yolov5训练步骤及安全帽检测

Posted 2022-08-08 韩搏

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环境部署问题、训练后无法识别问题都有介绍注意事项

一、说明

系统uname -ar：ubuntu18.0.4(Linux ubuntu 5.4.0-122-generic) 64bit
显卡lspci：GeForce GT 1030
nvidia版本：NVIDIA-Linux-x86_64-470.129.06.run (该网址搜索下载：https://www.nvidia.cn/geforce/drivers)
cuda版本： cuda_10.2.89_440.33.01_linux.run (历史版本：https://developer.nvidia.com/cuda-toolkit-archive)
yolov5: 代码是tag v6.1

二、PC机nvidia显卡(没有忽略用CPU方式)

禁用 nouveau驱动

lsmod | grep nouveau
		禁用:
		sudo vim /etc/modprobe.d/blacklist.conf
			blacklist nouveau
			options nouveau modeset=0
		
		sudo update-initramfs -u

nvidia驱动安装

sudo apt install dkms build-essential linux-headers-generic
	sudo apt-get install -y libc6-i386 lib32stdc++6 lib32gcc1 lib32ncurses5 lib32z1
	sudo ./NVIDIA-Linux-x86_64-470.129.06.run --dkms --no-opengl-files
	
	一定操作，否则会失败
		在Bios界面,禁用secure boot(安全模式)
	
	(安装失败重装)
	sudo nvidia-uninstall
	sudo apt-get remove --purge nvidia*

安装cuda10.2

A、sudo service lightdm stop

B、如果已安装nvidia驱动，安装时把驱动取消,建议先安装驱动，并且驱动版本大于cuda后缀的440.33.01，否则cuda安装失败)
		wget https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
		sudo sh cuda_10.2.89_440.33.01_linux.run --no-opengl-libs (如果开始安装了nvidia-390.151版本驱动会有问题)
C、vi ~/.bashrc
		export PATH="/usr/local/cuda-10.2/bin:$PATH" 
		export LD_LIBRARY_PATH="/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH"
	
D、sudo service lightdm start

nvidia-smi：失败

ls /usr/src | grep nvidia  #查看自己安装的nvidia版本，我的是470.129.06
sudo apt install dkms
sudo dkms install -m nvidia -v 470.129.06

三、python升级

python3.6–>pyhton3.9 (系统自带3.6)

安装依赖

sudo apt-get install libffi-dev zlib1g-dev libbz2-dev libssl-dev liblzma-dev

下载python3.9

wget https://www.python.org/ftp/python/3.9.0/Python-3.9.0.tgz

编译

./configure --with-ssl --enable-optimizations (--with-ssl 参数要加上否则使用中会出错)
	make
	sudo make install

设置软连接

sudo ln -s /usr/local/bin/python3 /usr/bin/python39 #不要修改系统python3软连接，否则一些命令无法使用
sudo ln -s /usr/local/bin/pip3 /usr/bin/pip3

四、pytorch安装

官网

https://pytorch.org/get-started/locally/

CPU

pip install torch==1.9.0+cpu torchvision==0.10.0+cpu torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
如果失败进入https://download.pytorch.org/whl/torch_stable.html 下载对应版本

GPU

pip install torch==1.9.0+cu102 torchvision==0.10.0+cu102 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
	
pip3 install torch-1.9.0+cu102-cp39-cp39-linux_x86_64.whl
pip3 install torchvision-0.10.0+cu102-cp39-cp39-linux_x86_64.whl
pip3 install torchaudio-0.9.0-cp39-cp39-linux_x86_64.whl

五、pip 依赖

absl-py==0.12.0
altgraph==0.17
backcall==0.2.0
backports.lzma==0.0.14
cachetools==4.2.1
certifi==2020.12.5
chardet==4.0.0
charset-normalizer==2.1.0
click==8.1.3
cycler==0.10.0
decorator==5.1.1
docker-pycreds==0.4.0
future==0.18.2
gitdb==4.0.9
GitPython==3.1.27
google-auth==1.28.1
google-auth-oauthlib==0.4.4
GPUtil==1.4.0
grpcio==1.37.0
idna==2.10
importlib-metadata==4.12.0
ipython==7.34.0
jedi==0.18.1
kiwisolver==1.3.1
lxml==4.9.1
Markdown==3.4.1
matplotlib==3.3.4
matplotlib-inline==0.1.3
numpy==1.21.6
oauthlib==3.2.0
opencv-python==4.5.1.48
pandas==1.3.5
parso==0.8.3
pathtools==0.1.2
pexpect==4.8.0
pickleshare==0.7.5
Pillow==9.2.0
promise==2.3
prompt-toolkit==3.0.30
protobuf==3.15.8
psutil==5.9.1
ptyprocess==0.7.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
Pygments==2.12.0
pyparsing==3.0.9
PyQt5==5.15.4
pyqt5-plugins==5.15.4.2.2
PyQt5-Qt5==5.15.2
PyQt5-sip==12.11.0
pyqt5-tools==5.15.4.3.2
PyQtChart==5.15.4
PyQtChart-Qt5==5.15.2
python-dateutil==2.8.2
python-dotenv==0.20.0
pytz==2022.1
PyYAML==6.0
qt5-applications==5.15.2.2.2
qt5-tools==5.15.2.1.2
requests==2.25.1
requests-oauthlib==1.3.1
rsa==4.9
scipy==1.6.1
seaborn==0.11.2
sentry-sdk==1.8.0
setproctitle==1.2.3
shortuuid==1.0.9
six==1.16.0
smmap==5.0.0
tensorboard==2.4.1
tensorboard-plugin-wit==1.8.1
tornado==6.1
tqdm==4.64.0
traitlets==5.3.0
typing-extensions==4.3.0
urllib3==1.26.5
wandb==0.12.21
wcwidth==0.2.5
Werkzeug==1.0.1
wincertstore==0.2
zipp==3.8.1

六、准备数据集

YOLOv5代码

git clone https://github.com/ultralytics/yolov5

图片资源，用飞桨安全帽资源，已经标注好了

HelmetDetection包括images(原始图片)和annotations(标注信息xml) 
下载地址：https://aistudio.baidu.com/aistudio/datasetdetail/50329

yolov5中创建目录(资源转成VOC格式)

helmet_source
		Annotations  #标注信息xml
		dataSet_path #
		images       #原始图片
		ImageSets    #数据集分类txt文件(自写make_voc_txt.py脚本生成)
		labels       #voc格式的标签文件(自写make_voc_label.py脚本生成)

注意

1. 按照上述目录结构训练结果可以检测出图片
2. 在data中创建的目录训练cls一直是0,检测图片也不识别
3. 尝试换环境版本,调参数都不行
4. 最后觉的可能哪里路径有问题

七、训练

将coco.yaml复制一份helmet.yaml修改如下：

train: helmet_source/dataSet_path/train.txt  # train images (relative to 'path') 118287 images
val: helmet_source/dataSet_path/val.txt  # val images (relative to 'path') 5000 images
# test: helmet_source/dataSet_path/test.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794

# Classes
nc: 2  # number of classes
names: ['helmet', 'head']  # class names

将models/yolov5s.yaml修改

nc: 2  # number of classes  改为自己的类别个数

训练指令(我的测试资源和脚本以及官网权重在文中后面有写)

python39 train.py --img 416 --batch 4 --epochs 100 --data data/helmet.yaml --cfg models/yolov5s.yaml --weights weights/yolov5s.pt --device 0 #--device cpu

说明

输出：runs/train/exp/weights/best.pt 和 last.pt
说明：训练100次,效果不太好,500次会好点,当然越多越好
YOLOv5 训练 ( train.py )、验证 ( val.py )、推理 ( detect.py ) 和导出 ( export.py ) 的正确操作

5.训练结果图

八、测试

检测指令

python39 detect.py --data data/helmet.yaml --weights runs/train/exp/weights/best.pt --source helmet_test.png #--conf-thres 0.1 --iou-thres 0.9

原始图片和结果

九、资源及脚本

测试资源下载点击这里下载

分类脚本 make_voc_txt.py

import os
import random
trainval_percent = 0.1
train_percent = 0.9
root_path = 'helmet_source'
xmlfilepath = '%s/Annotations' % root_path
txtsavepath = '%s/ImageSets' % root_path
total_xml = os.listdir(xmlfilepath)
num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
ftrainval = open('%s/trainval.txt' % txtsavepath, 'w')
ftest = open('%s/test.txt' % txtsavepath, 'w')
ftrain = open('%s/train.txt' % txtsavepath, 'w')
fval = open('%s/val.txt' % txtsavepath, 'w')
for i in list:
    name = total_xml[i][:-4] + '\\n'
    if i in trainval:
        ftrainval.write(name)
        if i in train:
            ftest.write(name)
        else:
            fval.write(name)
    else:
        ftrain.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()

生产yolo需要的标注数据格式make_voc_label.py（同时可以产生标注图片）

import xml.etree.ElementTree as ET
import pickle
import os, cv2
from os import listdir, getcwd
from os.path import join
from tqdm import tqdm

sets = ['train', 'test','val']
classes = ['helmet', 'head']
colors = 'helmet': (60, 60, 250), 'head': (250, 60, 60)

root_path = "helmet_source"
dataSet_path = "%s/dataSet_path" % root_path
image_path = "%s/images" % root_path
Annotations_path = "%s/Annotations" % root_path
ImageSets_path = "%s/ImageSets" % root_path
labels_path = "%s/labels" % root_path

def convert(size, box):
    dw = 1. / size[0]
    dh = 1. / size[1]
    x = (box[0] + box[1]) / 2.0 - 1
    y = (box[2] + box[3]) / 2.0 - 1
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh
    # x_center = (box[0]+box[1])/2.0
    # y_center = (box[2]+box[3])/2.0
    # x = x_center / size[0]
    # y = y_center / size[1]
    
    # w = (box[1] - box[0]) / size[0]
    # h = (box[3] - box[2]) / size[1]
    return (x, y, w, h)
def convert_annotation(image_id):
    in_file = open('%s/%s.xml' % (Annotations_path, image_id))
    out_file = open('%s/%s.txt' % (labels_path, image_id), 'w')
    im = cv2.imread('%s/%s.png' % (image_path, image_id))
    print('%s/%s.png' % (image_path, image_id))
	
    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')
        xmin = xmlbox.find('xmin').text
        xmax = xmlbox.find('xmax').text
        ymin = xmlbox.find('ymin').text
        ymax = xmlbox.find('ymax').text
        b = (float(xmin), float(xmax), float(ymin), float(ymax))
        b1, b2, b3, b4 = b
        # 标注越界修正
        if b2 > w:
            b2 = w
        if b4 > h:
            b4 = h
        b = (b1, b2, b3, b4)
        bb = convert((w, h), b)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\\n')
		
        print(cls, colors[cls], xmin, xmax, ymin, ymax)
        cv2.rectangle(im, (int(xmin), int(ymin)), (int(xmax), int(ymax)), colors[cls])
        cv2.putText(im, cls, (int(xmin), int(ymin) - 3), cv2.FONT_HERSHEY_SIMPLEX, 0.5, colors[cls])
    # cv2.imshow('result', im)
    # cv2.waitKey(0)
    cv2.imwrite('%s/%s_tag.png' % (image_path, image_id), im)
		
wd = getcwd()
print(wd)
for image_set in sets:
    if not os.path.exists(labels_path):
        os.makedirs(labels_path)
    image_ids = open('%s/%s.txt' % (ImageSets_path, image_set)).read().strip().split()
    list_file = open('%s/%s.txt' % (dataSet_path, image_set), 'w')
    for image_id in image_ids:
        # print(image_id)
        list_file.write('%s/%s.png\\n' % (image_path, image_id))
        convert_annotation(image_id)
    list_file.close()

文件重命名分序make_voc_file.py

import os
path = "./image"
filelist = os.listdir(path) #该文件夹下所有的文件（包括文件夹）
count=0 #从零开始
for file in filelist:
    print(file)
for file in filelist:   #遍历所有文件
    Olddir=os.path.join(path,file)   #原来的文件路径
    if os.path.isdir(Olddir):   #如果是文件夹则跳过
        continue
    filename=os.path.splitext(file)[0]   #文件名
    filetype=os.path.splitext(file)[1]   #文件扩展名
    Newdir=os.path.join(path,str(count).zfill(6)+filetype)  #用字符串函数zfill 以0补全所需位数
    os.rename(Olddir,Newdir)#重命名
    count+=1

5.下载官方权重(里面有download_weights.sh脚本)

from utils.downloads import attempt_download

models = ['n', 's', 'm', 'l', 'x']
models.extend([x + '6' for x in models])  # add P6 models

for x in models:
	attempt_download(f'yolov5x.pt')

附：

1、pip3 install backports.lzma (3.9忽略)
	  sudo vi /usr/local/lib/python3.7/lzma.py
		from _lzma import *
		from _lzma import _encode_filter_properties, _decode_filter_properties
		修改：
		try:
			from _lzma import *
			from _lzma import _encode_filter_properties, _decode_filter_properties
		except ImportError:
			from backports.lzma import *
			from backports.lzma import _encode_filter_properties, _decode_filter_properties
	
2、labelImg使用
		git clone https://github.com/tzutalin/labelImg
		pip3 install lxml
		pyrcc5 -o resources.py resources.qrc , 将Qt文件格式（.qrc）转为Python（.py）格式，将生成的resources.py拷贝到同级的libs目录下
		sudo apt-get install libxcb-xinerama0 (解决 qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "" even though it was found)
		
3、pip3 运行出错：subprocess.CalledProcessError: Command '('lsb_release', '-a')' returned non-zero exit status 1  
		sudo cp /usr/lib/python3/dist-packages/lsb_release.py /usr/local/lib/python3.7  (3.9忽略)

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