Python 官方文档：入门教程 => 点击学习
文章目录 0 前言1 课题介绍2 算法原理2.1 算法简介2.2 网络架构 3 关键代码4 数据集4.1 安装4.2 打开4.3 选择yolo标注格式4.4 打标签4.5 保存 5 训练6 实现效果6.1 pyqt实现简单G
🔥 优质竞赛项目系列，今天要分享的是
🚩 **基于YOLO实现的口罩佩戴检测 **
该项目较为新颖，适合作为竞赛课题方向，学长非常推荐！
🥇学长这里给一个题目综合评分(每项满分5分)
🧿 更多资料, 项目分享：
https://gitee.com/dancheng-senior/postgraduate
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。
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。
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。
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)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！
通过pip指令即可安装
pip install labelimg
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
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。
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
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。
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)## 2 图片识别效果
🧿 更多资料, 项目分享：
https://gitee.com/dancheng-senior/postgraduate
来源地址：https://blog.csdn.net/m0_43533/article/details/132472331
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本文标题: 计算机竞赛 基于YOLO实现的口罩佩戴检测 - python opemcv 深度学习
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