# -*- coding: utf-8 -*- # @Author : pan # @Description : python main.py # @Date : 2023年7月27日10:46:25 from PySide6.QtWidgets import QApplication, QMainWindow, QFileDialog, QMenu from PySide6.QtGui import QImage, QPixmap, QColor,QCursor from PySide6.QtCore import QTimer, QThread, Signal, QObject, Qt from ui.main_window import Ui_MainWindow from ui.pop.pop_box import MessageBox from ui.ui_function import * from ui.toast.toast import DialogOver from ui.dialog.rtsp_win import Window from ui.dialog.id_win import id_Window from utils.main_utils import check_url, check_path from utils.AtestCamera import Camera from classes.yolo import YoloPredictor from classes.main_config import MainConfig from classes.car_chart import WorkerThread from PIL import Image import numpy as np import supervision as sv import subprocess import sys import cv2 import os import datetime class MainWindow(QMainWindow, Ui_MainWindow): # 主窗口向yolo实例发送执行信号 main2yolo_begin_sgl = Signal() # 主函数 def __init__(self, parent=None): super(MainWindow, self).__init__() self.setupUi(self) # 背景设置为半透明 & 无边框窗口 self.setAttribute(Qt.WA_TranslucentBackground) self.setWindowFlags(Qt.FramelessWindowHint) # UI动作（动效） UIFuncitons.uiDefinitions(self) # 变量设置 self.car_threshold = 0 # 车辆阈值 self.web_flag = True # 可以开启 self.server_process = None # 服务器进程 self.image_id = 0 # 图片 id self.txt_id = 0 # 标签 id # 设置阴影 UIFuncitons.shadow_style(self, self.Class_QF, QColor(162,129,247)) UIFuncitons.shadow_style(self, self.Target_QF, QColor(251, 157, 139)) UIFuncitons.shadow_style(self, self.Fps_QF, QColor(170, 128, 213)) UIFuncitons.shadow_style(self, self.Model_QF, QColor(64, 186, 193)) # 设置 为 -- （好看） self.Class_num.setText('--') self.Target_num.setText('--') self.fps_label.setText('--') # 计时器：每2秒监视模型文件更改一次 self.Qtimer_ModelBox = QTimer(self) self.Qtimer_ModelBox.timeout.connect(self.ModelBoxRefre) self.Qtimer_ModelBox.start(2000) self.yolo_init() # 实例化YOLO self.model_bind() # 预测参数-数值变动绑定 主页面 # 主要功能绑定 @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ self.main_function_bind() self.load_config() # 配置加载 self.model_load() # 模型加载 # 画图线程 self.is_draw_thread = False self.draw_thread = WorkerThread() self.show_status('欢迎使用智能车流分析系统!') # 实例化YOLO def yolo_init(self): # Yolo-v8 thread self.yolo_predict = YoloPredictor() #实例化yolo检测 self.select_model = self.model_box.currentText() self.yolo_thread = QThread() # 显示预测视频（左，右） self.yolo_predict.yolo2main_trail_img.connect(lambda x: self.show_image(x, self.pre_video)) self.yolo_predict.yolo2main_box_img.connect(lambda x: self.show_image(x, self.res_video)) # 输出信息、FPS、类数、总数 self.yolo_predict.yolo2main_status_msg.connect(lambda x: self.show_status(x)) self.yolo_predict.yolo2main_fps.connect(lambda x: self.fps_label.setText(x)) self.yolo_predict.yolo2main_class_num.connect(lambda x:self.Class_num.setText(str(x))) self.yolo_predict.yolo2main_progress.connect(lambda x: self.progress_bar.setValue(x)) # 移动线程里面去（通过main2yolo_begin_sgl信号控制-先要开启yolo_thread线程，才能启动yolo_predict的run方法） self.yolo_predict.moveToThread(self.yolo_thread) self.main2yolo_begin_sgl.connect(self.yolo_predict.run) # 显示总车流量 self.yolo_predict.yolo2main_target_num.connect(lambda x:self.Target_setText(x)) # 模型加载 def model_load(self): # 创建模型文件夹 check_path(self.config.models_path) self.model_box.clear() self.pt_list = os.listdir(f'./{self.config.models_path}') self.pt_list = [file for file in self.pt_list if file.endswith('.pt') or file.endswith('.engine')] self.pt_list.sort(key=lambda x: os.path.getsize(f'./{self.config.models_path}/' + x)) #按文件大小排序 self.model_box.clear() self.model_box.addItems(self.pt_list) # 主页面各功能绑定 def main_function_bind(self): self.src_file_button.clicked.connect(self.open_src_file) # 打开文件夹 self.src_cam_button.clicked.connect(self.camera_select) # 摄像头 self.src_rtsp_button.clicked.connect(self.rtsp_seletction) # RTPS self.src_graph_button.clicked.connect(self.show_traffic_graph) # 流量图 self.src_lock_button.clicked.connect(self.lock_id_selection) # 单目追踪 self.src_web_button.clicked.connect(self.web_back_end) # 后端开启与关闭 self.run_button.clicked.connect(self.run_or_continue) # 开始 self.stop_button.clicked.connect(self.stop) # 终止 self.save_res_button.toggled.connect(self.is_save_res) # 是否 保存 视频 self.save_txt_button.toggled.connect(self.is_save_txt) # 是否 保存 标签 self.show_labels_checkbox.toggled.connect(self.is_show_labels) # 是否 显示标签 self.show_trace_checkbox.toggled.connect(self.is_show_trace) # 是否 显示轨迹 self.ToggleBotton.clicked.connect(lambda: UIFuncitons.toggleMenu(self, True)) self.settings_button.clicked.connect(lambda: UIFuncitons.settingBox(self, True)) # 模型参数绑定 def model_bind(self): self.model_box.currentTextChanged.connect(self.change_model) self.iou_spinbox.valueChanged.connect(lambda x:self.change_val(x, 'iou_spinbox')) # iou box self.iou_slider.valueChanged.connect(lambda x:self.change_val(x, 'iou_slider')) # iou scroll bar self.conf_spinbox.valueChanged.connect(lambda x:self.change_val(x, 'conf_spinbox')) # conf box self.conf_slider.valueChanged.connect(lambda x:self.change_val(x, 'conf_slider')) # conf scroll bar self.speed_spinbox.valueChanged.connect(lambda x:self.change_val(x, 'speed_spinbox'))# speed box self.speed_slider.valueChanged.connect(lambda x:self.change_val(x, 'speed_slider')) # speed scroll bar self.speed_sss.valueChanged.connect(lambda x: self.change_val(x, 'speed_sss')) # speed box self.speed_nnn.valueChanged.connect(lambda x:self.change_val(x, 'speed_nnn')) # speed scroll bar # JSON配置文件初始化 def load_config(self): self.config = MainConfig("./config/config.json") self.save_res_button.setChecked(self.config.save_res) self.save_txt_button.setChecked(self.config.save_txt) self.iou_slider.setValue(self.config.iou * 100) self.conf_slider.setValue(self.config.conf * 100) self.speed_slider.setValue(self.config.rate) self.speed_sss.setValue(self.config.car_threshold) self.yolo_predict.save_txt = self.config.save_txt # 保存标签 self.yolo_predict.save_res = self.config.save_res # 保存结果 self.yolo_predict.save_txt_path = self.config.save_txt_path self.yolo_predict.save_res_path = self.config.save_res_path self.yolo_predict.new_model_name = f"./{self.config.models_path}/%s" % self.select_model self.yolo_predict.show_trace = self.config.show_trace # 轨迹 self.show_trace_checkbox.setChecked(self.config.show_trace) # 轨迹 self.yolo_predict.show_labels = self.config.show_labels # 标签 self.show_labels_checkbox.setChecked(self.config.show_labels) # 标签 self.open_fold = self.config.open_fold self.rtsp_ip = self.config.rtsp_ip self.car_id = self.config.car_id self.run_button.setChecked(False) # 如果超出了车流阈值，那么就变红！！ def Target_setText(self, num): num = str(num) self.Target_num.setText(num) self.char_label.setText(f"当前车流量: {num}") if (int(num) > int(self.car_threshold)): self.char_label.setStyleSheet("color: red;") else: self.char_label.setStyleSheet("color: green;") #主窗口显示轨迹图像和检测图像 （缩放在这里） @staticmethod def show_image(img_src, label): try: # 检查图像的通道数，确定图像是否为彩色图像 if len(img_src.shape) == 3: ih, iw, _ = img_src.shape if len(img_src.shape) == 2: ih, iw = img_src.shape # 根据标签窗口的大小调整图像的大小 w = label.geometry().width() h = label.geometry().height() # 根据图像宽高比例进行缩放 if iw / w > ih / h: scal = w / iw nw = w nh = int(scal * ih) img_src_ = cv2.resize(img_src, (nw, nh)) else: scal = h / ih nw = int(scal * iw) nh = h img_src_ = cv2.resize(img_src, (nw, nh)) # 将OpenCV图像从BGR格式转换为RGB格式，并创建QImage对象 frame = cv2.cvtColor(img_src_, cv2.COLOR_BGR2RGB) img = QImage(frame.data, frame.shape[1], frame.shape[0], frame.shape[2] * frame.shape[1], QImage.Format_RGB888) # 在标签窗口中显示图像 label.setPixmap(QPixmap.fromImage(img)) except Exception as e: print(repr(e)) #控制开始|暂停 def run_or_continue(self): # 检测是否有模型 if self.yolo_predict.new_model_name == '' or self.yolo_predict.new_model_name == None: DialogOver(parent=self, text="请检测模型", title="运行失败", flags="danger") self.run_button.setChecked(False) return # 检测输入源 if self.yolo_predict.source == '' or self.yolo_predict.source == None: self.show_status('请在检测前选择输入源...') self.run_button.setChecked(False) DialogOver(parent=self, text="请检测输入源", title="运行失败", flags="danger") return self.yolo_predict.stop_dtc = False # 线程开始 # 开始 if self.run_button.isChecked(): # 图片预测 file_extension = self.yolo_predict.source[-3:].lower() if file_extension == 'png' or file_extension == 'jpg': self.img_predict() return # 视频预测 DialogOver(parent=self, text="开始检测...", title="运行成功", flags="success") self.run_button.setChecked(True) self.draw_thread.run_continue() # 折线图开始 # 不可再改变设置（config动态调整 关闭） self.save_txt_button.setEnabled(False) self.save_res_button.setEnabled(False) self.conf_slider.setEnabled(False) self.iou_slider.setEnabled(False) self.speed_slider.setEnabled(False) self.show_status('检测中...') if '0' in self.yolo_predict.source or 'rtsp' in self.yolo_predict.source: self.progress_bar.setFormat('实时视频流检测中...') if 'avi' in self.yolo_predict.source or 'mp4' in self.yolo_predict.source: self.progress_bar.setFormat("当前检测进度:%p%") self.yolo_predict.continue_dtc = True # 开始检测 if not self.yolo_thread.isRunning(): self.yolo_thread.start() self.main2yolo_begin_sgl.emit() # 暂停 else: self.draw_thread.pause() # 折线图暂停 self.yolo_predict.continue_dtc = False self.show_status("暂停...") DialogOver(parent=self, text="已暂停检测", title="运行暂停", flags="warning") self.run_button.setChecked(False) # 显示状态 （底部栏 def show_status(self, msg): self.status_bar.setText(msg) # 显示输出 if msg == '检测完成': self.save_res_button.setEnabled(True) self.save_txt_button.setEnabled(True) self.run_button.setChecked(False) self.progress_bar.setValue(0) # 终止yolo线程 if self.yolo_thread.isRunning(): self.yolo_thread.quit() # 修改画图线程状态 self.draw_thread.stop() self.is_draw_thread = False elif msg == '检测终止': self.save_res_button.setEnabled(True) self.save_txt_button.setEnabled(True) self.run_button.setChecked(False) self.progress_bar.setValue(0) # 终止yolo线程 if self.yolo_thread.isRunning(): self.yolo_thread.quit() # 修改画图线程状态 self.draw_thread.stop() self.is_draw_thread = False self.pre_video.clear() self.res_video.clear() self.Class_num.setText('--') self.Target_num.setText('--') self.fps_label.setText('--') # 打开文件 def open_src_file(self): name, _ = QFileDialog.getOpenFileName(self, 'Video/image', self.open_fold, "Pic File(*.mp4 *.mkv *.avi *.flv *.jpg *.png)") if name: self.yolo_predict.source = name self.show_status('加载文件：{}'.format(os.path.basename(name))) self.open_fold = os.path.dirname(name) # 终止事件 self.stop() DialogOver(parent=self, text=f"文件路径: {name}", title="加载成功", flags="success") # camera选择 def camera_select(self): #try: # 关闭YOLO线程 self.stop() #获取本地摄像头数量 _, cams = Camera().get_cam_num() popMenu = QMenu() popMenu.setFixedWidth(self.src_cam_button.width()) popMenu.setStyleSheet(''' QMenu { font-size: 20px; font-family: "Microsoft YaHei UI"; font-weight: light; color:white; padding-left: 5px; padding-right: 5px; padding-top: 4px; padding-bottom: 4px; border-style: solid; border-width: 0px; border-color: rgba(255, 212, 255, 255); border-radius: 3px; background-color: rgba(16,155,226,50); } ''') for cam in cams: exec("action_%s = QAction('%s 号摄像头')" % (cam, cam)) exec("popMenu.addAction(action_%s)" % cam) pos = QCursor.pos() action = popMenu.exec(pos) # 设置摄像头来源 if action: str_temp = '' selected_stream_source = str_temp.join(filter(str.isdigit, action.text())) #获取摄像头号，去除非数字字符 self.yolo_predict.source = selected_stream_source self.show_status(f'摄像头设备:{action.text()}') DialogOver(parent=self, text=f"当前摄像头为: {action.text()}", title="摄像头选择成功", flags="success") # 1、选择rtsp def rtsp_seletction(self): self.rtsp_window = Window() self.rtsp_window.rtspEdit.setText(self.rtsp_ip) self.rtsp_window.show() # 如果点击则加载RTSP self.rtsp_window.rtspButton.clicked.connect(lambda: self.load_rtsp(self.rtsp_window.rtspEdit.text())) # 2、加载RTSP def load_rtsp(self, ip): MessageBox(self.close_button, title='提示', text='加载 rtsp...', time=1000, auto=True).exec() self.stop() # 关闭YOLO线程 self.yolo_predict.source = ip self.rtsp_ip = ip # 写会ip self.rtsp_window.close() #状态显示 self.show_status(f'加载rtsp地址:{ip}') DialogOver(parent=self, text=f"rtsp地址为: {ip}", title="RTSP加载成功", flags="success") # 1、设置 单目标 def lock_id_selection(self): self.yolo_predict.lock_id = None self.id_window = id_Window() self.id_window.idEdit.setText(str(self.car_id)) self.id_window.show() self.id_window.idButton.clicked.connect(lambda: self.set_lock_id(self.id_window.idEdit.text())) # 2、设置 单目标 ID def set_lock_id(self,lock_id): self.yolo_predict.lock_id = None self.yolo_predict.lock_id = lock_id self.car_id = lock_id # 写回lock_id self.show_status('加载ID:{}'.format(lock_id)) self.id_window.close() #加载流量折线图 def show_traffic_graph(self): # 没有开始检测 if not self.run_button.isChecked(): DialogOver(parent=self, text="请先开始目标检测！", title="开启失败", flags="danger") return # 避免重复开启 if self.is_draw_thread: DialogOver(parent=self, text="流量图已经开启啦！", title="不能重复开启", flags="danger") return self.draw_thread.start() self.is_draw_thread = True # 开启Web后端 def web_back_end(self): # 终止事件 self.stop() base_dir = os.path.dirname(os.path.abspath(__file__)) flask_app_path = os.path.join(base_dir, 'app.py') # True是可以开启 if (self.web_flag): self.server_process = subprocess.Popen(['python', flask_app_path]) MessageBox(self.close_button, title='提示', text='正在开启网页端...', time=2000, auto=True).exec() # 检查子进程是否启动 try: if self.server_process.pid is not None: self.src_web_button.setText("关闭网页端") self.web_flag = False DialogOver(parent=self, text="网页端已开启", title="开启成功", flags="success") except Exception as e: DialogOver(parent=self, text=str(e), title="开启失败", flags="danger") # False是关闭后端 else: try: self.server_process.terminate() MessageBox(self.close_button, title='提示', text='正在关闭网页端...', time=2000, auto=True).exec() self.src_web_button.setText("开启网页端") self.web_flag = True DialogOver(parent=self, text="网页端已关闭", title="关闭成功", flags="success") except Exception as e: DialogOver(parent=self, text=str(e), title="关闭失败", flags="danger") #保存提示（MP4） def is_save_res(self): if self.save_res_button.checkState() == Qt.CheckState.Unchecked: self.show_status('提示: 监测结果不会被保存') self.yolo_predict.save_res = False elif self.save_res_button.checkState() == Qt.CheckState.Checked: self.show_status('提示: 监测结果将会被保存') self.yolo_predict.save_res = True #保存提示（txt） def is_save_txt(self): if self.save_txt_button.checkState() == Qt.CheckState.Unchecked: self.show_status('提示: 标签信息不会被保存') self.yolo_predict.save_txt = False elif self.save_txt_button.checkState() == Qt.CheckState.Checked: self.show_status('提示: 标签信息将会被保存') self.yolo_predict.save_txt = True # 是否显示 标签 def is_show_labels(self): if self.show_labels_checkbox.checkState() == Qt.CheckState.Unchecked: self.yolo_predict.show_labels = False self.show_status('提示: 不再显示标签') elif self.show_labels_checkbox.checkState() == Qt.CheckState.Checked: self.yolo_predict.show_labels = True self.show_status('提示: 显示标签') # 是否显示 轨迹 def is_show_trace(self): if self.show_trace_checkbox.checkState() == Qt.CheckState.Unchecked: self.yolo_predict.show_trace = False self.show_status('提示: 不再显示轨迹') elif self.show_trace_checkbox.checkState() == Qt.CheckState.Checked: self.yolo_predict.show_trace = True self.show_status('提示: 显示轨迹') #终止事件（按下终止按钮 or 输入源更换的时候） def stop(self): try: # 摄像头释放 self.yolo_predict.release_capture() # 这里是为了终止使用摄像头检测函数的线程，改了yolo源码 # 结束线程 self.yolo_thread.quit() except: pass self.yolo_predict.stop_dtc = True self.run_button.setChecked(False) #恢复按钮状态 # 终止后才可以修改设置 self.save_res_button.setEnabled(True) #把保存按钮设置为可用 self.save_txt_button.setEnabled(True) #把保存按钮设置为可用 self.iou_slider.setEnabled(True) #把滑块设置为可用 self.conf_slider.setEnabled(True) #把滑块设置为可用 self.speed_slider.setEnabled(True) #把滑块设置为可用 self.pre_video.clear() #清空视频显示 self.res_video.clear() #清空视频显示 self.progress_bar.setValue(0) #进度条清零 self.Class_num.setText('--') self.Target_num.setText('--') self.fps_label.setText('--') #检测参数设置 def change_val(self, x, flag): # 交互比 if flag == 'iou_spinbox': self.iou_slider.setValue(int(x*100)) elif flag == 'iou_slider': self.iou_spinbox.setValue(x/100) self.show_status('IOU Threshold: %s' % str(x/100)) self.yolo_predict.iou_thres = x/100 # 置信度 elif flag == 'conf_spinbox': self.conf_slider.setValue(int(x*100)) elif flag == 'conf_slider': self.conf_spinbox.setValue(x/100) self.show_status('Conf Threshold: %s' % str(x/100)) self.yolo_predict.conf_thres = x/100 # 延时 elif flag == 'speed_spinbox': self.speed_slider.setValue(x) elif flag == 'speed_slider': self.speed_spinbox.setValue(x) self.show_status('Delay: %s ms' % str(x)) self.yolo_predict.speed_thres = x # ms # 车辆数预测警报 elif flag == 'speed_nnn': self.speed_sss.setValue(x) elif flag == 'speed_sss': self.speed_nnn.setValue(x) self.show_status('流量阈值设置: %s 辆' % str(x)) self.car_threshold = x # ms #模型更换 def change_model(self,x): self.select_model = self.model_box.currentText() self.yolo_predict.new_model_name = f"./{self.config.models_path}/%s" % self.select_model self.show_status('更改模型：%s' % self.select_model) self.Model_name.setText(self.select_model) #循环监测文件夹的文件变化 def ModelBoxRefre(self): pt_list = os.listdir(f'./{self.config.models_path}') pt_list = [file for file in pt_list if file.endswith('.pt') or file.endswith('.engine')] pt_list.sort(key=lambda x: os.path.getsize(f'./{self.config.models_path}/' + x)) #必须排序后再比较，否则列表会一直刷新 if pt_list != self.pt_list: self.pt_list = pt_list self.model_box.clear() self.model_box.addItems(self.pt_list) #获取鼠标位置（用于按住标题栏拖动窗口） def mousePressEvent(self, event): p = event.globalPosition() globalPos = p.toPoint() self.dragPos = globalPos #拖动窗口大小时优化调整 def resizeEvent(self, event): # Update Size Grips UIFuncitons.resize_grips(self) # 退出时退出线程，保存设置 def closeEvent(self, event): try: self.stop() # 检测画图线程 self.draw_thread.close_exec() # self.draw_thread.deleteLater() # config.json self.config.save_res = self.yolo_predict.save_res self.config.save_txt = self.yolo_predict.save_txt self.config.show_labels = self.yolo_predict.show_labels self.config.show_trace = self.yolo_predict.show_trace self.config.iou = self.yolo_predict.iou_thres self.config.conf = self.yolo_predict.conf_thres self.config.rate = self.yolo_predict.speed_thres self.config.car_threshold = self.car_threshold # 车辆警报 阈值 self.config.rtsp_ip = self.rtsp_ip self.config.car_id = self.car_id self.config.open_fold = self.open_fold self.config.save_config() # 保存设置 # 退出弹窗 2s MessageBox( self.close_button, title='Note', text='退出中，请等待...', time=2000, auto=True).exec() # 检查服务器进程是否启动 if self.server_process is not None: if self.server_process.pid is not None: self.server_process.terminate() # 服务器进程关闭 sys.exit(0) except Exception as e: print(e) sys.exit(0) # 预测图片 def img_predict(self): if check_url(self.yolo_predict.source): DialogOver(parent=self, text="目标路径含有中文！", title="程序取消", flags="danger") return self.run_button.setChecked(False) # 按钮 # 读取照片 image = cv2.imread(self.yolo_predict.source) org_img = image.copy() # 加载模型 model = self.yolo_predict.load_yolo_model() # 获取数据源 iter_model = iter(model.track(source=image, show=False)) result = next(iter_model) # 这里是检测的核心， # 如果没有目标 if result.boxes.id is None: DialogOver(parent=self, text="该图片中没有要检测的目标哟！", title="运行完成", flags="warning") self.show_image(image, self.pre_video) self.show_image(image, self.res_video) self.yolo_predict.source = '' return # 如果有目标 detections = sv.Detections.from_yolov8(result) detections.tracker_id = result.boxes.id.cpu().numpy().astype(int) # 画标签 labels_write, img_box = self.yolo_predict.creat_labels(detections, image, model) # 显示信息 —— 类别数 & 总数 self.Class_num.setText(str(self.yolo_predict.get_class_number(detections))) self.Target_num.setText(str(len(detections.tracker_id))) # 显示图片 self.show_image(org_img, self.pre_video) # left self.show_image(img_box, self.res_video) # right self.yolo_predict.source = '' DialogOver(parent=self, text="图片检测完成", title="运行成功", flags="success") # 保存图片 if self.yolo_predict.save_res: check_path(self.config.save_res_path) # 检查保存路径 # 存在同名文件，自增 self.image_id 直至文件不存在 while os.path.exists(f"{self.config.save_res_path}/image_result_{self.image_id}.jpg"): self.image_id += 1 # 将 BGR 格式的 frame 转换为 RGB 格式 rgb_frame = cv2.cvtColor(img_box, cv2.COLOR_BGR2RGB) # 把 rgb_frame 转换为 numpy格式 就行了 numpy_frame = np.array(rgb_frame) Image.fromarray(numpy_frame).save(f"./{self.config.save_res_path}/image_result_{self.image_id}.jpg") # 存储labels里的信息 if self.yolo_predict.save_txt: check_path(self.config.save_txt_path) # 检查保存路径 # 存在同名文件，自增 self.txt_id 直至文件不存在 while os.path.exists(f"{self.config.save_txt_path}/result_{self.txt_id}.jpg"): self.txt_id += 1 with open(f'{self.config.save_txt_path}/result_{self.txt_id}.txt', 'a') as f: f.write('当前时刻屏幕信息:' + str(labels_write) + f'检测时间: {datetime.datetime.now().strftime("%Y-%m-%d-%H:%M:%S")}' + f' 路段通过的目标总数: {len(detections.tracker_id)}') f.write('\n') return if __name__ == "__main__": app = QApplication(sys.argv) Home = MainWindow() Home.show() sys.exit(app.exec())