Documentation to enable FastCV based OpenCV HAL and Extensions #26910
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Fix Logical defect in FilterSpecklesImpl #26996
Fixes : #24963
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Fix getPerspectiveTransform for singular case #26926
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Fix#26916
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Some minor fixes#26992
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Imgcodecs: gif: support Disposal Method #26930
Close https://github.com/opencv/opencv/issues/26924
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videoio: print test params instead of indexes #26948
_videoio_ test names changed - use string instead of index.
E.g. `videoio_read.threads/0` is now `videoio_read.threads/h264_0_RAW`.
It allows to filter tests independently of the platform.
**Notes:**
- not all tests has been updated - only simpler ones and those which have varying parameters depending on platform
Add a test related IMWRITE_PNG_COMPRESSION parameter #26973
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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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Added trackers factory with pre-loaded dnn models #26875
Replaces https://github.com/opencv/opencv/pull/26295
Allows to substitute custom models or initialize tracker from in-memory model.
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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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