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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3rdparty(mlas): add missing power (ppc64le) kernel headers #29516
Closes: https://github.com/opencv/opencv/issues/29465
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isMaskSupported now returns false when the matcher is
created with crossCheck enabled, because the mask
path is unsupported in that mode. knnMatchImpl also
drops the mask so it cannot reach batchDistance and
trigger its mask.empty() assertion.
https://github.com/opencv/opencv/issues/22093
core: SIMD optimizations for norm, distance and Hamming APIs (5.x) #29479
- Cache normHamming SIMD impl via a function pointer to cut per-call dispatch cost (improves ORB & BRISK)
- Add masked norm and norm-diff SIMD kernels (incl. cn==4 paths) and AVX-512 dispatch for norm/stat
- Add brute-force matcher perf test
- Alternative port of PR #29335 adapted to the 5.x norm.simd.hpp structure
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Distransform function IPP migration to HAL in 4.x #29463
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Fix calibration tutorial docs, decomposeProjectionMatrix, and convertMaps performance claims #29487Fixes#25655, #26791, #27277.
Three doc fixes:
1. Calibration tutorial: rows/cols swapped, fixed np.mgrid consistency
2. decomposeProjectionMatrix: clarified transVect is camera center in homogeneous coordinates
3. convertMaps: replaced overstated 2x speed claim
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- [x] I agree to contribute under Apache 2 License
- [x] Not based on GPL/incompatible license
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Gapi doc update #29149
This PR improves G-API documentation by clarifying the introduction and kernel API pages
- fixing some spelling errors
- operator|() can now link to the overloaded version that is actually called.
- supplemented and revised the doc based on the author's annotations.
The change is documentation-only and does not affect runtime behavior. I tested the doc reconstruction of this module.
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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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Replace "static inline" by "inline" in headers #29453
This fixes#29436
I also replaced CV_INLINE which can be removed in a later PR.
I can make a similar PR for 4.x if you want.
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Enable ipp calls for sort, sortIdx#29477
Follow up on https://github.com/opencv/opencv/pull/29184
Also introduced parallelization to sort similar to #29192
Performance is up to ~17x faster per our measurements
+ ~127KB to binary size.
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Documentation fixes, Added How to use pre-built opencv doc #29288
closes: https://github.com/opencv/opencv/issues/29263
co-authored by: @kirtijindal14 @Akansha-977
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dnn: add DynamicQuantizeLinear ONNX layer support #29018
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### Description
Implements the ONNX `DynamicQuantizeLinear` operator (opset 11) for the OpenCV DNN module.
**What it does:**
- Adds `QuantizeDynamicLayer` and `DequantizeDynamicLayer` layer classes
- Registers layers and adds ONNX importer dispatch for `DynamicQuantizeLinear`
- Computes scale and zero-point at runtime from activation min/max
- Quantizes FP32 input to int8 (stored as uint8 - 128, matching OpenCV convention)
**Framework limitation & workaround:**
Due to the single-dtype-per-layer constraint in `LayerData::dtype` (see #29017), the float32 scale output cannot be passed through the CV_8S blob pipeline directly. As a workaround, the scale is encoded as 4 raw bytes in a CV_8S `{1,4}` blob using `memcpy`, and decoded by the downstream `DequantizeDynamic` layer.
**Testing:**
- Custom accuracy tests reproduce all 3 ONNX conformance test cases: `test_dynamicquantizelinear`, `test_dynamicquantizelinear_max_adjusted`, `test_dynamicquantizelinear_min_adjusted`
- Each test verifies: quantized values, scale, zero point, and round-trip dequantize accuracy
- All existing quantization regression tests pass (42/42)
**Conformance tests:**
The 6 conformance tests for `DynamicQuantizeLinear` remain in the parser denylist (they were already denylisted before this PR) because the framework cannot produce mixed-type outputs. The custom tests provide equivalent coverage.
### Files changed
- `modules/dnn/include/opencv2/dnn/all_layers.hpp` — layer class declarations
- `modules/dnn/src/init.cpp` — layer registration
- `modules/dnn/src/onnx/onnx_importer.cpp` — ONNX import dispatch
- `modules/dnn/src/int8layers/quantization_utils.cpp` — layer implementations
- `modules/dnn/test/test_onnx_importer.cpp` — custom accuracy tests
- Count zero lanes via v_signmask()+popcount in AVX-512 dispatch units
- Alternative port of PR #29390 adapted to the 5.x macro-based kernels, including a SIMD path for 64F.
The previous commit bdf348c13a introduced
MLAS support including the s390x backend, but accidentally omitted the
required internal header files. This causes a compilation failure on
s390x:
fatal error: SgemmKernelZVECTOR.h: No such file or directory
This PR restores the build by adding the following missing headers from
upstream (microsoft/onnxruntime):
- FgemmKernelZVECTOR.h
- SgemmKernelZVECTOR.h
This is the same class of bug as #29422 (loongarch64), which was fixed
by PR #29423 for that architecture only.
Fix#29452: Remove <complex.h> to prevent _Complex macro conflicts #29455
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---
**Description:**
Resolves https://github.com/opencv/opencv/issues/29452
**Reason for the issue:**
The C99 `<complex.h>` header defines the macro `complex` on some platforms (like NetBSD with GCC 14). Because it was included before C++ `<complex>`, this caused conflicts where `std::complex<T>` was being expanded into `std::_Complex<T>`, resulting in the reported syntax errors.
**Changes made:**
- Removed the unnecessary C-style `#include <complex.h>`.
- Added a safety guard to `#undef complex` in case any transitive lapack headers attempt to define it, ensuring `std::complex` works cleanly without C-preprocessor interference.
Filter2D IPP extraction to HAL for 5.x #29428
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Alexander Smorkalov
Teddy-Yangjiale
Abhishek Gola
Alexander Smorkalov
Alessio Antochi
Prasadayus
Anand Mahesh
Varun Jaiswal
Madan mohan Manokar
Akansha-977
FAN YUCHEN
LiuChenjiayi
Prasad Ayush Kumar
Vincent Rabaud
Andrei Fedorov
velonica0
yyyisyyy11
Anu Oguntayo
Anushka