Impl RISC-V HAL for cv::flip | Add perf test for flip #26943
Implement through the existing `cv_hal_flip` interfaces.
Add perf test for `cv::flip`.
The reason why select these args for testing:
- **size**: copied from perf_lut
- **type**:
- U8C1: basic situation
- U8C3: unaligned element size
- U8C4: large element size
Tested on
- MUSE-PI (vlen=256)
- Compiler: gcc 14.2 (riscv-collab/riscv-gnu-toolchain Nightly: December 16, 2024)
```sh
$ opencv_test_core --gtest_filter="Core_Flip/ElemWiseTest.*"
$ opencv_perf_core --gtest_filter="Size_MatType_FlipCode*" --perf_min_samples=300 --perf_force_samples=300
```
```
Geometric mean (ms)
Name of Test scalar ui rvv ui rvv
vs vs
scalar scalar
(x-factor) (x-factor)
flip::Size_MatType_FlipCode::(320x240, 8UC1, FLIP_X) 0.026 0.033 0.031 0.81 0.84
flip::Size_MatType_FlipCode::(320x240, 8UC1, FLIP_XY) 0.206 0.212 0.091 0.97 2.26
flip::Size_MatType_FlipCode::(320x240, 8UC1, FLIP_Y) 0.185 0.189 0.082 0.98 2.25
flip::Size_MatType_FlipCode::(320x240, 8UC3, FLIP_X) 0.070 0.084 0.084 0.83 0.83
flip::Size_MatType_FlipCode::(320x240, 8UC3, FLIP_XY) 0.616 0.612 0.235 1.01 2.62
flip::Size_MatType_FlipCode::(320x240, 8UC3, FLIP_Y) 0.587 0.603 0.204 0.97 2.88
flip::Size_MatType_FlipCode::(320x240, 8UC4, FLIP_X) 0.263 0.110 0.109 2.40 2.41
flip::Size_MatType_FlipCode::(320x240, 8UC4, FLIP_XY) 0.930 0.831 0.316 1.12 2.95
flip::Size_MatType_FlipCode::(320x240, 8UC4, FLIP_Y) 1.175 1.129 0.313 1.04 3.75
flip::Size_MatType_FlipCode::(640x480, 8UC1, FLIP_X) 0.303 0.118 0.111 2.57 2.73
flip::Size_MatType_FlipCode::(640x480, 8UC1, FLIP_XY) 0.949 0.836 0.405 1.14 2.34
flip::Size_MatType_FlipCode::(640x480, 8UC1, FLIP_Y) 0.784 0.783 0.409 1.00 1.92
flip::Size_MatType_FlipCode::(640x480, 8UC3, FLIP_X) 1.084 0.360 0.355 3.01 3.06
flip::Size_MatType_FlipCode::(640x480, 8UC3, FLIP_XY) 3.768 3.348 1.364 1.13 2.76
flip::Size_MatType_FlipCode::(640x480, 8UC3, FLIP_Y) 4.361 4.473 1.296 0.97 3.37
flip::Size_MatType_FlipCode::(640x480, 8UC4, FLIP_X) 1.252 0.469 0.451 2.67 2.78
flip::Size_MatType_FlipCode::(640x480, 8UC4, FLIP_XY) 5.732 5.220 1.303 1.10 4.40
flip::Size_MatType_FlipCode::(640x480, 8UC4, FLIP_Y) 5.041 5.105 1.203 0.99 4.19
flip::Size_MatType_FlipCode::(1920x1080, 8UC1, FLIP_X) 2.382 0.903 0.903 2.64 2.64
flip::Size_MatType_FlipCode::(1920x1080, 8UC1, FLIP_XY) 8.606 7.508 2.581 1.15 3.33
flip::Size_MatType_FlipCode::(1920x1080, 8UC1, FLIP_Y) 8.421 8.535 2.219 0.99 3.80
flip::Size_MatType_FlipCode::(1920x1080, 8UC3, FLIP_X) 6.312 2.416 2.429 2.61 2.60
flip::Size_MatType_FlipCode::(1920x1080, 8UC3, FLIP_XY) 29.174 26.055 12.761 1.12 2.29
flip::Size_MatType_FlipCode::(1920x1080, 8UC3, FLIP_Y) 25.373 25.500 13.382 1.00 1.90
flip::Size_MatType_FlipCode::(1920x1080, 8UC4, FLIP_X) 7.620 3.204 3.115 2.38 2.45
flip::Size_MatType_FlipCode::(1920x1080, 8UC4, FLIP_XY) 32.876 29.310 12.976 1.12 2.53
flip::Size_MatType_FlipCode::(1920x1080, 8UC4, FLIP_Y) 28.831 29.094 14.919 0.99 1.93
```
The optimization for vlen <= 256 and > 256 are different, but I have no real hardware with vlen > 256. So accuracy tests for that like 512 and 1024 are conducted on QEMU built from the `riscv-collab/riscv-gnu-toolchain`.
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Enable SIMD_SCALABLE for exp and sqrt #26886
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```
CPU - Banana Pi k1, compiler - clang 18.1.4
```
```
Geometric mean (ms)
Name of Test baseline hal ui hal ui
vs vs
baseline baseline
(x-factor) (x-factor)
Exp::ExpFixture::(127x61, 32FC1) 0.358 -- 0.033 -- 10.70
Exp::ExpFixture::(640x480, 32FC1) 14.304 -- 1.167 -- 12.26
Exp::ExpFixture::(1280x720, 32FC1) 42.785 -- 3.538 -- 12.09
Exp::ExpFixture::(1920x1080, 32FC1) 96.206 -- 7.927 -- 12.14
Exp::ExpFixture::(127x61, 64FC1) 0.433 0.050 0.098 8.59 4.40
Exp::ExpFixture::(640x480, 64FC1) 17.315 1.935 3.813 8.95 4.54
Exp::ExpFixture::(1280x720, 64FC1) 52.181 5.877 11.519 8.88 4.53
Exp::ExpFixture::(1920x1080, 64FC1) 117.082 13.157 25.854 8.90 4.53
```
Additionally, this PR brings Sqrt optimization with UI:
```
Geometric mean (ms)
Name of Test baseline ui ui
vs
baseline
(x-factor)
Sqrt::SqrtFixture::(127x61, 5, false) 0.111 0.027 4.11
Sqrt::SqrtFixture::(127x61, 6, false) 0.149 0.053 2.82
Sqrt::SqrtFixture::(640x480, 5, false) 4.374 0.967 4.52
Sqrt::SqrtFixture::(640x480, 6, false) 5.885 2.046 2.88
Sqrt::SqrtFixture::(1280x720, 5, false) 12.960 2.915 4.45
Sqrt::SqrtFixture::(1280x720, 6, false) 17.648 6.107 2.89
Sqrt::SqrtFixture::(1920x1080, 5, false) 29.178 6.524 4.47
Sqrt::SqrtFixture::(1920x1080, 6, false) 39.709 13.670 2.90
```
Reference
Muller, J.-M. Elementary Functions: Algorithms and Implementation. 2nd ed. Boston: Birkhäuser, 2006.
https://www.springer.com/gp/book/9780817643720
core: vectorize cv::normalize / cv::norm #26885
Checklist:
| | normInf | normL1 | normL2 |
| ---- | ------- | ------ | ------ |
| bool | - | - | - |
| 8u | √ | √ | √ |
| 8s | √ | √ | √ |
| 16u | √ | √ | √ |
| 16s | √ | √ | √ |
| 16f | - | - | - |
| 16bf | - | - | - |
| 32u | - | - | - |
| 32s | √ | √ | √ |
| 32f | √ | √ | √ |
| 64u | - | - | - |
| 64s | - | - | - |
| 64f | √ | √ | √ |
*: Vectorization of data type bool, 16f, 16bf, 32u, 64u and 64s needs to be done on 5.x.
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Migrate remaning OpenVX integrations to OpenVX HAL (core) #26903
Tested with OpenVX 1.2 & 1.3 sample implementation.
Steps to build and test:
```
git clone git@github.com:KhronosGroup/OpenVX-sample-impl.git
cd OpenVX-sample-impl
python3 Build.py --os=Linux --conf=Release
cd ..
mkdir build
cmake -DWITH_OPENVX=ON -DOPENVX_ROOT=/mnt/Projects/Projects/OpenVX-sample-impl/install/Linux/x64/Release/ ../opencv
make -j8
```
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Add RISC-V HAL implementation for cv::norm and cv::normalize #26804
This patch implements `cv::norm` with norm types `NORM_INF/NORM_L1/NORM_L2/NORM_L2SQR` and `Mat::convertTo` function in RVV_HAL using native intrinsic, optimizing the performance for `cv::norm(src)`, `cv::norm(src1, src2)`, and `cv::normalize(src)` with data types `8UC1/8UC4/32FC1`.
`cv::normalize` also calls `minMaxIdx`, #26789 implements RVV_HAL for this.
Tested on MUSE-PI for both gcc 14.2 and clang 20.0.
```
$ opencv_test_core --gtest_filter="*Norm*"
$ opencv_perf_core --gtest_filter="*norm*" --perf_min_samples=300 --perf_force_samples=300
```
The head of the perf table is shown below since the table is too long.
View the full perf table here: [hal_rvv_norm.pdf](https://github.com/user-attachments/files/18468255/hal_rvv_norm.pdf)
<img width="1304" alt="Untitled" src="https://github.com/user-attachments/assets/3550b671-6d96-4db3-8b5b-d4cb241da650" />
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Add RISC-V HAL implementation for minMaxIdx #26789
On the RISC-V platform, `minMaxIdx` cannot benefit from Universal Intrinsics because the UI-optimized `minMaxIdx` only supports `CV_SIMD128` (and does not accept `CV_SIMD_SCALABLE` for RVV).
1d701d1690/modules/core/src/minmax.cpp (L209-L214)
This patch implements `minMaxIdx` function in RVV_HAL using native intrinsic, optimizing the performance for all data types with one channel.
Tested on MUSE-PI for both gcc 14.2 and clang 20.0.
```
$ opencv_test_core --gtest_filter="*MinMaxLoc*"
$ opencv_perf_core --gtest_filter="*minMaxLoc*"
```
<img width="1122" alt="Untitled" src="https://github.com/user-attachments/assets/6a246852-87af-42c5-a50b-c349c2765f3f" />
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Fix bug with int64 support for FileStorage #26846
### Pull Request Readiness Checklist
Fix#26829, https://github.com/opencv/opencv-python/issues/1078
In current implementation of `int64` support raw size of recorded integer is variable (`4` or `8` bytes depending on value). But then we iterate over nodes we need to know it exact value
dfad11aae7/modules/core/src/persistence.cpp (L2596-L2609)
Bug is that `rawSize` method still return `4` for any integer. I haven't figured out a way how to get variable raw size for integer in this method. I made raw size for integer is constant and equal to `8`.
Yes, after this patch memory consumption for integers will increase, but I don't know a better way to do it yet. At least this fixes bug and implementation becomes more correct
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Improved dumpVector, cv::Rect operator<< and exceptions #26602
- Applied format for vector element formatting to ensure consistent and clear output representation.
- Moved `operator<<` to the `cv` namespace to align with OpenCV's coding standards and improve maintainability.
- Enhanced error handling by including detailed exception messages using `e.what()` for better debugging.
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More convenient GpuMatND constructor #26472Closes#26471
For convenience, GpuMatND can now accept a step.size() equal to size.size(), as long as the last step is equal to elemSize()
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Support C++20 standard #26590
Close https://github.com/opencv/opencv/issues/26589
Related https://github.com/opencv/opencv_contrib/pull/3842
Related: https://github.com/opencv/opencv/issues/20269
- do not arithmetic enums and ( different enums or floating numeric)
- remove unused variable
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Emscripten build fixes#26537
- Corrects typo in Emscripten-only intrinsics header (Fixes https://github.com/opencv/opencv/issues/26536)
- Updates deprecated intrinsic title (as per LLVM final intrinsic name).
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HAL added for absdiff(array, scalar) + related fixes#26459
### This PR changes
* HAL for `absdiff` when one of arguments is a scalar, including multichannel arrays and scalars
* several channels support for HAL `addScalar`
* proper data type check for `addScalar` when one of arguments is a scalar
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HAL added for add(array, scalar) #25624
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int64 data type support for FileStorage. 1d and empty Mat with exact dimensions #26434
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Port of https://github.com/opencv/opencv/pull/26399 to 4.x branch
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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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Add support for v_sin and v_cos (Sine and Cosine) #25892
This PR aims to implement `v_sincos(v_float16 x)`, `v_sincos(v_float32 x)` and `v_sincos(v_float64 x)`.
Merged after https://github.com/opencv/opencv/pull/25891 and https://github.com/opencv/opencv/pull/26023
**NOTE:**
Also, the patch changes already added `v_exp`, `v_log` and `v_erf` to pass parameters by reference instead of by value, to match API of other universal intrinsics.
TODO:
- [x] double and half float precision
- [x] tests for them
- [x] doc to explain the implementation
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