Overview
Use an Intel® RealSense™ camera to create a solution that detects regular and irregularly shaped packages and tracks newly added and removed packages in the cargo area of a transportation vehicle.
Select Configure & Download to download the reference implementation and the software listed below.
- Time to Complete: Approximately 60 minutes
- Programming Language: Python*
Recommended Hardware
The below hardware is recommended for use with this reference implementation. For other suggestions, see Recommended Hardware.
Target System Requirements
-
Ubuntu* 20.04
-
6th to 10th Generation Intel® Core™ processors with Intel® Iris® Plus graphics or Intel® HD Graphics
-
Intel® RealSense™ D400 series camera
-
Minimum 20 GB free space
How It Works
Cargo Management Reference Implementation does the following:
-
Detects regularly shaped box’s Length, Width and Height dimensions up to 53 ft.
-
Detects an irregularly shaped package’s Length, Width and Height dimensions using an outline bounding box, up to 53 ft for each dimension and a minimum of 2 inches.
-
Provides a simple User Interface to interact with the driver for tracking and reviewing detected events.
-
Sends the detected packages and their dimensions as notifications to Amazon Web Services* (AWS) to display them on a Dashboard.
Figure 1: Architecture Diagram
Get Started
Prerequisites
-
Install Intel® RealSense™ SDK for Ubuntu using instructions on GitHub.
-
Print configuration chessboard from GitHub on page A4. Place it on camera view on a horizontal setup.
Step 1: Install the Reference Implementation
Select Configure & Download to download the reference implementation and then follow the steps below to install it.
NOTE: If the host system already has Docker images and containers, you might encounter errors while building the reference implementation packages. If you do encounter errors, refer to the Troubleshooting section at the end of this document before starting the reference implementation installation.
-
Open a new terminal, go to the downloaded folder and unzip the downloaded RI package.
unzip cargo_management.zip
-
Go to the
cargo_management/
directory.cd cargo_management/
-
Change permission of the executable edgesoftware file.
chmod 755 edgesoftware
-
Run the command below to install the Reference Implementation.
./edgesoftware install
-
During the installation, you will be prompted for the Product Key. The Product Key is contained in the email you received from Intel confirming your download.
Figure 2: Product Key
-
When the installation is complete, you see the message "Installation of package complete" and the installation status for each module.
Figure 3: Installation Success
NOTE: If you encounter any issues, refer to the Troubleshooting section at the end of this document. Installation failure logs will be available at the path:
/var/log/esb-cli/Cargo_Management_<version>/output.log
-
To start the application, you need to change the directory using the cd command printed at the end of the installation process:
cd /opt/intel/eif/EII-UseCaseManager
Step 2: Run the Application
Prerequisites
-
Run the application. Copy and run the
make webui
command from the end of the installation:make webui
-
Open the Web UI and go to 127.0.0.1:9090 on your web browser.
Figure 4: Reference Implementation Dashboard
-
If you installed your ThingsBoard Cloud Server and you have enabled S3 Bucket Server on your AWS account, you can provide your configured AWS Access Key ID, AWS Secret Access Key, Thingsboard IP, Thingsboard Port and Thingsboard Device token on the Cloud Data Configuration tab. After you complete the Cloud configuration, make sure you click on the Save Credentials and Save Token buttons. Now you can import the ThingsBoard dashboard as described at the end of the Set Up ThingsBoard* Cloud Data to enable all dashboard features, including the cloud storage.
Figure 5: Configuration Menu Option Contents
-
Access the Cargo Management Dashboard by selecting Run Application. The web application should detect the camera. Configure as below.
Figure 6: Select Run Application Menu Option
- Optionally, you can also set the simulation data that you want to use. You can choose between using the KnowGo Simulator or simply use the CSV pre-recorded simulation data.
-
After 30-40 seconds, the Visualizer should pop up and the camera should be configured. If the Visualizer does not pop up, make sure you have placed the calibration chessboard in camera view.
Figure 7: Visualizer Output
-
Place one object at a time on the chessboard calibration area to have the object measured. The images below show how the objects are measured.
Figure 8: Visualizer Output
Figure 9: Visualizer Output
-
After the visualizer starts, you can go to the ThingsBoard link and check the alerts sent by the reference implementation. If you configured the AWS credentials, you will also have access to pictures taken by the application on the video stream.
Figure 10: Intel Fleet Manager Dashboard shown in ThingsBoard
-
You can also check the cloud storage from the Reference Implementation Storage menu option.
Figure 11: Reference Implementation Storage Menu Option
Run in Parallel with Automated License Plate Recognition Reference Implementation
To run this task, you will need to download and install the Automated License Plate Recognition Reference Implementation.
For more details about parallel execution, see the Edge Insights for Fleet Use Case Manager documentation.
Prerequisites
- Follow the steps to install Automated License Plate Recognition after installing Cargo Management
Steps to Run the Application
-
Change directory to EII-UseCaseManager:
cd /opt/intel/eif/EII-UseCaseManager
-
Run the following command to start the web server application.
make webui
-
Open your browser and go to 127.0.0.1:9090.
NOTE: Configure each reference implementation by selecting the desired tab. For example, click the Run Application menu option, then click on CM to configure the Cargo Management RI. Next, click on ALPR to configure the Automated License Plate Recognition RI.
-
Access the Cargo Management Dashboard. The web application should detect the camera. Configure as below.
Figure 12: Configure Cargo Management Reference Implementation
-
Configure Automated License Plate Recognition Reference Implementation by setting the video source and the target (CPU, GPU or HETERO). Click on Run Application.
Figure 13: Configure Automated License Plate Recognition Reference Implementation
-
Wait for both Visualizers to get up and running.
Figure 14: Visualizer Output for 2 Reference Implementations
Summary and Next Steps
You successfully ran the Cargo Management application and displayed the result using Amazon Web Services* (AWS).
As next step, try the following: Using the current implementation, try to make the algorithm more reliable or make a preconfigured calibration. This will make it possible to detect multiple objects at the same time.
Learn More
To continue your learning, see the following guides and software resources:
- For additional reference implementations, visit Edge Insights for Fleet.
- Intel® Distribution of OpenVINO™ toolkit documentation
- Intel® RealSense™ SDK Documentation
Known Issues
Uninstall Reference Implementation
If you uninstall one of the reference implementations, you need to reinstall the other reference implementations because the Docker images will be cleared.
Troubleshooting
Installation Failure
If the host system already has Docker images and its containers running, you will have issues during the RI installation. You must stop/force stop existing containers and images.
-
To remove all stopped containers, dangling images, and unused networks:
sudo docker system prune --volumes
-
To stop Docker containers:
sudo docker stop $(sudo docker ps -aq)
-
To remove Docker containers:
sudo docker rm $(sudo docker ps -aq)
-
To remove all Docker images:
sudo docker rmi -f $(sudo docker images -aq)
Docker Image Build Failure
If Docker image build on corporate network fails, follow the steps below.
-
Get DNS server using the command:
nmcli dev show | grep 'IP4.DNS'
-
Configure Docker to use the server. Paste the line below in the
/etc/docker/daemon.json
file:{ "dns": ["<dns-server-from-above-command>"] }
-
Restart Docker:
sudo systemctl daemon-reload && sudo systemctl restart docker
Installation Failure Due to Ubuntu Timezone Setting
While building the reference implementation, if you see /etc/timezone && apt-get install -y tzdata && ln -sf /usr/share/zoneinfo/${HOST_TIME_ZONE} /etc/localtime && dpkg-reconfigure -f noninteractive tzdata' returned a non-zero code: 1 make: *** [config] Error 1
Run the following command in your terminal:
sudo timedatectl set-local-rtc 0
Installation Encoding Issue
While building the reference implementation, if you see ERROR: 'latin-1' codec can't encode character '\\u2615' in position 3: ordinal not in range(256)
Run the following command in your terminal:
export LANG=en_US.UTF-8
Can't Connect to Docker Daemon
If you can't connect to docker daemon at http+docker://localhost, run the following command in your terminal:
sudo usermod -aG docker $USER
Log out and log back in to Ubuntu.
Check before retry to install if group Docker is available for you by running the following command in a terminal:
groups
The output should contain Docker.
Visualizer Does Not Start
Make sure you have updated the camera firmware and the chessboard calibration is placed on camera view.
To update the camera firmware, download the latest firmware.
Installation Timeout When Using pip or apt Commands
You may experience a timeout issue when using the People's Republic of China (PRC) internet network.
Make sure that you have a stable internet connection while installing the packages. If you experience timeouts due to Linux* apt or Python* pip installation, try to reinstall the package.
Support Forum
If you're unable to resolve your issues, contact the Support Forum.