Use LMUL=2 in the RISC-V Vector (RVV) backend of Universal Intrinsic. #26318
The modification of this patch involves the RVV backend of Universal Intrinsic, replacing `LMUL=1` with `LMUL=2`.
Now each Universal Intrinsic type actually corresponds to two RVV vector registers, and each Intrinsic function also operates two vector registers. Considering that algorithms written using Universal Intrinsic usually do not use the maximum number of registers, this can help the RVV backend utilize more register resources without modifying the algorithm implementation
This patch is generally beneficial in performance.
We compiled OpenCV with `Clang-19.1.1` and `GCC-14.2.0` , ran it on `CanMV-k230` and `Banana-Pi F3`. Then we have four scenarios on combinations of compilers and devices. In `opencv_perf_core`, there are 3363 cases, of which:
- 901 (26.8%) cases achieved more than `5%` performance improvement in all four scenarios, and the average speedup of these test cases (compared to scalar) increased from `3.35x` to `4.35x`
- 75 (2.2%) cases had more than `5%` performance loss in all four scenarios, indicating that these cases are better with `LMUL=1` instead of `LMUL=2`. This involves `Mat_Transform`, `hasNonZero`, `KMeans`, `meanStdDev`, `merge` and `norm2`. Among them, `Mat_Transform` only has performance degradation in a few cases (`8UC3`), and the actual execution time of `hasNonZero` is so short that it can be ignored. For `KMeans`, `meanStdDev`, `merge` and `norm2`, we should be able to use the HAL to optimize/restore their performance. (In fact, we have already done this for `merge` #26216 )
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core: C-API cleanup: RNG algorithms in core(4.x) #26259
- replace CV_RAND_UNI and NORMAL to cv::RNG::UNIFORM and cv::RNG::NORMAL.
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Added offset for HAL as ofs2idx expects 1-based index #26080
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Fixed the simd bugs of iPow8u and iPow16u #26061
Add the following cases in opencv_perf_core:
* OCL_PowFixture_iPow.iPow/0, where GetParam() = (640x480, 8UC1)
* OCL_PowFixture_iPow.iPow/2, where GetParam() = (640x480, 16UC1)
iPow8u and iPow16u failed to call to simd accelerating while executing.
Fix the bug by changing the input type of iPow_SIMD function.
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imgproc: add specific error code when cvtColor is used on an image with an invalid number of channels #25981close#25971
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Add support for QNX #25832
Build and test instruction for QNX:
https://github.com/chachoi-world/qnx-ports/blob/main/opencv/README.md
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To be on par with `cv::Mat`, let's add `cv::cuda::GpuMat::getStdAllocator()`
This is useful anyway, because when a user wants to use custom allocators, he might want to resort to the standard default allocator behaviour, not some other allocator that could have been set by `setDefaultAllocator()`
HAL for dot product added #25936
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Added flag to GaussianBlur for faster but not bit-exact implementation #25792
Rationale:
Current implementation of GaussianBlur is almost always bit-exact. It helps to get predictable results according platforms, but prohibits most of approximations and optimization tricks.
The patch converts `borderType` parameter to more generic `flags` and introduces `GAUSS_ALLOW_APPROXIMATIONS` flag to allow not bit-exact implementation. With the flag IPP and generic HAL implementation are called first. The flag naming and location is a subject for discussion.
Replaces https://github.com/opencv/opencv/pull/22073
Possibly related issue: https://github.com/opencv/opencv/issues/24135
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Mark cv::Mat(Mat&&) as noexcept #25899
This fixes https://github.com/opencv/opencv/issues/25065
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Fill mean and stdDev tails with zeros for HAL branch in meanStdDev #25789
as it's done for other branches.
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Tests added for mixed type arithmetic operations #25671
### Changes
* added accuracy tests for mixed type arithmetic operations
_Note: div-by-zero values are removed from checking since the result is implementation-defined in common case_
* added perf tests for the same cases
* fixed a typo in `getMulExtTab()` function that lead to dead code
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core: try to solve warnings caused by Apple's new LAPACK interface #24804
Resolves https://github.com/opencv/opencv/issues/24660
Apple's BLAS documentation: https://developer.apple.com/documentation/accelerate/blas?language=objc
New interface since macOS >= 13.3, iOS >= 16.4.
Todo:
- [x] Detect macOS version.
- [x] ~Detect iOS versions (major and minor version).~ No calling of Accelerate New LAPACK on iOS.
- [x] Solve calling `cblas_cgemm` and `cblas_zgemm`.
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HAL mul8x8to16 added #25506Fixes#25034
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Check range for type-dependant function tables #25598
Address https://github.com/opencv/opencv/issues/24703
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Transform offset to indeces for MatND in minMaxIdx HAL #25563
Address comments in https://github.com/opencv/opencv/pull/25553
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Merge pull request #25554 from savuor:rv/hal_lut
HAL for LUT added #25554
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Fix HAL interface for hal_ni_minMaxIdx #25553
Fixes https://github.com/opencv/opencv/issues/25540
The original implementation call HAL with the same parameters independently from amount of channels. The patch uses HAL correctly for the case cn > 1.
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HAL for Hamming norm added #25491fixes#25474
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core: persistence: output reals as human-friendly expression. #25351Close#25073
Related https://github.com/opencv/opencv/pull/25087
This patch is need to merge same time with https://github.com/opencv/opencv_contrib/pull/3714
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Maksim Shabunin
Liutong HAN
Alexander Smorkalov
kallaballa
kallaballa
Kumataro
Alexander Smorkalov
penghuiho
James Choi
chacha21
Vincent Rabaud
Rostislav Vasilikhin
Richard Barnes
Yuantao Feng
Rostislav Vasilikhin