Video Respiration Rate Detector

This project is a Flask-based web application that processes uploaded video files to detect and calculate respiration rates. The application uses OpenCV for video processing and dlib for face detection.

Features

• Upload video files for processing
• Detect faces in the video
• Calculate respiration rates based on detected facial movements
• Display results including frames per second (FPS), total number of frames, respiration times, time length, and respiration rate (RR)

Requirements

• Python 3.x
• Flask
• OpenCV
• dlib
• scipy

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/video-respiration-rate-detector.git
   cd video-respiration-rate-detector

2. Install the required packages:

   pip install -r requirements.txt

3. Download and install dlib:

   # On Ubuntu or Debian
   sudo apt-get install -y cmake build-essential libgl1-mesa-glx
   
   # Install dlib using pip
   pip install dlib

Usage

1. Run the Flask application:

   python app.py

2. Open your web browser and go to http://0.0.0.0:5000.

3. Upload a video file and wait for the processing to complete.

4. The results will be displayed on the web page.

Docker

You can also run this application using Docker. Follow the steps below:

1. Build the Docker image:

   docker build -t video-respiration-rate-detector .

2. Run the Docker container:

   docker run -p 5000:5000 video-respiration-rate-detector

3. Open your web browser and go to http://0.0.0.0:5000.

Deploying on Azure Container Instances

You can deploy this application on Azure Container Instances for a scalable and managed environment. Follow these steps:

1. Ensure you have the Azure CLI installed and are logged in:

   az login

2. Create a resource group if you don't have one:

   az group create --name myResourceGroup --location eastus

3. Create a container instance:

   az container create --resource-group myResourceGroup --name videoRespirationRate --image yourdockerhubusername/video-respiration-rate-detector:latest --ports 5000 --dns-name-label video-respiration-rate --environment-variables 'FLASK_ENV'='production'

4. Find the FQDN of your container instance:

   az container show --resource-group myResourceGroup --name videoRespirationRate --query ipAddress.fqdn

5. Open your web browser and navigate to the FQDN retrieved from the previous step.

File Structure

• app.py: The main Flask application file
• templates/index.html: The HTML file for the web interface
• requirements.txt: The Python dependencies required for the project
• Dockerfile: The Dockerfile for containerizing the application

Endpoints

• GET /: The main page with the video upload form
• POST /process_video: Endpoint to handle the video file upload and process the video

Acknowledgements

• dlib
• OpenCV
• Flask
• Docker Hub
• Azure Container Instances
• Containers Tutorial

ScreenShots from The app

while Uploading

The results

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

Contact

For any questions or inquiries, please contact [email protected].