python实现疲劳驾驶检测系统

疲劳驾驶检测系统是一个基于人工智能的应用，可以通过分析驾驶员的眼睛、面部表情、姿势等特征，来检测是否存在疲劳驾驶的行为并及时提醒驾驶员。以下是一个基于Python的疲劳驾驶检测系统的示例代码：

import cv2
import dlib
import numpy as np
from scipy.spatial import distance

# 定义常量
EAR_THRESHOLD = 0.25
EAR_CONSEC_FRAMES = 30
YAWN_THRESH = 20
FRAME_COUNTER = 0
YAWN_COUNTER = 0

# 初始化dlib的人脸检测器和面部标志检测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# 定义函数计算两个二维坐标点之间的欧氏距离
def euclidean_dist(ptA, ptB):
    return distance.euclidean(ptA, ptB)

# 定义函数计算眼睛的EAR值
def eye_aspect_ratio(eye):
    # 计算垂直方向上的两个点之间的欧氏距离
    A = euclidean_dist(eye[1], eye[5])
    B = euclidean_dist(eye[2], eye[4])
    # 计算水平方向上的一个点和另外两个点之间的欧氏距离
    C = euclidean_dist(eye[0], eye[3])
    # 计算EAR值
    ear = (A + B) / (2.0 * C)
    return ear

# 加载摄像头
cap = cv2.VideoCapture(0)

while True:
    # 获取视频帧
    ret, frame = cap.read()
    if not ret:
        break
    
    # 将视频帧转换为灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 检测人脸
    faces = detector(gray, 0)

    # 遍历每个人脸
    for face in faces:
        # 获取面部标志点
        landmarks = predictor(gray, face)
        landmarks = np.array([[p.x, p.y] for p in landmarks.parts()])

        # 提取左右眼的坐标
        left_eye = landmarks[36:42]
        right_eye = landmarks[42:48]

        # 计算左右眼的EAR值
        left_ear = eye_aspect_ratio(left_eye)
        right_ear = eye_aspect_ratio(right_eye)

        # 计算平均EAR值
        ear = (left_ear + right_ear) / 2.0

        # 绘制眼部区域
        cv2.polylines(frame, [left_eye], True, (0, 255, 0), 1)
        cv2.polylines(frame, [right_eye], True, (0, 255, 0), 1)

        # 判断是否疲劳驾驶
        if ear < EAR_THRESHOLD:
            FRAME_COUNTER += 1
            if FRAME_COUNTER >= EAR_CONSEC_FRAMES:
                cv2.putText(frame, "Fatigue Alert!", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        else:
            FRAME_COUNTER = 0

        # 检测是否打哈欠
        # 计算嘴部区域的坐标
        mouth = landmarks[48:68]
        # 计算嘴部区域的高度
        mouth_height = euclidean_dist(mouth[2], mouth[10])
        if mouth_height > YAWN_THRESH:
            YAWN_COUNTER += 1
            cv2.putText(frame, "Yawn Alert!", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        else:
            YAWN_COUNTER = 0

        # 显示EAR值和打哈欠计数器
        cv2.putText(frame, "EAR: {:.2f}".format(ear), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
        cv2.putText(frame, "Yawn: {}".format(YAWN_COUNTER), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

    # 显示视频帧
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF
    if key == ord("q"):
        break

# 释放资源
cap.release()
cv2.destroyAllWindows()

这个代码使用了dlib库来检测人脸和面部标志点，通过计算眼睛的EAR值和嘴部区域高度来判断是否存在疲劳驾驶的行为，并在视频帧上显示警示信息。