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
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This PR updates the `BarcodeDetector` super-resolution API to support and utilize a single-file ONNX model format.
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little but important fix in bicubic warping 1-channel case #29229
merge together with https://github.com/opencv/opencv_contrib/pull/4136
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[OpenCV 5] Fix apriltag corners order and update the doc #28823
I have updated documentation for the AprilTag dictionaries.
Corresponding issues:
- https://github.com/opencv/opencv-python/issues/1195
- https://stackoverflow.com/questions/79044142/why-is-the-order-of-the-incoming-corners-different-between-apriltag-and-aruco-ma
This is a breaking change and is targeted only for OpenCV 5.
---
I have updated the ArUco doc with more information about fiducial markers detection.
I have tried to add some recommendations, best practices:
- `DICT_ARUCO_MIP_36h12` should be the recommended family, [see](https://stackoverflow.com/a/51511558)
- link to download pregenerated markers for `MIP_36h12` is [here](https://sourceforge.net/projects/aruco/files/. I have not found some other official links for the other ArUco family, but since `MIP_36h12` should be used, I guess it is fined.
- recommendation to have a white border when printing the marker
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Dedicated pointcloud module #29224
OpenCV contrib: https://github.com/opencv/opencv_contrib/pull/4134
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Fixed Out-of-Memory issue and added VLM sample #29221
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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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Caffe importer cleanup #28678
Merge with: https://github.com/opencv/opencv_extra/pull/1324
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Fixed the new chessboard detector by not using OpenCL #29211
This should hopefully fix test failures on macOS x64 builder in the latest 5.x branch.
The code of Chessboard detector (namely, the FastX part) runs tons of different-size box filters on the same image. Optimally, those filters need to be computed all together using once-computed integral image, but instead those multiple box filters, especially given how the current opencl path in box filter is organized, seem to 'overload' our OpenCL cache system and it breaks, at least on macOS. Given that boxfilter is relatively cheap operation, it will unlikely loose much by running on CPU vs GPU. So this is the current solution - switch to CPU path there. Local tests show that even on Apple M4 Max with very fast GPU we get better execution speed on CPU than on GPU.
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Revised bicubic interpolation kernels and updated the corresponding functions #29165
They should work more accurately and faster than the existing implementation. The PR follows the previous work (#26271 etc.) that brought faster bilinear kernels.
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Make cv::VideoCapture::get return cv::CAP_PROP_UNKNOWN (-1) for unsupported properties #21337
The return value indicating an unsupported property is not consistent across backends.
I stumbled on this issue because my code was determining if a property value is valid if it's non-zero (like the documentation says), which worked fine on macOS, but not on Android.
For example, auto-exposure is not supported on macOS, so get() returns 0. But it is supported on Android and 0 means auto-exposure is off.
I think -1 is the better return value to indicate unsupported properties.
I made changes to all the backends. I think I got every case. This breaks API compatibility for some backends, so I based this on branch 3.4.
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Homogeneize some calib/3d behavior #29077
- use CV_Check to validate input sizes (thus throwing for invalid inputs)
- return bool to validate a function result
This fixes#22746
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OpenCV New Documentation #29206
This PR introduces end to end working new Documentation in OpenCV.
Co-authored by: @abhishek-gola @omrope79 @Akansha-977 @Prasadayus @varun-jaiswal17
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WeChatQR-fix conversion Caffe to ONNX #28746
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Temporarily disabled RVV intrinsics in several layers of the new DNN tested on musebook K1.
Kernels will be re-enabled when we find some good solution for the current 'm2' issue in rvv_scalable intrinsics.
This should fix#28852
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Update default YuNet model to new dynamic inputs #29107
Update the default model in `face_detect.py` and `face_detect.cpp` to
`face_detection_yunet_2026may.onnx`, which has symbolic `height`/`width` input dims.
## Changes
- `samples/dnn/face_detect.py`: update default `--face_detection_model` to `face_detection_yunet_2026may.onnx`
- `samples/dnn/face_detect.cpp`: update default `fd_model` to `face_detection_yunet_2026may.onnx`
Companion PR :
- https://github.com/opencv/opencv_zoo/pull/310
- https://github.com/opencv/opencv_extra/pull/1373
Closes : https://github.com/opencv/opencv/issues/28769
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imgcodecs: fix Apple conversions build without AVFoundation #29138Fixes#28553.
This PR fixes Apple imgcodecs conversion builds when AVFoundation is disabled or unavailable on older macOS SDKs.
Changes:
- Guard AVFoundation import with HAVE_AVFOUNDATION.
- Use older macOS-compatible framework imports when ImageIO is unavailable.
- Add __bridge compatibility for older Objective-C++ compilers.
- Use NSMakeSize for older macOS instead of passing CGSize to NSImage API.
Testing:
- Not tested locally: no macOS environment available.
- Patch follows the fix confirmed in #28555 discussion.
Fixing python wrapper around ECCParameters to include itersPerLevel #29148
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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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### Summary
This PR improves SIMD coverage for masked `cv::accumulate()` on 4-channel images.
The existing masked accumulate SIMD implementation has special paths for `cn == 1` and `cn == 3`, while 4-channel images fall back to the general implementation. This PR adds `cn == 4` SIMD paths using OpenCV Universal Intrinsics.
Compare link:
https://github.com/opencv/opencv/compare/4.x...feitianduowen:opencv:4.x
Modified files:
- `modules/imgproc/src/accum.simd.hpp`
- Add masked `cn == 4` SIMD paths for:
- `CV_32FC4 -> CV_32FC4`
- `CV_8UC4 -> CV_32FC4`
- `modules/video/test/test_accum.cpp`
- Add deterministic correctness regression tests for the new masked 4-channel accumulate paths.
- `modules/imgproc/perf/perf_accumulate.cpp`
- Add performance coverage for masked 4-channel accumulate.
imgproc: add SIMD paths for masked 4-channel accumulate #29094
### Implementation
The new SIMD branches are added in:
- `modules/imgproc/src/accum.simd.hpp`
They handle:
- `src`: `CV_32FC4`, `dst`: `CV_32FC4`, `mask`: `CV_8UC1`
- `src`: `CV_8UC4`, `dst`: `CV_32FC4`, `mask`: `CV_8UC1`
For the `CV_32FC4` path, the implementation uses `v_load_deinterleave`, applies the pixel mask to all 4 channels, accumulates into `dst`, and stores the result with `v_store_interleave`.
For the `CV_8UC4 -> CV_32FC4` path, the implementation loads 4 interleaved uchar channels, applies the pixel mask, widens the values to float, accumulates into `dst`, and stores the results back as interleaved `CV_32FC4`.
The masked SIMD block is guarded with:
```cpp
if (x <= len - cVectorWidth)
{
...
}
```
This check ensures that SIMD setup and vector operations are only executed when at least one full vector iteration can run. For very small inputs or tail-only cases, the function skips SIMD initialization and lets the existing scalar fallback handle the data through `acc_general_(src, dst, mask, len, cn, x)`.
This keeps tail handling unchanged and avoids unnecessary SIMD initialization when the vector loop would be skipped.
### Accuracy tests
Added a deterministic regression test in:
* `modules/video/test/test_accum.cpp`
New test:
```bash
Video_Acc.accuracy_32FC4_masked_cn4
Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
```
Test commands:
```bash
opencv_test_video --gtest_filter=Video_Acc.accuracy_32FC4_masked_cn4
opencv_test_video --gtest_filter=Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
opencv_test_video --gtest_filter="Video_Acc.*:Video_AccSquared.*:Video_AccProduct.*:Video_RunningAvg.*"
```
The filtered accumulate-related tests passed locally.
I added dedicated deterministic tests instead of only extending the existing randomized accumulate tests because this PR only changes the masked `cv::accumulate()` paths for specific 4-channel type combinations. It does not add `cn == 4` SIMD coverage for `accumulateSquare`, `accumulateProduct`, or `accumulateWeighted`.
The existing base accumulate tests are shared by several accumulation functions. Extending the common randomized channel selection to include `cn == 4` would also affect tests for functions that are not optimized by this PR. The new deterministic tests directly cover the modified paths:
* `cv::accumulate(src, dst, mask)`
* `CV_32FC4 -> CV_32FC4`
* `CV_8UC4 -> CV_32FC4`
* `mask`: `CV_8UC1`
The test also covers small sizes and non-vector-multiple sizes, so both the new SIMD path and the existing scalar tail fallback are exercised.
### Performance tests
Added performance coverage in:
* `modules/imgproc/perf/perf_accumulate.cpp`
Perf command:
```bash
opencv_perf_imgproc --gtest_filter="*AccumulateMask32FC4*" --perf_min_samples=1000 --perf_force_samples=1000
opencv_perf_imgproc --gtest_filter="*AccumulateMask8UC4To32FC4*" --perf_min_samples=1000 --perf_force_samples=1000
```
Test environment:
* Release build
* AVX2 enabled
* IPP disabled
* OpenCL disabled
* Windows MinGW build
Performance results:
`CV_32FC4 -> CV_32FC4`
| Size | Before median | After median | Speedup |
| --------- | ------------- | ------------ | ------- |
| 1920x1080 | 3.07 ms | 2.74 ms | 1.12x |
| 1280x720 | 0.84 ms | 0.73 ms | 1.15x |
| 640x480 | 0.17 ms | 0.16 ms | 1.06x |
| 320x240 | 0.04 ms | 0.04 ms | ~1.00x |
`CV_8UC4 -> CV_32FC4`
| Size | Before median | After median | Speedup |
| --------- | ------------- | ------------ | ------- |
| 1920x1080 | 6.09 ms | 1.75 ms | 3.48x |
| 1280x720 | 2.69 ms | 0.82 ms | 3.28x |
| 640x480 | 0.96 ms | 0.28 ms | 3.43x |
| 320x240 | 0.24 ms | 0.06 ms | 4.00x |
| 127x61 | 0.02 ms | 0.01 ms | 2.00x |
### Build and test commands
The following commands were run from Windows `cmd.exe`.
```sh
cmake -S . -B build_release -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=ON -DBUILD_PERF_TESTS=OFF -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_release --target opencv_test_video -j 4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.accuracy_32FC4_masked_cn4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.accuracy_8UC4_to_32FC4_masked_cn4
build_release\bin\opencv_test_video.exe --gtest_filter=Video_Acc.*:Video_AccSquared.*:Video_AccProduct.*:Video_RunningAvg.*
```
Accuracy test commands passed locally.
For the performance comparison, I kept the new perf test in `modules/imgproc/perf/perf_accumulate.cpp` and only reverted `modules/imgproc/src/accum.simd.hpp` to measure the baseline. Then I restored the SIMD patch and measured the optimized version.
Baseline measurement:
```sh
git diff -- modules/imgproc/src/accum.simd.hpp > accum_cn4_simd.patch
git checkout -- modules/imgproc/src/accum.simd.hpp
mkdir perf_logs
cmake -S . -B build_perf_before -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign > perf_logs\before_cmake_config.txt 2>&1
cmake --build build_perf_before --target opencv_perf_imgproc -j 4 > perf_logs\before_build.txt 2>&1
build_perf_before\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\before_perf.txt 2>&1
```
```sh
git diff -- modules/imgproc/src/accum.simd.hpp > accum_cn4_u8_simd.patch
git checkout -- modules/imgproc/src/accum.simd.hpp
cmake -S . -B build_perf_before_u8 -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_perf_before_u8 --target opencv_perf_imgproc -j 4
build_perf_before_u8\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask8UC4To32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\before_perf1.txt 2>&1
```
Optimized measurement:
```sh
git apply accum_cn4_simd.patch
cmake -S . -B build_perf_after -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign > perf_logs\after_cmake_config.txt 2>&1
cmake --build build_perf_after --target opencv_perf_imgproc -j 4 > perf_logs\after_build.txt 2>&1
build_perf_after\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask32FC4* --perf_min_samples=500 --perf_force_samples=500 > perf_logs\after_perf.txt 2>&1
```
```sh
git apply accum_cn4_u8_simd.patch
cmake -S . -B build_perf_after_u8 -G Ninja -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=ON -DBUILD_EXAMPLES=OFF -DWITH_IPP=OFF -DWITH_OPENCL=OFF -DCMAKE_C_FLAGS=-mstackrealign -DCMAKE_CXX_FLAGS=-mstackrealign
cmake --build build_perf_after_u8 --target opencv_perf_imgproc -j 4
build_perf_after_u8\bin\opencv_perf_imgproc.exe --gtest_filter=*AccumulateMask8UC4To32FC4* --perf_min_samples=1000 --perf_force_samples=1000 > perf_logs\after_perf1.txt 2>&1
```
Performance comparison was measured by keeping the new perf tests and only reverting modules/imgproc/src/accum.simd.hpp for the baseline run.
`-mstackrealign` was used for the MinGW Windows build to avoid stack-alignment issues with AVX code generation during local testing.
### Notes
The implementation keeps the existing scalar fallback path unchanged. SIMD is used only for the newly covered masked `cn == 4` vectorizable part, and any remaining tail elements are still handled by `acc_general_()`.
The improvement is most visible on larger images. Small images are dominated by overhead and do not always show meaningful speedup.
Added SDPA layer (Scaled Dot Product Attention) #29104
Merge with: https://github.com/opencv/opencv_extra/pull/1374
### 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
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
### 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
fix MSVC error: use name constexp for alignas array size in INT8 kernel #29156
### Problem
Windows CI fails with MSVC error C2131 in `conv2_int8_kernels.simd.hpp`:
error C2131: expression did not evaluate to a constant
failure was caused by a read of a variable outside its lifetime
see usage of 'this'
Two array declarations inside `parallel_for_` lambdas used `constexpr`
variables defined inside the lambda body as array sizes:
alignas(32) int8_t wbuf[128 * K0]; // K0 defined inside lambda
alignas(32) int32_t sumbuf[SPAT_BLOCK_SIZE * K0]; // both defined inside lambda
### Fix
Move the combined size constants to function scope (before the lambda),
where they are true compile-time constants with no `this` involvement:
constexpr int WBUF_SIZE = 128 * 8; // 128 * K0
constexpr int SUMBUF_SIZE = 8 * 8; // SPAT_BLOCK_SIZE * K0
### Related
Fixes Windows CI failure introduced by #29126.
### 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
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Attention graph fusion with MLAS FlashAttention #29126
Performance numbers for Owl-v2 model on intel i9:
```
ORT: Average inference time over 10 runs: 1411.55 ms (min 1399.75, max 1438.89)
NEW: Average inference time over 10 runs: 1078 ms (min 1048.04, max 1110.61)
```
### 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
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