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大数据毕设选题 – 深度学习口罩佩戴检测系统(python opemcv yolo)

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文章目录

 

 Hi,大家好,这里是丹成学长的毕设系列文章!

 

 对毕设有任何疑问都可以问学长哦!

 

这两年开始,各个学校对毕设的要求越来越高,难度也越来越大… 毕业设计耗费时间,耗费精力,甚至有些题目即使是专业的老师或者硕士生也需要很长时间,所以一旦发现问题,一定要提前准备,避免到后面措手不及,草草了事。

 

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,今天要分享的新项目是

 

 **基于YOLO实现的口罩佩戴检测 **

 

磊学长这里给一个题目综合评分(每项满分5分)

难度系数:4分
工作量:4分
创新点:3分

刺 选题指导, 项目分享:

 

https://blog.csdn.net/Mr_DC_IT/article/details/126460477

 

 

 

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"
{
   '':>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。

 

 

 

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)\";
"
"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(" 
"
"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

 

 

 

刺 选题指导, 项目分享:

 

https://blog.csdn.net/Mr_DC_IT/article/details/126460477

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