基于YOLOV5的头盔佩戴检测识别系统源码+训练好的数据+权重文件

# yolov5训练自己的数据集 ## 1.下载`yolo v5`源码 在GitHub中搜索`yolov5`，找到[ultralytics](https://github.com/ultralytics)/**[yolov5](https://github.com/ultralytics/yolov5)**项目，下载`.zip`文件，这里我下载的是`5.0`版本，或者使用git bash 输入https://github.com/ultralytics/yolov5.git下载代码。若下载的是`yolov5-5.0.zip`文件，下载完成后解压至代码编辑的地方。 ## 2.使用Anaconda创建虚拟环境 若无anaconda环境，也可直接使用python环境 在Anaconda Prompt中输入`conda create --name yolov5 python=3.8` 输入y回车，然后输入命令`conda activate yolov5`进入虚拟环境。 yoloV5[要求](https://github.com/ultralytics/yolov5/blob/master/requirements.txt)[**在Python>= 3.7.0**](https://www.python.org/)环境中，包括[**PyTorch> = 1.7。**](https://pytorch.org/get-started/locally/) 然后我们进入解压后的YOLO V5项目文件夹，使用`pip install -r requirements.txt`命令下载项目所需依赖包（无anaconda可直接使用本命令安装依赖库，默认你安装好了python） 安装完成后,我们进入[PyTorch](https://pytorch.org/)官网,这里我选择以下配置: `PyTorch Build`选择`Stable (1.10.2)` `Your OS`选择`Windows` 系统 `Package`选择`Pip` 注意这里最好选用pip,conda会一直出现报错 `Language`选择`Python` `Compute Platform`选择`CUDA 10.2`有显卡建议选这个,没有显卡选择`CPU` `Run this Command:`显示 ```python pip3 install torch==1.10.2+cu102 torchvision==0.11.3+cu102 torchaudio===0.10.2+cu102 -f https://download.pytorch.org/whl/cu102/torch_stable.html ``` 将上面命令复制到控制台,安装pytorch,显示Successful即可 ## 3.建立VOC格式标准文件夹 在`yolov5-5.0\`下创建`make_voc_dir.py` ```python import os os.makedirs('VOCdevkit/VOC2007/Annotations') os.makedirs('VOCdevkit/VOC2007/JPEGImages') ``` 运行`make_voc_dir.py` 在`\yolov5-5.0\VOCdevkit\VOC2007\Annotations`中存放`xml`格式文件 在`\yolov5-5.0\VOCdevkit\VOC2007\JPEGImages`中存放`JPG`格式文件 ## 4.将xml格式转换成yolo格式 在`yolov5-5.0\`下创建`voc_to_yolo.py` ```python import xml.etree.ElementTree as ET import pickle import os from os import listdir, getcwd from os.path import join import random from shutil import copyfile classes = ["crack","helmet"] # 这个列表里存放的是你的类别 TRAIN_RATIO = 90 # 训练的比例 # 遍历文件夹 def clear_hidden_files(path): dir_list = os.listdir(path) for i in dir_list: abspath = os.path.join(os.path.abspath(path), i) if os.path.isfile(abspath): if i.startswith("._"): os.remove(abspath) else: clear_hidden_files(abspath) # 对宽高进行归一化操作 size:原图的宽和高 def convert(size, box): dw = 1. / size[0] dh = 1. / size[1] x = (box[0] + box[1]) / 2.0 y = (box[2] + box[3]) / 2.0 w = box[1] - box[0] h = box[3] - box[2] x = x * dw w = w * dw y = y * dh h = h * dh return (x, y, w, h) # 解析XML def convert_annotation(image_id): in_file = open('VOCdevkit/VOC2007/Annotations/%s.xml' % image_id,'rb') out_file = open('VOCdevkit/VOC2007/YOLOLabels/%s.txt' % image_id, 'w') tree = ET.parse(in_file) root = tree.getroot() size = root.find('size') w = int(size.find('width').text) h = int(size.find('height').text) for obj in root.iter('object'): difficult = obj.find('difficult').text cls = obj.find('name').text if cls not in classes or int(difficult) == 1: continue cls_id = classes.index(cls) xmlbox = obj.find('bndbox') b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text)) bb = convert((w, h), b) out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') in_file.close() out_file.close() wd = os.getcwd() wd = os.getcwd() data_base_dir = os.path.join(wd, "VOCdevkit/") if not os.path.isdir(data_base_dir): os.mkdir(data_base_dir) work_sapce_dir = os.path.join(data_base_dir, "VOC2007/") if not os.path.isdir(work_sapce_dir): os.mkdir(work_sapce_dir) annotation_dir = os.path.join(work_sapce_dir, "Annotations/") if not os.path.isdir(annotation_dir): os.mkdir(annotation_dir) clear_hidden_files(annotation_dir) image_dir = os.path.join(work_sapce_dir, "JPEGImages/") if not os.path.isdir(image_dir): os.mkdir(image_dir) clear_hidden_files(image_dir) yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/") if not os.path.isdir(yolo_labels_dir): os.mkdir(yolo_labels_dir) clear_hidden_files(yolo_labels_dir) yolov5_images_dir = os.path.join(data_base_dir, "images/") if not os.path.isdir(yolov5_images_dir): os.mkdir(yolov5_images_dir) clear_hidden_files(yolov5_images_dir) yolov5_labels_dir = os.path.join(data_base_dir, "labels/") if not os.path.isdir(yolov5_labels_dir): os.mkdir(yolov5_labels_dir) clear_hidden_files(yolov5_labels_dir) yolov5_images_train_dir = os.path.join(yolov5_images_dir, "train/") if not os.path.isdir(yolov5_images_train_dir): os.mkdir(yolov5_images_train_dir) clear_hidden_files(yolov5_images_train_dir) yolov5_images_test_dir = os.path.join(yolov5_images_dir, "val/") if not os.path.isdir(yolov5_images_test_dir): os.mkdir(yolov5_images_test_dir) clear_hidden_files(yolov5_images_test_dir) yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, "train/") if not os.path.isdir(yolov5_labels_train_dir): os.mkdir(yolov5_labels_train_dir) clear_hidden_files(yolov5_labels_train_dir) yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, "val/") if not os.path.isdir(yolov5_labels_test_dir): os.mkdir(yolov5_labels_test_dir) clear_hidden_files(yolov5_labels_test_dir) train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w') test_file = open(os.path.join(wd, "yolov5_val.txt"), 'w') train_file.close() test_file.close() train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a') test_file = open(os.path.join(wd, "yolov5_val.txt"), 'a') list_imgs = os.listdir(image_dir) # list image_one files prob = random.randint(1, 100) print("Probability: %d" % prob) for i in range(0, len(list_imgs)): path = os.path.join(image_dir, list_imgs[i]) if os.path.isfile(path): image_path = image_dir + list_imgs[i] voc_path = list_imgs[i] (nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path)) (voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path)) annotation_name = nameWithoutExtention + '.xml' annotation_path = os.path.join(annotation_dir, annotation_name) label_name = nameWithoutExtention + '.txt' label_path = os.path.join(yolo_labels_dir, label_name) prob = random.randint(1, 100) print("Probability: %d" % prob) if (prob < TRAIN_RATIO): # train dataset if os.path.exists(annotation_path): train_file.write(image_path + '\n') convert_annotation(nameWithoutExtention) # convert label copyfile(image_path, yolov5_images_train_dir + voc_path) copyfile(label_path, yolov5_labels_train_dir + label_name) else: # test dataset if os.path.exists(annotation_path): test_file.write(image_path + '\n') convert_annotation(nameWithoutExtention) # convert label copyfile(image_path, yolov5_images_test_dir + voc_path) copyfile(label_path, yolov5_labels_test_dir + label_name) train_file.close() test_file.close() ``` 执行一下`voc_to_yolo.py` ```shell (yoloV5) E:\PythonCode\yoloV5_toukui\yolov5-5.0>python voc_to_yolo.py Probability: 6 Probability: 79 Probability: 26 Probability: 19 Probability: 64 Probability: 5 Probability: 80 Probability: 40 Probability: 46 Probability: 23 Probability: 87 Probability: 19 Probabilit