OpenVINO? C# API 項(xiàng)目介紹與Blazeface模型部署

OpenVINO? C# API 是一個(gè) OpenVINO? 的 .Net wrapper，應(yīng)用最新的 OpenVINO? 庫開發(fā)，通過 OpenVINO? C API 實(shí)現(xiàn) .Net 對 OpenVINO? Runtime 調(diào)用，使用習(xí)慣與 OpenVINO? C++ API 一致。OpenVINO? C# API 由于是基于 OpenVINO? 開發(fā)，所支持的平臺(tái)與 OpenVINO? 完全一致，具體信息可以參考 OpenVINO?。通過使用 OpenVINO? C# API，可以在 .NET、.NET Framework等框架下使用 C# 語言實(shí)現(xiàn)深度學(xué)習(xí)模型在指定平臺(tái)推理加速。

OpenVINO? C# API 項(xiàng)目鏈接為：
https://github.com/guojin-yan/OpenVINO-CSharp-API.git

項(xiàng)目源碼鏈接為：
https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples.git

Blazeface模型是Google推出的一款專為移動(dòng)GPU推理量身定制的輕量級(jí)且性能卓越的人臉檢測器，BlazeFace 在旗艦移動(dòng)設(shè)備上以200-1000 + FPS的速度運(yùn)行。 在本文中，我們將使用OpenVINO? C# API 部署 Blazeface 實(shí)現(xiàn)人臉檢測。

該項(xiàng)目中所需依賴已經(jīng)支持通過NuGet Package進(jìn)行安裝，在該項(xiàng)目中，需要安裝以下NuGet Package：
OpenVINO C# API NuGet Package：
OpenVINO.CSharp.API
OpenVINO.runtime.win
OpenVINO.CSharp.API.Extensions

OpenCvSharp NuGet Package：
OpenCvSharp4
OpenCvSharp4.Extensions
OpenCvSharp4.runtime.win

項(xiàng)目使用的是控制臺(tái)輸出，運(yùn)行后輸出如下所示：
<00:00:00> Sending http request to https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples/releases/download/Model/blazeface_1000e.tar.
<00:00:01> Http Response Accquired.
<00:00:01> Total download length is 0.47 Mb.
<00:00:01> Download Started.
<00:00:01> File created.
<00:00:01> Downloading: [■■■■■■■■■■] 100% <00:00:01 0.24 Mb/s> 0.47 Mb/0.47 Mb downloaded.
<00:00:01> File Downloaded, saved in E:\GitSpace\OpenVINO-CSharp-API-Samples\model_samples\face_detection\blazeface_opencvsharp\bin\Release\net6.0\model\blazeface_1000e.tar.
<00:00:00> Sending http request to https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples/releases/download/Image/face1.jpg.
<00:00:01> Http Response Accquired.
<00:00:01> Total download length is 0.14 Mb.
<00:00:01> Download Started.
<00:00:01> File created.
<00:00:01> Downloading: [■■■■■■■■■■] 100% <00:00:01 0.08 Mb/s> 0.14 Mb/0.14 Mb downloaded.
<00:00:01> File Downloaded, saved in E:\GitSpace\OpenVINO-CSharp-API-Samples\model_samples\face_detection\blazeface_opencvsharp\bin\Release\net6.0\model\face1.jpg.
[ INFO ] ---- OpenVINO INFO----
[ INFO ] Description : OpenVINO Runtime
[ INFO ] Build number: 2024.0.0-14509-34caeefd078-releases/2024/0
[ INFO ] Predict model files: ./model/blazeface_1000e.xml
[ INFO ] Predict image files: ./model/face1.jpg
[ INFO ] Inference device: CPU
[ INFO ] Start RT-DETR model inference.
[ INFO ] 1. Initialize OpenVINO Runtime Core success, time spend: 3.2045ms.
[ INFO ] 2. Read inference model success, time spend: 46.1753ms.
[ INFO ] Inference Model
[ INFO ] Model name: Model0
[ INFO ] Input:
[ INFO ] name: scale_factor
[ INFO ] type: float
[ INFO ] shape: Shape : {1,2}
[ INFO ] name: image
[ INFO ] type: float
[ INFO ] shape: Shape : {1,3,640,640}
[ INFO ] name: im_shape
[ INFO ] type: float
[ INFO ] shape: Shape : {1,2}
[ INFO ] Output:
[ INFO ] name: multiclass_nms3_0.tmp_0
[ INFO ] type: float
[ INFO ] shape: Shape : {750,6}
[ INFO ] name: multiclass_nms3_0.tmp_2
[ INFO ] type: int32_t
[ INFO ] shape: Shape : {1}
[ INFO ] 3. Loading a model to the device success, time spend:213.1551ms.
[ INFO ] 4. Create an infer request success, time spend:0.4506ms.
[ INFO ] 5. Process input images success, time spend:74.6076ms.
[ INFO ] 6. Set up input data success, time spend:1.7392ms.
[ INFO ] 7. Do inference synchronously success, time spend:21.3498ms.
[ INFO ] 8. Get infer result data success, time spend:1.1302ms.
[ INFO ] 9. Process reault success, time spend:0.4035ms.
[ INFO ] The result save to E:\GitSpace\OpenVINO-CSharp-API-Samples\model_samples\face_detection\blazeface_opencvsharp\bin\Release\net6.0\model\face1_result.jpg

圖像預(yù)測結(jié)果如下圖所示：

以下為嘛中所使用的命名空間代碼：
using OpenCvSharp.Dnn;
using OpenCvSharp;
using OpenVinoSharp;
using OpenVinoSharp.Extensions;
using OpenVinoSharp.Extensions.utility;
using System.Runtime.InteropServices;
using OpenVinoSharp.preprocess;
using OpenVinoSharp.Extensions.result;
using OpenVinoSharp.Extensions.process;
using System;
using System.Reflection.Metadata;

下面為定義的模型預(yù)測代碼：
static void face_detection(string model_path, string image_path, string device)
{
// -------- Step 1. Initialize OpenVINO Runtime Core --------
Core core = new Core();
// -------- Step 2. Read inference model --------
Model model = core.read_model(model_path);
Dictionary pairs = new Dictionary();
pairs.Add("scale_factor", new PartialShape(new Shape(1, 2)));
pairs.Add("im_shape", new PartialShape(new Shape(1, 2)));
pairs.Add("image", new PartialShape(new Shape(1, 3, 640, 640));
model.reshape(pairs);
OvExtensions.printf_model_info(model);
// -------- Step 3. Loading a model to the device --------
CompiledModel compiled_model = core.compile_model(model, device);
// -------- Step 4. Create an infer request --------
InferRequest infer_request = compiled_model.create_infer_request();
// -------- Step 5. Process input images --------
Mat image = new Mat(image_path); // Read image by opencvsharp
Mat mat = new Mat();
Cv2.Resize(image, mat, new Size(640, 640));
mat = Normalize.run(mat, new float[] { 123f, 117f, 104f }, new float[] { 1 / 127.502231f, 1 / 127.502231f, 1 / 127.502231f }, false);
float[] input_data = Permute.run(mat);
// -------- Step 6. Set up input data --------
Tensor input_tensor_data = infer_request.get_tensor("image");
input_tensor_data.set_data(input_data);
Tensor input_tensor_shape = infer_request.get_tensor("im_shape");
input_tensor_shape.set_shape(new Shape(1, 2));
input_tensor_shape.set_data(new float[] { 640, 640 });
Tensor input_tensor_factor = infer_request.get_tensor("scale_factor");
input_tensor_factor.set_shape(new Shape(1, 2));
input_tensor_factor.set_data(new float[] { ((float)640.0f / image.Rows), ((float)640.0 / image.Cols) });
// -------- Step 7. Do inference synchronously --------
infer_request.infer();
// -------- Step 8. Get infer result data --------
Tensor output_tensor = infer_request.get_output_tensor(0);
Shape output_shape = output_tensor.get_shape();
int output_length = (int)output_tensor.get_size();
float[] result_data = output_tensor.get_data(output_length);
Tensor output_tensor1 = infer_request.get_output_tensor(1);
int output_length1 = (int)output_tensor1.get_size();
int[] result_len = output_tensor1.get_data(output_length1);
// -------- Step 9. Process reault --------
List position_boxes = new List();
List confidences = new List();
// Preprocessing output results
for (int i = 0; i < result_len[0]; i++)
{
double confidence = result_data[6 * i + 1];
if (confidence > 0.5)
{
float tlx = result_data[6 * i + 2];
float tly = result_data[6 * i + 3];
float brx = result_data[6 * i + 4];
float bry = result_data[6 * i + 5];
Rect box = new Rect((int)tlx, (int)tly, (int)(brx - tlx), (int)(bry - tly));
position_boxes.Add(box);
confidences.Add((float)confidence);
}
}
for (int i = 0; i < position_boxes.Count; i++)
{
int index = i;
Cv2.Rectangle(image, position_boxes[index], new Scalar(255, 0, 0), 1, LineTypes.Link8);
Cv2.PutText(image, confidences[index].ToString("0.00"), new OpenCvSharp.Point(position_boxes[index].TopLeft.X, position_boxes[index].TopLeft.Y - 5), HersheyFonts.HersheySimplex, 0.4, new Scalar(255, 0, 0), 1);
}
string output_path = Path.Combine(Path.GetDirectoryName(Path.GetFullPath(image_path)), Path.GetFileNameWithoutExtension(image_path) + "_result.jpg");
Cv2.ImWrite(output_path, image);
Slog.INFO("The result save to " + output_path);
Cv2.ImShow("Result", image);
Cv2.WaitKey(0);
}

下面為程序運(yùn)行的主函數(shù)代碼，該代碼會(huì)下載轉(zhuǎn)換好的預(yù)測模型，并調(diào)用預(yù)測方法進(jìn)行預(yù)測：
static void Main(string[] args)
{
string model_path = "";
string image_path = "";
string device = "CPU";
if (args.Length == 0)
{
if (!Directory.Exists("./model"))
{
Directory.CreateDirectory("./model");
}
if (!File.Exists("./model/blazeface_1000e.xml") && !File.Exists("./model/blazeface_1000e.bin"))
{
if (!File.Exists("./model/blazeface_1000e.tar"))
{
_ = Download.download_file_async("https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples/releases/download/Model/blazeface_1000e.tar", "./model/blazeface_1000e.tar").Result;
}
Download.unzip("./model/blazeface_1000e.tar", "./model/");
}
if (!File.Exists("./model/face1.jpg"))
{
_ = Download.download_file_async("https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples/releases/download/Image/face1.jpg", "./model/face1.jpg").Result;
}
model_path = "./model/blazeface_1000e.xml";
image_path = "./model/face1.jpg";
}
else if (args.Length >= 2)
{
model_path = args[0];
image_path = args[1];
device = args[2];
}
else
{
Console.WriteLine("Please enter the correct command parameters, for example:");
Console.WriteLine("> 1. dotnet run");
Console.WriteLine("> 2. dotnet run ");
}
// -------- Get OpenVINO runtime version --------
OpenVinoSharp.Version version = Ov.get_openvino_version();
Slog.INFO("---- OpenVINO INFO----");
Slog.INFO("Description : " + version.description);
Slog.INFO("Build number: " + version.buildNumber);
Slog.INFO("Predict model files: " + model_path);
Slog.INFO("Predict image files: " + image_path);
Slog.INFO("Inference device: " + device);
Slog.INFO("Start RT-DETR model inference.");
face_detection(model_path, image_path, device);
}

在該項(xiàng)目中，我們結(jié)合之前開發(fā)的 OpenVINO? C# API 項(xiàng)目部署 Blazeface 模型，成功實(shí)現(xiàn)了人臉檢測。

項(xiàng)目完整代碼鏈接為：
https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples/blob/master/model_samples/face_detection/blazeface_opencvsharp/Program.cs

為了方便EmguCV用戶使用需求，同時(shí)開發(fā)了EmguCV版本，項(xiàng)目鏈接為：
https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples/blob/master/model_samples/face_detection/blazeface_emgucv/Program.cs

最后如果各位開發(fā)者在使用中有任何問題，歡迎大家與我聯(lián)系。

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