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# -*- coding: utf-8 -*-
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# 应该在界面启动的时候就将模型加载出来，设置tmp的目录来放中间的处理结果
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import shutil
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import PyQt5.QtCore
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from PyQt5.QtGui import *
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from PyQt5.QtCore import *
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from PyQt5.QtWidgets import *
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import threading
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import argparse
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import os
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import sys
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from pathlib import Path
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import cv2
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import torch
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import torch.backends.cudnn as cudnn
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import os.path as osp
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[0] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.common import DetectMultiBackend
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from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
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from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
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increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
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from utils.plots import Annotator, colors, save_one_box
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from utils.torch_utils import select_device, time_sync
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# 添加一个关于界面
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# 窗口主类
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class MainWindow(QTabWidget):
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# 基本配置不动，然后只动第三个界面
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def __init__(self):
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# 初始化界面
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super().__init__()
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self.setWindowTitle('Target detection system')
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self.resize(1200, 800)
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self.setWindowIcon(QIcon("images/UI/lufei.png"))
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# 图片读取进程
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self.output_size = 480
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self.img2predict = ""
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self.device = 'cpu'
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# # 初始化视频读取线程
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self.vid_source = '0' # 初始设置为摄像头
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self.stopEvent = threading.Event()
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self.webcam = True
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self.stopEvent.clear()
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self.model = self.model_load(weights="runs/train/exp_yolov5s/weights/best.pt",
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device=self.device) # todo 指明模型加载的位置的设备
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self.initUI()
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self.reset_vid()
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'''
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***模型初始化***
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'''
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@torch.no_grad()
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def model_load(self, weights="", # model.pt path(s)
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device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
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half=False, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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):
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device = select_device(device)
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half &= device.type != 'cpu' # half precision only supported on CUDA
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device = select_device(device)
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model = DetectMultiBackend(weights, device=device, dnn=dnn)
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stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
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# Half
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half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
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if pt:
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model.model.half() if half else model.model.float()
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print("模型加载完成!")
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return model
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'''
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***界面初始化***
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'''
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def initUI(self):
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# 图片检测子界面
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font_title = QFont('楷体', 16)
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font_main = QFont('楷体', 14)
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# 图片识别界面, 两个按钮，上传图片和显示结果
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img_detection_widget = QWidget()
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img_detection_layout = QVBoxLayout()
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img_detection_title = QLabel("图片识别功能")
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img_detection_title.setFont(font_title)
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mid_img_widget = QWidget()
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mid_img_layout = QHBoxLayout()
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self.left_img = QLabel()
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self.right_img = QLabel()
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self.left_img.setPixmap(QPixmap("images/UI/up.jpeg"))
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self.right_img.setPixmap(QPixmap("images/UI/right.jpeg"))
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self.left_img.setAlignment(Qt.AlignCenter)
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self.right_img.setAlignment(Qt.AlignCenter)
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mid_img_layout.addWidget(self.left_img)
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mid_img_layout.addStretch(0)
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mid_img_layout.addWidget(self.right_img)
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mid_img_widget.setLayout(mid_img_layout)
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up_img_button = QPushButton("上传图片")
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det_img_button = QPushButton("开始检测")
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up_img_button.clicked.connect(self.upload_img)
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det_img_button.clicked.connect(self.detect_img)
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up_img_button.setFont(font_main)
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det_img_button.setFont(font_main)
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up_img_button.setStyleSheet("QPushButton{color:white}"
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"QPushButton:hover{background-color: rgb(2,110,180);}"
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"QPushButton{background-color:rgb(48,124,208)}"
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"QPushButton{border:2px}"
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"QPushButton{border-radius:5px}"
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"QPushButton{padding:5px 5px}"
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"QPushButton{margin:5px 5px}")
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det_img_button.setStyleSheet("QPushButton{color:white}"
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"QPushButton:hover{background-color: rgb(2,110,180);}"
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"QPushButton{background-color:rgb(48,124,208)}"
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"QPushButton{border:2px}"
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"QPushButton{border-radius:5px}"
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"QPushButton{padding:5px 5px}"
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"QPushButton{margin:5px 5px}")
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img_detection_layout.addWidget(img_detection_title, alignment=Qt.AlignCenter)
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img_detection_layout.addWidget(mid_img_widget, alignment=Qt.AlignCenter)
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img_detection_layout.addWidget(up_img_button)
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img_detection_layout.addWidget(det_img_button)
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img_detection_widget.setLayout(img_detection_layout)
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# todo 视频识别界面
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# 视频识别界面的逻辑比较简单，基本就从上到下的逻辑
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vid_detection_widget = QWidget()
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vid_detection_layout = QVBoxLayout()
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vid_title = QLabel("视频检测功能")
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vid_title.setFont(font_title)
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self.vid_img = QLabel()
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self.vid_img.setPixmap(QPixmap("images/UI/up.jpeg"))
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vid_title.setAlignment(Qt.AlignCenter)
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self.vid_img.setAlignment(Qt.AlignCenter)
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self.webcam_detection_btn = QPushButton("摄像头实时监测")
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self.mp4_detection_btn = QPushButton("视频文件检测")
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self.vid_stop_btn = QPushButton("停止检测")
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self.webcam_detection_btn.setFont(font_main)
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self.mp4_detection_btn.setFont(font_main)
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self.vid_stop_btn.setFont(font_main)
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self.webcam_detection_btn.setStyleSheet("QPushButton{color:white}"
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"QPushButton:hover{background-color: rgb(2,110,180);}"
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"QPushButton{background-color:rgb(48,124,208)}"
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"QPushButton{border:2px}"
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"QPushButton{border-radius:5px}"
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"QPushButton{padding:5px 5px}"
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"QPushButton{margin:5px 5px}")
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self.mp4_detection_btn.setStyleSheet("QPushButton{color:white}"
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"QPushButton:hover{background-color: rgb(2,110,180);}"
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"QPushButton{background-color:rgb(48,124,208)}"
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"QPushButton{border:2px}"
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"QPushButton{border-radius:5px}"
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"QPushButton{padding:5px 5px}"
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"QPushButton{margin:5px 5px}")
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self.vid_stop_btn.setStyleSheet("QPushButton{color:white}"
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"QPushButton:hover{background-color: rgb(2,110,180);}"
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"QPushButton{background-color:rgb(48,124,208)}"
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"QPushButton{border:2px}"
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"QPushButton{border-radius:5px}"
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"QPushButton{padding:5px 5px}"
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"QPushButton{margin:5px 5px}")
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self.webcam_detection_btn.clicked.connect(self.open_cam)
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self.mp4_detection_btn.clicked.connect(self.open_mp4)
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self.vid_stop_btn.clicked.connect(self.close_vid)
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# 添加组件到布局上
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vid_detection_layout.addWidget(vid_title)
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vid_detection_layout.addWidget(self.vid_img)
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vid_detection_layout.addWidget(self.webcam_detection_btn)
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vid_detection_layout.addWidget(self.mp4_detection_btn)
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vid_detection_layout.addWidget(self.vid_stop_btn)
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vid_detection_widget.setLayout(vid_detection_layout)
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# todo 关于界面
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about_widget = QWidget()
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about_layout = QVBoxLayout()
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about_title = QLabel('欢迎使用目标检测系统\n\n 提供付费指导：有需要的好兄弟加下面的QQ即可') # todo 修改欢迎词语
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about_title.setFont(QFont('楷体', 18))
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about_title.setAlignment(Qt.AlignCenter)
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about_img = QLabel()
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about_img.setPixmap(QPixmap('images/UI/qq.png'))
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about_img.setAlignment(Qt.AlignCenter)
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# label4.setText("<a href='https://oi.wiki/wiki/学习率的调整'>如何调整学习率</a>")
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label_super = QLabel() # todo 更换作者信息
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label_super.setText("<a href='https://blog.csdn.net/ECHOSON'>或者你可以在这里找到我-->肆十二</a>")
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label_super.setFont(QFont('楷体', 16))
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label_super.setOpenExternalLinks(True)
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# label_super.setOpenExternalLinks(True)
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label_super.setAlignment(Qt.AlignRight)
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about_layout.addWidget(about_title)
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about_layout.addStretch()
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about_layout.addWidget(about_img)
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about_layout.addStretch()
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about_layout.addWidget(label_super)
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about_widget.setLayout(about_layout)
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self.left_img.setAlignment(Qt.AlignCenter)
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self.addTab(img_detection_widget, '图片检测')
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self.addTab(vid_detection_widget, '视频检测')
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self.setTabIcon(0, QIcon('images/UI/lufei.png'))
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self.setTabIcon(1, QIcon('images/UI/lufei.png'))
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'''
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***上传图片***
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'''
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def upload_img(self):
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# 选择录像文件进行读取
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fileName, fileType = QFileDialog.getOpenFileName(self, 'Choose file', '', '*.jpg *.png *.tif *.jpeg')
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if fileName:
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suffix = fileName.split(".")[-1]
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save_path = osp.join("images/tmp", "tmp_upload." + suffix)
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shutil.copy(fileName, save_path)
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# 应该调整一下图片的大小，然后统一防在一起
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im0 = cv2.imread(save_path)
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resize_scale = self.output_size / im0.shape[0]
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im0 = cv2.resize(im0, (0, 0), fx=resize_scale, fy=resize_scale)
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cv2.imwrite("images/tmp/upload_show_result.jpg", im0)
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# self.right_img.setPixmap(QPixmap("images/tmp/single_result.jpg"))
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self.img2predict = fileName
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self.left_img.setPixmap(QPixmap("images/tmp/upload_show_result.jpg"))
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# todo 上传图片之后右侧的图片重置，
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self.right_img.setPixmap(QPixmap("images/UI/right.jpeg"))
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'''
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***检测图片***
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'''
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def detect_img(self):
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model = self.model
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output_size = self.output_size
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source = self.img2predict # file/dir/URL/glob, 0 for webcam
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imgsz = [640,640] # inference size (pixels)
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conf_thres = 0.25 # confidence threshold
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iou_thres = 0.45 # NMS IOU threshold
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max_det = 1000 # maximum detections per image
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device = self.device # cuda device, i.e. 0 or 0,1,2,3 or cpu
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view_img = False # show results
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save_txt = False # save results to *.txt
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save_conf = False # save confidences in --save-txt labels
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save_crop = False # save cropped prediction boxes
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nosave = False # do not save images/videos
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classes = None # filter by class: --class 0, or --class 0 2 3
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agnostic_nms = False # class-agnostic NMS
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augment = False # ugmented inference
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visualize = False # visualize features
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line_thickness = 3 # bounding box thickness (pixels)
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hide_labels = False # hide labels
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hide_conf = False # hide confidences
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half = False # use FP16 half-precision inference
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dnn = False # use OpenCV DNN for ONNX inference
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print(source)
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if source == "":
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QMessageBox.warning(self, "请上传", "请先上传图片再进行检测")
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else:
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source = str(source)
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device = select_device(self.device)
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webcam = False
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stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
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imgsz = check_img_size(imgsz, s=stride) # check image size
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save_img = not nosave and not source.endswith('.txt') # save inference images
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# Dataloader
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if webcam:
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view_img = check_imshow()
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cudnn.benchmark = True # set True to speed up constant image size inference
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dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
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bs = len(dataset) # batch_size
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else:
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
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bs = 1 # batch_size
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vid_path, vid_writer = [None] * bs, [None] * bs
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# Run inference
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if pt and device.type != 'cpu':
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model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters()))) # warmup
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dt, seen = [0.0, 0.0, 0.0], 0
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for path, im, im0s, vid_cap, s in dataset:
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t1 = time_sync()
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im = torch.from_numpy(im).to(device)
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im = im.half() if half else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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t2 = time_sync()
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dt[0] += t2 - t1
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# Inference
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# visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
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pred = model(im, augment=augment, visualize=visualize)
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t3 = time_sync()
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dt[1] += t3 - t2
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# NMS
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pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
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dt[2] += time_sync() - t3
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# Second-stage classifier (optional)
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# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
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# Process predictions
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for i, det in enumerate(pred): # per image
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seen += 1
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if webcam: # batch_size >= 1
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p, im0, frame = path[i], im0s[i].copy(), dataset.count
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s += f'{i}: '
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else:
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p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
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p = Path(p) # to Path
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s += '%gx%g ' % im.shape[2:] # print string
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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imc = im0.copy() if save_crop else im0 # for save_crop
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annotator = Annotator(im0, line_width=line_thickness, example=str(names))
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if len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
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# Print results
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum() # detections per class
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
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# Write results
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for *xyxy, conf, cls in reversed(det):
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if save_txt: # Write to file
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(
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-1).tolist() # normalized xywh
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line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
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# with open(txt_path + '.txt', 'a') as f:
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# f.write(('%g ' * len(line)).rstrip() % line + '\n')
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if save_img or save_crop or view_img: # Add bbox to image
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c = int(cls) # integer class
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label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
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annotator.box_label(xyxy, label, color=colors(c, True))
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# if save_crop:
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# save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg',
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# BGR=True)
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# Print time (inference-only)
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LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
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# Stream results
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im0 = annotator.result()
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# if view_img:
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# cv2.imshow(str(p), im0)
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# cv2.waitKey(1) # 1 millisecond
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# Save results (image with detections)
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resize_scale = output_size / im0.shape[0]
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im0 = cv2.resize(im0, (0, 0), fx=resize_scale, fy=resize_scale)
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cv2.imwrite("images/tmp/single_result.jpg", im0)
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# 目前的情况来看，应该只是ubuntu下会出问题，但是在windows下是完整的，所以继续
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self.right_img.setPixmap(QPixmap("images/tmp/single_result.jpg"))
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# 视频检测，逻辑基本一致，有两个功能，分别是检测摄像头的功能和检测视频文件的功能，先做检测摄像头的功能。
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'''
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### 界面关闭事件 ###
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'''
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def closeEvent(self, event):
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reply = QMessageBox.question(self,
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'quit',
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"Are you sure?",
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QMessageBox.Yes | QMessageBox.No,
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QMessageBox.No)
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if reply == QMessageBox.Yes:
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self.close()
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event.accept()
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else:
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event.ignore()
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'''
|
||||
### 视频关闭事件 ###
|
||||
'''
|
||||
|
||||
def open_cam(self):
|
||||
self.webcam_detection_btn.setEnabled(False)
|
||||
self.mp4_detection_btn.setEnabled(False)
|
||||
self.vid_stop_btn.setEnabled(True)
|
||||
self.vid_source = '0'
|
||||
self.webcam = True
|
||||
# 把按钮给他重置了
|
||||
# print("GOGOGO")
|
||||
th = threading.Thread(target=self.detect_vid)
|
||||
th.start()
|
||||
|
||||
'''
|
||||
### 开启视频文件检测事件 ###
|
||||
'''
|
||||
|
||||
def open_mp4(self):
|
||||
fileName, fileType = QFileDialog.getOpenFileName(self, 'Choose file', '', '*.mp4 *.avi')
|
||||
if fileName:
|
||||
self.webcam_detection_btn.setEnabled(False)
|
||||
self.mp4_detection_btn.setEnabled(False)
|
||||
# self.vid_stop_btn.setEnabled(True)
|
||||
self.vid_source = fileName
|
||||
self.webcam = False
|
||||
th = threading.Thread(target=self.detect_vid)
|
||||
th.start()
|
||||
|
||||
'''
|
||||
### 视频开启事件 ###
|
||||
'''
|
||||
|
||||
# 视频和摄像头的主函数是一样的，不过是传入的source不同罢了
|
||||
def detect_vid(self):
|
||||
# pass
|
||||
model = self.model
|
||||
output_size = self.output_size
|
||||
# source = self.img2predict # file/dir/URL/glob, 0 for webcam
|
||||
imgsz = [640, 640] # inference size (pixels)
|
||||
conf_thres = 0.25 # confidence threshold
|
||||
iou_thres = 0.45 # NMS IOU threshold
|
||||
max_det = 1000 # maximum detections per image
|
||||
# device = self.device # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
view_img = False # show results
|
||||
save_txt = False # save results to *.txt
|
||||
save_conf = False # save confidences in --save-txt labels
|
||||
save_crop = False # save cropped prediction boxes
|
||||
nosave = False # do not save images/videos
|
||||
classes = None # filter by class: --class 0, or --class 0 2 3
|
||||
agnostic_nms = False # class-agnostic NMS
|
||||
augment = False # ugmented inference
|
||||
visualize = False # visualize features
|
||||
line_thickness = 3 # bounding box thickness (pixels)
|
||||
hide_labels = False # hide labels
|
||||
hide_conf = False # hide confidences
|
||||
half = False # use FP16 half-precision inference
|
||||
dnn = False # use OpenCV DNN for ONNX inference
|
||||
source = str(self.vid_source)
|
||||
webcam = self.webcam
|
||||
device = select_device(self.device)
|
||||
stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
save_img = not nosave and not source.endswith('.txt') # save inference images
|
||||
# Dataloader
|
||||
if webcam:
|
||||
view_img = check_imshow()
|
||||
cudnn.benchmark = True # set True to speed up constant image size inference
|
||||
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
|
||||
bs = len(dataset) # batch_size
|
||||
else:
|
||||
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
|
||||
bs = 1 # batch_size
|
||||
vid_path, vid_writer = [None] * bs, [None] * bs
|
||||
# Run inference
|
||||
if pt and device.type != 'cpu':
|
||||
model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters()))) # warmup
|
||||
dt, seen = [0.0, 0.0, 0.0], 0
|
||||
for path, im, im0s, vid_cap, s in dataset:
|
||||
t1 = time_sync()
|
||||
im = torch.from_numpy(im).to(device)
|
||||
im = im.half() if half else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
if len(im.shape) == 3:
|
||||
im = im[None] # expand for batch dim
|
||||
t2 = time_sync()
|
||||
dt[0] += t2 - t1
|
||||
# Inference
|
||||
# visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||||
pred = model(im, augment=augment, visualize=visualize)
|
||||
t3 = time_sync()
|
||||
dt[1] += t3 - t2
|
||||
# NMS
|
||||
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
|
||||
dt[2] += time_sync() - t3
|
||||
# Second-stage classifier (optional)
|
||||
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
|
||||
# Process predictions
|
||||
for i, det in enumerate(pred): # per image
|
||||
seen += 1
|
||||
if webcam: # batch_size >= 1
|
||||
p, im0, frame = path[i], im0s[i].copy(), dataset.count
|
||||
s += f'{i}: '
|
||||
else:
|
||||
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
|
||||
p = Path(p) # to Path
|
||||
# save_path = str(save_dir / p.name) # im.jpg
|
||||
# txt_path = str(save_dir / 'labels' / p.stem) + (
|
||||
# '' if dataset.mode == 'image' else f'_{frame}') # im.txt
|
||||
s += '%gx%g ' % im.shape[2:] # print string
|
||||
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
||||
imc = im0.copy() if save_crop else im0 # for save_crop
|
||||
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
|
||||
if len(det):
|
||||
# Rescale boxes from img_size to im0 size
|
||||
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
|
||||
|
||||
# Print results
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||
|
||||
# Write results
|
||||
for *xyxy, conf, cls in reversed(det):
|
||||
if save_txt: # Write to file
|
||||
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(
|
||||
-1).tolist() # normalized xywh
|
||||
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
|
||||
# with open(txt_path + '.txt', 'a') as f:
|
||||
# f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||
|
||||
if save_img or save_crop or view_img: # Add bbox to image
|
||||
c = int(cls) # integer class
|
||||
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
|
||||
annotator.box_label(xyxy, label, color=colors(c, True))
|
||||
# if save_crop:
|
||||
# save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg',
|
||||
# BGR=True)
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
|
||||
# Stream results
|
||||
# Save results (image with detections)
|
||||
im0 = annotator.result()
|
||||
frame = im0
|
||||
resize_scale = output_size / frame.shape[0]
|
||||
frame_resized = cv2.resize(frame, (0, 0), fx=resize_scale, fy=resize_scale)
|
||||
cv2.imwrite("images/tmp/single_result_vid.jpg", frame_resized)
|
||||
self.vid_img.setPixmap(QPixmap("images/tmp/single_result_vid.jpg"))
|
||||
# self.vid_img
|
||||
# if view_img:
|
||||
# cv2.imshow(str(p), im0)
|
||||
# self.vid_img.setPixmap(QPixmap("images/tmp/single_result_vid.jpg"))
|
||||
# cv2.waitKey(1) # 1 millisecond
|
||||
if cv2.waitKey(25) & self.stopEvent.is_set() == True:
|
||||
self.stopEvent.clear()
|
||||
self.webcam_detection_btn.setEnabled(True)
|
||||
self.mp4_detection_btn.setEnabled(True)
|
||||
self.reset_vid()
|
||||
break
|
||||
# self.reset_vid()
|
||||
|
||||
'''
|
||||
### 界面重置事件 ###
|
||||
'''
|
||||
|
||||
def reset_vid(self):
|
||||
self.webcam_detection_btn.setEnabled(True)
|
||||
self.mp4_detection_btn.setEnabled(True)
|
||||
self.vid_img.setPixmap(QPixmap("images/UI/up.jpeg"))
|
||||
self.vid_source = '0'
|
||||
self.webcam = True
|
||||
|
||||
'''
|
||||
### 视频重置事件 ###
|
||||
'''
|
||||
|
||||
def close_vid(self):
|
||||
self.stopEvent.set()
|
||||
self.reset_vid()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app = QApplication(sys.argv)
|
||||
mainWindow = MainWindow()
|
||||
mainWindow.show()
|
||||
sys.exit(app.exec_())
|
||||
Loading…
Reference in New Issue