Welcome to the EnAcuity Coding Challenge’s documentation!

Source code is available at enAcuity-coding-challenge.

Overview

This project is a coding challenge focused on building an interactive application for analysing surgical videos. The application is designed to perform frame-by-frame analysis and support real-time processing for in-depth exploration of surgical procedures.

Features

• Frame-by-frame video analysis: Allows users to step through each frame of a surgical video.

• Real-time processing: Provides an efficient interface for real-time surgical video review.

• Simple and interactive GUI: Built using PySimpleGUI for a user-friendly experience.

Prerequisites

Ensure you have the following installed on your machine:

1. Python 3.9+

2. pip for installing Python packages

Installation Instructions

1. Clone the repository

git clone https://github.com/solenedietsch/enAcuity-coding-challenge
cd enAcuity-coding-challenge

2. Set up a virtual environment (optional but recommended)

python -m venv venv
source venv/bin/activate  # On Windows: venv\\Scripts\\activate

3. Install dependencies

The project requires PySimpleGUI and other libraries for video processing. Run the following command:

pip install -r requirements.txt

Running the Application

Once the setup is complete, you can run the application with the following command:

python app.py

Usage Instructions

1. Start the application: Launch the GUI by running main.py.

2. Load video: Use the file picker to load a video from the Cholec80 dataset.

3. Frame controls:

   • Navigate frame-by-frame using the provided buttons.

   • Pause and play for real-time processing.

4. Analysis Tools: The interface provides basic real-time processing tools for video exploration.

Troubleshooting

If you encounter GUI issues:

• Make sure PySimpleGUI is correctly installed.

• Update PySimpleGUI using:

• pip install –upgrade pysimplegui

• Create a PySimpleGUI account: https://www.pysimplegui.com/pricing.

• Create a Hobbyist account.

Future Enhancements

• Add annotation features for labeling surgical phases.

• Integrate machine learning models for automatic event detection.

Contributing

Contributions are welcome! Feel free to submit issues or pull requests.

License

This project is licensed under the MIT License. See LICENSE for details.