//M*/
-#ifndef __OPENCV_CORE_EIGEN_HPP__
-#define __OPENCV_CORE_EIGEN_HPP__
+#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
+/** @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, Mat& dst )
+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(), DataType<_Tp>::type,
- (void*)src.data(), src.stride()*sizeof(_Tp));
+ 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(), DataType<_Tp>::type,
- (void*)src.data(), src.stride()*sizeof(_Tp));
+ Mat _src(src.rows(), src.cols(), traits::Type<_Tp>::value,
+ (void*)src.data(), src.outerStride()*sizeof(_Tp));
_src.copyTo(dst);
}
}
CV_DbgAssert(src.rows == _rows && src.cols == _cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
- const Mat _dst(src.cols, src.rows, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ 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 )
}
else
{
- const Mat _dst(src.rows, src.cols, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
+ dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
}
}
{
if( !(dst.Flags & Eigen::RowMajorBit) )
{
- const Mat _dst(_cols, _rows, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ 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, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ const Mat _dst(_rows, _cols, traits::Type<_Tp>::value,
+ dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
Mat(src).copyTo(_dst);
}
}
dst.resize(src.rows, src.cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
- const Mat _dst(src.cols, src.rows, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ 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 )
}
else
{
- const Mat _dst(src.rows, src.cols, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
+ dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
}
}
dst.resize(_rows, _cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
- const Mat _dst(_cols, _rows, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ 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, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ const Mat _dst(_rows, _cols, traits::Type<_Tp>::value,
+ dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
Mat(src).copyTo(_dst);
}
}
if( !(dst.Flags & Eigen::RowMajorBit) )
{
- const Mat _dst(src.cols, src.rows, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ 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
}
else
{
- const Mat _dst(src.rows, src.cols, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
+ dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
}
}
if( !(dst.Flags & Eigen::RowMajorBit) )
{
- const Mat _dst(1, _rows, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ 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, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ const Mat _dst(_rows, 1, traits::Type<_Tp>::value,
+ dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
src.copyTo(_dst);
}
}
dst.resize(src.cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
- const Mat _dst(src.cols, src.rows, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ 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
}
else
{
- const Mat _dst(src.rows, src.cols, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ const Mat _dst(src.rows, src.cols, traits::Type<_Tp>::value,
+ dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
src.convertTo(_dst, _dst.type());
}
}
dst.resize(_cols);
if( !(dst.Flags & Eigen::RowMajorBit) )
{
- const Mat _dst(_cols, 1, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ 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, DataType<_Tp>::type,
- dst.data(), (size_t)(dst.stride()*sizeof(_Tp)));
+ const Mat _dst(1, _cols, traits::Type<_Tp>::value,
+ dst.data(), (size_t)(dst.outerStride()*sizeof(_Tp)));
Mat(src).copyTo(_dst);
}
}
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