Added Attention fusion and thin gemm support #28859
Merge with: https://github.com/opencv/opencv_extra/pull/1350
Performance numbers after these optimizations:
For Device: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW (Before)` | `ENGINE_NEW (After)` | `ENGINE_ORT` |
| :--- | :--- | :--- | :--- |
| **BERT** |26.3 ms| 9.15 ms| 9.13 ms|
| **ViT** | 79.65 ms| 63.23 ms| 32.3 ms|
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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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Add Gemma3 tokenizer support for dnn #28837
- Adds Gemma3 tokenizer support
- Implements character-level BPE
- Adds 6 tests covering English, phrase, mixed case, numbers, special tokens, and encode/decode
- add gemma3_inference.py
Merge with:
- **Companion PR** : https://github.com/opencv/opencv_extra/pull/1346
- forward pass bug in gemma3_inference.py : https://github.com/opencv/opencv/pull/28836
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- Emscripten 5.0.1+ changed version macros to uppercase and deprecated lowercase ones.
- Updated CMake to detect the version from both cases.
- Added macro aliases in intrin_wasm.hpp to avoid deprecated warnings and ensure compatibility.
Added QLinear layer support #28811
closes: https://github.com/opencv/opencv/issues/26310
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videoio: fix C4576 build error with older ffmpeg versions #28894Fixes#28875
### Description
This PR fixes a build failure (`error C4576`) that occurs when building the `videoio` module with older FFmpeg versions (e.g., 4.4.4) using MSVC under strict C++17 mode.
**Root Cause:**
In older FFmpeg headers, the `AV_TIME_BASE_Q` macro is defined using a C99 compound literal `(AVRational){1, AV_TIME_BASE}`. This syntax is non-standard in C++ and is strictly rejected by MSVC when `/std:c++17` is enabled.
**Solution:**
Replaced the `AV_TIME_BASE_Q` macro with FFmpeg's standard `av_make_q(1, AV_TIME_BASE)` function. This resolves the C4576 compiler error and ensures modern C++ compatibility.
*(Note: `av_make_q` is already smoothly polyfilled in `cap_ffmpeg_impl.hpp` for extremely old FFmpeg versions, making this a highly safe, consistent, and zero-overhead replacement.)*
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Add CAP_PROP_IMAGE_SEQ_START #28844
Resolves https://github.com/opencv/opencv/issues/28843
New CAP_PROP_IMAGE_SEQ_START as an open only property for setting the first frame index when a VideoCap is opening with pattern, squashes test_images TODO.
`CAP_FFMPEG`: forwarded to the image2 demuxers "start_number" option.
`CAP_IMAGES`: Now a WithParams variant so the value reaches open() before probe runs, replaces 0 or 1 auto probe for sequence start.
Property is opt in so default behavior is not affected.
Please note that G_STREAMER is not considered here as I think its out of scope, it already has a "start-index" property and its backend is driven by the pipeline strings rather then these printf patterns.
If an alternative solution is preferred that will not involve a new property or promoting CAP_IMAGES to a "WithParams" variant I don't mind implementing.
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Bump some calib3d files to C++ #28825
This is just copy/pasting files from 5.x (first commit) and applying some light patch for compilation (second commit).
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Add Prior Box Layer to support the WeChat QR model #28855
OpenCV extra: https://github.com/opencv/opencv_extra/pull/1349
### Pull Request Readiness Checklist
This PR is part 1 of the split from the original PR: WeChatQR-fix conversion Caffe to ONNX #28746.
It focuses on adding the PriorBox layer and its related unit tests. The WeChatQR specific changes will be submitted in a separate PR.
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Avoid lossy Latin-1 conversions when creating/looking up Qt HighGUI windows and convert C-string entry points to UTF-8 explicitly. This fixes non-ASCII title handling paths that could lead to mismatched window lookup and silent display failures.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
objdetect: clear stale outputs in CharucoDetector::detectDiamonds #28818
## Summary
- clear `diamondCorners`/`diamondIds` outputs at the beginning of `CharucoDetector::detectDiamonds`
- prevent stale detections from previous calls when the current call has fewer than 4 markers or finds no diamonds
- add a regression test that pre-fills outputs, calls `detectDiamonds` with 3 markers, and verifies outputs are empty
Fixes#28783.
## Testing
- built `opencv_test_objdetect` locally
- ran `opencv_test_objdetect --gtest_filter=Charuco.detectDiamondsClearsOutputsWithLessThanFourMarkers`
- result: PASS
Parallelize DNN layers using chunking #28821
At a high level, I replaced tensor-level parallelism with chunk-level parallelism. Previously, parallel_for_ was dispatched over the number of input or output tensors i.e. one thread handled one whole tensor's copy.
The new approach precomputes each tensor's destination offset and per-slice size upfront, then slices the total byte work into fixed 64 KB chunks. The full chunk count is handed to parallel_for_ as a single flat range, and each worker decodes its chunk index back into (tensor, slice, byte_offset) using a prefix-sum table before running a plain memcpy on its piece. A small-size threshold falls back to the sequential path so we don't get threading overhead on small tensors.
Performance numbers after these optimizations:
For Device: Intel(R) Core(TM) i9-14900KS, x86, 32 Cores, ubuntu 24.04,
| Model | `ENGINE_NEW` | `ENGINE_ORT` |
| :--- | :--- | :--- |
| **YOLOv8n** |10.9 ms| 12.15 ms|
| **YOLOv5n** | 8.36 ms| 9.23 ms|
| **YOLOX-S** | 23.46 ms| 25.16 ms|
For Device: Macbook M1 Air
| Model | `ENGINE_NEW` | `ENGINE_ORT` |
| :--- | :--- | :--- |
| **YOLOv8n** |34.45 ms| 42.52 ms|
| **YOLOv5n** | 31.62 ms| 25.52 ms|
| **YOLOX-S** | 88.9 ms| 116.7 ms|
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Fix dnn NaN bugs in GELU SIMD and softmax for large inputs #28836
### Bug Description
- **GELU SIMD bug:** `exp(2*inner)` overflows to `inf` for large inputs (e.g. `x=10.6` → `inner≈51`), causing `inf/inf = NaN`; fixed by clamping `inner` to [-9, 9] as `tanh` already saturates to ±1.0 beyond this range.
- **Softmax bug:** All `-inf` inputs (masked attention rows) produce `sum=0`, then `1/0 = inf` and `0*inf = NaN`;
fixed by outputting zeros when `sum == 0`.
- Add regression tests for both fixes
Depends on : https://github.com/opencv/opencv/pull/28837
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Multiscale ECC #28802
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1338
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Previously, OpenCL in-place flip kernels (rows/cols/both) could produce
incorrect results compared to the CPU implementation on strict OpenCL
drivers such as Mesa. The kernels relied on implicit load–load–store–store
(LLSS) ordering when src and dst alias, which is not guaranteed by the
OpenCL memory model and may be reordered.
Some vendor drivers happened to preserve the expected ordering, masking
the issue, but Mesa correctly exposes the undefined behavior.
This change introduces dedicated in-place flip kernels that:
- Explicitly detect in-place execution (src == dst)
- Stage data through local memory tiles
- Enforce correct ordering with work-group barriers
- Avoid global memory read/write aliasing hazards
The non in-place path is unchanged.
With this fix, OpenCL in-place flip produces correct and consistent results
across drivers, matches CPU behavior, and complies with the OpenCL memory
model.
Signed-off-by: jiajia Qian <jiajia.qian@nxp.com>
Avfoundation fractional fps seek fix#28833
Accompanied by PR on [opencv_extra](https://github.com/opencv/opencv_extra/pull/1345)
Fixes https://github.com/opencv/opencv/issues/28831
AVFoundation backend seeking slowly drifts for any video which has a non-integer frame rate.
Narrowing of a float `mAssetTrack.nominalFrameRate` to int32_t means fractional rates are treated as integer when seeking by frame number.
Solution implemented in this branch is to use the tracks native rational frame duration when seeking (and available).
I've also included an AVF only test case which uses a new test file in opencv_extra. This test just compares the video caps expected ms vs actual for a known rate file (23.976).
Could consider extending this test to more then just AVF, as from what I can see there are no fractional rate tests / files.
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Fix issues with MultiViewTest.OneLineInitialGuess and MultiViewTest.OneLine #28817
To fix issues caused by https://github.com/opencv/opencv/issues/28789
This probably can be fixed for 4.x, but honestly I don't believe it is worth it to have to deal with users tests failing because it was 0.02% off so we can save them like 2 minutes, so I believe it is best to leave the optimization for 5.x.
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imgcodecs(png): support cICP metadata for imreadWithMetadata() #28615
Close https://github.com/opencv/opencv/issues/28606
Related https://github.com/opencv/opencv/pull/27741 (support for imwriteWithMetadata)
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The existing 2-pass cross-check implementation requires both forward and
reverse distance computations on GPU, followed by CPU-side filtering.
This adds a single-pass kernel using int64 atomics to eliminate the
reverse pass. The BruteForceMatch_CrossCheckMatch kernel performs forward
matching and tracks best train->query mappings in one GPU pass, reducing
memory transfers. Falls back to 2-pass approach on devices without
cl_khr_int64_base_atomics extension.
Tested on NVIDIA RTX 5060 Ti: 1.65x-1.97x faster than 2-pass GPU,
3.49x-7.33x faster than CPU at 640x480 to 1920x1080 resolutions.
Fix flatten onnx axis==rank case #28812
fix ONNX Flatten layer incorrect output when axis equals input rank edge case
Resolves : https://github.com/opencv/opencv/pull/28781#discussion_r3060088247
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1342
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Alexander Smorkalov
kirilllapi
Alexander Smorkalov
vrooomy
Abhishek Gola
Kumataro
Varun Jaiswal
Vincent Rabaud
Teddy-Yangjiale
Alex
omrope79
Mulham Fetna
Samaresh Kumar Singh
SheliaJimenez
Jeevan Mohan Pawar
kjg0724
4ekmah
jiajia Qian
Rohan Mistry
Anand Mahesh