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
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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
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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
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Patch to opencv_extra has the same branch name.
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Rvv core norm #29057Fixesopencv/opencv#29052
### Problem
`Core_Norm/ElemWiseTest.accuracy/0` failed on RISC-V with RVV enabled when computing norm for `CV_16S` data.
The reported failing case was:
```text
src[0] ~ 16sC4 3-dim (1 x 116 x 40)
```
The expected norm result was a large positive `double`, but the RVV path returned an incorrect value:
```text
expected: 3370900308417
actual: -173296
```
This indicates that the problem was not in the public `cv::norm()` API, but in the RVV HAL implementation used for the `CV_16S` L2/L2SQR accumulation path.
### Root Cause
The RVV HAL has a specialized implementation for `CV_16S` L2 norm:
```cpp
NormL2_RVV<short, double>
```
The implementation widens `int16` values, squares them, converts the widened products to `float64`, accumulates them in an `f64m8` vector, and finally reduces the vector to a scalar `double`:
```cpp
auto s = __riscv_vfmv_v_f_f64m8(0, vlmax);
...
auto v_mul = __riscv_vwmul(v, v, vl);
s = __riscv_vfadd_tu(s, s, __riscv_vfwcvt_f(v_mul, vl), vl);
...
return __riscv_vfmv_f(__riscv_vfredosum(...));
```
The bug was in the scalar initializer passed to `__riscv_vfredosum`.
Before this patch, the code created an `f64m1` scalar vector but used the maximum vector length for `e32m1`:
```cpp
__riscv_vfmv_s_f_f64m1(0, __riscv_vsetvlmax_e32m1())
```
This is inconsistent: the vector type is `f64m1`, so the VL used to initialize it must correspond to `e64m1`, not `e32m1`.
## Summary
Replace the existing RVV HAL FAST-16 implementation (`hal/riscv-rvv/src/features2d/fast.cpp`) with a score-first approach, achieving **2-4x speedup** over the previous HAL implementation on RISC-V hardware.
## Approach
The previous HAL implementation used a count-based approach (XOR trick + per-pixel bitmask scanning + separate cornerScore computation). This PR replaces it with a **score-first** approach that processes multiple horizontal pixels in parallel using RVV vectors.
Key optimizations:
1. **Score-first approach**: Directly compute corner scores via min/max reduction over all 9-arc windows, then threshold on score. This eliminates the arc-counting pass and the separate `cornerScore` function.
2. **u8 to i16 zero-extension**: Zero-extend pixel values to int16 for natural signed comparison, eliminating the XOR-delta trick and preventing score overflow.
3. **vcompress batch output**: Extract all corner indices in one vector operation instead of per-lane bitmask scanning.
4. **vlen-adaptive**: Uses `vsetvl_e16m1` for dynamic vector length — works on any RVV 1.0 implementation regardless of VLEN (128, 256, 512, etc.) without code changes.
## Performance
Benchmarked by maintainer on **Muse Pi v3.0** (GCC, SpacemiT toolchain v1.0.4):
| Test | Previous HAL (ms) | This PR (ms) | Speedup |
|------|-------------------|--------------|---------|
| FAST-20 NMS=true | 23.06 | 7.99 | **2.89x** |
| FAST-20 NMS=true (orig) | 7.31 | 1.80 | **4.06x** |
| FAST-30 NMS=false | 20.10 | 7.75 | **2.59x** |
| FAST_DEFAULT s2.jpg | 76.94 | 19.06 | **4.04x** |
| ORB_DEFAULT chess9 | 64.46 | 25.43 | **2.53x** |
Both NMS=true and NMS=false cases are supported.
## Files Changed
- **`hal/riscv-rvv/src/features2d/fast.cpp`**: Replaced count-based implementation with score-first approach
- No changes to `modules/features2d/src/fast.cpp` or any other files
## Test Plan
- [x] Built with RISC-V cross-compilation toolchain (GCC 14.2, RVV 1.0)
- [x] Tested on QEMU (rv64, v=true, vlen=256) — correct results for both NMS modes
- [x] Maintainer tested on Muse Pi v3.0 hardware — 2-4x speedup confirmed
- [ ] CI passes on all platforms
Add ARMPL support for DFT Function #28664
- This PR introduces hal/armpl/ with implementation of 1D, 2D DFT and DCT routines using ARM Performance Libraries as a custom HAL replacement for OpenCV's DFT & DCT Function.
- ArmPL MSI package is automatically downloaded and extracted via CMake when building on Windows ARM64, with a WITH_ARMPL option that defaults to ON for that platform.
- Forward and inverse real DFT calls in dxt.cpp are routed through ArmPL when available, with scaling applied only when needed.
- Test error thresholds in test_dxt.cpp are relaxed (from 1e-5 to 2e-4 for float ) to account for numerical differences between ArmPL and OpenCV's reference DFT results.
**Performance Benchmarks :**
<img width="993" height="835" alt="image" src="https://github.com/user-attachments/assets/76def647-6d20-4bce-8bc9-7363e723669f" />
- [x] I agree to contribute to the project under Apache 2 License.
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fix: Guard ndsrvp filter center copy when source span is empty #28915
## Summary
This adds a defensive guard around the centre-region `memcpy` in the ndsrvp HAL filter padding path.
**Description**: Multiple memcpy calls in the ndsrvp HAL filter, bilateral filter, and median blur routines use computed sizes (e.g., cnes * (rborder - j), cn * (rborder - j), src_step * height) without validating that the computed byte count fits within the destination buffer. An attacker supplying a crafted image with manipulated dimensions, channel counts, or border parameters can cause the computed copy size to exceed the allocated destination buffer, resulting in a heap buffer overflow that corrupts adjacent memory.
## Changes
- `hal/ndsrvp/src/filter.cpp`
## Verification
- [x] Build passes
- [x] Scanner re-scan confirms fix
- [x] LLM code review passed
---
*Automated security fix by [OrbisAI Security](https://orbisappsec.com)*
hal/riscv-rvv: implement laplacian #28887
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1353
An implementation of laplacian for riscv-rvv hal
Added benchmark in perf_laplacian.cpp
The performance is evaluated on K1 by running
./opencv_perf_imgproc --gtest_filter="Perf_Laplacian*"
Results are attached in the figure below.
CV_8U fast path supports ksize=3 with BORDER_CONSTANT/BORDER_REPLICATE; unsupported combinations fall back to default implementation. See performance results bellow.
### Pull Request Readiness Checklist
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Fix IPP warpAffine #28562
The issue is that we compute the inverse transformation and pass that to the HAL, but tell IPP that we use the actual transformation.
https://github.com/opencv/opencv/blob/a269a489b94b84717691533a04f79d9a0e5f479a/modules/imgproc/src/imgwarp.cpp#L2399-L2412
I have added an additional test with `CV_16S` Mats to test the IPP implementation, because right now no other `warpAffine` test enters the IPP branch.
This fixes#28554.
### Pull Request Readiness Checklist
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hal/riscv-rvv: Fix test failures on hardware that implements tail-agnostic as all 1s #28375
### 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.
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### Summary
Fix test failures in `opencv_test_core` and `opencv_test_imgproc` on hardware that implements the tail-agnostic policy by overwriting tail elements with all 1s
### Root Cause
According to the [RISC-V Vector Extension Specification v1.0](https://github.com/riscvarchive/riscv-v-spec/blob/master/v-spec.adoc#343-vector-tail-agnostic-and-vector-mask-agnostic-vta-and-vma):
> "When a set is marked agnostic, the corresponding set of destination elements in any vector destination operand can either retain the value they previously held, or are overwritten with 1s."
Both `dotProd_32f` and `dotProd_32s` functions use accumulator variables(`s`) that persist across loop iterations, but were using vector intrinsics with tail-agnostic policy. The tail elements can be overwritten with 1s, corrupting the accumulator and producing NaN during the final reduction sum.
### Solution
Changed both functions to use `tail-undisturbed`(`tu`) policy to ensure tail elements remain unchanged across loop iterations.
### Reproduce Steps
The failures can be reproduced using [QEMU](https://gitlab.com/qemu-project/qemu) with `rvv_ma_all_1s=true` and `rvv_ta_all_1s=true` options to emulate hardware implementing tail-agnostic policy as all 1s.
#### Reproduce **`opencv_test_core`** failures:
```
qemu-riscv64 -cpu rv64,zba=true,zbb=true,zbc=true,zbs=true,v=true,vlen=512,elen=64,vext_spec=v1.0,rvv_ma_all_1s=true,rvv_ta_all_1s=true -L '/PATH_TO_SYSROOT/sysroot/' ./opencv_test_core --gtest_filter="Core_DotProduct*"
```
Before the patch:
```
[ FAILED ] Core_DotProduct.accuracy (24 ms)
```
After the patch:
```
[ PASSED ] 1 test.
```
#### Reproduce **`opencv_test_imgproc`** failures:
```
qemu-riscv64 -cpu rv64,zba=true,zbb=true,zbc=true,zbs=true,v=true,vlen=512,elen=64,vext_spec=v1.0,rvv_ma_all_1s=true,rvv_ta_all_1s=true -L '/workspace/riscv/sysroot/' ./opencv_test_imgproc --gtest_filter="fitLine_Modes.accuracy/*"
```
Before the patch
```
[ FAILED ] 24 tests, listed below:
[ FAILED ] fitLine_Modes.accuracy/0, where GetParam() = (13, 1)
[ FAILED ] fitLine_Modes.accuracy/1, where GetParam() = (13, 2)
[ FAILED ] fitLine_Modes.accuracy/2, where GetParam() = (13, 4)
[ FAILED ] fitLine_Modes.accuracy/3, where GetParam() = (13, 5)
[ FAILED ] fitLine_Modes.accuracy/4, where GetParam() = (13, 6)
[ FAILED ] fitLine_Modes.accuracy/5, where GetParam() = (13, 7)
[ FAILED ] fitLine_Modes.accuracy/6, where GetParam() = (21, 1)
[ FAILED ] fitLine_Modes.accuracy/7, where GetParam() = (21, 2)
[ FAILED ] fitLine_Modes.accuracy/8, where GetParam() = (21, 4)
[ FAILED ] fitLine_Modes.accuracy/9, where GetParam() = (21, 5)
[ FAILED ] fitLine_Modes.accuracy/10, where GetParam() = (21, 6)
[ FAILED ] fitLine_Modes.accuracy/11, where GetParam() = (21, 7)
[ FAILED ] fitLine_Modes.accuracy/12, where GetParam() = (12, 1)
[ FAILED ] fitLine_Modes.accuracy/13, where GetParam() = (12, 2)
[ FAILED ] fitLine_Modes.accuracy/14, where GetParam() = (12, 4)
[ FAILED ] fitLine_Modes.accuracy/15, where GetParam() = (12, 5)
[ FAILED ] fitLine_Modes.accuracy/16, where GetParam() = (12, 6)
[ FAILED ] fitLine_Modes.accuracy/17, where GetParam() = (12, 7)
[ FAILED ] fitLine_Modes.accuracy/18, where GetParam() = (20, 1)
[ FAILED ] fitLine_Modes.accuracy/19, where GetParam() = (20, 2)
[ FAILED ] fitLine_Modes.accuracy/20, where GetParam() = (20, 4)
[ FAILED ] fitLine_Modes.accuracy/21, where GetParam() = (20, 5)
[ FAILED ] fitLine_Modes.accuracy/22, where GetParam() = (20, 6)
[ FAILED ] fitLine_Modes.accuracy/23, where GetParam() = (20, 7)
```
After the patch
`[ PASSED ] 24 tests.`
rvv_hal: fix flip inplace #28180
Fixes https://github.com/opencv/opencv/issues/28124
### Pull Request Readiness Checklist
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Added CMake define option to enforce IPP calls in IPP HAL when building with IPP integration #27925
Extra build option needed to simplify custom builds with IPP integration to ensure the IPP calls done even in cases when the results are not bitwise compliant to the reference OpenCV implementation. Option name is ```WITH_IPP_CALLS_ENFORCED```, and it is disabled by default.
Requested by some IPP customers and might be used in general as a way providing calls with better performance for the cases the aligned precision is not as important aspect.
Supposed to be used in HAL only, so added only in IPP HAL CMake, currently it affects only Warp Affine, Warp Perspective and Remap integrations since the results may vary depending on HW and algorithm implementations.
### Pull Request Readiness Checklist
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- Changed splitting logic to improved performance for all warp related functions in IPP HAL for multithreaded mode.
- Removed IPP 7.0 preprocessor condition from warp in IPP HAL, since unsupported.
- Added preprocessor condition to enforce IPP warp calls for custom builds.
Add canny, scharr and sobel for riscv-rvv hal. #27378
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Alexander Smorkalov
ozhanghe
Alexander Smorkalov
Shengyu Wu
Andrei Fedorov
Ziyuan Li
Kevin Lin
Teddy-Yangjiale
Tiansuanyu
Shengyu-Wu
Pratham Kumar
orbisai0security
Alex Wu
Connor Tench
nmizonov
Adrian Kretz
Arnie Chang
Yuantao Feng
victorget
damon-spacemit
Kumataro
Haosonn
eplankin
ekharkov
amane-ame