C#使用Openvino.Csharp推理Yolov9c.xml

using OpenCvSharp;
using OpenVinoSharp;
using OpenCvSharp.Dnn;
using System.Runtime.InteropServices;
float sigmoid(float a)
{
    float b = 1.0f / (1.0f + (float)Math.Exp(-a));
    return b;
}
string[] read_class_names(string path)
{
    string[] class_names;
    List<string> str = new List<string>();
    StreamReader sr = new StreamReader(path);
    string line;
    while ((line = sr.ReadLine()) != null)
    {
        str.Add(line);
    }
    class_names = str.ToArray();
    return class_names;
}
float conf_threshold = 0.25f;
float nms_threshold = 0.5f;
List<Rect> position_boxes = new List<Rect>();
List<int> class_ids = new List<int>();
List<float> confidences = new List<float>();
string model_path = "yolov9c.xml";
string device = "AUTO";
string image_path = "bus.jpg";
string[] classes_names = read_class_names("coco.names");
Core core = new Core();
Model model = core.read_model(model_path);
CompiledModel compiled_model = core.compile_model(model, device);
InferRequest infer_request = compiled_model.create_infer_request();
Mat image = new Mat(image_path);
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);
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);
infer_request.infer();
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);
Mat result_data = new Mat(84, 8400, MatType.CV_32F, output_data);
result_data = result_data.T();
for (int i = 0; i < result_data.Rows; i++)
{
    Mat classes_scores = new Mat(result_data, new Rect(4, i, 80, 1));
    OpenCvSharp.Point max_classId_point, min_classId_point;
    double max_score, min_score;
    Cv2.MinMaxLoc(classes_scores, out min_score, out max_score,
        out min_classId_point, out max_classId_point);
    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);
        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();
        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);
    }
}
int[] indexes = new int[position_boxes.Count];
CvDnn.NMSBoxes(position_boxes, confidences, conf_threshold, nms_threshold, out indexes);
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, classes_names[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);
}
Cv2.ImShow("Result", image);
Cv2.WaitKey(0);