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add API to reinterpret Mat type

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gaohaoyuan 2024-07-11 17:55:08 +08:00 committed by gaohaoyuan
parent 1ca526dcdb
commit 603344fa54
4 changed files with 62 additions and 0 deletions
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10
modules/core/include/opencv2/core/mat.hpp
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@ -371,6 +371,7 @@ public:
void release() const;
void clear() const;
void setTo(const _InputArray& value, const _InputArray & mask = _InputArray()) const;
Mat reinterpret( int type ) const;
void assign(const UMat& u) const;
void assign(const Mat& m) const;
@ -1322,6 +1323,15 @@ public:
*/
Mat reshape(int cn, const std::vector<int>& newshape) const;
/** @brief Reset the type of matrix.
The methods reset the data type of matrix. If the new type and the old type of the matrix
have the same element size, the current buffer can be reused. The method needs to consider whether the
current mat is a submatrix or has any references.
@param type New data type.
*/
Mat reinterpret( int type ) const;
/** @brief Transposes a matrix.
The method performs matrix transposition by means of matrix expressions. It does not perform the

10
modules/core/src/matrix.cpp
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@ -1261,6 +1261,16 @@ Mat Mat::reshape(int _cn, const std::vector<int>& _newshape) const
return reshape(_cn, (int)_newshape.size(), &_newshape[0]);
}
Mat Mat::reinterpret(int type) const
{
type = CV_MAT_TYPE(type);
CV_Assert(CV_ELEM_SIZE(this->type()) == CV_ELEM_SIZE(type));
Mat m = *this;
m.flags = (m.flags & ~CV_MAT_TYPE_MASK) | type;
m.updateContinuityFlag();
return m;
}
Mat Mat::diag(const Mat& d)
{
CV_Assert( d.cols == 1 || d.rows == 1 );

6
modules/core/src/matrix_wrap.cpp
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@ -1656,6 +1656,12 @@ void _OutputArray::create(int d, const int* sizes, int mtype, int i,
CV_Error(Error::StsNotImplemented, "Unknown/unsupported array type");
}
Mat _OutputArray::reinterpret(int mtype) const
{
mtype = CV_MAT_TYPE(mtype);
return getMat().reinterpret(mtype);
}
void _OutputArray::createSameSize(const _InputArray& arr, int mtype) const
{
int arrsz[CV_MAX_DIM], d = arr.sizend(arrsz);

36
modules/core/test/test_mat.cpp
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@ -1303,6 +1303,42 @@ TEST(Core_Mat, reshape_ndims_4)
}
}
TEST(Core_Mat, reinterpret_Mat_8UC3_8SC3)
{
cv::Mat A(8, 16, CV_8UC3, cv::Scalar(1, 2, 3));
cv::Mat B = A.reinterpret(CV_8SC3);
EXPECT_EQ(A.data, B.data);
EXPECT_EQ(B.type(), CV_8SC3);
}
TEST(Core_Mat, reinterpret_Mat_8UC4_32FC1)
{
cv::Mat A(8, 16, CV_8UC4, cv::Scalar(1, 2, 3, 4));
cv::Mat B = A.reinterpret(CV_32FC1);
EXPECT_EQ(A.data, B.data);
EXPECT_EQ(B.type(), CV_32FC1);
}
TEST(Core_Mat, reinterpret_OutputArray_8UC3_8SC3) {
cv::Mat A(8, 16, CV_8UC3, cv::Scalar(1, 2, 3));
cv::OutputArray C(A);
cv::Mat B = C.reinterpret(CV_8SC3);
EXPECT_EQ(A.data, B.data);
EXPECT_EQ(B.type(), CV_8SC3);
}
TEST(Core_Mat, reinterpret_OutputArray_8UC4_32FC1) {
cv::Mat A(8, 16, CV_8UC4, cv::Scalar(1, 2, 3, 4));
cv::OutputArray C(A);
cv::Mat B = C.reinterpret(CV_32FC1);
EXPECT_EQ(A.data, B.data);
EXPECT_EQ(B.type(), CV_32FC1);
}
TEST(Core_Mat, push_back)
{
Mat a = (Mat_<float>(1,2) << 3.4884074f, 1.4159607f);