The aim of this project is to provide a simple, easy to use, application that enables field scientists to quickly sort through camera trap images. Camera traps are often set off by moving leaves and consequentlly take lots of empty shots. Weeding out empty images is a mundane task that would typically take several hours of sorting through hundreds of images.
Using machine learning we can reduce the time it takes to sort these images by making use of object detection. This application categorises your images in folders depending on what it thinks it has detected.
The only currently supported platform is Windows (64-bit).
Go to the Releases page and download the provided installer for the latest available version.
Read the instructions here on how to enable significantly faster processing.
The app is built using ElectronJS. The UI framework is Svelte, and components are styled using Fomantic-UI
The brains of the app are in Python. I have adapted MegaDetector's code into a slightly more organised API. We use this API to build a basic CLI app that compiles to an executable. The executable is called by the Electron app whenever we need to use the model for inference.
If you wish to use this project for development purposes follow these steps:
Pre-requsites:
Steps:
npm install
npm run semantic-build
cd engine/
pip install -r requirements.txt
engine/models/
folderpyinstaller -F cli_wrapper/cli.py
Running the application:
npm run start
npm run build