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attempt to add 0d/1d mat support to OpenCV (#23473)

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* attempt to add 0d/1d mat support to OpenCV

* revised the patch; now 1D mat is treated as 1xN 2D mat rather than Nx1.

* a step towards 'green' tests

* another little step towards 'green' tests

* calib test failures seem to be fixed now

* more fixes _core & _dnn

* another step towards green ci; even 0D mat's (a.k.a. scalars) are now partly supported!

* * fixed strange bug in aruco/charuco detector, not sure why it did not work
* also fixed a few remaining failures (hopefully) in dnn & core

* disabled failing GAPI tests - too complex to dig into this compiler pipeline

* hopefully fixed java tests

* trying to fix some more tests

* quick followup fix

* continue to fix test failures and warnings

* quick followup fix

* trying to fix some more tests

* partly fixed support for 0D/scalar UMat's

* use updated parseReduce() from upstream

* trying to fix the remaining test failures

* fixed [ch]aruco tests in Python

* still trying to fix tests

* revert "fix" in dnn's CUDA tensor

* trying to fix dnn+CUDA test failures

* fixed 1D umat creation

* hopefully fixed remaining cuda test failures

* removed training whitespaces
This commit is contained in:
Vadim Pisarevsky
2023-09-21 18:24:38 +03:00
committed by GitHub
parent fdab565711
commit 416bf3253d
80 changed files with 1013 additions and 561 deletions
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modules/core/src/lda.cpp
+10 -10
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@@ -959,7 +959,7 @@ void eigenNonSymmetric(InputArray _src, OutputArray _evals, OutputArray _evects)
Mat src = _src.getMat();
int type = src.type();
size_t n = (size_t)src.rows;
int n = src.rows;
CV_Assert(src.rows == src.cols);
CV_Assert(type == CV_32F || type == CV_64F);
@@ -976,26 +976,26 @@ void eigenNonSymmetric(InputArray _src, OutputArray _evals, OutputArray _evects)
// EigenvalueDecomposition returns transposed and non-sorted eigenvalues
std::vector<double> eigenvalues64f;
eigensystem.eigenvalues().copyTo(eigenvalues64f);
CV_Assert(eigenvalues64f.size() == n);
CV_Assert(eigenvalues64f.size() == (size_t)n);
std::vector<int> sort_indexes(n);
cv::sortIdx(eigenvalues64f, sort_indexes, SORT_EVERY_ROW | SORT_DESCENDING);
std::vector<double> sorted_eigenvalues64f(n);
for (size_t i = 0; i < n; i++) sorted_eigenvalues64f[i] = eigenvalues64f[sort_indexes[i]];
for (int i = 0; i < n; i++) sorted_eigenvalues64f[i] = eigenvalues64f[sort_indexes[i]];
Mat(sorted_eigenvalues64f).convertTo(_evals, type);
Mat(n, 1, CV_64F, &sorted_eigenvalues64f[0]).convertTo(_evals, type);
if( _evects.needed() )
{
Mat eigenvectors64f = eigensystem.eigenvectors().t(); // transpose
CV_Assert((size_t)eigenvectors64f.rows == n);
CV_Assert((size_t)eigenvectors64f.cols == n);
Mat_<double> sorted_eigenvectors64f((int)n, (int)n, CV_64FC1);
for (size_t i = 0; i < n; i++)
CV_Assert(eigenvectors64f.rows == n);
CV_Assert(eigenvectors64f.cols == n);
Mat_<double> sorted_eigenvectors64f(n, n, CV_64FC1);
for (int i = 0; i < n; i++)
{
double* pDst = sorted_eigenvectors64f.ptr<double>((int)i);
double* pSrc = eigenvectors64f.ptr<double>(sort_indexes[(int)i]);
double* pDst = sorted_eigenvectors64f.ptr<double>(i);
double* pSrc = eigenvectors64f.ptr<double>(sort_indexes[i]);
CV_Assert(pSrc != NULL);
memcpy(pDst, pSrc, n * sizeof(double));
}