The aim of this project is to create neural network models for forest Fire detection using Flask and TensorFlow, and integrate them into a website for convenient use.
Note: This code is not optimized for production enviroment.
For documentation of the code in this repository, please see the Wiki. See below for instructions to install and run the web server.
- Python => 3.7 and =< 3.10
- pip
sudo apt install python3-pip - git
sudo apt install git
git clone git@github.com:akdasUAF/ForestFireDetection.git
If you do not have access to the repository from your command line, but your github account does have access, then follow the instructions at this link to set up ssh keys to provide access. Then, run the git clone command again.
Then, download the three files in this google drive folder (these are too large for github to accept, and one contains another git repo). Move yolov5.tar.gz and dataset.tar.gz into the repo directory and extract them using
gunzip yolov5.tar.gz
gunzip dataset.tar.gz
tar -xf yolov5.tar
tar -xf dataset.tar
Then, move dbn_pipeline_model.joblib.gz to the Models/weights directory inside of the repo, and extract with
gunzip dbn_pipeline_model.joblib.gz
pip install -r requirements.txt
If you get warnings, you may need to add the following line to your .bashrc file (the path being added to PATH may vary depending on your operating system):
export PATH="/home/$USER/.local/bin:$PATH"
To do this, run
echo 'export PATH="/home/$USER/.local/bin:$PATH"' >> ~/.bashrc
Then, close and re-open your terminal or server connection, or just run
source ~/.bashrc
To run the web server, just do:
python3 app.py
If you want it to run persistantly (staying up after you close your terminal connection) do:
nohup python3 app.py &