HumanEyeDetector.py

import cv2

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

# Initialize the camera (you can specify the camera index, e.g., 0 for the default camera)
cap = cv2.VideoCapture(0)

while True:
    # Capture a frame from the camera
    ret, frame = cap.read()

    # Convert the frame to grayscale for better performance
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the frame
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5, minSize=(30, 30))

    for (x, y, w, h) in faces:
        # Draw a rectangle around the detected face
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

        # Extract the region of interest (ROI) in the face
        roi_gray = gray[y:y + h, x:x + w]
        roi_color = frame[y:y + h, x:x + w]

        # Detect eyes in the ROI
        eyes = eye_cascade.detectMultiScale(roi_gray)
        for (ex, ey, ew, eh) in eyes:
            # Draw a rectangle around the detected eyes
            cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 0, 255), 2)

    # Display the frame with detected faces and eyes
    cv2.imshow('Eye Detection', frame)

    # Exit the loop if the 'q' key is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the camera and close all OpenCV windows
cap.release()
# cv2.destroyAllWindows()