Added Hannwindow, Hammingwindow & Blackmanwindow support #28075
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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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Added RandomNormalLike layer for fixing ViTs parsing issue #28110
closes: https://github.com/opencv/opencv/issues/27603
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RMSNorm: reference cpu impl #28104https://onnx.ai/onnx/operators/onnx__RMSNormalization.html
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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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Resolving failing tests of rotary embedding layer on win32 #28080
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Onnx importer2 dispatch map #28058
I have noticed that PR[28032]( https://github.com/opencv/opencv/pull/28032) was incomplete - it fixed only the new ONNX importer. Also, building the dispatch map based on the parsed opset version hypotetically can cause trouble if the graph simplifier inserts a node with a different opset version which is not included in the dispatch map. So I removed this parameter in `buildDispatchMap_COM_MICROSOFT` and `buildDispatchMap_ONNX_AI` for now. It was marked as `CV_UNUSED` anyway.
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Rotary position layer #28031
Implemented https://onnx.ai/onnx/operators/onnx__RotaryEmbedding.html#rotaryembedding-23
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Switch to the new Github actions pipeline for Windows in 5.x too #28035
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Onnx importer2 dispatch map #28032
in the new onnx_importer all domains in the dispatch map should be included per default.
See https://github.com/opencv/opencv/pull/27988#issuecomment-3521140872
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resolves https://github.com/opencv/opencv/issues/27580
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Added affine grid layer to new DNN engine #27894
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Added DFT layer to new DNN engine #27941
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Added Onehot layer support to new DNN engine #27902
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Added center crop pad layer to new DNN engine #27892
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Added support for SCE and NLL losses #27809
This pull request adds the support for Negative Log-Likelihood loss and Softmax Cross-Entropy loss in new DNN engine.
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Extended Activation layers support #27882
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Extended Reduce layer support in new DNN engine #27816
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Added Bitwise layer to new DNN engine #27845
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Added cast and castlike layers support in new DNN engine #27698
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Cleaned up parser denylist and removed passing tests. #27857
Merge with: https://github.com/opencv/opencv_extra/pull/1278
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dnn: added neon intrinsics implementation of fastGEMM1T function #27785
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- This PR improves the performance of the LSTM function on ARM64 targets.
- Added a NEON intrinsics implementation of the fastGEMM1T function and enabled its use in fully connected and recurrent layers file.
- As a result, ARM64 now benefits from vectorized matrix–vector multiplications, leading to measurable performance improvements in the LSTM layer.
- This change is limited to ARM64 and does not affect other architectures.
**Performance impact:**
- The optimization significantly improves the performance of lstm functions on ARM64 targets.
<img width="930" height="313" alt="image" src="https://github.com/user-attachments/assets/92e251cd-dc6c-4cda-9586-acc19bf16dfd" />
Updated ONNX conformance tests and parser denylist #27827
Merge with https://github.com/opencv/opencv_extra/pull/1277
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dnn: improve performance of softmax_3d with loop unrolling #27777
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- This PR applies loop unrolling in the softmax function.
- The change does not affect functional correctness.
**Performance Improvements**
- The optimization significantly improves the performance of softmax_3d on Windows ARM64 targets.
<img width="703" height="203" alt="image" src="https://github.com/user-attachments/assets/85997c15-f543-432c-95e5-69099d71fe71" />