【OpenVINO™】在 C# 中使用OpenVINO™ 部署PP-YOLOE实现物体检测

 前言

OpenVINO™ C# API 是一个 OpenVINO™ 的 .Net wrapper，应用最新的 OpenVINO™ 库开发，通过 OpenVINO™ C API 实现 .Net 对 OpenVINO™ Runtime 调用，使用习惯与 OpenVINO™ C++ API 一致。OpenVINO™ C# API 由于是基于 OpenVINO™ 开发，所支持的平台与 OpenVINO™ 完全一致，具体信息可以参考 OpenVINO™。通过使用 OpenVINO™ C# API，可以在 .NET、.NET Framework等框架下使用 C# 语言实现深度学习模型在指定平台推理加速。

OpenVINO™ C# API 项目链接为：

https://github.com/guojin-yan/OpenVINO-CSharp-API.git

项目源码链接为：

https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples.git

1. 简介

   PP-YOLOE是基于PP-YOLOv2的优秀单级无锚模型，超越了各种流行的YOLO模型。PP-YOLOE有一系列型号，命名为s/m/l/x，通过宽度乘数和深度乘数进行配置。PP-YOLOE避免使用特殊的运算符，如可变形卷积或矩阵NMS，以便友好地部署在各种硬件上。 在本文中，我们将使用OpenVINO™ C# API 部署 PP-YOLOE实现物体检测。

2. 项目环境与依赖

  该项目中所需依赖已经支持通过NuGet Package进行安装，在该项目中，需要安装以下NuGet Package：

• OpenVINO C# API NuGet Package：

OpenVINO.CSharp.API
OpenVINO.runtime.win
OpenVINO.CSharp.API.Extensions
OpenVINO.CSharp.API.Extensions.OpenCvSharp

• OpenCvSharp NuGet Package：

OpenCvSharp4
OpenCvSharp4.Extensions
OpenCvSharp4.runtime.win

3. 项目输出

  项目使用的是控制台输出，运行后输出如下所示：

<00:00:00> Sending http request to https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples/releases/download/Model/ppyoloe_plus_crn_l_80e_coco.tar.
<00:00:02> Http Response Accquired.
<00:00:02> Total download length is 199.68 Mb.
<00:00:02> Download Started.
<00:00:02> File created.
<00:02:03> Downloading: [■■■■■■■■■■] 100% <00:02:03 1.81 Mb/s> 199.68 Mb/199.68 Mb downloaded.
<00:02:03> File Downloaded, saved in E:\GitSpace\OpenVINO-CSharp-API-Samples\model_samples\ppyoloe\ppyoloe_opencvsharp\bin\Release\net6.0\model\ppyoloe_plus_crn_l_80e_coco.tar.
<00:00:00> Sending http request to https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples/releases/download/Image/test_det_02.jpg.
<00:00:02> Http Response Accquired.
<00:00:02> Total download length is 0.16 Mb.
<00:00:02> Download Started.
<00:00:02> File created.
<00:00:02> Downloading: [■■■■■■■■■■] 100% <00:00:02 0.06 Mb/s> 0.16 Mb/0.16 Mb downloaded.
<00:00:02> File Downloaded, saved in E:\GitSpace\OpenVINO-CSharp-API-Samples\model_samples\ppyoloe\ppyoloe_opencvsharp\bin\Release\net6.0\model\test_image.jpg.
[ INFO ] Inference device: CPU
[ INFO ] Start RT-DETR model inference.
[ INFO ] 1. Initialize OpenVINO Runtime Core success, time spend: 4.5204ms.
[ INFO ] 2. Read inference model success, time spend: 228.4451ms.
[ INFO ] Inference Model
[ INFO ]   Model name: Model0
[ INFO ]   Input:
[ INFO ]      name: scale_factor
[ INFO ]      type: float
[ INFO ]      shape: Shape : {
   
   ?,2}
[ INFO ]      name: image
[ INFO ]      type: float
[ INFO ]      shape: Shape : {
   
   ?,3,640,640}
[ INFO ]   Output:
[ INFO ]      name: multiclass_nms3_0.tmp_0
[ INFO ]      type: float
[ INFO ]      shape: Shape : {
   
   ?,6}
[ INFO ]      name: multiclass_nms3_0.tmp_2
[ INFO ]      type: int32_t
[ INFO ]      shape: Shape : {
   
   ?}
[ INFO ] 3. Loading a model to the device success, time spend:501.0716ms.
[ INFO ] 4. Create an infer request success, time spend:0.2663ms.
[ INFO ] 5. Process input images success, time spend:30.1001ms.
[ INFO ] 6. Set up input data success, time spend:2.3631ms.
[ INFO ] 7. Do inference synchronously success, time spend:286.1085ms.
[ INFO ] 8. Get infer result data success, time spend:0.5189ms.
[ INFO ] 9. Process reault  success, time spend:0.4425ms.
[ INFO ] The result save to E:\GitSpace\OpenVINO-CSharp-API-Samples\model_samples\ppyoloe\ppyoloe_opencvsharp\bin\Release\net6.0\model\test_image_result.jpg

  图像预测结果如下图所示：

4. 代码展示

  以下为嘛中所使用的命名空间代码：

using OpenCvSharp.Dnn;
using OpenCvSharp;