YOLOv11 - Flask

Object Detection Web App using YOLOv11 and Flask. Supports RTSP Streams and Video Uploads. A study case for a Fall Detection System for the Elderly.

Features

1. User Authentication:

• Secure login and registration functionality using Flask-Login.
• Supports session management for user access. 2. Object Detection:
• Utilitizes YOLOv11 for real-time object detection.
• Allows video uploads for processing
• Provides RTSP stream integration for live detection 3. File Management:
• Uploaded videos and detected frames are stored in a structured directory.
• Automatically cleanup of old detection files to maintain storage efficiency. 4. Real-Time Notifications:
• Displays the count of new logins since the current day for authenticated users. 5. Cross-Origin Resource Sharing (CORS):
• Configured to allow specific resources to be accessed from external domains.

Installation

1. Clone the repository: git clone https://github.com/protheeuz/YOLOv11-Flask.git 2. Install dependencies: pip install -r requirements.txt 3. Create and configure a .env file:

• Define environment variables such as database credentials, Flask secret key, and other required settings. 4. Initialize the database:
• Execute the necessary SQL scripts or ORM migrations to set up the database schema. 5. Run the application: python app.py 6. Access the web app:
• Open your browser and navigate to http://localhost:PORT.

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Directory Structure

.
├── config.py            # Configuration for Flask and other services
├── database.py          # Database connection and teardown handlers
├── detection.py         # Cleanup and detection-specific logic
├── models.py            # Database models and user-related operations
├── views/
│   ├── auth.py          # Routes for authentication
│   ├── main.py          # Main application routes
├── static/
│   ├── uploads/         # Uploaded videos and detection files
├── templates/           # HTML templates for the Flask app
├── main.py              # Entry point for the Flask app
├── requirements.txt     # Python dependencies
├── auth_bp.py           # Authentication logic and routes
├── additional_logic.py  # Auxiliary functions and custom features
└── rtsp_handler.py      # RTSP streaming and fall detection logic

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Configuration

• UPLOAD_FOLDER: Path to the directory where uloaded files are stored.
• DETECTION_IMAGES_FOLDER: Path to the directory for detection result images.
• PUBLIC_URL: Publicly accessible URL for the application.
• ALLOWED_EXTENSIONS: Set of allowed file extensions for upload.
• CONFIDENCE_THRESHOLD: Confidence threshold for object detection labels.

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Cleanup Task

A background thread is implemented to periodically clean up old detection files. The task runs every 30 seconds to ensure storage is managed efficiently.

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Usage

1. Login:

• Create an account or log in using your credentials. 2. Upload Video:
• Navigate to the upload page and upload a video file. 3. Real-Time Detection:
• Monitor object detection output directly on the application. 4. RTSP Stream Monitoring:
• Add RTSP stream URLs for live monitoring and fall detection.