在 Intel Ultra AI PC 装备上使用 OpenVINO™ C# API本地部署YOLOv11与YOLO - IT评测·应用市场-qidao123.com

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

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https://github.com/guojin-yan/YoloDeployCsharp/blob/yolov1/demo/yolo_openvino_demo/

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conda create -n yolo python=3.10
conda activate yolo
pip install ultralytics

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yolo export model=yolo11s.pt format=onnx

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dotnet new console --framework net8.0 --use-program-main -o yolo_sample

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dotnet add package OpenVINO.CSharp.API
dotnet add package OpenVINO.runtime.win
dotnet add package OpenVINO.CSharp.API.Extensions
dotnet add package OpenVINO.CSharp.API.Extensions.OpenCvSharp

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dotnet add package OpenCvSharp4
dotnet add package OpenCvSharp4.Extensions
dotnet add package OpenCvSharp4.runtime.win

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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFramework>net8.0</TargetFramework>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="OpenCvSharp4" Version="4.10.0.20241108" />
<PackageReference Include="OpenCvSharp4.Extensions" Version="4.10.0.20241108" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.10.0.20241108" />
<PackageReference Include="OpenVINO.CSharp.API" Version="2025.0.0.1" />
<PackageReference Include="OpenVINO.runtime.win" Version="2025.0.0.1" />
</ItemGroup>
</Project>

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internal class YoloDet
{
public static void predict(string model_path, string image_path, string device)
{
DateTime start = DateTime.Now;
// -------- Step 1. Initialize OpenVINO Runtime Core --------
Core core = new Core();
DateTime end = DateTime.Now;
Console.WriteLine("1. Initialize OpenVINO Runtime Core success, time spend: " + (end - start).TotalMilliseconds + "ms.");
// -------- Step 2. Read inference model --------
start = DateTime.Now;
Model model = core.read_model(model_path);
end = DateTime.Now;
Console.WriteLine("2. Read inference model success, time spend: " + (end - start).TotalMilliseconds + "ms.");
// -------- Step 3. Loading a model to the device --------
start = DateTime.Now;
CompiledModel compiled_model = core.compile_model(model, device);
end = DateTime.Now;
Console.WriteLine("3. Loading a model to the device success, time spend:" + (end - start).TotalMilliseconds + "ms.");
// -------- Step 4. Create an infer request --------
start = DateTime.Now;
InferRequest infer_request = compiled_model.create_infer_request();
end = DateTime.Now;
Console.WriteLine("4. Create an infer request success, time spend:" + (end - start).TotalMilliseconds + "ms.");
// -------- Step 5. Process input images --------
start = DateTime.Now;
Mat image = new Mat(image_path); // Read image by opencvsharp
int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
Rect roi = new Rect(0, 0, image.Cols, image.Rows);
image.CopyTo(new Mat(max_image, roi));
float factor = (float)(max_image_length / 640.0);
end = DateTime.Now;
Console.WriteLine("5. Process input images success, time spend:" + (end - start).TotalMilliseconds + "ms.");
// -------- Step 6. Set up input data --------
start = DateTime.Now;
Tensor input_tensor = infer_request.get_input_tensor();
Shape input_shape = input_tensor.get_shape();
Mat input_mat = CvDnn.BlobFromImage(max_image, 1.0 / 255.0, new OpenCvSharp.Size(input_shape[2], input_shape[3]), new Scalar(), true, false);
float[] input_data = new float[input_shape[1] * input_shape[2] * input_shape[3]];
Marshal.Copy(input_mat.Ptr(0), input_data, 0, input_data.Length);
input_tensor.set_data<float>(input_data);
end = DateTime.Now;
Console.WriteLine("6. Set up input data success, time spend:" + (end - start).TotalMilliseconds + "ms.");
// -------- Step 7. Do inference synchronously --------
infer_request.infer();
start = DateTime.Now;
infer_request.infer();
end = DateTime.Now;
Console.WriteLine("7. Do inference synchronously success, time spend:" + (end - start).TotalMilliseconds + "ms.");
// -------- Step 8. Get infer result data --------
start = DateTime.Now;
Tensor output_tensor = infer_request.get_output_tensor();
int output_length = (int)output_tensor.get_size();
float[] output_data = output_tensor.get_data<float>(output_length);
end = DateTime.Now;
Console.WriteLine("8. Get infer result data success, time spend:" + (end - start).TotalMilliseconds + "ms.");
// -------- Step 9. Process reault --------
start = DateTime.Now;
// Storage results list
List<Rect> position_boxes = new List<Rect>();
List<int> class_ids = new List<int>();
List<float> confidences = new List<float>();
// Preprocessing output results
for (int i = 0; i < 8400; i++)
{
for (int j = 4; j < 84; j++)
{
float conf = output_data[8400 * j + i];
int label = j - 4;
if (conf > 0.2)
{
float cx = output_data[8400 * 0 + i];
float cy = output_data[8400 * 1 + i];
float ow = output_data[8400 * 2 + i];
float oh = output_data[8400 * 3 + i];
int x = (int)((cx - 0.5 * ow) * factor);
int y = (int)((cy - 0.5 * oh) * factor);
int width = (int)(ow * factor);
int height = (int)(oh * factor);
Rect box = new Rect(x, y, width, height);
position_boxes.Add(box);
class_ids.Add(label);
confidences.Add(conf);
}
}
}
// NMS non maximum suppression
int[] indexes = new int[position_boxes.Count];
CvDnn.NMSBoxes(position_boxes, confidences, 0.5f, 0.5f, out indexes);
end = DateTime.Now;
Console.WriteLine("9. Process reault success, time spend:" + (end - start).TotalMilliseconds + "ms.");
for (int i = 0; i < indexes.Length; i++)
{
int index = indexes[i];
Cv2.Rectangle(image, position_boxes[index], new Scalar(0, 0, 255), 2, LineTypes.Link8);
Cv2.Rectangle(image, new OpenCvSharp.Point(position_boxes[index].TopLeft.X, position_boxes[index].TopLeft.Y + 30),
new OpenCvSharp.Point(position_boxes[index].BottomRight.X, position_boxes[index].TopLeft.Y), new Scalar(0, 255, 255), -1);
Cv2.PutText(image, class_ids[index] + "-" + confidences[index].ToString("0.00"),
new OpenCvSharp.Point(position_boxes[index].X, position_boxes[index].Y + 25),
HersheyFonts.HersheySimplex, 0.8, new Scalar(0, 0, 0), 2);
}
string output_path = Path.Combine(Path.GetDirectoryName(Path.GetFullPath(image_path)),
Path.GetFileNameWithoutExtension(image_path) + "_result.jpg");
Cv2.ImWrite(output_path, image);
Console.WriteLine("The result save to " + output_path);
Cv2.ImShow("Result", image);
Cv2.WaitKey(0);
}
}

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YoloDet.predict("E:/Model/Yolo/yolo11x.onnx", "./demo_2.jpg", "NPU");
YoloDet.predict("E:/Model/Yolo/yolo12x.onnx", "./demo_2.jpg", "CPU");

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dotnet build

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dotnet run --no-build

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