PyroEngine: Wildfire detection on edge devices

PyroEngine provides a high-level interface to use Deep learning models in production while being connected to the alert API.

Quick Tour

Running your engine locally

You can use the library like any other python package to detect wildfires as follows:

from pyroengine.core import Engine
from PIL import Image

engine = Engine("pyronear/rexnet1_3x")

im = Image.open("path/to/your/image.jpg").convert('RGB')

prediction = engine.predict(image) 

Setup

Python 3.6 (or higher) and pip/conda are required to install PyroVision.

Stable release

You can install the last stable release of the package using pypi as follows:

pip install pyroengine

Developer installation

Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source:

git clone https://github.com/pyronear/pyro-engine.git
pip install -e pyro-engine/.

Full docker orchestration

Finally, you will need a .env file to enable camera & Alert API interactions. Your file should include a few mandatory entries:

API_URL=http://my-api.myhost.com
LAT=48.88
LON=2.38
CAM_USER=my_dummy_login
CAM_PWD=my_dummy_pwd

Additionally, you'll need a ./data folder which contains:

• credentials.json: a dictionary with the IP address of your cameras as key, and dictionary with entries login & password for their Alert API credentials
• model.onnx: optional, will overrides the model weights download from HuggingFace Hub
• config.json: optional, will overrides the model config download from HuggingFace Hub

Documentation

The full package documentation is available here for detailed specifications.

Contributing

Please refer to CONTRIBUTING if you wish to contribute to this project.

Credits

This project is developed and maintained by the repo owner and volunteers from Data for Good.

License

Distributed under the Apache 2 License. See LICENSE for more information.