Getting Started
This guide provides technical users with instructions on setting up and using filters within an edge computing image processing system. The system architecture includes K3s, Helm, and NanoMQ. The process is illustrated using the example of a Face Blur filter.
- Familiarity with Kubernetes, Helm, and command-line interfaces.
- Familiarity with MQTT
- K3s: A lightweight Kubernetes distribution for managing containerized applications.
- Helm: A package manager for Kubernetes used for deploying applications.
- NanoMQ: A lightweight MQTT message broker for efficient message handling.
For an efficient installation of FilterBox, it's essential to adhere to the following prerequisites and understand the system compatibility requirements:
- Linux is the preferred platform: It offers unparalleled hardware access and seamless Docker integration. This synergy is vital for FilterBox's high-performance demands, particularly for tasks involving direct hardware interaction like USB camera processing. Deployment on a Linux-based system is highly recommended.
- Support for Windows and Mac: Official support for Windows and Mac OS is in development in upcoming releases. Users on these platforms may face challenges, especially with direct USB camera usage, due to the inherent OS limitations and Docker's virtualization nuances on these systems.
- Docker Installation: The Docker must be pre-installed on the system. This requirement is crucial as FilterBox leverages Docker's containerization technology for deploying and managing its filters.
- Camera Integration Considerations: For Linux users, integrating USB cameras with FilterBox is generally straightforward, thanks to Linux's support for such devices. However, we recommend utilizing network cameras that support RTSP feeds for those using FilterBox within a VM environment or on non-Linux platforms. This approach simplifies the setup and avoids the complexities of USB device compatibility and access in virtualized or non-Linux contexts.
The first step in setting up your edge computing environment involves initializing the system with
filterbox
, a powerful command-line tool designed for ease of use. By downloading and extracting filterbox
, users can set up their environment with a single command, filterbox init
. This process abstracts the complexity of configuring each component individually, offering a streamlined setup experience.Highlights |
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ARM Install
X86 Install
Download the tool:
1
ARCH=arm64
2
VERSION=1.0.0
3
wget "https://github.com/PlainsightAI/filterbox/releases/download/v$VERSION/filterbox_Linux_$ARCH.tar.gz"
Extract the tarball:
1
tar -xf filterbox_Linux_$ARCH.tar.gz
Move
filterbox
to the bin directorymv ./filterbox /usr/local/bin
Initialize
filterbox
:1
filterbox init
Download the tool:
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ARCH=x86_64
2
VERSION=1.0.0
3
wget "https://github.com/PlainsightAI/filterbox/releases/download/v$VERSION/filterbox_Linux_$ARCH.tar.gz"
Extract the tarball:
1
tar -xf filterbox_Linux_$ARCH.tar.gz
Move
filterbox
to the bin directorymv ./filterbox /usr/local/bin
Initialize edgectl:
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filterbox init
With the system initialized, installing a filter becomes a straightforward task. By running
filterbox filter install
, users can deploy their desired image processing filters, such as the Face Blur filter, onto their edge computing system. This step leverages Helm's capabilities to manage the deployment, ensuring that the filters are correctly installed and configured within the Kubernetes environment.Highlights |
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1
filterbox filter install
When using
filterbox filter install
, you'll be prompted to select your filter and version from available options. You can also specify your video source, whether it's a USB device number (e.g., 0, 1) or an RTSP address (e.g., rtsp://10.100.10.20:8000/UniqueID), ensuring a customized setup tailored to your needs.The final step involves managing the installed filters using
filterbox
. Users can start, stop, list, and uninstall filters with simple commands, offering complete control over the filter lifecycle. For example, filterbox filter run
starts a filter, enabling real-time image processing on edge devices. This level of control allows for dynamic management of filters, adapting to changing processing needs or system constraints.Highlights |
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With the
filterbox filter stop
, list
, or uninstall
commands, you'll experience the convenience of a straightforward selection interface. This feature enables you to manage your filters efficiently, allowing quick actions like stopping a specific filter, viewing all active filters, or removing a filter from your system, all through an intuitive process.1
filterbox filter list
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filterbox filter run
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filterbox filter stop
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filterbox filter uninstall
As part of our video processing service, the Plainsight watermark is automatically included as a standard signature. For watermark removal or options, please get in touch with our Marketing Team.
- Ensure all prerequisites are met before beginning installation.
- Regularly update Helm and Kubernetes to their latest versions.
- Monitor system performance and adjust configurations as necessary.
K3s Official Documentation Site: Comprehensive guide on installation, configuration, and cluster management.
Helm Official Documentation: Detailed instructions on chart development, repository management, and usage.
Last modified 1mo ago