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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Extended Attention layer support #29333
Implemented present/past KV support.
Merge with: https://github.com/opencv/opencv_extra/pull/1381
Co-authored by: @Akansha-977
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dnn: add Winograd F(6,3) RVV implementation for RISC-V (VLEN≥256) #29411
### Summary
Adds a RISC-V Vector (RVV) backend for the Winograd F(6,3) convolution path
in the DNN module, targeting VLEN≥256 processors (tested on SpacemiT K1,
rv64gcv).
### Background
The Winograd F(6,3) path was already gated on `useSIMD128 || useAVX ||
useAVX2 || useNEON` at runtime, and protected by the same set of compile-time
macros. RVV was simply missing from both guards, so it always fell through to
generic convolution even when the hardware supports it.
A second issue: `conv_winograd_f63` was registered with
`ocv_add_dispatched_file`, which skips ISA variants not present in the host
toolchain during cross-compilation. Changing to `ocv_add_dispatched_file_force_all`
forces the `.rvv.cpp` translation unit to be generated unconditionally, matching
how every other RVV-enabled kernel in the DNN module is registered.
### Implementation notes
**Atom width.** `vsetvlmax_e32m1()` returns 8 on VLEN=256, so `winoAtomF32=8`
is chosen, matching the AVX2 atom width. The `impl_accum_F32` and transform
functions are structured identically to the AVX2 path (4 output channels ×
6 input tiles per atom).
**Input/output transform.** AVX2 uses `_mm256_unpacklo/hi_ps` + `permute2f128`
for an in-register 8×8 transpose. RVV has no equivalent cross-lane shuffle at
this width without `vrgather`, which adds index-vector overhead for a
non-bottleneck step. Instead the 8×8 intermediate matrix is transposed
scalar-in-memory between the two `wino_bt8x8_rvv` / `wino_at8x6_rvv` passes.
This keeps the transform code simple and correct; the GEMM in `impl_accum_F32`
dominates runtime.
**VLEN guard.** `getWinofunc_F32` checks `vsetvlmax_e32m1() >= 8` at runtime
and returns an empty functor on narrower implementations, so the code is safe
on VLEN=128 targets without a separate code path.
### Testing
**Correctness:** `ConvolutionWinograd.Accuracy` passes on the K1 board (9 ms).
**Performance:** `opencv_perf_dnn`, SpacemiT K1 (rv64gcv, VLEN=256),
GCC 13, `-O2 -march=rv64gcv`. Reported times are per-iteration medians from
`[ PERFSTAT ]` output (`--perf_min_samples=5`). "Generic" is the same build
with Winograd disabled via `OPENCV_DNN_DISABLE_WINOGRAD=1`.
| Input → Output C | Winograd (ms) | Generic (ms) | Speedup |
|--------------------------|--------------|-------------|---------|
| {1,128,52,52} → 256 | 35.97 | 62.86 | 1.75× |
| {1,512,13,13} → 1024 | 65.18 | 112.50 | 1.73× |
| {1,256,75,75} → 256 | 159.75 | 271.65 | 1.70× |
| {1,64,300,300} → 64 | 174.21 | 295.32 | 1.69× |
| {1,1152,16,16} → 1152 | 176.12 | 245.33 | 1.39× |
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dnn: implement LpPool ONNX operator #29217
## Summary
Implements the `LpPool` ONNX operator (opset 1–18), which was previously unregistered and caused a parse failure. `LpPool` computes the Lp-norm pooling: `(sum(|x|^p))^(1/p)` over a sliding window.
## Changes
- New `LpPoolLayer` in `modules/dnn/src/layers/lppool_layer.cpp`
- Supports `kernel_shape`, `strides`, `dilations`, `pads`, `auto_pad` (NOTSET/SAME_UPPER), `ceil_mode`, and `p` (default 2)
- SIMD fast paths for p=1 (abs + accumulate) and p=2 (square + accumulate + sqrt); scalar fallback for other values of p
- Registered `LpPool` dispatch entry in both `onnx_importer.cpp` (classic engine) and `onnx_importer2.cpp` (new graph engine)
- Added `LpPoolLayer` declaration to `modules/dnn/include/opencv2/dnn/all_layers.hpp`
- Registered layer class in `modules/dnn/src/init.cpp`
- Re-enabled 8 lppool conformance tests in `test_onnx_conformance.cpp` (previously in parser denylist)
- `test_lppool_2d_same_lower` added to the global conformance denylist — same known SAME_LOWER padding bug that affects `averagepool` and `maxpool`
## Testing
All applicable ONNX conformance tests pass:
| Test | Result |
|------|--------|
| test_lppool_1d_default | PASSED |
| test_lppool_2d_default | PASSED |
| test_lppool_2d_dilations | PASSED |
| test_lppool_2d_pads | PASSED |
| test_lppool_2d_same_lower | SKIPPED (known SAME_LOWER padding bug, consistent with avgpool/maxpool) |
| test_lppool_2d_same_upper | PASSED |
| test_lppool_2d_strides | PASSED |
| test_lppool_3d_default | PASSED |
Tested on: macOS (x86_64/SSE4, Rosetta 2) and Linux x86_64 (AVX2/AVX-512, GCC 13.3.0), Release build
OpenCV version: 5.0.0-pre
## Related Issues
None
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Port of #29377 to 5.x. Wires Log, Erf, Exp, Sin, Cos, Sinh, Cosh, Tan,
Softplus, BNLL, Asinh, Acosh, Atanh into the dispatched activation_kernels
registry, plus a Layer_Activation perf test. 1.4-12.3x across
M4/A76/Threadripper/Xeon, correct to <=5e-7 vs scalar.
Validate onnx tensor payload size in getMatFromTensor (5.x) #29370
5.x port of #29314. The DNN ONNX importer diverged here, so the change is ported manually.
`getMatFromTensor()` sizes the output blob from `TensorProto.dims` and then reads that many elements out of the tensor payload, but the payload is sized independently in the model and never checked against the shape. In 5.x the payload reaches the read through three sources: a typed `*_data` field, `raw_data`, or external-file data routed through `getTensorRAWData()`. An initializer whose dims claim more elements than the payload holds makes the `Mat` copy/convert read past the buffer.
### Before
A FLOAT tensor declaring `[1000000]` backed by a 4-byte `raw_data` reads about 4 MB out of bounds (ASan flags a heap over-read in `cv::Mat::copyTo`). The same mismatch is present in every datatype branch and for both the typed-field and `rawdata` sources, reachable from `readNetFromONNX`/`readNetFromONNXBuffer` for every initializer.
### After
Compute the element count the shape implies once, then check each payload source holds at least that many elements before the read. `getTensorRAWData()` now reports the byte count it returns (covering both `raw_data` and external-file data), and `getMatFromTensor()` validates the typed fields and the raw byte count per datatype. A short payload throws a clear `cv::Exception` instead of over-reading.
### Tradeoffs
The check lives in `getMatFromTensor` because that is the one place the shape and the payload meet, so every initializer and attribute tensor is covered without each caller repeating it. The added cost is one multiply-accumulate over the dims plus a comparison per tensor at load time; well-formed models, where the payload already matches the shape, are unaffected. The element-count accumulation saturates on overflow so an oversized shape can't wrap to a small total and slip past the check.
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fixed Dynamic quantized linear layer error #29386
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dnn: vectorize fp32 convolution on RISC-V RVV #29357
### Summary
Vectorizes the new DNN engine's fp32 convolution on RISC-V **RVV**. Convolution was the last major block-layout operator still running scalar on RVV; this brings it to parity with the already-vectorized depthwise/pool/batchnorm path.
Single file, **purely additive** (`#elif CV_SIMD_SCALABLE` branches only) — x86/AVX2, ARM/NEON and AArch64 codegen are unchanged.
### Background
- **#28585** introduced block-layout conv with fp32 kernels specialized for AVX2 (`C0=8`) and NEON, scalar `#else` for everything else, and explicitly noted *"we could add the respective kernels later."*
- On RVV that `#else` meant conv ran **scalar at every VLEN** — the C0=8 fixed width approach doesn't transfer to a variable-VLEN ISA.
- **#29304** made the block size track the hardware width (`C0 = vlanes()`), fixing the VLEN≥256 assertion but leaving conv scalar.
- **This PR** supplies the deferred RVV conv vectorization, on top of that `C0=vlanes()` foundation.
### Approach
- Re-enable the `SPAT_BLOCK_SIZE=10` **blocked path** on RVV. Because `K0 == vlanes()`, one `v_float32` accumulator covers a full output-channel block, so 10 accumulators process 10 output positions together. A single K0-wide weight `vx_load` is reused across all 10 — amortizing the per-output-point load that bottlenecks the scalar path (this is what enables multi-core scaling; the scalar/per-FMA-load path is bandwidth-bound).
- Vectorize the **scalar tail** (the `<10`-position remainder) the same way.
- **Vector-length-agnostic:** `C0` is runtime, the kernel is written in terms of `v_float32`/`vlanes()`, so the same binary runs at full width on VLEN 128/256/512/1024 with no recompile. No per-VLEN kernels.
- The six `C0=8` specialized kernels stay `#if !CV_SIMD_SCALABLE`-gated (they can't run at `C0=vlanes()`); on RVV all shapes use the generic kernel.
### Performance
Convolution throughput, blocked (this PR) vs scalar, same machine/engine (VLEN=256, 8× rv64 @ 2.4 GHz), GFLOP/s:
| layer | scalar 1T | this 1T | scalar 8T | this 8T | 8T speedup |
|---|---|---|---|---|---|
| 3×3, 256→256, 64² | 1.13 | 13.23 | 8.28 | 75.62 | **9.1×** |
| 1×1, 256→256, 64² | 1.08 | 11.73 | 8.02 | 71.70 | 8.9× |
| 3×3, 128→128, 128² | 1.10 | 13.02 | 8.45 | 89.14 | **10.6×** |
≈ **12× single-thread, 9–11× at 8 threads**. Convolution dominates CNN inference time, so this is a large end-to-end win on RVV hardware.
### Validation
K3-class RVV board, VLEN=256: `Test_ONNX_layers` **264/264**, `Test_ONNX_conformance` **1641/1641**, **0 assertions**. Output is bit-identical to the scalar path (same accumulation order, just vectorized over `K0`).
Vector-length-agnosticism confirmed by running the VLEN=256 binary unchanged at **VLEN=1024**.
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Extended support for Resize and Split layers #29323
Merge with: https://github.com/opencv/opencv_extra/pull/1379
Key changes:
Resize layer:
_antialias_ (linear & cubic) :- PIL-style separable resampling with stretched filter support and edge-clamped, renormalized weights.
_axes_ (incl. reversed [3,2]) :- getOutShape, the scale override, and runtime _tf_crop_and_resize_ ROI parsing now map 2-element sizes/scales/roi by the axes order instead of assuming [2,3].
_keep_aspect_ratio_policy_ (not_larger/not_smaller) :- output size from min/max per-axis scale.
_half_pixel_symmetric_ :- new coordinate-transform mode + importer mapping.
_align_corners_ downsampling :- coordinate scale uses the unfloored scaled length (in−1)/(in·x_scale−1).
Split Layer:
_convertTo empty 1-D Mat:_ the empty-Mat branch collapsed a 1-D [0] to 2-D [1,0] via cv::Size(); now uses allowTransposed like the non-empty path.
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predictOptimalVectorWidth() built its vectorWidths table with 8 entries
(depths CV_8U..CV_16F), but checkOptimalVectorWidth() indexes it by depth.
The 5.x depths CV_16BF, CV_Bool, CV_64U, CV_64S and CV_32U therefore read
past the end of the array; the garbage value can slip past the ckercn <= 0
guard, and the divider normalization loop then shifts the divider to zero,
so "offsets[i] % dividers[i]" raises SIGFPE. The failure is allocation
dependent and shows up as a sequence-dependent crash, e.g. in the OpenCL
Norm tests for CV_32U (cv::norm on a UMat reaches this via ocl_sum, which
calls predictOptimalVectorWidth before its own depth guard bails out).
Size the table to CV_DEPTH_MAX so every depth is in bounds and map the new
fixed-size integer depths to their natural OpenCL vector widths; CV_16BF
has no OpenCL vector type and stays scalar. Add a regression test covering
all depths.
Fix ONNXRuntime dll path mismatch #29309
**1. C2664 build error in `net_impl_backend.cpp`**
`EnableProfiling()` expects `const wchar_t*` on Windows (`ORTCHAR_T`), but was passed `const char*`.
Fixed by converting to `std::wstring`, as suggested in #29278
---
**2. Wrong ORT DLL loaded at runtime (`modules/dnn/CMakeLists.txt`)**
With `DOWNLOAD_ONNXRUNTIME=ON`, the DLL glob only searched `bin/` but the downloaded package places DLLs in `lib/`. This left the build tree with no ORT DLL, causing Windows to fall back to the stale `System32\onnxruntime.dll` (1.17.1), crashing against the ORT 1.25.1 API.
Fixed by adding `lib/` as fallback and staging DLLs into the build bin directory at configure time.
Closes : #29278
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dnn: fix new-engine block layout for RVV when VLEN > 128 #29304
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#### Problem
Fixes#28852. The new DNN engine packs activations in a blocked NCHWc layout with a fixed channel-block size `C0 = 8`. The blocked-layout kernels (MaxPool, AveragePool, BatchNorm, depthwise Conv, ConvTranspose, generic Conv) load one block into a SIMD vector and assert: `C0 == nlanes || C0 == nlanes*2 || C0 % (nlanes*4) == 0`
On RISC-V the universal intrinsics are **scalable with LMUL=2**, so `nlanes = (VLEN/32)*2` — **16 at VLEN=256, 64 at VLEN=1024** — which exceeds the fixed `C0=8`. The assertion fails with `-215` and the ONNX pooling/conv conformance tests abort. #29180 worked around it by disabling the RVV SIMD path in those kernels.
#### Fix
Make the block size track the hardware vector width on RVV instead of assuming 8:
- `net_impl.cpp`: `defaultC0 = max(8, VTraits<v_float32>::vlanes())` (16/64 on RVV).
- The fp32 blocked kernels read `C0` at runtime and size scratch buffers by `max_nlanes` (bounded by the compile-time `CV_RVV_MAX_VLEN`, default 1024).
- All changes are guarded by `CV_SIMD_SCALABLE`, so **x86/NEON are unchanged**.
- The int8 quantized kernels are hardwired to `C0=8` (VNNI/NEON packing + per-channel quant) and are scalar on RVV, so quantized graphs are pinned to `C0=8` in `prepareForInference()` (detected via a `CV_8S` arg); only fp32 graphs use the wider block.
#### Validation (native RISC-V)
I have already conducted tests on the Spacemit K3, covering both the X100 (VLEN=256) and the A100 (VLEN=1024).
`opencv_test_dnn`, ENGINE_AUTO (new engine), no assertions:
| VLEN | Test_ONNX_conformance | Test_ONNX_layers (conv/pool/depthwise/quantized/MobileNet_v4) |
|------|-----------------------|--------------------------------------------------------------|
| 256 | 1641 / 1641 | all pass (incl. `Quantized_Convolution`) |
| 1024 | 1641 / 1641 | all pass (incl. `Quantized_Convolution`) |
Added harfbuzz; use it instead of STB to render text #29300
Merge with https://github.com/opencv/opencv_extra/pull/1378.
This is the next big step to improve font rendering in OpenCV 5.x. See #18760, #26301. See also OpenCV 5.0 release notes, where it was pointed out that complex scripts are not rendered correctly, and HarfBuzz integration is needed to fix it. So, here it is — HarfBuzz integration:
* Removed tweaked STB engine. STB truetype rendering engine was included into OpenCV 5.0-pre/5.0 and it was extended to instantiate and render variable fonts, but the support was incomplete and immature.
* Instead, we now use [HarfBuzz](https://github.com/harfbuzz/harfbuzz) — famous font shaping library and now also font rendering library, used by many big companies and organizations.
* As a result, we now correctly render complex scripts, such as arabic or devanagari, correctly handle font ligatures (ff, fi, ft etc.)
<img width="1300" height="600" alt="text_test" src="https://github.com/user-attachments/assets/a7d2c754-0fcf-40f8-8104-578f3a14850b" />
* Potentially, we could also support color emoji, but that would be a next step.
* As with STB-based engine, glyphs are cached (the same glyph from the same font is not rendered twice), so the performance should be on par with the previous engine.
* When OpenCV is built with `-DWITH_HARFBUZZ=ON`, which is set by default, it tries to detect HarfBuzz in the system and use it. If it's not detected, OpenCV builds our own small subset of HarfBuzz.
The following table compares size of libopencv_imgproc.dylib.5.0.0 (Release mode) in different configurations:
| Configuration | libopencv_imgproc (stripped) | delta vs 5.0 |
|----------------------------------------|------------------------------|----------|
| imgproc without text rendering (`-DWITH_HARFBUZZ=OFF`) | 4.27 MB | −2.35 MB |
| imgproc with stb-based engine (5.0) | 6.62 MB | 0.0 Mb |
| imgproc with HarfBuzz-based engine (this PR) | 7.07 MB | +0.45 MB |
The biggest source of the size increase when OpenCV is built with text rendering support (STB- or Harfbuzz-based) is that imgproc in this case includes .ttf fonts (Rubik and 'Wen Quan Yi Micro Hei'), which are gzip-compressed, but still have noticeable size, especially 'Wen Quan Yi' (~2Mb). HarfBuzz itself, compared to STB engine, adds just 0.45Mb, or ~7% to imgproc size. On other platforms (Linux x64, Windows), where IPP or other acceleration libraries are linked into libopencv_imgproc, the harfbuzz footprint is even less noticeable.
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objc: Fix VolumeType enum name clash on MacOS #29262
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- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake
**Problem**
When the ObjC wrapper is generated, the global enum `cv::VolumeType` from the ptcloud module is emitted as `typedef NS_ENUM(int, VolumeType)`. macOS defines `typedef OSType VolumeType` in CoreServices, which leads to a typedef-redefinition error during the framework build.
**Fix**
`add_enum()` in `gen_objc.py` now handles namespace-global enums by falling back to a module-level lookup (`self.Module`) when the enum has no enclosing class. A new `gen_dict.json` entry maps `VolumeType` -> `PtcloudVolumeType`, and the import generation automatically uses the renamed enum.
Fixes#29260
doc: remove duplicated bib #29298
### 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
- [ ] 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
objc: fix conflicting Swift name for LightGlueMatcher.create #29257Fixes#29256
`cv::LightGlueMatcher` (a `DescriptorMatcher` subclass) declares a static
`create(const String& modelPath, ...)` overload. The Objective-C bindings
generator emits it as the selector `+create:`, which is the same selector as
the inherited `DescriptorMatcher::create(MatcherType)` but carries a different
`swift_name`, so clang refuses to build the `opencv2` Objective-C module:
```
LightGlueMatcher.h: error: 'swift_name' and 'swift_name' attributes are not compatible
```
Wrap the file-path overload with `CV_WRAP_AS(createFromFile)` so it gets a
distinct selector, mirroring the sibling `createFromMemory` overload, while
keeping the Swift name `create(modelPath:...)`.
### 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
- [ ] 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
imgproc: fix INTER_NEAREST_EXACT to match Pillow (5.x retarget of #28661) #28873
## Summary
Fixes `INTER_NEAREST_EXACT` producing different results from Pillow for images with dimensions that are multiples of 64 (e.g., 128, 192).
Retargets #28661 to 5.x per core team request.
## Root cause
The old 16-bit fixed-point arithmetic (`ifx`, `ifx0`, `>> 16`) rounds differently from Pillow's pixel-center coordinate mapping. At exact pixel boundaries (e.g., y=7 with 128 to 160: `0.4 + 7*0.8 = 5.999999999999999`), Pillow's truncation drops to 5 while the old opencv formula rounds to 6.
## Changes
- `resize.cpp`: Replace `ifx/ifx0/ify/ify0` 16-bit fixed-point with an int64 arithmetic formula equivalent to `floor((i + 0.5) * src / dst)`, computed as `((int64_t)(i * 2 + 1) * src) / (dst * 2)`. This matches Pillow's pixel-center mapping exactly, is deterministic across platforms, and avoids FP rounding divergence entirely.
- `test_resize_bitexact.cpp`: Add `NearestExact_PillowCompat` covering 4 dimension pairs including the reproducer from #28429.
## Testing
- All 3 `Resize_Bitexact` tests pass (Linear8U, Nearest8U, NearestExact_PillowCompat) on this branch built against 5.x
- Verified bit-exact match to PIL output (Python 3.14, Pillow) on 49 dimension combinations including random pairs
- Built on macOS ARM64 with HAL (carotene/KleidiCV) active
Fixes#28429
[GSOC] feat: Add ALIKED feature extractor and LightGlue matcher with DNN integration #28986
## PR Description
### Summary
Integrate ALIKED and LightGlue into OpenCV's `features` module as native`Feature2D` and `DescriptorMatcher` implementations, enabling end-to-end neural feature matching within OpenCV's ecosystem.
---
### What's included
#### New classes
- **`cv::ALIKED`** extends `Feature2D`
- CNN-based keypoint detection
- 128-D descriptor extraction via ONNX Runtime
- **`cv::LightGlueMatcher`** extends `DescriptorMatcher`
- Deep feature matching with spatial context
- Uses keypoints and image sizes during matching
---
#### API design
- Standard OpenCV patterns:
- `detectAndCompute()`
- `match()`
- `knnMatch()`
- Multiple factory methods:
- ONNX model path
- In-memory model buffer
- Pre-loaded `dnn::Net`
- `Params` structs use `CV_EXPORTS_W_SIMPLE`
for Python/Java bindings support
- Optional DNN dependency:
- `HAVE_OPENCV_DNN` guards
- Stub implementations throw `StsNotImplemented`
---
### Files added
| File | Description |
|------|-------------|
| `src/feature2d_aliked.cpp` | ALIKED implementation |
| `src/matchers_lightglue.cpp` | LightGlueMatcher implementation |
| `src/aliked_context.hpp` | Shared internal context struct |
| `test/test_aliked_lightglue.cpp` | Unit tests (9 test cases) |
| `samples/cpp/example_features_aliked_lightglue.cpp` | Demo application |
---
### Files modified
- `CMakeLists.txt`
- Add `opencv_dnn` as optional dependency
- `features.hpp`
- Add ALIKED and LightGlueMatcher declarations
- `precomp.hpp`
- Add DNN include guard
---
### Usage
```cpp
// Feature extraction
Ptr<ALIKED> aliked =
ALIKED::create("aliked-n16rot-top1k-640.onnx");
vector<KeyPoint> kpts;
Mat descs;
aliked->detectAndCompute(image, Mat(), kpts, descs);
// Feature matching
Ptr<LightGlueMatcher> lg =
LightGlueMatcher::create("aliked_lightglue.onnx");
lg->setPairInfo(
kpts1Mat,
kpts2Mat,
img1.size(),
img2.size()
);
vector<DMatch> matches;
lg->match(descs1, descs2, matches);
````
please refer to samples/cpp/example_features_aliked_lightglue.cpp
---
### Test plan
* Build with `BUILD_LIST=features,dnn`
* Build without DNN:
* Verify stubs compile
* Verify `StsNotImplemented` is thrown
* Run:
* `ctest -R Features2d_ALIKED`
* `ctest -R Features2d_LightGlueMatcher`
* Run sample application with:
* Real images
* Real ONNX models
* Verify Python/Java bindings compile and work
---
### Related
Phase 1 of the
"End-to-End AI Feature Extraction and LightGlue Matching Pipeline"
GSoC project.
Designed to be extensible to:
* XFeat
* SuperPoint
* Other neural feature extractors
### test dependency
Depends on the opencv_extra PR adding ALIKED and LightGlue test models:
- [opencv_extra PR](https://github.com/opencv/opencv_extra/pull/1366)
This PR adds the following ONNX models to `download_models.py`:
- `aliked-n16rot-top1k-640.onnx`
- `aliked_lightglue.onnx`
These models are required for the `features2d` tests in the main OpenCV repository to validate the ALIKED and LightGlue feature extraction and matching pipeline.
### 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
- [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
[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220
### Pull Request Readiness Checklist
This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206)
Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17
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
Inverted imgproc-geometry dependency and moved more functions to geometry #29230
Fixes: https://github.com/opencv/opencv/issues/20267
Continues: https://github.com/opencv/opencv/pull/29175
OpenCV Contrib: https://github.com/opencv/opencv_contrib/pull/4137
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1377
Summary:
- LSD returned back to imgproc
- drawing functions moved to imgproc
- undistort image and related perf-pixel functions moved to imgproc
- moments moved to geometry
- estimateXXXtransform moved to geometry
After the patch the geometry module depends on code and Flann and may be used everywhere without potential circular dependencies
### 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
Alexander Smorkalov
Prasad Ayush Kumar
Alexander Smorkalov
Teddy-Yangjiale
Abhishek Gola
Saravana Balaji Mohan Balaji
Vincent Rabaud
Tonu Samuel
uwezkhan
Savya Sanchi Sharma
velonica0
Gold856
Jesus Armando Anaya
Varun Jaiswal
Vadim Pisarevsky
Haris shakeel
Kumataro
Peter Rekdal Khan-Sunde
Yang Chao
Matt Van Horn
Vibhor
Yang Guanyuhan
omrope79