基于深度学习神经网络YOLOv4目标检测的口罩识别系统

第一步：YOLOv4介绍

YOLOv4是一种目标检测算法，它在精度和速度之间取得了最佳的平衡。它是YOLO（You Only Look Once）系列算法的最新版本，通过将目标检测任务转化为一个回归问题，实现了实时目标检测。YOLOv4采用了一系列的调优手段，使得其在目标检测任务中表现出色。

YOLOv4的框架原理主要包括以下几个方面：

1. BackBone：YOLOv4使用了CSPDarknet53作为其主干网络，该网络结构具有较强的特征提取能力。
2. 训练策略：YOLOv4采用了多尺度训练和数据增强等策略来提高模型的泛化能力和检测精度。
3. 推理策略：YOLOv4使用了多尺度推理和后处理技术来提高检测速度和准确性。
4. 检测头训练策略：YOLOv4使用了Mosaic数据增强和CIoU损失函数等策略来提高小目标的检测精度。
5. 检测头推理策略：YOLOv4使用了YOLOv3和YOLOv4的检测头结合策略，提高了模型的检测能力。

总之，YOLOv4是一种高效准确的目标检测算法，具有较好的精度和速度表现。它在目标检测领域具有广泛的应用前景。

标注数据，YOLOv4的训练和测试步骤，各路大神都已经做了很多工作，我就不再写了，这里有几个写的比较好的博客可以参考：

【项目实践】YOLO V4万字原理详细讲解并训练自己的数据集（pytorch完整项目打包下载）-腾讯云开发者社区-腾讯云

YOLOv4 的各种新实现、配置、测试、训练资源汇总

第二步：YOLOv4网络结构

第三步：代码展示

#-------------------------------------# # 创建YOLO类 #-------------------------------------# import cv2 import numpy as np import colorsys import os import torch import torch.nn as nn from nets.yolo4 import YoloBody import torch.backends.cudnn as cudnn from PIL import Image, ImageFont, ImageDraw from torch.autograd import Variable from utils.utils import non_max_suppression, bbox_iou, DecodeBox, letterbox_image, yolo_correct_boxes class YOLO(object): _defaults = { "model_path": 'model_data/yolov4_maskdetect_weights1.pth', "anchors_path": 'model_data/yolo_anchors.txt', "classes_path": 'model_data/mask_classes.txt', "model_image_size" : (608, 608, 3), "confidence": 0.5, "cuda": True } @classmethod def get_defaults(cls, n): if n in cls._defaults: return cls._defaults[n] else: return "Unrecognized attribute name '" + n + "'" #---------------------------------------------------# # 初始化YOLO #---------------------------------------------------# def __init__(self, **kwargs): self.__dict__.update(self._defaults) self.class_names = self._get_class() self.anchors = self._get_anchors() self.generate() #---------------------------------------------------# # 获得所有的分类 #---------------------------------------------------# def _get_class(self): classes_path = os.path.expanduser(self.classes_path) with open(classes_path) as f: class_names = f.readlines() class_names = [c.strip() for c in class_names] return class_names #---------------------------------------------------# # 获得所有的先验框 #---------------------------------------------------# def _get_anchors(self): anchors_path = os.path.expanduser(self.anchors_path) with open(anchors_path) as f: anchors = f.readline() anchors = [float(x) for x in anchors.split(',')] return np.array(anchors).reshape([-1, 3, 2])[::-1,:,:] #---------------------------------------------------# # 获得所有的分类 #---------------------------------------------------# def generate(self): self.net = YoloBody(len(self.anchors[0]), len(self.class_names)).eval() # 加快模型训练的效率 print('Loading pretrained weights.') model_dict = self.net.state_dict() pretrained_dict = torch.load(self.model_path) pretrained_dict = {k: v for k, v in pretrained_dict.items() if np.shape(model_dict[k]) == np.shape(v)} model_dict.update(pretrained_dict) self.net.load_state_dict(model_dict) if self.cuda: os.environ["CUDA_VISIBLE_DEVICES"] = '0' self.net = nn.DataParallel(self.net) self.net = self.net.cuda() print('Finish loading!') self.yolo_decodes = [] for i in range(3): self.yolo_decodes.append(DecodeBox(self.anchors[i], len(self.class_names), (self.model_image_size[1], self.model_image_size[0]))) print('{} model, anchors, and classes loaded.'.format(self.model_path)) # 画框设置不同的颜色 hsv_tuples = [(x / len(self.class_names), 1., 1.) for x in range(len(self.class_names))] self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples)) self.colors = list( map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), self.colors)) #---------------------------------------------------# # 检测图片 #---------------------------------------------------# def detect_image(self, image): image_shape = np.array(np.shape(image)[0:2]) crop_img = np.array(letterbox_image(image, (self.model_image_size[0],self.model_image_size[1]))) photo = np.array(crop_img,dtype = np.float32) photo /= 255.0 photo = np.transpose(photo, (2, 0, 1)) photo = photo.astype(np.float32) images = [] images.append(photo) images = np.asarray(images) with torch.no_grad(): images = torch.from_numpy(images) if self.cuda: images = images.cuda() outputs = self.net(images) output_list = [] for i in range(3): output_list.append(self.yolo_decodes[i](outputs[i])) output = torch.cat(output_list, 1) batch_detections = non_max_suppression(output, len(self.class_names), conf_thres=self.confidence, nms_thres=0.3) try: batch_detections = batch_detections[0].cpu().numpy() except: return image top_index = batch_detections[:,4]*batch_detections[:,5] > self.confidence top_conf = batch_detections[top_index,4]*batch_detections[top_index,5] top_label = np.array(batch_detections[top_index,-1],np.int32) top_bboxes = np.array(batch_detections[top_index,:4]) top_xmin, top_ymin, top_xmax, top_ymax = np.expand_dims(top_bboxes[:,0],-1),np.expand_dims(top_bboxes[:,1],-1),np.expand_dims(top_bboxes[:,2],-1),np.expand_dims(top_bboxes[:,3],-1) # 去掉灰条 boxes = yolo_correct_boxes(top_ymin,top_xmin,top_ymax,top_xmax,np.array([self.model_image_size[0],self.model_image_size[1]]),image_shape) font = ImageFont.truetype(font='model_data/simhei.ttf',size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32')) thickness = (np.shape(image)[0] + np.shape(image)[1]) // self.model_image_size[0] mask_num = 0 nomask_num = 0 with open("test.txt", "w") as f: for i, c in enumerate(top_label): predicted_class = self.class_names[c] score = top_conf[i] top, left, bottom, right = boxes[i] top = top - 5 left = left - 5 bottom = bottom + 5 right = right + 5 top = max(0, np.floor(top + 0.5).astype('int32')) left = max(0, np.floor(left + 0.5).astype('int32')) bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32')) right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32')) # 画框框 label = '{}: {:.2f}'.format(predicted_class, score) draw = ImageDraw.Draw(image) label_size = draw.textsize(label, font) label = label.encode('utf-8') f.write(str(label)) f.write('\n') f.write("top: " + str(top) + " left: " + str(left) + " bottom: " + str(bottom) + " right: " + str(right)) f.write('\n') print(label) print("top: " + str(top) + " left: " + str(left) + " bottom: " + str(bottom) + " right: " + str(right)) if top - label_size[1] >= 0: text_origin = np.array([left, top - label_size[1]]) else: text_origin = np.array([left, top + 1]) for i in range(thickness): draw.rectangle( [left + i, top + i, right - i, bottom - i], outline=self.colors[self.class_names.index(predicted_class)]) draw.rectangle( [tuple(text_origin), tuple(text_origin + label_size)], fill=self.colors[self.class_names.index(predicted_class)]) draw.text(text_origin, str(label,'UTF-8'), fill=(0, 0, 0), font=font) if str(label)[2:8]=='nomask': nomask_num += 1 else: mask_num += 1 del draw return image, nomask_num, mask_num

第四步：运行

运行界面：

识别效果：

第五步：整个工程的内容

项目完整文件下载请见演示与介绍视频的简介处给出：➷➷➷

https://www.bilibili.com/video/BV1o3U6YqEd6/