Process masked integer norms chunk by chunk and reuse each loaded mask
and predicate across channels, avoiding repeated mask processing.
Extend norm_mask performance coverage with three-channel 8U, 8S, 16U,
16S, and 32S inputs for INF, L1, and L2 norms.
Co-authored-by: Yang Wang <yangwang@iscas.ac.cn>
Co-authored-by: Yuansheng <yuansheng@isrc.iscas.ac.cn>
Replace separate widening multiplication and FP64 addition with
widening FMA in the unmasked and masked FP32 L2 norm kernels.
Finite FP32 products are exactly representable in FP64, so this
preserves numerical results without changing the accumulation order.
Tail and mask undisturbed policies are retained.
Co-authored-by: Yang Wang <yangwang@iscas.ac.cn>
Co-authored-by: Yuansheng <yuansheng@isrc.iscas.ac.cn>
Add an RVV HAL implementation for CV_32F to CV_16S conversion.
The RISC-V convertScale HAL already handles several source/destination
depth combinations, but CV_32F to CV_16S currently returns
CV_HAL_ERROR_NOT_IMPLEMENTED and falls back to the generic core path.
Implement the missing conversion using native RVV intrinsics. The
identity-scale case skips the multiply-add, while scaled conversions
apply alpha and beta before narrowing to signed 16-bit output.
The implementation is VLEN-agnostic and uses vsetvl for tail handling.
Functional test:
```text
./build-rvv/bin/opencv_test_core \
--gtest_filter='*ConvertScale*'
```
Performance was measured with opencv_perf_core on SpacemiT K3
(RVV 1.0, VLEN=256), comparing against an unmodified 5.x baseline.
CV_32F -> CV_16S median time:
```text
1920x1080 C1, alpha=1:
26.16 ms -> 1.46 ms (17.92x)
1920x1080 C1, alpha=1/255:
24.10 ms -> 1.45 ms (16.62x)
1920x1080 C4, alpha=1:
103.76 ms -> 6.06 ms (17.12x)
1920x1080 C4, alpha=1/255:
96.03 ms -> 5.68 ms (16.91x)
```
All four corresponding opencv_perf_core cases pass.
Co-authored-by: Yang Wang [yangwang@iscas.ac.cn](mailto:yangwang@iscas.ac.cn)
Co-authored-by: Yuansheng [yuansheng@iscas.ac.cn](mailto:yuansheng@iscas.ac.cn)
The generic odd-radix DFT path created the index vector with
vsetvlmax_e32mf2() and kept it live across a subsequent setvl() call used
for the current processing chunk.
This code pattern can produce incorrect results with GCC 15.2 using the
default optimized VSETVL strategy. Core_DFT.accuracy and
Core_DCT.accuracy fail with the RVV HAL enabled, while disabling VSETVL
global fusion makes the same tests pass.
Generate the index vector after setting the vector length for each
q-loop chunk instead. Keep all vector operations within the same local
qvl and advance q explicitly by that value.
This preserves the existing odd-radix DFT algorithm while avoiding
vector values that span changes of VL.
Tested on SpaceMIT K3 with GCC 15.2.0, RVV 1.0 and VLEN=256:
RISCV_RVV_SCALABLE=ON
WITH_HAL_RVV=ON
CMAKE_BUILD_TYPE=Release
Core_DFT and Core_DCT accuracy tests pass with GCC's default VSETVL
optimization enabled.
Co-authored-by: Yuansheng <yuansheng@iscas.ac.cn>
Co-authored-by: Yang Wang <yangwang@iscas.ac.cn>
Extracted IPP to HAL for getRectSubPix function in 5.x - #29514
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Refactoring and moving IPP functions to HAL for bilateral_filter in Imgproc - #29409
**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1hnH2aGmc3D88HGnvM34xczQbRZUgsKsAHorcB-DpLq4/edit?usp=sharing
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Enabled WarpAffine and WarpPerspective for IPP HAL - #29771
PR enables Warp* functions that were switched off before. The thing is that I added clear 1 for places that agrees with OpenCV due to the changes in OpenCV. Also restrict the transparent border since OpenCV and IPP differently process transparent border with a workaround for current ICV package since it fails on Windows as it was mentioned in https://github.com/opencv/opencv/pull/29669#issuecomment-5339331722.
Locally the `Imgproc_WarpPerspective_Test.accuracy` test passed with ICV that is automatically downloaded by CMake.
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dnn: add HAL hook for general convolution and an RVV kernel - #29689
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### Summary
Adds cv_hal_dnn_conv32f and a RISC-V RVV implementation.
The hook uses the same flat C ABI as the existing DNN hooks and is behaviour-neutral without a backend. The RVV kernel runs e32m2 at vl = K0 = 8 with 10 output positions in flight, and on wide registers packs P = VLMAX/K0 output channel blocks into one vector. It declines to the built-in when an output channel block is only partially filled: K % 8, K/ngroups % 8, or grouped C/ngroups % 8.
### Verification — K3 board, VLEN 256 and 1024, GCC 15.2 + Clang 22
- Standalone harness, 22 configurations (kernel sizes, strides, dilation, asymmetric pads, 1D/2D/3D, groups, residual, all five activations): pass at both VLENs, bit-identical to a scalar reference
- opencv_test_dnn under OPENCV_FORCE_DNN_ENGINE=2: 960 passed / 29 failed, failure set identical with the hook on and off and at both VLENs; the 29 are pre-existing
- Fault injection flips exactly 19 tests, confirming the hook is on the execution path
### Performance – speedup over the built-in
| Network | 8 threads, VLEN 256 | 1 thread, VLEN 256 | 1 thread, VLEN 1024 |
| :--- | :--- | :--- | :--- |
| **SqueezeNet_v1_1** | 2.91× | 7.8× | 19.4× |
| **Inception_v1** | 2.00× | 7.0× | 13.7× |
| **Squeezenet** | 2.17× | 6.9× | 14.5× |
| **TinyYolov2** | 2.14× | 6.8× | 14.2× |
| **ResNet_50** | 2.56× | 5.7× | 9.7× |
| **LResNet100E_IR** | 1.94× | 5.5× | 11.2× |
Enabled WarpAffine and WarpPerspective for IPP HAL - #29669
PR enables Warp* functions that were switched off before. The thing is that I added clear `1` for places that agrees with OpenCV due to the changes in OpenCV. Also restrict the transparent border since OpenCV and IPP differently process transparent border.
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Extracting IPP to HAL for matchTemplate function in 5.x #29466
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imgproc: extend boxFilter / gaussianBlurBinomial coverage in RISC-V RVV HAL #29440
### Summary
This PR extends RVV HAL coverage of the two most frequently used smoothing filters. Previously, `cv_hal_boxFilter` was only implemented for single-channel types (plus 32FC3) at 3×3/5×5, and `cv_hal_gaussianBlurBinomial` for 8UC1/16UC1/8UC4. The most common cases in practice — interleaved **8UC3** images and **7×7** box kernels — fell back to the core `FilterEngine` path, which runs single-threaded, costing 6–19× on multi-core RISC-V hardware.
New coverage:
- `boxFilter`: **8UC3 / 8UC4** (3×3, 5×5, 7×7); **7×7** for 8UC1, 16SC1, 32FC1, 32FC3 and the 8U→16U variant
- `gaussianBlurBinomial`: **8UC3** (3×3, 5×5)
Combinations already covered upstream keep their existing kernels byte-for-byte; net diff is +274/−162 across two files.
### Implementation
1. **Channel-blind "flat" kernels** (`boxFilterFlat8U<ksize, cn>`, `gaussianBlurFlat8U<ksize, cn>`). An interleaved row is processed as `width*cn` flat u8 elements whose horizontal taps sit at strides of `cn` elements: element *i* sums exactly its own channel's taps at `i, i+cn, …, i+(ksize−1)·cn`. One template therefore serves all channel counts, and:
- the horizontal pass becomes `ksize` independent unaligned `vle8` loads combined with widening adds — no `vlseg`/`vsseg` and no serial `vslide1down` chains, which are microcoded/serialized on current RVV hardware;
- the vertical pass is a single contiguous u16 stream processed at LMUL=8;
- the output is a plain `vse8` after saturating narrowing.
2. **Incremental vertical sliding window** for box with `ksize > 3`: the column-sum ring buffer keeps `ksize+1` rows so the window slides in O(1) per output row (add the entering row, subtract the leaving row) instead of re-summing `ksize` rows. u16 modular arithmetic stays exact because window sums never exceed 49·255 = 12495.
3. **Division-free normalization**: normalized box output uses multiply-high + shift (`m = ceil(2^18 / k²)`, Granlund–Montgomery; exact for divisors 9/25/49 over the full value range, verified per divisor) instead of long-latency vector integer division. Rounding is bit-identical to the previous `(sum + k²/2) / k²`.
4. **Existing kernels are extended, not rewritten**: `boxFilterC1` and the float `boxFilterC3` gain a 7×7 step in their existing unrolled style, so the 3×3/5×5 instantiations compile to the same code as before. The previous per-channel gaussian 8UC4 segment kernel is replaced by the flat kernel at measured parity, which is why the diff removes more gaussian lines than it adds.
5. Constraints respected: no hard-coded VLEN, RVV 1.0 intrinsics only, no scalable vector types in arrays.
### Performance
SpacemiT K1 (X60, rv64gcv, VLEN=256, 8×1.6 GHz), 1280×720, against a clean build of current 5.x HEAD.
**Measurement protocol:** each case is timed for **30 iterations** (after 2 warm-up runs) and the minimum is taken; the whole benchmark is executed in **2 interleaved rounds per library** (baseline/candidate alternated to cancel frequency/thermal drift, measured at up to 2× per run on this board), reporting the per-case minimum across rounds.
| Case | Generic | HAL | Speedup |
|------|--------:|----:|--------:|
| boxFilter 8UC1 7×7 | 6.539 ms | 0.330 ms | 19.8× |
| boxFilter 8UC3 3×3 | 7.325 ms | 0.994 ms | 7.4× |
| boxFilter 8UC3 5×5 | 8.118 ms | 1.142 ms | 7.1× |
| boxFilter 8UC3 7×7 | 15.930 ms | 1.328 ms | 12.0× |
| boxFilter 8UC4 3×3 | 9.552 ms | 1.466 ms | 6.5× |
| boxFilter 8UC4 5×5 | 10.775 ms | 1.841 ms | 5.9× |
| boxFilter 8UC4 7×7 | 14.268 ms | 2.112 ms | 6.8× |
| gaussianBlur 8UC3 3×3 | 2.382 ms | 1.023 ms | 2.3× |
| gaussianBlur 8UC3 5×5 | 2.746 ms | 1.684 ms | 1.6× |
**Geometric mean: ~6.1×** across the newly covered cases. Most of the box gain is the HAL's row-parallel execution (core `FilterEngine` has no internal parallelism) multiplied by the flat-layout kernels; the incremental window gives 7×7 a further ~1.9× over the plain flat version.
Regression check: the full `opencv_perf_imgproc` sweep (5610 timed cases, `--perf_min_samples=10`, one round per library) shows a geomean of 1.015× with no regressions beyond run-to-run noise — every case initially below 0.90× was re-verified with 2×2 interleaved rounds and per-case minima, and none persisted. The blur fixtures independently confirm 4.7–8.6× on 8UC4 across sizes and border types.
### Accuracy
- **Bit-exact against the core fallback**: for every newly covered combination (all 15 type/ksize pairs exercised), the HAL output was checksummed against the generic implementation on identical random inputs — all checksums match exactly. This includes the multiply-high normalization (bit-identical rounding to the previous division) and the incremental vertical window (u16 modular add/subtract cancels exactly).
- **Full accuracy suites**: `opencv_test_imgproc` filter/blur/smooth tests (`*BoxFilter*:*GaussianBlur*:*Blur*:*blur*:*Smooth*:*smooth*`) were run on K1 against a clean upstream HEAD build: **819 tests pass on both libraries, and the failure sets (pre-existing upstream failures, unrelated to this PR) are line-for-line identical**. These suites cover random anchors, all border modes, ROI offsets, and in-place operation.
- Combinations already covered upstream are unchanged by construction (same kernels, same codegen), so their accuracy behavior is inherited.
Note for reviewers: the large baseline gap also reflects that core `FilterEngine` runs single-threaded; that affects all non-HAL targets and may deserve a separate issue.
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Distransform function IPP migration to HAL in 4.x #29463
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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
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Extracting IPP to HAL for matchTemplate function in 4.x #29464
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Filter2D IPP extraction to HAL for 5.x #29428
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Filter2D IPP migration to HAL for 4.x #29427
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Merge pull request #29414 from Prasadayus:box_filter_refactor
Moving IPP functions to HAL for box_filter in Imgproc #29414
**Performance Numbers on Intel(R) Core(TM) i9-11900K:** https://docs.google.com/spreadsheets/d/1puWmOSTtAFwWjPu8J1SpuccPQvxUJU8NlFQs7mAZwL8/edit?usp=sharing
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Refactoring and optimizing cvtcolor in Imgproc #29389
**Key Changes:**
1. Unified three divergent SIMD swap implementations into a single shared `v_swap` helper.
2. Consolidated the repeated `CV_8U/CV_16U/CV_32F` depth-dispatch chains into a single `CvtColorLoopDepth` template.
3. Extracted the duplicated coefficient-selection loops in the YCrCb/YUV constructors into `selectYuvCoeffs`.
4. Collapsed the shared YUV420 store logic duplicated across both decode invokers into `storeYUV420block`.
5. Generalized the inline blue-channel coefficient swaps in `color_lab.cpp` into `swapBlueCoeffsCols`/`swapBlueCoeffsRows`.
6. Added a `v_dotprod`-based SIMD path (`v_RGB2Y`/`v_RGB2UV`) for the previously scalar `RGB8toYUV422Invoker`.
7. Migrated all inline `CV_IPP_CHECK` cvtColor paths out of the dispatch files and into the IPP HAL plugin (`hal/ipp/src/color_ipp.cpp`), replacing each call site with `CALL_HAL`.
**Performance Numbers on Intel(R) Core(TM) i9-11900K**: https://docs.google.com/spreadsheets/d/1pz0aHlTeG4Ao8RT7LipgdNSjhCJz92QfZG3GmNa63hU/edit?usp=sharing
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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
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Alexander Smorkalov
Pratham Kumar
Abhishek Gola
Prasadayus
Akansha Mallick
Andrei Fedorov
Prasad Ayush Kumar
Sean McBride
velonica0
velonica0
Teddy-Yangjiale
Alexander Smorkalov
orbisai0security