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 typos #27338
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videoio: add Orbbec Gemini 330 camera support #27230
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### Description of Changes
#### motivated：
- Orbbec has launched a new RGB-D camera — the Gemini 330. To fully leverage the capabilities of the Gemini 330, Orbbec simultaneously released version 2 of the open-source OrbbecSDK. This PR adapts the support for the Gemini 330 series cameras to better meet and respond to users’ application requirements.
#### change：
- Add support for the Orbbec Gemini330 camera.
- Fixed an issue with Femto Mega on Windows 10/11; for details, see [issue](https://github.com/opencv/opencv/pull/23237#issuecomment-2242347295).
- When enabling `HAVE_OBSENSOR_ORBBEC_SDK`, the build now fetches version 2 of the OrbbecSDK, and the sample API calls have been updated to the v2 format.
### Testing
| OS | Compiler | Camera | Result |
|:----------:|:---------------------------------------:|:-----------------:|:------:|
| Windows 11 | (VS2022) MSVC runtime library version 14.40 | Gemini 335/336L | Pass |
| Windows 11 | (VS2022) MSVC runtime library version 14.19 | Gemini 335/336L | Pass |
| Ubuntu22.04| GCC 11.4 | Gemini 335/336L | Pass |
| Ubuntu18.04| GCC 7.5 | Gemini 335/336L | Pass |
### Acknowledgements
Thank you to the OpenCV team for the continuous support and for creating such a robust open source project. I appreciate the valuable feedback from the community and reviewers, which has helped improve the quality of this contribution!
Minor changes in calib3d docs for clarity #27221
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build: Check supported C++ standard features and user setting #27107Close#27105
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Update tutorials #26441
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Documentation to enable FastCV based OpenCV HAL and Extensions #26910
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doc: update supporting imgcodec format settings #26889
Close https://github.com/opencv/opencv/issues/26877
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Add direct pdf links in the bibliography #26754
Update and add pdf links in the bibliography.
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OpenEXR 2.2 or earlier cannot be used with C++17 or later #26678
Close https://github.com/opencv/opencv/issues/26673
Close https://github.com/opencv/opencv/issues/25313
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Feature: weighted Hough Transform #21407
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Animated WebP Support #25608
related issues #24855#22569
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Improvement of macOS installation guide in documentation #26564
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Update Documentation #26260
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doc: remove duplicated OpenCV Theory at ToC in Basic Drawing #26018Close#26017
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Add tutorial on using Orbbec 3D cameras (UVC) #25907
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Added flag to GaussianBlur for faster but not bit-exact implementation #25792
Rationale:
Current implementation of GaussianBlur is almost always bit-exact. It helps to get predictable results according platforms, but prohibits most of approximations and optimization tricks.
The patch converts `borderType` parameter to more generic `flags` and introduces `GAUSS_ALLOW_APPROXIMATIONS` flag to allow not bit-exact implementation. With the flag IPP and generic HAL implementation are called first. The flag naming and location is a subject for discussion.
Replaces https://github.com/opencv/opencv/pull/22073
Possibly related issue: https://github.com/opencv/opencv/issues/24135
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Add sample support of YOLOv9 and YOLOv10 in OpenCV #25794
This PR adds sample support of [`YOLOv9`](https://github.com/WongKinYiu/yolov9) and [`YOLOv10`](https://github.com/THU-MIG/yolov10/tree/main)) in OpenCV. Models for this test are located in this [PR](https://github.com/opencv/opencv_extra/pull/1186).
**Running YOLOv10 using OpenCV.**
1. In oder to run `YOLOv10` one needs to cut off postporcessing with dynamic shapes from torch and then convert it to ONNX. If someone is looking for ready solution, there is [this forked branch](https://github.com/Abdurrahheem/yolov10/tree/ash/opencv-export) from official YOLOv10. Particularty follow this proceduce.
```bash
git clone git@github.com:Abdurrahheem/yolov10.git
conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
python export_opencv.py --model=<model-name> --imgsz=<input-img-size>
```
By default `model="yolov10s"` and `imgsz=(480,640)`. This will generate file `yolov10s.onnx`, which can be use for inference in OpenCV
2. For inference part on OpenCV. one can use `yolo_detector.cpp` [sample](https://github.com/opencv/opencv/blob/4.x/samples/dnn/yolo_detector.cpp). If you have followed above exporting procedure, then you can use following command to run the model.
``` bash
build opencv from source
cd build
./bin/example_dnn_yolo_detector --model=<path-to-yolov10s.onnx-file> --yolo=yolov10 --width=640 --height=480 --input=<path-to-image> --scale=0.003921568627 --padvalue=114
```
If you do not specify `--input` argument, OpenCV will grab first camera that is avaliable on your platform.
For more deatils on how to run the `yolo_detector.cpp` file see this [guide](https://docs.opencv.org/4.x/da/d9d/tutorial_dnn_yolo.html#autotoc_md443)
**Running YOLOv9 using OpenCV**
1. Export model following [official guide](https://github.com/WongKinYiu/yolov9)of the YOLOv9 repository. Particularly you can do following for converting.
```bash
git clone https://github.com/WongKinYiu/yolov9.git
cd yolov9
conda create -n yolov9 python=3.9
conda activate yolov9
pip install -r requirements.txt
wget https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-t-converted.pt
python export.py --weights=./yolov9-t-converted.pt --include=onnx --img-size=(480,640)
```
This will generate <yolov9-t-converted.onnx> file.
2. Inference on OpenCV.
```bash
build opencv from source
cd build
./bin/example_dnn_yolo_detector --model=<path-to-yolov9-t-converted.onnx> --yolo=yolov9 --width=640 --height=480 --scale=0.003921568627 --padvalue=114 --path=<path-to-image>
```
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Update the tutorial of using Orbbec Astra cameras #25813
This PR is the backport of Orbbec OpenNI-based Astra camera related changes from #25410 to the 4.x branch, which includes updating the tutorial of Orbbec Astra cameras, renaming `orbbec_astra.cpp`.
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Integrate ARM KleidiCV as OpenCV HAL #25443
The library source code with license: https://gitlab.arm.com/kleidi/kleidicv/
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highgui: wayland: show "NO" status if dependency is missing #25496Close#25495
- [doc] Add document to enable Wayland highgui-backend in ubuntu 24.04.
- [build] Show "NO" status instead of version if dependency library is missing.
- [build] Fix to find Wayland EGL.
- [fix] Add some callback stub functions to suppress build warning.
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Move Charuco/Calib tutorials and samples to main repo #25378
Merge with https://github.com/opencv/opencv_contrib/pull/3708
Move Charuco/Calib tutorials and samples to main repo:
- [x] update/fix charuco_detection.markdown and samples
- [x] update/fix charuco_diamond_detection.markdown and samples
- [x] update/fix aruco_calibration.markdown and samples
- [x] update/fix aruco_faq.markdown
- [x] move tutorials, samples and tests to main repo
- [x] remove old tutorials, samples and tests from contrib
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Documentation transition to fresh Doxygen #25042
* current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584)
* Doxyfile adapted to new version
* MathJax updated to 3.x
* `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings)
* refactored matx.hpp - extracted matx.inl.hpp
* opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638
Move Aruco tutorials and samples to main repo #23018
merge with https://github.com/opencv/opencv_contrib/pull/3401
merge with https://github.com/opencv/opencv_extra/pull/1143
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---------
Co-authored-by: AleksandrPanov <alexander.panov@xperience.ai>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
Documentation for Yolo usage in Opencv #24898
This PR introduces documentation for the usage of yolo detection model family in open CV. This is not to be merge before #24691, as the sample will need to be changed.
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Removed all pre-C++11 code, workarounds, and branches #23736
This removes a bunch of pre-C++11 workrarounds that are no longer necessary as C++11 is now required.
It is a nice clean up and simplification.
* No longer unconditionally #include <array> in cvdef.h, include explicitly where needed
* Removed deprecated CV_NODISCARD, already unused in the codebase
* Removed some pre-C++11 workarounds, and simplified some backwards compat defines
* Removed CV_CXX_STD_ARRAY
* Removed CV_CXX_MOVE_SEMANTICS and CV_CXX_MOVE
* Removed all tests of CV_CXX11, now assume it's always true. This allowed removing a lot of dead code.
* Updated some documentation consequently.
* Removed all tests of CV_CXX11, now assume it's always true
* Fixed links.
---------
Co-authored-by: Maksim Shabunin <maksim.shabunin@gmail.com>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
