疲劳驾驶检测python

对于疲劳驾驶检测，你可以使用Python来进行相关的开发。以下是一个简单的示例代码，用于检测驾驶员的疲劳程度：

import cv2
import dlib
from scipy.spatial import distance

def calculate_eye_aspect_ratio(eye):
    # 计算眼睛纵横比
    A = distance.euclidean(eye[1], eye[5])
    B = distance.euclidean(eye[2], eye[4])
    C = distance.euclidean(eye[0], eye[3])
    ear = (A + B) / (2.0 * C)
    return ear

def detect_fatigue(frame, detector, predictor, ear_threshold):
    # 检测疲劳驾驶
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = detector(gray)
    for face in faces:
        landmarks = predictor(gray, face)

        left_eye = []
        right_eye = []
        for n in range(36, 42):
            x = landmarks.part(n).x
            y = landmarks.part(n).y
            left_eye.append((x, y))
        for n in range(42, 48):
            x = landmarks.part(n).x
            y = landmarks.part(n).y
            right_eye.append((x, y))

        left_ear = calculate_eye_aspect_ratio(left_eye)
        right_ear = calculate_eye_aspect_ratio(right_eye)

        avg_ear = (left_ear + right_ear) / 2

        if avg_ear < ear_threshold:
            return True

    return False

# 加载人脸检测器和人脸特征点检测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # 设置疲劳阈值，根据实际情况调整
    ear_threshold = 0.25

    if detect_fatigue(frame, detector, predictor, ear_threshold):
        cv2.putText(frame, "Fatigue Detected", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

    cv2.imshow("Frame", frame)

    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

这段代码使用了dlib库进行人脸检测和人脸特征点检测，并计算了眼睛纵横比(eye aspect ratio, EAR)来判断驾驶员是否疲劳。你需要下载dlib库和shape_predictor_68_face_landmarks.dat文件，并将其放在代码同一目录下。

代码中通过摄像头实时获取图像，然后检测人脸并计算眼睛纵横比。如果眼睛纵横比小于设定的阈值，将会显示"Fatigue Detected"的提示。

请注意，这只是一个简单的示例代码，实际应用中可能需要更复杂的算法和模型来进行疲劳驾驶检测。