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
### Pull Request Readiness Checklist
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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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Fix typos in tutorial documentation #29299
This PR fixes spelling typos across several tutorial documentation files (dnn, calib3d, objdetect, app, js_tutorials), including `export=dowload` -> `export=download` in the DNN text-spotting tutorial's Google Drive URLs.
Documentation-only change; no functional code is affected.
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doc: remove duplicated bib #29298
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[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
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[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
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Update BarcodeDetector super-resolution API to use single-file ONNX #29227
### Pull Request Readiness Checklist
This PR updates the `BarcodeDetector` super-resolution API to support and utilize a single-file ONNX model format.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Dedicated pointcloud module #29224
OpenCV contrib: https://github.com/opencv/opencv_contrib/pull/4134
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Added DISK feature extractor support #29073
closes: https://github.com/opencv/opencv/issues/27083
Merge with: https://github.com/opencv/opencv_extra/pull/1368/
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Moved geometry transformations from imgproc to 3d, future geometry module #29101
The first step of 2d geometry operations migration to the future geometry module.
I created 2d.hpp to isolate the moved functions for now. I propose to create geometry.hpp when the module is renamed and include all things there.
OpenCV contrib: https://github.com/opencv/opencv_contrib/pull/4126
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Update ArUco doc #28824
See https://github.com/opencv/opencv/pull/28823
Update of the ArUco doc but targeted for OpenCV 4.
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imgproc: add findTRUContours - lock-free parallel contour extraction #28773
Integrates the TRUCO algorithm (Threaded Raster Unrestricted Contour Ownership, @cite TRUCO2026) as a transparent fast path inside `cv::findContours`. No API change is required: existing code automatically benefits when `mode=RETR_LIST` and no hierarchy output is requested.
### How it works
When `mode=RETR_LIST` and the caller does not request hierarchy, `findContours` delegates to the TRUCO parallel engine instead of Suzuki-Abe. All ContourApproximationModes are supported; approximation is applied in parallel after extraction. In all other cases the original Suzuki-Abe path is used unchanged.
### Key design ideas
- Row-strip domain decomposition parallelised via `cv::parallel_for_`
- Start-point ownership rule + speculative downward tracing eliminate tile stitching and synchronisation primitives (lock-free)
- 8-bit state space instead of the 32-bit integer labeling required by Suzuki-Abe, giving higher SIMD throughput and lower memory-bandwidth pressure
- Paged contour buffer (`TRUCOPagedContour`) avoids heap reallocation on the hot tracing path
- SIMD-accelerated row scanning via `cv::v_uint8` intrinsics
- Thread count controlled globally via `cv::setNumThreads()`, consistent with OpenCV conventions
### Output correctness
Significant effort has gone into ensuring the output is identical to the original `findContours` in every respect: same contour set, same ordering, and same approximation results for all methods. A dedicated test suite (`test_contours_truco.cpp`) verifies exact match against Suzuki-Abe across all four `ContourApproximationModes` and thread counts from 1 to 39, using noise images, circles, nested rectangles, and mixed scenes.
### Performance vs. Suzuki-Abe (from submitted paper)
- single-thread: ~1.8–1.9× faster (8-bit SIMD advantage)
- 20 threads: ~14–20× faster on i7-13700H (6P+8E, 20T)
- 20 threads: ~10–12× faster on Xeon Silver 4510 (12C/24T)
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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" />
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Added custom layer support in new DNN engine #28963
Closes: https://github.com/opencv/opencv/issues/26200
Merge with: https://github.com/opencv/opencv_extra/pull/1358
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update default ONNX Runtime version to 1.25.1 #28969
Bump ONNX Runtime default version to latest version: 1.25.1
Includes the following update:
https://github.com/microsoft/onnxruntime/pull/27164 : Adds LpNormalization opset-22 kernel support missing in 1.24.2.
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doc: modernize Doxygen comments to support for v1.15.0 #28903
This PR addresses several documentation build failures encountered with modern Doxygen versions, particularly v1.15.0 (shipped with Ubuntu 26.04).
- flann module: Updated license headers from /**** to /*M****. This prevents Doxygen from misinterpreting the license text (specifically the unclosed backticks in ``AS IS'') as documentation blocks, which previously caused "Reached end of file" errors.
- core module: Fixed a typo in operations.hpp where a doubled backtick (``) caused parsing to fail.
- tutorials: Fixed a missing backtick in real_time_pose.markdown (around line 108) and corrected typos in the RobustMatcher class name.
- Links: Resolved explicit link request failures in calib3d.hpp by ensuring proper namespace resolution.
These fixes ensure that the documentation can be generated without errors on the latest toolchains.
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Added OnnxRuntime GPU wrapper #28588
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Optimize calibrateCamera with Schur‑complement LM and parallel Jacobian accumulation #28461
## Summary
- Optimized `calibrateCamera` for faster runtime without changing outputs using Schur‑complement LM, Parallel Jacobian accumulation, alongside other optimizations.
- Reduced time complexity from O(n^3) to O(n)
- Add a perf test that uses a 500-image chessboard dataset for performance testing.
## Performance
<img width="1200" height="800" alt="base_vs_fast_results" src="https://github.com/user-attachments/assets/6dafa19f-f9cb-4f7f-ba40-0940373712e8" />
<img width="1200" height="800" alt="fast_vs_ceres_results" src="https://github.com/user-attachments/assets/7157af27-8a2b-4810-8b53-3cc9972a8493" />
<img width="1200" height="800" alt="base_vs_fast_param_deviation" src="https://github.com/user-attachments/assets/fe4f954c-34f9-4b9a-b1b2-46e4c76ce08c" />
[Testing repo
](https://github.com/Ron12777/OpenCV-benchmarking)
## Testing
- All local tests pass
## Related
- [opencv_extra PR with test images](https://github.com/opencv/opencv_extra/pull/1312)
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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The tutorial code used cv.NORM_L2 (which takes a square root) and then
averaged those values. The correct RMSE formula should use NORM_L2SQR
to get squared errors, average them, and take the square root at the
end. Updated the explanatory text to match.
Fixes https://github.com/opencv/opencv/issues/28651
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## This tutorial describe how to build opencv from source in windows using VSCode and MSYS2
Added ONNX Runtime as an optional wrapper #28444
This PR adds ONNXRuntime (ORT) as an _optional_ wrapper, which can be enabled by adding **WITH_ONNXRUNTIME** flag in CMake command.
Using ORT wrapper the inference time for _resnet50.onnx model_ has come to _**~7ms**_ from _**~14ms**_.
Also, we are able to run models like `ssd_mobilenet_v1.onnx`.
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docs(calib3d): fix grammar and clarify radial distortion formula in camera calibration tutorial #28422
This PR improves the camera calibration tutorial documentation by:
- Fixing minor grammatical issues for better readability.
- Clarifying the radial distortion model by explicitly defining r² = x² + y².
These changes do not affect functionality and are intended to make the mathematical explanation clearer for readers and new users.
doc: update image codec configuration reference (add AVIF, fix JPEG XL) #28393
This PR updates the build configuration documentation to:
- Add AVIF to the supported format list (it was missing).
- Clarify that some codecs (AVIF, JPEG XL) do not support BUILD_* options because OpenCV does not bundle their source code.
- Update the description to include "write" capabilities for these formats.
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Alexander Smorkalov
FAN YUCHEN
Abhishek Gola
rmsalinas
Gold856
Ayush Das
Kumataro
Yang Guanyuhan
omrope79
Alexander Smorkalov
kirtijindal
s-trinh
Maksim Shabunin
Pratham Kumar
Varun Jaiswal
Rohan Mistry
Matt Van Horn
rajmahadev422
steaphenai
Jake Floch
Shrutikasri04
Luis Adame Rodríguez
Vincent Rabaud