Face & Eye Detection using OpenCV

This project uses OpenCV’s Haar Cascade Classifiers to perform real-time face and eye detection via your webcam. It also allows you to capture snapshots of the video feed.

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Features

• Real-time group face detection using Haar cascades
• Automatically opens your default webcam (VideoCapture(0))
• Draws bounding boxes around detected faces
• Displays total number of faces detected

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Requirements

• Python 3.8 or later
• OpenCV library

Install OpenCV using pip:

pip install opencv-python

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How to Run

1. Clone or download this repository.

2. Run it using:

   python detect_room.py

3. A window will open showing the webcam feed.

   • Press esc to quit the program

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Code Overview

import cv2

# Load pre-trained Haar cascades for face and eye detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

...

# Initialize webcam
cap = cv2.VideoCapture(0)

1. Cascade Loading – Loads the Haar cascade XML files that contain pre-trained models for detecting faces and eyes.

2. Video Stream – Opens your webcam using cv2.VideoCapture(0).

3. Frame Loop – Continuously reads frames, converts them to grayscale, and runs detectMultiScale() to find faces and eyes.

4. Drawing & Display – Rectangles are drawn around detected regions and displayed using cv2.imshow().

5. Key Controls –

   • esc: exits the loop and closes all windows

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Parameters

You can tweak these parameters inside detectMultiScale() to fine-tune performance:

faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=3)

• scaleFactor: How much the image size is reduced at each image scale (lower = more accurate but slower)
• minNeighbors: How many neighbors each rectangle should have to be retained (higher = fewer detections)

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Output

• Window Preview: Real-time video stream with bounding boxes and total number of detected faces.