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import cv2
import numpy as np
class FaceAnalyzer:
def __init__(self):
# Load OpenCV's face detector and eye detector
self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
self.eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
def _get_eye_aspect_ratio(self, eye_region):
"""
Calculate eye aspect ratio (EAR)
:param eye_region: Image of eye region
:return: EAR value
"""
# Convert eye region to grayscale
gray_eye = cv2.cvtColor(eye_region, cv2.COLOR_BGR2GRAY)
# Detect eyes
eyes = self.eye_cascade.detectMultiScale(gray_eye)
if len(eyes) != 2: # If not detected two eyes
return 0.0
# Get eye width and height
eye1 = eyes[0]
eye2 = eyes[1]
# Calculate eye width-height ratio
ear1 = eye1[2] / eye1[3]
ear2 = eye2[2] / eye2[3]
# Return average EAR
return (ear1 + ear2) / 2.0
def is_drowsy(self, face_image):
"""
Detect drowsiness
:param face_image: Face image
:return: Whether drowsy (True/False)
"""
# Convert image to grayscale
gray = cv2.cvtColor(face_image, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = self.face_cascade.detectMultiScale(gray, 1.3, 5)
if len(faces) == 0:
return False
# Get the largest face region
(x, y, w, h) = faces[0]
face_roi = face_image[y:y+h, x:x+w]
# Calculate eye aspect ratio
ear = self._get_eye_aspect_ratio(face_roi)
# If EAR is less than the threshold, consider it drowsy
EAR_THRESHOLD = 0.25
return ear < EAR_THRESHOLD |