_AVX512_COMMON 0
#endif
#ifndef CV_AVX512_KNL
# define CV_AVX512_KNL 0
#endif
#ifndef CV_AVX512_KNM
# define CV_AVX512_KNM 0
#endif
#ifndef CV_AVX512_SKX
# define CV_AVX512_SKX 0
#endif
#ifndef CV_AVX512_CNL
# define CV_AVX512_CNL 0
#endif
#ifndef CV_AVX512_CLX
# define CV_AVX512_CLX 0
#endif
#ifndef CV_AVX512_ICL
# define CV_AVX512_ICL 0
#endif
#ifndef CV_NEON
# define CV_NEON 0
#endif
#ifndef CV_VSX
# define CV_VSX 0
#endif
#ifndef CV_VSX3
# define CV_VSX3 0
#endif
#ifndef CV_MSA
# define CV_MSA 0
#endif
#ifndef CV_WASM_SIMD
# define CV_WASM_SIMD 0
#endif

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// AUTOGENERATED, DO NOT EDIT
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE
# define CV_TRY_SSE 1
# define CV_CPU_FORCE_SSE 1
# define CV_CPU_HAS_SUPPORT_SSE 1
# define CV_CPU_CALL_SSE(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_SSE_(fn, args) return (opt_SSE::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE
# define CV_TRY_SSE 1
# define CV_CPU_FORCE_SSE 0
# define CV_CPU_HAS_SUPPORT_SSE (cv::checkHardwareSupport(CV_CPU_SSE))
# define CV_CPU_CALL_SSE(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args)
# define CV_CPU_CALL_SSE_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE) return (opt_SSE::fn args)
#else
# define CV_TRY_SSE 0
# define CV_CPU_FORCE_SSE 0
# define CV_CPU_HAS_SUPPORT_SSE 0
# define CV_CPU_CALL_SSE(fn, args)
# define CV_CPU_CALL_SSE_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_SSE(fn, args, mode, ...) CV_CPU_CALL_SSE(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE2
# define CV_TRY_SSE2 1
# define CV_CPU_FORCE_SSE2 1
# define CV_CPU_HAS_SUPPORT_SSE2 1
# define CV_CPU_CALL_SSE2(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_SSE2_(fn, args) return (opt_SSE2::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE2
# define CV_TRY_SSE2 1
# define CV_CPU_FORCE_SSE2 0
# define CV_CPU_HAS_SUPPORT_SSE2 (cv::checkHardwareSupport(CV_CPU_SSE2))
# define CV_CPU_CALL_SSE2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args)
# define CV_CPU_CALL_SSE2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE2) return (opt_SSE2::fn args)
#else
# define CV_TRY_SSE2 0
# define CV_CPU_FORCE_SSE2 0
# define CV_CPU_HAS_SUPPORT_SSE2 0
# define CV_CPU_CALL_SSE2(fn, args)
# define CV_CPU_CALL_SSE2_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_SSE2(fn, args, mode, ...) CV_CPU_CALL_SSE2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE3
# define CV_TRY_SSE3 1
# define CV_CPU_FORCE_SSE3 1
# define CV_CPU_HAS_SUPPORT_SSE3 1
# define CV_CPU_CALL_SSE3(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_SSE3_(fn, args) return (opt_SSE3::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE3
# define CV_TRY_SSE3 1
# define CV_CPU_FORCE_SSE3 0
# define CV_CPU_HAS_SUPPORT_SSE3 (cv::checkHardwareSupport(CV_CPU_SSE3))
# define CV_CPU_CALL_SSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args)
# define CV_CPU_CALL_SSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE3) return (opt_SSE3::fn args)
#else
# define CV_TRY_SSE3 0
# define CV_CPU_FORCE_SSE3 0
# define CV_CPU_HAS_SUPPORT_SSE3 0
# define CV_CPU_CALL_SSE3(fn, args)
# define CV_CPU_CALL_SSE3_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_SSE3(fn, args, mode, ...) CV_CPU_CALL_SSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSSE3
# define CV_TRY_SSSE3 1
# define CV_CPU_FORCE_SSSE3 1
# define CV_CPU_HAS_SUPPORT_SSSE3 1
# define CV_CPU_CALL_SSSE3(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_SSSE3_(fn, args) return (opt_SSSE3::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSSE3
# define CV_TRY_SSSE3 1
# define CV_CPU_FORCE_SSSE3 0
# define CV_CPU_HAS_SUPPORT_SSSE3 (cv::checkHardwareSupport(CV_CPU_SSSE3))
# define CV_CPU_CALL_SSSE3(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args)
# define CV_CPU_CALL_SSSE3_(fn, args) if (CV_CPU_HAS_SUPPORT_SSSE3) return (opt_SSSE3::fn args)
#else
# define CV_TRY_SSSE3 0
# define CV_CPU_FORCE_SSSE3 0
# define CV_CPU_HAS_SUPPORT_SSSE3 0
# define CV_CPU_CALL_SSSE3(fn, args)
# define CV_CPU_CALL_SSSE3_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_SSSE3(fn, args, mode, ...) CV_CPU_CALL_SSSE3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_1
# define CV_TRY_SSE4_1 1
# define CV_CPU_FORCE_SSE4_1 1
# define CV_CPU_HAS_SUPPORT_SSE4_1 1
# define CV_CPU_CALL_SSE4_1(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_SSE4_1_(fn, args) return (opt_SSE4_1::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_1
# define CV_TRY_SSE4_1 1
# define CV_CPU_FORCE_SSE4_1 0
# define CV_CPU_HAS_SUPPORT_SSE4_1 (cv::checkHardwareSupport(CV_CPU_SSE4_1))
# define CV_CPU_CALL_SSE4_1(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args)
# define CV_CPU_CALL_SSE4_1_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_1) return (opt_SSE4_1::fn args)
#else
# define CV_TRY_SSE4_1 0
# define CV_CPU_FORCE_SSE4_1 0
# define CV_CPU_HAS_SUPPORT_SSE4_1 0
# define CV_CPU_CALL_SSE4_1(fn, args)
# define CV_CPU_CALL_SSE4_1_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_SSE4_1(fn, args, mode, ...) CV_CPU_CALL_SSE4_1(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_SSE4_2
# define CV_TRY_SSE4_2 1
# define CV_CPU_FORCE_SSE4_2 1
# define CV_CPU_HAS_SUPPORT_SSE4_2 1
# define CV_CPU_CALL_SSE4_2(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_SSE4_2_(fn, args) return (opt_SSE4_2::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_SSE4_2
# define CV_TRY_SSE4_2 1
# define CV_CPU_FORCE_SSE4_2 0
# define CV_CPU_HAS_SUPPORT_SSE4_2 (cv::checkHardwareSupport(CV_CPU_SSE4_2))
# define CV_CPU_CALL_SSE4_2(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args)
# define CV_CPU_CALL_SSE4_2_(fn, args) if (CV_CPU_HAS_SUPPORT_SSE4_2) return (opt_SSE4_2::fn args)
#else
# define CV_TRY_SSE4_2 0
# define CV_CPU_FORCE_SSE4_2 0
# define CV_CPU_HAS_SUPPORT_SSE4_2 0
# define CV_CPU_CALL_SSE4_2(fn, args)
# define CV_CPU_CALL_SSE4_2_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_SSE4_2(fn, args, mode, ...) CV_CPU_CALL_SSE4_2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_POPCNT
# define CV_TRY_POPCNT 1
# define CV_CPU_FORCE_POPCNT 1
# define CV_CPU_HAS_SUPPORT_POPCNT 1
# define CV_CPU_CALL_POPCNT(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_POPCNT_(fn, args) return (opt_POPCNT::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_POPCNT
# define CV_TRY_POPCNT 1
# define CV_CPU_FORCE_POPCNT 0
# define CV_CPU_HAS_SUPPORT_POPCNT (cv::checkHardwareSupport(CV_CPU_POPCNT))
# define CV_CPU_CALL_POPCNT(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args)
# define CV_CPU_CALL_POPCNT_(fn, args) if (CV_CPU_HAS_SUPPORT_POPCNT) return (opt_POPCNT::fn args)
#else
# define CV_TRY_POPCNT 0
# define CV_CPU_FORCE_POPCNT 0
# define CV_CPU_HAS_SUPPORT_POPCNT 0
# define CV_CPU_CALL_POPCNT(fn, args)
# define CV_CPU_CALL_POPCNT_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_POPCNT(fn, args, mode, ...) CV_CPU_CALL_POPCNT(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX
# define CV_TRY_AVX 1
# define CV_CPU_FORCE_AVX 1
# define CV_CPU_HAS_SUPPORT_AVX 1
# define CV_CPU_CALL_AVX(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX_(fn, args) return (opt_AVX::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX
# define CV_TRY_AVX 1
# define CV_CPU_FORCE_AVX 0
# define CV_CPU_HAS_SUPPORT_AVX (cv::checkHardwareSupport(CV_CPU_AVX))
# define CV_CPU_CALL_AVX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args)
# define CV_CPU_CALL_AVX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX) return (opt_AVX::fn args)
#else
# define CV_TRY_AVX 0
# define CV_CPU_FORCE_AVX 0
# define CV_CPU_HAS_SUPPORT_AVX 0
# define CV_CPU_CALL_AVX(fn, args)
# define CV_CPU_CALL_AVX_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX(fn, args, mode, ...) CV_CPU_CALL_AVX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FP16
# define CV_TRY_FP16 1
# define CV_CPU_FORCE_FP16 1
# define CV_CPU_HAS_SUPPORT_FP16 1
# define CV_CPU_CALL_FP16(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_FP16_(fn, args) return (opt_FP16::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FP16
# define CV_TRY_FP16 1
# define CV_CPU_FORCE_FP16 0
# define CV_CPU_HAS_SUPPORT_FP16 (cv::checkHardwareSupport(CV_CPU_FP16))
# define CV_CPU_CALL_FP16(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args)
# define CV_CPU_CALL_FP16_(fn, args) if (CV_CPU_HAS_SUPPORT_FP16) return (opt_FP16::fn args)
#else
# define CV_TRY_FP16 0
# define CV_CPU_FORCE_FP16 0
# define CV_CPU_HAS_SUPPORT_FP16 0
# define CV_CPU_CALL_FP16(fn, args)
# define CV_CPU_CALL_FP16_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_FP16(fn, args, mode, ...) CV_CPU_CALL_FP16(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX2
# define CV_TRY_AVX2 1
# define CV_CPU_FORCE_AVX2 1
# define CV_CPU_HAS_SUPPORT_AVX2 1
# define CV_CPU_CALL_AVX2(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX2_(fn, args) return (opt_AVX2::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX2
# define CV_TRY_AVX2 1
# define CV_CPU_FORCE_AVX2 0
# define CV_CPU_HAS_SUPPORT_AVX2 (cv::checkHardwareSupport(CV_CPU_AVX2))
# define CV_CPU_CALL_AVX2(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args)
# define CV_CPU_CALL_AVX2_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX2) return (opt_AVX2::fn args)
#else
# define CV_TRY_AVX2 0
# define CV_CPU_FORCE_AVX2 0
# define CV_CPU_HAS_SUPPORT_AVX2 0
# define CV_CPU_CALL_AVX2(fn, args)
# define CV_CPU_CALL_AVX2_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX2(fn, args, mode, ...) CV_CPU_CALL_AVX2(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_FMA3
# define CV_TRY_FMA3 1
# define CV_CPU_FORCE_FMA3 1
# define CV_CPU_HAS_SUPPORT_FMA3 1
# define CV_CPU_CALL_FMA3(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_FMA3_(fn, args) return (opt_FMA3::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_FMA3
# define CV_TRY_FMA3 1
# define CV_CPU_FORCE_FMA3 0
# define CV_CPU_HAS_SUPPORT_FMA3 (cv::checkHardwareSupport(CV_CPU_FMA3))
# define CV_CPU_CALL_FMA3(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args)
# define CV_CPU_CALL_FMA3_(fn, args) if (CV_CPU_HAS_SUPPORT_FMA3) return (opt_FMA3::fn args)
#else
# define CV_TRY_FMA3 0
# define CV_CPU_FORCE_FMA3 0
# define CV_CPU_HAS_SUPPORT_FMA3 0
# define CV_CPU_CALL_FMA3(fn, args)
# define CV_CPU_CALL_FMA3_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_FMA3(fn, args, mode, ...) CV_CPU_CALL_FMA3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX_512F
# define CV_TRY_AVX_512F 1
# define CV_CPU_FORCE_AVX_512F 1
# define CV_CPU_HAS_SUPPORT_AVX_512F 1
# define CV_CPU_CALL_AVX_512F(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX_512F_(fn, args) return (opt_AVX_512F::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX_512F
# define CV_TRY_AVX_512F 1
# define CV_CPU_FORCE_AVX_512F 0
# define CV_CPU_HAS_SUPPORT_AVX_512F (cv::checkHardwareSupport(CV_CPU_AVX_512F))
# define CV_CPU_CALL_AVX_512F(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args)
# define CV_CPU_CALL_AVX_512F_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX_512F) return (opt_AVX_512F::fn args)
#else
# define CV_TRY_AVX_512F 0
# define CV_CPU_FORCE_AVX_512F 0
# define CV_CPU_HAS_SUPPORT_AVX_512F 0
# define CV_CPU_CALL_AVX_512F(fn, args)
# define CV_CPU_CALL_AVX_512F_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX_512F(fn, args, mode, ...) CV_CPU_CALL_AVX_512F(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_COMMON
# define CV_TRY_AVX512_COMMON 1
# define CV_CPU_FORCE_AVX512_COMMON 1
# define CV_CPU_HAS_SUPPORT_AVX512_COMMON 1
# define CV_CPU_CALL_AVX512_COMMON(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX512_COMMON_(fn, args) return (opt_AVX512_COMMON::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_COMMON
# define CV_TRY_AVX512_COMMON 1
# define CV_CPU_FORCE_AVX512_COMMON 0
# define CV_CPU_HAS_SUPPORT_AVX512_COMMON (cv::checkHardwareSupport(CV_CPU_AVX512_COMMON))
# define CV_CPU_CALL_AVX512_COMMON(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_COMMON) return (opt_AVX512_COMMON::fn args)
# define CV_CPU_CALL_AVX512_COMMON_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_COMMON) return (opt_AVX512_COMMON::fn args)
#else
# define CV_TRY_AVX512_COMMON 0
# define CV_CPU_FORCE_AVX512_COMMON 0
# define CV_CPU_HAS_SUPPORT_AVX512_COMMON 0
# define CV_CPU_CALL_AVX512_COMMON(fn, args)
# define CV_CPU_CALL_AVX512_COMMON_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX512_COMMON(fn, args, mode, ...) CV_CPU_CALL_AVX512_COMMON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_KNL
# define CV_TRY_AVX512_KNL 1
# define CV_CPU_FORCE_AVX512_KNL 1
# define CV_CPU_HAS_SUPPORT_AVX512_KNL 1
# define CV_CPU_CALL_AVX512_KNL(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX512_KNL_(fn, args) return (opt_AVX512_KNL::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_KNL
# define CV_TRY_AVX512_KNL 1
# define CV_CPU_FORCE_AVX512_KNL 0
# define CV_CPU_HAS_SUPPORT_AVX512_KNL (cv::checkHardwareSupport(CV_CPU_AVX512_KNL))
# define CV_CPU_CALL_AVX512_KNL(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNL) return (opt_AVX512_KNL::fn args)
# define CV_CPU_CALL_AVX512_KNL_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNL) return (opt_AVX512_KNL::fn args)
#else
# define CV_TRY_AVX512_KNL 0
# define CV_CPU_FORCE_AVX512_KNL 0
# define CV_CPU_HAS_SUPPORT_AVX512_KNL 0
# define CV_CPU_CALL_AVX512_KNL(fn, args)
# define CV_CPU_CALL_AVX512_KNL_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX512_KNL(fn, args, mode, ...) CV_CPU_CALL_AVX512_KNL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_KNM
# define CV_TRY_AVX512_KNM 1
# define CV_CPU_FORCE_AVX512_KNM 1
# define CV_CPU_HAS_SUPPORT_AVX512_KNM 1
# define CV_CPU_CALL_AVX512_KNM(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX512_KNM_(fn, args) return (opt_AVX512_KNM::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_KNM
# define CV_TRY_AVX512_KNM 1
# define CV_CPU_FORCE_AVX512_KNM 0
# define CV_CPU_HAS_SUPPORT_AVX512_KNM (cv::checkHardwareSupport(CV_CPU_AVX512_KNM))
# define CV_CPU_CALL_AVX512_KNM(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNM) return (opt_AVX512_KNM::fn args)
# define CV_CPU_CALL_AVX512_KNM_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_KNM) return (opt_AVX512_KNM::fn args)
#else
# define CV_TRY_AVX512_KNM 0
# define CV_CPU_FORCE_AVX512_KNM 0
# define CV_CPU_HAS_SUPPORT_AVX512_KNM 0
# define CV_CPU_CALL_AVX512_KNM(fn, args)
# define CV_CPU_CALL_AVX512_KNM_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX512_KNM(fn, args, mode, ...) CV_CPU_CALL_AVX512_KNM(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_SKX
# define CV_TRY_AVX512_SKX 1
# define CV_CPU_FORCE_AVX512_SKX 1
# define CV_CPU_HAS_SUPPORT_AVX512_SKX 1
# define CV_CPU_CALL_AVX512_SKX(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX512_SKX_(fn, args) return (opt_AVX512_SKX::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_SKX
# define CV_TRY_AVX512_SKX 1
# define CV_CPU_FORCE_AVX512_SKX 0
# define CV_CPU_HAS_SUPPORT_AVX512_SKX (cv::checkHardwareSupport(CV_CPU_AVX512_SKX))
# define CV_CPU_CALL_AVX512_SKX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args)
# define CV_CPU_CALL_AVX512_SKX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_SKX) return (opt_AVX512_SKX::fn args)
#else
# define CV_TRY_AVX512_SKX 0
# define CV_CPU_FORCE_AVX512_SKX 0
# define CV_CPU_HAS_SUPPORT_AVX512_SKX 0
# define CV_CPU_CALL_AVX512_SKX(fn, args)
# define CV_CPU_CALL_AVX512_SKX_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX512_SKX(fn, args, mode, ...) CV_CPU_CALL_AVX512_SKX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_CNL
# define CV_TRY_AVX512_CNL 1
# define CV_CPU_FORCE_AVX512_CNL 1
# define CV_CPU_HAS_SUPPORT_AVX512_CNL 1
# define CV_CPU_CALL_AVX512_CNL(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX512_CNL_(fn, args) return (opt_AVX512_CNL::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_CNL
# define CV_TRY_AVX512_CNL 1
# define CV_CPU_FORCE_AVX512_CNL 0
# define CV_CPU_HAS_SUPPORT_AVX512_CNL (cv::checkHardwareSupport(CV_CPU_AVX512_CNL))
# define CV_CPU_CALL_AVX512_CNL(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CNL) return (opt_AVX512_CNL::fn args)
# define CV_CPU_CALL_AVX512_CNL_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CNL) return (opt_AVX512_CNL::fn args)
#else
# define CV_TRY_AVX512_CNL 0
# define CV_CPU_FORCE_AVX512_CNL 0
# define CV_CPU_HAS_SUPPORT_AVX512_CNL 0
# define CV_CPU_CALL_AVX512_CNL(fn, args)
# define CV_CPU_CALL_AVX512_CNL_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX512_CNL(fn, args, mode, ...) CV_CPU_CALL_AVX512_CNL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_CLX
# define CV_TRY_AVX512_CLX 1
# define CV_CPU_FORCE_AVX512_CLX 1
# define CV_CPU_HAS_SUPPORT_AVX512_CLX 1
# define CV_CPU_CALL_AVX512_CLX(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX512_CLX_(fn, args) return (opt_AVX512_CLX::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_CLX
# define CV_TRY_AVX512_CLX 1
# define CV_CPU_FORCE_AVX512_CLX 0
# define CV_CPU_HAS_SUPPORT_AVX512_CLX (cv::checkHardwareSupport(CV_CPU_AVX512_CLX))
# define CV_CPU_CALL_AVX512_CLX(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CLX) return (opt_AVX512_CLX::fn args)
# define CV_CPU_CALL_AVX512_CLX_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_CLX) return (opt_AVX512_CLX::fn args)
#else
# define CV_TRY_AVX512_CLX 0
# define CV_CPU_FORCE_AVX512_CLX 0
# define CV_CPU_HAS_SUPPORT_AVX512_CLX 0
# define CV_CPU_CALL_AVX512_CLX(fn, args)
# define CV_CPU_CALL_AVX512_CLX_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX512_CLX(fn, args, mode, ...) CV_CPU_CALL_AVX512_CLX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_AVX512_ICL
# define CV_TRY_AVX512_ICL 1
# define CV_CPU_FORCE_AVX512_ICL 1
# define CV_CPU_HAS_SUPPORT_AVX512_ICL 1
# define CV_CPU_CALL_AVX512_ICL(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_AVX512_ICL_(fn, args) return (opt_AVX512_ICL::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_AVX512_ICL
# define CV_TRY_AVX512_ICL 1
# define CV_CPU_FORCE_AVX512_ICL 0
# define CV_CPU_HAS_SUPPORT_AVX512_ICL (cv::checkHardwareSupport(CV_CPU_AVX512_ICL))
# define CV_CPU_CALL_AVX512_ICL(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_ICL) return (opt_AVX512_ICL::fn args)
# define CV_CPU_CALL_AVX512_ICL_(fn, args) if (CV_CPU_HAS_SUPPORT_AVX512_ICL) return (opt_AVX512_ICL::fn args)
#else
# define CV_TRY_AVX512_ICL 0
# define CV_CPU_FORCE_AVX512_ICL 0
# define CV_CPU_HAS_SUPPORT_AVX512_ICL 0
# define CV_CPU_CALL_AVX512_ICL(fn, args)
# define CV_CPU_CALL_AVX512_ICL_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_AVX512_ICL(fn, args, mode, ...) CV_CPU_CALL_AVX512_ICL(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_NEON
# define CV_TRY_NEON 1
# define CV_CPU_FORCE_NEON 1
# define CV_CPU_HAS_SUPPORT_NEON 1
# define CV_CPU_CALL_NEON(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_NEON_(fn, args) return (opt_NEON::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_NEON
# define CV_TRY_NEON 1
# define CV_CPU_FORCE_NEON 0
# define CV_CPU_HAS_SUPPORT_NEON (cv::checkHardwareSupport(CV_CPU_NEON))
# define CV_CPU_CALL_NEON(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args)
# define CV_CPU_CALL_NEON_(fn, args) if (CV_CPU_HAS_SUPPORT_NEON) return (opt_NEON::fn args)
#else
# define CV_TRY_NEON 0
# define CV_CPU_FORCE_NEON 0
# define CV_CPU_HAS_SUPPORT_NEON 0
# define CV_CPU_CALL_NEON(fn, args)
# define CV_CPU_CALL_NEON_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_NEON(fn, args, mode, ...) CV_CPU_CALL_NEON(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_MSA
# define CV_TRY_MSA 1
# define CV_CPU_FORCE_MSA 1
# define CV_CPU_HAS_SUPPORT_MSA 1
# define CV_CPU_CALL_MSA(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_MSA_(fn, args) return (opt_MSA::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_MSA
# define CV_TRY_MSA 1
# define CV_CPU_FORCE_MSA 0
# define CV_CPU_HAS_SUPPORT_MSA (cv::checkHardwareSupport(CV_CPU_MSA))
# define CV_CPU_CALL_MSA(fn, args) if (CV_CPU_HAS_SUPPORT_MSA) return (opt_MSA::fn args)
# define CV_CPU_CALL_MSA_(fn, args) if (CV_CPU_HAS_SUPPORT_MSA) return (opt_MSA::fn args)
#else
# define CV_TRY_MSA 0
# define CV_CPU_FORCE_MSA 0
# define CV_CPU_HAS_SUPPORT_MSA 0
# define CV_CPU_CALL_MSA(fn, args)
# define CV_CPU_CALL_MSA_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_MSA(fn, args, mode, ...) CV_CPU_CALL_MSA(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX
# define CV_TRY_VSX 1
# define CV_CPU_FORCE_VSX 1
# define CV_CPU_HAS_SUPPORT_VSX 1
# define CV_CPU_CALL_VSX(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_VSX_(fn, args) return (opt_VSX::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX
# define CV_TRY_VSX 1
# define CV_CPU_FORCE_VSX 0
# define CV_CPU_HAS_SUPPORT_VSX (cv::checkHardwareSupport(CV_CPU_VSX))
# define CV_CPU_CALL_VSX(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args)
# define CV_CPU_CALL_VSX_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX) return (opt_VSX::fn args)
#else
# define CV_TRY_VSX 0
# define CV_CPU_FORCE_VSX 0
# define CV_CPU_HAS_SUPPORT_VSX 0
# define CV_CPU_CALL_VSX(fn, args)
# define CV_CPU_CALL_VSX_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_VSX(fn, args, mode, ...) CV_CPU_CALL_VSX(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#if !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_COMPILE_VSX3
# define CV_TRY_VSX3 1
# define CV_CPU_FORCE_VSX3 1
# define CV_CPU_HAS_SUPPORT_VSX3 1
# define CV_CPU_CALL_VSX3(fn, args) return (cpu_baseline::fn args)
# define CV_CPU_CALL_VSX3_(fn, args) return (opt_VSX3::fn args)
#elif !defined CV_DISABLE_OPTIMIZATION && defined CV_ENABLE_INTRINSICS && defined CV_CPU_DISPATCH_COMPILE_VSX3
# define CV_TRY_VSX3 1
# define CV_CPU_FORCE_VSX3 0
# define CV_CPU_HAS_SUPPORT_VSX3 (cv::checkHardwareSupport(CV_CPU_VSX3))
# define CV_CPU_CALL_VSX3(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args)
# define CV_CPU_CALL_VSX3_(fn, args) if (CV_CPU_HAS_SUPPORT_VSX3) return (opt_VSX3::fn args)
#else
# define CV_TRY_VSX3 0
# define CV_CPU_FORCE_VSX3 0
# define CV_CPU_HAS_SUPPORT_VSX3 0
# define CV_CPU_CALL_VSX3(fn, args)
# define CV_CPU_CALL_VSX3_(fn, args)
#endif
#define __CV_CPU_DISPATCH_CHAIN_VSX3(fn, args, mode, ...) CV_CPU_CALL_VSX3(fn, args); __CV_EXPAND(__CV_CPU_DISPATCH_CHAIN_ ## mode(fn, args, __VA_ARGS__))
#define CV_CPU_CALL_BASELINE(fn, args) return (cpu_baseline::fn args)
#define __CV_CPU_DISPATCH_CHAIN_BASELINE(fn, args, mode, ...) CV_CPU_CALL_BASELINE(fn, args) /* last in sequence */

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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.
// Copyright (C) 2015, Itseez 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_CORE_CVDEF_H
#define OPENCV_CORE_CVDEF_H
//! @addtogroup core_utils
//! @{
#ifdef OPENCV_INCLUDE_PORT_FILE // User-provided header file with custom platform configuration
#include OPENCV_INCLUDE_PORT_FILE
#endif
#if !defined CV_DOXYGEN && !defined CV_IGNORE_DEBUG_BUILD_GUARD
#if (defined(_MSC_VER) && (defined(DEBUG) || defined(_DEBUG))) || \
(defined(_GLIBCXX_DEBUG) || defined(_GLIBCXX_DEBUG_PEDANTIC))
// Guard to prevent using of binary incompatible binaries / runtimes
// https://github.com/opencv/opencv/pull/9161
#define CV__DEBUG_NS_BEGIN namespace debug_build_guard {
#define CV__DEBUG_NS_END }
namespace cv { namespace debug_build_guard { } using namespace debug_build_guard; }
#endif
#endif
#ifndef CV__DEBUG_NS_BEGIN
#define CV__DEBUG_NS_BEGIN
#define CV__DEBUG_NS_END
#endif
#ifdef __OPENCV_BUILD
#include "cvconfig.h"
#endif
#ifndef __CV_EXPAND
#define __CV_EXPAND(x) x
#endif
#ifndef __CV_CAT
#define __CV_CAT__(x, y) x ## y
#define __CV_CAT_(x, y) __CV_CAT__(x, y)
#define __CV_CAT(x, y) __CV_CAT_(x, y)
#endif
#define __CV_VA_NUM_ARGS_HELPER(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, N, ...) N
#define __CV_VA_NUM_ARGS(...) __CV_EXPAND(__CV_VA_NUM_ARGS_HELPER(__VA_ARGS__, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0))
#if defined __GNUC__
#define CV_Func __func__
#elif defined _MSC_VER
#define CV_Func __FUNCTION__
#else
#define CV_Func ""
#endif
//! @cond IGNORED
//////////////// static assert /////////////////
#define CVAUX_CONCAT_EXP(a, b) a##b
#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b)
#if defined(__clang__)
# ifndef __has_extension
# define __has_extension __has_feature /* compatibility, for older versions of clang */
# endif
# if __has_extension(cxx_static_assert)
# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition)
# elif __has_extension(c_static_assert)
# define CV_StaticAssert(condition, reason) _Static_assert((condition), reason " " #condition)
# endif
#elif defined(__GNUC__)
# if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L)
# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition)
# endif
#elif defined(_MSC_VER)
# if _MSC_VER >= 1600 /* MSVC 10 */
# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition)
# endif
#endif
#ifndef CV_StaticAssert
# if !defined(__clang__) && defined(__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 302)
# define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); })
# else
namespace cv {
template <bool x> struct CV_StaticAssert_failed;
template <> struct CV_StaticAssert_failed<true> { enum { val = 1 }; };
template<int x> struct CV_StaticAssert_test {};
}
# define CV_StaticAssert(condition, reason)\
typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast<bool>(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__)
# endif
#endif
// Suppress warning "-Wdeprecated-declarations" / C4996
#if defined(_MSC_VER)
#define CV_DO_PRAGMA(x) __pragma(x)
#elif defined(__GNUC__)
#define CV_DO_PRAGMA(x) _Pragma (#x)
#else
#define CV_DO_PRAGMA(x)
#endif
#ifdef _MSC_VER
#define CV_SUPPRESS_DEPRECATED_START \
CV_DO_PRAGMA(warning(push)) \
CV_DO_PRAGMA(warning(disable: 4996))
#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop))
#elif defined (__clang__) || ((__GNUC__) && (__GNUC__*100 + __GNUC_MINOR__ > 405))
#define CV_SUPPRESS_DEPRECATED_START \
CV_DO_PRAGMA(GCC diagnostic push) \
CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations")
#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop)
#else
#define CV_SUPPRESS_DEPRECATED_START
#define CV_SUPPRESS_DEPRECATED_END
#endif
#define CV_UNUSED(name) (void)name
//! @endcond
// undef problematic defines sometimes defined by system headers (windows.h in particular)
#undef small
#undef min
#undef max
#undef abs
#undef Complex
#include <limits.h>
#include "opencv2/core/hal/interface.h"
#if defined __ICL
# define CV_ICC __ICL
#elif defined __ICC
# define CV_ICC __ICC
#elif defined __ECL
# define CV_ICC __ECL
#elif defined __ECC
# define CV_ICC __ECC
#elif defined __INTEL_COMPILER
# define CV_ICC __INTEL_COMPILER
#endif
#ifndef CV_INLINE
# if defined __cplusplus
# define CV_INLINE static inline
# elif defined _MSC_VER
# define CV_INLINE __inline
# else
# define CV_INLINE static
# endif
#endif
#ifndef CV_ALWAYS_INLINE
#if defined(__GNUC__) && (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 1))
#define CV_ALWAYS_INLINE inline __attribute__((always_inline))
#elif defined(_MSC_VER)
#define CV_ALWAYS_INLINE __forceinline
#else
#define CV_ALWAYS_INLINE inline
#endif
#endif
#if defined CV_DISABLE_OPTIMIZATION || (defined CV_ICC && !defined CV_ENABLE_UNROLLED)
# define CV_ENABLE_UNROLLED 0
#else
# define CV_ENABLE_UNROLLED 1
#endif
#ifdef __GNUC__
# define CV_DECL_ALIGNED(x) __attribute__ ((aligned (x)))
#elif defined _MSC_VER
# define CV_DECL_ALIGNED(x) __declspec(align(x))
#else
# define CV_DECL_ALIGNED(x)
#endif
/* CPU features and intrinsics support */
#define CV_CPU_NONE 0
#define CV_CPU_MMX 1
#define CV_CPU_SSE 2
#define CV_CPU_SSE2 3
#define CV_CPU_SSE3 4
#define CV_CPU_SSSE3 5
#define CV_CPU_SSE4_1 6
#define CV_CPU_SSE4_2 7
#define CV_CPU_POPCNT 8
#define CV_CPU_FP16 9
#define CV_CPU_AVX 10
#define CV_CPU_AVX2 11
#define CV_CPU_FMA3 12
#define CV_CPU_AVX_512F 13
#define CV_CPU_AVX_512BW 14
#define CV_CPU_AVX_512CD 15
#define CV_CPU_AVX_512DQ 16
#define CV_CPU_AVX_512ER 17
#define CV_CPU_AVX_512IFMA512 18 // deprecated
#define CV_CPU_AVX_512IFMA 18
#define CV_CPU_AVX_512PF 19
#define CV_CPU_AVX_512VBMI 20
#define CV_CPU_AVX_512VL 21
#define CV_CPU_AVX_512VBMI2 22
#define CV_CPU_AVX_512VNNI 23
#define CV_CPU_AVX_512BITALG 24
#define CV_CPU_AVX_512VPOPCNTDQ 25
#define CV_CPU_AVX_5124VNNIW 26
#define CV_CPU_AVX_5124FMAPS 27
#define CV_CPU_NEON 100
#define CV_CPU_MSA 150
#define CV_CPU_VSX 200
#define CV_CPU_VSX3 201
// CPU features groups
#define CV_CPU_AVX512_SKX 256
#define CV_CPU_AVX512_COMMON 257
#define CV_CPU_AVX512_KNL 258
#define CV_CPU_AVX512_KNM 259
#define CV_CPU_AVX512_CNL 260
#define CV_CPU_AVX512_CLX 261
#define CV_CPU_AVX512_ICL 262
// when adding to this list remember to update the following enum
#define CV_HARDWARE_MAX_FEATURE 512
/** @brief Available CPU features.
*/
enum CpuFeatures {
CPU_MMX = 1,
CPU_SSE = 2,
CPU_SSE2 = 3,
CPU_SSE3 = 4,
CPU_SSSE3 = 5,
CPU_SSE4_1 = 6,
CPU_SSE4_2 = 7,
CPU_POPCNT = 8,
CPU_FP16 = 9,
CPU_AVX = 10,
CPU_AVX2 = 11,
CPU_FMA3 = 12,
CPU_AVX_512F = 13,
CPU_AVX_512BW = 14,
CPU_AVX_512CD = 15,
CPU_AVX_512DQ = 16,
CPU_AVX_512ER = 17,
CPU_AVX_512IFMA512 = 18, // deprecated
CPU_AVX_512IFMA = 18,
CPU_AVX_512PF = 19,
CPU_AVX_512VBMI = 20,
CPU_AVX_512VL = 21,
CPU_AVX_512VBMI2 = 22,
CPU_AVX_512VNNI = 23,
CPU_AVX_512BITALG = 24,
CPU_AVX_512VPOPCNTDQ= 25,
CPU_AVX_5124VNNIW = 26,
CPU_AVX_5124FMAPS = 27,
CPU_NEON = 100,
CPU_MSA = 150,
CPU_VSX = 200,
CPU_VSX3 = 201,
CPU_AVX512_SKX = 256, //!< Skylake-X with AVX-512F/CD/BW/DQ/VL
CPU_AVX512_COMMON = 257, //!< Common instructions AVX-512F/CD for all CPUs that support AVX-512
CPU_AVX512_KNL = 258, //!< Knights Landing with AVX-512F/CD/ER/PF
CPU_AVX512_KNM = 259, //!< Knights Mill with AVX-512F/CD/ER/PF/4FMAPS/4VNNIW/VPOPCNTDQ
CPU_AVX512_CNL = 260, //!< Cannon Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI
CPU_AVX512_CLX = 261, //!< Cascade Lake with AVX-512F/CD/BW/DQ/VL/VNNI
CPU_AVX512_ICL = 262, //!< Ice Lake with AVX-512F/CD/BW/DQ/VL/IFMA/VBMI/VNNI/VBMI2/BITALG/VPOPCNTDQ
CPU_MAX_FEATURE = 512 // see CV_HARDWARE_MAX_FEATURE
};
#include "cv_cpu_dispatch.h"
/* fundamental constants */
#define CV_PI 3.1415926535897932384626433832795
#define CV_2PI 6.283185307179586476925286766559
#define CV_LOG2 0.69314718055994530941723212145818
#if defined __ARM_FP16_FORMAT_IEEE \
&& !defined __CUDACC__
# define CV_FP16_TYPE 1
#else
# define CV_FP16_TYPE 0
#endif
typedef union Cv16suf
{
short i;
ushort u;
#if CV_FP16_TYPE
__fp16 h;
#endif
}
Cv16suf;
typedef union Cv32suf
{
int i;
unsigned u;
float f;
}
Cv32suf;
typedef union Cv64suf
{
int64 i;
uint64 u;
double f;
}
Cv64suf;
#define OPENCV_ABI_COMPATIBILITY 400
#ifdef __OPENCV_BUILD
# define DISABLE_OPENCV_3_COMPATIBILITY
# define OPENCV_DISABLE_DEPRECATED_COMPATIBILITY
#endif
#ifndef CV_EXPORTS
# if (defined _WIN32 || defined WINCE || defined __CYGWIN__) && defined(CVAPI_EXPORTS)
# define CV_EXPORTS __declspec(dllexport)
# elif defined __GNUC__ && __GNUC__ >= 4 && (defined(CVAPI_EXPORTS) || defined(__APPLE__))
# define CV_EXPORTS __attribute__ ((visibility ("default")))
# endif
#endif
#ifndef CV_EXPORTS
# define CV_EXPORTS
#endif
#ifdef _MSC_VER
# define CV_EXPORTS_TEMPLATE
#else
# define CV_EXPORTS_TEMPLATE CV_EXPORTS
#endif
#ifndef CV_DEPRECATED
# if defined(__GNUC__)
# define CV_DEPRECATED __attribute__ ((deprecated))
# elif defined(_MSC_VER)
# define CV_DEPRECATED __declspec(deprecated)
# else
# define CV_DEPRECATED
# endif
#endif
#ifndef CV_DEPRECATED_EXTERNAL
# if defined(__OPENCV_BUILD)
# define CV_DEPRECATED_EXTERNAL /* nothing */
# else
# define CV_DEPRECATED_EXTERNAL CV_DEPRECATED
# endif
#endif
#ifndef CV_EXTERN_C
# ifdef __cplusplus
# define CV_EXTERN_C extern "C"
# else
# define CV_EXTERN_C
# endif
#endif
/* special informative macros for wrapper generators */
#define CV_EXPORTS_W CV_EXPORTS
#define CV_EXPORTS_W_SIMPLE CV_EXPORTS
#define CV_EXPORTS_AS(synonym) CV_EXPORTS
#define CV_EXPORTS_W_MAP CV_EXPORTS
#define CV_IN_OUT
#define CV_OUT
#define CV_PROP
#define CV_PROP_RW
#define CV_WRAP
#define CV_WRAP_AS(synonym)
#define CV_WRAP_MAPPABLE(mappable)
#define CV_WRAP_PHANTOM(phantom_header)
#define CV_WRAP_DEFAULT(val)
/****************************************************************************************\
* Matrix type (Mat) *
\****************************************************************************************/
#define CV_MAT_CN_MASK ((CV_CN_MAX - 1) << CV_CN_SHIFT)
#define CV_MAT_CN(flags) ((((flags) & CV_MAT_CN_MASK) >> CV_CN_SHIFT) + 1)
#define CV_MAT_TYPE_MASK (CV_DEPTH_MAX*CV_CN_MAX - 1)
#define CV_MAT_TYPE(flags) ((flags) & CV_MAT_TYPE_MASK)
#define CV_MAT_CONT_FLAG_SHIFT 14
#define CV_MAT_CONT_FLAG (1 << CV_MAT_CONT_FLAG_SHIFT)
#define CV_IS_MAT_CONT(flags) ((flags) & CV_MAT_CONT_FLAG)
#define CV_IS_CONT_MAT CV_IS_MAT_CONT
#define CV_SUBMAT_FLAG_SHIFT 15
#define CV_SUBMAT_FLAG (1 << CV_SUBMAT_FLAG_SHIFT)
#define CV_IS_SUBMAT(flags) ((flags) & CV_MAT_SUBMAT_FLAG)
/** Size of each channel item,
0x28442211 = 0010 1000 0100 0100 0010 0010 0001 0001 ~ array of sizeof(arr_type_elem) */
#define CV_ELEM_SIZE1(type) ((0x28442211 >> CV_MAT_DEPTH(type)*4) & 15)
#define CV_ELEM_SIZE(type) (CV_MAT_CN(type)*CV_ELEM_SIZE1(type))
#ifndef MIN
# define MIN(a,b) ((a) > (b) ? (b) : (a))
#endif
#ifndef MAX
# define MAX(a,b) ((a) < (b) ? (b) : (a))
#endif
///////////////////////////////////////// Enum operators ///////////////////////////////////////
/**
Provides compatibility operators for both classical and C++11 enum classes,
as well as exposing the C++11 enum class members for backwards compatibility
@code
// Provides operators required for flag enums
CV_ENUM_FLAGS(AccessFlag)
// Exposes the listed members of the enum class AccessFlag to the current namespace
CV_ENUM_CLASS_EXPOSE(AccessFlag, ACCESS_READ [, ACCESS_WRITE [, ...] ]);
@endcode
*/
#define __CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST) \
static const EnumType MEMBER_CONST = EnumType::MEMBER_CONST; \
#define __CV_ENUM_CLASS_EXPOSE_2(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_1(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_3(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_2(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_4(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_3(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_5(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_4(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_6(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_5(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_7(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_6(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_8(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_7(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_CLASS_EXPOSE_9(EnumType, MEMBER_CONST, ...) \
__CV_ENUM_CLASS_EXPOSE_1(EnumType, MEMBER_CONST); \
__CV_EXPAND(__CV_ENUM_CLASS_EXPOSE_8(EnumType, __VA_ARGS__)); \
#define __CV_ENUM_FLAGS_LOGICAL_NOT(EnumType) \
static inline bool operator!(const EnumType& val) \
{ \
typedef std::underlying_type<EnumType>::type UnderlyingType; \
return !static_cast<UnderlyingType>(val); \
} \
#define __CV_ENUM_FLAGS_LOGICAL_NOT_EQ(Arg1Type, Arg2Type) \
static inline bool operator!=(const Arg1Type& a, const Arg2Type& b) \
{ \
return static_cast<int>(a) != static_cast<int>(b); \
} \
#define __CV_ENUM_FLAGS_LOGICAL_EQ(Arg1Type, Arg2Type) \
static inline bool operator==(const Arg1Type& a, const Arg2Type& b) \
{ \
return static_cast<int>(a) == static_cast<int>(b); \
} \
#define __CV_ENUM_FLAGS_BITWISE_NOT(EnumType) \
static inline EnumType operator~(const EnumType& val) \
{ \
typedef std::underlying_type<EnumType>::type UnderlyingType; \
return static_cast<EnumType>(~static_cast<UnderlyingType>(val)); \
} \
#define __CV_ENUM_FLAGS_BITWISE_OR(EnumType, Arg1Type, Arg2Type) \
static inline EnumType operator|(const Arg1Type& a, const Arg2Type& b) \
{ \
typedef std::underlying_type<EnumType>::type UnderlyingType; \
return static_cast<EnumType>(static_cast<UnderlyingType>(a) | static_cast<UnderlyingType>(b)); \
} \
#define __CV_ENUM_FLAGS_BITWISE_AND(EnumType, Arg1Type, Arg2Type) \
static inline EnumType operator&(const Arg1Type& a, const Arg2Type& b) \
{ \
typedef std::underlying_type<EnumType>::type UnderlyingType; \
return static_cast<EnumType>(static_cast<UnderlyingType>(a) & static_cast<UnderlyingType>(b)); \
} \
#define __CV_ENUM_FLAGS_BITWISE_XOR(EnumType, Arg1Type, Arg2Type) \
static inline EnumType operator^(const Arg1Type& a, const Arg2Type& b) \
{ \
typedef std::underlying_type<EnumType>::type UnderlyingType; \
return static_cast<EnumType>(static_cast<UnderlyingType>(a) ^ static_cast<UnderlyingType>(b)); \
} \
#define __CV_ENUM_FLAGS_BITWISE_OR_EQ(EnumType, Arg1Type) \
static inline EnumType& operator|=(EnumType& _this, const Arg1Type& val) \
{ \
_this = static_cast<EnumType>(static_cast<int>(_this) | static_cast<int>(val)); \
return _this; \
} \
#define __CV_ENUM_FLAGS_BITWISE_AND_EQ(EnumType, Arg1Type) \
static inline EnumType& operator&=(EnumType& _this, const Arg1Type& val) \
{ \
_this = static_cast<EnumType>(static_cast<int>(_this) & static_cast<int>(val)); \
return _this; \
} \
#define __CV_ENUM_FLAGS_BITWISE_XOR_EQ(EnumType, Arg1Type) \
static inline EnumType& operator^=(EnumType& _this, const Arg1Type& val) \
{ \
_this = static_cast<EnumType>(static_cast<int>(_this) ^ static_cast<int>(val)); \
return _this; \
} \
#define CV_ENUM_CLASS_EXPOSE(EnumType, ...) \
__CV_EXPAND(__CV_CAT(__CV_ENUM_CLASS_EXPOSE_, __CV_VA_NUM_ARGS(__VA_ARGS__))(EnumType, __VA_ARGS__)); \
#define CV_ENUM_FLAGS(EnumType) \
__CV_ENUM_FLAGS_LOGICAL_NOT (EnumType) \
__CV_ENUM_FLAGS_LOGICAL_EQ (EnumType, int) \
__CV_ENUM_FLAGS_LOGICAL_NOT_EQ (EnumType, int) \
\
__CV_ENUM_FLAGS_BITWISE_NOT (EnumType) \
__CV_ENUM_FLAGS_BITWISE_OR (EnumType, EnumType, EnumType) \
__CV_ENUM_FLAGS_BITWISE_AND (EnumType, EnumType, EnumType) \
__CV_ENUM_FLAGS_BITWISE_XOR (EnumType, EnumType, EnumType) \
\
__CV_ENUM_FLAGS_BITWISE_OR_EQ (EnumType, EnumType) \
__CV_ENUM_FLAGS_BITWISE_AND_EQ (EnumType, EnumType) \
__CV_ENUM_FLAGS_BITWISE_XOR_EQ (EnumType, EnumType) \
/****************************************************************************************\
* static analysys *
\****************************************************************************************/
// In practice, some macro are not processed correctly (noreturn is not detected).
// We need to use simplified definition for them.
#ifndef CV_STATIC_ANALYSIS
# if defined(__KLOCWORK__) || defined(__clang_analyzer__) || defined(__COVERITY__)
# define CV_STATIC_ANALYSIS 1
# endif
#else
# if defined(CV_STATIC_ANALYSIS) && !(__CV_CAT(1, CV_STATIC_ANALYSIS) == 1) // defined and not empty
# if 0 == CV_STATIC_ANALYSIS
# undef CV_STATIC_ANALYSIS
# endif
# endif
#endif
/****************************************************************************************\
* Thread sanitizer *
\****************************************************************************************/
#ifndef CV_THREAD_SANITIZER
# if defined(__has_feature)
# if __has_feature(thread_sanitizer)
# define CV_THREAD_SANITIZER
# endif
# endif
#endif
/****************************************************************************************\
* exchange-add operation for atomic operations on reference counters *
\****************************************************************************************/
#ifdef CV_XADD
// allow to use user-defined macro
#elif defined __GNUC__ || defined __clang__
# if defined __clang__ && __clang_major__ >= 3 && !defined __ANDROID__ && !defined __EMSCRIPTEN__ && !defined(__CUDACC__) && !defined __INTEL_COMPILER
# ifdef __ATOMIC_ACQ_REL
# define CV_XADD(addr, delta) __c11_atomic_fetch_add((_Atomic(int)*)(addr), delta, __ATOMIC_ACQ_REL)
# else
# define CV_XADD(addr, delta) __atomic_fetch_add((_Atomic(int)*)(addr), delta, 4)
# endif
# else
# if defined __ATOMIC_ACQ_REL && !defined __clang__
// version for gcc >= 4.7
# define CV_XADD(addr, delta) (int)__atomic_fetch_add((unsigned*)(addr), (unsigned)(delta), __ATOMIC_ACQ_REL)
# else
# define CV_XADD(addr, delta) (int)__sync_fetch_and_add((unsigned*)(addr), (unsigned)(delta))
# endif
# endif
#elif defined _MSC_VER && !defined RC_INVOKED
# include <intrin.h>
# define CV_XADD(addr, delta) (int)_InterlockedExchangeAdd((long volatile*)addr, delta)
#else
#ifdef OPENCV_FORCE_UNSAFE_XADD
CV_INLINE CV_XADD(int* addr, int delta) { int tmp = *addr; *addr += delta; return tmp; }
#else
#error "OpenCV: can't define safe CV_XADD macro for current platform (unsupported). Define CV_XADD macro through custom port header (see OPENCV_INCLUDE_PORT_FILE)"
#endif
#endif
/****************************************************************************************\
* CV_NORETURN attribute *
\****************************************************************************************/
#ifndef CV_NORETURN
# if defined(__GNUC__)
# define CV_NORETURN __attribute__((__noreturn__))
# elif defined(_MSC_VER) && (_MSC_VER >= 1300)
# define CV_NORETURN __declspec(noreturn)
# else
# define CV_NORETURN /* nothing by default */
# endif
#endif
/****************************************************************************************\
* CV_NODISCARD attribute *
* encourages the compiler to issue a warning if the return value is discarded (C++17) *
\****************************************************************************************/
#ifndef CV_NODISCARD
# if defined(__GNUC__)
# define CV_NODISCARD __attribute__((__warn_unused_result__)) // at least available with GCC 3.4
# elif defined(__clang__) && defined(__has_attribute)
# if __has_attribute(__warn_unused_result__)
# define CV_NODISCARD __attribute__((__warn_unused_result__))
# endif
# endif
#endif
#ifndef CV_NODISCARD
# define CV_NODISCARD /* nothing by default */
#endif
/****************************************************************************************\
* C++ 11 *
\****************************************************************************************/
#ifndef CV_CXX11
# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1800)
# define CV_CXX11 1
# endif
#else
# if CV_CXX11 == 0
# undef CV_CXX11
# endif
#endif
#ifndef CV_CXX11
# error "OpenCV 4.x+ requires enabled C++11 support"
#endif
#define CV_CXX_MOVE_SEMANTICS 1
#define CV_CXX_MOVE(x) std::move(x)
#define CV_CXX_STD_ARRAY 1
#include <array>
#ifndef CV_OVERRIDE
# define CV_OVERRIDE override
#endif
#ifndef CV_FINAL
# define CV_FINAL final
#endif
#ifndef CV_NOEXCEPT
# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
# define CV_NOEXCEPT noexcept
# endif
#endif
#ifndef CV_NOEXCEPT
# define CV_NOEXCEPT
#endif
#ifndef CV_CONSTEXPR
# if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
# define CV_CONSTEXPR constexpr
# endif
#endif
#ifndef CV_CONSTEXPR
# define CV_CONSTEXPR
#endif
// Integer types portatibility
#ifdef OPENCV_STDINT_HEADER
#include OPENCV_STDINT_HEADER
#elif defined(__cplusplus)
#if defined(_MSC_VER) && _MSC_VER < 1600 /* MSVS 2010 */
namespace cv {
typedef signed char int8_t;
typedef unsigned char uint8_t;
typedef signed short int16_t;
typedef unsigned short uint16_t;
typedef signed int int32_t;
typedef unsigned int uint32_t;
typedef signed __int64 int64_t;
typedef unsigned __int64 uint64_t;
}
#elif defined(_MSC_VER) || __cplusplus >= 201103L
#include <cstdint>
namespace cv {
using std::int8_t;
using std::uint8_t;
using std::int16_t;
using std::uint16_t;
using std::int32_t;
using std::uint32_t;
using std::int64_t;
using std::uint64_t;
}
#else
#include <stdint.h>
namespace cv {
typedef ::int8_t int8_t;
typedef ::uint8_t uint8_t;
typedef ::int16_t int16_t;
typedef ::uint16_t uint16_t;
typedef ::int32_t int32_t;
typedef ::uint32_t uint32_t;
typedef ::int64_t int64_t;
typedef ::uint64_t uint64_t;
}
#endif
#else // pure C
#include <stdint.h>
#endif
#ifdef __cplusplus
namespace cv
{
class float16_t
{
public:
#if CV_FP16_TYPE
float16_t() : h(0) {}
explicit float16_t(float x) { h = (__fp16)x; }
operator float() const { return (float)h; }
static float16_t fromBits(ushort w)
{
Cv16suf u;
u.u = w;
float16_t result;
result.h = u.h;
return result;
}
static float16_t zero()
{
float16_t result;
result.h = (__fp16)0;
return result;
}
ushort bits() const
{
Cv16suf u;
u.h = h;
return u.u;
}
protected:
__fp16 h;
#else
float16_t() : w(0) {}
explicit float16_t(float x)
{
#if CV_AVX2
__m128 v = _mm_load_ss(&x);
w = (ushort)_mm_cvtsi128_si32(_mm_cvtps_ph(v, 0));
#else
Cv32suf in;
in.f = x;
unsigned sign = in.u & 0x80000000;
in.u ^= sign;
if( in.u >= 0x47800000 )
w = (ushort)(in.u > 0x7f800000 ? 0x7e00 : 0x7c00);
else
{
if (in.u < 0x38800000)
{
in.f += 0.5f;
w = (ushort)(in.u - 0x3f000000);
}
else
{
unsigned t = in.u + 0xc8000fff;
w = (ushort)((t + ((in.u >> 13) & 1)) >> 13);
}
}
w = (ushort)(w | (sign >> 16));
#endif
}
operator float() const
{
#if CV_AVX2
float f;
_mm_store_ss(&f, _mm_cvtph_ps(_mm_cvtsi32_si128(w)));
return f;
#else
Cv32suf out;
unsigned t = ((w & 0x7fff) << 13) + 0x38000000;
unsigned sign = (w & 0x8000) << 16;
unsigned e = w & 0x7c00;
out.u = t + (1 << 23);
out.u = (e >= 0x7c00 ? t + 0x38000000 :
e == 0 ? (static_cast<void>(out.f -= 6.103515625e-05f), out.u) : t) | sign;
return out.f;
#endif
}
static float16_t fromBits(ushort b)
{
float16_t result;
result.w = b;
return result;
}
static float16_t zero()
{
float16_t result;
result.w = (ushort)0;
return result;
}
ushort bits() const { return w; }
protected:
ushort w;
#endif
};
}
#endif
//! @}
#ifndef __cplusplus
#include "opencv2/core/fast_math.hpp" // define cvRound(double)
#endif
#endif // OPENCV_CORE_CVDEF_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*/
#ifndef OPENCV_CORE_CVSTD_HPP
#define OPENCV_CORE_CVSTD_HPP
#ifndef __cplusplus
# error cvstd.hpp header must be compiled as C++
#endif
#include "opencv2/core/cvdef.h"
#include <cstddef>
#include <cstring>
#include <cctype>
#include <string>
// import useful primitives from stl
# include <algorithm>
# include <utility>
# include <cstdlib> //for abs(int)
# include <cmath>
namespace cv
{
static inline uchar abs(uchar a) { return a; }
static inline ushort abs(ushort a) { return a; }
static inline unsigned abs(unsigned a) { return a; }
static inline uint64 abs(uint64 a) { return a; }
using std::min;
using std::max;
using std::abs;
using std::swap;
using std::sqrt;
using std::exp;
using std::pow;
using std::log;
}
#include "cvstd_wrapper.hpp"
namespace cv {
//! @addtogroup core_utils
//! @{
//////////////////////////// memory management functions ////////////////////////////
/** @brief Allocates an aligned memory buffer.
The function allocates the buffer of the specified size and returns it. When the buffer size is 16
bytes or more, the returned buffer is aligned to 16 bytes.
@param bufSize Allocated buffer size.
*/
CV_EXPORTS void* fastMalloc(size_t bufSize);
/** @brief Deallocates a memory buffer.
The function deallocates the buffer allocated with fastMalloc . If NULL pointer is passed, the
function does nothing. C version of the function clears the pointer *pptr* to avoid problems with
double memory deallocation.
@param ptr Pointer to the allocated buffer.
*/
CV_EXPORTS void fastFree(void* ptr);
/*!
The STL-compliant memory Allocator based on cv::fastMalloc() and cv::fastFree()
*/
template<typename _Tp> class Allocator
{
public:
typedef _Tp value_type;
typedef value_type* pointer;
typedef const value_type* const_pointer;
typedef value_type& reference;
typedef const value_type& const_reference;
typedef size_t size_type;
typedef ptrdiff_t difference_type;
template<typename U> class rebind { typedef Allocator<U> other; };
explicit Allocator() {}
~Allocator() {}
explicit Allocator(Allocator const&) {}
template<typename U>
explicit Allocator(Allocator<U> const&) {}
// address
pointer address(reference r) { return &r; }
const_pointer address(const_reference r) { return &r; }
pointer allocate(size_type count, const void* =0) { return reinterpret_cast<pointer>(fastMalloc(count * sizeof (_Tp))); }
void deallocate(pointer p, size_type) { fastFree(p); }
void construct(pointer p, const _Tp& v) { new(static_cast<void*>(p)) _Tp(v); }
void destroy(pointer p) { p->~_Tp(); }
size_type max_size() const { return cv::max(static_cast<_Tp>(-1)/sizeof(_Tp), 1); }
};
//! @} core_utils
//! @endcond
//! @addtogroup core_basic
//! @{
//////////////////////////////// string class ////////////////////////////////
class CV_EXPORTS FileNode; //for string constructor from FileNode
typedef std::string String;
#ifndef OPENCV_DISABLE_STRING_LOWER_UPPER_CONVERSIONS
//! @cond IGNORED
namespace details {
// std::tolower is int->int
static inline char char_tolower(char ch)
{
return (char)std::tolower((int)ch);
}
// std::toupper is int->int
static inline char char_toupper(char ch)
{
return (char)std::toupper((int)ch);
}
} // namespace details
//! @endcond
static inline std::string toLowerCase(const std::string& str)
{
std::string result(str);
std::transform(result.begin(), result.end(), result.begin(), details::char_tolower);
return result;
}
static inline std::string toUpperCase(const std::string& str)
{
std::string result(str);
std::transform(result.begin(), result.end(), result.begin(), details::char_toupper);
return result;
}
#endif // OPENCV_DISABLE_STRING_LOWER_UPPER_CONVERSIONS
//! @} core_basic
} // cv
#endif //OPENCV_CORE_CVSTD_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_CORE_CVSTDINL_HPP
#define OPENCV_CORE_CVSTDINL_HPP
#include <complex>
#include <ostream>
//! @cond IGNORED
#ifdef _MSC_VER
#pragma warning( push )
#pragma warning( disable: 4127 )
#endif
namespace cv
{
template<typename _Tp> class DataType< std::complex<_Tp> >
{
public:
typedef std::complex<_Tp> value_type;
typedef value_type work_type;
typedef _Tp channel_type;
enum { generic_type = 0,
depth = DataType<channel_type>::depth,
channels = 2,
fmt = DataType<channel_type>::fmt + ((channels - 1) << 8),
type = CV_MAKETYPE(depth, channels) };
typedef Vec<channel_type, channels> vec_type;
};
static inline
std::ostream& operator << (std::ostream& out, Ptr<Formatted> fmtd)
{
fmtd->reset();
for(const char* str = fmtd->next(); str; str = fmtd->next())
out << str;
return out;
}
static inline
std::ostream& operator << (std::ostream& out, const Mat& mtx)
{
return out << Formatter::get()->format(mtx);
}
static inline
std::ostream& operator << (std::ostream& out, const UMat& m)
{
return out << m.getMat(ACCESS_READ);
}
template<typename _Tp> static inline
std::ostream& operator << (std::ostream& out, const Complex<_Tp>& c)
{
return out << "(" << c.re << "," << c.im << ")";
}
template<typename _Tp> static inline
std::ostream& operator << (std::ostream& out, const std::vector<Point_<_Tp> >& vec)
{
return out << Formatter::get()->format(Mat(vec));
}
template<typename _Tp> static inline
std::ostream& operator << (std::ostream& out, const std::vector<Point3_<_Tp> >& vec)
{
return out << Formatter::get()->format(Mat(vec));
}
template<typename _Tp, int m, int n> static inline
std::ostream& operator << (std::ostream& out, const Matx<_Tp, m, n>& matx)
{
return out << Formatter::get()->format(Mat(matx));
}
template<typename _Tp> static inline
std::ostream& operator << (std::ostream& out, const Point_<_Tp>& p)
{
out << "[" << p.x << ", " << p.y << "]";
return out;
}
template<typename _Tp> static inline
std::ostream& operator << (std::ostream& out, const Point3_<_Tp>& p)
{
out << "[" << p.x << ", " << p.y << ", " << p.z << "]";
return out;
}
template<typename _Tp, int n> static inline
std::ostream& operator << (std::ostream& out, const Vec<_Tp, n>& vec)
{
out << "[";
if (cv::traits::Depth<_Tp>::value <= CV_32S)
{
for (int i = 0; i < n - 1; ++i) {
out << (int)vec[i] << ", ";
}
out << (int)vec[n-1] << "]";
}
else
{
for (int i = 0; i < n - 1; ++i) {
out << vec[i] << ", ";
}
out << vec[n-1] << "]";
}
return out;
}
template<typename _Tp> static inline
std::ostream& operator << (std::ostream& out, const Size_<_Tp>& size)
{
return out << "[" << size.width << " x " << size.height << "]";
}
template<typename _Tp> static inline
std::ostream& operator << (std::ostream& out, const Rect_<_Tp>& rect)
{
return out << "[" << rect.width << " x " << rect.height << " from (" << rect.x << ", " << rect.y << ")]";
}
static inline std::ostream& operator << (std::ostream& out, const MatSize& msize)
{
int i, dims = msize.dims();
for( i = 0; i < dims; i++ )
{
out << msize[i];
if( i < dims-1 )
out << " x ";
}
return out;
}
static inline std::ostream &operator<< (std::ostream &s, cv::Range &r)
{
return s << "[" << r.start << " : " << r.end << ")";
}
} // cv
#ifdef _MSC_VER
#pragma warning( pop )
#endif
//! @endcond
#endif // OPENCV_CORE_CVSTDINL_HPP

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_CVSTD_WRAPPER_HPP
#define OPENCV_CORE_CVSTD_WRAPPER_HPP
#include "opencv2/core/cvdef.h"
#include <string>
#include <memory> // std::shared_ptr
#include <type_traits> // std::enable_if
namespace cv {
using std::nullptr_t;
//! @addtogroup core_basic
//! @{
#ifdef CV_DOXYGEN
template <typename _Tp> using Ptr = std::shared_ptr<_Tp>; // In ideal world it should look like this, but we need some compatibility workarounds below
template<typename _Tp, typename ... A1> static inline
Ptr<_Tp> makePtr(const A1&... a1) { return std::make_shared<_Tp>(a1...); }
#else // cv::Ptr with compatibility workarounds
// It should be defined for C-API types only.
// C++ types should use regular "delete" operator.
template<typename Y> struct DefaultDeleter;
#if 0
{
void operator()(Y* p) const;
};
#endif
namespace sfinae {
template<typename C, typename Ret, typename... Args>
struct has_parenthesis_operator
{
private:
template<typename T>
static CV_CONSTEXPR std::true_type check(typename std::is_same<typename std::decay<decltype(std::declval<T>().operator()(std::declval<Args>()...))>::type, Ret>::type*);
template<typename> static CV_CONSTEXPR std::false_type check(...);
typedef decltype(check<C>(0)) type;
public:
#if __cplusplus >= 201103L || (defined(_MSC_VER) && _MSC_VER >= 1900/*MSVS 2015*/)
static CV_CONSTEXPR bool value = type::value;
#else
// support MSVS 2013
static const int value = type::value;
#endif
};
} // namespace sfinae
template <typename T, typename = void>
struct has_custom_delete
: public std::false_type {};
// Force has_custom_delete to std::false_type when NVCC is compiling CUDA source files
#ifndef __CUDACC__
template <typename T>
struct has_custom_delete<T, typename std::enable_if< sfinae::has_parenthesis_operator<DefaultDeleter<T>, void, T*>::value >::type >
: public std::true_type {};
#endif
template<typename T>
struct Ptr : public std::shared_ptr<T>
{
#if 0
using std::shared_ptr<T>::shared_ptr; // GCC 5.x can't handle this
#else
inline Ptr() CV_NOEXCEPT : std::shared_ptr<T>() {}
inline Ptr(nullptr_t) CV_NOEXCEPT : std::shared_ptr<T>(nullptr) {}
template<typename Y, typename D> inline Ptr(Y* p, D d) : std::shared_ptr<T>(p, d) {}
template<typename D> inline Ptr(nullptr_t, D d) : std::shared_ptr<T>(nullptr, d) {}
template<typename Y> inline Ptr(const Ptr<Y>& r, T* ptr) CV_NOEXCEPT : std::shared_ptr<T>(r, ptr) {}
inline Ptr(const Ptr<T>& o) CV_NOEXCEPT : std::shared_ptr<T>(o) {}
inline Ptr(Ptr<T>&& o) CV_NOEXCEPT : std::shared_ptr<T>(std::move(o)) {}
template<typename Y> inline Ptr(const Ptr<Y>& o) CV_NOEXCEPT : std::shared_ptr<T>(o) {}
template<typename Y> inline Ptr(Ptr<Y>&& o) CV_NOEXCEPT : std::shared_ptr<T>(std::move(o)) {}
#endif
inline Ptr(const std::shared_ptr<T>& o) CV_NOEXCEPT : std::shared_ptr<T>(o) {}
inline Ptr(std::shared_ptr<T>&& o) CV_NOEXCEPT : std::shared_ptr<T>(std::move(o)) {}
// Overload with custom DefaultDeleter: Ptr<IplImage>(...)
template<typename Y>
inline Ptr(const std::true_type&, Y* ptr) : std::shared_ptr<T>(ptr, DefaultDeleter<Y>()) {}
// Overload without custom deleter: Ptr<std::string>(...);
template<typename Y>
inline Ptr(const std::false_type&, Y* ptr) : std::shared_ptr<T>(ptr) {}
template<typename Y = T>
inline Ptr(Y* ptr) : Ptr(has_custom_delete<Y>(), ptr) {}
// Overload with custom DefaultDeleter: Ptr<IplImage>(...)
template<typename Y>
inline void reset(const std::true_type&, Y* ptr) { std::shared_ptr<T>::reset(ptr, DefaultDeleter<Y>()); }
// Overload without custom deleter: Ptr<std::string>(...);
template<typename Y>
inline void reset(const std::false_type&, Y* ptr) { std::shared_ptr<T>::reset(ptr); }
template<typename Y>
inline void reset(Y* ptr) { Ptr<T>::reset(has_custom_delete<Y>(), ptr); }
template<class Y, class Deleter>
void reset(Y* ptr, Deleter d) { std::shared_ptr<T>::reset(ptr, d); }
void reset() CV_NOEXCEPT { std::shared_ptr<T>::reset(); }
Ptr& operator=(const Ptr& o) { std::shared_ptr<T>::operator =(o); return *this; }
template<typename Y> inline Ptr& operator=(const Ptr<Y>& o) { std::shared_ptr<T>::operator =(o); return *this; }
T* operator->() const CV_NOEXCEPT { return std::shared_ptr<T>::get();}
typename std::add_lvalue_reference<T>::type operator*() const CV_NOEXCEPT { return *std::shared_ptr<T>::get(); }
// OpenCV 3.x methods (not a part of standard C++ library)
inline void release() { std::shared_ptr<T>::reset(); }
inline operator T* () const { return std::shared_ptr<T>::get(); }
inline bool empty() const { return std::shared_ptr<T>::get() == nullptr; }
template<typename Y> inline
Ptr<Y> staticCast() const CV_NOEXCEPT { return std::static_pointer_cast<Y>(*this); }
template<typename Y> inline
Ptr<Y> constCast() const CV_NOEXCEPT { return std::const_pointer_cast<Y>(*this); }
template<typename Y> inline
Ptr<Y> dynamicCast() const CV_NOEXCEPT { return std::dynamic_pointer_cast<Y>(*this); }
};
template<typename _Tp, typename ... A1> static inline
Ptr<_Tp> makePtr(const A1&... a1)
{
static_assert( !has_custom_delete<_Tp>::value, "Can't use this makePtr with custom DefaultDeleter");
return (Ptr<_Tp>)std::make_shared<_Tp>(a1...);
}
#endif // CV_DOXYGEN
//! @} core_basic
} // cv
#endif //OPENCV_CORE_CVSTD_WRAPPER_HPP

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_ASYNC_PROMISE_HPP
#define OPENCV_CORE_ASYNC_PROMISE_HPP
#include "../async.hpp"
#include "exception_ptr.hpp"
namespace cv {
/** @addtogroup core_async
@{
*/
/** @brief Provides result of asynchronous operations
*/
class CV_EXPORTS AsyncPromise
{
public:
~AsyncPromise() CV_NOEXCEPT;
AsyncPromise() CV_NOEXCEPT;
explicit AsyncPromise(const AsyncPromise& o) CV_NOEXCEPT;
AsyncPromise& operator=(const AsyncPromise& o) CV_NOEXCEPT;
void release() CV_NOEXCEPT;
/** Returns associated AsyncArray
@note Can be called once
*/
AsyncArray getArrayResult();
/** Stores asynchronous result.
@param[in] value result
*/
void setValue(InputArray value);
// TODO "move" setters
#if CV__EXCEPTION_PTR
/** Stores exception.
@param[in] exception exception to be raised in AsyncArray
*/
void setException(std::exception_ptr exception);
#endif
/** Stores exception.
@param[in] exception exception to be raised in AsyncArray
*/
void setException(const cv::Exception& exception);
#ifdef CV_CXX11
explicit AsyncPromise(AsyncPromise&& o) { p = o.p; o.p = NULL; }
AsyncPromise& operator=(AsyncPromise&& o) CV_NOEXCEPT { std::swap(p, o.p); return *this; }
#endif
// PImpl
typedef struct AsyncArray::Impl Impl; friend struct AsyncArray::Impl;
inline void* _getImpl() const CV_NOEXCEPT { return p; }
protected:
Impl* p;
};
//! @}
} // namespace
#endif // OPENCV_CORE_ASYNC_PROMISE_HPP

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef OPENCV_CORE_DETAILS_EXCEPTION_PTR_H
#define OPENCV_CORE_DETAILS_EXCEPTION_PTR_H
#ifndef CV__EXCEPTION_PTR
# if defined(__ANDROID__) && defined(ATOMIC_INT_LOCK_FREE) && ATOMIC_INT_LOCK_FREE < 2
# define CV__EXCEPTION_PTR 0 // Not supported, details: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=58938
# elif defined(CV_CXX11)
# define CV__EXCEPTION_PTR 1
# elif defined(_MSC_VER)
# define CV__EXCEPTION_PTR (_MSC_VER >= 1600)
# elif defined(__clang__)
# define CV__EXCEPTION_PTR 0 // C++11 only (see above)
# elif defined(__GNUC__) && defined(__GXX_EXPERIMENTAL_CXX0X__)
# define CV__EXCEPTION_PTR (__GXX_EXPERIMENTAL_CXX0X__ > 0)
# endif
#endif
#ifndef CV__EXCEPTION_PTR
# define CV__EXCEPTION_PTR 0
#elif CV__EXCEPTION_PTR
# include <exception> // std::exception_ptr
#endif
#endif // OPENCV_CORE_DETAILS_EXCEPTION_PTR_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) 2010-2013, Advanced Micro Devices, 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 copyright holders 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_CORE_DIRECTX_HPP
#define OPENCV_CORE_DIRECTX_HPP
#include "mat.hpp"
#include "ocl.hpp"
#if !defined(__d3d11_h__)
struct ID3D11Device;
struct ID3D11Texture2D;
#endif
#if !defined(__d3d10_h__)
struct ID3D10Device;
struct ID3D10Texture2D;
#endif
#if !defined(_D3D9_H_)
struct IDirect3DDevice9;
struct IDirect3DDevice9Ex;
struct IDirect3DSurface9;
#endif
namespace cv { namespace directx {
namespace ocl {
using namespace cv::ocl;
//! @addtogroup core_directx
// This section describes OpenCL and DirectX interoperability.
//
// To enable DirectX support, configure OpenCV using CMake with WITH_DIRECTX=ON . Note, DirectX is
// supported only on Windows.
//
// To use OpenCL functionality you should first initialize OpenCL context from DirectX resource.
//
//! @{
// TODO static functions in the Context class
//! @brief Creates OpenCL context from D3D11 device
//
//! @param pD3D11Device - pointer to D3D11 device
//! @return Returns reference to OpenCL Context
CV_EXPORTS Context& initializeContextFromD3D11Device(ID3D11Device* pD3D11Device);
//! @brief Creates OpenCL context from D3D10 device
//
//! @param pD3D10Device - pointer to D3D10 device
//! @return Returns reference to OpenCL Context
CV_EXPORTS Context& initializeContextFromD3D10Device(ID3D10Device* pD3D10Device);
//! @brief Creates OpenCL context from Direct3DDevice9Ex device
//
//! @param pDirect3DDevice9Ex - pointer to Direct3DDevice9Ex device
//! @return Returns reference to OpenCL Context
CV_EXPORTS Context& initializeContextFromDirect3DDevice9Ex(IDirect3DDevice9Ex* pDirect3DDevice9Ex);
//! @brief Creates OpenCL context from Direct3DDevice9 device
//
//! @param pDirect3DDevice9 - pointer to Direct3Device9 device
//! @return Returns reference to OpenCL Context
CV_EXPORTS Context& initializeContextFromDirect3DDevice9(IDirect3DDevice9* pDirect3DDevice9);
//! @}
} // namespace cv::directx::ocl
//! @addtogroup core_directx
//! @{
//! @brief Converts InputArray to ID3D11Texture2D. If destination texture format is DXGI_FORMAT_NV12 then
//! input UMat expected to be in BGR format and data will be downsampled and color-converted to NV12.
//
//! @note Note: Destination texture must be allocated by application. Function does memory copy from src to
//! pD3D11Texture2D
//
//! @param src - source InputArray
//! @param pD3D11Texture2D - destination D3D11 texture
CV_EXPORTS void convertToD3D11Texture2D(InputArray src, ID3D11Texture2D* pD3D11Texture2D);
//! @brief Converts ID3D11Texture2D to OutputArray. If input texture format is DXGI_FORMAT_NV12 then
//! data will be upsampled and color-converted to BGR format.
//
//! @note Note: Destination matrix will be re-allocated if it has not enough memory to match texture size.
//! function does memory copy from pD3D11Texture2D to dst
//
//! @param pD3D11Texture2D - source D3D11 texture
//! @param dst - destination OutputArray
CV_EXPORTS void convertFromD3D11Texture2D(ID3D11Texture2D* pD3D11Texture2D, OutputArray dst);
//! @brief Converts InputArray to ID3D10Texture2D
//
//! @note Note: function does memory copy from src to
//! pD3D10Texture2D
//
//! @param src - source InputArray
//! @param pD3D10Texture2D - destination D3D10 texture
CV_EXPORTS void convertToD3D10Texture2D(InputArray src, ID3D10Texture2D* pD3D10Texture2D);
//! @brief Converts ID3D10Texture2D to OutputArray
//
//! @note Note: function does memory copy from pD3D10Texture2D
//! to dst
//
//! @param pD3D10Texture2D - source D3D10 texture
//! @param dst - destination OutputArray
CV_EXPORTS void convertFromD3D10Texture2D(ID3D10Texture2D* pD3D10Texture2D, OutputArray dst);
//! @brief Converts InputArray to IDirect3DSurface9
//
//! @note Note: function does memory copy from src to
//! pDirect3DSurface9
//
//! @param src - source InputArray
//! @param pDirect3DSurface9 - destination D3D10 texture
//! @param surfaceSharedHandle - shared handle
CV_EXPORTS void convertToDirect3DSurface9(InputArray src, IDirect3DSurface9* pDirect3DSurface9, void* surfaceSharedHandle = NULL);
//! @brief Converts IDirect3DSurface9 to OutputArray
//
//! @note Note: function does memory copy from pDirect3DSurface9
//! to dst
//
//! @param pDirect3DSurface9 - source D3D10 texture
//! @param dst - destination OutputArray
//! @param surfaceSharedHandle - shared handle
CV_EXPORTS void convertFromDirect3DSurface9(IDirect3DSurface9* pDirect3DSurface9, OutputArray dst, void* surfaceSharedHandle = NULL);
//! @brief Get OpenCV type from DirectX type
//! @param iDXGI_FORMAT - enum DXGI_FORMAT for D3D10/D3D11
//! @return OpenCV type or -1 if there is no equivalent
CV_EXPORTS int getTypeFromDXGI_FORMAT(const int iDXGI_FORMAT); // enum DXGI_FORMAT for D3D10/D3D11
//! @brief Get OpenCV type from DirectX type
//! @param iD3DFORMAT - enum D3DTYPE for D3D9
//! @return OpenCV type or -1 if there is no equivalent
CV_EXPORTS int getTypeFromD3DFORMAT(const int iD3DFORMAT); // enum D3DTYPE for D3D9
//! @}
} } // namespace cv::directx
#endif // OPENCV_CORE_DIRECTX_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_CORE_EIGEN_HPP
#define OPENCV_CORE_EIGEN_HPP
#include "opencv2/core.hpp"
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4714 ) //__forceinline is not inlined
#pragma warning( disable: 4127 ) //conditional expression is constant
#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data
#endif
namespace cv
{
//! @addtogroup core_eigen
//! @{
template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, OutputArray dst )
{
if( !(src.Flags & Eigen::RowMajorBit) )
{
Mat _src(src.cols(), src.rows(), traits::Type<_Tp>::value,
(void*)src.data(), src.outerStride()*sizeof(_Tp));
transpose(_src, dst);
}
else
{
Mat _src(src.rows(), src.cols(), traits::Type<_Tp>::value,
(void*)src.data(), src.outerStride()*sizeof(_Tp));
_src.copyTo(dst);
}
}
// Matx case
template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src,
Matx<_Tp, _rows, _cols>& dst )
{
if( !(src.Flags & Eigen::RowMajorBit) )
{
dst = Matx<_Tp, _cols, _rows>(static_cast<const _Tp*>(src.data())).t();
}
else
{
dst = Matx<_Tp, _rows, _cols>(static_cast<const _Tp*>(src.data()));
}
}
template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
void cv2eigen( const Mat& src,
Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst )
{
CV_DbgAssert(src.rows == _rows && src.cols == _cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
if( src.type() == _dst.type() )
transpose(src, _dst);
else if( src.cols == src.rows )
{
src.convertTo(_dst, _dst.type());
transpose(_dst, _dst);
}
else
Mat(src.t()).convertTo(_dst, _dst.type());
}
else
{
const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
}
}
// Matx case
template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
void cv2eigen( const Matx<_Tp, _rows, _cols>& src,
Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst )
{
if( !(dst.Flags & Eigen::RowMajorBit) )
{
const Mat _dst(_cols, _rows, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
transpose(src, _dst);
}
else
{
const Mat _dst(_rows, _cols, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
Mat(src).copyTo(_dst);
}
}
template<typename _Tp> static inline
void cv2eigen( const Mat& src,
Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
{
dst.resize(src.rows, src.cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
if( src.type() == _dst.type() )
transpose(src, _dst);
else if( src.cols == src.rows )
{
src.convertTo(_dst, _dst.type());
transpose(_dst, _dst);
}
else
Mat(src.t()).convertTo(_dst, _dst.type());
}
else
{
const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
}
}
// Matx case
template<typename _Tp, int _rows, int _cols> static inline
void cv2eigen( const Matx<_Tp, _rows, _cols>& src,
Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
{
dst.resize(_rows, _cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
const Mat _dst(_cols, _rows, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
transpose(src, _dst);
}
else
{
const Mat _dst(_rows, _cols, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
Mat(src).copyTo(_dst);
}
}
template<typename _Tp> static inline
void cv2eigen( const Mat& src,
Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst )
{
CV_Assert(src.cols == 1);
dst.resize(src.rows);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
if( src.type() == _dst.type() )
transpose(src, _dst);
else
Mat(src.t()).convertTo(_dst, _dst.type());
}
else
{
const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
}
}
// Matx case
template<typename _Tp, int _rows> static inline
void cv2eigen( const Matx<_Tp, _rows, 1>& src,
Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst )
{
dst.resize(_rows);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
const Mat _dst(1, _rows, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
transpose(src, _dst);
}
else
{
const Mat _dst(_rows, 1, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
src.copyTo(_dst);
}
}
template<typename _Tp> static inline
void cv2eigen( const Mat& src,
Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst )
{
CV_Assert(src.rows == 1);
dst.resize(src.cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
if( src.type() == _dst.type() )
transpose(src, _dst);
else
Mat(src.t()).convertTo(_dst, _dst.type());
}
else
{
const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
}
}
//Matx
template<typename _Tp, int _cols> static inline
void cv2eigen( const Matx<_Tp, 1, _cols>& src,
Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst )
{
dst.resize(_cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
const Mat _dst(_cols, 1, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
transpose(src, _dst);
}
else
{
const Mat _dst(1, _cols, traits::Type<_Tp>::value,
dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
Mat(src).copyTo(_dst);
}
}
//! @}
} // cv
#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.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez 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_CORE_FAST_MATH_HPP
#define OPENCV_CORE_FAST_MATH_HPP
#include "opencv2/core/cvdef.h"
//! @addtogroup core_utils
//! @{
/****************************************************************************************\
* fast math *
\****************************************************************************************/
#ifdef __cplusplus
# include <cmath>
#else
# ifdef __BORLANDC__
# include <fastmath.h>
# else
# include <math.h>
# endif
#endif
#if defined(__CUDACC__)
// nothing, intrinsics/asm code is not supported
#else
#if ((defined _MSC_VER && defined _M_X64) \
|| (defined __GNUC__ && defined __x86_64__ && defined __SSE2__)) \
&& !defined(OPENCV_SKIP_INCLUDE_EMMINTRIN_H)
#include <emmintrin.h>
#endif
#if defined __PPC64__ && defined __GNUC__ && defined _ARCH_PWR8 \
&& !defined(OPENCV_SKIP_INCLUDE_ALTIVEC_H)
#include <altivec.h>
#endif
#if defined(CV_INLINE_ROUND_FLT)
// user-specified version
// CV_INLINE_ROUND_DBL should be defined too
#elif defined __GNUC__ && defined __arm__ && (defined __ARM_PCS_VFP || defined __ARM_VFPV3__ || defined __ARM_NEON__) && !defined __SOFTFP__
// 1. general scheme
#define ARM_ROUND(_value, _asm_string) \
int res; \
float temp; \
CV_UNUSED(temp); \
__asm__(_asm_string : [res] "=r" (res), [temp] "=w" (temp) : [value] "w" (_value)); \
return res
// 2. version for double
#ifdef __clang__
#define CV_INLINE_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %[value] \n vmov %[res], %[temp]")
#else
#define CV_INLINE_ROUND_DBL(value) ARM_ROUND(value, "vcvtr.s32.f64 %[temp], %P[value] \n vmov %[res], %[temp]")
#endif
// 3. version for float
#define CV_INLINE_ROUND_FLT(value) ARM_ROUND(value, "vcvtr.s32.f32 %[temp], %[value]\n vmov %[res], %[temp]")
#elif defined __PPC64__ && defined __GNUC__ && defined _ARCH_PWR8
// P8 and newer machines can convert fp32/64 to int quickly.
#define CV_INLINE_ROUND_DBL(value) \
int out; \
double temp; \
__asm__( "fctiw %[temp],%[in]\n\tmfvsrwz %[out],%[temp]\n\t" : [out] "=r" (out), [temp] "=d" (temp) : [in] "d" ((double)(value)) : ); \
return out;
// FP32 also works with FP64 routine above
#define CV_INLINE_ROUND_FLT(value) CV_INLINE_ROUND_DBL(value)
#endif
#ifdef CV_INLINE_ISINF_FLT
// user-specified version
// CV_INLINE_ISINF_DBL should be defined too
#elif defined __PPC64__ && defined _ARCH_PWR9 && defined(scalar_test_data_class)
#define CV_INLINE_ISINF_DBL(value) return scalar_test_data_class(value, 0x30);
#define CV_INLINE_ISINF_FLT(value) CV_INLINE_ISINF_DBL(value)
#endif
#ifdef CV_INLINE_ISNAN_FLT
// user-specified version
// CV_INLINE_ISNAN_DBL should be defined too
#elif defined __PPC64__ && defined _ARCH_PWR9 && defined(scalar_test_data_class)
#define CV_INLINE_ISNAN_DBL(value) return scalar_test_data_class(value, 0x40);
#define CV_INLINE_ISNAN_FLT(value) CV_INLINE_ISNAN_DBL(value)
#endif
#if !defined(OPENCV_USE_FASTMATH_BUILTINS) \
&& ( \
defined(__x86_64__) || defined(__i686__) \
|| defined(__arm__) \
|| defined(__PPC64__) \
)
/* Let builtin C math functions when available. Dedicated hardware is available to
round and convert FP values. */
#define OPENCV_USE_FASTMATH_BUILTINS 1
#endif
/* Enable builtin math functions if possible, desired, and available.
Note, not all math functions inline equally. E.g lrint will not inline
without the -fno-math-errno option. */
#if defined(CV_ICC)
// nothing
#elif defined(OPENCV_USE_FASTMATH_BUILTINS) && OPENCV_USE_FASTMATH_BUILTINS
#if defined(__clang__)
#define CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS
#if !defined(CV_INLINE_ISNAN_DBL) && __has_builtin(__builtin_isnan)
#define CV_INLINE_ISNAN_DBL(value) return __builtin_isnan(value);
#endif
#if !defined(CV_INLINE_ISNAN_FLT) && __has_builtin(__builtin_isnan)
#define CV_INLINE_ISNAN_FLT(value) return __builtin_isnan(value);
#endif
#if !defined(CV_INLINE_ISINF_DBL) && __has_builtin(__builtin_isinf)
#define CV_INLINE_ISINF_DBL(value) return __builtin_isinf(value);
#endif
#if !defined(CV_INLINE_ISINF_FLT) && __has_builtin(__builtin_isinf)
#define CV_INLINE_ISINF_FLT(value) return __builtin_isinf(value);
#endif
#elif defined(__GNUC__)
#define CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS
#if !defined(CV_INLINE_ISNAN_DBL)
#define CV_INLINE_ISNAN_DBL(value) return __builtin_isnan(value);
#endif
#if !defined(CV_INLINE_ISNAN_FLT)
#define CV_INLINE_ISNAN_FLT(value) return __builtin_isnanf(value);
#endif
#if !defined(CV_INLINE_ISINF_DBL)
#define CV_INLINE_ISINF_DBL(value) return __builtin_isinf(value);
#endif
#if !defined(CV_INLINE_ISINF_FLT)
#define CV_INLINE_ISINF_FLT(value) return __builtin_isinff(value);
#endif
#elif defined(_MSC_VER)
#if !defined(CV_INLINE_ISNAN_DBL)
#define CV_INLINE_ISNAN_DBL(value) return isnan(value);
#endif
#if !defined(CV_INLINE_ISNAN_FLT)
#define CV_INLINE_ISNAN_FLT(value) return isnan(value);
#endif
#if !defined(CV_INLINE_ISINF_DBL)
#define CV_INLINE_ISINF_DBL(value) return isinf(value);
#endif
#if !defined(CV_INLINE_ISINF_FLT)
#define CV_INLINE_ISINF_FLT(value) return isinf(value);
#endif
#endif
#endif
#endif // defined(__CUDACC__)
/** @brief Rounds floating-point number to the nearest integer
@param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
result is not defined.
*/
CV_INLINE int
cvRound( double value )
{
#if defined CV_INLINE_ROUND_DBL
CV_INLINE_ROUND_DBL(value);
#elif ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \
&& defined __SSE2__ && !defined __APPLE__) || CV_SSE2) \
&& !defined(__CUDACC__)
__m128d t = _mm_set_sd( value );
return _mm_cvtsd_si32(t);
#elif defined _MSC_VER && defined _M_IX86
int t;
__asm
{
fld value;
fistp t;
}
return t;
#elif defined CV_ICC || defined __GNUC__
return (int)(lrint(value));
#else
/* it's ok if round does not comply with IEEE754 standard;
the tests should allow +/-1 difference when the tested functions use round */
return (int)(value + (value >= 0 ? 0.5 : -0.5));
#endif
}
/** @brief Rounds floating-point number to the nearest integer not larger than the original.
The function computes an integer i such that:
\f[i \le \texttt{value} < i+1\f]
@param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
result is not defined.
*/
CV_INLINE int cvFloor( double value )
{
#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
&& ( \
defined(__PPC64__) \
)
return __builtin_floor(value);
#else
int i = (int)value;
return i - (i > value);
#endif
}
/** @brief Rounds floating-point number to the nearest integer not smaller than the original.
The function computes an integer i such that:
\f[i \le \texttt{value} < i+1\f]
@param value floating-point number. If the value is outside of INT_MIN ... INT_MAX range, the
result is not defined.
*/
CV_INLINE int cvCeil( double value )
{
#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
&& ( \
defined(__PPC64__) \
)
return __builtin_ceil(value);
#else
int i = (int)value;
return i + (i < value);
#endif
}
/** @brief Determines if the argument is Not A Number.
@param value The input floating-point value
The function returns 1 if the argument is Not A Number (as defined by IEEE754 standard), 0
otherwise. */
CV_INLINE int cvIsNaN( double value )
{
#if defined CV_INLINE_ISNAN_DBL
CV_INLINE_ISNAN_DBL(value);
#else
Cv64suf ieee754;
ieee754.f = value;
return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) +
((unsigned)ieee754.u != 0) > 0x7ff00000;
#endif
}
/** @brief Determines if the argument is Infinity.
@param value The input floating-point value
The function returns 1 if the argument is a plus or minus infinity (as defined by IEEE754 standard)
and 0 otherwise. */
CV_INLINE int cvIsInf( double value )
{
#if defined CV_INLINE_ISINF_DBL
CV_INLINE_ISINF_DBL(value);
#elif defined(__x86_64__) || defined(_M_X64) || defined(__aarch64__) || defined(_M_ARM64) || defined(__PPC64__)
Cv64suf ieee754;
ieee754.f = value;
return (ieee754.u & 0x7fffffff00000000) ==
0x7ff0000000000000;
#else
Cv64suf ieee754;
ieee754.f = value;
return ((unsigned)(ieee754.u >> 32) & 0x7fffffff) == 0x7ff00000 &&
(unsigned)ieee754.u == 0;
#endif
}
#ifdef __cplusplus
/** @overload */
CV_INLINE int cvRound(float value)
{
#if defined CV_INLINE_ROUND_FLT
CV_INLINE_ROUND_FLT(value);
#elif ((defined _MSC_VER && defined _M_X64) || (defined __GNUC__ && defined __x86_64__ \
&& defined __SSE2__ && !defined __APPLE__) || CV_SSE2) \
&& !defined(__CUDACC__)
__m128 t = _mm_set_ss( value );
return _mm_cvtss_si32(t);
#elif defined _MSC_VER && defined _M_IX86
int t;
__asm
{
fld value;
fistp t;
}
return t;
#elif defined CV_ICC || defined __GNUC__
return (int)(lrintf(value));
#else
/* it's ok if round does not comply with IEEE754 standard;
the tests should allow +/-1 difference when the tested functions use round */
return (int)(value + (value >= 0 ? 0.5f : -0.5f));
#endif
}
/** @overload */
CV_INLINE int cvRound( int value )
{
return value;
}
/** @overload */
CV_INLINE int cvFloor( float value )
{
#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
&& ( \
defined(__PPC64__) \
)
return __builtin_floorf(value);
#else
int i = (int)value;
return i - (i > value);
#endif
}
/** @overload */
CV_INLINE int cvFloor( int value )
{
return value;
}
/** @overload */
CV_INLINE int cvCeil( float value )
{
#if (defined CV__FASTMATH_ENABLE_GCC_MATH_BUILTINS || defined CV__FASTMATH_ENABLE_CLANG_MATH_BUILTINS) \
&& ( \
defined(__PPC64__) \
)
return __builtin_ceilf(value);
#else
int i = (int)value;
return i + (i < value);
#endif
}
/** @overload */
CV_INLINE int cvCeil( int value )
{
return value;
}
/** @overload */
CV_INLINE int cvIsNaN( float value )
{
#if defined CV_INLINE_ISNAN_FLT
CV_INLINE_ISNAN_FLT(value);
#else
Cv32suf ieee754;
ieee754.f = value;
return (ieee754.u & 0x7fffffff) > 0x7f800000;
#endif
}
/** @overload */
CV_INLINE int cvIsInf( float value )
{
#if defined CV_INLINE_ISINF_FLT
CV_INLINE_ISINF_FLT(value);
#else
Cv32suf ieee754;
ieee754.f = value;
return (ieee754.u & 0x7fffffff) == 0x7f800000;
#endif
}
#endif // __cplusplus
//! @} core_utils
#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.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez 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_HAL_HPP
#define OPENCV_HAL_HPP
#include "opencv2/core/cvdef.h"
#include "opencv2/core/cvstd.hpp"
#include "opencv2/core/hal/interface.h"
namespace cv { namespace hal {
//! @addtogroup core_hal_functions
//! @{
CV_EXPORTS int normHamming(const uchar* a, int n);
CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n);
CV_EXPORTS int normHamming(const uchar* a, int n, int cellSize);
CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize);
CV_EXPORTS int LU32f(float* A, size_t astep, int m, float* b, size_t bstep, int n);
CV_EXPORTS int LU64f(double* A, size_t astep, int m, double* b, size_t bstep, int n);
CV_EXPORTS bool Cholesky32f(float* A, size_t astep, int m, float* b, size_t bstep, int n);
CV_EXPORTS bool Cholesky64f(double* A, size_t astep, int m, double* b, size_t bstep, int n);
CV_EXPORTS void SVD32f(float* At, size_t astep, float* W, float* U, size_t ustep, float* Vt, size_t vstep, int m, int n, int flags);
CV_EXPORTS void SVD64f(double* At, size_t astep, double* W, double* U, size_t ustep, double* Vt, size_t vstep, int m, int n, int flags);
CV_EXPORTS int QR32f(float* A, size_t astep, int m, int n, int k, float* b, size_t bstep, float* hFactors);
CV_EXPORTS int QR64f(double* A, size_t astep, int m, int n, int k, double* b, size_t bstep, double* hFactors);
CV_EXPORTS void gemm32f(const float* src1, size_t src1_step, const float* src2, size_t src2_step,
float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags);
CV_EXPORTS void gemm64f(const double* src1, size_t src1_step, const double* src2, size_t src2_step,
double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags);
CV_EXPORTS void gemm32fc(const float* src1, size_t src1_step, const float* src2, size_t src2_step,
float alpha, const float* src3, size_t src3_step, float beta, float* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags);
CV_EXPORTS void gemm64fc(const double* src1, size_t src1_step, const double* src2, size_t src2_step,
double alpha, const double* src3, size_t src3_step, double beta, double* dst, size_t dst_step,
int m_a, int n_a, int n_d, int flags);
CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n);
CV_EXPORTS float normL1_(const float* a, const float* b, int n);
CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n);
CV_EXPORTS void exp32f(const float* src, float* dst, int n);
CV_EXPORTS void exp64f(const double* src, double* dst, int n);
CV_EXPORTS void log32f(const float* src, float* dst, int n);
CV_EXPORTS void log64f(const double* src, double* dst, int n);
CV_EXPORTS void fastAtan32f(const float* y, const float* x, float* dst, int n, bool angleInDegrees);
CV_EXPORTS void fastAtan64f(const double* y, const double* x, double* dst, int n, bool angleInDegrees);
CV_EXPORTS void magnitude32f(const float* x, const float* y, float* dst, int n);
CV_EXPORTS void magnitude64f(const double* x, const double* y, double* dst, int n);
CV_EXPORTS void sqrt32f(const float* src, float* dst, int len);
CV_EXPORTS void sqrt64f(const double* src, double* dst, int len);
CV_EXPORTS void invSqrt32f(const float* src, float* dst, int len);
CV_EXPORTS void invSqrt64f(const double* src, double* dst, int len);
CV_EXPORTS void split8u(const uchar* src, uchar** dst, int len, int cn );
CV_EXPORTS void split16u(const ushort* src, ushort** dst, int len, int cn );
CV_EXPORTS void split32s(const int* src, int** dst, int len, int cn );
CV_EXPORTS void split64s(const int64* src, int64** dst, int len, int cn );
CV_EXPORTS void merge8u(const uchar** src, uchar* dst, int len, int cn );
CV_EXPORTS void merge16u(const ushort** src, ushort* dst, int len, int cn );
CV_EXPORTS void merge32s(const int** src, int* dst, int len, int cn );
CV_EXPORTS void merge64s(const int64** src, int64* dst, int len, int cn );
CV_EXPORTS void add8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
CV_EXPORTS void add64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
CV_EXPORTS void sub64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
CV_EXPORTS void max64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
CV_EXPORTS void min64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* );
CV_EXPORTS void absdiff64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* );
CV_EXPORTS void and8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void or8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void xor8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void not8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* );
CV_EXPORTS void cmp8u(const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp8s(const schar* src1, size_t step1, const schar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp16u(const ushort* src1, size_t step1, const ushort* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp16s(const short* src1, size_t step1, const short* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp32s(const int* src1, size_t step1, const int* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp32f(const float* src1, size_t step1, const float* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void cmp64f(const double* src1, size_t step1, const double* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _cmpop);
CV_EXPORTS void mul8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void mul64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void div64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip8u( const uchar *, size_t, const uchar * src2, size_t step2, uchar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip8s( const schar *, size_t, const schar * src2, size_t step2, schar* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip16u( const ushort *, size_t, const ushort * src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip16s( const short *, size_t, const short * src2, size_t step2, short* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip32s( const int *, size_t, const int * src2, size_t step2, int* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip32f( const float *, size_t, const float * src2, size_t step2, float* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void recip64f( const double *, size_t, const double * src2, size_t step2, double* dst, size_t step, int width, int height, void* scale);
CV_EXPORTS void addWeighted8u( const uchar* src1, size_t step1, const uchar* src2, size_t step2, uchar* dst, size_t step, int width, int height, void* _scalars );
CV_EXPORTS void addWeighted8s( const schar* src1, size_t step1, const schar* src2, size_t step2, schar* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void addWeighted16u( const ushort* src1, size_t step1, const ushort* src2, size_t step2, ushort* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void addWeighted16s( const short* src1, size_t step1, const short* src2, size_t step2, short* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void addWeighted32s( const int* src1, size_t step1, const int* src2, size_t step2, int* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void addWeighted32f( const float* src1, size_t step1, const float* src2, size_t step2, float* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void addWeighted64f( const double* src1, size_t step1, const double* src2, size_t step2, double* dst, size_t step, int width, int height, void* scalars );
CV_EXPORTS void cvt16f32f( const float16_t* src, float* dst, int len );
CV_EXPORTS void cvt32f16f( const float* src, float16_t* dst, int len );
CV_EXPORTS void addRNGBias32f( float* arr, const float* scaleBiasPairs, int len );
CV_EXPORTS void addRNGBias64f( double* arr, const double* scaleBiasPairs, int len );
struct CV_EXPORTS DFT1D
{
static Ptr<DFT1D> create(int len, int count, int depth, int flags, bool * useBuffer = 0);
virtual void apply(const uchar *src, uchar *dst) = 0;
virtual ~DFT1D() {}
};
struct CV_EXPORTS DFT2D
{
static Ptr<DFT2D> create(int width, int height, int depth,
int src_channels, int dst_channels,
int flags, int nonzero_rows = 0);
virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0;
virtual ~DFT2D() {}
};
struct CV_EXPORTS DCT2D
{
static Ptr<DCT2D> create(int width, int height, int depth, int flags);
virtual void apply(const uchar *src_data, size_t src_step, uchar *dst_data, size_t dst_step) = 0;
virtual ~DCT2D() {}
};
//! @} core_hal
//=============================================================================
// for binary compatibility with 3.0
//! @cond IGNORED
CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n);
CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n);
CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n);
CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n);
CV_EXPORTS void exp(const float* src, float* dst, int n);
CV_EXPORTS void exp(const double* src, double* dst, int n);
CV_EXPORTS void log(const float* src, float* dst, int n);
CV_EXPORTS void log(const double* src, double* dst, int n);
CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees);
CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n);
CV_EXPORTS void magnitude(const double* x, const double* y, double* dst, int n);
CV_EXPORTS void sqrt(const float* src, float* dst, int len);
CV_EXPORTS void sqrt(const double* src, double* dst, int len);
CV_EXPORTS void invSqrt(const float* src, float* dst, int len);
CV_EXPORTS void invSqrt(const double* src, double* dst, int len);
//! @endcond
}} //cv::hal
#endif //OPENCV_HAL_HPP

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#ifndef OPENCV_CORE_HAL_INTERFACE_H
#define OPENCV_CORE_HAL_INTERFACE_H
//! @addtogroup core_hal_interface
//! @{
//! @name Return codes
//! @{
#define CV_HAL_ERROR_OK 0
#define CV_HAL_ERROR_NOT_IMPLEMENTED 1
#define CV_HAL_ERROR_UNKNOWN -1
//! @}
#ifdef __cplusplus
#include <cstddef>
#else
#include <stddef.h>
#include <stdbool.h>
#endif
//! @name Data types
//! primitive types
//! - schar - signed 1 byte integer
//! - uchar - unsigned 1 byte integer
//! - short - signed 2 byte integer
//! - ushort - unsigned 2 byte integer
//! - int - signed 4 byte integer
//! - uint - unsigned 4 byte integer
//! - int64 - signed 8 byte integer
//! - uint64 - unsigned 8 byte integer
//! @{
#if !defined _MSC_VER && !defined __BORLANDC__
# if defined __cplusplus && __cplusplus >= 201103L && !defined __APPLE__
# include <cstdint>
# ifdef __NEWLIB__
typedef unsigned int uint;
# else
typedef std::uint32_t uint;
# endif
# else
# include <stdint.h>
typedef uint32_t uint;
# endif
#else
typedef unsigned uint;
#endif
typedef signed char schar;
#ifndef __IPL_H__
typedef unsigned char uchar;
typedef unsigned short ushort;
#endif
#if defined _MSC_VER || defined __BORLANDC__
typedef __int64 int64;
typedef unsigned __int64 uint64;
# define CV_BIG_INT(n) n##I64
# define CV_BIG_UINT(n) n##UI64
#else
typedef int64_t int64;
typedef uint64_t uint64;
# define CV_BIG_INT(n) n##LL
# define CV_BIG_UINT(n) n##ULL
#endif
#define CV_USRTYPE1 (void)"CV_USRTYPE1 support has been dropped in OpenCV 4.0"
#define CV_CN_MAX 512
#define CV_CN_SHIFT 3
#define CV_DEPTH_MAX (1 << CV_CN_SHIFT)
#define CV_8U 0
#define CV_8S 1
#define CV_16U 2
#define CV_16S 3
#define CV_32S 4
#define CV_32F 5
#define CV_64F 6
#define CV_16F 7
#define CV_MAT_DEPTH_MASK (CV_DEPTH_MAX - 1)
#define CV_MAT_DEPTH(flags) ((flags) & CV_MAT_DEPTH_MASK)
#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT))
#define CV_MAKE_TYPE CV_MAKETYPE
#define CV_8UC1 CV_MAKETYPE(CV_8U,1)
#define CV_8UC2 CV_MAKETYPE(CV_8U,2)
#define CV_8UC3 CV_MAKETYPE(CV_8U,3)
#define CV_8UC4 CV_MAKETYPE(CV_8U,4)
#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n))
#define CV_8SC1 CV_MAKETYPE(CV_8S,1)
#define CV_8SC2 CV_MAKETYPE(CV_8S,2)
#define CV_8SC3 CV_MAKETYPE(CV_8S,3)
#define CV_8SC4 CV_MAKETYPE(CV_8S,4)
#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n))
#define CV_16UC1 CV_MAKETYPE(CV_16U,1)
#define CV_16UC2 CV_MAKETYPE(CV_16U,2)
#define CV_16UC3 CV_MAKETYPE(CV_16U,3)
#define CV_16UC4 CV_MAKETYPE(CV_16U,4)
#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n))
#define CV_16SC1 CV_MAKETYPE(CV_16S,1)
#define CV_16SC2 CV_MAKETYPE(CV_16S,2)
#define CV_16SC3 CV_MAKETYPE(CV_16S,3)
#define CV_16SC4 CV_MAKETYPE(CV_16S,4)
#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n))
#define CV_32SC1 CV_MAKETYPE(CV_32S,1)
#define CV_32SC2 CV_MAKETYPE(CV_32S,2)
#define CV_32SC3 CV_MAKETYPE(CV_32S,3)
#define CV_32SC4 CV_MAKETYPE(CV_32S,4)
#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n))
#define CV_32FC1 CV_MAKETYPE(CV_32F,1)
#define CV_32FC2 CV_MAKETYPE(CV_32F,2)
#define CV_32FC3 CV_MAKETYPE(CV_32F,3)
#define CV_32FC4 CV_MAKETYPE(CV_32F,4)
#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n))
#define CV_64FC1 CV_MAKETYPE(CV_64F,1)
#define CV_64FC2 CV_MAKETYPE(CV_64F,2)
#define CV_64FC3 CV_MAKETYPE(CV_64F,3)
#define CV_64FC4 CV_MAKETYPE(CV_64F,4)
#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n))
#define CV_16FC1 CV_MAKETYPE(CV_16F,1)
#define CV_16FC2 CV_MAKETYPE(CV_16F,2)
#define CV_16FC3 CV_MAKETYPE(CV_16F,3)
#define CV_16FC4 CV_MAKETYPE(CV_16F,4)
#define CV_16FC(n) CV_MAKETYPE(CV_16F,(n))
//! @}
//! @name Comparison operation
//! @sa cv::CmpTypes
//! @{
#define CV_HAL_CMP_EQ 0
#define CV_HAL_CMP_GT 1
#define CV_HAL_CMP_GE 2
#define CV_HAL_CMP_LT 3
#define CV_HAL_CMP_LE 4
#define CV_HAL_CMP_NE 5
//! @}
//! @name Border processing modes
//! @sa cv::BorderTypes
//! @{
#define CV_HAL_BORDER_CONSTANT 0
#define CV_HAL_BORDER_REPLICATE 1
#define CV_HAL_BORDER_REFLECT 2
#define CV_HAL_BORDER_WRAP 3
#define CV_HAL_BORDER_REFLECT_101 4
#define CV_HAL_BORDER_TRANSPARENT 5
#define CV_HAL_BORDER_ISOLATED 16
//! @}
//! @name DFT flags
//! @{
#define CV_HAL_DFT_INVERSE 1
#define CV_HAL_DFT_SCALE 2
#define CV_HAL_DFT_ROWS 4
#define CV_HAL_DFT_COMPLEX_OUTPUT 16
#define CV_HAL_DFT_REAL_OUTPUT 32
#define CV_HAL_DFT_TWO_STAGE 64
#define CV_HAL_DFT_STAGE_COLS 128
#define CV_HAL_DFT_IS_CONTINUOUS 512
#define CV_HAL_DFT_IS_INPLACE 1024
//! @}
//! @name SVD flags
//! @{
#define CV_HAL_SVD_NO_UV 1
#define CV_HAL_SVD_SHORT_UV 2
#define CV_HAL_SVD_MODIFY_A 4
#define CV_HAL_SVD_FULL_UV 8
//! @}
//! @name Gemm flags
//! @{
#define CV_HAL_GEMM_1_T 1
#define CV_HAL_GEMM_2_T 2
#define CV_HAL_GEMM_3_T 4
//! @}
//! @}
#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.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2015, Itseez 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_HAL_INTRIN_HPP
#define OPENCV_HAL_INTRIN_HPP
#include <cmath>
#include <float.h>
#include <stdlib.h>
#include "opencv2/core/cvdef.h"
#define OPENCV_HAL_ADD(a, b) ((a) + (b))
#define OPENCV_HAL_AND(a, b) ((a) & (b))
#define OPENCV_HAL_NOP(a) (a)
#define OPENCV_HAL_1ST(a, b) (a)
namespace {
inline unsigned int trailingZeros32(unsigned int value) {
#if defined(_MSC_VER)
#if (_MSC_VER < 1700) || defined(_M_ARM) || defined(_M_ARM64)
unsigned long index = 0;
_BitScanForward(&index, value);
return (unsigned int)index;
#elif defined(__clang__)
// clang-cl doesn't export _tzcnt_u32 for non BMI systems
return value ? __builtin_ctz(value) : 32;
#else
return _tzcnt_u32(value);
#endif
#elif defined(__GNUC__) || defined(__GNUG__)
return __builtin_ctz(value);
#elif defined(__ICC) || defined(__INTEL_COMPILER)
return _bit_scan_forward(value);
#elif defined(__clang__)
return llvm.cttz.i32(value, true);
#else
static const int MultiplyDeBruijnBitPosition[32] = {
0, 1, 28, 2, 29, 14, 24, 3, 30, 22, 20, 15, 25, 17, 4, 8,
31, 27, 13, 23, 21, 19, 16, 7, 26, 12, 18, 6, 11, 5, 10, 9 };
return MultiplyDeBruijnBitPosition[((uint32_t)((value & -value) * 0x077CB531U)) >> 27];
#endif
}
}
// unlike HAL API, which is in cv::hal,
// we put intrinsics into cv namespace to make its
// access from within opencv code more accessible
namespace cv {
namespace hal {
enum StoreMode
{
STORE_UNALIGNED = 0,
STORE_ALIGNED = 1,
STORE_ALIGNED_NOCACHE = 2
};
}
template<typename _Tp> struct V_TypeTraits
{
};
#define CV_INTRIN_DEF_TYPE_TRAITS(type, int_type_, uint_type_, abs_type_, w_type_, q_type_, sum_type_, nlanes128_) \
template<> struct V_TypeTraits<type> \
{ \
typedef type value_type; \
typedef int_type_ int_type; \
typedef abs_type_ abs_type; \
typedef uint_type_ uint_type; \
typedef w_type_ w_type; \
typedef q_type_ q_type; \
typedef sum_type_ sum_type; \
enum { nlanes128 = nlanes128_ }; \
\
static inline int_type reinterpret_int(type x) \
{ \
union { type l; int_type i; } v; \
v.l = x; \
return v.i; \
} \
\
static inline type reinterpret_from_int(int_type x) \
{ \
union { type l; int_type i; } v; \
v.i = x; \
return v.l; \
} \
}
CV_INTRIN_DEF_TYPE_TRAITS(uchar, schar, uchar, uchar, ushort, unsigned, unsigned, 16);
CV_INTRIN_DEF_TYPE_TRAITS(schar, schar, uchar, uchar, short, int, int, 16);
CV_INTRIN_DEF_TYPE_TRAITS(ushort, short, ushort, ushort, unsigned, uint64, unsigned, 8);
CV_INTRIN_DEF_TYPE_TRAITS(short, short, ushort, ushort, int, int64, int, 8);
CV_INTRIN_DEF_TYPE_TRAITS(unsigned, int, unsigned, unsigned, uint64, void, unsigned, 4);
CV_INTRIN_DEF_TYPE_TRAITS(int, int, unsigned, unsigned, int64, void, int, 4);
CV_INTRIN_DEF_TYPE_TRAITS(float, int, unsigned, float, double, void, float, 4);
CV_INTRIN_DEF_TYPE_TRAITS(uint64, int64, uint64, uint64, void, void, uint64, 2);
CV_INTRIN_DEF_TYPE_TRAITS(int64, int64, uint64, uint64, void, void, int64, 2);
CV_INTRIN_DEF_TYPE_TRAITS(double, int64, uint64, double, void, void, double, 2);
#ifndef CV_DOXYGEN
#ifndef CV_CPU_OPTIMIZATION_HAL_NAMESPACE
#ifdef CV_CPU_DISPATCH_MODE
#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE __CV_CAT(hal_, CV_CPU_DISPATCH_MODE)
#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace __CV_CAT(hal_, CV_CPU_DISPATCH_MODE) {
#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
#else
#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE hal_baseline
#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN namespace hal_baseline {
#define CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END }
#endif
#endif // CV_CPU_OPTIMIZATION_HAL_NAMESPACE
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
using namespace CV_CPU_OPTIMIZATION_HAL_NAMESPACE;
#endif
}
#ifdef CV_DOXYGEN
# undef CV_AVX2
# undef CV_SSE2
# undef CV_NEON
# undef CV_VSX
# undef CV_FP16
# undef CV_MSA
#endif
#if (CV_SSE2 || CV_NEON || CV_VSX || CV_MSA || CV_WASM_SIMD) && !defined(CV_FORCE_SIMD128_CPP)
#define CV__SIMD_FORWARD 128
#include "opencv2/core/hal/intrin_forward.hpp"
#endif
#if CV_SSE2 && !defined(CV_FORCE_SIMD128_CPP)
#include "opencv2/core/hal/intrin_sse_em.hpp"
#include "opencv2/core/hal/intrin_sse.hpp"
#elif CV_NEON && !defined(CV_FORCE_SIMD128_CPP)
#include "opencv2/core/hal/intrin_neon.hpp"
#elif CV_VSX && !defined(CV_FORCE_SIMD128_CPP)
#include "opencv2/core/hal/intrin_vsx.hpp"
#elif CV_MSA && !defined(CV_FORCE_SIMD128_CPP)
#include "opencv2/core/hal/intrin_msa.hpp"
#elif CV_WASM_SIMD && !defined(CV_FORCE_SIMD128_CPP)
#include "opencv2/core/hal/intrin_wasm.hpp"
#else
#define CV_SIMD128_CPP 1
#include "opencv2/core/hal/intrin_cpp.hpp"
#endif
// AVX2 can be used together with SSE2, so
// we define those two sets of intrinsics at once.
// Most of the intrinsics do not conflict (the proper overloaded variant is
// resolved by the argument types, e.g. v_float32x4 ~ SSE2, v_float32x8 ~ AVX2),
// but some of AVX2 intrinsics get v256_ prefix instead of v_, e.g. v256_load() vs v_load().
// Correspondingly, the wide intrinsics (which are mapped to the "widest"
// available instruction set) will get vx_ prefix
// (and will be mapped to v256_ counterparts) (e.g. vx_load() => v256_load())
#if CV_AVX2
#define CV__SIMD_FORWARD 256
#include "opencv2/core/hal/intrin_forward.hpp"
#include "opencv2/core/hal/intrin_avx.hpp"
#endif
// AVX512 can be used together with SSE2 and AVX2, so
// we define those sets of intrinsics at once.
// For some of AVX512 intrinsics get v512_ prefix instead of v_, e.g. v512_load() vs v_load().
// Wide intrinsics will be mapped to v512_ counterparts in this case(e.g. vx_load() => v512_load())
#if CV_AVX512_SKX
#define CV__SIMD_FORWARD 512
#include "opencv2/core/hal/intrin_forward.hpp"
#include "opencv2/core/hal/intrin_avx512.hpp"
#endif
//! @cond IGNORED
namespace cv {
#ifndef CV_DOXYGEN
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
#endif
#ifndef CV_SIMD128
#define CV_SIMD128 0
#endif
#ifndef CV_SIMD128_64F
#define CV_SIMD128_64F 0
#endif
#ifndef CV_SIMD256
#define CV_SIMD256 0
#endif
#ifndef CV_SIMD256_64F
#define CV_SIMD256_64F 0
#endif
#ifndef CV_SIMD512
#define CV_SIMD512 0
#endif
#ifndef CV_SIMD512_64F
#define CV_SIMD512_64F 0
#endif
#ifndef CV_SIMD128_FP16
#define CV_SIMD128_FP16 0
#endif
#ifndef CV_SIMD256_FP16
#define CV_SIMD256_FP16 0
#endif
#ifndef CV_SIMD512_FP16
#define CV_SIMD512_FP16 0
#endif
//==================================================================================================
#define CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \
inline vtyp vx_setall_##short_typ(typ v) { return prefix##_setall_##short_typ(v); } \
inline vtyp vx_setzero_##short_typ() { return prefix##_setzero_##short_typ(); } \
inline vtyp vx_##loadsfx(const typ* ptr) { return prefix##_##loadsfx(ptr); } \
inline vtyp vx_##loadsfx##_aligned(const typ* ptr) { return prefix##_##loadsfx##_aligned(ptr); } \
inline vtyp vx_##loadsfx##_low(const typ* ptr) { return prefix##_##loadsfx##_low(ptr); } \
inline vtyp vx_##loadsfx##_halves(const typ* ptr0, const typ* ptr1) { return prefix##_##loadsfx##_halves(ptr0, ptr1); } \
inline void vx_store(typ* ptr, const vtyp& v) { return v_store(ptr, v); } \
inline void vx_store_aligned(typ* ptr, const vtyp& v) { return v_store_aligned(ptr, v); } \
inline vtyp vx_lut(const typ* ptr, const int* idx) { return prefix##_lut(ptr, idx); } \
inline vtyp vx_lut_pairs(const typ* ptr, const int* idx) { return prefix##_lut_pairs(ptr, idx); }
#define CV_INTRIN_DEFINE_WIDE_LUT_QUAD(typ, vtyp, prefix) \
inline vtyp vx_lut_quads(const typ* ptr, const int* idx) { return prefix##_lut_quads(ptr, idx); }
#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \
inline wtyp vx_load_expand(const typ* ptr) { return prefix##_load_expand(ptr); }
#define CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix) \
inline qtyp vx_load_expand_q(const typ* ptr) { return prefix##_load_expand_q(ptr); }
#define CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(typ, vtyp, short_typ, wtyp, qtyp, prefix, loadsfx) \
CV_INTRIN_DEFINE_WIDE_INTRIN(typ, vtyp, short_typ, prefix, loadsfx) \
CV_INTRIN_DEFINE_WIDE_LUT_QUAD(typ, vtyp, prefix) \
CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(typ, wtyp, prefix) \
CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND_Q(typ, qtyp, prefix)
#define CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(prefix) \
CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(uchar, v_uint8, u8, v_uint16, v_uint32, prefix, load) \
CV_INTRIN_DEFINE_WIDE_INTRIN_WITH_EXPAND(schar, v_int8, s8, v_int16, v_int32, prefix, load) \
CV_INTRIN_DEFINE_WIDE_INTRIN(ushort, v_uint16, u16, prefix, load) \
CV_INTRIN_DEFINE_WIDE_LUT_QUAD(ushort, v_uint16, prefix) \
CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(ushort, v_uint32, prefix) \
CV_INTRIN_DEFINE_WIDE_INTRIN(short, v_int16, s16, prefix, load) \
CV_INTRIN_DEFINE_WIDE_LUT_QUAD(short, v_int16, prefix) \
CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(short, v_int32, prefix) \
CV_INTRIN_DEFINE_WIDE_INTRIN(int, v_int32, s32, prefix, load) \
CV_INTRIN_DEFINE_WIDE_LUT_QUAD(int, v_int32, prefix) \
CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(int, v_int64, prefix) \
CV_INTRIN_DEFINE_WIDE_INTRIN(unsigned, v_uint32, u32, prefix, load) \
CV_INTRIN_DEFINE_WIDE_LUT_QUAD(unsigned, v_uint32, prefix) \
CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(unsigned, v_uint64, prefix) \
CV_INTRIN_DEFINE_WIDE_INTRIN(float, v_float32, f32, prefix, load) \
CV_INTRIN_DEFINE_WIDE_LUT_QUAD(float, v_float32, prefix) \
CV_INTRIN_DEFINE_WIDE_INTRIN(int64, v_int64, s64, prefix, load) \
CV_INTRIN_DEFINE_WIDE_INTRIN(uint64, v_uint64, u64, prefix, load) \
CV_INTRIN_DEFINE_WIDE_LOAD_EXPAND(float16_t, v_float32, prefix)
template<typename _Tp> struct V_RegTraits
{
};
#define CV_DEF_REG_TRAITS(prefix, _reg, lane_type, suffix, _u_reg, _w_reg, _q_reg, _int_reg, _round_reg) \
template<> struct V_RegTraits<_reg> \
{ \
typedef _reg reg; \
typedef _u_reg u_reg; \
typedef _w_reg w_reg; \
typedef _q_reg q_reg; \
typedef _int_reg int_reg; \
typedef _round_reg round_reg; \
}
#if CV_SIMD128 || CV_SIMD128_CPP
CV_DEF_REG_TRAITS(v, v_uint8x16, uchar, u8, v_uint8x16, v_uint16x8, v_uint32x4, v_int8x16, void);
CV_DEF_REG_TRAITS(v, v_int8x16, schar, s8, v_uint8x16, v_int16x8, v_int32x4, v_int8x16, void);
CV_DEF_REG_TRAITS(v, v_uint16x8, ushort, u16, v_uint16x8, v_uint32x4, v_uint64x2, v_int16x8, void);
CV_DEF_REG_TRAITS(v, v_int16x8, short, s16, v_uint16x8, v_int32x4, v_int64x2, v_int16x8, void);
CV_DEF_REG_TRAITS(v, v_uint32x4, unsigned, u32, v_uint32x4, v_uint64x2, void, v_int32x4, void);
CV_DEF_REG_TRAITS(v, v_int32x4, int, s32, v_uint32x4, v_int64x2, void, v_int32x4, void);
#if CV_SIMD128_64F
CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, v_float64x2, void, v_int32x4, v_int32x4);
#else
CV_DEF_REG_TRAITS(v, v_float32x4, float, f32, v_float32x4, void, void, v_int32x4, v_int32x4);
#endif
CV_DEF_REG_TRAITS(v, v_uint64x2, uint64, u64, v_uint64x2, void, void, v_int64x2, void);
CV_DEF_REG_TRAITS(v, v_int64x2, int64, s64, v_uint64x2, void, void, v_int64x2, void);
#if CV_SIMD128_64F
CV_DEF_REG_TRAITS(v, v_float64x2, double, f64, v_float64x2, void, void, v_int64x2, v_int32x4);
#endif
#endif
#if CV_SIMD256
CV_DEF_REG_TRAITS(v256, v_uint8x32, uchar, u8, v_uint8x32, v_uint16x16, v_uint32x8, v_int8x32, void);
CV_DEF_REG_TRAITS(v256, v_int8x32, schar, s8, v_uint8x32, v_int16x16, v_int32x8, v_int8x32, void);
CV_DEF_REG_TRAITS(v256, v_uint16x16, ushort, u16, v_uint16x16, v_uint32x8, v_uint64x4, v_int16x16, void);
CV_DEF_REG_TRAITS(v256, v_int16x16, short, s16, v_uint16x16, v_int32x8, v_int64x4, v_int16x16, void);
CV_DEF_REG_TRAITS(v256, v_uint32x8, unsigned, u32, v_uint32x8, v_uint64x4, void, v_int32x8, void);
CV_DEF_REG_TRAITS(v256, v_int32x8, int, s32, v_uint32x8, v_int64x4, void, v_int32x8, void);
CV_DEF_REG_TRAITS(v256, v_float32x8, float, f32, v_float32x8, v_float64x4, void, v_int32x8, v_int32x8);
CV_DEF_REG_TRAITS(v256, v_uint64x4, uint64, u64, v_uint64x4, void, void, v_int64x4, void);
CV_DEF_REG_TRAITS(v256, v_int64x4, int64, s64, v_uint64x4, void, void, v_int64x4, void);
CV_DEF_REG_TRAITS(v256, v_float64x4, double, f64, v_float64x4, void, void, v_int64x4, v_int32x8);
#endif
#if CV_SIMD512
CV_DEF_REG_TRAITS(v512, v_uint8x64, uchar, u8, v_uint8x64, v_uint16x32, v_uint32x16, v_int8x64, void);
CV_DEF_REG_TRAITS(v512, v_int8x64, schar, s8, v_uint8x64, v_int16x32, v_int32x16, v_int8x64, void);
CV_DEF_REG_TRAITS(v512, v_uint16x32, ushort, u16, v_uint16x32, v_uint32x16, v_uint64x8, v_int16x32, void);
CV_DEF_REG_TRAITS(v512, v_int16x32, short, s16, v_uint16x32, v_int32x16, v_int64x8, v_int16x32, void);
CV_DEF_REG_TRAITS(v512, v_uint32x16, unsigned, u32, v_uint32x16, v_uint64x8, void, v_int32x16, void);
CV_DEF_REG_TRAITS(v512, v_int32x16, int, s32, v_uint32x16, v_int64x8, void, v_int32x16, void);
CV_DEF_REG_TRAITS(v512, v_float32x16, float, f32, v_float32x16, v_float64x8, void, v_int32x16, v_int32x16);
CV_DEF_REG_TRAITS(v512, v_uint64x8, uint64, u64, v_uint64x8, void, void, v_int64x8, void);
CV_DEF_REG_TRAITS(v512, v_int64x8, int64, s64, v_uint64x8, void, void, v_int64x8, void);
CV_DEF_REG_TRAITS(v512, v_float64x8, double, f64, v_float64x8, void, void, v_int64x8, v_int32x16);
#endif
#if CV_SIMD512 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 512)
#define CV__SIMD_NAMESPACE simd512
namespace CV__SIMD_NAMESPACE {
#define CV_SIMD 1
#define CV_SIMD_64F CV_SIMD512_64F
#define CV_SIMD_FP16 CV_SIMD512_FP16
#define CV_SIMD_WIDTH 64
typedef v_uint8x64 v_uint8;
typedef v_int8x64 v_int8;
typedef v_uint16x32 v_uint16;
typedef v_int16x32 v_int16;
typedef v_uint32x16 v_uint32;
typedef v_int32x16 v_int32;
typedef v_uint64x8 v_uint64;
typedef v_int64x8 v_int64;
typedef v_float32x16 v_float32;
CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v512)
#if CV_SIMD512_64F
typedef v_float64x8 v_float64;
CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v512, load)
#endif
inline void vx_cleanup() { v512_cleanup(); }
} // namespace
using namespace CV__SIMD_NAMESPACE;
#elif CV_SIMD256 && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 256)
#define CV__SIMD_NAMESPACE simd256
namespace CV__SIMD_NAMESPACE {
#define CV_SIMD 1
#define CV_SIMD_64F CV_SIMD256_64F
#define CV_SIMD_FP16 CV_SIMD256_FP16
#define CV_SIMD_WIDTH 32
typedef v_uint8x32 v_uint8;
typedef v_int8x32 v_int8;
typedef v_uint16x16 v_uint16;
typedef v_int16x16 v_int16;
typedef v_uint32x8 v_uint32;
typedef v_int32x8 v_int32;
typedef v_uint64x4 v_uint64;
typedef v_int64x4 v_int64;
typedef v_float32x8 v_float32;
CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v256)
#if CV_SIMD256_64F
typedef v_float64x4 v_float64;
CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v256, load)
#endif
inline void vx_cleanup() { v256_cleanup(); }
} // namespace
using namespace CV__SIMD_NAMESPACE;
#elif (CV_SIMD128 || CV_SIMD128_CPP) && (!defined(CV__SIMD_FORCE_WIDTH) || CV__SIMD_FORCE_WIDTH == 128)
#define CV__SIMD_NAMESPACE simd128
namespace CV__SIMD_NAMESPACE {
#define CV_SIMD CV_SIMD128
#define CV_SIMD_64F CV_SIMD128_64F
#define CV_SIMD_WIDTH 16
typedef v_uint8x16 v_uint8;
typedef v_int8x16 v_int8;
typedef v_uint16x8 v_uint16;
typedef v_int16x8 v_int16;
typedef v_uint32x4 v_uint32;
typedef v_int32x4 v_int32;
typedef v_uint64x2 v_uint64;
typedef v_int64x2 v_int64;
typedef v_float32x4 v_float32;
CV_INTRIN_DEFINE_WIDE_INTRIN_ALL_TYPES(v)
#if CV_SIMD128_64F
typedef v_float64x2 v_float64;
CV_INTRIN_DEFINE_WIDE_INTRIN(double, v_float64, f64, v, load)
#endif
inline void vx_cleanup() { v_cleanup(); }
} // namespace
using namespace CV__SIMD_NAMESPACE;
#endif
#ifndef CV_SIMD_64F
#define CV_SIMD_64F 0
#endif
#ifndef CV_SIMD_FP16
#define CV_SIMD_FP16 0 //!< Defined to 1 on native support of operations with float16x8_t / float16x16_t (SIMD256) types
#endif
#ifndef CV_SIMD
#define CV_SIMD 0
#endif
#include "simd_utils.impl.hpp"
#ifndef CV_DOXYGEN
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
#endif
} // cv::
//! @endcond
#endif

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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#ifndef CV__SIMD_FORWARD
#error "Need to pre-define forward width"
#endif
namespace cv
{
//! @cond IGNORED
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_BEGIN
/** Types **/
#if CV__SIMD_FORWARD == 1024
// [todo] 1024
#error "1024-long ops not implemented yet"
#elif CV__SIMD_FORWARD == 512
// 512
#define __CV_VX(fun) v512_##fun
#define __CV_V_UINT8 v_uint8x64
#define __CV_V_INT8 v_int8x64
#define __CV_V_UINT16 v_uint16x32
#define __CV_V_INT16 v_int16x32
#define __CV_V_UINT32 v_uint32x16
#define __CV_V_INT32 v_int32x16
#define __CV_V_UINT64 v_uint64x8
#define __CV_V_INT64 v_int64x8
#define __CV_V_FLOAT32 v_float32x16
#define __CV_V_FLOAT64 v_float64x8
struct v_uint8x64;
struct v_int8x64;
struct v_uint16x32;
struct v_int16x32;
struct v_uint32x16;
struct v_int32x16;
struct v_uint64x8;
struct v_int64x8;
struct v_float32x16;
struct v_float64x8;
#elif CV__SIMD_FORWARD == 256
// 256
#define __CV_VX(fun) v256_##fun
#define __CV_V_UINT8 v_uint8x32
#define __CV_V_INT8 v_int8x32
#define __CV_V_UINT16 v_uint16x16
#define __CV_V_INT16 v_int16x16
#define __CV_V_UINT32 v_uint32x8
#define __CV_V_INT32 v_int32x8
#define __CV_V_UINT64 v_uint64x4
#define __CV_V_INT64 v_int64x4
#define __CV_V_FLOAT32 v_float32x8
#define __CV_V_FLOAT64 v_float64x4
struct v_uint8x32;
struct v_int8x32;
struct v_uint16x16;
struct v_int16x16;
struct v_uint32x8;
struct v_int32x8;
struct v_uint64x4;
struct v_int64x4;
struct v_float32x8;
struct v_float64x4;
#else
// 128
#define __CV_VX(fun) v_##fun
#define __CV_V_UINT8 v_uint8x16
#define __CV_V_INT8 v_int8x16
#define __CV_V_UINT16 v_uint16x8
#define __CV_V_INT16 v_int16x8
#define __CV_V_UINT32 v_uint32x4
#define __CV_V_INT32 v_int32x4
#define __CV_V_UINT64 v_uint64x2
#define __CV_V_INT64 v_int64x2
#define __CV_V_FLOAT32 v_float32x4
#define __CV_V_FLOAT64 v_float64x2
struct v_uint8x16;
struct v_int8x16;
struct v_uint16x8;
struct v_int16x8;
struct v_uint32x4;
struct v_int32x4;
struct v_uint64x2;
struct v_int64x2;
struct v_float32x4;
struct v_float64x2;
#endif
/** Value reordering **/
// Expansion
void v_expand(const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&);
void v_expand(const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&);
void v_expand(const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&);
void v_expand(const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&);
void v_expand(const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&);
void v_expand(const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&);
// Low Expansion
__CV_V_UINT16 v_expand_low(const __CV_V_UINT8&);
__CV_V_INT16 v_expand_low(const __CV_V_INT8&);
__CV_V_UINT32 v_expand_low(const __CV_V_UINT16&);
__CV_V_INT32 v_expand_low(const __CV_V_INT16&);
__CV_V_UINT64 v_expand_low(const __CV_V_UINT32&);
__CV_V_INT64 v_expand_low(const __CV_V_INT32&);
// High Expansion
__CV_V_UINT16 v_expand_high(const __CV_V_UINT8&);
__CV_V_INT16 v_expand_high(const __CV_V_INT8&);
__CV_V_UINT32 v_expand_high(const __CV_V_UINT16&);
__CV_V_INT32 v_expand_high(const __CV_V_INT16&);
__CV_V_UINT64 v_expand_high(const __CV_V_UINT32&);
__CV_V_INT64 v_expand_high(const __CV_V_INT32&);
// Load & Low Expansion
__CV_V_UINT16 __CV_VX(load_expand)(const uchar*);
__CV_V_INT16 __CV_VX(load_expand)(const schar*);
__CV_V_UINT32 __CV_VX(load_expand)(const ushort*);
__CV_V_INT32 __CV_VX(load_expand)(const short*);
__CV_V_UINT64 __CV_VX(load_expand)(const uint*);
__CV_V_INT64 __CV_VX(load_expand)(const int*);
// Load lower 8-bit and expand into 32-bit
__CV_V_UINT32 __CV_VX(load_expand_q)(const uchar*);
__CV_V_INT32 __CV_VX(load_expand_q)(const schar*);
// Saturating Pack
__CV_V_UINT8 v_pack(const __CV_V_UINT16&, const __CV_V_UINT16&);
__CV_V_INT8 v_pack(const __CV_V_INT16&, const __CV_V_INT16&);
__CV_V_UINT16 v_pack(const __CV_V_UINT32&, const __CV_V_UINT32&);
__CV_V_INT16 v_pack(const __CV_V_INT32&, const __CV_V_INT32&);
// Non-saturating Pack
__CV_V_UINT32 v_pack(const __CV_V_UINT64&, const __CV_V_UINT64&);
__CV_V_INT32 v_pack(const __CV_V_INT64&, const __CV_V_INT64&);
// Pack signed integers with unsigned saturation
__CV_V_UINT8 v_pack_u(const __CV_V_INT16&, const __CV_V_INT16&);
__CV_V_UINT16 v_pack_u(const __CV_V_INT32&, const __CV_V_INT32&);
/** Arithmetic, bitwise and comparison operations **/
// Non-saturating multiply
#if CV_VSX
template<typename Tvec>
Tvec v_mul_wrap(const Tvec& a, const Tvec& b);
#else
__CV_V_UINT8 v_mul_wrap(const __CV_V_UINT8&, const __CV_V_UINT8&);
__CV_V_INT8 v_mul_wrap(const __CV_V_INT8&, const __CV_V_INT8&);
__CV_V_UINT16 v_mul_wrap(const __CV_V_UINT16&, const __CV_V_UINT16&);
__CV_V_INT16 v_mul_wrap(const __CV_V_INT16&, const __CV_V_INT16&);
#endif
// Multiply and expand
#if CV_VSX
template<typename Tvec, typename Twvec>
void v_mul_expand(const Tvec& a, const Tvec& b, Twvec& c, Twvec& d);
#else
void v_mul_expand(const __CV_V_UINT8&, const __CV_V_UINT8&, __CV_V_UINT16&, __CV_V_UINT16&);
void v_mul_expand(const __CV_V_INT8&, const __CV_V_INT8&, __CV_V_INT16&, __CV_V_INT16&);
void v_mul_expand(const __CV_V_UINT16&, const __CV_V_UINT16&, __CV_V_UINT32&, __CV_V_UINT32&);
void v_mul_expand(const __CV_V_INT16&, const __CV_V_INT16&, __CV_V_INT32&, __CV_V_INT32&);
void v_mul_expand(const __CV_V_UINT32&, const __CV_V_UINT32&, __CV_V_UINT64&, __CV_V_UINT64&);
void v_mul_expand(const __CV_V_INT32&, const __CV_V_INT32&, __CV_V_INT64&, __CV_V_INT64&);
#endif
// Conversions
__CV_V_FLOAT32 v_cvt_f32(const __CV_V_INT32& a);
__CV_V_FLOAT32 v_cvt_f32(const __CV_V_FLOAT64& a);
__CV_V_FLOAT32 v_cvt_f32(const __CV_V_FLOAT64& a, const __CV_V_FLOAT64& b);
__CV_V_FLOAT64 v_cvt_f64(const __CV_V_INT32& a);
__CV_V_FLOAT64 v_cvt_f64_high(const __CV_V_INT32& a);
__CV_V_FLOAT64 v_cvt_f64(const __CV_V_FLOAT32& a);
__CV_V_FLOAT64 v_cvt_f64_high(const __CV_V_FLOAT32& a);
__CV_V_FLOAT64 v_cvt_f64(const __CV_V_INT64& a);
/** Cleanup **/
#undef CV__SIMD_FORWARD
#undef __CV_VX
#undef __CV_V_UINT8
#undef __CV_V_INT8
#undef __CV_V_UINT16
#undef __CV_V_INT16
#undef __CV_V_UINT32
#undef __CV_V_INT32
#undef __CV_V_UINT64
#undef __CV_V_INT64
#undef __CV_V_FLOAT32
#undef __CV_V_FLOAT64
CV_CPU_OPTIMIZATION_HAL_NAMESPACE_END
//! @endcond
} // cv::

1856
face_num_recongation/src/3rdParty/opencv/include/opencv2/core/hal/intrin_msa.hpp vendored Normal file
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