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Merge pull request #17320 from jgbradley1:add-eigen-tensor-conversions

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* add eigen tensor conversion functions

* add eigen tensor conversion tests

* add support for column major order

* update eigen tensor tests

* fix coding style and add conditional compilation

* fix conditional compilation checks

* remove whitespace

* rearrange functions for easier reading

* reformat function documentation and add tensormap unit test

* cleanup documentation of unit test

* remove condition duplication

* check Eigen major version, not minor version

* restrict to Eigen v3.3.0+

* add documentation note and add type checking to cv2eigen_tensormap()
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Josh Bradley 2020-05-23 14:25:01 -04:00 committed by GitHub
parent a9b030591b
commit 9fef09fe89
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106
modules/core/include/opencv2/core/eigen.hpp
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@ -47,6 +47,11 @@
#include "opencv2/core.hpp"
#if EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3
#include <unsupported/Eigen/CXX11/Tensor>
#define OPENCV_EIGEN_TENSOR_SUPPORT
#endif // EIGEN_WORLD_VERSION == 3 && EIGEN_MAJOR_VERSION >= 3
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4714 ) //__forceinline is not inlined
#pragma warning( disable: 4127 ) //conditional expression is constant
@ -59,6 +64,107 @@ namespace cv
//! @addtogroup core_eigen
//! @{
#ifdef OPENCV_EIGEN_TENSOR_SUPPORT
/** @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(const cv::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 )
{

80
modules/core/test/test_mat.cpp
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@ -2084,6 +2084,86 @@ TEST(Core_Eigen, eigen2cv_check_Mat_type)
}
#endif // HAVE_EIGEN
#ifdef OPENCV_EIGEN_TENSOR_SUPPORT
TEST(Core_Eigen, cv2eigen_check_tensor_conversion)
{
Mat A(2, 3, CV_32FC3);
float value = 0;
for(int row=0; row<A.rows; row++)
for(int col=0; col<A.cols; col++)
for(int ch=0; ch<A.channels(); ch++)
A.at<Vec3f>(row,col)[ch] = value++;
Eigen::Tensor<float, 3, Eigen::RowMajor> row_tensor;
cv2eigen(A, row_tensor);
float* mat_ptr = (float*)A.data;
float* tensor_ptr = row_tensor.data();
for (int i=0; i< row_tensor.size(); i++)
ASSERT_FLOAT_EQ(mat_ptr[i], tensor_ptr[i]);
Eigen::Tensor<float, 3, Eigen::ColMajor> col_tensor;
cv2eigen(A, col_tensor);
value = 0;
for(int row=0; row<A.rows; row++)
for(int col=0; col<A.cols; col++)
for(int ch=0; ch<A.channels(); ch++)
ASSERT_FLOAT_EQ(value++, col_tensor(row,col,ch));
}
#endif // OPENCV_EIGEN_TENSOR_SUPPORT
#ifdef OPENCV_EIGEN_TENSOR_SUPPORT
TEST(Core_Eigen, eigen2cv_check_tensor_conversion)
{
Eigen::Tensor<float, 3, Eigen::RowMajor> row_tensor(2,3,3);
Eigen::Tensor<float, 3, Eigen::ColMajor> col_tensor(2,3,3);
float value = 0;
for(int row=0; row<row_tensor.dimension(0); row++)
for(int col=0; col<row_tensor.dimension(1); col++)
for(int ch=0; ch<row_tensor.dimension(2); ch++)
{
row_tensor(row,col,ch) = value;
col_tensor(row,col,ch) = value;
value++;
}
Mat A;
eigen2cv(row_tensor, A);
float* tensor_ptr = row_tensor.data();
float* mat_ptr = (float*)A.data;
for (int i=0; i< row_tensor.size(); i++)
ASSERT_FLOAT_EQ(tensor_ptr[i], mat_ptr[i]);
Mat B;
eigen2cv(col_tensor, B);
value = 0;
for(int row=0; row<B.rows; row++)
for(int col=0; col<B.cols; col++)
for(int ch=0; ch<B.channels(); ch++)
ASSERT_FLOAT_EQ(value++, B.at<Vec3f>(row,col)[ch]);
}
#endif // OPENCV_EIGEN_TENSOR_SUPPORT
#ifdef OPENCV_EIGEN_TENSOR_SUPPORT
TEST(Core_Eigen, cv2eigen_tensormap_check_tensormap_access)
{
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_tensor = cv2eigen_tensormap<float>(a_mat);
for(int i=0; i<a_mat.rows; i++) {
for (int j=0; j<a_mat.cols; j++) {
for (int ch=0; ch<a_mat.channels(); ch++) {
ASSERT_FLOAT_EQ(a_mat.at<Vec3f>(i,j)[ch], a_tensor(i,j,ch));
ASSERT_EQ(&a_mat.at<Vec3f>(i,j)[ch], &a_tensor(i,j,ch));
}
}
}
}
#endif // OPENCV_EIGEN_TENSOR_SUPPORT
TEST(Mat, regression_12943) // memory usage: ~4.5 Gb
{
applyTestTag(CV_TEST_TAG_MEMORY_6GB);