pyroengine: Deploy Pyronear wildfire detection

The increasing adoption of mobile phones have significantly shortened the time required for firefighting agents to be alerted of a starting wildfire. In less dense areas, limiting and minimizing this duration remains critical to preserve forest areas.

aims at providing the means to create a wildfire early detection system with state-of-the-art performances at minimal deployment costs.

pyroengine aims to deploy pyrovision wildfire detection system

Table of Contents

• Getting Started
  • Prerequisites
  • Installation
• Documentation
• Contributing
• Credits
• License

Getting started

Prerequisites

• Python 3.6 (or more recent)
• pip

Installation

You can install the package using pypi as follows:

pip install pyroengine

Environment files

The pyroengine/pi_utils/python.env file must contain:

• WEBSERVER_IP: the IP address of the main rpi once it is installed on site
• WEBSERVER_PORT: the port exposed on the main rpi for the local webserver

Test Engine

You can test to run a prediction using our Pyronear Engine using the following:

from pyroengine.engine import PyronearEngine
from PIL import Image

engine = PyronearEngine()

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

prediction = engine.predict(image) 

This is a quick demo without api setup, so without sending the alert

Documentation

The full package documentation is available here for detailed specifications. The documentation was built with Sphinx using a theme provided by Read the Docs.

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.