Video Frame Extraction and Fire Detection

This project is a Flask-based web application that extracts frames from an uploaded video and uses a pre-trained TensorFlow model to detect fire in the frames. If fire is detected, a notification is sent via Telegram. The application is also containerized using Docker for easy deployment.

Features

• Upload video files and extract frames at regular intervals.
• Use a pre-trained TensorFlow model to predict the presence of fire in each frame.
• Send a notification to a specified Telegram chat if fire is detected.
• Containerized using Docker for easy deployment and reproducibility.

Workflow

1. Upload Video: Users can upload a video file through a simple web interface.
2. Extract Frames: The uploaded video is processed, and frames are extracted at regular intervals.
3. Fire Detection: Each extracted frame is analyzed using a TensorFlow model to detect the presence of fire.
4. Telegram Notification: If fire is detected in any frame, a message is sent to a specified Telegram chat.

Screenshots

Extracted Frames and Fire Detection

Installation

Prerequisites

• Docker
• Docker Compose

Steps

1. Clone the repository:

   git clone https://github.com/afnamal/Fire-Detection
   cd Fire-Detection

2. Download the pre-trained model from Kaggle:

   • Visit the following Kaggle page to download the model: Fire Detection Model
   • Place the downloaded model file (final_model.h5) into the prediction directory.

3. Build and run the Docker containers:

   docker-compose up --build

4. The application will be available at:

   • http://localhost:5001 for video upload
   • http://localhost:5000 for fire detection
   • http://localhost:15672 for RabbitMQ management interface (default credentials: guest/guest).

API Endpoints

• Upload Video: POST /upload_video

  • Uploads a video file and starts the frame extraction process.
  • Example using curl:

    curl -F "video=@path/to/your/video.ts" http://localhost:5001/upload_video

• List Frames: GET /frames

  • Returns a list of all extracted frames.

• Get Frame: GET /frames/<filename>

  • Retrieves a specific frame image.

• Predict Fire: POST /predict

  • Analyzes a given image for the presence of fire.
  • Example using curl:

    curl -F "image=@path/to/frame.jpg" http://localhost:5000/predict

Usage

Upload a Video

1. Open your browser and go to http://localhost:5001.
2. Use the interface to upload a .ts video file.
3. Frames will be extracted and analyzed for fire detection.

Fire Detection

1. Once frames are extracted, they will be automatically analyzed.
2. If fire is detected in any frame, a message will be sent to the specified Telegram chat.

Docker

This project uses Docker to ensure consistent environments and easy deployment. The Dockerfile and docker-compose.yml files are configured to set up the application with all necessary dependencies.

RabbitMQ Management Interface

The RabbitMQ management interface is accessible at http://localhost:15672 with the default credentials guest/guest. You can monitor the message queue and other RabbitMQ parameters from this interface.

Project Structure

.
├── extraction             # Backend for frame extraction
│   ├── extrac_frames.py   # Frame extraction Flask application
│   ├── Dockerfile         # Docker configuration for frame extraction
│   └── requirements.txt   # Python dependencies for frame extraction
├── prediction             # Backend for fire prediction
│   ├── fire_detection.py  # Fire detection Flask application
│   ├── Dockerfile         # Docker configuration for fire detection
│   ├── final_model.h5     # Pre-trained model for fire detection (downloaded from Kaggle)
│   └── requirements.txt   # Python dependencies for fire detection
├── frontend               # Frontend Vue.js application
│   ├── app.vue            # Main Vue.js component
│   ├── Dockerfile         # Docker configuration for frontend
│   └── package.json       # JavaScript dependencies for frontend
├── docker-compose.yml     # Docker Compose configuration
└── README.md              # This file