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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### 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.
Multiscale ECC #28802
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1338
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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[video] Add setCoarsestScale to DISOpticalFlow (Fixes#25068) #28217
### Description
This PR addresses issue #25068 regarding the number of scales in `DISOpticalFlow`.
Currently, `DISOpticalFlow` automatically computes the `coarsest_scale` based on the image size. While generally effective, this behavior can cause errors in specific use cases (e.g., mechanics/speckle pattern analysis) where high pyramid levels degrade quality.
This change introduces a manual override:
- Added `setCoarsestScale(int val)` and `getCoarsestScale()` to the public API.
- Updated `DISOpticalFlowImpl::calc` and `ocl_calc` to use the user-defined scale if set.
- Preserved the existing "automatic" behavior as the default (when set to -1).
### Changes
- **`modules/video/include/opencv2/video/tracking.hpp`**: Added virtual method declarations.
- **`modules/video/src/dis_flow.cpp`**: Implemented `set/getCoarsestScale` in `DISOpticalFlowImpl` and updated calculation logic.
- **`modules/video/test/test_OF_accuracy.cpp`**: Added `DenseOpticalFlow_DIS.ManualCoarsestScale` regression test.
Fixes#25068
Add optional template mask for findTransformECC #27952
Supersedes #22997
**Summary**
Add optional template mask support to findTransformECC so that only pixels valid in both the template and the image are used in ECC. Backward compatibility is preserved (existing signatures unchanged; one new overload adds templateMask).
**Motivation**
- Real-world frames often contain moving foreground artifacts (e.g., a football over a static field). Masking the object in one frame only is insufficient because its position changes independently of the background. Since we don’t know the warp a priori, we can’t back-project a single mask across frames. The correct approach is to supply both masks and take their intersection.
- Templates may include uninformative/low-texture or noisy regions, or partial overlaps with other objects. Excluding such regions from the alignment improves robustness and convergence.
This PR completes and replaces https://github.com/opencv/opencv/pull/22997
### Pull Request Readiness Checklist
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2025-10-14T05:53:31.5387050Z C:\GHA-OCV-1\_work\ci-gha-workflow\ci-gha-workflow\opencv\modules\imgcodecs\src\bitstrm.cpp(156,57): warning C4244: 'argument': conversion from 'int64_t' to 'ptrdiff_t', possible loss of data [C:\GHA-OCV-1\_work\ci-gha-workflow\ci-gha-workflow\build\modules\imgcodecs\opencv_imgcodecs.vcxproj]
### Pull Request Readiness Checklist
Optional Known Foreground Mask for Background Subtractors #27810
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.
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### Description
This adds an optional foreground input mask parameter to the MOG2 and KNN background subtractors, in line with issue https://github.com/opencv/opencv/issues/26476
4 tests are added under test_bgfg2.cpp:
2 for each subtractor type (1 with shadow detection and 1 without)
A demo shows the feature with only 3 parameters and with a 4th optional foreground mask for both core subtractor types.
Note: To patch contrib inheritance of the background subtraction class, empty apply method which throws a not implemented error is added to contrib subclasses. This is done to keep the overloaded apply function as pure virtual. Contrib PR to be made and linked shortly.
Contrib Repo Paired Pull Request: https://github.com/opencv/opencv_contrib/pull/4017
video: fix vittrack in the case where crop size grows until out-of-memory when the input is black #25771
Fixes https://github.com/opencv/opencv/issues/25760
### Pull Request Readiness Checklist
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Remove goturn caffe model #25503
**Merged with:** https://github.com/opencv/opencv_extra/pull/1174
**Merged with:** https://github.com/opencv/opencv_contrib/pull/3729
Part of https://github.com/opencv/opencv/issues/25314
This PR aims to remove goturn tracking model because Caffe importer will be remove in 5.0
The GOTURN model will take **388 MB** of traffic for each download if converted to onnx. If the user wants to use the tracking method, we can recommend they use Vit or dasimRPN.
### 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.
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libjpeg upgrade to version 9f #25092
Upgrade libjpeg dependency from version 9d to 9f.
- [X] I agree to contribute to the project under Apache 2 License.
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VIT track(gsoc realtime object tracking model) #24201
Vit tracker(vision transformer tracker) is a much better model for real-time object tracking. Vit tracker can achieve speeds exceeding nanotrack by 20% in single-threaded mode with ARM chip, and the advantage becomes even more pronounced in multi-threaded mode. In addition, on the dataset, vit tracker demonstrates better performance compared to nanotrack. Moreover, vit trackerprovides confidence values during the tracking process, which can be used to determine if the tracking is currently lost.
opencv_zoo: https://github.com/opencv/opencv_zoo/pull/194
opencv_extra: [https://github.com/opencv/opencv_extra/pull/1088](https://github.com/opencv/opencv_extra/pull/1088)
# Performance comparison is as follows:
NOTE: The speed below is tested by **onnxruntime** because opencv has poor support for the transformer architecture for now.
ONNX speed test on ARM platform(apple M2)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| nanotrack| 5.25| 4.86| 4.72| 4.49|
| vit tracker| 4.18| 2.41| 1.97| **1.46 (3X)**|
ONNX speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| nanotrack|3.20|2.75|2.46|2.55|
| vit tracker|3.84|2.37|2.10|2.01|
opencv speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| vit tracker|31.3|31.4|31.4|31.4|
preformance test on lasot dataset(AUC is the most important data. Higher AUC means better tracker):
|LASOT | AUC| P| Pnorm|
|--------|--------|--------|--------|
| nanotrack| 46.8| 45.0| 43.3|
| vit tracker| 48.6| 44.8| 54.7|
[https://youtu.be/MJiPnu1ZQRI](https://youtu.be/MJiPnu1ZQRI)
In target tracking tasks, the score is an important indicator that can indicate whether the current target is lost. In the video, vit tracker can track the target and display the current score in the upper left corner of the video. When the target is lost, the score drops significantly. While nanotrack will only return 0.9 score in any situation, so that we cannot determine whether the target is lost.
### Pull Request Readiness Checklist
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[teset data in opencv_extra](https://github.com/opencv/opencv_extra/pull/1016)
NanoTrack is an extremely lightweight and fast object-tracking model.
The total size is **1.1 MB**.
And the FPS on M1 chip is **150**, on Raspberry Pi 4 is about **30**. (Float32 CPU only)
With this model, many users can run object tracking on the edge device.
The author of NanoTrack is @HonglinChu.
The original repo is https://github.com/HonglinChu/NanoTrack.
### 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
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Replaced sprintf with safer snprintf
* Straightforward replacement of sprintf with safer snprintf
* Trickier replacement of sprintf with safer snprintf
Some functions were changed to take another parameter: the size of the buffer, so that they can pass that size on to snprintf.
Fix unsigned int bug in computeECC
* address issue with unsigned ints in computeEcc
* remove additional logic checking firstOctave
* use swap instead of same src/dst
* simplify the unsigned check logic
Alexander Smorkalov
Alexander Smorkalov
Siyu Wang
4ekmah
raimbekovm
satyam yadav
Dmytro Dadyka
Dave Merchant
Maksim Shabunin
Alexander Smorkalov
Yuantao Feng
Wanli
Michael Klatis
Alexander Alekhin
Sean McBride
lpylpy0514
Alexander Alekhin
zihaomu
Kumataro
Anna Prigarina
Danny
Thang Tran