Fow now, it is possible to define valid rectangle for which some
functions overflow (e.g. br(), ares() ...).
This patch fixes the intersection operator so that it works with
any rectangle.
`PyObject*` to `std::vector<T>` conversion logic:
- If user passed Numpy Array
- If array is planar and T is a primitive type (doesn't require
constructor call) that matches with the element type of array, then
copy element one by one with the respect of the step between array
elements. If compiler is lucky (or brave enough) copy loop can be
vectorized.
For classes that require constructor calls this path is not
possible, because we can't begin an object lifetime without hacks.
- Otherwise fall-back to general case
- Otherwise - execute the general case:
If PyObject* corresponds to Sequence protocol - iterate over the
sequence elements and invoke the appropriate `pyopencv_to` function.
`std::vector<T>` to `PyObject*` conversion logic:
- If `std::vector<T>` is empty - return empty tuple.
- If `T` has a corresponding `Mat` `DataType` than return
Numpy array instance of the matching `dtype` e.g.
`std::vector<cv::Rect>` is returned as `np.ndarray` of shape `Nx4` and
`dtype=int`.
This branch helps to optimize further evaluations in user code.
- Otherwise - execute the general case:
Construct a tuple of length N = `std::vector::size` and insert
elements one by one.
Unnecessary functions were removed and code was rearranged to allow
compiler select the appropriate conversion function specialization.
Improve performance on Arm64
* Improve performance on Apple silicon
This patch will
- Enable dot product intrinsics for macOS arm64 builds
- Enable for macOS arm64 builds
- Improve HAL primitives
- reduction (sum, min, max, sad)
- signmask
- mul_expand
- check_any / check_all
Results on a M1 Macbook Pro
* Updates to #20011 based on feedback
- Removes Apple Silicon specific workarounds
- Makes #ifdef sections smaller for v_mul_expand cases
- Moves dot product optimization to compiler optimization check
- Adds 4x4 matrix transpose optimization
* Remove dotprod and fix v_transpose
Based on the latest, we've removed dotprod entirely and will revisit in a future PR.
Added explicit cats with v_transpose4x4()
This should resolve all opens with this PR
* Remove commented out lines
Remove two extraneous comments
* Updated cpp reference implementations for a few intrinsics to address wide universal intrinsics as well
* Updated cpp reference implementations for a few more universal intrinsics
* Update polynom_solver.cpp
This pull request is in the response to Issue #19526. I have fixed the problem with the cube root calculation of 2*R. The Issue was in the usage of pow function with negative values of R, but if it is calculated for only positive values of R then changing x0 according to the parity of R, the Issue is resolved. Kindly consider it, Thanks!
* add cv::cubeRoot(double)
Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
- follows iso c++ guideline C.44
- enables default compiler-created constructors to
also be noexcept
original commit: 77e26a7db3
- handled KernelArg, Image2D
* [hal][neon] Optimize the v_dotprod_fast intrinsics for aarch64.
On Armv8 in AArch64 execution mode, we can skip the sequence
v<op>_<ty>(vget_high_<ty>(x), vget_high_<ty>(y))
in favour of
v<op>_high_<ty>(x, y)
This has better changes for recent compilers to use less data movement
operations and better register allocation. See for example:
https://godbolt.org/z/bPq7vd
* [hal][neon] Fix build failure on armv7.
* [hal][neon] Address review comments in PR.
PR: https://github.com/opencv/opencv/pull/19486
* [hal][neon] Define macro to check for the AArch64 execution state of Armv8.
* [hal][neon] Fix macro definition for AArch64.
The fix is needed to prevent warnings when building for Armv7.
they might be thrown from third-party code (notably Ogre in the ovis
module).
While Linux is kind enough to print them, they cause instant termination
on Windows.
Arguably, they do not origin from OpenCV itself, but still this helps
understanding what went wrong when calling an OpenCV function.
Alexander Alekhin