KleidiCV update to verison 26.03 #28744
KleidiCV release: https://gitlab.arm.com/kleidi/kleidicv/-/releases/26.03
Tuned DNN test threshold as resize linear produces slightly different result with the new KleidiCV version.
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randomnormallike_layer.cpp registered itself via CV_DNN_REGISTER_LAYER_CLASS_STATIC at file scope. Nothing else in the translation unit was referenced from elsewhere, so when OpenCV is built statically (BUILD_SHARED_LIBS=OFF, as in the reporter's MSVC build) the linker drops the object file and the static-init registration never runs. The ONNX importer then sets layerParams.type = "RandomNormalLike" but getLayerInstance has no factory for that type, and parsing fails with "Can't create layer of type RandomNormalLike".
Switch to the standard pattern used by every other layer in the module: declare RandomNormalLikeLayer in all_layers.hpp, expose a static create() factory from the .cpp, and register it explicitly from initializeLayerFactory in init.cpp. Because init.cpp is referenced by the DNN module init path, this pulls in the layer .cpp regardless of build mode and the registration always runs.
The failure was incorrectly attributed to MSVC in the bug report. The bug is build-mode sensitive (static vs shared), not platform sensitive.
Verified locally on linux/gcc-13 by building modules/dnn and
opencv_test_dnn against the patched tree, then running opencv_test_dnn --gtest_filter='Test_ONNX_layers.RandomNormalLike_basic/0:Test_ONNX_layers.RandomNormalLike_complex/0'
Both tests pass; without the patch the same binary reproduces the
"Can't create layer of type RandomNormalLike" error from the report.
dnn: fix Resize initNgraph for two-input case #28724
## Summary
Fixes the issue #28707
When a Resize/Upsample layer has two inputs, the data tensor and a reference tensor whose **shape** defines the output spatial size, the OpenVINO/NGRAPH backend's `initNgraph()` was ignoring `nodes[1]` entirely and relying solely on the `outHeight`/`outWidth` member variables.
These variables are set by `finalize()` from the pre-computed output blob dimensions. However, when the output shape is determined dynamically at runtime from the second input, `finalize()` sets them from the live tensor, but the OpenVINO backend calls `initNgraph()` to build a static compiled graph. If the member variables are 0 at that point, the compiled `Interpolate` node gets hardcoded with `{0, 0}` output dimensions, causing CV_Assert failure: {N,C,0,0} vs {N,C,H2,W2}
DNN: Fix OpenVINO 2026 build failure due to ov::Tensor::data() const change #28592
Fixes: https://github.com/opencv/opencv/issues/28586
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### PR Description
OpenVINO 2026 changed ov::Tensor::data() to return `const void*`
instead of `void*`. This causes a build error in
modules/dnn/src/op_inf_engine.cpp when constructing a cv::Mat
wrapper over Tensor memory.
Tensor memory for input/output blobs remains mutable, but the API
now enforces const-correct access. This patch casts away const with
an explicit comment to preserve existing zero-copy semantics and
restore compatibility with OpenVINO 2026.
Preserves existing zero-copy semantics of the OpenVINO backend without altering runtime behavior.
Tested by building OpenCV 4.x against OpenVINO 2026.0.0 on Ubuntu 24.04.
DNN: Fix Squeeze to remove all size-1 dims when axes is empty #28425Fixes#28424
OpenCV Extra: [opencv/opencv_extra#1308](https://github.com/opencv/opencv_extra/pull/1308)
This PR fixes the ONNX Squeeze operator to correctly remove all size-1 dimensions when `axes` is not provided, conforming to the ONNX specification.
### Details
Per [ONNX Squeeze specification](https://onnx.ai/onnx/operators/onnx__Squeeze.html):
> 'If axes is not provided, all the single dimensions will be removed from the shape.'
Previously, OpenCV DNN would not remove any dimensions in this case, causing shape mismatch errors with models like LaMa (inpainting).
### Example
```python
# Input: [1, 1, 2, 4]
# Squeeze with no axes attribute
# Before: [1, 1, 2, 4] ✗ (No change)
# After: [2, 4] ✓ (matches ONNX Runtime)
```
### Tests
Added `testONNXModels("squeeze_no_axes")` which validates this behavior with new test data.
opencv_extra_pr=opencv/opencv_extra#1324
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optimize(dnn): parallelize Slice layer implementation (4.x) #28511
### Summary
Backport of PR #28447 to 4.x branch.
### Description
This PR optimizes the SliceLayer implementation for strided inputs (where step > 1). The original implementation used a recursive element-wise copy (getSliceRecursive) for any strided slice, which was extremely inefficient.
This PR introduces:
- **Parallelization**: Uses `cv::parallel_for_` to parallelize the outermost dimension of the slice operation.
- **Memcpy Optimization**: Automatically detects "pseudo-contiguous" blocks in strided slices (e.g., slicing an outer dimension but keeping inner dimensions intact) and uses `std::memcpy` instead of scalar loops.
- **Refactoring**: Replaces the recursive function with a dedicated `ParallelSlice` loop body.
### Impact
Significant performance improvement for strided slice operations (common in detection heads, strided sampling, etc.).
### Benchmark Results
Tested on CPU with 20 threads.
| Test Case | Baseline (ms) | Optimized (ms) | Speedup |
| :--- | :--- | :--- | :--- |
| Strided Axis 0 [::2, ...] | 1.10 | 0.02 | **~55x** |
| Strided Axis 2 [..., ::2] | 1.15 | 0.11 | **~10.5x** |
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dnn: improve robustness of MVN layer#28308
This change adds small defensive improvements to the MVN layer implementation:
->Guard against zero-sized OpenCL kernel launches
->Add input validation in finalize()
->Use int64 for FLOPS computation to avoid overflow
No functional or performance changes are intended.
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The TopK layer was incorrectly rejecting K values equal to the input
dimension size. According to ONNX specification, K should be allowed
to equal the dimension size (to retrieve all elements).
Changed validation from 'K < input_shape[axis]' to 'K <= input_shape[axis]'
This fixes the error when loading YOLOv10 ONNX models that use TopK
with K equal to the dimension size.
Fixes#28445
docs: fix spelling errors in documentation and code #28301
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### Description
Fixed multiple spelling errors across documentation, comments, and code:
- 'colummn' → 'column' (cublas.hpp, 3 occurrences)
- 'points_per_colum' → 'points_per_column' (calib3d.hpp, 3 occurrences)
- 'Asignee' → 'Assignee' (sift files, 2 occurrences)
- 'compability' → 'compatibility' (face.hpp, 2 occurrences)
- 'orignal' → 'original' (aruco_detector.cpp)
- 'refrence' → 'reference' (chessboard.cpp)
- 'indeces' → 'indices' (stitching.hpp)
- 'OutputPrecison' → 'OutputPrecision' (test)
- 'tranform' → 'transform' (slice_layer.cpp, 3 occurrences)
Total: 24 fixes across 14 files. Documentation and comment changes only, no functional impact.
Added randomNormalLike layer to 4.x branch #28164
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1297
Backport of https://github.com/opencv/opencv/pull/28110 to 4.x
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dnn(tflite): add support for MAXIMUM layer #28171Fixes#26433
This PR adds support for the `MAXIMUM` layer in the TFLite importer.
It maps the TFLite `MAXIMUM` opcode to the existing OpenCV Element-wise `Max` operation.
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Enable KleidiCV on Linux and Mac Mx by default #27640
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1296
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resolves https://github.com/opencv/opencv/issues/27580
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