This coding example is part of a Udemy Python course using Python to detect faces in images.

• Python3
  • Used to create the main application functionality

• OpenCV
  • open-source library that includes several hundreds of computer vision algorithms.
• OpenCV haar-cascade
  • OpenCV xml data containing trained classifiers for detecting objects of a particular type e.g. faces, people, vehicles etc.

• VS Code
  • Code Editor

The website was developed using VS Code & Git pushed to GitHub, which hosts the repository. I made the following steps to deploy the site:

Ensure the following are installed locally on your computer:

• Python 3.6 or higher
• PIP3 Python package installer
• Git Version Control
• PostgreSQL database with pgAdmin management tool

• navigate to simonjvardy/python-image-face-detection GitHub repository.
• Click the Code button
• Copy the clone url in the dropdown menu
• Using your favourite IDE open up your preferred terminal.
• Navigate to your desired file location.

Copy the following code and input it into your terminal to clone Sportswear-Online:

Note: The process may be different depending upon your own OS - please follow this Python help guide to understand how to create a virtual environment.

• To run the face detection application, enter the following command into the terminal window:

• the output image file contains the image with a green rectangle surrounding the detected face.

• To run the face detection application for multiple faces, enter the following command into the terminal window:

• the output image file contains the image with a green rectangle surrounding the detected faces. In the example image below, OpenCv was unable to detect the face on the right chewing the newspaper page as it is partially obscured.

• Udemy: The Python Mega Course - Build 10 Real World Applications Credit: Ardit Sulce
• OpenCV haarcascades