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OpenVINO? C# API 项目介绍与Blazeface模型部署

时间：2024-05-08　|　来源：　|　阅读：103

Blazeface模型是Google推出的一款专为移动GPU推理量身定制的轻量级且性能卓越的人脸检测器，BlazeFace 在旗舰移动设备上以200-1000 + FPS的速度运行。在本文中，我们将使用OpenVINO? C# API 部署 Blazeface 实现人脸检测。

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

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

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

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/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

图像预测结果如下图所示：
OpenVINO? C# API 项目介绍与Blazeface模型部署

以下为嘛中所使用的命名空间代码：
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;

下面为定义的模型预测代码：
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); }

下面为程序运行的主函数代码，该代码会下载转换好的预测模型，并调用预测方法进行预测：
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); }