TomaTect: A GUI for Fresh / Rotten Tomato Detection with Combined Fuzzy and YOLOv8 Algorithms

Description:

The model used in this GUI (TomaTect App) is a combined Fuzzy and YOLOv8 algorithms named Fuzzy YOLOv8.pt that could detect fresh / rotten tomato from a picture of tomato.

Steps to use the GUI:

1. Your Python's version must be: 3.10 <= Python's Version < 3.12
2. Open Windows Terminal / Powershell
3. pip install ultralytics==8.0.48
4. pip install pyside6==6.4.2
5. Go to your preferred directory path to clone this Repository using cd [directory path]
6. git clone [repository URL]
7. cd Tomato-Detection-Fuzzy-YOLOv8
8. python main.py

Notes:

1. If you want to use your own trained YOLOv8 model, you should put your .pt file (which you usually know as best.pt file) into the models folder.
2. If you checked the Save settings, the detection results will be saved in the run folder.
3. The UI design file is home.ui file, structured in PySide6. If you modify this file, you should run pyside6-uic -o ui/home.py home.ui in your Windows Terminal / Powershell, then change import resources_rc into import ui.resources_rc in line 23 of the home.py file, then run python main.py again to see the result.
4. The resource file is resources.qrc file. If you modify, delete, or add new images files path in this file, you should run pyside6-rcc -o ui/resources_rc.py resources.qrc in your Windows Terminal / Powershell, then run python main.py to see the result.
5. The version of your Python, Ultralytics, and PySide6 must be the same as mentioned in "Steps to use the GUI".
6. The ultralytics library follows the GPL-3.0 License (GNU General Public License Version 3), so if you need commercial use, you need to obtain its license first.

References:

1. https://ieeexplore.ieee.org/document/7103952
2. https://github.com/ultralytics/ultralytics
3. https://docs.ultralytics.com/
4. https://github.com/Jai-wei/YOLOv8-PySide6-GUI/