Face and Body Detection using OpenCV

This Python script utilizes OpenCV to perform real-time detection of frontal faces, side faces, and full bodies using a webcam. The program captures video frames, processes them to detect various features, and displays the results with visual annotations.

Features

• Real-time face and body detection: Detects frontal faces, side faces, and full bodies using Haar Cascade classifiers.
• Visual annotations: Draws rectangles, lines, and text around detected faces and bodies.
• Midpoint calculation: Computes and displays the midpoint of detected frontal faces.
• Dynamic updates: Continuously processes the video stream and updates the display in real-time.

Installation

1. Clone the repository:

   git clone https://github.com/Rcode879/Haar-cascade-detector.git
   cd Haar-cascade-detector

2. Install the required libraries:

   Ensure you have Python installed (preferably Python 3.6 or higher) and install the required dependencies:

   pip install opencv-python

Usage

1. Run the script:

   Execute the script to start the webcam and begin detecting faces and bodies:

   python detector.py

2. Interact with the application:

   • The webcam feed will open in a window named frame.
   • The program will highlight detected faces and bodies with annotations.
   • To stop the program, press the q key.

How It Works

• Capture Video: Initializes the video capture from the default webcam.
• Set Resolution: Configures the capture to HD resolution (1280x720).
• Load Haar Cascades: Loads pre-trained Haar Cascade classifiers for detecting frontal faces, side faces, and full bodies.
• Process Each Frame:
  • Converts each frame to grayscale for efficient detection.
  • Uses the Haar Cascade classifiers to detect frontal faces, side faces, and bodies.
  • Draws rectangles, lines, and text annotations around the detected objects.
  • Displays the annotated frame in a window.
• Exit on Key Press: Ends the loop and releases resources when the q key is pressed.

Dependencies

• OpenCV (cv2)

Acknowledgements

This project utilizes the OpenCV library for image processing and computer vision tasks. Haar Cascade classifiers are provided by OpenCV's pre-trained models.