MediTracker Footage Analyzer

Overview

MediTracker Footage Analyzer is a system designed to analyze video footage in medical environments. It automatically detects and tracks medical equipment, logging their movements in real-time. This tool is part of a larger system that updates equipment locations on a dashboard. Here are the links to the related repositories:

• Frontend: https://github.com/Ashani-Sansala/MediTrack_System_Frontend
• Backend: https://github.com/Ashani-Sansala/MediTrack_System_Backend

Features

• Real-time detection of hospital equipment using YOLOv5
• Movement tracking and direction analysis
• Integration with MySQL database for logging detections
• Firebase integration for storing and sharing detected frames
• Configurable parameters for optimizing performance and accuracy

Requirements

• Python 3.10 or higher (recommended)
• CUDA-capable GPU (recommended for faster processing)
• Sufficient storage for video files and temporary data

Installation

1. Clone the repository:

https://github.com/Ashani-Sansala/MediTracker_FootageAnalyzer.git
cd MediTracker_FootageAnalyzer

2. Install required dependencies:

pip install -r requirements.txt

3. Ensure you have the necessary credentials for MySQL database and Firebase.

Configuration

Before running the analyzer, configure the config.json file. Open it in a text editor and set the following parameters:

• Database Settings (MySQL)
• Firebase Settings
• Video Processing Settings

Refer to the user manual for detailed explanations of each parameter.

Usage

1. Ensure the backend system is running:

   • The MediTracker Footage Analyzer depends on a backend system for database and storage operations.
   • Make sure the backend is set up and running before proceeding.

2. Prepare your video footage files:

• Name format: camX_locY_Z.mp4 (X: Camera ID, Y: Location ID, Z: Footage number/name)
• Place the video file in the directory specified by local_video_path in config.json.

3. Run the main script:

python main.py

4. The system will start analyzing the video footage file specified in config.json.

Note: It's crucial to have the backend system operational before running the Footage Analyzer. The analyzer relies on the backend for database connections.

Monitoring Progress

• If enable_preview is set to true, a window will show the video with detection boxes.
• The console will display messages about detected equipment and their movements.

Viewing Results

• Detection logs are stored in the MySQL database specified in config.json.
• Frames with detected equipment are uploaded to Firebase Storage.

Performance Optimization

Refer to the user manual for detailed information on optimizing performance through configuration parameters such as:

• Frame skip
• Frame resizing
• Confidence threshold
• Buffer size
• Movement threshold

Troubleshooting

If you encounter issues:

• Verify all paths in config.json are correct and accessible.
• Check database and Firebase credentials.
• For detection inaccuracies, adjust confidence_threshold or use a better-trained model.
• For performance issues, try increasing frame_skip or reducing frame_width and frame_height.