pip install PyQt5
pip install PyQt5-tools

# -*- 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", "演示系統(tǒng)"))
        self.pushButton.setText(_translate("MainWindow", "圖片檢測(cè)"))
        self.pushButton_2.setText(_translate("MainWindow", "攝像頭檢測(cè)"))
        self.pushButton_3.setText(_translate("MainWindow", "視頻檢測(cè)"))
        self.groupBox.setTitle(_translate("MainWindow", "檢測(cè)結(jié)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;\">演示系統(tǒng)</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, "打開圖片", "", "*.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, "打開視頻", "", "*.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"打開視頻失敗", 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():
            # 默認(rèn)使用第一個(gè)本地camera
            flag = self.cap.open(0)
            if flag == False:
                QtWidgets.QMessageBox.warning(
                    self, u"Warning", u"打開攝像頭失敗", 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"關(guān)閉攝像頭")
        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"攝像頭檢測(cè)")
 
    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_())

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_())