An OpenHack codebase to support a Bee Health (mite) tracking IoT App. This app uses a TensorFlow model to perform image classification on-device. Telemetry data is sent to Azure IoT Central.
Use the requirements.txt file to install required libraries, using the command: pip install -r requirements.txt
- Raspberry Pi 0+
- Raspberry Pi PIR Motion Sensor
- Raspberry Pi Camera
- Some knowledge of how to use/configure a Raspberry Pi
- Azure IoT Hub: Your device will need to be enrolled, and it's connection string updated in the creds.json file discussed below
- Azure Storage: Optionally used to archive telemetry data from the device
- Azure Stream Analytics: Optionally used for telemetry analysis
The following wiring is required:
- LED light at GPIO 18
- Motion Sensor at GPIO 17
- Raspberry Pi Camera connected to the camera port
This app needs a creds.json file to store certain required credential and status info. It's not contained in the repo for obvious reasons, but here's the structure you'll need:
{
"device_id" : "<your device id>",
"latitude" : "",
"longitude" : "",
"owner_email" : "",
"provisioning_host": "global.azure-devices-provisioning.net",
"registration_id": "<same as device id>",
"id_scope": "",
"symmetric_key": "",
"blob_token":"<blob_sas_token_for_images>",
"tf_models":"<blob_token_for_tfmodels>"
}Save this in a file called creds.json in the root folder of the application.
Run the app using the command: python motion.py
Note that the initial startup time for the app is approx 30-45 seconds, as it loads the TensorFlow model. Performing image classification on the first image takes substantially longer (30s), than every subsequent image (1.1s).
Test mode tells the app to use the sample images in the img_test folder instead of ones it takes with the camera. These images are not ones used to train the model used, but ones that will test the classification and allow the app to run without the need for Bees to look at!
To run in TEST Mode:
python motion.py --test True