Yolov5(v5.0) + pyqt5界面设计

1.下载安装pyqt5工具包以及配置ui界面开发环境

pip install PyQt5pip 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.

 2.1 添加QtDesigner

 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

2.2 添加PyUIC

 PyUIC主要是把Qt Designer生成的.ui文件换成.py文件

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.

 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。

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 sysimport cv2import argparseimport randomimport torchimport numpy as npimport torch.backends.cudnn as cudnnfrom PyQt5 import QtCore, QtGui, QtWidgetsfrom utils.torch_utils import select_devicefrom models.experimental import attempt_loadfrom utils.general import check_img_size, non_max_suppression, scale_coordsfrom utils.datasets import letterboxfrom utils.plots import plot_one_boxclass 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')        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.groupBox.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")        self.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()  # 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_())

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 = Ui_MainWindow()    ui.show()    sys.exit(app.exec_())

6.reference

http://t.csdn.cn/ZVtSKhttp://t.csdn.cn/ZVtSKPyQt5系列教程(三)利用QtDesigner设计UI界面 - 迷途小书童的Note迷途小书童的Note (xugaoxiang.com)https://xugaoxiang.com/2019/12/04/pyqt5-3-qtdesigner/