Real-time Image Processing using Flask and OpenCV

About

This is a real-time image processing application built using Flask and OpenCV. The application receives a video stream from the client and processes it using OpenCV. The processed video is then sent back to the client for display.

Repository Structure

├── Dockerfile
├── README.md
├── app.py
├── docker-compose.yml
├── requirements.txt
└── templates
    ├── index.html
    └── static
        └── logo.ico

Tools Used

The application was built using the following tools:

• Flask: A micro web framework written in Python.
• Socket.IO: A library that enables real-time, bidirectional communication between web clients and servers.
• OpenCV: A computer vision library with Python bindings.
• Docker: A tool designed to make it easier to create, deploy, and run applications by using containers.

How to run the application (using virtual environment)

1. Clone the repository: git clone https://github.com/Nneji123/flask-client-camera.git
2. Create a virtual environment: python3 -m venv env
3. Activate the virtual environment: source env/bin/activate or source env/Scripts/activate if you use windows os.
4. Install the required packages: pip install -r requirements.txt
5. Start the application: python app.py

How to run the application (using gitpod)

How to run the application (using Docker and Docker Compose)

1. Clone the repository: git clone https://github.com/your_username/real-time-image-processing.git
2. Install Docker and Docker Compose on your machine
3. Build the Docker image: docker build -t image-processing .
4. Start the Docker container: docker run -p 5000:5000 -it image-processing

Alternatively, you can use Docker Compose to start the application: docker-compose up -d --build

How to deploy the application

Click the button below to deploy the application to render.com

License

MIT