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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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" />
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" />
dnn: Tune CONV_NR_FP32 size for WASM #27773
We can see ~20% inference time reduction on local benchmark.
The local benchmark includes face detection with res10_300x300_ssd_iter_140000_fp16.caffemodel and image classification with squeezenet.onnx .
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- Added vector_MatShape and vector_vector_MatShape to gen_dict.json
- Implemented MatShape_to_vector_MatShape, vector_MatShape_to_MatShape, MatShape_to_vector_vector_MatShape, and vector_vector_MatShape_to_MatShape conversion functions in dnn_converters.h/cpp and Converters.java
- Added testGetLayersShapes test to verify List<List<MatShape>> conversion
- Added vector_vector_Mat to gen_dict.json
- Implemented Mat_to_vector_vector_Mat and vector_vector_Mat_to_Mat conversion functions in converters.h/cpp and Converters.java
- Added DnnForwardAndRetrieve.java test to verify List<List<Mat>> conversion : Reference: C++ test in modules/dnn/test/test_misc.cpp - TEST(Net, forwardAndRetrieve)
Close https://github.com/opencv/opencv/issues/27413
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Feature: Add OpenVINO NPU support #27363
## Why
- OpenVINO now supports inference on integrated NPU devices in intel's Core Ultra series processors.
- Sometimes as fast as GPU, but should use considerably less power.
## How
- The NPU plugin is now available as "NPU" in openvino `ov::Core::get_available_devices()`.
- Removed the guards and checks for NPU in available targets for Inference Engine backend.
## Test example
### Pre-requisites
- Intel [Core Ultra series processor](https://www.intel.com/content/www/us/en/products/details/processors/core-ultra/edge.html#tab-blade-1-0)
- [Intel NPU driver](https://github.com/intel/linux-npu-driver/releases)
- OpenVINO 2023.3.0+ (Tested on 2025.1.0)
### Example
```cpp
#include <opencv2/dnn.hpp>
#include <iostream>
int main(){
cv::dnn::Net net = cv::dnn::readNet("../yolov8s-openvino/yolov8s.xml", "../yolov8s-openvino/yolov8s.bin");
cv::Size net_input_shape = cv::Size(640, 480);
std::cout << "Setting backend to DNN_BACKEND_INFERENCE_ENGINE and target to DNN_TARGET_NPU" << std::endl;
net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(cv::dnn::DNN_TARGET_NPU);
cv::Mat image(net_input_shape, CV_8UC3);
cv::randu(image, cv::Scalar(0, 0, 0), cv::Scalar(255, 255, 255));
cv::Mat blob = cv::dnn::blobFromImage(
image, 1, net_input_shape, cv::Scalar(0, 0, 0), true, false, CV_32F);
net.setInput(blob);
std::cout << "Running forward" << std::endl;
cv::Mat result = net.forward();
std::cout << "Output shape: " << result.size << std::endl; // Output shape: 1 x 84 x 6300
}
```
model files [here](https://limewire.com/d/bPgiA#BhUeSTBnMc)
docker image used to build opencv: [ghcr.io/mro47/opencv-builder](https://github.com/MRo47/opencv-builder/blob/main/Dockerfile)
Closes#26240
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Fix#27352: Add checks before getting latest pin in Net::Impl::getLatestLayerPin() #27353
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Fixes#27352
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Fix typos #27338
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build: fix warnings from recent gcc versions #27337
This PR addresses the following found warnings:
- [x] -Wmaybe-uninitialized
- [x] -Wunused-variable
- [x] -Wsign-compare
Tested building with GCC 14.2 (RISC-V 64).
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TFLite fixes for Face Blendshapes V2 #27307
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* Scalars support
* Better handling of 1D tensors
* New ops import: SUB, SQRT, DIV, NEG, SQUARED_DIFFERENCE, SUM
* Number of NHWC<->NCHW layouts compatibility improvements
resolves#27211
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1257
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Explicitly specify enum type scopes to improve Java wrapper generation #27228
Changed DataLayout and ImagePaddingMode to dnn::DataLayout and dnn::ImagePaddingMode to explicitly specify their scopes. This allows gen_java.py to correctly register disc_type, preventing constructors and methods using these enum types from being skipped during Java wrapper generation.
Similarly updated QRCodeEncoder::CorrectionLevel and QRCodeEncoder::EncodeMode with explicit scope declarations.
Also added a new Java test class `DnnBlobFromImageWithParamsTest` based on: https://github.com/opencv/opencv/blob/4.x/modules/dnn/test/test_misc.cpp#L133-L243
Related issues
#23753
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