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 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.

 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

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
Program	designer.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

 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
Program	python.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.

 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 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')
        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",
            "\n"
            "\n"
            "
演示系统
"))

    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/