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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Finally dropped convertFp16 function in favor of cv::Mat::convertTo() #26327
Partially address https://github.com/opencv/opencv/issues/24909
Related PR to contrib: https://github.com/opencv/opencv_contrib/pull/3812
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Modified TFLite parser for the new dnn engine #26330
The new dnn graph is creating just by defining input and output names of each layer.
Some TFLite layers has fused activation, which doesn't have layer name and input and output names. Also some layers require additional preprocessing layers (e.g. NHWC -> NCHW). All these layers should be added to the graph with some unique layer and input and output names.
I solve this problem by adding additionalPreLayer and additionalPostLayer layers.
If a layer has a fused activation, I add additionalPostLayer and change input and output names this way:
**original**: conv_relu(conv123, conv123_input, conv123_output)
**new**: conv(conv123, conv123_input, conv123_output_additional_post_layer) + relu(conv123_relu, conv1_output_additional_post_layer, conv123_output)
If a layer has additional preprocessing layer, I change input and output names this way:
**original**: permute_reshape(reshape345, reshape345_input, reshape345_output)
**new**: permute(reshape345_permute, reshape345_input, reshape345_input_additional_pre_layer) + reshape(reshape345, reshape345_input_additional_pre_layer, reshape345_output)
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Fix#26322: construction of another Mat header for empty matrix #26333
The PR fixes#26322
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2x more accurate float => bfloat conversion #26321
There is a magic trick to make float => bfloat conversion more accurate (_original reference needed, is it done this way in PyTorch?_). In simplified form it looks like:
```
uint16_t f2bf(float x) {
union {
unsigned u;
float f;
} u;
u.f = x;
// return (uint16_t)(u.u >> 16); <== the old method before this patch
return (uint16_t)((u.u + 0x8000) >> 16);
}
```
it works correctly for almost all valid floating-point values, positive, zero or negative, and even for some extreme cases, like `+/-inf`, `nan` etc. The addition of `0x8000` to integer representation of 32-bit float before retrieving the highest 16 bits reduces the rounding error by ~2x.
The slight problem with this improved method is that the numbers very close to or equal to `+/-FLT_MAX` are mistakenly converted to `+/-inf`, respectively.
This patch implements improved algorithm for `float => bfloat` conversion in scalar and vector form; it fixes the above-mentioned problem using some extra bit magic, i.e. 0x8000 is not added to very big (by absolute value) numbers:
```
// the actual implementation is more efficient,
// without conditions or floating-point operations, see the source code
return (uint16_t)(u.u + (fabsf(x) <= big_threshold ? 0x8000 : 0)) >> 16);
```
The corresponding test has been added as well and this is output from the test:
```
[----------] 1 test from Core_BFloat
[ RUN ] Core_BFloat.convert
maxerr0 = 0.00774842, mean0 = 0.00190643, stddev0 = 0.00186063
maxerr1 = 0.00389057, mean1 = 0.000952614, stddev1 = 0.000931268
[ OK ] Core_BFloat.convert (7 ms)
```
Here `maxerr0, mean0, stddev0` are for the original method and `maxerr1, mean1, stddev1` are for the new method. As you can see, there is a significant improvement in accuracy.
**Note:**
_Actually, on ~32,000,000 random FP32 numbers with uniformly distributed sign, exponent and mantissa the new method is always at least as accurate as the old one._
The test also checks all the corner cases, where we see no degradation either vs the original method.
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imgcodecs: implement imencodemulti() #26211Close#26207
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G-API: Introduce level optimization flag for ONNXRT backend #26293
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Use border value in ipp version of warp affine #26313
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New dnn engine #26056
This is the 1st PR with the new engine; CI is green and PR is ready to be merged, I think.
Merge together with https://github.com/opencv/opencv_contrib/pull/3794
---
**Known limitations:**
* [solved] OpenVINO is temporarily disabled, but is probably easy to restore (it's not a deal breaker to merge this PR, I guess)
* The new engine does not support any backends nor any targets except for the default CPU implementation. But it's possible to choose the old engine when loading a model, then all the functionality is available.
* [Caffe patch is here: #26208] The new engine only supports ONNX. When a model is constructed manually or is loaded from a file of different format (.tf, .tflite, .caffe, .darknet), the old engine is used.
* Even in the case of ONNX some layers are not supported by the new engine, such as all quantized layers (including DequantizeLinear, QuantizeLinear, QLinearConv etc.), LSTM, GRU, .... It's planned, of course, to have full support for ONNX by OpenCV 5.0 gold release. When a loaded model contains unsupported layers, we switch to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* Some layers , e.g. Expat, are only partially supported by the new engine. In the case of unsupported flavours it switches to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* 'Concat' graph optimization is disabled. The optimization eliminates Concat layer and instead makes the layers that generate tensors to be concatenated to write the outputs to the final destination. Of course, it's only possible when `axis=0` or `axis=N=1`. The optimization is not compatible with dynamic shapes since we need to know in advance where to store the tensors. Because some of the layer implementations have been modified to become more compatible with the new engine, the feature appears to be broken even when the old engine is used.
* Some `dnn::Net` API is not available with the new engine. Also, shape inference may return false if some of the output or intermediate tensors' shapes cannot be inferred without running the model. Probably this can be fixed by a dummy run of the model with zero inputs.
* Some overloads of `dnn::Net::getFLOPs()` and `dnn::Net::getMemoryConsumption()` are not exposed any longer in wrapper generators; but the most useful overloads are exposed (and checked by Java tests).
* [in progress] A few Einsum tests related to empty shapes have been disabled due to crashes in the tests and in Einsum implementations. The code and the tests need to be repaired.
* OpenCL implementation of Deconvolution is disabled. It's very bad and very slow anyway; need to be completely revised.
* Deconvolution3D test is now skipped, because it was only supported by CUDA and OpenVINO backends, both of which are not supported by the new engine.
* Some tests, such as FastNeuralStyle, checked that the in the case of CUDA backend there is no fallback to CPU. Currently all layers in the new engine are processed on CPU, so there are many fallbacks. The checks, therefore, have been temporarily disabled.
---
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Proposed solution for the issue 26297 #26298closes#26297
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1) numerals are now monospace, e.g. '1' has the same width as '0',
2) '0' is different from capital 'o',
3) new glyphs added
2. stb_truetype upgraded from 1.24 to 1.26 with some fixes in rendering.
imgproc: update warpAffine opencl kernel to be in sync with cpu one #26292
Relates https://github.com/opencv/opencv/pull/26242
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Features2d cleanup: Move several feature detectors and descriptors to opencv_contrib #25292
features2d cleanup: #24999
The PR moves KAZE, AKAZE, AgastFeatureDetector, BRISK and BOW to opencv_contrib/xfeatures2d.
Related PR: opencv/opencv_contrib#3709
In that function, the floats are cast to int to be compared to 0.
But a float can be -0 or +0, hence
define CHECK_NZ_FP(x) ((x)*2 != 0)
to remove the sign bit. Except that can trigger the sanitizer:
runtime error: signed integer overflow: -1082130432 * 2 cannot be represented in type 'int'
Doing everything in uint instead of int is properly defined by the
standard.
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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- Implemented a new `create` method in `FaceRecognizerSF` to allow model and configuration loading from memory buffers (std::vector<uchar>), similar to the existing functionality in `FaceDetectorYN`.
- Updated `face_recognize.cpp` with a new constructor in `FaceRecognizerSFImpl` that supports buffer-based loading for both model weights and network configuration.
- Ensured compatibility with both file-based and buffer-based model loading by maintaining consistent backend and target settings across both constructors.
- This change improves flexibility, allowing FaceRecognizerSF to be instantiated from memory buffers, which is useful for dynamic model loading scenarios such as embedded systems or applications where models are loaded in-memory.
Update Documentation #26260
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Update intrin_wasm.hpp #25909
See https://github.com/microsoft/vcpkg/issues/33443 for some build context when using
```vcpkg install opencv4:wasm32-emscripten```
`__EMSCRIPTEN_major__`, `__EMSCRIPTEN_minor__` and `__EMSCRIPTEN_tiny__` in `emsdk` >= 3.1.4 are in a header, as opposed to command line.
We could potentially be more aggressive with how I'm checking this property; let me know if I should make the change.
It should also be suggested that `-msimd128` is auto-included in the associated portfile for opencv, but that's a separate issue. Someone let me know if I should also make that change as well.
Special thanks to https://github.com/youar for supporting this work; please inform if applying a copyright-header is appropriate attribution.
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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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extended Norm tests to prove that cv::norm() already supports all the types.
cv::norm() already provides enough functionality; just extended tests to prove it. See #24887
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- Added function overload for the simple case
- Added CV_Bool type support for masks
- `parallel_for_` for intrinsics calibration for faster inference
- Homogenize parameters order with other calibrateXXX functions
imgproc: add optimized warpAffine kernels for 8U/16U/32F + C1/C3/C4 inputs #25984
Merge wtih https://github.com/opencv/opencv_extra/pull/1198.
Merge with https://github.com/opencv/opencv_contrib/pull/3787.
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* use 2 parms for now to identify the error
* Revert "use 2 parms for now to identify the error"
This reverts commit 86faf993a7.
* replace += with =
* add v_log ref
* refactor intrin_math code
* Add include guard to `intrin_math.hpp` to prevent multiple inclusions
* rename VX to V; make fp64 impl in neon be optional
* add v_setall, v_setzero for all backends; rewrite the intrin_math
* fix error on rvv_scalable
* let v_erf use v_exp_default_32f function
* 1. replaced 'v_setzero(VecType dummy)' with 'v_setzero_<VecType>()'
2. replaced 'v_setall(LaneType x, VecType dummy)' with 'v_setall_<VecType>(LaneType x)'
3. added tests for the new v_setzero_<> and v_setall_<>.
* gcc does not seem to like static_assert in functions even when they are not used
* trying to fix compile errors in Debug mode on Linux
---------
Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>
Reworked multiview calibration interface #26221
- Use InputArray / OutputArray
- Use enum for camera type
- Sort parameters according guidelines
- Made more outputs optional
- Introduce flags and added tests for intrinsics and extrinsics guess.
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Mirror most recent changes from https://github.com/terzakig/sqpnp/pull/24
- rank revealing QR in nullspace computation
- sqrt-free Cholesky (i.e., L*D*Lt) in the SQP solution
- replaced divisions with multiplications by inverses
- simplified checks in computeRowAndNullspace()
- removed unnecessary negations
- broke some dependency chains with parentheses
- minor other changes
HAL interface for Sharr derivatives needed for Lukas-Kanade algorithm #26163
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Documentation update for imagecodecs #26152
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Added HAL interface for Lukas-Kanade optical flow #26143
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Documentation update for minMaxLoc #25785Fixes#25784
Update documentation for minMaxLoc to be more specific about when multi-channel images are and are not supported.
Testing:
Built documentation locally to check that updates were incorporated correctly.
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Replace operators with wrapper functions on universal intrinsics backends #26109
This PR aims to replace the operators(logic, arithmetic, bit) with wrapper functions(v_add, v_eq, v_and...)
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❗ Potential conflicts with #25958
C-API cleanup: highgui, videoio #26025❗ Merge with: opencv/opencv_contrib#3780
This PR removes usage of C-API from highgui and videoio modules. Only source code is affected, tests were not using obsolete API.
It should be possible to backport these changes to 4.x branch preserving removed public headers and source files (`*_c.h` and `*_c.cpp`).
#### Checklist
I tried to verify as many backends as possible, though these checks were not as thorough as I'd like them to be. Below is the checklist covering all modified backends with their statuses.
> 🔹 - small changes
> 🟢 - consider working
> ⚪ - considered untested
##### highgui
Pass | Backend | Local check | CI check
-----|---------|-------------|---------
🟢 | GTK2 | build + test, plugin build | build + test ❔🟢 | GTK3 | build + test, plugin build | build + test
🟢 | QT | build + test, plugin build |
⚪ | Wayland 🔹 | |
🟢 | WIN32 🔹 | | build + test
🟢 | Cocoa 🔹 | | build + test
⚪ | WinRT | |
##### videoio
Pass | Backend | Local check | CI check
-----|---------|-------------|---------
🟢 | Android Camera/MediaNDK 🔹 | | build
🟢 | Aravis | build |
🟢 | AVFoundation OSX | | build + test
⚪ | AVFoundation iOS | | build
🟢 | DC1394 | build |
🟢 | DShow 🔹 | | build
🟢 | FFMpeg | build, plugin build | build + test
🟢 | GPhoto 🔹 | build |
🟢 | GStreamer | build, plugin build | build + test
🟢 | Images | build | build + test
🟢 | MSMF 🔹 | | build + test
🟢 | OpenNI | build |
🟢 | PVAPI | build |
🟢 | V4L | build + test | build
🟢 | XIMEA | build |
🟢 | XINE 🔹 | build |
#### Notes
- local linux build checks performed using [this framework](https://github.com/mshabunin/opencv-videoio-build-check)
- minor extra changes made in both `cap_avfoundation*.mm` to make them slightly more synchronized - it would be better to combine them into a single one in the future
- configurations with plugins have been build but not tested
- **moved unrelated changes to separate PRs** ~two issues have been fixed in separate commits:~
- ~imgproc: missing `cv::hal::` color conversion functions has been used in MediaSDK backend~
- ~videoio/V4L: wrong color conversion mode caused bad colors for NV12 camera input format (RGB instead of BGR)~
It would be nice to check following functionality manually:
- [ ] OSX: camera input
- [ ] iOS: camera and file input
- [ ] WinRT: build, some testing
- [x] Linux/Wayland: build
dnn(5.x): handle topk data type #26124
Resolves https://github.com/opencv/opencv/issues/26076
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Support for GatherND layer #26106
This PR adds support for GatherND layer. The layer was in comformance deny list initially.
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Add size() to CUDA PtrStepSz #26042
According to [cppreference.com compiler support table](https://en.cppreference.com/w/cpp/compiler_support/17), `nvcc` supports `[[nodiscard]]` from version 11.
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Related: https://github.com/opencv/opencv/pull/25659
Switch off OpenVINO backend for Hardmax #26110
Fix Hardmax Issue with backend mentioned in #26107
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#25006#25314
This pull request removes hed_pretrained caffe model to the SOTA dexined onnx model for edge detection. Usage of conventional methods like canny has also been added
The obsolete cpp and python sample has been removed
TODO:
- [ ] Remove temporary hack for quantized models. Refer issue https://github.com/opencv/opencv_zoo/issues/273
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C-API cleanup: moved cvErrorStr to new interface, minor ts changes #26101
Merge with opencv/opencv_contrib#3786
**Note:** `toString` might be too generic name (even though it is in `cv::Error::` namespace), another variant is `codeToString` (we have `typeToString` and `depthToString` in check.hpp).
**Note:** _ts_ module seem to have no other C API usage except for `ArrayTest` class which requires refactoring.
Added more data types to OCL flip() and rotate() perf tests #26115
Connected PR with updated sanity data: https://github.com/opencv/opencv_extra/pull/1206
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Avoid uninitialized value read in resize. #26084
When there is no point falling right, an hypothetical value is computed (but unused) using an uninitialized ofst. This triggers warnings in the sanitizers.
Including those values in the for loops is also possible but messy when SIMD is involved.
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Remove the redundant codes of cv::convertMaps and mRGBA2RGBA<uchar> #26071
(1) cv::convertMaps: the branch [else if( m1type == CV_32FC2 && dstm1type == CV_16SC2 ) if( nninterpolate )] is unreachable,
as the condition is satisfied in lines 1959 to 1961, calculated in advance and return directly.
(2) mRGBA2RGBA<uchar>: dst[0], dst[1], dst[2] and dst[3] is calculated repeatedly. Introduced in https://github.com/opencv/opencv/pull/13440
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Update test_tiff.cpp #26093
related #22090
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Fix hfloat, float16_t, float collision in 5.x about v_exp and v_log #26023
This PR try to fix https://github.com/opencv/opencv/issues/25922
Because the `hfloat` is defined as explicit conversion, the test code should be modified as it. The `vx_setall_f16` problem has already been fixed.
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Add Support for Hardmax Layer #26079
This PR add support for `Hardmax` layer, which as previously listed in conformance deny list.
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Added offset for HAL as ofs2idx expects 1-based index #26080
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DNN(ONNX): Enabled several OpenCL conformance tests #26053
The tests also work in 5.x
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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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Einsum buffer allocation fix#26059
This PR fixed buffer allocation issue in Einsum layer that causes segmentation fault on 32bit platforms. Related issue #26008
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Imgproc: use double to determine whether the corners points are within src #26022close#26016
Related https://github.com/opencv/opencv_contrib/pull/3778
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Split Javascript white-list to support contrib modules #25986
Single whitelist converted to several per-module json files. They are concatenated automatically and can be overriden by user config.
Related to https://github.com/opencv/opencv/pull/25656
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Increase neighbors search radius for corners in ChessBoardDetector:findQuadNeighbors #26014
I didn't do everything right the way I wanted at #25991. I forgot that `edge_len` is edge **squared** length as well as `thresh_scale` is threshold for **squared** scale. So, I wanted to increase scale by `sqrt(2)` times (idea is to use quad diagonal instead of quad side) and therefore `thresh_scale` should be equal to `sqrt(2)^2 = 2`.
And refactor variables names to explicitly indicate that they are squared, so that no one else falls into this trap
I tested this PR with benchmark
```
python3 objdetect_benchmark.py --configuration=generate_run --board_x=7 --path=res_chessboard --synthetic_object=chessboard
```
PR increases detected chessboards number by `1/2%`:
```
cell_img_size = 100 (default)
before
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.941667 13560 14400 0.596726
Total detected time: 136.68963200000007 sec
after
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.952083 13710 14400 0.595984
Total detected time: 136.55770600000014 sec
----------------------------------------------------------------------------------------------------------------------------------------------
cell_img_size = 10
before
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.579167 8340 14400 4.198448
Total detected time: 2.535998999999999 sec
after
category detected chessboard total detected chessboard total chessboard average detected error
all 0.591389 8516 14400 4.155250
Total detected time: 2.700832999999997 sec
```
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dnn: add ONNX TopK #23279
Merge with https://github.com/opencv/opencv_extra/pull/1200
Partially fixes#22890 and #20258
To-do:
- [x] TopK forward impl
- [x] add tests
- [x] support Opset 1 & 10 if possible
- [ ] ~Support other backends~ (TopK has two outputs, which is not supported by other backends, such as openvino)
Perf:
M1 (time in millisecond)
| input shape | axis | dnn | ort |
| --------------- | ---- | ---- | ---- |
| (1000, 100) | 0 | 1.68 | 4.07 |
| (1000, 100) K5 | 0 | 1.13 | 0.12 |
| (1000, 100) | 1 | 0.96 | 0.77 |
| (100, 100, 100) | 0 | 10.00 | 31.13 |
| (100, 100, 100) | 1 | 7.33 | 9.17 |
| (100, 100, 100) | 2 | 7.52 | 9.48 |
M2 (time in milisecond)
| input shape | axis | dnn | ort |
| --------------- | ---- | ---- | ---- |
| (1000, 100) | 0 | 0.76 | 2.44 |
| (1000, 100) K5 | 0 | 0.68 | 0.07 |
| (1000, 100) | 1 | 0.41 | 0.50 |
| (100, 100, 100) | 0 | 4.83 | 17.52|
| (100, 100, 100) | 1 | 3.60 | 5.08 |
| (100, 100, 100) | 2 | 3.73 | 5.10 |
ONNXRuntime performance testing script: https://gist.github.com/fengyuentau/a119f94fd16721ec9974b8c7b0a45d4c
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Correct Bayer2Gray u8 SIMD #25968
SIMD version of CV_DESCALE is not correct. It should be implemented using v_dotprod.
What's more, the stop condition of vector operation should be `bayer < bayer_end - 14` because we just need to make sure result is safely stored into `dst`.
Closes: https://github.com/opencv/opencv/issues/25823
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