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Computer vision library for wildfire detection 🌲 Deep learning models in PyTorch & ONNX for inference on edge devices (e.g. Raspberry Pi)

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![PyroNear Logo](https://raw.githubusercontent.com/pyronear/pyro-vision/main/docs/source/_static/images/logo.png)

# Pyrovision: wildfire early 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.

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

## Quick Tour

### Automatic wildfire detection in PyTorch

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

```python
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() >= 0.5
```

## Setup

Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install PyroVision.

### Stable release

You can install the last stable release of the package using [pypi](https://pypi.org/project/pyrovision/) as follows:

```shell
pip install pyrovision
```

or using [conda](https://anaconda.org/pyronear/pyrovision):

```shell
conda install -c pyronear pyrovision
```

### 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:

```shell
git clone https://github.com/pyronear/pyro-vision.git
pip install -e pyro-vision/.
```

## What else

### Documentation

The full package documentation is available [here](https://pyronear.org/pyro-vision/) for detailed specifications.

### Demo app

The project includes a minimal demo app using [Gradio](https://gradio.app/)

![demo_app](https://user-images.githubusercontent.com/26927750/179017766-326fbbff-771d-4680-a230-b2785ee89c4d.png)

You can check the live demo, hosted on :hugs: [HuggingFace Spaces](https://huggingface.co/spaces) :hugs: over here :point_down:
[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/pyronear/vision)

### Docker container

If you wish to deploy containerized environments, a Dockerfile is provided for you build a docker image:

```shell
docker build . -t <YOUR_IMAGE_TAG>
```

### Minimal API template

Looking for a boilerplate to deploy a model from PyroVision with a REST API? Thanks to the wonderful [FastAPI](https://github.com/tiangolo/fastapi) framework, you can do this easily. Follow the instructions in [`./api`](api) to get your own API running!

### Reference scripts

If you wish to train models on your own, we provide training scripts for multiple tasks!
Please refer to the [`./references`](references) folder if that's the case.

## Citation

If you wish to cite this project, feel free to use this [BibTeX](http://www.bibtex.org/) reference:

```bibtex
@misc{pyrovision2019,
    title={Pyrovision: wildfire early detection},
    author={Pyronear contributors},
    year={2019},
    month={October},
    publisher = {GitHub},
    howpublished = {\url{https://github.com/pyronear/pyro-vision}}
}
```

## Contributing

Please refer to [`CONTRIBUTING`](CONTRIBUTING.md) to help grow this project!

## License

Distributed under the Apache 2 License. See [`LICENSE`](LICENSE) for more information.

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