利用opencv 做一个疲劳检测系统（2）
文章目录
- 杂谈
- 实现步骤
- 核心算法
- 交互界面
- 界面代码
- 检测效果
- 源代码
杂谈
最近发现视力下降严重， 可能跟我的过度用眼有关，于是想着能不能做一个检测用眼疲劳的，灵感来自特斯拉的疲劳检测系统。
效果如下：
实现步骤
- 实现核心算法
- 制作交互界面
- 设计交互逻辑
核心算法
疲劳检测算法讲解：
利用dlib 人脸检测算法来捕获人脸的关键点数（68个关键点）
参考文章：https://blog.csdn.net/monk96/article/details/127751414?spm=1001.2014.3001.5502
获取眼睛和嘴巴的点位置
眼睛疲劳计算公式
利用欧拉距离计算
dist = (||P2 - P6|| + ||P3 - P5||)/ 2 * ||P1 - P4||
对应就是上下距离 与左右的比值， 然后我们设定一个阈值，比如说0.3， 另外设置帧数，如3帧，超过3帧则 检测为闭眼， 在闭眼总数上加1，如果闭眼次数超过设定的阈值（6次），判断为疲劳状态。
哈欠疲劳计算公式
哈欠用于眼睛相同的计算方式来计算打哈欠， 同样设置阈值和帧数， 不同点是在于哈欠是设定为0.8。
tips： 这里需要设置多少时间内没闭眼，这去除计数器，不然长时间的检测，肯定会超过阈值
这里需要拿到眼睛的位置进行计算，引入欧拉距离工具
from scipy.spatial import distance as dist
from collections import OrderedDict
设定点位（固定的）
self.LANDMARKS = OrderedDict([
("mouth", (48, 68)),
("right_eyebrow", (17, 22)),
("left_eyebrow", (22, 27)),
("right_eye", (36, 42)),
("left_eye", (42, 48)),
("nose", (27, 36)),
("jaw", (0, 17))
])
self.EYE_THRESH = 0.3
self.EYE_FRAMES = 3
self.COUNTER_FRAMES = 0
self.TOTAL = 0
self.MOUSE_UP_FRAMES = 5
self.MOUSE_COUNTER_FRAMES = 0
self.MOUSE_RATE = 0.8
(self.lStart, self.lEnd) = self.LANDMARKS['left_eye']
(self.rStart, self.rEnd) = self.LANDMARKS['right_eye']
(self.mStart, self.mEnd) = self.LANDMARKS['mouth']
计算大小 这里计算欧拉距离，然后再把距离进行平均，减少误差
def eye_aspect_ratio(self, eye):
A = dist.euclidean(eye[1], eye[5])
B = dist.euclidean(eye[2], eye[4])
C = dist.euclidean(eye[0], eye[3])
return (A + B) / (2 * C)
def cal_height(self, points):
leftEye = points[self.lStart: self.lEnd]
rightEye = points[self.rStart: self.rEnd]
leftEAR = self.eye_aspect_ratio(leftEye)
rightEAR = self.eye_aspect_ratio(rightEye)
return (leftEAR + rightEAR)/2
设定检测的方法：这里用来每一帧检测图片，并返回信息给到交互界面
def skim_video(self, img, ha, eye, warn):
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 人脸数rects
rects = self.detector(img_gray, 0)
close_eye = False
for i in range(len(rects)):
faces = self.predictor(img, rects[i]).parts()
points = np.matrix([[p.x, p.y] for p in faces])
rate = self.cal_height(points) # 闭眼
rate_mouse = self.cal_mouse_height(points) # 哈欠
if rate_mouse > self.MOUSE_RATE and ha:
self.MOUSE_COUNTER_FRAMES += 1
if self.MOUSE_COUNTER_FRAMES >= self.MOUSE_UP_FRAMES:
print('打哈欠')
cv2.putText(img, "haha", (rects[i].left(), rects[i].top() - 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255))
if rate < self.EYE_THRESH and eye:
self.COUNTER_FRAMES += 1
# print('闭眼检测到了，第%s次'%COUNTER_FRAMES)
if self.COUNTER_FRAMES >= 5:
self.TOTAL += 1
self.COUNTER_FRAMES = 0
close_eye = True
else:
self.COUNTER_FRAMES = 0
# for idx, point in enumerate(points):
# pos = (point[0, 0], point[0, 1])
# cv2.circle(img, pos, 2, (0, 0, 255), 1)
# cv2.putText(img, str(idx + 1), pos, cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 255, 255))
if self.TOTAL >= 10 and warn:
cv2.rectangle(img, (rects[i].left(), rects[i].top()), (rects[i].right(), rects[i].bottom()), color= (255, 0, 255))
cv2.putText(img, "tired", (rects[i].left(), rects[i].top() - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255))
print('warning， 您已疲劳，请尽快休息')
return "%s闭眼"%time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) if close_eye else "", img
完整的数据处理类
import numpy as np
import dlib
import cv2
import sys
import time
sys.path.append("..")
from scipy.spatial import distance as dist
from collections import OrderedDict
class Recognize():
def __init__(self):
self.init_data()
self.init_model()
def init_data(self):
self.LANDMARKS = OrderedDict([
("mouth", (48, 68)),
("right_eyebrow", (17, 22)),
("left_eyebrow", (22, 27)),
("right_eye", (36, 42)),
("left_eye", (42, 48)),
("nose", (27, 36)),
("jaw", (0, 17))
])
self.EYE_THRESH = 0.3
self.EYE_FRAMES = 3
self.COUNTER_FRAMES = 0
self.TOTAL = 0
self.MOUSE_UP_FRAMES = 5
self.MOUSE_COUNTER_FRAMES = 0
self.MOUSE_RATE = 0.8
(self.lStart, self.lEnd) = self.LANDMARKS['left_eye']
(self.rStart, self.rEnd) = self.LANDMARKS['right_eye']
(self.mStart, self.mEnd) = self.LANDMARKS['mouth']
def cal_height(self, points):
leftEye = points[self.lStart: self.lEnd]
rightEye = points[self.rStart: self.rEnd]
leftEAR = self.eye_aspect_ratio(leftEye)
rightEAR = self.eye_aspect_ratio(rightEye)
return (leftEAR + rightEAR)/2
def cal_mouse_height(self, points):
mouse = points[self.mStart: self.mEnd]
mouse_rate = self.mouse_aspect_ratio(mouse)
return mouse_rate
def mouse_aspect_ratio(self, mouse):
A = dist.euclidean(mouse[2],mouse[9])
B = dist.euclidean(mouse[4],mouse[7])
C = dist.euclidean(mouse[0],mouse[6])
return (A+ B) / (2 * C)
def eye_aspect_ratio(self, eye):
A = dist.euclidean(eye[1], eye[5])
B = dist.euclidean(eye[2], eye[4])
C = dist.euclidean(eye[0], eye[3])
return (A + B) / (2 * C)
def init_model(self):
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor('F:/python/ML/11-learn/tired/model_data/shape_predictor_68_face_landmarks.dat')
def init_video_capture(self, method):
if method == 0:
self.capture = cv2.VideoCapture(0)
else:
self.capture = cv2.VideoCapture(method)
def skim_video(self, img, ha, eye, warn):
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 人脸数rects
rects = self.detector(img_gray, 0)
close_eye = False
for i in range(len(rects)):
faces = self.predictor(img, rects[i]).parts()
points = np.matrix([[p.x, p.y] for p in faces])
rate = self.cal_height(points) # 闭眼
rate_mouse = self.cal_mouse_height(points) # 哈欠
if rate_mouse > self.MOUSE_RATE and ha:
self.MOUSE_COUNTER_FRAMES += 1
if self.MOUSE_COUNTER_FRAMES >= self.MOUSE_UP_FRAMES:
print('打哈欠')
cv2.putText(img, "haha", (rects[i].left(), rects[i].top() - 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255))
if rate < self.EYE_THRESH and eye:
self.COUNTER_FRAMES += 1
# print('闭眼检测到了，第%s次'%COUNTER_FRAMES)
if self.COUNTER_FRAMES >= 5:
self.TOTAL += 1
self.COUNTER_FRAMES = 0
close_eye = True
else:
self.COUNTER_FRAMES = 0
# for idx, point in enumerate(points):
# pos = (point[0, 0], point[0, 1])
# cv2.circle(img, pos, 2, (0, 0, 255), 1)
# cv2.putText(img, str(idx + 1), pos, cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 255, 255))
if self.TOTAL >= 10 and warn:
cv2.rectangle(img, (rects[i].left(), rects[i].top()), (rects[i].right(), rects[i].bottom()), color= (255, 0, 255))
cv2.putText(img, "tired", (rects[i].left(), rects[i].top() - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255))
print('warning， 您已疲劳，请尽快休息')
return "%s闭眼"%time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) if close_eye else "", img
# print('结束检测，检测到了%s次疲劳闭眼'%TOTAL)
交互界面
交互界面利用qtdesigner， 可视化的设计， 再转化成python语言进行操作。
交互代码
# 控件绑定相关操作
def init_slots(self):
self.ui.tired_time.setValue(3)
self.ui.tired_count.setValue(6)
self.ui.eye.setChecked(True)
self.ui.video.setChecked(True)
self.ui.select_video.clicked.connect(self.button_video_open)
self.ui.start_skim.clicked.connect(self.toggleState)
self.ui.camera.clicked.connect(partial(self.change_method, METHOD.CAMERA))
self.ui.video.clicked.connect(partial(self.change_method, METHOD.VIDEO))
暂停和开始： 这里利用了QtCore.QTimer() 的方法，里面有开始和暂停的api可以调用
import argparse
import random
import sys
import time
sys.path.append("..")
from ui import detect
from logic.recognize import Recognize
import torch
from PyQt5.QtWidgets import *
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtWidgets import QApplication,QMainWindow
from functools import partial
import torch.backends.cudnn as cudnn
import cv2 as cv
import numpy as np
class METHOD():
CAMERA = 0
VIDEO = 1
# pyuic5 -o name.py test.ui
class UI_Logic_Window(QtWidgets.QMainWindow):
def __init__(self, parent = None):
super(UI_Logic_Window, self).__init__(parent)
self.timer_video = QtCore.QTimer() # 创建定时器
#创建一个窗口
self.w = QMainWindow()
self.ui = detect.Ui_DREAM_EYE()
self.ui.setupUi(self)
self.init_slots()
self.output_folder = 'output/'
self.cap = cv.VideoCapture()
# 日志
self.logging = ''
self.recognize = Recognize()
# 控件绑定相关操作
def init_slots(self):
self.ui.tired_time.setValue(3)
self.ui.tired_count.setValue(6)
self.ui.eye.setChecked(True)
self.ui.video.setChecked(True)
self.ui.select_video.clicked.connect(self.button_video_open)
self.ui.start_skim.clicked.connect(self.toggleState)
self.ui.camera.clicked.connect(partial(self.change_method, METHOD.CAMERA))
self.ui.video.clicked.connect(partial(self.change_method, METHOD.VIDEO))
# self.ui.capScan.clicked.connect(self.button_camera_open)
# self.ui.loadWeight.clicked.connect(self.open_model)
# self.ui.initModel.clicked.connect(self.model_init)
# self.ui.start_skim.clicked.connect(self.toggleState)
# self.ui.end.clicked.connect(self.endVideo)
# # self.ui.pushButton_stop.clicked.connect(self.button_video_stop)
# # self.ui.pushButton_finish.clicked.connect(self.finish_detect)
self.timer_video.timeout.connect(self.show_video_frame) # 定时器超时，将槽绑定至show_video_frame
def change_method(self, type):
if type == METHOD.CAMERA:
self.ui.select_video.setDisabled(True)
else:
self.ui.select_video.setDisabled(False)
def button_image_open(self):
print('button_image_open')
name_list = []
try:
img_name, _ = QtWidgets.QFileDialog.getOpenFileName(self, "选择文件")
except OSError as reason:
print('文件出错啦')
QtWidgets.QMessageBox.warning(self, 'Warning', '文件出错', buttons=QtWidgets.QMessageBox.Ok)
else:
if not img_name:
QtWidgets.QMessageBox.warning(self,"Warning", '文件出错', buttons=QtWidgets.QMessageBox.Ok)
self.log('文件出错')
else:
img = cv.imread(img_name)
info_show = self.recognize.skim_video(img)
date = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time())) # 当前时间
file_extaction = img_name.split('.')[-1]
new_fileName = date + '.' + file_extaction
file_path = self.output_folder + 'img_output/' + new_fileName
cv.imwrite(file_path, img)
self.show_img(info_show, img)
# self.log(info_show) #检测信息
# self.result = cv.cvtColor(img, cv.COLOR_BGR2BGRA)
# self.result = letterbox(self.result, new_shape=self.opt.img_size)[0] #cv.resize(self.result, (640, 480), interpolation=cv.INTER_AREA)
# self.QtImg = QtGui.QImage(self.result.data, self.result.shape[1], self.result.shape[0], QtGui.QImage.Format_RGB32)
# print(type(self.ui.show))
# self.ui.show.setPixmap(QtGui.QPixmap.fromImage(self.QtImg))
# self.ui.show.setScaledContents(True) # 设置图像自适应界面大小
def show_img(self, info_show, img):
if info_show:
self.log(info_show)
show = cv.resize(img, (640, 480)) # 直接将原始img上的检测结果进行显示
self.result = cv.cvtColor(show, cv.COLOR_BGR2RGB)
showImage = QtGui.QImage(self.result.data, self.result.shape[1], self.result.shape[0],
QtGui.QImage.Format_RGB888)
self.ui.capture.setPixmap(QtGui.QPixmap.fromImage(showImage))
self.ui.capture.setScaledContents(True) # 设置图像自适应界面大小
def toggleState(self):
print('toggle')
state = self.timer_video.signalsBlocked()
self.timer_video.blockSignals(not state)
text = '继续' if not state else '暂停'
self.ui.start_skim.setText(text)
def endVideo(self):
print('end')
self.timer_video.blockSignals(True)
self.releaseRes()
def button_video_open(self):
video_path, _ = QtWidgets.QFileDialog.getOpenFileName(self, '选择检测视频', './', filter="*.mp4;;*.avi;;All Files(*)")
self.ui.video_path.setText(video_path)
flag = self.cap.open(video_path)
if not flag:
QtWidgets.QMessageBox.warning(self,"Warning", '打开视频失败', buttons=QtWidgets.QMessageBox.Ok)
else:
self.timer_video.start(1000/self.cap.get(cv.CAP_PROP_FPS)) # 以30ms为间隔，启动或重启定时器
# if self.opt.save:
# fps, w, h, path = self.set_video_name_and_path()
# self.vid_writer = cv.VideoWriter(path, cv.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
def set_video_name_and_path(self):
# 获取当前系统时间，作为img和video的文件名
now = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime(time.time()))
# if vid_cap: # video
fps = self.cap.get(cv.CAP_PROP_FPS)
w = int(self.cap.get(cv.CAP_PROP_FRAME_WIDTH))
h = int(self.cap.get(cv.CAP_PROP_FRAME_HEIGHT))
# 视频检测结果存储位置
save_path = self.output_folder + 'video/' + now + '.mp4'
return fps, w, h, save_path
def button_camera_open(self):
camera_num = 0
self.cap = cv.VideoCapture(camera_num)
if not self.cap.isOpened():
QtWidgets.QMessageBox.warning(self, u"Warning", u'摄像头打开失败', buttons=QtWidgets.QMessageBox.Ok)
else:
self.timer_video.start(1000/60)
if self.opt.save:
fps, w, h, path = self.set_video_name_and_path()
self.vid_writer = cv.VideoWriter(path, cv.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
def open_model(self):
self.openfile_name_model, _ = QFileDialog.getOpenFileName(self, '选择权重文件', directory='./yolov5\yolo\YoloV5_PyQt5-main\weights')
print(self.openfile_name_model)
if not self.openfile_name_model:
# QtWidgets.QMessageBox.warning(self, u"Warning" u'未选择权重文件，请重试', buttons=QtWidgets.QMessageBox.Ok)
self.log("warining 未选择权重文件，请重试")
else :
print(self.openfile_name_model)
self.log("权重文件路径为：%s"%self.openfile_name_model)
pass
def show_video_frame(self):
name_list = []
flag, img = self.cap.read()
if img is None:
self.releaseRes()
else:
close_eye, img = self.recognize.skim_video(img, self.ui.ha.checkState(), self.ui.eye.checkState(), self.ui.tired.checkState())
# if self.opt.save:
# self.vid_writer.write(img) # 检测结果写入视频
self.show_img(close_eye, img)
def releaseRes(self):
print('读取结束')
self.log('检测结束')
self.timer_video.stop()
self.cap.release() # 释放video_capture资源
self.ui.show.clear()
if self.opt.save:
self.vid_writer.release()
def log(self, msg):
self.logging += '%s\n'%msg
self.ui.log.setText(self.logging)
self.ui.log.moveCursor(QtGui.QTextCursor.End)
if __name__=='__main__':
# 创建QApplication实例
app=QApplication(sys.argv)#获取命令行参数
current_ui = UI_Logic_Window()
current_ui.show()
sys.exit(app.exec_())
界面代码
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'detect_ui.ui'
#
# Created by: PyQt5 UI code generator 5.9.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_DREAM_EYE(object):
def setupUi(self, DREAM_EYE):
DREAM_EYE.setObjectName("DREAM_EYE")
DREAM_EYE.resize(936, 636)
icon = QtGui.QIcon()
icon.addPixmap(QtGui.QPixmap("../../ui_img/icon.jpg"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
DREAM_EYE.setWindowIcon(icon)
self.centralwidget = QtWidgets.QWidget(DREAM_EYE)
self.centralwidget.setEnabled(True)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Fixed)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.centralwidget.sizePolicy().hasHeightForWidth())
self.centralwidget.setSizePolicy(sizePolicy)
self.centralwidget.setObjectName("centralwidget")
self.groupBox = QtWidgets.QGroupBox(self.centralwidget)
self.groupBox.setGeometry(QtCore.QRect(640, 10, 311, 621))
font = QtGui.QFont()
font.setFamily("Microsoft YaHei")
font.setPointSize(11)
self.groupBox.setFont(font)
self.groupBox.setObjectName("groupBox")
self.groupBox_2 = QtWidgets.QGroupBox(self.groupBox)
self.groupBox_2.setGeometry(QtCore.QRect(10, 270, 271, 151))
self.groupBox_2.setObjectName("groupBox_2")
self.camera = QtWidgets.QRadioButton(self.groupBox_2)
self.camera.setGeometry(QtCore.QRect(20, 30, 89, 16))
self.camera.setObjectName("camera")
self.video = QtWidgets.QRadioButton(self.groupBox_2)
self.video.setGeometry(QtCore.QRect(140, 30, 89, 16))
self.video.setObjectName("video")
self.label = QtWidgets.QLabel(self.groupBox_2)
self.label.setGeometry(QtCore.QRect(20, 60, 81, 31))
self.label.setObjectName("label")
self.select_video = QtWidgets.QPushButton(self.groupBox_2)
self.select_video.setGeometry(QtCore.QRect(30, 110, 81, 31))
self.select_video.setObjectName("select_video")
self.start_skim = QtWidgets.QPushButton(self.groupBox_2)
self.start_skim.setGeometry(QtCore.QRect(150, 110, 81, 31))
self.start_skim.setObjectName("start_skim")
self.video_path = QtWidgets.QTextEdit(self.groupBox_2)
self.video_path.setGeometry(QtCore.QRect(110, 60, 151, 31))
self.video_path.setObjectName("video_path")
self.groupBox_3 = QtWidgets.QGroupBox(self.groupBox)
self.groupBox_3.setGeometry(QtCore.QRect(10, 30, 271, 111))
self.groupBox_3.setObjectName("groupBox_3")
self.eye = QtWidgets.QCheckBox(self.groupBox_3)
self.eye.setGeometry(QtCore.QRect(20, 30, 91, 21))
self.eye.setObjectName("eye")
self.ha = QtWidgets.QCheckBox(self.groupBox_3)
self.ha.setGeometry(QtCore.QRect(150, 30, 91, 21))
self.ha.setObjectName("ha")
self.head = QtWidgets.QCheckBox(self.groupBox_3)
self.head.setGeometry(QtCore.QRect(20, 70, 91, 21))
self.head.setObjectName("head")
self.tired = QtWidgets.QCheckBox(self.groupBox_3)
self.tired.setGeometry(QtCore.QRect(150, 70, 91, 21))
self.tired.setObjectName("tired")
self.groupBox_5 = QtWidgets.QGroupBox(self.groupBox)
self.groupBox_5.setGeometry(QtCore.QRect(10, 430, 271, 181))
self.groupBox_5.setObjectName("groupBox_5")
self.log = QtWidgets.QTextBrowser(self.groupBox_5)
self.log.setGeometry(QtCore.QRect(10, 30, 251, 141))
self.log.setObjectName("log")
self.groupBox_4 = QtWidgets.QGroupBox(self.groupBox)
self.groupBox_4.setGeometry(QtCore.QRect(10, 150, 271, 111))
self.groupBox_4.setObjectName("groupBox_4")
self.label_3 = QtWidgets.QLabel(self.groupBox_4)
self.label_3.setGeometry(QtCore.QRect(20, 30, 81, 21))
self.label_3.setObjectName("label_3")
self.tired_time = QtWidgets.QSpinBox(self.groupBox_4)
self.tired_time.setGeometry(QtCore.QRect(100, 30, 42, 22))
self.tired_time.setObjectName("tired_time")
self.label_4 = QtWidgets.QLabel(self.groupBox_4)
self.label_4.setGeometry(QtCore.QRect(20, 70, 81, 21))
self.label_4.setObjectName("label_4")
self.tired_count = QtWidgets.QSpinBox(self.groupBox_4)
self.tired_count.setGeometry(QtCore.QRect(100, 70, 42, 22))
self.tired_count.setObjectName("tired_count")
self.capture = QtWidgets.QLabel(self.centralwidget)
self.capture.setGeometry(QtCore.QRect(10, 10, 611, 611))
self.capture.setText("")
self.capture.setObjectName("capture")
DREAM_EYE.setCentralWidget(self.centralwidget)
self.retranslateUi(DREAM_EYE)
QtCore.QMetaObject.connectSlotsByName(DREAM_EYE)
def retranslateUi(self, DREAM_EYE):
_translate = QtCore.QCoreApplication.translate
DREAM_EYE.setWindowTitle(_translate("DREAM_EYE", "疲劳检测系统"))
self.groupBox.setTitle(_translate("DREAM_EYE", "参数设置"))
self.groupBox_2.setTitle(_translate("DREAM_EYE", "视频源"))
self.camera.setText(_translate("DREAM_EYE", "摄像头"))
self.video.setText(_translate("DREAM_EYE", "视频文件"))
self.label.setText(_translate("DREAM_EYE", "视频地址："))
self.select_video.setText(_translate("DREAM_EYE", "选择文件"))
self.start_skim.setText(_translate("DREAM_EYE", "确定"))
self.groupBox_3.setTitle(_translate("DREAM_EYE", "疲劳检测"))
self.eye.setText(_translate("DREAM_EYE", "闭眼检测"))
self.ha.setText(_translate("DREAM_EYE", "哈欠检测"))
self.head.setText(_translate("DREAM_EYE", "瞌睡检测"))
self.tired.setText(_translate("DREAM_EYE", "疲劳预警"))
self.groupBox_5.setTitle(_translate("DREAM_EYE", "输出"))
self.groupBox_4.setTitle(_translate("DREAM_EYE", "检测设置"))
self.label_3.setText(_translate("DREAM_EYE", "疲劳时间："))
self.label_4.setText(_translate("DREAM_EYE", "疲劳次数："))
检测效果
效果还不错，不过对于不是那么明显的情况可能就不是很好了，如光线不好，人脸不全的情况。
接下来可以放到你笔记本上，让他定时提醒你休息。
源代码
https://github.com/cdmstrong/tried