Merge pull request #27363 from MRo47:openvino-npu-support

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

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

doc/tutorials/dnn/dnn_openvino/dnn_openvino.markdown

@@ -26,3 +26,14 @@ There are 2 approaches how to get OpenCV:
 - Build OpenCV from source code against specific version of OpenVINO. This approach solves the limitations mentioned above.
 
 The instruction how to follow both approaches is provided in [OpenCV wiki](https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO).
+
+## Supported targets
+
+OpenVINO backend (DNN_BACKEND_INFERENCE_ENGINE) supports the following [targets](https://docs.opencv.org/4.x/d6/d0f/group__dnn.html#ga709af7692ba29788182cf573531b0ff5):
+
+- **DNN_TARGET_CPU:** Runs on the CPU, no additional dependencies required.
+- **DNN_TARGET_OPENCL, DNN_TARGET_OPENCL_FP16:** Runs on the iGPU, requires OpenCL drivers. Install [intel-opencl-icd](https://launchpad.net/ubuntu/jammy/+package/intel-opencl-icd) on Ubuntu.
+- **DNN_TARGET_MYRIAD:** Runs on Intel® VPU like the [Neural Compute Stick](https://www.intel.com/content/www/us/en/products/sku/140109/intel-neural-compute-stick-2/specifications.html), to set up [see](https://www.intel.com/content/www/us/en/developer/archive/tools/neural-compute-stick.html).
+- **DNN_TARGET_HDDL:** Runs on the Intel® Movidius™ Myriad™ X High Density Deep Learning VPU, for details [see](https://intelsmartedge.github.io/ido-specs/doc/building-blocks/enhanced-platform-awareness/smartedge-open_hddl/).
+- **DNN_TARGET_FPGA:** Runs on Intel® Altera® series FPGAs [see](https://www.intel.com/content/www/us/en/docs/programmable/768970/2025-1/getting-started-guide.html).
+- **DNN_TARGET_NPU:** Runs on the integrated Intel® AI Boost processor, requires [Linux drivers](https://github.com/intel/linux-npu-driver/releases/tag/v1.17.0) OR [Windows drivers](https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html).

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -72,7 +72,7 @@ CV__DNN_INLINE_NS_BEGIN
         //! DNN_BACKEND_DEFAULT equals to OPENCV_DNN_BACKEND_DEFAULT, which can be defined using CMake or a configuration parameter
         DNN_BACKEND_DEFAULT = 0,
         DNN_BACKEND_HALIDE,
-        DNN_BACKEND_INFERENCE_ENGINE,            //!< Intel OpenVINO computational backend
+        DNN_BACKEND_INFERENCE_ENGINE,            //!< Intel OpenVINO computational backend, supported targets: CPU, OPENCL, OPENCL_FP16, MYRIAD, HDDL, NPU
                                                  //!< @note Tutorial how to build OpenCV with OpenVINO: @ref tutorial_dnn_openvino
         DNN_BACKEND_OPENCV,
         DNN_BACKEND_VKCOM,

modules/dnn/src/ie_ngraph.cpp

@@ -223,6 +223,9 @@ void InfEngineNgraphNet::init(Target targetId)
         case DNN_TARGET_FPGA:
             device_name = "FPGA";
             break;
+        case DNN_TARGET_NPU:
+            device_name = "NPU";
+            break;
         default:
             CV_Error(Error::StsNotImplemented, "Unknown target");
     };

modules/dnn/src/net_openvino.cpp

@@ -125,7 +125,8 @@ public:
               preferableTarget == DNN_TARGET_OPENCL_FP16 ||
               preferableTarget == DNN_TARGET_MYRIAD ||
               preferableTarget == DNN_TARGET_HDDL ||
-              preferableTarget == DNN_TARGET_FPGA,
+              preferableTarget == DNN_TARGET_FPGA ||
+              preferableTarget == DNN_TARGET_NPU,
               "Unknown OpenVINO target"
         );
     }

modules/dnn/src/op_inf_engine.cpp

@@ -275,6 +275,8 @@ bool checkTarget(Target target)
             return true;
         else if (std::string::npos != i->find("GPU") && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
             return true;
+        else if (std::string::npos != i->find("NPU") && target == DNN_TARGET_NPU)
+            return true;
     }
     return false;
 }