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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。

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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

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

git clone https://github.com/AlexeyAB/darknet
cd darknet

代码结构

Image text

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

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设置Windows SDK版本和平台工具集为当前系统安装版本

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设置Release和x64

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然后执行以下操作:Build-> Build yolo_cpp_dll

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==========

在打包DLL的过程中可能遇到如下问题

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	1	
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

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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

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封装YOLOv4编译后的DLL

  • 1、进入 darknet\build\darknet\x64 目录,将 pthreadGC2.dllpthreadVC2.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

Image text

代码

YOLOv4封装项目

YoloWrapper.cs - 封装主文件,调用 YOLOv4 的动态链接库

using System;
using System.Runtime.InteropServices;

namespace YoloWrapper
{

    public class YoloWrapper : IDisposable
    {
        private const string YoloLibraryName = @"\Dlls\yolo_cpp_dll.dll";

        [DllImport(YoloLibraryName, EntryPoint = "init")]
        private static extern int InitializeYolo(string configurationFilename, string weightsFilename, int gpu);

        [DllImport(YoloLibraryName, EntryPoint = "detect_image")]
        private static extern int DetectImage(string filename, ref BboxContainer container);

        [DllImport(YoloLibraryName, EntryPoint = "detect_mat")]
        private static extern int DetectImage(IntPtr pArray, int nSize, ref BboxContainer container);

        [DllImport(YoloLibraryName, EntryPoint = "dispose")]
        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[0]) * 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

using System.Runtime.InteropServices;

namespace YoloWrapper
{
    [StructLayout(LayoutKind.Sequential)]
    public struct BboxContainer
    {
        [MarshalAs(UnmanagedType.ByValArray, SizeConst = 1000)]
        public BoundingBox[] candidates;
    }
}

BoundingBox.cs

using System;
using System.Runtime.InteropServices;

namespace YoloWrapper
{
    [StructLayout(LayoutKind.Sequential)]
    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

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[i]);
        }


        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

Type Confidence X Y Width Height
mouse 0.25446844 1206 633 78 30
laptop 0.5488589 907 451 126 148
laptop 0.51734066 688 455 53 37
laptop 0.48207012 980 423 113 99
person 0.58085686 429 293 241 469
bottle 0.22032459 796 481 43 48
bottle 0.24873751 659 491 32 53
cup 0.5715177 868 453 55 70
bottle 0.29916075 1264 459 31 89
cup 0.2782725 685 503 35 40
cup 0.28154427 740 539 78 44
person 0.94347733 81 199 541 880
person 0.9496539 1187 368 233 155
chair 0.22458112 624 442 45 48
person 0.97528315 655 389 86 100
bottle 0.9407686 1331 436 33 107
bottle 0.9561032 1293 434 37 113
chair 0.4784215 1 347 386 730
cup 0.8945817 822 586 112 69
cup 0.6422996 1265 472 31 72
laptop 0.9833646 802 700 639 216
cup 0.9216428 828 521 115 71
chair 0.88087356 1124 416 111 70
diningtable 0.3222557 531 585 951 472

控制台

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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。

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