【OpenVINO™】在 Windows 上使用 OpenVINO™ C# API 部署 Yolov8-obb 实现 - ToB企服应用市场:ToB评测及商务社交产业平台

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

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yolo export model=yolov8s-obb.pt format=onnx

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ovc yolov8s-obb.onnx

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dotnet new console --framework net6.0 --use-program-main -o yolov8_obb_opencvsharp
cd yolov8_obb_opencvsharp

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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>net6.0</TargetFramework>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="OpenCvSharp4" Version="4.9.0.20240103" />
<PackageReference Include="OpenCvSharp4.Extensions" Version="4.9.0.20240103" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.9.0.20240103" />
<PackageReference Include="OpenVINO.CSharp.API" Version="2023.2.0.4" />
<PackageReference Include="OpenVINO.CSharp.API.Extensions" Version="1.0.1" />
<PackageReference Include="OpenVINO.CSharp.API.Extensions.OpenCvSharp" Version="1.0.4" />
<PackageReference Include="OpenVINO.runtime.win" Version="2023.3.0.1" />
</ItemGroup>
</Project>

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static void yolov8_obb(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);
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
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 / 1024.0);
// -------- Step 6. Set up input data --------
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]), 0, 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);
// -------- Step 7. Do inference synchronously --------
infer_request.infer();
// -------- Step 8. Get infer result data --------
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);
// -------- Step 9. Process reault --------
Mat result_data = new Mat(20, 21504, MatType.CV_32F, output_data);
result_data = result_data.T();
float[] d = new float[output_length];
result_data.GetArray<float>(out d);
// Storage results list
List<Rect2d> position_boxes = new List<Rect2d>();
List<int> class_ids = new List<int>();
List<float> confidences = new List<float>();
List<float> rotations = new List<float>();
// Preprocessing output results
for (int i = 0; i < result_data.Rows; i++)
{
Mat classes_scores = new Mat(result_data, new Rect(4, i, 15, 1));
OpenCvSharp.Point max_classId_point, min_classId_point;
double max_score, min_score;
// Obtain the maximum value and its position in a set of data
Cv2.MinMaxLoc(classes_scores, out min_score, out max_score,
out min_classId_point, out max_classId_point);
// Confidence level between 0 ~ 1
// Obtain identification box information
if (max_score > 0.25)
{
float cx = result_data.At<float>(i, 0);
float cy = result_data.At<float>(i, 1);
float ow = result_data.At<float>(i, 2);
float oh = result_data.At<float>(i, 3);
double x = (cx - 0.5 * ow) * factor;
double y = (cy - 0.5 * oh) * factor;
double width = ow * factor;
double height = oh * factor;
Rect2d box = new Rect2d();
box.X = x;
box.Y = y;
box.Width = width;
box.Height = height;
position_boxes.Add(box);
class_ids.Add(max_classId_point.X);
confidences.Add((float)max_score);
rotations.Add(result_data.At<float>(i, 19));
}
}
// NMS non maximum suppression
int[] indexes = new int[position_boxes.Count];
CvDnn.NMSBoxes(position_boxes, confidences, 0.25f, 0.7f, out indexes);
List<RotatedRect> rotated_rects = new List<RotatedRect>();
for (int i = 0; i < indexes.Length; i++)
{
int index = indexes[i];
float w = (float)position_boxes[index].Width;
float h = (float)position_boxes[index].Height;
float x = (float)position_boxes[index].X + w / 2;
float y = (float)position_boxes[index].Y + h / 2;
float r = rotations[index];
float w_ = w > h ? w : h;
float h_ = w > h ? h : w;
r = (float)((w > h ? r : (float)(r + Math.PI / 2)) % Math.PI);
RotatedRect rotate = new RotatedRect(new Point2f(x, y), new Size2f(w_, h_), (float)(r * 180.0 / Math.PI));
rotated_rects.Add(rotate);
}
for (int i = 0; i < indexes.Length; i++)
{
int index = indexes[i];
Point2f[] points = rotated_rects[i].Points();
for (int j = 0; j < 4; j++)
{
Cv2.Line(image, (Point)points[j], (Point)points[(j + 1) % 4], new Scalar(255, 100, 200), 2);
}
Cv2.PutText(image, class_lables[class_ids[index]] + "-" + confidences[index].ToString("0.00"),
(Point)points[0], 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);
Slog.INFO("The result save to " + output_path);
Cv2.ImShow("Result", image);
Cv2.WaitKey(0);
}

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yolov8_obb("yolov8s-obb.xml", "test_image.png", "AUTO");

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static void Main(string[] args)
{
string model_path = "";
string image_path = "";
string device = "AUTO";
if (args.Length == 0)
{
if (!Directory.Exists("./model"))
{
Directory.CreateDirectory("./model");
}
if (!File.Exists("./model/yolov8s-obb.bin") && !File.Exists("./model/yolov8s-obb.bin"))
{
if (!File.Exists("./model/yolov8s-obb.tar"))
{
_ = Download.download_file_async("https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples/releases/download/Model/yolov8s-obb.tar",
"./model/yolov8s-obb.tar").Result;
}
Download.unzip("./model/yolov8s-obb.tar", "./model/");
}
if (!File.Exists("./model/plane.png"))
{
_ = Download.download_file_async("https://github.com/guojin-yan/OpenVINO-CSharp-API-Samples/releases/download/Image/plane.png",
"./model/plane.png").Result;
}
model_path = "./model/yolov8s-obb.xml";
image_path = "./model/plane.png";
}
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 <model path> <image path> <device name>");
}
// -------- 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 yolov8 model inference.");
yolov8_obb(model_path, image_path, device);
}

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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.EmguCV

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dotnet add package Emgu.CV
dotnet add package Emgu.CV.runtime.windows

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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFramework>net6.0</TargetFramework>
<ImplicitUsings>enable</ImplicitUsings>
<Nullable>enable</Nullable>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="Emgu.CV" Version="4.8.1.5350" />
<PackageReference Include="Emgu.CV.runtime.windows" Version="4.8.1.5350" />
<PackageReference Include="OpenVINO.CSharp.API" Version="2023.2.0.4" />
<PackageReference Include="OpenVINO.CSharp.API.Extensions" Version="1.0.1" />
<PackageReference Include="OpenVINO.CSharp.API.Extensions.EmguCV" Version="1.0.4.1" />
<PackageReference Include="OpenVINO.runtime.win" Version="2023.3.0.1" />
</ItemGroup>
</Project>

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static void yolov8_obb(string model_path, string image_path, string device)
{
// -------- Step 1. Initialize OpenVINO Runtime Core --------
Core core = new Core();
// -------- Step 2. Read inference model --------
OpenVinoSharp.Model model = core.read_model(model_path);
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
int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
Mat max_image = Mat.Zeros(max_image_length, max_image_length, DepthType.Cv8U, 3);
Rectangle roi = new Rectangle(0, 0, image.Cols, image.Rows);
image.CopyTo(new Mat(max_image, roi));
float factor = (float)(max_image_length / 1024.0);
// -------- Step 6. Set up input data --------
Tensor input_tensor = infer_request.get_input_tensor();
Shape input_shape = input_tensor.get_shape();
Mat input_mat = DnnInvoke.BlobFromImage(max_image, 1.0 / 255.0, new Size((int)input_shape[2], (int)input_shape[3]), new MCvScalar(0), true, false);
float[] input_data = new float[input_shape[1] * input_shape[2] * input_shape[3]];
//Marshal.Copy(input_mat.Ptr, input_data, 0, input_data.Length);
input_mat.CopyTo<float>(input_data);
input_tensor.set_data<float>(input_data);
// -------- Step 7. Do inference synchronously --------
infer_request.infer();
// -------- Step 8. Get infer result data --------
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);
// -------- Step 9. Process reault --------
Mat result_data = new Mat(20, 21504, DepthType.Cv32F, 1,
Marshal.UnsafeAddrOfPinnedArrayElement(output_data, 0), 4 * 21504);
result_data = result_data.T();
List<Rectangle> position_boxes = new List<Rectangle>();
List<int> class_ids = new List<int>();
List<float> confidences = new List<float>();
List<float> rotations = new List<float>();
// Preprocessing output results
for (int i = 0; i < result_data.Rows; i++)
{
Mat classes_scores = new Mat(result_data, new Rectangle(4, i, 15, 1));//GetArray(i, 5, classes_scores);
Point max_classId_point = new Point(), min_classId_point = new Point();
double max_score = 0, min_score = 0;
CvInvoke.MinMaxLoc(classes_scores, ref min_score, ref max_score,
ref min_classId_point, ref max_classId_point);
if (max_score > 0.25)
{
Mat mat = new Mat(result_data, new Rectangle(0, i, 20, 1));
float[,] data = (float[,])mat.GetData();
float cx = data[0, 0];
float cy = data[0, 1];
float ow = data[0, 2];
float oh = data[0, 3];
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);
Rectangle box = new Rectangle();
box.X = x;
box.Y = y;
box.Width = width;
box.Height = height;
position_boxes.Add(box);
class_ids.Add(max_classId_point.X);
confidences.Add((float)max_score);
rotations.Add(data[0, 19]);
}
}
// NMS non maximum suppression
int[] indexes = DnnInvoke.NMSBoxes(position_boxes.ToArray(), confidences.ToArray(), 0.5f, 0.5f);
List<RotatedRect> rotated_rects = new List<RotatedRect>();
for (int i = 0; i < indexes.Length; i++)
{
int index = indexes[i];
float w = (float)position_boxes[index].Width;
float h = (float)position_boxes[index].Height;
float x = (float)position_boxes[index].X + w / 2;
float y = (float)position_boxes[index].Y + h / 2;
float r = rotations[index];
float w_ = w > h ? w : h;
float h_ = w > h ? h : w;
r = (float)((w > h ? r : (float)(r + Math.PI / 2)) % Math.PI);
RotatedRect rotate = new RotatedRect(new PointF(x, y), new SizeF(w_, h_), (float)(r * 180.0 / Math.PI));
rotated_rects.Add(rotate);
}
for (int i = 0; i < indexes.Length; i++)
{
int index = indexes[i];
PointF[] points = rotated_rects[i].GetVertices();
for (int j = 0; j < 4; j++)
{
CvInvoke.Line(image, new Point((int)points[j].X, (int)points[j].Y),
new Point((int)points[(j + 1) % 4].X, (int)points[(j + 1) % 4].Y), new MCvScalar(255, 100, 200), 2);
}
CvInvoke.PutText(image, class_lables[class_ids[index]] + "-" + confidences[index].ToString("0.00"),
new Point((int)points[0].X, (int)points[0].Y), FontFace.HersheySimplex, 0.8, new MCvScalar(0, 0, 0), 2);
}
string output_path = Path.Combine(Path.GetDirectoryName(Path.GetFullPath(image_path)),
Path.GetFileNameWithoutExtension(image_path) + "_result.jpg");
CvInvoke.Imwrite(output_path, image);
Slog.INFO("The result save to " + output_path);
CvInvoke.Imshow("Result", image);
CvInvoke.WaitKey(0);
}

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dotnet build

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dotnet run --no-build

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