C# OpenVINO Det 物体检测-CSDN博客

效果

耗时

elephant:89%
Preprocess: 0.00ms
Infer: 47.21ms
Postprocess: 11.63ms
Total: 58.84ms

项目

代码

using OpenCvSharp;
using Sdcb.OpenVINO;
using Sdcb.OpenVINO.Natives;
using System;
using System.Diagnostics;
using System.Drawing;
using System.Text;
using System.Windows.Forms;
using System.Xml.Linq;
using System.Xml.XPath;

namespace OpenVINO_Det_物体检测
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string startupPath;
        string model_path;
        Mat src;
        string[] dicts;

        StringBuilder sb = new StringBuilder();

        private void button1_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;
            pictureBox1.Image = null;
            image_path = ofd.FileName;
            pictureBox1.Image = new Bitmap(image_path);
            textBox1.Text = "";
            src = new Mat(image_path);
            pictureBox2.Image = null;
        }

        unsafe private void button2_Click(object sender, EventArgs e)
        {
            if (pictureBox1.Image == null)
            {
                return;
            }

            pictureBox2.Image = null;
            textBox1.Text = "";
            sb.Clear();

            Model rawModel = OVCore.Shared.ReadModel(model_path);
            PrePostProcessor pp = rawModel.CreatePrePostProcessor();
            PreProcessInputInfo inputInfo = pp.Inputs.Primary;

            inputInfo.TensorInfo.Layout = Sdcb.OpenVINO.Layout.NHWC;
            inputInfo.ModelInfo.Layout = Sdcb.OpenVINO.Layout.NCHW;

            Model m = pp.BuildModel();
            CompiledModel cm = OVCore.Shared.CompileModel(m, "CPU");
            InferRequest ir = cm.CreateInferRequest();

            Shape inputShape = m.Inputs.Primary.Shape;
            Size2f sizeRatio = new Size2f(1f * src.Width / inputShape[2], 1f * src.Height / inputShape[1]);

            Stopwatch stopwatch = new Stopwatch();
            Mat resized = src.Resize(new OpenCvSharp.Size(inputShape[2], inputShape[1]));
            Mat f32 = new Mat();
            resized.ConvertTo(f32, MatType.CV_32FC3, 1.0 / 255);

            using (Tensor input = Tensor.FromRaw(
                 new ReadOnlySpan<byte>((void*)f32.Data, (int)((long)f32.DataEnd - (long)f32.DataStart)),
                new Shape(1, f32.Rows, f32.Cols, 3),
                ov_element_type_e.F32))
            {
                ir.Inputs.Primary = input;
            }
            double preprocessTime = stopwatch.Elapsed.TotalMilliseconds;
            stopwatch.Restart();

            ir.Run();
            double inferTime = stopwatch.Elapsed.TotalMilliseconds;
            stopwatch.Restart();

            using (Tensor output = ir.Outputs.Primary)
            {
                ReadOnlySpan<float> data = output.GetData<float>();
                DetectionResult[] results = DetectionResult.FromYolov8DetectionResult(data, output.Shape, sizeRatio, dicts);
                double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;
                stopwatch.Stop();
                double totalTime = preprocessTime + inferTime + postprocessTime;

                Mat result_image = src.Clone();
                //Cv2.PutText(result_image, $"Preprocess: {preprocessTime:F2}ms", new Point(10, 20), HersheyFonts.HersheyPlain, 1, Scalar.Red);
                //Cv2.PutText(result_image, $"Infer: {inferTime:F2}ms", new Point(10, 40), HersheyFonts.HersheyPlain, 1, Scalar.Red);
                //Cv2.PutText(result_image, $"Postprocess: {postprocessTime:F2}ms", new Point(10, 60), HersheyFonts.HersheyPlain, 1, Scalar.Red);
                //Cv2.PutText(result_image, $"Total: {totalTime:F2}ms", new Point(10, 80), HersheyFonts.HersheyPlain, 1, Scalar.Red);

                foreach (DetectionResult r in results)
                {
                    sb.AppendLine($"{r.Class}:{r.Confidence:P0}");
                    Cv2.PutText(result_image, $"{r.Class}:{r.Confidence:P0}", new OpenCvSharp.Point(r.Rect.TopLeft.X, r.Rect.TopLeft.Y - 10), HersheyFonts.HersheySimplex, 1, Scalar.Red, 2);
                    Cv2.Rectangle(result_image, r.Rect, Scalar.Red, thickness: 2);
                }

                sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");
                sb.AppendLine($"Infer: {inferTime:F2}ms");
                sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");
                sb.AppendLine($"Total: {totalTime:F2}ms");

                pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
                textBox1.Text = sb.ToString();
            }
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            startupPath = Application.StartupPath;
            model_path = startupPath + "\\yolov8n.xml";
            dicts = XDocument.Load(model_path)
            .XPathSelectElement(@"/net/rt_info/model_info/labels").Attribute("value").Value
            .Split(' ');
        }
    }
}

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