Real-Time Face Mask Detection with Streamlit and YOLOv8

Demo Link: FaceMask-Detection

How to Run Locally

System Requirements

• Operating System: Windows, MacOS, or Linux
• Python 3.10
• Git
• Disk Space: At least 3GB of available disk space

Pre-Installation Checks

1. Check Python Installation:

   • Open Command Prompt (cmd).
   • Check if Python is installed by running:

   python --version

   • If Python is not installed, download and install from python.org. Make sure to choose a version compatible with your operating system.

2. Check Git Installation:

   • Check if Git is installed by running:

     git --version

   • If Git is not installed, download and install from git-scm.com.

Installation Steps

3. Navigate to Project Folder via Command Prompt:

• Change the current directory to your project folder using the cd command.

4. Create Virtual Environment and Project Folder:

• Create a new folder named "facemaskdetection-project" using the following command:

  mkdir facemaskdetection-project

• Navigate to the "facemaskdetection-project" folder:

  cd facemaskdetection-project

• Create a new Python virtual environment named "facemaskEnv" using the following command:

  python -m venv facemaskEnv 

  or

  py -m venv facemaskEnv

  • Activate the virtual environment:
    • On Windows:

      .\facemaskEnv\Scripts\activate

    • On MacOS/Linux:

      source facemaskEnv/bin/activate

Now you have set up the project folder named "facemaskdetection-project" with a Python virtual environment named "facemaskEnv". Next, proceed with the remaining installation steps within this project folder.

5. Clone Project Files:

• Clone the project repository containing notebook files, models, and Streamlit script from GitHub with the following command:

  git clone https://github.com/dinosptr/facemask-detection.git

• Navigate to the cloned repository folder:

  cd facemask-detection

6. Install Project Dependencies:

• Install the project's required dependencies using pip and the provided requirements.txt file:

  pip install -r requirements.txt

7. Run the Streamlit App:

To launch the Streamlit application, execute the following command in the project directory:

streamlit run Welcome.py

After the command to run the Streamlit script, a new line is added informing the user that they will be directed to open the app in their web browser at localhost:8501.

8. Tampilan Website

Contoh output Image