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https://github.com/pyronear/pyro-vision
Computer vision library for wildfire detection 🌲 Deep learning models in PyTorch & ONNX for inference on edge devices (e.g. Raspberry Pi)
https://github.com/pyronear/pyro-vision
computer-vision deep-learning image-classification keypoint-detection object-detection onnx python pytorch wildfire
Last synced: 12 days ago
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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)
- Host: GitHub
- URL: https://github.com/pyronear/pyro-vision
- Owner: pyronear
- License: apache-2.0
- Created: 2019-09-08T13:15:41.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-04-12T00:53:18.000Z (7 months ago)
- Last Synced: 2024-04-14T00:59:07.206Z (7 months ago)
- Topics: computer-vision, deep-learning, image-classification, keypoint-detection, object-detection, onnx, python, pytorch, wildfire
- Language: Python
- Homepage: https://pyronear.org/pyro-vision/
- Size: 7.66 MB
- Stars: 49
- Watchers: 10
- Forks: 24
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- open-sustainable-technology - Pyrovision - Computer vision library for wildfire detection. (Biosphere / Wildfire)
README
# 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/)
You can check the live demo, hosted on :hugs: [HuggingFace Spaces](https://huggingface.co/spaces) :hugs: over here :point_down:
[](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
```
### 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.