前言
这篇博客针对《Python路面车道线识别偏离预警》编写代码，代码整洁，规则，易读。 学习与应用推荐首选。
运行结果
文章目录
一、所需工具软件
二、使用步骤
1. 主要代码
2. 运行结果
三、在线协助
一、所需工具软件
1. Python
2. Pycharm
二、使用步骤
代码如下（示例）：
import os
from PyQt5.QtWidgets import QMainWindow
import cv2
from .form.mainwindow import Ui_MainWindow
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *
import qdarkstyle
import json
import time
class MainWinddow(QMainWindow):
print_debug_signal = pyqtSignal(str) # 用于触发打印调试信息函数
show_image_signal = pyqtSignal(str) # 用于触发图像处理结果函数
# 初始化函数
def __init__(self,parent=None):
super(MainWinddow,self).__init__(parent)
self.main_ui = Ui_MainWindow()
self.main_ui.setupUi(self)
# 设置软件名称
self.setWindowTitle("基于TWINLITENET算法的车道线与可行驶区域感知系统")
# 设置登录界面第一行，第二行文字
self.main_ui.label_up.setText("基于TWINLITENET算法的")
self.main_ui.label_down.setText("车道线与可行驶区域感知系统")
self.main_ui.label_up_2.setText("基于TWINLITENET算法的")
self.main_ui.label_9.setText("车道线与可行驶区域感知系统")
# 设置界面起始页
self.main_ui.stackedWidget.setCurrentIndex(0)
# # 设置界面初始状态
# self.clear_label()
# # 设置背景颜色
# palette = qdarkstyle.palette.Palette()
# palette.ID = "dark"
# # 修改背景颜色为红色
# palette.background = "#FF0000" # 这里是十六进制颜色代码，代表红色
#
# self.setStyleSheet(qdarkstyle.load_stylesheet(qt_api="pyqt5", palette=palette))
self.main_ui.textBrowser_debug.document().setMaximumBlockCount(10)
# 读取系统人员名单（采用本地保存格式）
# 账号：英文字母、数字组合或中文（最大长度10）
# 密码：英文字母、数字组合（最大长度6）
self.count_dict = {}
with open(r"UI\form\namelist.json","r", encoding='utf-8' ) as f:
self.count_dict = json.load(f)
# 深度学习模型推理类
self.videoprocess = videoprocess(self.print_debug_signal,self.show_image_signal)
# 背景图平铺
def paintEvent(self, event):
painter = QPainter(self)
pixmap = QPixmap("background/background5.jpg")
painter.drawPixmap(self.rect(), pixmap)
# 背景图平铺结束
# 登录槽函数
def login_function(self):
# 获取界面账户、密码
count = self.main_ui.lineEdit_count.text()
secret = self.main_ui.lineEdit_secret.text()
# 判断用户信息是否存在
if count in self.count_dict.keys():
# 判断密码是否正确
if self.count_dict[count] == secret:
# 弹出登录成功窗口
QMessageBox.information(self,"Tip","登录成功！")
# 切换页面
self.main_ui.stackedWidget.setCurrentIndex(2)
else:
# 弹出密码错误窗口
QMessageBox.critical(self,"error","密码错误！")
else:
# 弹出账户未注册窗口
QMessageBox.critical(self,"error","账户未注册！")
def registe_interface_change(self):
# 切换页面
self.main_ui.stackedWidget.setCurrentIndex(1)
print("registe_interface_change")
# 注册槽函数
def registe_function(self):
# 获取界面账户、密码
count = self.main_ui.lineEdit_count_2.text()
secret = self.main_ui.lineEdit_secret_2.text()
# 判断账户、密码输入是否为空字符串
if len(count) == 0 or len(secret) == 0:
# 提示用户重新输入
QMessageBox.warning(self,"warning","信息无效！")
else:
# 忽视用户信息是否存在，重新设置用户信息
self.count_dict[count] = secret
# 保存至本地，(如需删除账户信息可直接在UI\form\namelist.json中删除即可)
with open(r"UI\form\namelist.json","w", encoding='utf-8') as f: ## 设置'utf-8'编码
f.write(json.dumps(self.count_dict, ensure_ascii=False ,indent=4))
QMessageBox.information(self,"Tip","注册成功！")
def backLoginWindow(self):
# 切换页面
self.main_ui.stackedWidget.setCurrentIndex(0)
# 视频源改变槽函数
def videosoure_change(self):
#当视频源发生切换如果视频正在推理需要停止
if not self.videoprocess.stopped:
self.videoprocess.stopped = True
self.clear_label()
if self.main_ui.comboBox_source.currentIndex() == 0:
self.main_ui.lineEdit_filepath.setVisible(True)
self.main_ui.pushButton_open.setVisible(True)
elif self.main_ui.comboBox_source.currentIndex() == 1:
self.main_ui.lineEdit_filepath.setVisible(True)
self.main_ui.pushButton_open.setVisible(True)
# 打开视频与图片槽函数
def open_video_function(self):
# 打开本地路径
if self.main_ui.comboBox_source.currentIndex() == 0:
fileName, filetype = QFileDialog.getOpenFileName(self,"选取视频","./videos", "'Video Files (*.mp4 *.avi *.mkv)'")
elif self.main_ui.comboBox_source.currentIndex() == 1:
fileName, filetype = QFileDialog.getOpenFileName(self, '选择图片',"./images", 'Image Files (*.png *.jpg *.jpeg *.gif)')
# 显示本地路径
self.main_ui.lineEdit_filepath.setText(fileName)
# 打印信息
self.print_debug_signal.emit("{}打开成功，请点击开始按钮！！！".format(fileName))
# 开始测试
def predicte_function(self):
if not self.videoprocess.stopped:
self.print_debug_signal.emit("已经开启，请关闭后再次开启！！")
return
self.main_ui.pushButton_pause.setVisible(True)
self.main_ui.pushButton_pause.setText("暂停测试")
# 是否保存视频
if self.main_ui.checkBox_save.isChecked():
self.videoprocess.save_out = True
else:
self.videoprocess.save_out = False
self.videoprocess.filename = self.main_ui.lineEdit_filepath.text()
# 启动深度学习推理线程
self.videoprocess.start()
self.main_ui.pushButton_alarm.setStyleSheet("background-color:rgb(0,255,0);border-radius: 10px; border: 2px groove black;border-style: outset;")
# 暂停测试
def setpause_function(self):
if self.videoprocess.is_pause:
# 当前状态已经暂定
self.videoprocess.is_pause = False
self.main_ui.pushButton_pause.setText("暂停测试")
else:
self.videoprocess.is_pause = True
self.main_ui.pushButton_pause.setText("继续测试")
# 停止测试
def stop_function(self):
# 停止深度学习推理线程
if self.videoprocess.stopped:
self.print_debug_signal.emit("已经关闭！！")
else:
self.videoprocess.stopped = True
self.clear_label()
# 清空标签,恢复初始状态
def clear_label(self):
self.main_ui.label_image_source.clear()
self.main_ui.label_image_source.setText("原视频")
self.main_ui.label_image_lane.clear()
self.main_ui.label_image_lane.setText("车道线")
self.main_ui.label_image_driving.clear()
self.main_ui.label_image_driving.setText("可行驶区域")
self.main_ui.label_image_result.clear()
self.main_ui.label_image_result.setText("识别结果")
self.main_ui.pushButton_alarm.setStyleSheet("background-color:rgb(150, 150, 150);border-radius: 10px; border: 2px groove black;border-style: outset;")
self.main_ui.label_fps.setText("FPS: 00.00")
self.main_ui.pushButton_pause.setVisible(False) # 停止运行后暂停按钮失效
# 显示界面函数
def showimgae(self,fps):
if not self.videoprocess.stopped:
# 转换格式
img_source = cv2.cvtColor(self.videoprocess.img_source, cv2.COLOR_BGR2RGB)
color_lane = cv2.cvtColor(self.videoprocess.color_lane, cv2.COLOR_BGR2RGB)
color_driving = cv2.cvtColor(self.videoprocess.color_driving, cv2.COLOR_BGR2RGB)
img_rs = cv2.cvtColor(self.videoprocess.img_rs, cv2.COLOR_BGR2RGB)
# 显示原图
self.main_ui.label_image_source.setPixmap(QPixmap(QImage(img_source.data,img_source.shape[1],img_source.shape[0],QImage.Format_RGB888)))
self.main_ui.label_image_source.setScaledContents(True)
# 显示车道线
self.main_ui.label_image_lane.setPixmap(QPixmap(QImage(color_lane.data,color_lane.shape[1],color_lane.shape[0],QImage.Format_RGB888)))
self.main_ui.label_image_lane.setScaledContents(True)
运行结果
三、在线协助：
如需安装运行环境或远程调试，见文章底部个人 QQ 名片，由专业技术人员远程协助！
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