### Python 实现疲劳驾驶检测系统 #### 使用 Mediapipe 和 Eye Aspect Ratio 进行疲劳检测 为了构建一个有效的疲劳驾驶检测系统，可以利用 `Mediapipe` 库中提供的面部网格解决方案来追踪驾驶员的眼睛状态。通过计算眼睛纵横比(Eye Aspect Ratio, EAR)，能够判断驾驶员是否存在闭眼行为。 定义一个用于计数眼睛眨眼次数的函数[^1]： ```python import cv2 import mediapipe as mp from math import hypot def calculate_EAR(eye_points, facial_landmarks): """ 计算单只眼睛的眼球纵横比 """ horizontal_line_length = hypot( (facial_landmarks.part(eye_points[0]).x - facial_landmarks.part(eye_points[3]).x), (facial_landmarks.part(eye_points[0]).y - facial_landmarks.part(eye_points[3]).y)) vertical_line_1_length = hypot( (facial_landmarks.part(eye_points[1]).x - facial_landmarks.part(eye_points[5]).x), (facial_landmarks.part(eye_points[1]).y - facial_landmarks.part(eye_points[5]).y)) vertical_line_2_length = hypot( (facial_landmarks.part(eye_points[2]).x - facial_landmarks.part(eye_points[4]).x), (facial_landmarks.part(eye_points[2]).y - facial_landmarks.part(eye_points[4]).y)) ear_aspect_ratio = (vertical_line_1_length + vertical_line_2_length) / (2.0 * horizontal_line_length) return ear_aspect_ratio mp_face_mesh = mp.solutions.face_mesh.FaceMesh() cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) results = mp_face_mesh.process(rgb_frame) if results.multi_face_landmarks: for face_landmarks in results.multi_face_landmarks: # 获取左眼和右眼的关键点索引位置 left_eye_indices = [362, 385, 387, 263, 373, 380] right_eye_indices = [33, 160, 158, 133, 153, 145] # 提取左右眼坐标 left_eye_coords = [(int(face_landmarks.landmark[i].x * frame.shape[1]), int(face_landmarks.landmark[i].y * frame.shape[0])) for i in left_eye_indices] right_eye_coords = [(int(face_landmarks.landmark[i].x * frame.shape[1]), int(face_landmarks.landmark[i].y * frame.shape[0])) for i in right_eye_indices] # 计算EAR值 left_ear = calculate_EAR(left_eye_indices, face_landmarks) right_ear = calculate_EAR(right_eye_indices, face_landmarks) avg_ear = (left_ear + right_ear) / 2.0 # 如果EAR低于阈值，则认为是在打瞌睡 if avg_ear < 0.2: print('Driver is drowsy!') key = cv2.waitKey(1) if key == ord('q'): break cv2.destroyAllWindows() cap.release() ``` 此代码片段展示了如何使用 `mediapipe` 来获取面部特征点，并据此计算双眼的EAR比率。当该比例小于设定阈值时（此处设为0.2），则触发警告提示司机可能处于困倦状态[^2]。