Feat: add OpenCL SIFT detector and descriptor #29450
Add OpenCL implementation of SIFT detector and
descriptor extractor, triggered via T-API when
the caller passes a UMat. Targets the features
module (5.x directory layout).
Implementation:
- 4 OpenCL kernels in sift.cl: gaussian_blur_h, gaussian_blur_v, detect_and_orient, and compute_descriptor.
- Host-side dispatch in sift.dispatch.cpp using CV_OCL_RUN_ macro, pure additions to the existing CPU path.
- T-API entry: detectAndCompute, detect, compute.
Gating and fallback:
- 64-bit only. 32-bit falls back to CPU via sizeof(void*) > 4 guard.
- OpenCL 1.1 or later required. Devices below 1.1 fall back to CPU.
- No-OpenCL builds fall back to CPU. Verified with WITH_OPENCL=OFF: 79 tests pass, same 3 pre-existing failures (DISK/AFFINE_FEATURE missing .npy data files).
Correctness:
- nOctaveLayers > 3 OOB fix: gk_coeffs and gk_radius resized to nOctaveLayers+2 via std::vector (were fixed size 5).
- Regression test for nOctaveLayers > 3.
- 10 OCL SIFT tests pass, 60 OCL tests pass, 139 features tests pass (3 pre-existing failures unrelated to SIFT).
Performance optimizations:
- native_exp, native_sqrt, native_recip for hardware approximations (OpenCL 1.1 builtins).
- Eliminate intermediate rawDst[128] buffer; normalize in-place from hist[].
- Non-blocking kernel launches with single ocl::finish() before host reads keypoint count.
- Custom separable Gaussian blur for init image bypassing T-API GaussianBlur dispatch overhead.
- exp2() replaces pow(2.0f, x); scl_octv reused.
- Hoist UMat tmp allocation out of pyramid loop.
- Interleaved keypoint output buffer (6 arrays to 1) for coalesced writes and fewer copies.
- Consolidated descriptor keypoint copies (2N to 2 host-to-device transfers).
- std::map replaced with flat vector indexed by pyramid level (O(1) vs O(log n)).
- DoG computed on-the-fly in detect kernel via READ_DOG macro, eliminating 45 subtract() calls and dog_pack allocation.
- Shared gauss_packs between detect and descriptor, eliminating duplicate copyTo.
- Cross-level packing per octave: one descriptor kernel launch per octave instead of per level. Keypoint buffer expanded to 5 floats (added layer index). Levels packed into single UMat. Reduces kernel launch overhead and improves GPU utilization for octaves with few keypoints.
- __local memory tiling for Gaussian blur kernels with 16x16 workgroups and cooperative halo loading. Reduces global memory traffic.
- Direct uchar descriptor output for CV_8U: kernel writes convert_uchar_sat_rte directly instead of float buffer + host convertTo pass. Eliminates temp allocation and extra kernel launch.
Performance result (stitching/s2.jpg resized):
- DetectAndCompute: OCL wins at >=960x540 (1.32x at 960x540, 1.80x at 1280x720, 1.87x at 1920x1080).
- Compute-only: OCL wins at >=1280x720 (1.60x at 1280x720, 1.78x at 1920x1080).
Tests:
- modules/features/test/ocl/test_feature2d.cpp with OCL SIFT correctness tests on real images (leuven img1.png, a3.png, s2.jpg).
- DescriptorType, Regression_26139, and Batch tests mirror CPU coverage (CV_8U descriptor type, single-keypoint edge case, 6-image detect+compute).
- modules/features/perf/opencl/perf_sift.cpp with real image and 5-size sweep.
- CPU SIFT_Scaled fixture added to perf_feature2d.cpp for aligned OCL vs CPU comparison.
### 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
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
[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
Added DISK feature extractor support #29073
closes: https://github.com/opencv/opencv/issues/27083
Merge with: https://github.com/opencv/opencv_extra/pull/1368/
### 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
core: add YAML 1.2 support for FileStorage #28482Fixes: #26363
CI Update: https://github.com/opencv/ci-gha-workflow/pull/306
Summary:
- Bool true/false literals support
- Header-less YAMLs support for Python YAML module compatibility. Header-less files are parsed as YAMLs by default
- Added FORMAT_YAML_1_0 flag to FileStorage for fallback.
- New YAML1.2 header
Testing:
- Added Core_InputOutput.YAML_1_2_Compatibility test case in test_io.cpp.
- Added YAML interop test in Python
Anand Mahesh
Alexander Smorkalov
Abhishek Gola
Yang Guanyuhan
satyam yadav
WU Jia