Remove Tensorflow Importer - #29474
[PR OpenCV Extra](https://github.com/opencv/opencv_extra/pull/1390)
[PR OpenCV Contrib](https://github.com/opencv/opencv_contrib/pull/4171)
Removed tensorflow importers
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Remove deprecated CommaInitializer API - #29948
This goes hand in hand with https://github.com/opencv/opencv_contrib/pull/4217
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The XFeat keypoint demo was added to samples/cpp/aliked_lightglue.cpp
upstream. That sample is replaced here by features_lightglue.cpp, which
now covers ALIKED/DISK LightGlue matching and the XFeat demo.
Xfeat feature - #29361
## PR Description
### Summary
Integrate XFeat into OpenCV's `features` module as a native `Feature2D` implementation, enabling lightweight neural feature detection and descriptor extraction through OpenCV's standard feature extraction API.
---
### What's included
#### New class
- **`cv::XFeat`** extends `Feature2D`
- CNN-based keypoint detection
- 64-D descriptor extraction via ONNX/DNN
- Score-map based keypoint selection
- Descriptor sampling from the dense feature map
---
### Files added
| File | Description |
|------|-------------|
| `src/feature2d_xfeat.cpp` | XFeat `Feature2D` implementation |
| `test/test_xfeat.cpp` | XFeat unit and regression tests |
---
### Files modified
- `features.hpp`
- Add `cv::XFeat` declaration and public factory APIs
---
### Usage
```cpp
#include <opencv2/features.hpp>
using namespace cv;
// Feature extraction
Ptr<XFeat> xfeat =
XFeat::create("xfeat.onnx", 2000, 0.5f, 640);
std::vector<KeyPoint> keypoints;
Mat descriptors;
xfeat->detectAndCompute(image, noArray(), keypoints, descriptors);
```
---
### Test dependency
Depends on the opencv_extra changes adding the XFeat ONNX model and reference outputs.
Required test data:
https://github.com/opencv/opencv_extra/pull/1383
- `xfeat.onnx`
- `xfeat_lena_640_kpts.npy`
- `xfeat_lena_640_desc.npy`
These files are required for the `Features2d_XFeat` tests in the main OpenCV repository to validate XFeat feature extraction and descriptor generation.
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Enable collectContours in detect_blob.cpp and draw the per-blob
contours returned by getBlobContours(), so the sample exercises the
public contour-collection API added in #21942.
Refs #25904
[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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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
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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
### Pull Request Readiness Checklist
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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
### Pull Request Readiness Checklist
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Migrated chessboard and circles grid detectors to objdetect #28804
OpenCV Contrib: https://github.com/opencv/opencv_contrib/pull/4125
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1375
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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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Chromatic aberration correction #27491
Merge with https://github.com/opencv/opencv_extra/pull/1266
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Parent issue: https://github.com/opencv/opencv/issues/27206
Related PR: [#27490](https://github.com/opencv/opencv/pull/27490)
This PR adds chromatic aberration correction in C++ based on calibration data from the python app.
This code adds a function for chromatic aberration correction based on the calibration file (Mat correctChromaticAberration(InputArray image, const String& calibration_file)), and a class ChromaticAberrationCorrector which can be used to correct images of the same camera under the same settings (so for the same calibration data that is initialized in the beginning).
Also, basic functionality and performance tests are added.
3D object pose estimation tutorial update #27874
1. Use cv::loadMesh instead of custom csv-based reader.
2. Use code snippets
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The Kullback-Leibler divergence works with histogram that have integral = 1,
otherwise it can return negative values. The normalization of the histograms
have been changed accordingly, and all the six comparison methods have been
used in the histogram comparison tutorial.
Fix Typos in Comments and Documentation #27455
Description:
This pull request corrects minor typos in comments and documentation within the codebase:
- Replaces "representitive" with "representative" in kmeans.cpp.
- Replaces "indices" with the correct spelling in a comment in main.cu.
Savya Sanchi Sharma