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Yolov5(v5.0)+pyqt5界面设计图文教程

目录
  • 1.下载安装pyqt5工具包以及配置ui界面开发环境
  • 2.点击File->Settings->External Tools进行工具添加,依次进行Qt Designer、PyUIC环境配置.
    •  2.1 添加QtDesigner
    • 2.2 添加PyUIC
  • 3. QtDesigner建立图形化窗口界面 
    • 3.1 在根目录下新建UI文件夹进行UI文件的专门存储
    • 3.2 创建一个Main Window窗口
    • 3.3 完成基本界面开发后,保存其为Detect.ui,放置在UI文件夹下,利用PyUic工具将其转化为Detect.py文件。
  • 4. demo
    • 5.添加背景图片
      • 6.reference
        • 总结

          1.下载安装pyqt5工具包以及配置ui界面开发环境

          pip install PyQt5
          pip install PyQt5-tools

          2.点击File->Settings->External Tools进行工具添加,依次进行Qt Designer、PyUIC环境配置.

          Yolov5(v5.0)+pyqt5界面设计图文教程

           2.1 添加QtDesigner

          Yolov5(v5.0)+pyqt5界面设计图文教程

           Qt Designer 是通过拖拽的方式放置控件,并实时查看控件效果进行快速UI设计

          位置内容
          name可以随便命名,只要便于记忆就可以,本次采取通用命名:Qt Designer
          Progphpramdesigner.exe路径,一般在python中.\Library\bin\designer.exe
          Arguments固定格式,直接复制也可:$FileDir$\$FileName$
          Working directory固定格式,直接复制也可:$FileDir$

          2.2 添加PyUIC

          Yolov5(v5.0)+pyqt5界面设计图文教程

           PyUIC主要是把Qt Designer生成的.ui文件换成.py文件

          位置内容
          name可以随便命名,只要便于记忆就可以,本次采取通用命名:PyUiC
          Programpython.exe路径,一般在python安装根目录中
          Arguments固定格式,直接复制也可:-m PyQt5.uic.pyuic $FileName$ -o $FileNameWithoutExtension$.py
          Working directory固定格式,直接复制也可:$FileDir$

          3. QtDesigner建立图形化窗口界面 

          3.1 在根目录下新建UI文件夹进行UI文件的专门存储

          点击Tools->External Tools->Qt Designer进行图形界面创建.

          Yolov5(v5.0)+pyqt5界面设计图文教程

          3.2 创建一个Main Window窗口

          Yolov5(v5.0)+pyqt5界面设计图文教程

          Yolov5(v5.0)+pyqt5界面设计图文教程

          Yolov5(v5.0)+pyqt5界面设计图文教程

          3.3 完成基本界面开发后,保存其为Detect.ui,放置在UI文件夹下,利用PyUic工具将其转化为Detect.py文件。

          Yolov5(v5.0)+pyqt5界面设计图文教程

          Yolov5(v5.0)+pyqt5界面设计图文教程

          转换完成后,进行相应的槽函数的建立与修改,此处建议直接看我后面给出的demo。

          4. demo

          使用时只需将parser.add_argument中的'--weights'设为响应权重即可。

          # -*- coding: utf-8 -*-
           
          # Form implementation generated from reading ui file '.\project.ui'
          #
          # Created by: PyQt5 UI code generator 5.9.2
          #
          # WARNING! All changes made in this file will be lost!
          import sys
          import cv2
          import argparse
          import random
          import torch
          import numpy as np
          import torch.backends.cudnn as cudnn
           
          from PyQt5 import QtCore, QtGui, QtWidgets
           
          from utils.torch_utils import select_device
          from models.experimental import attempt_load
          from utils.general import check_img_size, non_max_suppression, scale_coords
          from utils.datasets import letterbox
          from utils.plots import plot_one_box
           
           
          class Ui_MainWindow(QtWidgets.QMainWindow):
              def __init__(self, parent=None):
                  super(Ui_MainWindow, self).__init__(parent)
                  self.timer_video = QtCore.QTimer()
                  self.setupUi(self)
                  self.init_logo()
                  self.init_slots()
                  self.cap = cv2.VideoCapture()
                  self.out = None
                  # self.out = cv2.VideoWriter('prediction.avi', cv2.VideoWriter_fourcc(*'XVID'), 20.0, (640, 480))
           
                  parser = argparse.ArgumentParser()
                  parser.add_argument('--weights', nargs='+', type=str,
                                      default='weights/best.pt', help='model.pt path(s)')
                  # file/folder, 0 for webcam
                  parser.add_argument('--source', type=str,
                                      default='data/images', help='source')
                  parser.add_argument('--img-size', type=int,
                                      default=640, help='inference size (pixels)')
                  parser.add_argument('--conf-thres', type=float,
                                      default=0.25, help='object confidence threshold')
                  parser.add_argument('--iou-thres', type=float,
                                      default=0.45, help='IOU threshold for NMS')
          js        parser.add_argument('--device', default='',
                                      help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
                  parser.add_argument(
                      '--view-img', action='store_true', help='display results')
                  parser.add_argument('--save-txt', action='store_true',
                                      help='save results to *.txt')
                  parser.add_argument('--save-conf', action='store_true',
                                      help='save confidences in --save-txt labels')
                  parser.add_argument('--nosave', action='store_true',
                                      help='do not save images/videos')
                  parser.add_argument('--classes', nargs='+', type=int,
                                      help='filter by class: --class 0, or --class 0 2 3')
                  parser.add_argument(
                      '--agnostic-nms', action='store_true', help='class-agnostic NMS')
                  parser.add_argument('--augment', action='store_true',
                                      help='augmented inference')
                  parser.add_argument('--update', action='store_true',
                                      help='update all models')
                  parser.add_argument('--project', default='runs/detect',
                                      help='save results to project/name')
                  parser.add_argument('--name', default='exp',
                                      help='save results to project/name')
                  parser.add_argument('--exist-ok', action='store_true',
                                      help='existing project/name ok, do not increment')
                  self.opt = parser.parse_args()
                  print(self.opt)
           
                  source, weights, view_img, save_txt, imgsz = self.opt.source, self.opt.weights, self.opt.view_img, self.opt.save_txt, self.opt.img_size
           
                  self.device = select_device(self.opt.device)
                  self.half = self.device.type != 'cpu'  # half precision only supported on CUDA
           
                  cudnn.benchmark = True
           
                  # Load model
                  self.model = attempt_load(
                      weights, map_location=self.device)  # load FP32 model
                  stride = int(self.model.stride.max())  # model stride
                  self.imgsz = check_img_size(imgsz, s=stride)  # check img_size
                  if self.half:
                      self.model.half()  # to FP16
           
                  # Get names and colors
                  self.names = self.model.module.names if hasattr(
                      self.model, 'module') else self.model.names
                  self.colors = [[random.randint(0, 255)
                                  for _ in range(3)] for _ in self.names]
           
              def setupUi(self, MainWindow):
                  MainWindow.setObjectName("MainWindow")
                  MainWindow.resize(800, 600)
                  self.centralwidget = QtWidgets.QWidget(MainWindow)
                  self.centralwidget.setObjectName("centralwidget")
                  self.pushButton = QtWidgets.QPushButton(self.centralwidget)
                  self.pushButton.setGeometry(QtCore.QRect(20, 130, 112, 34))
                  self.pushButton.setObjectName("pushButton")
                  self.pushButton_2 = QtWidgets.QPushButton(self.centralwidget)
                  self.pushButton_2.setGeometry(QtCore.QRect(20, 220, 112, 34))
                  self.pushButton_2.setObjectName("pushButton_2")
                  self.pushButton_3 = QtWidgets.QPushButton(self.centralwidget)
                  self.pushButton_3.setGeometry(QtCore.QRect(20, 300, 112, 34))
                  self.pushButton_3.setObjectName("pushButton_3")
                  self.groupBox = QtWidgets.QGroupBox(self.centralwidget)
                  self.groujavascriptpBox.setGeometry(QtCore.QRect(160, 90, 611, 411))
                  self.groupBox.setObjectName("groupBox")
                  self.label = QtWidgets.QLabel(self.groupBox)
                  self.label.setGeometry(QtCore.QRect(10, 40, 561, 331))
                  self.label.setObjectName("label")
                  s编程客栈elf.textEdit = QtWidgets.QTextEdit(self.centralwidget)
                  self.textEdit.setGeometry(QtCore.QRect(150, 10, 471, 51))
                  self.textEdit.setObjectName("textEdit")
                  MainWindow.setCentralWidget(self.centralwidget)
                  self.menubar = QtWidgets.QMenuBar(MainWindow)
                  self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 30))
                  self.menubar.setObjectName("menubar")
                  MainWindow.setMenuBar(self.menubar)
                  self.statusbar = QtWidgets.QStatusBar(MainWindow)
                  self.statusbar.setObjectName("statusbar")
                  MainWindow.setStatusBar(self.statusbar)
           
                  self.retranslateUi(MainWindow)
                  QtCore.QMetaObject.connectSlotsByName(MainWindow)
           
              def retranslateUi(self, MainWindow):
                  _translate = QtCore.QCoreApplication.translate
                  MainWindow.setWindowTitle(_translate("MainWindow", "演示系统"))
                  self.pushButton.setText(_translate("MainWindow", "图片检测"))
                  self.pushButton_2.setText(_translate("MainWindow", "摄像头检测"))
                  self.pushButton_3.setText(_translate("MainWindow", "视频检测"))
                  self.groupBox.setTitle(_translate("MainWindow", "检测结果"))
                  self.label.setText(_translate("MainWindow", "TextLabel"))
                  self.textEdit.sethtml(_translate("MainWindow",
                      "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" \"http://www.w3.org/TR/REC-html40/strict.dtd\">\n"
                      "<html><head><meta name=\"qrichtext\" content=\"1\" /><style type=\"text/css\">\n"
                      "p, li { white-space: pre-wrap; }\n"
                      "</style></head><body style=\" font-family:\'SimSun\'; font-size:9pt; font-weight:400; font-style:normal;\">\n"
                      "<p align=\"center\" style=\" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-size:18pt; font-weight:600;\">演示系统</span></p></body></html>"))
           
              def init_slots(self):
                  self.pushButton.clicked.connect(self.button_image_open)
                  self.pushButton_3.clicked.connect(self.button_video_open)
                  self.pushButton_2.clicked.connect(self.button_camera_open)
                  self.timer_video.timeout.connect(self.show_video_frame)
           
              def init_logo(self):
                  pix = QtGui.QPixmap('wechat.jpg')
                  self.label.setScaledContents(True)
                  self.label.setPixmap(pix)
           
              def button_image_open(self):
                  print('button_image_open')
                  name_list = []
           
                  img_name, _ = QtWidgets.QFileDialog.getOpenFileName(
                      self, "打开图片", "", "*.jpg;;*.png;;All Files(*)")
                  if not img_name:
                      return
           
                  img = cv2.imread(img_name)
                  print(img_name)
                  showimg = img
                  with torch.no_grad():
                      img = letterbox(img, new_shape=self.opt.img_size)[0]
                      # Convert
                      # BGR to RGB, to 3x416x416
                      img = img[:, :, ::-1].transpose(2, 0, 1)
                      img = np.ascontiguousarray(img)
                      img = torch.from_numpy(img).to(self.device)
                      img = img.half() if self.half else img.float()  # uint8 to fp16/32
                      img /= 255.0  # 0 - 255 to 0.0 - 1.0
                      if img.ndimension() == 3:
                          img = img.unsqueeze(0)
                      # Inference
                      pred = self.model(img, augment=self.opt.augment)[0]
                      # Apply NMS
                      pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes,
                                                 agnostic=self.opt.agnostic_nms)
                      print(pred)
                      # Process detections
                      for i, det in enumerate(pred):
                          if det is not None and len(det):
                              # Rescale boxes from img_size to im0 size
                              det[:, :4] = scale_coords(
                                  img.shape[2:], det[:, :4], showimg.shape).round()
           
                              for *xyxy, conf, cls in reversed(det):
                                  label = '%s %.2f' % (self.names[int(cls)], conf)
                                  name_list.append(self.names[int(cls)])
                                  plot_one_box(xyxy, showimg, label=label,
                                               color=self.colors[int(cls)], line_thickness=2)
           
                  cv2.imwrite('prediction.jpg', showimg)
                  self.result = cv2.cvtColor(showimg, cv2.COLOR_BGR2BGRA)
                  self.result = cv2.resize(
                      self.result, (640, 480), interpolation=cv2.INTER_AREA)
                  self.QtImg = QtGui.QImage(
                      self.result.data, self.result.shape[1], self.result.shape[0], QtGui.QImage.Format_RGB32)
                  self.label.setPixmap(QtGui.QPixmap.fromImage(self.QtImg))
           
              def button_video_open(self):
                  video_name, _ = QtWidgets.QFileDialog.getOpenFileName(
                      self, "打开视频", "", "*.mp4;;*.avi;;All Files(*)")
           
                  if not video_name:
                      return
           
                  flag = self.cap.open(video_name)
                  if flag == False:
                      QtWidgets.QMessageBox.warning(
                          self, u"Warning", u"打开视频失败", buttons=QtWidgets.QMessageBox.Ok, defaultButton=QtWidgets.QMessageBox.Ok)
                  else:
                      self.out = cv2.VideoWriter('prediction.avi', cv2.VideoWriter_fourcc(
                          *'MJPG'), 20, (int(self.cap.get(3)), int(self.cap.get(4))))
                      self.timer_video.start(30)
                      self.pushButton_3.setDisabled(True)
                      self.pushButton.setDisabled(True)
                      self.pushButton_2.setDisabled(True)
           
              def button_camera_open(self):
                  if not self.timer_video.isActive():
                      # 默认使用第一个本地camera
                      flag = self.cap.open(0)
                      if flag == False:
                          QtWidgets.QMessageBox.warning(
                              self, u"Warning", u"打开摄像头失败", buttons=QtWidgets.QMessageBox.Ok,
                              defaultButton=QtWidgets.QMessageBox.Ok)
                      else:
                          self.out = cv2.VideoWriter('prediction.avi', cv2.VideoWriter_fourcc(
                              *'MJPG'), 20, (int(self.cap.get(3)), int(self.cap.get(4))))
                          self.timer_video.start(30)
                          self.pushButton_3.setDisabled(True)
                          self.pushButton.setDisabled(True)
                          self.pushButton_2.setText(u"关闭摄像头")
                  else:
                      self.timer_video.stop()
                      self.cap.release()
                      self.out.release()
                      self.label.clear()
                      self.init_logo()
                      self.pushButton_3.setDisabled(False)
                      self.pushButton.setDisabled(False)
                      self.pushButton_2.setText(u"摄像头检测")
           
              def show_video_frame(self):
                  name_list = []
           
                  flag, img = self.cap.read()
                  if img is not None:
                      showimg = img
                      with torch.no_grad():
                          img = letterbox(img, new_shape=self.opt.img_size)[0]
                          # Convert
                          # BGR to RGB, to 3x416x416
                          img = img[:, :, ::-1].transpose(2, 0, 1)
                          img = np.ascontiguousarray(img)
                          img = torch.from_numpy(img).to(self.device)
                          img = img.half() if self.half else img.float()开发者_JAVA入门  # uint8 to fp16/32
                          img /= 255.0  # 0 - 255 to 0.0 - 1.0
                          if img.ndimension() == 3:
                              img = img.unsqueeze(0)
                          # Inference
                          pred = self.model(img, augment=self.opt.augment)[0]
           
                          # Apply NMS
                          pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes,
                                                     agnostic=self.opt.agnostic_nms)
                          # Process detections
                          for i, det in enumerate(pred):  # detections per image
                              if det is not None and len(det):
                                  # Rescale boxes from img_size to im0 size
                                  det[:, :4] = scale_coords(
                                      img.shape[2:], det[:, :4], showimg.shape).round()
                                  # Write results
                                  for *xyxy, conf, cls in reversed(det):
                                      label = '%s %.2f' % (self.names[int(cls)], conf)
                                      name_list.append(self.names[int(cls)])
                                      print(label)
                                      plot_one_box(
                                          xyxy, showimg, label=label, color=self.colors[int(cls)], line_thickness=2)
           
                      self.out.write(showimg)
                      show = cv2.resize(showimg, (640, 480))
                      self.result = cv2.cvtColor(show, cv2.COLOR_BGR2RGB)
                      showImage = QtGui.QImage(self.result.data, self.result.shape[1], self.result.shape[0],
                                               QtGui.QImage.Format_RGB888)
                      self.label.setPixmap(QtGui.QPixmap.fromImage(showImage))
           
                  else:
                      self.timer_video.stop()
                      self.cap.release()
                      self.out.release()
                      self.label.clear()
                      self.pushButton_3.setDisabled(False)
                      self.pushButton.setDisabled(False)
                      self.pushButton_2.setDisabled(False)
                      self.init_logo()
           
          if __name__ == '__main__':
              app = QtWidgets.QApplication(sys.argv)
              ui = Ui_MainWindow()
              ui.show()
              sys.exit(app.exec_())

          Yolov5(v5.0)+pyqt5界面设计图文教程

          5.添加背景图片

          将demo中最后一段代码改为如下,其中background-image为背景图片地址。

          if __name__ == '__main__':
              stylesheet = """
                      Ui_MainWindow {
                          background-image: url("4K.jpg");
                          background-repeat: no-repeat;
                          background-position: center;
                      }
                  """
              app = QtWidgets.QApplication(sys.argv)
              app.setStyleSheet(stylesheet)
              ui = Uwww.devze.comi_MainWindow()
              ui.show()
              sys.exit(app.exec_())

          Yolov5(v5.0)+pyqt5界面设计图文教程

          6.reference

          链接一

          总结

          到此这篇关于Yolov5(v5.0)+pyqt5界面设计的文章就介绍到这了,更多相关Yolov5+pyqt5界面设计内容请搜索我们以前的文章或继续浏览下面的相关文章希望大家以后多多支持我们!

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