Python如何实现人脸识别系统

小编给大家分享一下Python如何实现人脸识别系统，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！

基本原理

人脸识别和目标检测这些还不太一样，比如大家传统的训练一个目标检测模型，你只有对这个目标训练了之后，你的模型才能找到这样的目标，比如你的目标检测模型如果是检测植物的，那显然就不能检测动物。但是人脸识别就不一样，以你的手机为例，你发现你只录入了一次你的人脸信息，不需要训练，他就能准确的识别你，这里识别的原理是通过人脸识别的模型提取你脸部的特征向量，然后将实时检测到的你的人脸同数据库中保存的人脸进行比对，如果相似度超过一定的阈值之后，就认为比对成功。不过我这里说的只是简化版本的人脸识别，现在手机和门禁这些要复杂和安全的多，也不是简单平面上的人脸识别。

总结下来可以分为下面的步骤：

1.上传人脸到数据库

2.人脸检测

3.数据库比对并返回结果

这里我做了一个简答的示意图，可以帮助大家简单理解一下。

代码实现

废话不多说，这里就是我们的代码实现，代码我已经上传到码云，大家直接下载就行，地址就在博客开头。

不会安装python环境的兄弟请看这里：如何在pycharm中配置anaconda的虚拟环境

创建虚拟环境

创建虚拟环境前请大家先下载博客开头的码云源码到本地。

本次我们需要使用到python3.7的虚拟环境，命令如下：

conda create -n face python==3.7.3conda activate face

安装必要的库

pip install -r requirements.txt

愉快地开始你的人脸识别吧！

执行下面的主文件即可

python UI.py

或者在pycharm中按照下面的方式直接运行即可

首先将你需要识别的人脸上传到数据库中

通过第二个视频检测功能识别实时的人脸

详细的代码如下：

# -*- coding: utf-8 -*-"""-------------------------------------------------Project Name: yolov5-jungongFile Name: window.py.pyAuthor: chenmingCreate Date: 2021/11/8Description：图形化界面，可以检测摄像头、视频和图片文件-------------------------------------------------"""# 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import shutilimport PyQt5.QtCorefrom PyQt5.QtGui import *from PyQt5.QtCore import *from PyQt5.QtWidgets import *import threadingimport argparseimport osimport sysfrom pathlib import Pathimport cv2import torchimport torch.backends.cudnn as cudnnimport os.path as ospFILE = Path(__file__).resolve()ROOT = FILE.parents[0]  # YOLOv5 root directoryif str(ROOT) not in sys.path:    sys.path.append(str(ROOT))  # add ROOT to PATHROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relativefrom models.common import DetectMultiBackendfrom utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreamsfrom utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)from utils.plots import Annotator, colors, save_one_boxfrom utils.torch_utils import select_device, time_sync# 添加一个关于界面# 窗口主类class MainWindow(QTabWidget):    # 基本配置不动，然后只动第三个界面    def __init__(self):        # 初始化界面        super().__init__()        self.setWindowTitle('Target detection system')        self.resize(1200, 800)        self.setWindowIcon(QIcon("images/UI/lufei.png"))        # 图片读取进程        self.output_size = 480        self.img2predict = ""        self.device = 'cpu'        # # 初始化视频读取线程        self.vid_source = '0'  # 初始设置为摄像头        self.stopEvent = threading.Event()        self.webcam = True        self.stopEvent.clear()        self.model = self.model_load(weights="runs/train/exp_yolov5s/weights/best.pt",                                     device="cpu")  # todo 指明模型加载的位置的设备        self.initUI()        self.reset_vid()    '''    ***模型初始化***    '''    @torch.no_grad()    def model_load(self, weights="",  # model.pt path(s)                   device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu                   half=False,  # use FP16 half-precision inference                   dnn=False,  # use OpenCV DNN for ONNX inference                   ):        device = select_device(device)        half &= device.type != 'cpu'  # half precision only supported on CUDA        device = select_device(device)        model = DetectMultiBackend(weights, device=device, dnn=dnn)        stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx        # Half        half &= pt and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA        if pt:            model.model.half() if half else model.model.float()        print("模型加载完成!")        return model    '''    ***界面初始化***    '''    def initUI(self):        # 图片检测子界面        font_title = QFont('楷体', 16)        font_main = QFont('楷体', 14)        # 图片识别界面, 两个按钮，上传图片和显示结果        img_detection_widget = QWidget()        img_detection_layout = QVBoxLayout()        img_detection_title = QLabel("图片识别功能")        img_detection_title.setFont(font_title)        mid_img_widget = QWidget()        mid_img_layout = QHBoxLayout()        self.left_img = QLabel()        self.right_img = QLabel()        self.left_img.setPixmap(QPixmap("images/UI/up.jpeg"))        self.right_img.setPixmap(QPixmap("images/UI/right.jpeg"))        self.left_img.setAlignment(Qt.AlignCenter)        self.right_img.setAlignment(Qt.AlignCenter)        mid_img_layout.addWidget(self.left_img)        mid_img_layout.addStretch(0)        mid_img_layout.addWidget(self.right_img)        mid_img_widget.setLayout(mid_img_layout)        up_img_button = QPushButton("上传图片")        det_img_button = QPushButton("开始检测")        up_img_button.clicked.connect(self.upload_img)        det_img_button.clicked.connect(self.detect_img)        up_img_button.setFont(font_main)        det_img_button.setFont(font_main)        up_img_button.setStyleSheet("QPushButton{color:white}"                                    "QPushButton:hover{background-color: rgb(2,110,180);}"                                    "QPushButton{background-color:rgb(48,124,208)}"                                    "QPushButton{border:2px}"                                    "QPushButton{border-radius:5px}"                                    "QPushButton{padding:5px 5px}"                                    "QPushButton{margin:5px 5px}")        det_img_button.setStyleSheet("QPushButton{color:white}"                                     "QPushButton:hover{background-color: rgb(2,110,180);}"                                     "QPushButton{background-color:rgb(48,124,208)}"                                     "QPushButton{border:2px}"                                     "QPushButton{border-radius:5px}"                                     "QPushButton{padding:5px 5px}"                                     "QPushButton{margin:5px 5px}")        img_detection_layout.addWidget(img_detection_title, alignment=Qt.AlignCenter)        img_detection_layout.addWidget(mid_img_widget, alignment=Qt.AlignCenter)        img_detection_layout.addWidget(up_img_button)        img_detection_layout.addWidget(det_img_button)        img_detection_widget.setLayout(img_detection_layout)        # todo 视频识别界面        # 视频识别界面的逻辑比较简单，基本就从上到下的逻辑        vid_detection_widget = QWidget()        vid_detection_layout = QVBoxLayout()        vid_title = QLabel("视频检测功能")        vid_title.setFont(font_title)        self.vid_img = QLabel()        self.vid_img.setPixmap(QPixmap("images/UI/up.jpeg"))        vid_title.setAlignment(Qt.AlignCenter)        self.vid_img.setAlignment(Qt.AlignCenter)        self.webcam_detection_btn = QPushButton("摄像头实时监测")        self.mp4_detection_btn = QPushButton("视频文件检测")        self.vid_stop_btn = QPushButton("停止检测")        self.webcam_detection_btn.setFont(font_main)        self.mp4_detection_btn.setFont(font_main)        self.vid_stop_btn.setFont(font_main)        self.webcam_detection_btn.setStyleSheet("QPushButton{color:white}"                                                "QPushButton:hover{background-color: rgb(2,110,180);}"                                                "QPushButton{background-color:rgb(48,124,208)}"                                                "QPushButton{border:2px}"                                                "QPushButton{border-radius:5px}"                                                "QPushButton{padding:5px 5px}"                                                "QPushButton{margin:5px 5px}")        self.mp4_detection_btn.setStyleSheet("QPushButton{color:white}"                                             "QPushButton:hover{background-color: rgb(2,110,180);}"                                             "QPushButton{background-color:rgb(48,124,208)}"                                             "QPushButton{border:2px}"                                             "QPushButton{border-radius:5px}"                                             "QPushButton{padding:5px 5px}"                                             "QPushButton{margin:5px 5px}")        self.vid_stop_btn.setStyleSheet("QPushButton{color:white}"                                        "QPushButton:hover{background-color: rgb(2,110,180);}"                                        "QPushButton{background-color:rgb(48,124,208)}"                                        "QPushButton{border:2px}"                                        "QPushButton{border-radius:5px}"                                        "QPushButton{padding:5px 5px}"                                        "QPushButton{margin:5px 5px}")        self.webcam_detection_btn.clicked.connect(self.open_cam)        self.mp4_detection_btn.clicked.connect(self.open_mp4)        self.vid_stop_btn.clicked.connect(self.close_vid)        # 添加组件到布局上        vid_detection_layout.addWidget(vid_title)        vid_detection_layout.addWidget(self.vid_img)        vid_detection_layout.addWidget(self.webcam_detection_btn)        vid_detection_layout.addWidget(self.mp4_detection_btn)        vid_detection_layout.addWidget(self.vid_stop_btn)        vid_detection_widget.setLayout(vid_detection_layout)        # todo 关于界面        about_widget = QWidget()        about_layout = QVBoxLayout()        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        about_title.setFont(QFont('楷体', 18))        about_title.setAlignment(Qt.AlignCenter)        about_img = QLabel()        about_img.setPixmap(QPixmap('images/UI/qq.png'))        about_img.setAlignment(Qt.AlignCenter)        # label4.setText("如何调整学习率")        label_super = QLabel()  # todo 更换作者信息        label_super.setText("或者你可以在这里找到我-->肆十二")        label_super.setFont(QFont('楷体', 16))        label_super.setOpenExternalLinks(True)        # label_super.setOpenExternalLinks(True)        label_super.setAlignment(Qt.AlignRight)        about_layout.addWidget(about_title)        about_layout.addStretch()        about_layout.addWidget(about_img)        about_layout.addStretch()        about_layout.addWidget(label_super)        about_widget.setLayout(about_layout)        self.left_img.setAlignment(Qt.AlignCenter)        self.addTab(img_detection_widget, '图片检测')        self.addTab(vid_detection_widget, '视频检测')        self.addTab(about_widget, '联系我')        self.setTabIcon(0, QIcon('images/UI/lufei.png'))        self.setTabIcon(1, QIcon('images/UI/lufei.png'))        self.setTabIcon(2, QIcon('images/UI/lufei.png'))    '''    ***上传图片***    '''    def upload_img(self):        # 选择录像文件进行读取        fileName, fileType = QFileDialog.getOpenFileName(self, 'Choose file', '', '*.jpg *.png *.tif *.jpeg')        if fileName:            suffix = fileName.split(".")[-1]            save_path = osp.join("images/tmp", "tmp_upload." + suffix)            shutil.copy(fileName, save_path)            # 应该调整一下图片的大小，然后统一防在一起            im0 = cv2.imread(save_path)            resize_scale = self.output_size / im0.shape[0]            im0 = cv2.resize(im0, (0, 0), fx=resize_scale, fy=resize_scale)            cv2.imwrite("images/tmp/upload_show_result.jpg", im0)            # self.right_img.setPixmap(QPixmap("images/tmp/single_result.jpg"))            self.img2predict = fileName            self.left_img.setPixmap(QPixmap("images/tmp/upload_show_result.jpg"))            # todo 上传图片之后右侧的图片重置，            self.right_img.setPixmap(QPixmap("images/UI/right.jpeg"))    '''    ***检测图片***    '''    def detect_img(self):        model = self.model        output_size = self.output_size        source = self.img2predict  # file/dir/URL/glob, 0 for webcam        imgsz = 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        print(source)        if source == "":            QMessageBox.warning(self, "请上传", "请先上传图片再进行检测")        else:            source = str(source)            device = select_device(self.device)            webcam = False            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                    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                    im0 = annotator.result()                    # if view_img:                    #     cv2.imshow(str(p), im0)                    #     cv2.waitKey(1)  # 1 millisecond                    # Save results (image with detections)                    resize_scale = output_size / im0.shape[0]                    im0 = cv2.resize(im0, (0, 0), fx=resize_scale, fy=resize_scale)                    cv2.imwrite("images/tmp/single_result.jpg", im0)                    # 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                    self.right_img.setPixmap(QPixmap("images/tmp/single_result.jpg"))    # 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。    '''    ### 界面关闭事件 ###     '''    def closeEvent(self, event):        reply = QMessageBox.question(self,                                     'quit',                                     "Are you sure?",                                     QMessageBox.Yes | QMessageBox.No,                                     QMessageBox.No)        if reply == QMessageBox.Yes:            self.close()            event.accept()        else:            event.ignore()    '''    ### 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        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  # 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_())

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