Get Started with the JWIPC iShow* Developer Kit

Published: 10/16/2019  

Last Updated: 10/16/2019

Follow these steps to connect your JWIPC iShow* Developer Kit and begin working with your development kit. This guide assumes you've already set up and powered your system according to the guide included in the box.

ishow small image

You’ll walk through the following basic steps:

Run and Modify an Intel® Distribution of OpenVINO™ toolkit Application

Modify and Run a Project

Projects and Tutorials

Run and Modify an Intel® Distribution of OpenVINO™ toolkit Application

In this section, you'll run a vehicle detection application. By default, this sample application runs on the CPU and detects vehicles in a static image, using the Intel® Distribution for OpenVINO™ toolkit. You'll modify the project to run on a sample image and offload the processing to your GPU.

Intel® technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. Check with your system manufacturer or retailer or learn more at software.intel.com.

Before You Begin

These steps were tested with the following configuration:

  • JWIPC iShow Developer Kit
  • Version 2019 R2 of the Intel® Distribution of OpenVINO™ toolkit

The models used in this example are pre-trained models included with the Intel® Distribution of OpenVINO™ toolkit.
For additional information about each of these models, refer to the model descriptions, located by default in the appropriate directories in:

/opt/intel/openvino/deployment_tools/intel_models/

Model

Description

license-plate-recognition-barrier-0001 A model that uses a small-footprint network, trained to recognize Chinese license plates in traffic.
vehicle-attributes-recognition-barrier-0039 This model presents a vehicle attributes classification algorithm for a traffic analysis scenario.
vehicle-license-plate-detection-barrier-0106 This is a MobileNetV2 and SSD-based vehicle and (Chinese) license plate detector for the security barrier example.

Set Up Environment Variables

To source the setupvars script in the current shell run the following command.

. /opt/intel/openvino/bin/setupvars.sh

Run a Sample Project on the CPU

Your sample project has already been included in the Intel® Distribution of OpenVINO™ toolkit for you. Follow the steps below to run it for the first time.

  1. Next, you'll change the existing program arguments to run the proiject. This information and more details about the arguments you can supply to the sample can be found in the README.md file in the security barrier camera demo source files.
  2. Navigate to the script in the following directory path:
  3. /opt/intel/openvino/deployment_tools/demo

  4. Copy the following command to the terminal and run the script.

    ./demo_security_barrier_camera.sh

    The directory structure and command should look similar to the image below. The sample runs on an image, highlighting the vehicles it detects in bounding boxes. You'll see labels displayed next to the cars it detects, such as "black car".

  5. In the text displayed over the image, you can see data about the sample. Note the fps value, which you can use to estimate performance; a higher fps value corresponds to better performance.

  6. The image used in the example is a simple .bmp image:

    Format .bmp
    Dimension 749x637 pixels
    Size 1.36 MB

Congratulations, you've successfully run the sample! To close the image, type ctl+C in the terminal.

View CPU Utilization

You can view CPU utilization on your device, then compare the performance change when you move processing to the GPU or a vision accelerator.

  1. Click the Ubuntu* icon in your taskbar. From the search bar, search for and open the System Monitor application.
  2. Click the Resources tab to bring up the CPU History chart. You can see how the CPU utilization changes when you run the sample on the CPU, as described below.

Afterwards, keep the application open so you can compare the utilization numbers when you offload application processing to the GPU or an accelerator.

Modify Your Project to Run on the GPU

Next, you'll modify the project to run on a GPU.

  1. If this is a new terminal, make sure the environment is initialized.

    /opt/intel/openvino/bin/setupvars.sh

  2. Begin in the demo directory, you will run the demo script on the GPU.

    /opt/intel/openvino/deployment_tools/demo

  3. Next, you'll change the device command option (-d) to run the project on the GPU (-d GPU).

    This information and more details about the arguments you can supply to the sample can be found in the README.md file in the security_barrier_camera_demo source files.

  4. Run the following command from the demo directory:

    ./demo_security_barrier_camera.sh -d GPU

    The directory structure and command should look similar to the image below.  

  5. Your project runs. The results appear in the terminal and in a separate window.

  6. In the image, you'll see a car.

  7. The application will highlight the vehicle it detects in bounding boxes and display a label next to the car ("white car", "red car", etc.).

    Intel® technologies features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. Check with your system manufacturer or retailer or learn more at software.intel.com.

    If you have the System Monitor open, you can see the CPU utilization spike as the project runs. Compared to running the project on the CPU only, only about one CPU core is utilized at 100% at a time, as work is being offloaded to the GPU.

    To close the image, type ctrl+c in the terminal or close the terminal window.

    Next Steps

    Next, try some similar projects.

    • Projects and Tutorials

Projects and Tutorials

This section contains tutorials for developing projects for and working with your system.

Reference Implementations, Code Samples, and Additional Documentation

You can check out the following reference implementations for steps to leverage your system to create some real-world solutions:

You can also view additional code samples and OpenVINO™ toolkit documentation.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.