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.
Pyrovision aims at providing the means to create a wildfire early detection system with state-of-the-art performances at minimal deployment costs.
You can use the library like any other python package to detect wildfires as follows:
from pyrovision.models import rexnet1_0x
from torchvision import transforms
import torch
from PIL import Image
# Init
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
tf = transforms.Compose([transforms.Resize(size=(448)), transforms.CenterCrop(size=448),
transforms.ToTensor(), normalize])
model = rexnet1_0x(pretrained=True).eval()
# Predict
im = tf(Image.open("path/to/your/image.jpg").convert('RGB'))
with torch.no_grad():
pred = model(im.unsqueeze(0))
is_wildfire = torch.sigmoid(pred).item() >= 0.5Python 3.6 (or higher) and pip/conda are required to install PyroVision.
You can install the last stable release of the package using pypi as follows:
pip install pyrovisionor using conda:
conda install -c pyronear pyrovisionAlternatively, 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-vision.git
pip install -e pyro-vision/.The full package documentation is available here for detailed specifications.
The project includes a minimal demo app using Gradio
You can check the live demo, hosted on 🤗 HuggingFace Spaces 🤗 over here 👇
If you wish to deploy containerized environments, a Dockerfile is provided for you build a docker image:
docker build . -t <YOUR_IMAGE_TAG>Looking for a boilerplate to deploy a model from PyroVision with a REST API? Thanks to the wonderful FastAPI framework, you can do this easily. Follow the instructions in ./api to get your own API running!
If you wish to train models on your own, we provide training scripts for multiple tasks!
Please refer to the ./references folder if that's the case.
If you wish to cite this project, feel free to use this BibTeX reference:
@misc{pyrovision2019,
title={Pyrovision: wildfire early detection},
author={Pyronear contributors},
year={2019},
month={October},
publisher = {GitHub},
howpublished = {\url{https://github.com/pyronear/pyro-vision}}
}Please refer to CONTRIBUTING to help grow this project!
Distributed under the Apache 2 License. See LICENSE for more information.
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