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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 | |