Coral Labeler

This repo tracks the Coral Labeler application, which is in development by Dylan Chapell, Khawla Douah, Mai Nguyen, and Calvin Than. The goal of this application is to streamline the labeling of datasets containing images of corals. It provides manual labeling tools as well as a machine learning model to help predict labels.

Building the application

On MacOS, this process is automated by the ./build-macos.sh script

Installing Dependencies

Our application has the following dependencies:

• Python 3
• PySide6 - Qt (and qt-quick) bindings for python
• numpy - Working with the data structure that holds the labels for each pixel
• Scikit Image - Provides flood fill functionality, as well as image dims
• torch and torchvision - Used to train and run the machine learning model that provides predicted labels
• rdp - Implementation of the Ramer–Douglas–Peucker algorithm to reduce the number of points in a line
• pyinstaller - Packages our application into the format to be distributed
• opencv

To install the dependencies in a virtual environment:

1. Move into the program directory cd CoralLabeler

2. Create a virual environment in this directory: python3 -m venv venv

3. Activate the venv if it is not already:

   • source venv/bin/activate on POSIX

   • venv/bin/activate.bat on Windows

4. Install the dependencies: pip install PySide6 scikit-image numpy torch torchvision rdp pyinstaller opencv-python

If you would like to run the application without building it, you can do so now by running python3 main.py

Compiling with pyinstaller

1. Inspect CoralLabeler.spec to ensure that the build options are set in the way you want. (pyinstaller documentation)
   • Specifically, make sure all files you want bundled with the application are listed in datas. If you have added another AI descriptor file besides defaultAI.py, it will need to be added here.
2. Run pyinstaller CoralLabeler.spec

The compiled application will now be in the ./dist/CoralLabeler directory.

Packaging

MacOS

Compress CoralLabeler.app into a zip file

cd dist
zip -ry CoralLabeler-macos.zip CoralLabeler.app

Upload this zip file to a draft Github Release

Windows

1. Install and open the InstallForge program
2. Open build-windows-installforge.ifp
3. Adjust the files tab to point to the produced CoralLabeler.exe and _internal folder
4. Build the installer
5. Upload the produced installer to a draft Github Release

Linux

Compress dist/CoralLabeler/CoralLabeler and dist/CoralLabeler/_internal into a zip file

cd dist/
zip -ry CoralLabeler-linux.zip CoralLabeler/

Upload this zip file to a draft Github Release