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403 lines
14 KiB
C++
403 lines
14 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef OPENCV_CORE_EIGEN_HPP
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#define OPENCV_CORE_EIGEN_HPP
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#ifndef EIGEN_WORLD_VERSION
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#error "Wrong usage of OpenCV's Eigen utility header. Include Eigen's headers first. See https://github.com/opencv/opencv/issues/17366"
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#endif
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#include "opencv2/core.hpp"
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#if defined _MSC_VER && _MSC_VER >= 1200
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#define NOMINMAX // fix https://github.com/opencv/opencv/issues/17548
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#pragma warning( disable: 4714 ) //__forceinline is not inlined
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#pragma warning( disable: 4127 ) //conditional expression is constant
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#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data
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#endif
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#if !defined(OPENCV_DISABLE_EIGEN_TENSOR_SUPPORT)
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#if EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3 \
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&& defined(CV_CXX11) && defined(CV_CXX_STD_ARRAY)
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#include <unsupported/Eigen/CXX11/Tensor>
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#define OPENCV_EIGEN_TENSOR_SUPPORT 1
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#endif // EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3
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#endif // !defined(OPENCV_DISABLE_EIGEN_TENSOR_SUPPORT)
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namespace cv
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{
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/** @addtogroup core_eigen
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These functions are provided for OpenCV-Eigen interoperability. They convert `Mat`
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objects to corresponding `Eigen::Matrix` objects and vice-versa. Consult the [Eigen
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documentation](https://eigen.tuxfamily.org/dox/group__TutorialMatrixClass.html) for
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information about the `Matrix` template type.
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@note Using these functions requires the `Eigen/Dense` or similar header to be
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included before this header.
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*/
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//! @{
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#if defined(OPENCV_EIGEN_TENSOR_SUPPORT) || defined(CV_DOXYGEN)
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/** @brief Converts an Eigen::Tensor to a cv::Mat.
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The method converts an Eigen::Tensor with shape (H x W x C) to a cv::Mat where:
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H = number of rows
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W = number of columns
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C = number of channels
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Usage:
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\code
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Eigen::Tensor<float, 3, Eigen::RowMajor> a_tensor(...);
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// populate tensor with values
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Mat a_mat;
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eigen2cv(a_tensor, a_mat);
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\endcode
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*/
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template <typename _Tp, int _layout> static inline
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void eigen2cv( const Eigen::Tensor<_Tp, 3, _layout> &src, OutputArray dst )
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{
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if( !(_layout & Eigen::RowMajorBit) )
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{
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const std::array<int, 3> shuffle{2, 1, 0};
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Eigen::Tensor<_Tp, 3, !_layout> row_major_tensor = src.swap_layout().shuffle(shuffle);
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Mat _src(src.dimension(0), src.dimension(1), CV_MAKETYPE(DataType<_Tp>::type, src.dimension(2)), row_major_tensor.data());
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_src.copyTo(dst);
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}
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else
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{
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Mat _src(src.dimension(0), src.dimension(1), CV_MAKETYPE(DataType<_Tp>::type, src.dimension(2)), (void *)src.data());
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_src.copyTo(dst);
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}
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}
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/** @brief Converts a cv::Mat to an Eigen::Tensor.
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The method converts a cv::Mat to an Eigen Tensor with shape (H x W x C) where:
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H = number of rows
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W = number of columns
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C = number of channels
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Usage:
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\code
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Mat a_mat(...);
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// populate Mat with values
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Eigen::Tensor<float, 3, Eigen::RowMajor> a_tensor(...);
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cv2eigen(a_mat, a_tensor);
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\endcode
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*/
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template <typename _Tp, int _layout> static inline
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void cv2eigen( const Mat &src, Eigen::Tensor<_Tp, 3, _layout> &dst )
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{
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if( !(_layout & Eigen::RowMajorBit) )
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{
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Eigen::Tensor<_Tp, 3, !_layout> row_major_tensor(src.rows, src.cols, src.channels());
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Mat _dst(src.rows, src.cols, CV_MAKETYPE(DataType<_Tp>::type, src.channels()), row_major_tensor.data());
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if (src.type() == _dst.type())
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src.copyTo(_dst);
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else
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src.convertTo(_dst, _dst.type());
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const std::array<int, 3> shuffle{2, 1, 0};
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dst = row_major_tensor.swap_layout().shuffle(shuffle);
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}
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else
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{
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dst.resize(src.rows, src.cols, src.channels());
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Mat _dst(src.rows, src.cols, CV_MAKETYPE(DataType<_Tp>::type, src.channels()), dst.data());
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if (src.type() == _dst.type())
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src.copyTo(_dst);
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else
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src.convertTo(_dst, _dst.type());
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}
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}
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/** @brief Maps cv::Mat data to an Eigen::TensorMap.
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The method wraps an existing Mat data array with an Eigen TensorMap of shape (H x W x C) where:
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H = number of rows
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W = number of columns
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C = number of channels
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Explicit instantiation of the return type is required.
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@note Caller should be aware of the lifetime of the cv::Mat instance and take appropriate safety measures.
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The cv::Mat instance will retain ownership of the data and the Eigen::TensorMap will lose access when the cv::Mat data is deallocated.
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The example below initializes a cv::Mat and produces an Eigen::TensorMap:
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\code
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float arr[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
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Mat a_mat(2, 2, CV_32FC3, arr);
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Eigen::TensorMap<Eigen::Tensor<float, 3, Eigen::RowMajor>> a_tensormap = cv2eigen_tensormap<float>(a_mat);
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\endcode
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*/
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template <typename _Tp> static inline
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Eigen::TensorMap<Eigen::Tensor<_Tp, 3, Eigen::RowMajor>> cv2eigen_tensormap(InputArray src)
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{
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Mat mat = src.getMat();
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CV_CheckTypeEQ(mat.type(), CV_MAKETYPE(traits::Type<_Tp>::value, mat.channels()), "");
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return Eigen::TensorMap<Eigen::Tensor<_Tp, 3, Eigen::RowMajor>>((_Tp *)mat.data, mat.rows, mat.cols, mat.channels());
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}
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#endif // OPENCV_EIGEN_TENSOR_SUPPORT
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template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
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void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src, OutputArray dst )
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{
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if( !(src.Flags & Eigen::RowMajorBit) )
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{
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Mat _src(src.cols(), src.rows(), traits::Type<_Tp>::value,
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(void*)src.data(), src.outerStride()*sizeof(_Tp));
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transpose(_src, dst);
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}
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else
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{
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Mat _src(src.rows(), src.cols(), traits::Type<_Tp>::value,
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(void*)src.data(), src.outerStride()*sizeof(_Tp));
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_src.copyTo(dst);
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}
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}
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// Matx case
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template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
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void eigen2cv( const Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& src,
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Matx<_Tp, _rows, _cols>& dst )
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{
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if( !(src.Flags & Eigen::RowMajorBit) )
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{
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dst = Matx<_Tp, _cols, _rows>(static_cast<const _Tp*>(src.data())).t();
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}
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else
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{
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dst = Matx<_Tp, _rows, _cols>(static_cast<const _Tp*>(src.data()));
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}
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}
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template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
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void cv2eigen( const Mat& src,
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Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst )
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{
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CV_DbgAssert(src.rows == _rows && src.cols == _cols);
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if( !(dst.Flags & Eigen::RowMajorBit) )
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{
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const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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if( src.type() == _dst.type() )
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transpose(src, _dst);
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else if( src.cols == src.rows )
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{
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src.convertTo(_dst, _dst.type());
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transpose(_dst, _dst);
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}
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else
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Mat(src.t()).convertTo(_dst, _dst.type());
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}
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else
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{
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const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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src.convertTo(_dst, _dst.type());
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}
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}
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// Matx case
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template<typename _Tp, int _rows, int _cols, int _options, int _maxRows, int _maxCols> static inline
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void cv2eigen( const Matx<_Tp, _rows, _cols>& src,
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Eigen::Matrix<_Tp, _rows, _cols, _options, _maxRows, _maxCols>& dst )
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{
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if( !(dst.Flags & Eigen::RowMajorBit) )
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{
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const Mat _dst(_cols, _rows, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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transpose(src, _dst);
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}
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else
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{
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const Mat _dst(_rows, _cols, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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Mat(src).copyTo(_dst);
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}
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}
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template<typename _Tp> static inline
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void cv2eigen( const Mat& src,
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Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
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{
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dst.resize(src.rows, src.cols);
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if( !(dst.Flags & Eigen::RowMajorBit) )
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{
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const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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if( src.type() == _dst.type() )
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transpose(src, _dst);
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else if( src.cols == src.rows )
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{
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src.convertTo(_dst, _dst.type());
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transpose(_dst, _dst);
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}
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else
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Mat(src.t()).convertTo(_dst, _dst.type());
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}
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else
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{
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const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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src.convertTo(_dst, _dst.type());
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}
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}
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// Matx case
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template<typename _Tp, int _rows, int _cols> static inline
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void cv2eigen( const Matx<_Tp, _rows, _cols>& src,
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Eigen::Matrix<_Tp, Eigen::Dynamic, Eigen::Dynamic>& dst )
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{
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dst.resize(_rows, _cols);
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if( !(dst.Flags & Eigen::RowMajorBit) )
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{
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const Mat _dst(_cols, _rows, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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transpose(src, _dst);
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}
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else
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{
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const Mat _dst(_rows, _cols, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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Mat(src).copyTo(_dst);
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}
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}
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template<typename _Tp> static inline
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void cv2eigen( const Mat& src,
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Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst )
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{
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CV_Assert(src.cols == 1);
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dst.resize(src.rows);
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if( !(dst.Flags & Eigen::RowMajorBit) )
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{
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const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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if( src.type() == _dst.type() )
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transpose(src, _dst);
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else
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Mat(src.t()).convertTo(_dst, _dst.type());
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}
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else
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{
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const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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src.convertTo(_dst, _dst.type());
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}
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}
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// Matx case
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template<typename _Tp, int _rows> static inline
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void cv2eigen( const Matx<_Tp, _rows, 1>& src,
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Eigen::Matrix<_Tp, Eigen::Dynamic, 1>& dst )
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{
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dst.resize(_rows);
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if( !(dst.Flags & Eigen::RowMajorBit) )
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{
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const Mat _dst(1, _rows, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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transpose(src, _dst);
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}
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else
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{
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const Mat _dst(_rows, 1, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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src.copyTo(_dst);
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}
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}
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template<typename _Tp> static inline
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void cv2eigen( const Mat& src,
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Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst )
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{
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CV_Assert(src.rows == 1);
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dst.resize(src.cols);
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if( !(dst.Flags & Eigen::RowMajorBit) )
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{
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const Mat _dst(src.cols, src.rows, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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if( src.type() == _dst.type() )
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transpose(src, _dst);
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else
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Mat(src.t()).convertTo(_dst, _dst.type());
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}
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else
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{
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const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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src.convertTo(_dst, _dst.type());
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}
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}
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//Matx
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template<typename _Tp, int _cols> static inline
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void cv2eigen( const Matx<_Tp, 1, _cols>& src,
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Eigen::Matrix<_Tp, 1, Eigen::Dynamic>& dst )
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{
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dst.resize(_cols);
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if( !(dst.Flags & Eigen::RowMajorBit) )
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{
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const Mat _dst(_cols, 1, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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transpose(src, _dst);
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}
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else
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{
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const Mat _dst(1, _cols, traits::Type<_Tp>::value,
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dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
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Mat(src).copyTo(_dst);
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}
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}
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//! @}
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} // cv
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#endif
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