Brainy Pi

Available to select audience (currently beta)

Artificial Intelligence (AI) has become a key technology in various industries because it enables automation, optimization, and enhanced decision-making. OpenVINO (Open Visual Inference and Neural Network Optimization) is a powerful toolkit developed by Intel. Its purpose is to accelerate deep learning inference across a wide range of Intel architectures, including CPUs, GPUs, VPUs, and FPGAs. In this blog post, we will explore how to install OpenVINO on Brainy Pi and run some demos. This will allow you to get started with AI development for industrial products, thus leveraging the potential of OpenVINO for your specific needs.

Installing OpenVINO on Brainy Pi

To begin, we need to install OpenVINO and its dependencies on Brainy Pi. Open a terminal and run the following command:
sudo apt install openvino-toolkit libopencv-dev
This command will install OpenVINO and the necessary OpenCV development files on your system.

Compiling Demos

Once OpenVINO is installed on Brainy Pi, we can proceed to compile the demos. These demos provide a great starting point for understanding and exploring the capabilities of OpenVINO. Follow the steps below:
  1. Set up the OpenVINO environment by sourcing the setupvars.sh script:
    source /opt/openvino/setupvars.sh
  2. Clone the Open Model Zoo repository, which contains the demos, using the following command:
    git clone --recurse-submodules https://github.com/openvinotoolkit/open_model_zoo.git
    cd open_model_zoo/demos/
  3. Build the demos by executing the build_demos.sh script:
    ./build_demos.sh
This will compile the demo applications and make them ready for execution.

Running the Demos

With the demos compiled, we can now download the required models and run them using OpenVINO. Follow these steps:
  1. Download the models needed for the demo by running the following command:
    omz_downloader --name person-vehicle-bike-detection-2000 -o ~/models/ --precision FP16
  2. Download the test video
    cd ~/
    wget https://raw.githubusercontent.com/intel-iot-devkit/sample-videos/master/car-detection.mp4
  3. Once the models and the test video are downloaded, you can run the object dection demo using the following command:
    ~/omz_demos_build/aarch64/Release/object_detection_demo -m ~/models/intel/person-vehicle-bike-detection-2000/FP16/person-vehicle-bike-detection-2000.xml -at ssd -i ~/car-detection.mp4

Conclusion

By following the steps mentioned above, you have successfully installed OpenVINO and run the object detection demo. This is a significant achievement because it serves as a starting point for AI development in industrial applications. Additionally, it provides you with the opportunity to explore and customize the demos according to your specific requirements. So, you can now delve deeper into the capabilities of OpenVINO and expand your knowledge in this field.
In subsequent blog posts, we will delve deeper into different use cases, and deployment options for OpenVINO on Brainy Pi, empowering you to build sophisticated AI-powered industrial products.
Stay tuned for more exciting content on AI development with OpenVINO on ARM !
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