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code_app/third_party/opencv/kylin/loongarch64/include/opencv2/core/eigen.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.
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// (including, but not limited to, procurement of substitute goods or services;
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//M*/
#ifndef OPENCV_CORE_EIGEN_HPP
#define OPENCV_CORE_EIGEN_HPP
#ifndef EIGEN_WORLD_VERSION
#error "Wrong usage of OpenCV's Eigen utility header. Include Eigen's headers first. See https://github.com/opencv/opencv/issues/17366"
#endif
#include "opencv2/core.hpp"
#if defined _MSC_VER && _MSC_VER >= 1200
#define NOMINMAX // fix https://github.com/opencv/opencv/issues/17548
#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
#if !defined(OPENCV_DISABLE_EIGEN_TENSOR_SUPPORT)
#if EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3 \
&& defined(CV_CXX11) && defined(CV_CXX_STD_ARRAY)
#include <unsupported/Eigen/CXX11/Tensor>
#define OPENCV_EIGEN_TENSOR_SUPPORT 1
#endif // EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3
#endif // !defined(OPENCV_DISABLE_EIGEN_TENSOR_SUPPORT)
namespace cv
{
/** @addtogroup core_eigen
These functions are provided for OpenCV-Eigen interoperability. They convert `Mat`
objects to corresponding `Eigen::Matrix` objects and vice-versa. Consult the [Eigen
documentation](https://eigen.tuxfamily.org/dox/group__TutorialMatrixClass.html) for
information about the `Matrix` template type.
@note Using these functions requires the `Eigen/Dense` or similar header to be
included before this header.
*/
//! @{
#if defined(OPENCV_EIGEN_TENSOR_SUPPORT) || defined(CV_DOXYGEN)
/** @brief Converts an Eigen::Tensor to a cv::Mat.
The method converts an Eigen::Tensor with shape (H x W x C) to a cv::Mat where:
H = number of rows
W = number of columns
C = number of channels
Usage:
\code
Eigen::Tensor<float, 3, Eigen::RowMajor> a_tensor(...);
// populate tensor with values
Mat a_mat;
eigen2cv(a_tensor, a_mat);
\endcode
*/
template <typename _Tp, int _layout> static inline
void eigen2cv( const Eigen::Tensor<_Tp, 3, _layout> &src, OutputArray dst )
{
if( !(_layout & Eigen::RowMajorBit) )
{
const std::array<int, 3> shuffle{2, 1, 0};
Eigen::Tensor<_Tp, 3, !_layout> row_major_tensor = src.swap_layout().shuffle(shuffle);
Mat _src(src.dimension(0), src.dimension(1), CV_MAKETYPE(DataType<_Tp>::type, src.dimension(2)), row_major_tensor.data());
_src.copyTo(dst);
}
else
{
Mat _src(src.dimension(0), src.dimension(1), CV_MAKETYPE(DataType<_Tp>::type, src.dimension(2)), (void *)src.data());
_src.copyTo(dst);
}
}
/** @brief Converts a cv::Mat to an Eigen::Tensor.
The method converts a cv::Mat to an Eigen Tensor with shape (H x W x C) where:
H = number of rows
W = number of columns
C = number of channels
Usage:
\code
Mat a_mat(...);
// populate Mat with values
Eigen::Tensor<float, 3, Eigen::RowMajor> a_tensor(...);
cv2eigen(a_mat, a_tensor);
\endcode
*/
template <typename _Tp, int _layout> static inline
void cv2eigen( const Mat &src, Eigen::Tensor<_Tp, 3, _layout> &dst )
{
if( !(_layout & Eigen::RowMajorBit) )
{
Eigen::Tensor<_Tp, 3, !_layout> row_major_tensor(src.rows, src.cols, src.channels());
Mat _dst(src.rows, src.cols, CV_MAKETYPE(DataType<_Tp>::type, src.channels()), row_major_tensor.data());
if (src.type() == _dst.type())
src.copyTo(_dst);
else
src.convertTo(_dst, _dst.type());
const std::array<int, 3> shuffle{2, 1, 0};
dst = row_major_tensor.swap_layout().shuffle(shuffle);
}
else
{
dst.resize(src.rows, src.cols, src.channels());
Mat _dst(src.rows, src.cols, CV_MAKETYPE(DataType<_Tp>::type, src.channels()), dst.data());
if (src.type() == _dst.type())
src.copyTo(_dst);
else
src.convertTo(_dst, _dst.type());
}
}
/** @brief Maps cv::Mat data to an Eigen::TensorMap.
The method wraps an existing Mat data array with an Eigen TensorMap of shape (H x W x C) where:
H = number of rows
W = number of columns
C = number of channels
Explicit instantiation of the return type is required.
@note Caller should be aware of the lifetime of the cv::Mat instance and take appropriate safety measures.
The cv::Mat instance will retain ownership of the data and the Eigen::TensorMap will lose access when the cv::Mat data is deallocated.
The example below initializes a cv::Mat and produces an Eigen::TensorMap:
\code
float arr[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
Mat a_mat(2, 2, CV_32FC3, arr);
Eigen::TensorMap<Eigen::Tensor<float, 3, Eigen::RowMajor>> a_tensormap = cv2eigen_tensormap<float>(a_mat);
\endcode
*/
template <typename _Tp> static inline
Eigen::TensorMap<Eigen::Tensor<_Tp, 3, Eigen::RowMajor>> cv2eigen_tensormap(InputArray src)
{
Mat mat = src.getMat();
CV_CheckTypeEQ(mat.type(), CV_MAKETYPE(traits::Type<_Tp>::value, mat.channels()), "");
return Eigen::TensorMap<Eigen::Tensor<_Tp, 3, Eigen::RowMajor>>((_Tp *)mat.data, mat.rows, mat.cols, mat.channels());
}
#endif // OPENCV_EIGEN_TENSOR_SUPPORT
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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