C#封装YOLOv4算法进行目标检测
概述官网：https://pjreddie.com/darknet/
Darknet：【Github】
C#封装代码：【Github】
YOLO: 是实现实时物体检测的系统，Darknet是基于YOLO的框架
采用C#语言对 YOLOv4 目标检测算法封装，将模型在实际应用系统中落地，实现模型在线远程调用。
环境准备
本章只讲解如何对YOLOv4封装进行详解，具体环境安装过程不做介绍
查看你的GPU计算能力是否支持 >= 3.0：【点击查看】
Windows运行要求
[*]CMake >= 3.12: 【点击下载】
[*]CUDA >= 10.0: 【点击下载】
[*]OpenCV >= 2.4: 【点击下载】
[*]cuDNN >= 7.0: 【点击下载】
[*]Visual Studio 2017/2019: 【点击下载】
我所使用的环境
[*]系统版本：Windows 10 专业版
[*]显卡：GTX 1050 Ti
[*]CMake版本：3.18.2
[*]CUDA版本：10.1
[*]OpenCV版本：4.4.0
[*]cuDNN版本：10.1
[*]MSVC 2017/2019: Visual Studio 2019
程序代码准备
源代码下载#
下载地址：【Darknet】
使用Git
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git clone https://github.com/AlexeyAB/darknet cd darknet代码结构
将YOLOv4编译为DLL#
详细教程：【点击查看】，这个教程描述的很详细。
进入 darknet\build\darknet 目录，打开解决方案 yolo_cpp_dll.sln
设置Windows SDK版本和平台工具集为当前系统安装版本
设置Release和x64
然后执行以下操作：Build-> Build yolo_cpp_dll
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已完成生成项目“yolo_cpp_dll.vcxproj”的操作。 ========== 生成: 成功 1 个，失败 0 个，最新 0 个，跳过 0 个 ==========在打包DLL的过程中可能遇到如下问题#
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C1041
无法打开程序数据库“D:\代码管理\C\darknet\build\darknet\x64\DLL_Release\vc142.pdb”；如果要将多个 CL.EXE 写入同一个 .PDB 文件，请使用 /FS yolo_cpp_dll C:\Users\administrator\AppData\Local\Temp\tmpxft_00005db0_00000000-6_dropout_layer_kernels.compute_75.cudafe1.cpp 1Copy
MSB3721
命令“"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin\nvcc.exe" -gencode=arch=compute_30,code=\"sm_30,compute_30\" -gencode=arch=compute_75,code=\"sm_75,compute_75\" --use-local-env -ccbin "C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.27.29110\bin\HostX86\x64" -x cu -IC:\opencv\build\include -IC:\opencv_3.0\opencv\build\include -I..\..\include -I..\..\3rdparty\stb\include -I..\..\3rdparty\pthreads\include -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include" -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include" -I\include -I\include -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include" --keep-dir x64\Release -maxrregcount=0 --machine 64 --compile -cudart static -DCUDNN_HALF -DCUDNN -DGPU -DLIB_EXPORTS -D_TIMESPEC_DEFINED -D_SCL_SECURE_NO_WARNINGS -D_CRT_SECURE_NO_WARNINGS -DWIN32 -DNDEBUG -D_CONSOLE -D_LIB -D_WINDLL -D_MBCS -Xcompiler "/EHsc /W3 /nologo /O2 /Fdx64\DLL_Release\vc142.pdb /Zi /MD " -o x64\DLL_Release\dropout_layer_kernels.cu.obj "D:\darknet\src\dropout_layer_kernels.cu"”已退出，返回代码为 2。 yolo_cpp_dll C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Microsoft\VC\v160\BuildCustomizations\CUDA 10.1.targets 757解决方法#
在VS 2019 工具》选项》项目和解决方案》生成并运行 中最大并行项目生成数设为 1
在VS 2019 项目-》属性-》配置属性-》常规 将Windows SDK版本设置为系统当前版本即可
封装YOLOv4编译后的DLL
[*]1、进入 darknet\build\darknet\x64 目录，将 pthreadGC2.dll 和 pthreadVC2.dll 拷贝到项目 Dll 文件夹
[*]2、将编译后的YOLOv4 DLL文件拷贝到项目 Dll 文件夹
[*]3、进入 darknet\build\darknet\x64\cfg 目录，将 yolov4.cfg 拷贝到项目 Cfg 文件夹
[*]4、进入 darknet\build\darknet\x64\data 目录，将 coco.names 拷贝到项目 Data 文件夹
[*]5、下载 yolov4.weights 权重文件 拷贝到 Weights 文件夹，文件245 MB 【点击下载】
项目文件
代码下载：【Github】
[*]YoloWrapper - YOLOv4封装项目
[*]Cfg - 配置文件夹
[*]Data - label文件夹
[*]Dll - YOLOv4 编译后的DLL文件夹
[*]Weights - YOLOv4 权重文件夹
[*]BboxContainer.cs
[*]BoundingBox.cs
[*]YoloWrapper.cs - 封装主文件，调用 YOLOv4 的动态链接库
[*]YoloWrapperConsole - 调用封装DLL控制台程序
[*]Program.cs - 控制台主程序，调用 YOLOv4 封装文件
代码
YOLOv4封装项目
YoloWrapper.cs - 封装主文件，调用 YOLOv4 的动态链接库
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using System;
using System.Runtime.InteropServices;
namespace YoloWrapper
{
public class YoloWrapper : IDisposable
{
private const string YoloLibraryName = @"\Dlls\yolo_cpp_dll.dll";
private static extern int InitializeYolo(string configurationFilename, string weightsFilename, int gpu);
private static extern int DetectImage(string filename, ref BboxContainer container);
private static extern int DetectImage(IntPtr pArray, int nSize, ref BboxContainer container);
private static extern int DisposeYolo();
public YoloWrapper(string configurationFilename, string weightsFilename, int gpu)
{
InitializeYolo(configurationFilename, weightsFilename, gpu);
}
public void Dispose()
{
DisposeYolo();
}
public BoundingBox[] Detect(string filename)
{
var container = new BboxContainer();
var count = DetectImage(filename, ref container);
return container.candidates;
}
public BoundingBox[] Detect(byte[] imageData)
{
var container = new BboxContainer();
var size = Marshal.SizeOf(imageData) * imageData.Length;
var pnt = Marshal.AllocHGlobal(size);
try
{
Marshal.Copy(imageData, 0, pnt, imageData.Length);
var count = DetectImage(pnt, imageData.Length, ref container);
if (count == -1)
{
throw new NotSupportedException($"{YoloLibraryName} has no OpenCV support");
}
}
catch (Exception exception)
{
return null;
}
finally
{
Marshal.FreeHGlobal(pnt);
}
return container.candidates;
}
}
}
BboxContainer.cs
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using System.Runtime.InteropServices; namespace YoloWrapper { public struct BboxContainer { public BoundingBox[] candidates; } }BoundingBox.cs
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using System; using System.Runtime.InteropServices; namespace YoloWrapper { public struct BoundingBox { public UInt32 x, y, w, h; public float prob; public UInt32 obj_id; public UInt32 track_id; public UInt32 frames_counter; public float x_3d, y_3d, z_3d; } }调用封装DLL控制台程序
BoundingBox.cs
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using ConsoleTables;
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using YoloWrapper;
namespace YoloWrapperConsole
{
class Program
{
private const string configurationFilename = @".\Cfg\yolov4.cfg";
private const string weightsFilename = @".\Weights\yolov4.weights";
private const string namesFile = @".\Data\coco.names";
private static Dictionary<int, string> _namesDic = new Dictionary<int, string>();
private static YoloWrapper.YoloWrapper _wrapper;
static void Main(string[] args)
{
Initilize();
Console.Write("ImagePath：");
string imagePath = Console.ReadLine();
var bbox = _wrapper.Detect(imagePath);
Convert(bbox);
Console.ReadKey();
}
private static void Initilize()
{
_wrapper = new YoloWrapper.YoloWrapper(configurationFilename, weightsFilename, 0);
var lines = File.ReadAllLines(namesFile);
for (var i = 0; i < lines.Length; i++)
_namesDic.Add(i, lines);
}
private static void Convert(BoundingBox[] bbox)
{
Console.WriteLine("Result：");
var table = new ConsoleTable("Type", "Confidence", "X", "Y", "Width", "Height");
foreach (var item in bbox.Where(o => o.h > 0 || o.w > 0))
{
var type = _namesDic[(int)item.obj_id];
table.AddRow(type, item.prob, item.x, item.y, item.w, item.h);
}
table.Write(Format.MarkDown);
}
}
}测试返回结果#
TypeConfidenceXYWidthHeightmouse0.2544684412066337830laptop0.5488589907451126148laptop0.517340666884555337laptop0.4820701298042311399person0.58085686429293241469bottle0.220324597964814348bottle0.248737516594913253cup0.57151778684535570bottle0.2991607512644593189cup0.27827256855033540cup0.281544277405397844person0.9434773381199541880person0.94965391187368233155chair0.224581126244424548person0.9752831565538986100bottle0.9407686133143633107bottle0.9561032129343437113chair0.47842151347386730cup0.894581782258611269cup0.642299612654723172laptop0.9833646802700639216cup0.921642882852111571chair0.88087356112441611170diningtable0.3222557531585951472控制台
文档来源：51CTO技术博客https://blog.51cto.com/shanyou/3184082
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