【毕业设计】基于YOLO实现的口罩佩戴检测 - python opemcv 深度学习

---

0 前言

这两年开始毕业设计和毕业答辩的要求和难度不断提升，传统的毕设题目缺少创新和亮点，往往达不到毕业答辩的要求，这两年不断有学弟学妹告诉学长自己做的项目系统达不到老师的要求。

为了大家能够顺利以及最少的精力通过毕设，学长分享优质毕业设计项目，今天要分享的是

**基于YOLO实现的口罩佩戴检测 **

学长这里给一个题目综合评分(每项满分5分)

• 难度系数：3分
• 工作量：4分
• 创新点：4分

选题指导, 项目分享：

https://gitee.com/dancheng-senior/project-sharing-1/blob/master/%E6%AF%95%E8%AE%BE%E6%8C%87%E5%AF%BC/README.md

---

1 课题介绍

受全球新冠肺炎疫情影响，虽然目前中国疫情防控取 得了良好效果，绝大多数地区处于疫情低风险，但个别地 区仍有零星散发病例和局部聚集性疫情。在机场、地 铁 站、医院等公共服务和重点机构场所规定必须佩戴口罩， 口罩佩戴检查已成为疫情防控的必备操作。目前，口罩 佩戴检查多为人工检查方式，如高铁上会有乘务人员一节 节车厢巡逻检查提醒乘客佩戴口罩，在医院等高危场所也 会有医务人员提醒时刻戴好口罩。人工检查方式存在检 查效率低下、难以及时发现错误佩戴口罩以及未佩戴口罩 行为等弊端。采用深度学习目标检测方法设计一个具有口罩识别功能的防疫系统，可以大大提高检测效率。

2 算法原理

2.1 算法简介

YOLOv5是一种单阶段目标检测算法，该算法在YOLOv4的基础上添加了一些新的改进思路，使其速度与精度都得到了极大的性能提升。主要的改进思路如下所示：

输入端：在模型训练阶段，提出了一些改进思路，主要包括Mosaic数据增强、自适应锚框计算、自适应图片缩放；
基准网络：融合其它检测算法中的一些新思路，主要包括：Focus结构与CSP结构；
Neck网络：目标检测网络在BackBone与最后的Head输出层之间往往会插入一些层，Yolov5中添加了FPN+PAN结构；
Head输出层：输出层的锚框机制与YOLOv4相同，主要改进的是训练时的损失函数GIOU_Loss，以及预测框筛选的DIOU_nms。

2.2 网络架构

上图展示了YOLOv5目标检测算法的整体框图。对于一个目标检测算法而言，我们通常可以将其划分为4个通用的模块，具体包括：输入端、基准网络、Neck网络与Head输出端，对应于上图中的4个红色模块。YOLOv5算法具有4个版本，具体包括：YOLOv5s、YOLOv5m、YOLOv5l、YOLOv5x四种，本文重点讲解YOLOv5s，其它的版本都在该版本的基础上对网络进行加深与加宽。

• 输入端-输入端表示输入的图片。该网络的输入图像大小为608*608，该阶段通常包含一个图像预处理阶段，即将输入图像缩放到网络的输入大小，并进行归一化等操作。在网络训练阶段，YOLOv5使用Mosaic数据增强操作提升模型的训练速度和网络的精度；并提出了一种自适应锚框计算与自适应图片缩放方法。
• 基准网络-基准网络通常是一些性能优异的分类器种的网络，该模块用来提取一些通用的特征表示。YOLOv5中不仅使用了CSPDarknet53结构，而且使用了Focus结构作为基准网络。
• Neck网络-Neck网络通常位于基准网络和头网络的中间位置，利用它可以进一步提升特征的多样性及鲁棒性。虽然YOLOv5同样用到了SPP模块、FPN+PAN模块，但是实现的细节有些不同。
• Head输出端-Head用来完成目标检测结果的输出。针对不同的检测算法，输出端的分支个数不尽相同，通常包含一个分类分支和一个回归分支。YOLOv4利用GIOU_Loss来代替Smooth L1 Loss函数，从而进一步提升算法的检测精度。

3 关键代码

class Detect(nn.Module):
    stride = None  # strides computed during build
    onnx_dynamic = False  # ONNX export parameter

    def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
        super().__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        self.anchor_grid = [torch.zeros(1)] * self.nl  # init anchor grid
        self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
        self.inplace = inplace  # use in-place ops (e.g. slice assignment)

    def forward(self, x):
        z = []  # inference output
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            if not self.training:  # inference
                if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)

                y = x[i].sigmoid()
                if self.inplace:
                    y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xy
                    y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
                    xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xy
                    wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                    y = torch.cat((xy, wh, y[..., 4:]), -1)
                z.append(y.view(bs, -1, self.no))

        return x if self.training else (torch.cat(z, 1), x)

    def _make_grid(self, nx=20, ny=20, i=0):
        d = self.anchors[i].device
        if check_version(torch.__version__, '1.10.0'):  # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
            yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)], indexing='ij')
        else:
            yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)])
        grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()
        anchor_grid = (self.anchors[i].clone() * self.stride[i]) \
            .view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()
        return grid, anchor_grid


class Model(nn.Module):
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
        super().__init__()
        if isinstance(cfg, dict):
            self.yaml = cfg  # model dict
        else:  # is *.yaml
            import yaml  # for torch hub
            self.yaml_file = Path(cfg).name
            with open(cfg, encoding='ascii', errors='ignore') as f:
                self.yaml = yaml.safe_load(f)  # model dict

        # Define model
        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
        if nc and nc != self.yaml['nc']:
            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml['nc'] = nc  # override yaml value
        if anchors:
            LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
            self.yaml['anchors'] = round(anchors)  # override yaml value
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
        self.inplace = self.yaml.get('inplace', True)

        # Build strides, anchors
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):
            s = 256  # 2x min stride
            m.inplace = self.inplace
            m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
            m.anchors /= m.stride.view(-1, 1, 1)
            check_anchor_order(m)
            self.stride = m.stride
            self._initialize_biases()  # only run once

        # Init weights, biases
        initialize_weights(self)
        self.info()
        LOGGER.info('')

    def forward(self, x, augment=False, profile=False, visualize=False):
        if augment:
            return self._forward_augment(x)  # augmented inference, None
        return self._forward_once(x, profile, visualize)  # single-scale inference, train

    def _forward_augment(self, x):
        img_size = x.shape[-2:]  # height, width
        s = [1, 0.83, 0.67]  # scales
        f = [None, 3, None]  # flips (2-ud, 3-lr)
        y = []  # outputs
        for si, fi in zip(s, f):
            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
            yi = self._forward_once(xi)[0]  # forward
            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
            yi = self._descale_pred(yi, fi, si, img_size)
            y.append(yi)
        y = self._clip_augmented(y)  # clip augmented tails
        return torch.cat(y, 1), None  # augmented inference, train

    def _forward_once(self, x, profile=False, visualize=False):
        y, dt = [], []  # outputs
        for m in self.model:
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            x = m(x)  # run
            y.append(x if m.i in self.save else None)  # save output
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
        return x

    def _descale_pred(self, p, flips, scale, img_size):
        # de-scale predictions following augmented inference (inverse operation)
        if self.inplace:
            p[..., :4] /= scale  # de-scale
            if flips == 2:
                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
            elif flips == 3:
                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
        else:
            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
            if flips == 2:
                y = img_size[0] - y  # de-flip ud
            elif flips == 3:
                x = img_size[1] - x  # de-flip lr
            p = torch.cat((x, y, wh, p[..., 4:]), -1)
        return p

    def _clip_augmented(self, y):
        # Clip YOLOv5 augmented inference tails
        nl = self.model[-1].nl  # number of detection layers (P3-P5)
        g = sum(4 ** x for x in range(nl))  # grid points
        e = 1  # exclude layer count
        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
        y[0] = y[0][:, :-i]  # large
        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
        y[-1] = y[-1][:, i:]  # small
        return y

    def _profile_one_layer(self, m, x, dt):
        c = isinstance(m, Detect)  # is final layer, copy input as inplace fix
        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
        t = time_sync()
        for _ in range(10):
            m(x.copy() if c else x)
        dt.append((time_sync() - t) * 100)
        if m == self.model[0]:
            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  {'module'}")
        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
        if c:
            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")

    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
        # https://arxiv.org/abs/1708.02002 section 3.3
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
        m = self.model[-1]  # Detect() module
        for mi, s in zip(m.m, m.stride):  # from
            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # cls
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)

    def _print_biases(self):
        m = self.model[-1]  # Detect() module
        for mi in m.m:  # from
            b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
            LOGGER.info(
                ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))

    # def _print_weights(self):
    #     for m in self.model.modules():
    #         if type(m) is Bottleneck:
    #             LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights

    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
        LOGGER.info('Fusing layers... ')
        for m in self.model.modules():
            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                delattr(m, 'bn')  # remove batchnorm
                m.forward = m.forward_fuse  # update forward
        self.info()
        return self

    def autoshape(self):  # add AutoShape module
        LOGGER.info('Adding AutoShape... ')
        m = AutoShape(self)  # wrap model
        copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
        return m

    def info(self, verbose=False, img_size=640):  # print model information
        model_info(self, verbose, img_size)

    def _apply(self, fn):
        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
        self = super()._apply(fn)
        m = self.model[-1]  # Detect()
        if isinstance(m, Detect):
            m.stride = fn(m.stride)
            m.grid = list(map(fn, m.grid))
            if isinstance(m.anchor_grid, list):
                m.anchor_grid = list(map(fn, m.anchor_grid))
        return self


def parse_model(d, ch):  # model_dict, input_channels(3)
    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)

    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
        m = eval(m) if isinstance(m, str) else m  # eval strings
        for j, a in enumerate(args):
            try:
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
            except NameError:
                pass

        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
        if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
                 BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]:
            c1, c2 = ch[f], args[0]
            if c2 != no:  # if not output
                c2 = make_divisible(c2 * gw, 8)

            args = [c1, c2, *args[1:]]
            if m in [BottleneckCSP, C3, C3TR, C3Ghost]:
                args.insert(2, n)  # number of repeats
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        elif m is Detect:
            args.append([ch[x] for x in f])
            if isinstance(args[1], int):  # number of anchors
                args[1] = [list(range(args[1] * 2))] * len(f)
        elif m is Contract:
            c2 = ch[f] * args[0] ** 2
        elif m is Expand:
            c2 = ch[f] // args[0] ** 2
        else:
            c2 = ch[f]

        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace('__main__.', '')  # module type
        np = sum(x.numel() for x in m_.parameters())  # number params
        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
        LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)

4 数据集

大家可采用公开标注好的数据集。如果为了更深入的学习也可自己标注，但过程相对比较繁琐，麻烦。

以下简单介绍数据标注的相关方法，数据标注这里推荐的软件是labelimg，学长以火灾数据集为例！

4.1 安装

通过pip指令即可安装

pip install labelimg

4.2 打开

在命令行中输入labelimg即可打开

打开你所需要进行标注的文件夹

4.3 选择yolo标注格式

点击红色框区域进行标注格式切换，我们需要yolo格式，因此切换到yolo。

4.4 打标签

点击Create RectBo -> 拖拽鼠标框选目标 -> 给上标签 -> 点击ok。

注：若要删除目标，右键目标区域，delete即可

4.5 保存

点击save，保存txt。

打开具体的标注文件，你将会看到下面的内容，txt文件中每一行表示一个目标，以空格进行区分，分别表示目标的类别id，归一化处理之后的中心点x坐标、y坐标、目标框的w和h。

5 训练

修改train.py中的weights、cfg、data、epochs、batch_size、imgsz、device、workers等参数

训练代码成功执行之后会在命令行中输出下列信息，接下来就是安心等待模型训练结束即可。

6 实现效果

6.1 pyqt实现简单GUI

from PyQt5 import QtCore, QtGui, QtWidgets


class Ui_Win_mask(object):
    def setupUi(self, Win_mask):
        Win_mask.setObjectName("Win_mask")
        Win_mask.resize(1107, 868)
        Win_mask.setStyleSheet("QString qstrStylesheet = \"background-color:rgb(43, 43, 255)\";\n"
"ui.pushButton->setStyleSheet(qstrStylesheet);")
        self.frame = QtWidgets.QFrame(Win_mask)
        self.frame.setGeometry(QtCore.QRect(10, 140, 201, 701))
        self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
        self.frame.setFrameShadow(QtWidgets.QFrame.Raised)
        self.frame.setObjectName("frame")
        self.pushButton = QtWidgets.QPushButton(self.frame)
        self.pushButton.setGeometry(QtCore.QRect(10, 40, 161, 51))
        font = QtGui.QFont()
        font.setBold(True)
        font.setUnderline(True)
        font.setWeight(75)
        self.pushButton.setFont(font)
        self.pushButton.setStyleSheet("QPushButton{background-color:rgb(151, 191, 255);}")
        self.pushButton.setObjectName("pushButton")
        self.pushButton_2 = QtWidgets.QPushButton(self.frame)
        self.pushButton_2.setGeometry(QtCore.QRect(10, 280, 161, 51))
        font = QtGui.QFont()
        font.setBold(True)
        font.setUnderline(True)
        font.setWeight(75)
        self.pushButton_2.setFont(font)
        self.pushButton_2.setStyleSheet("QPushButton{background-color:rgb(151, 191, 255);}")
        self.pushButton_2.setObjectName("pushButton_2")
        self.pushButton_3 = QtWidgets.QPushButton(self.frame)
        self.pushButton_3.setGeometry(QtCore.QRect(10, 500, 161, 51))
        font = QtGui.QFont()
        font.setBold(True)
        font.setUnderline(True)
        font.setWeight(75)
        font.setStrikeOut(False)
        self.pushButton_3.setFont(font)
        self.pushButton_3.setStyleSheet("QPushButton{background-color:rgb(151, 191, 255);}")
        self.pushButton_3.setObjectName("pushButton_3")
        self.frame_2 = QtWidgets.QFrame(Win_mask)
        self.frame_2.setGeometry(QtCore.QRect(230, 110, 1031, 861))
        self.frame_2.setStyleSheet("")
        self.frame_2.setFrameShape(QtWidgets.QFrame.StyledPanel)
        self.frame_2.setFrameShadow(QtWidgets.QFrame.Raised)
        self.frame_2.setObjectName("frame_2")
        self.show_picture_page = QtWidgets.QStackedWidget(self.frame_2)
        self.show_picture_page.setGeometry(QtCore.QRect(-10, 0, 871, 731))
        font = QtGui.QFont()
        font.setBold(True)
        font.setWeight(75)
        self.show_picture_page.setFont(font)
        self.show_picture_page.setObjectName("show_picture_page")
        self.photo = QtWidgets.QWidget()
        self.photo.setObjectName("photo")
        self.label = QtWidgets.QLabel(self.photo)
        self.label.setGeometry(QtCore.QRect(10, 30, 641, 641))
        font = QtGui.QFont()
        font.setFamily("Arial")
        font.setPointSize(36)
        self.label.setFont(font)
        self.label.setText("")
        self.label.setPixmap(QtGui.QPixmap("./images/UI/up.jpeg"))
        self.label.setObjectName("label")
        self.pushButton_4 = QtWidgets.QPushButton(self.photo)
        self.pushButton_4.setGeometry(QtCore.QRect(680, 220, 171, 61))
        font = QtGui.QFont()
        font.setBold(True)
        font.setUnderline(True)
        font.setWeight(75)
        self.pushButton_4.setFont(font)
        self.pushButton_4.setStyleSheet("QPushButton{background-color:rgb(85, 170, 255);}")
        self.pushButton_4.setObjectName("pushButton_4")
        self.pushButton_5 = QtWidgets.QPushButton(self.photo)
        self.pushButton_5.setGeometry(QtCore.QRect(680, 400, 171, 61))
        font = QtGui.QFont()
        font.setUnderline(True)
        self.pushButton_5.setFont(font)
        self.pushButton_5.setStyleSheet("QPushButton{background-color:rgb(85, 170, 255);}")
        self.pushButton_5.setObjectName("pushButton_5")
        self.show_picture_page.addWidget(self.photo)
        self.videos = QtWidgets.QWidget()
        self.videos.setObjectName("videos")
        self.vid_img = QtWidgets.QLabel(self.videos)
        self.vid_img.setGeometry(QtCore.QRect(10, 30, 640, 640))
        font = QtGui.QFont()
        font.setFamily("Arial")
        font.setPointSize(36)
        self.vid_img.setFont(font)
        self.vid_img.setText("")
        self.vid_img.setPixmap(QtGui.QPixmap("./images/UI/up.jpeg"))
        self.vid_img.setObjectName("vid_img")
        self.mp4_detection_btn = QtWidgets.QPushButton(self.videos)
        self.mp4_detection_btn.setGeometry(QtCore.QRect(680, 220, 171, 61))
        font = QtGui.QFont()
        font.setBold(True)
        font.setUnderline(True)
        font.setWeight(75)
        self.mp4_detection_btn.setFont(font)
        self.mp4_detection_btn.setStyleSheet("QPushButton{background-color:rgb(85, 170, 255);}")
        self.mp4_detection_btn.setObjectName("mp4_detection_btn")
        self.vid_stop_btn = QtWidgets.QPushButton(self.videos)
        self.vid_stop_btn.setGeometry(QtCore.QRect(680, 400, 171, 61))
        font = QtGui.QFont()
        font.setBold(True)
        font.setUnderline(True)
        font.setWeight(75)
        self.vid_stop_btn.setFont(font)
        self.vid_stop_btn.setStyleSheet("QPushButton{background-color:rgb(85, 170, 255);}")
        self.vid_stop_btn.setObjectName("vid_stop_btn")
        self.show_picture_page.addWidget(self.videos)
        self.camera = QtWidgets.QWidget()
        self.camera.setObjectName("camera")
        self.webcam_detection_btn = QtWidgets.QPushButton(self.camera)
        self.webcam_detection_btn.setGeometry(QtCore.QRect(680, 220, 171, 61))
        self.webcam_detection_btn.setBaseSize(QtCore.QSize(2, 2))
        font = QtGui.QFont()
        font.setBold(True)
        font.setUnderline(True)
        font.setWeight(75)
        self.webcam_detection_btn.setFont(font)
        self.webcam_detection_btn.setStyleSheet("QPushButton{background-color:rgb(85, 170, 255);}")
        self.webcam_detection_btn.setObjectName("webcam_detection_btn")
        self.cam_img = QtWidgets.QLabel(self.camera)
        self.cam_img.setGeometry(QtCore.QRect(10, 30, 640, 640))
        font = QtGui.QFont()
        font.setFamily("Arial")
        font.setPointSize(36)
        self.cam_img.setFont(font)
        self.cam_img.setText("")
        self.cam_img.setPixmap(QtGui.QPixmap("./images/UI/up.jpeg"))
        self.cam_img.setObjectName("cam_img")
        self.vid_stop_btn_cma = QtWidgets.QPushButton(self.camera)
        self.vid_stop_btn_cma.setGeometry(QtCore.QRect(680, 400, 171, 61))
        font = QtGui.QFont()
        font.setBold(True)
        font.setUnderline(True)
        font.setWeight(75)
        self.vid_stop_btn_cma.setFont(font)
        self.vid_stop_btn_cma.setStyleSheet("QPushButton{background-color:rgb(85, 170, 255);}")
        self.vid_stop_btn_cma.setObjectName("vid_stop_btn_cma")
        self.show_picture_page.addWidget(self.camera)
        self.label_2 = QtWidgets.QLabel(Win_mask)
        self.label_2.setGeometry(QtCore.QRect(430, 40, 251, 71))
        font = QtGui.QFont()
        font.setPointSize(24)
        font.setBold(True)
        font.setItalic(False)
        font.setUnderline(True)
        font.setWeight(75)
        self.label_2.setFont(font)
        self.label_2.setStyleSheet("Font{background-color:rgb(85, 170, 255);}")
        self.label_2.setObjectName("label_2")
        self.listView = QtWidgets.QListView(Win_mask)
        self.listView.setGeometry(QtCore.QRect(-5, 1, 1121, 871))
        self.listView.setStyleSheet(" \n"
"background-image: url(:/bg.png);")
        self.listView.setObjectName("listView")
        self.listView.raise_()
        self.frame.raise_()
        self.frame_2.raise_()
        self.label_2.raise_()

        self.retranslateUi(Win_mask)
        self.show_picture_page.setCurrentIndex(0)
        QtCore.QMetaObject.connectSlotsByName(Win_mask)

6.2 图片识别效果

6.3 视频识别效果

6.4 摄像头实时识别