python疲劳检测代码

引用中提到了基于Percols算法的疲劳检测，但是并没有给出具体的代码实现。因此，以下是一份基于OpenCV和dlib库的Python疲劳检测代码实现：

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

# 定义常量
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 48

# 初始化dlib的人脸检测器和关键点检测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# 计算两个眼睛的EAR值
def eye_aspect_ratio(eye):
    A = dist.euclidean(eye[1], eye[5])
    B = dist.euclidean(eye[2], eye[4])
    C = dist.euclidean(eye[0], eye[3])
    ear = (A + B) / (2.0 * C)
    return ear

# 初始化EAR值和连续帧数
ear = 0
frame_count = 0

# 打开本地摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取视频帧
    ret, frame = cap.read()

    # 转换为灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

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

    # 遍历检测到的人脸
    for rect in rects:
        # 检测关键点
        shape = predictor(gray, rect)
        shape = np.array([(shape.part(i).x, shape.part(i).y) for i in range(68)])

        # 计算左眼和右眼的EAR值
        left_eye = shape[36:42]
        right_eye = shape[42:48]
        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

        # 绘制眼睛区域
        cv2.drawContours(frame, [cv2.convexHull(left_eye)], -1, (0, 255, 0), 1)
        cv2.drawContours(frame, [cv2.convexHull(right_eye)], -1, (0, 255, 0), 1)

        # 判断是否疲劳
        if ear < EYE_AR_THRESH:
            frame_count += 1
            if frame_count >= EYE_AR_CONSEC_FRAMES:
                cv2.putText(frame, "Fatigue Detected", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        else:
            frame_count = 0

        # 绘制EAR值
        cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

    # 显示视频帧
    cv2.imshow("Frame", frame)

    # 按下q键退出循环
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头并关闭窗口
cap.release()
cv2.destroyAllWindows()

该代码使用了dlib库进行人脸检测和关键点检测，使用了OpenCV库进行图像处理和显示。通过计算眼睛的EAR值来判断是否疲劳，并在视频帧中绘制眼睛区域和EAR值。