Usage¶
If you run into any problems, please create a GitHub issue.
RuntimeError: X_LINK_DEVICE_ALREADY_IN_USE
If you run into the error shown above, the most common cause is that a previously started script is already communicating with the OAK camera (e.g. cron job at boot). You can show all currently running processes by using the process manager htop.
Start htop by running:
If you see one of the Python scripts in the list of processes, you can hit
F9 with the script selected. This will open the SIGTERM option and by
confirming with Enter the process will be stopped. Close htop by pressing
Q and you should now be able to connect to the OAK camera again.
Web App¶
You can use the Insect Detect web app to stream frames from the OAK camera via HTTP to the browser-based interface (based on the NiceGUI framework). It includes real-time camera control (e.g. manual focus) and customization of all configuration parameters, as well as selecting the active config that is used by both the web app and the recording script.
Run the webapp.py
script with the Python interpreter from the virtual environment where you
installed the required packages (e.g. env_insdet):
Use the link that is printed to the Terminal to open the web app in your browser. You can use any device that is connected to the same network as the Raspberry Pi (e.g. phone or tablet) to open the web app. If the link based on your hostname does not work, use the RPi's IP address instead.
If the Raspberry Pi is connected to a hotspot from your phone, you can use the Network Analyzer app to find its IP address.
Stop the web app by either using the button in your browser window or by pressing Ctrl+C in the Terminal.
Recording Script¶
The recording script
can be used for fully automated insect monitoring in autonomous camera trap deployments.
All configuration parameters can be customized in the web app or by directly modifying the
config_custom.yaml
file. You can generate multiple custom configuration files and select the active
config either in the web app or by modifying the
config_selector.yaml.
Run the
yolo_tracker_save_hqsync.py
script with the Python interpreter from the virtual environment where you
installed the required packages (e.g. env_insdet):
Stop the script by pressing Ctrl+C in the Terminal.
For fully automated monitoring in the field, set up a cron job that will run the script automatically after each boot (triggered by the power management board).
Processing pipeline:
- A custom YOLO insect detection model is run in real time on device (OAK) and uses a continuous stream of downscaled LQ frames as input.
- An object tracker uses the bounding box coordinates of detected insects to assign a unique tracking ID to each individual present in the frame and track its movement through time.
- The tracker + model output from inference on LQ frames is synchronized with MJPEG-encoded HQ frames (default: 3840x2160 px) on device (OAK).
- The HQ frames are saved to the microSD card at the configured capture intervals while an insect is detected (triggered capture) and independent of detections (time-lapse capture).
- Corresponding metadata from the detection model and tracker output is saved to a metadata .csv file for each detected and tracked insect (including timestamp, label, confidence score, tracking ID, tracking status and bounding box coordinates).
- The bounding box coordinates can be used to crop detected insects from the corresponding HQ frames and save them as individual .jpg images. Depending on the post-processing configuration, the original HQ frames are optionally deleted to save storage space.
- If a power management board (Witty Pi 4 L3V7 or PiJuice Zero) is connected and enabled in the configuration, intelligent power management is activated which includes battery charge level monitoring with conditional recording durations.
- With the default configuration, running the recording consumes ~3.8 W of power.