Merge remote-tracking branch 'upstream/3.4' into merge-3.4
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@@ -47,6 +47,11 @@
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#include "opencv2/core.hpp"
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#if EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3
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#include <unsupported/Eigen/CXX11/Tensor>
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#define OPENCV_EIGEN_TENSOR_SUPPORT
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#endif // EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3
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#if defined _MSC_VER && _MSC_VER >= 1200
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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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@@ -59,6 +64,107 @@ namespace cv
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//! @addtogroup core_eigen
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//! @{
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#ifdef OPENCV_EIGEN_TENSOR_SUPPORT
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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(const cv::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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