Extract IPP integration as HAL function for bilateral_filter - #29410
Backport of https://github.com/opencv/opencv/pull/29409
**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1rmNB3X_V8rWttUGBqXRs1FmkxeKR0O93x_ez5tVjutY/edit?usp=sharing
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Extracted IPP to HAL for getRectSubPix function in 4.x - #29512
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Implemented a check to enable instrumentation for IPP HAL #29542
To enable instrumentation for IPP HAL correctly it needed to generalize a part of `private.hpp` and put it to the separate file that can be included in `private.hpp` and `ipp_utils.hpp` both to avoid duplication.
Built with `-DENABLE_INSTRUMENTATION=ON` and ran `opencv_perf_imgproc --perf_instrument=1`:
| Test | IPP weight before | IPP weight after |
|---|---|---|
| `distanceTransform` (8U->32F) | 0.0% | ~76% |
| `ippSobel` | 0.0% | ~98% |
| `scharrViaSobelFilter` | 0.0% | ~90% |
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
hal_rvv core: Cover more data types for split with native RVV intrinsics #29557
Covering 16-bit, 32-bit and 64-bit split.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
The IPP HAL (hal/ipp/src/warp_ipp.cpp) routed cv::warpAffine with
INTER_NEAREST to iwiWarpAffine for CV_16S{C1,C3,C4}, CV_64F{C1,C3,C4}
and CV_16UC4 via the non-enforced (default) impl[] dispatch table,
whose comment promises results "strictly aligned to OpenCV
implementation". That contract was broken: IPP rounds source
coordinates at the half-pixel boundary differently from the native
fixed-point kernel (warpAffineBlocklineNN, 10-bit AB_BITS), so under
rotation or fractional translation about 0.05-0.07% of destination
pixels resolve to a different source pixel. Because INTER_NEAREST does
no blending, those pixels take entirely different values, so warping
identical data as e.g. CV_16S (IPP path) versus CV_16U (native path)
produced non-identical output, and x86_64 (IPP) silently diverged from
ARM (no IPP). This is the same "Different results" behavior for which
IPP warpAffine was disabled from ~2017 through 4.11.
Zero the INTER_NEAREST column of the non-enforced dispatch table for
the affected types so the dispatch guard falls back to the native
kernel, restoring the "strictly aligned" contract. The LINEAR and
CUBIC columns for these rows were already 0, so IPP was only ever used
for NEAREST here. The IPP_CALLS_ENFORCED table is left untouched so the
opt-in performance-benchmarking build still exercises IPP.
Adds a regression test (Imgproc_Warp.regression_29279) that warps a
gradient image with INTER_NEAREST as each affected type and asserts
bit-exact equality with a native (CV_32F) reference across several
rotation angles and a fractional translation.
Fixes#29279
Distransform function IPP migration to HAL in 4.x #29463
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Extracting IPP integaration as HAL for Canny #29434
Backport of : https://github.com/opencv/opencv/pull/29433
**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1RMvUZP1tSQtdjuNQY9JasYmlx1L5iN9XWGhXtG5u1uI/edit?usp=sharing
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Extracting IPP to HAL for matchTemplate function in 4.x #29464
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Filter2D IPP migration to HAL for 4.x #29427
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Extract IPP integration as HAL function for box_filter #29420
Backport of https://github.com/opencv/opencv/pull/29414
**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1kMKiZWh--pH30hqsQo6j1suNw1lfQiKzSankMCMa2FI/edit?usp=sharing
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Defer the u8→i16 widening (vzext) in the pre-screen phase until after
the quick-reject check passes. This avoids 5 unnecessary widen
operations per pixel-strip when the pre-screen rejects (which happens
~70-80% of the time at the default threshold=20).
The approach uses u8mf2 (same VL as i16m1) for the pre-screen
comparison with vssubu/vsaddu for bounds and vmsgtu for unsigned
comparison. When the pre-screen passes, the already-loaded u8mf2
variables are widened to i16m1 for the score computation, reusing
the same data without redundant memory loads.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Reenable ICV and fix#29166#29183
Fixing https://github.com/opencv/opencv/issues/29166 and return back the 2026.0.0 ICV.
Tests are passed locally on Windows machine. Feel free to check if they are passed in bigger test systems.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
KleidiCV update to verison 26.03 #28744
KleidiCV release: https://gitlab.arm.com/kleidi/kleidicv/-/releases/26.03
Tuned DNN test threshold as resize linear produces slightly different result with the new KleidiCV version.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
RISC-V: add RVV convertScale paths for selected depth conversions #29058
### Summary
This PR extends the RISC-V RVV HAL `convertScale` implementation with optimized paths for selected `convertTo()` depth conversions:
- `CV_32F -> CV_8U`
- `CV_16U -> CV_32F`
- `CV_16S -> CV_32F`
The change is confined to `hal/riscv-rvv/src/core/convert_scale.cpp`. It does not modify the generic `convertTo()` implementation or affect dispatch for non-RISC-V targets.
### Rationale
`Mat::convertTo()` reaches architecture-specific implementations through the HAL `convertScale` interface. Adding these paths in the RVV HAL keeps the optimization in the existing backend layer and avoids introducing RISC-V-specific branches into common OpenCV code.
The selected conversions were chosen based on measured benefit and practical relevance. `CV_32F -> CV_8U` provides the largest speedup, while `CV_16U -> CV_32F` and `CV_16S -> CV_32F` provide consistent improvements for common image-processing data paths.
### Benchmark
Tested on Banana Pi BPI-F3, an 8-core RISC-V board with RVV 1.0 support.
Build and test configuration:
- OpenCV `4.14.0-pre`
- Release build
- RVV HAL enabled
- Compiler: `riscv64-unknown-linux-gnu-g++ 14.2.1`
- Test binary: `opencv_perf_core`
- Test filter: `*convertTo*`
- Samples: `--perf_force_samples=20 --perf_min_samples=20`
Benchmark command:
```bash
./opencv_perf_core \
--gtest_filter=*convertTo* \
--perf_force_samples=20 \
--perf_min_samples=20
```
Each benchmark case was measured with 20 forced samples. The baseline/after comparison was repeated to check run-to-run stability. The optimized conversion pairs showed consistent improvements.
Second-run comparison for the optimized pairs:
```text
OPTIMIZED_PAIRS: 24 cases, gmean 2.6009x
CV_32F -> CV_8U: 16.0718x
CV_16U -> CV_32F: 1.2334x
CV_16S -> CV_32F: 1.1376x
```
`CV_32F -> CV_8U` showed the largest improvement, with speedups in the `14.5x` to `18.8x` range across the tested image sizes, channel counts, and alpha values.
### Notes on Measurement
The initial benchmark run showed noise in conversion pairs not touched by this PR, likely due to board-level variance such as frequency scaling, thermal state, or background scheduling.
A second run showed untouched pairs close to neutral:
```text
UNTOUCHED_OTHER: gmean 0.9941x
```
The optimized conversion pairs remained consistently faster across both runs, with no observed regression in the added RVV paths.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
