Deploying to Embedded Targets

This guide will walk you through installing the EdgeFirst Middleware in "user-mode" on a target device. If you're using a Maivin or Raivin refer to the Deploying to the Maivin guide instead.

Installation

Warning

This workflow is currently only supported on NXP i.MX 8M Plus EVK platforms running the NXP Yocto BSP.

The EdgeFirst Middleware can be easily installed on embedded targets using the Python package manager to install the middleware binaries and sample models. Simply run the following command to install the middleware.

pip3 install edgefirst

This can also be done through a Python virtual environment to keep all the packages in a user-defined location.

pip3 install virtualenv
python3 -m virtualenv venv
source venv/bin/activate
pip3 install edgefirst

Usage

Once installed, the applications can be launched using the edgefirst launcher which handles running the various services. The applications can also be run directly, for example edgefirst-camera to run the camera service. The launcher is an optional but convenient way to run all the applications in "user-mode" in contrast to having the middleware integrated into the system as "system-mode" where the applications will be managed by systemd.

Live Camera Mode

The live camera mode is launched using the following command. By default it will run a person detection and segmentation model, if you wish to run your own model simply use the --model mymodel.tflite parameter and provide the path to your TFLite model. Currently only ModelPack for Detection & Segmentation is supported by the launcher.

edgefirst live

Note

The live camera mode uses the camera found at /dev/video3 by default. If your camera is on another device entry you can use the --camera /dev/videoX parameter to select the correct camera, replace X with the correct camera index.

Once launched, you can access the live camera view and model overlays by pointing your browser to your device's IP address using https://MY_DEVICE_IP. You'll see something like the following.

If testing a model trained using a few images collected on a phone you'll notice the performance is probably poor. With small datasets it is especially important to include samples captured with the target device's camera which is luckily easy to do from the web interface. There is a toggle button in the top-right of the screen which will start an MCAP recording.

You can also access the MCAP recordings from the working directory where the edgefirst live command was run.

Once you have your MCAP download to your PC you may upload it to EdgeFirst Studio to annotate and further train your model. Uploading MCAP recordings is done through the Snapshot Dashboard.