快速上手YOLOv5

快速上手YOLOv5
- 一、YOLOv5算法
- - 1. 算法对比
  - - （1）传统目标检测方法
    - （2）基于深度学习的目标检测算法
    - （2-1）Two-Stage（R-CNN/Fast R-CNN/Faster R-CNN）
    - （2-2）One-Stage（SSD/YOLO）
    - （2-3）One-Stage VS Two-Stage
- 二、环境配置
- - 1. NVIDIA独立显卡
  - 2. 环境要求
  - 3. 安装Visual Studio
  - 4. 安装Anaconda
  - 5. 安装CUDA Toolkit
  - 6. 安装cuDNN
  - 7. 安装TensorRT
  - 8. 安装OpenCV
  - 9. 安装CMake
  - 10. 安装Pytorch
  - 11. 安装YOLOv5必要依赖包
  - 12. YOLOv5使用TensorRT加速
- 三、获取数据集
- - 1. 公共数据集Kaggle、MakeML等：
  - 2. 百度图片爬虫：
  - 3. FFmpeg视频帧截取：
- 四、图像标注
- - 1. Labelme图片标注工具：
  - - 半自动辅助标注：
  - 2. AI标注：
- 五、训练平台
- - 1. 免费算力平台：
- 六、Demo
- 七、参考资料

快速上手YOLOv5

一、YOLOv5算法

1. 算法对比

（1）传统目标检测方法

（2）基于深度学习的目标检测算法

（2-1）Two-Stage（R-CNN/Fast R-CNN/Faster R-CNN）

Two-Stage：先产生候选区域，后对目标分类与位置精修，准确率高但速度较慢

（2-2）One-Stage（SSD/YOLO）

One-Stage：直接回归目标类别与位置，速度快但准确率较低

（2-3）One-Stage VS Two-Stage

YOLO v5s：模型小、推理时间短、准确率较高

二、环境配置

1. NVIDIA独立显卡

打开设备管理器查看是否拥有NVIDIA独立显卡：

查看NVIDIA独立显卡是否支持CUDA：

https://developer.nvidia.com/cuda-gpus

2. 环境要求

环境	版本	地址
Visual Studio	Visual Studio 2019 版本 16.11 Community	点击下载
CMake	cmake-3.24.0-rc2-windows-x86_64	点击下载
OpenCV	OpenCV – 3.4.16	点击下载
Anaconda	64-Bit Graphical Installer (594 MB)	点击下载
CUDA Toolkit	CUDA Toolkit 11.3.1	点击下载
cuDNN	cuDNN v8.2.1 (June 7th, 2021), for CUDA 11.x	点击下载
TensorRT	TensorRT 8.2 GA Update 4 for x86_64 Architecture	点击下载

3. 安装Visual Studio

安装Visual Studio 2019勾选C++桌面开发工具：

4. 安装Anaconda

添加系统环境变量：

测试：

换国内源：

新建.condarc文件保存以下代码，并替换C:\Users\XXX下的.condarc文件

channels:- defaults
show_channel_urls: true
default_channels:- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r- http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:conda-forge: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloudmsys2: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloudbioconda: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloudmenpo: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloudpytorch: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloudpytorch-lts: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloudsimpleitk: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud

5. 安装CUDA Toolkit

选择自定义

全部安装

添加系统环境变量：

上述两项是安装完成Cuda后，已自动生成的环境变量的配置，我们需要自行添加的下述变量：

CUDA_BIN_PATH: %CUDA_PATH%\bin

CUDA_LIB_PATH: %CUDA_PATH%\lib\x64

CUDA_SDK_PATH: XXX\NVIDIA Corporation\CUDA Samples\v11.1

CUDA_SDK_BIN_PATH: %CUDA_SDK_PATH%\bin\win64

CUDA_SDK_LIB_PATH: %CUDA_SDK_PATH%\common\lib\x64

并在系统变量Path中，添加一下四个信息：

%CUDA_BIN_PATH%

%CUDA_LIB_PATH%

%CUDA_SDK_BIN_PATH%

%CUDA_SDK_LIB_PATH%

测试：

nvcc -V

6. 安装cuDNN

复制XXX\cudnn-11.3-windows-x64-v8.2.1.32\cuda全部内容到XXX\NVIDIA GPU Computing Toolkit\CUDA\v11.3

7. 安装TensorRT

添加环境变量：

复制XXX\TensorRT-8.2.5.1\lib以下所有dll文件到XXX\NVIDIA GPU Computing Toolkit\CUDA\v11.3\bin下

安装XXXTensorRT\TensorRT-8.2.5.1\python目录下tensorrt-8.2.5.1-cp39-none-win_amd64.whl：

activate pytorch
pip install tensorrt-8.2.5.1-cp39-none-win_amd64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple

8. 安装OpenCV

添加环境变量：

9. 安装CMake

添加环境变量：

10. 安装Pytorch

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

以管理员模式在命令行中运行：

conda create -n pytorch python=3.9
activate pytorch
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

测试：

# cuda test
import torch
print(torch.__version__)
x = torch.Tensor([1.0])
xx = x.cuda()
print(xx)
print(torch.cuda.is_available())# cudnn test
from torch.backends import cudnn
print(cudnn.is_acceptable(xx))

11. 安装YOLOv5必要依赖包

下载YOLOv5源代码：

进入XXX\yolov5-master目录执行：

activate pytorch
pip install -r requirements.txt

测试：

activate pytorch
python detect.py

运行结果保存在XXX\yolov5-master\runs\detect\exp下

12. YOLOv5使用TensorRT加速

下载YOLOv5 TensorRT加速版本：

https://github.com/wang-xinyu/tensorrtx

在XXX\tensorrtx下创建include文件夹，下载文件dirent.h放置在include文件夹中，
下载地址 https://github.com/tronkko/dirent/tree/master/include/dirent.h

修改XXX\tensorrtx\yolov5目录下CMakeLists.txt内容为（按自己的安装目录修改#1 #2 #3 #4）：

cmake_minimum_required(VERSION 2.6)project(yolov5)
set(OpenCV_DIR "D:/Environment/OpenCV/opencv/build")    ##1
set(OpenCV_INCLUDE_DIRS "D:/Environment/OpenCV/opencv/build/include")    ##2
set(OpenCV_LIBS "D:\\Environment\\OpenCV\\opencv\\build\\x64\\vc14\\lib\\opencv_world3416.lib")    ##3
set(TRT_DIR "D:/Environment/TensorRT/TensorRT-8.2.5.1")    ##4add_definitions(-DAPI_EXPORTS)
add_definitions(-std=c++11)
option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_BUILD_TYPE Debug)set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads)# setup CUDA
find_package(CUDA REQUIRED)
message(STATUS "libraries: ${CUDA_LIBRARIES}")
message(STATUS "include path: ${CUDA_INCLUDE_DIRS}")include_directories(${CUDA_INCLUDE_DIRS})####
enable_language(CUDA)  # add this line, then no need to setup cuda path in vs
####
include_directories(${PROJECT_SOURCE_DIR}/include)
include_directories(${TRT_DIR}\\include)
include_directories(D:\\Environment\\TensorRT\\tensorrtx\\include)    ##5#find_package(OpenCV)
include_directories(${OpenCV_INCLUDE_DIRS})
include_directories(${OpenCV_INCLUDE_DIRS}\\opencv2) #6# -D_MWAITXINTRIN_H_INCLUDED for solving error: identifier "__builtin_ia32_mwaitx" is undefined
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -D_MWAITXINTRIN_H_INCLUDED")# setup opencv
find_package(OpenCV QUIETNO_MODULENO_DEFAULT_PATHNO_CMAKE_PATHNO_CMAKE_ENVIRONMENT_PATHNO_SYSTEM_ENVIRONMENT_PATHNO_CMAKE_PACKAGE_REGISTRYNO_CMAKE_BUILDS_PATHNO_CMAKE_SYSTEM_PATHNO_CMAKE_SYSTEM_PACKAGE_REGISTRY
)message(STATUS "OpenCV library status:")
message(STATUS "version: ${OpenCV_VERSION}")
message(STATUS "libraries: ${OpenCV_LIBS}")
message(STATUS "include path: ${OpenCV_INCLUDE_DIRS}")include_directories(${OpenCV_INCLUDE_DIRS})
link_directories(${TRT_DIR}\\lib)
link_directories(${OpenCV_DIR}\\x64\\vc14\\lib) #8add_executable(yolov5 ${PROJECT_SOURCE_DIR}/calibrator.cpp yolov5 ${PROJECT_SOURCE_DIR}/yolov5.cpp ${PROJECT_SOURCE_DIR}/yololayer.cu ${PROJECT_SOURCE_DIR}/yololayer.h ${PROJECT_SOURCE_DIR}/preprocess.cu ${PROJECT_SOURCE_DIR}/preprocess.h)
target_link_libraries(yolov5 "nvinfer" "nvinfer_plugin") #9
target_link_libraries(yolov5 ${OpenCV_LIBS})        #10
target_link_libraries(yolov5 ${CUDA_LIBRARIES}) #11
target_link_libraries(yolov5 Threads::Threads) #12

在XXX\tensorrtx\yolov5目录下新建build文件夹

运行XXX\CMake\bin\cmake-gui.exe配置：

Where is the source code: XXX\tensorrtx\yolov5

Where to build the binaries: XXX\tensorrtx\yolov5\build

依次执行Configure -> Generate -> Open Project

下载YOLOv5 TensorRT Win10加速版本：

https://github.com/Monday-Leo/Yolov5_Tensorrt_Win10

复制XXX\Yolov5_Tensorrt_Win10-master\yolov5.cpp文件内容，替换XXX\tensorrtx\yolov5\yolov5.cpp内容

修改yololayer.h文件20行中的类别数（按需修改）

static constexpr int CLASS_NUM = 4;     # 修改类别数

选择重新生成yolov5项目

复制XXX\tensorrtx\yolov5\gen_wts.py到XXX\yolov5-master目录，执行以下代码：

activate pytorch
python gen_wts.py -w yolov5s.pt -o yolov5s.wts

复制XXX\yolov5-master\yolov5s.wts到XXX\tensorrtx\yolov5\build\Release下，执行以下代码：

yolov5.exe -s yolov5s.wts yolov5s.engine s

执行XXX\tensorrtx\yolov5\build下yolov5.sln

右键选择yolov5项目属性

修改配置属性->常规->配置类型为动态库(.dll)

修改配置属性->高级->目标文件扩展名为.dll

选择重新生成yolov5项目

复制XXX\tensorrtx\yolov5\build\Release下yolov5.dll和yolov5s.wts到XXX\Yolov5_Tensorrt_Win10-master

复制XXX\yolov5-master\data\images到XXX\Yolov5_Tensorrt_Win10-master

修改python_trt.py：

img = cv2.imread("./images/bus.jpg")

测试：

activate pytorch
python python_trt.py

三、获取数据集

1. 公共数据集Kaggle、MakeML等：

https://www.kaggle.com/datasets

https://makeml.app/dataset-store

2. 百度图片爬虫：

import json
import itertools
import urllib
import requests
import os
import re
import sysword = input("请输入关键字：")
pageNum = input("爬取页面数（每个页面含有30张图片）：")path = "D:/Data/CrawlImg"
if not os.path.exists(path):os.mkdir(path)urls = []
word = urllib.parse.quote(word)headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36'
}url = r"https://image.baidu.com/search/acjson?tn=resultjson_com&ipn=rj&ct=201326592&is=0%2C0&fp=detail&logid=7181752122064024928&cl=2&lm=-1&ie=utf-8&oe=utf-8&adpicid=0&lpn=0&st=-1&word={word}&ic=0&hd=undefined&latest=undefined&copyright=undefined&s=undefined&se=&tab=0&width=&height=&face=undefined&istype=2&qc=&nc=&fr=&simics=&srctype=&bdtype=0&rpstart=0&rpnum=0&cs=604827192%2C704143832&catename=&force=undefined&album_id=&album_tab=&cardserver=&tabname=&pn={pn}&rn=30&gsm=1&1619190981089="for i in range(int(pageNum)):urls.append(url.format(word=word,pn=i*30))str_table = {'_z2C$q': ':','AzdH3F': '/','_z&e3B': '.'
}char_table = {'w': 'a','k': 'b','v': 'c','1': 'd','j': 'e','u': 'f','2': 'g','i': 'h','t': 'i','3': 'j','h': 'k','s': 'l','4': 'm','g': 'n','5': 'o','r': 'p','q': 'q','6': 'r','f': 's','p': 't','7': 'u','e': 'v','o': 'w','8': '1','d': '2','n': '3','9': '4','c': '5','m': '6','0': '7','b': '8','l': '9','a': '0'
}i=1
char_table = {ord(key): ord(value) for key, value in char_table.items()}
for url in urls:html=requests.get(url,headers=headers).texta=re.compile(r'"objURL":"(.*?)"')downURL=re.findall(a,html)for t in downURL:for key, value in str_table.items():t = t.replace(key, value)t=t.translate(char_table)print(t)try:html_1=requests.get(t)if str(html_1.status_code)[0]=="4":print('请求失败')continueexcept Exception as e:print('图片链接不存在')continuewith open(path+"/"+str(i)+".jpg",'wb') as f:f.write(html_1.content)i=i+1

3. FFmpeg视频帧截取：

ffmpeg -i [video] -r [interval] -q:v [quality] -f image2 [name]%03d.jpeg

-r 表示每一秒几帧

-q:v表示存储jpeg的图像质量，一般2是高质量

ffmpeg -i input.mp4 -r 1 -q:v 2 -f image2 pic-%03d.jpeg

如此，ffmpeg会把input.mp4，每隔一秒，存一张图片下来。假设有60s，那会有60张

四、图像标注

1. Labelme图片标注工具：

下载地址https://github.com/wkentaro/labelme/releases/download/v5.0.1/Labelme.exe

Open Dir设置为图片所在文件夹

Edit -> Change Output Dir设置lJSON格式标签保存位置

创建数据集目录树

Annotation文件夹保存labelme标注格式标签

images文件夹存放图片

labels文件夹保存YOLO格式标签

classes.txt存储类别名，必须按顺序一行一个类别名

labelme标注格式为JSON需转换为YOLO格式，并划分train、val、test数据集

labelme to yolo

# coding:utf-8import os
import shutil
import json
import argparse
from sklearn.model_selection import train_test_split'''
1. One row per object
2. Each row is class x_center y_center width height format.
3. Box coordinates must be in normalized xywh format (from 0 - 1).
If your boxes are in pixels, divide x_center and width by image width, and y_center and height by image height.
4. Class numbers are zero-indexed (start from 0).
'''parser = argparse.ArgumentParser()
parser.add_argument('--img_dir', default='./datasets/CSGO2/images',type=str, help="path of images")
parser.add_argument('--json_dir', default='./datasets/CSGO2/Annotations', type=str, help="path of json")
parser.add_argument('--classes_path', default='./datasets/CSGO2/classes.txt', type=str, help="path of classes.txt")
parser.add_argument('--txt_dir', default='./datasets/CSGO2/labels', type=str, help="save path of txt")
parser.add_argument('--split_ratio', default=[0.8, 0.1, 0.1], type=str, help="random split the dataset [train, val, test], default ratio is 8:1:1")
arg = parser.parse_args()sets = ['train', 'val', 'test']def train_test_val_split_random(img_dir, json_dir, classes_path, txt_dir, split_ratio):ratio_train = split_ratio[0]ratio_val = split_ratio[1]ratio_test = split_ratio[2]assert int(ratio_train+ratio_test+ratio_val) == 1imgset_path_list = {}imgset_path_list[0], middle_img_path = train_test_split(os.listdir(img_dir),test_size=1-ratio_train, random_state=233)ratio=ratio_val/(1-ratio_train)imgset_path_list[1], imgset_path_list[2] = train_test_split(middle_img_path,test_size=ratio, random_state=233)print("NUMS of train:val:test = {}:{}:{}".format(len(imgset_path_list[0]), len(imgset_path_list[1]), len(imgset_path_list[2])))for index, set in enumerate(sets, 0):split_imgset_dir = os.path.join(img_dir, set)if not os.path.exists(split_imgset_dir):os.makedirs(split_imgset_dir)for imgset_path in imgset_path_list[index]:shutil.copy(os.path.join(img_dir, imgset_path), split_imgset_dir)split_txt_dir = os.path.join(txt_dir, set)if not os.path.exists(split_txt_dir):os.makedirs(split_txt_dir)labelme_to_yolo(split_imgset_dir, json_dir, classes_path, split_txt_dir)def labelme_to_yolo(split_imgset_dir, json_dir, classes_path, split_txt_dir):with open(classes_path) as f:classes = f.read().strip().split()img_path_list = os.listdir(split_imgset_dir)for img_path in img_path_list:if img_path.startswith('.'):continuename, suffix = os.path.splitext(img_path)save_path = os.path.join(split_txt_dir, name + '.txt')json_path = os.path.join(json_dir, name + '.json')print('processing -> %s' % json_path)label_dict = json.load(open(json_path, 'r'))height = label_dict['imageHeight']width = label_dict['imageWidth']loc_info_list = label_dict['shapes']label_info_list = list()for loc_info in loc_info_list:obj_name = loc_info.get('label')label_id = classes.index(obj_name)loc = loc_info.get('points')x0, y0 = loc[0]  # 左上角点x1, y1 = loc[1]  # 右下角点x_center = (x0 + x1) / 2 / widthy_center = (y0 + y1) / 2 / heightbox_w = (abs(x1 - x0)) / width  # 这里使用绝对值是因为有时候先标注的右下角点box_h = (abs(y1 - y0)) / heightassert box_w > 0, print((int(x0), int(y0)), (int(x1), int(y1)))assert box_h > 0label_info_list.append([str(label_id), str(x_center), str(y_center), str(box_w), str(box_h)])with open(save_path, 'a') as f:for label_info in label_info_list:label_str = ' '.join(label_info)f.write(label_str)f.write('\n')if __name__ == '__main__':train_test_val_split_random(arg.img_dir, arg.json_dir, arg.classes_path, arg.txt_dir, arg.split_ratio)

复制images和labels文件夹到XXX\yolov5-master\datasets\XXX目录

设置训练数据配置文件xxx.yaml即可用YOLOv5对数据集进行训练

path设置为数据集文件夹

nc设置为类别数

names设置为类别名，必须按顺序

修改XXX\yolov5-master\train.py

parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')    # 训练已有模型绝对路径，为空时即重新训练新的模型
parser.add_argument('--data', type=str, default=ROOT / 'datasets/CSGO/csgo.yaml', help='dataset.yaml path')    # 训练数据配置文件xxx.yaml绝对路径
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')    # 批大小，提示显存不够设置数值减少一半
parser.add_argument('--workers', type=int, default=0, help='max dataloader workers (per RANK in DDP mode)')  # Windows系统下设置为0

开始训练：

activate pytorch
python train.py

半自动辅助标注：

利用XXX\yolov5-master\detect.py半自动辅助标注，设置detect.py中222行为：

parser.add_argument('--save-txt', default=True, action='store_true', help='save results to *.txt')

运行detect.py：

activate pytorch
python detect.py

标签结果保存在XXX\yolov5-master\runs\detect\expX\labels中，复制labels到数据集目录下的lables中，执行以下代码即可在Annotation文件夹下生成labelme的标注格式JSON文件

yolo to labelme

# coding:utf-8import os
import cv2
import json
import argparse
import base64parser = argparse.ArgumentParser()
parser.add_argument('--img_dir', default='./datasets/CSGO2/images',type=str, help="path of images")
parser.add_argument('--txt_dir', default='./datasets/CSGO2/labels', type=str, help="path of txt")
parser.add_argument('--classes_path', default='./datasets/CSGO2/classes.txt', type=str, help="path of classes.txt")
parser.add_argument('--json_dir', default='./datasets/CSGO2/Annotations', type=str, help="save path of json")
arg = parser.parse_args()def parse_tta_label(img_path, txt_path, classes_path, json_dir):file_name = img_path.split('\\')[-1].split('.')[0]imagePath = img_path.split('/')[-1]img = cv2.imread(img_path)h, w = img.shape[:2]with open(img_path, 'rb') as f:image = f.read()image_base64 = str(base64.b64encode(image), encoding='utf-8')version = '5.0.1'data_dict = dict()data_dict.__setitem__('version', version)data_dict.__setitem__('imagePath', imagePath)data_dict.__setitem__("imageData", image_base64)data_dict.__setitem__('imageHeight', h)data_dict.__setitem__('imageWidth', w)data_dict.__setitem__('flags', {})data_dict['shapes'] = list()with open(txt_path, 'r') as f:label_info_list = f.readlines()for label_info in label_info_list:label_info = label_info.strip()label_info = label_info.split(' ')class_name = label_info[0]c_x = float(label_info[1]) * wc_y = float(label_info[2]) * hb_w = float(label_info[3]) * wb_h = float(label_info[4]) * hx1 = c_x - b_w / 2x2 = c_x + b_w / 2y1 = c_y - b_h / 2y2 = c_y + b_h / 2points = [[x1, y1], [x2, y2]]shape_type = 'rectangle'shape = {}with open(classes_path) as f:classes = f.read().strip().split()shape.__setitem__('label', classes[int(class_name)])shape.__setitem__('points', points)shape.__setitem__('shape_type', shape_type)shape.__setitem__('flags', {})shape.__setitem__('group_id', None)data_dict['shapes'].append(shape)save_json_path = os.path.join(json_dir, '%s.json' % file_name)json.dump(data_dict, open(save_json_path, 'w'), indent=4)def generate_labelme_prelabel(img_dir, txt_dir, classes_path, json_dir):img_name_list = os.listdir(img_dir)for img_name in img_name_list:if img_name.startswith('.'):continuetxt_name = img_name.replace('images','txt').replace('.jpg','.txt').replace('.jpeg','.txt').replace('.png','.txt')print('processing -> %s' % txt_name)img_path = os.path.join(img_dir, img_name)txt_path = os.path.join(txt_dir, txt_name)parse_tta_label(img_path, txt_path, classes_path, json_dir)if __name__ == '__main__':generate_labelme_prelabel(arg.img_dir, arg.txt_dir, arg.classes_path, arg.json_dir)

Open Dir设置为图片所在文件夹

Edit -> Change Output Dir设置lJSON格式标签保存位置

利用labelme进行人工修整后，按labelme to yolo方法转换标签格式再给YOLOv5训练

2. AI标注：

飞桨BML：

https://blog.csdn.net/weixin_42217041/article/details/119250959

五、训练平台

1. 免费算力平台：

Google Colab：

利用Google Colab训练YOLOv5

教程：

https://blog.csdn.net/djstavav/article/details/112261905

MultCloud：

利用MultCloud把OneDrive数据集传出到Google Colab

教程：

https://blog.csdn.net/AWhiteDongDong/article/details/107928536

六、Demo

https://github.com/johnhillross/YOLOv5_TensorRT_Win

顺便臭不要脸要个Star⭐️

七、参考资料

[1] https://github.com/ultralytics/yolov5

[2] https://github.com/wang-xinyu/tensorrtx

[3] https://github.com/Monday-Leo/Yolov5_Tensorrt_Win10

[4] https://blog.csdn.net/qq_41319718/article/details/123075288

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