Segmentation App

Table of Contents

• About
  • manualSegmentation.py
  • semanticSegmentation.py
• Installation
  • Installing the Executable
  • Installing Python
  • Installing required packages
• Manual
• Notice

About

Based on the bachelor-thesis "Microstructure analysis of materials with the assistance of artificial technology" by Kerim Yalcin from Feb 2024.

Features:

• read and save image files
• use filters to change brightness and gaussian-blur amount of loaded images
• apply binarization and tresholding
• trace boundaries using resizable brush tool in black or white
• create image labels for a Resnet34 model (max. 2 classifications)
• create and train a ResNet34 model by using the labels
• predict images with a trained ResNet34 model

manualSegmentation.py

Use this script for manual label creation. After that, create labels using the image crop tool.

semanticSegmentation.py

Use this script for creating and training a ResNet34 model. After that, predict images using the trained model.

Installation

The application can either be started using the executable or directly by running the scripts after installing Python and the required packages.

Installing the Executable

The executables are created using auto-py-to-exe by Brent Vollebregt and can be downloaded here under Assets.

Installing Python

Installing required packages

Manual

Notice

• Reading and saving image file names are not supported in Unicode due to OpenCV imread and imwrite function
• Cancelling the file dialog without selecting a path can lead to termination of the app
• an internet connection is required to create the ResNet32 model