疲劳驾驶检测python行为记录

可以使用Python来记录疲劳驾驶的行为。下面是一个简单的示例代码：

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

# 加载人脸检测器和眼睛检测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# 设置阈值，判断疲劳驾驶
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 48

# 初始化帧计数器和疲劳状态
COUNTER = 0
ALARM_ON = False

# 通过视频流逐帧检测疲劳驾驶行为
video_capture = cv2.VideoCapture(0)
while True:
    ret, frame = video_capture.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

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

    for face in faces:
        # 识别人脸特征点
        shape = predictor(gray, face)
        shape = shape_to_np(shape)

        # 提取左眼和右眼坐标
        leftEye = shape[lStart:lEnd]
        rightEye = shape[rStart:rEnd]

        # 计算眼睛纵横比
        leftEAR = calculate_eye_aspect_ratio(leftEye)
        rightEAR = calculate_eye_aspect_ratio(rightEye)

        # 计算平均纵横比
        ear = (leftEAR + rightEAR) / 2.0

        # 检测是否闭眼
        if ear < EYE_AR_THRESH:
            COUNTER += 1

            if COUNTER >= EYE_AR_CONSEC_FRAMES:
                if not ALARM_ON:
                    ALARM_ON = True
                    # 触发疲劳驾驶警报，例如播放声音、发送警报等
                    print("疲劳驾驶警报")

                # 在视频中标记疲劳驾驶
                cv2.putText(frame, "DROWSINESS ALERT!", (10, 30),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        else:
            COUNTER = 0
            ALARM_ON = False

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

# 清理资源
video_capture.release()
cv2.destroyAllWindows()

这段代码使用dlib库进行人脸检测和眼睛检测，通过计算眼睛的纵横比来判断是否闭眼从而检测疲劳驾驶行为。当闭眼帧连续达到一定阈值时，会触发疲劳驾驶警报。你可以根据实际需求对代码进行修改和优化。需要注意的是，该代码只是一个简单的示例，实际应用中可能需要更多的处理和优化。