C# OpenVINO Crack Seg 裂缝分割 裂缝检测

目录

效果

模型信息

项目

代码

数据集

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C# OpenVINO Crack Seg 裂缝分割  裂缝检测

效果

模型信息

Model Properties
-------------------------
date：2024-02-29T16:35:48.364242
author：Ultralytics
task：segment
version：8.1.18
stride：32
batch：1
imgsz：[640, 640]
names：{0: 'crack'}
---------------------------------------------------------------

Inputs
-------------------------
name：images
tensor：Float[1, 3, 640, 640]
---------------------------------------------------------------

Outputs
-------------------------
name：output0
tensor：Float[1, 37, 8400]
name：output1
tensor：Float[1, 32, 160, 160]
---------------------------------------------------------------

项目

代码

using OpenCvSharp;
using Sdcb.OpenVINO;
using Sdcb.OpenVINO.Natives;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Drawing;
using System.IO;
using System.Text;
using System.Windows.Forms;

namespace OpenVINO_Seg
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string startupPath;
        string model_path;
        string classer_path;

        Mat src;

        SegmentationResult result_pro;
        Mat result_image;
        Result seg_result;

        StringBuilder sb = new StringBuilder();

        float[] det_result_array = new float[8400 * 37];
        float[] proto_result_array = new float[32 * 160 * 160];

        // 识别结果类型
        public string[] class_names;

        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();

            src = new Mat(image_path);

            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[0].Shape;

            float[] factors = new float[4];
            factors[0] = 1f * src.Width / inputShape[2];
            factors[1] = 1f * src.Height / inputShape[1];
            factors[2] = src.Rows;
            factors[3] = src.Cols;

            result_pro = new SegmentationResult(class_names, factors,0.3f,0.5f);

            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((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_det = ir.Outputs[0])
            using (Tensor output_proto = ir.Outputs[1])
            {
                det_result_array = output_det.GetData().ToArray();
                proto_result_array = output_proto.GetData().ToArray();

                seg_result = result_pro.process_result(det_result_array, proto_result_array);

                double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;
                stopwatch.Stop();

                double totalTime = preprocessTime + inferTime + postprocessTime;

                result_image = src.Clone();
                Mat masked_img = new Mat();

                // 将识别结果绘制到图片上
                for (int i = 0; i < seg_result.length; i++)
                {
                    Cv2.Rectangle(result_image, seg_result.rects[i], new Scalar(0, 0, 255), 2, LineTypes.Link8);
                    Cv2.Rectangle(result_image, new OpenCvSharp.Point(seg_result.rects[i].TopLeft.X, seg_result.rects[i].TopLeft.Y - 20),
                        new OpenCvSharp.Point(seg_result.rects[i].BottomRight.X, seg_result.rects[i].TopLeft.Y), new Scalar(0, 255, 255), -1);
                    Cv2.PutText(result_image, seg_result.classes[i] + "-" + seg_result.scores[i].ToString("0.00"),
                        new OpenCvSharp.Point(seg_result.rects[i].X, seg_result.rects[i].Y - 5),
                        HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);
                    Cv2.AddWeighted(result_image, 0.5, seg_result.masks[i], 0.5, 0, masked_img);

                    sb.AppendLine($"{seg_result.classes[i]}:{seg_result.scores[i]:P0}");
                }

                if (seg_result.length > 0)
                {
                    if (pictureBox2.Image != null)
                    {
                        pictureBox2.Image.Dispose();
                    }
                    pictureBox2.Image = new Bitmap(masked_img.ToMemoryStream());
                    sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");
                    sb.AppendLine($"Infer: {inferTime:F2}ms");
                    sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");
                    sb.AppendLine($"Total: {totalTime:F2}ms");
                    textBox1.Text = sb.ToString();
                }
                else
                {
                    textBox1.Text = "无信息";
                }

                masked_img.Dispose();
                result_image.Dispose();
            }
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            image_path = "1.jpg";
            pictureBox1.Image = new Bitmap(image_path);

            startupPath = Application.StartupPath;

            model_path = startupPath + "\\crack_m_best.onnx";
            classer_path = startupPath + "\\lable.txt";

            List str = new List();
            StreamReader sr = new StreamReader(classer_path);
            string line;
            while ((line = sr.ReadLine()) != null)
            {
                str.Add(line);
            }
            class_names = str.ToArray();

        }
    }
}

  1. using OpenCvSharp;
  2. using Sdcb.OpenVINO;
  3. using Sdcb.OpenVINO.Natives;
  4. using System;
  5. using System.Collections.Generic;
  6. using System.Diagnostics;
  7. using System.Drawing;
  8. using System.IO;
  9. using System.Text;
  10. using System.Windows.Forms;
  11.  
  12. namespace OpenVINO_Seg
  13. {
  14.     public partial class Form1 : Form
  15.     {
  16.         public Form1()
  17.         {
  18.             InitializeComponent();
  19.         }
  20.  
  21.         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
  22.         string image_path = "";
  23.         string startupPath;
  24.         string model_path;
  25.         string classer_path;
  26.  
  27.         Mat src;
  28.  
  29.         SegmentationResult result_pro;
  30.         Mat result_image;
  31.         Result seg_result;
  32.  
  33.         StringBuilder sb = new StringBuilder();
  34.  
  35.         float[] det_result_array = new float[8400 * 37];
  36.         float[] proto_result_array = new float[32 * 160 * 160];
  37.  
  38.         // 识别结果类型
  39.         public string[] class_names;
  40.  
  41.         private void button1_Click(object sender, EventArgs e)
  42.         {
  43.             OpenFileDialog ofd = new OpenFileDialog();
  44.             ofd.Filter = fileFilter;
  45.             if (ofd.ShowDialog() != DialogResult.OK) return;
  46.             pictureBox1.Image = null;
  47.             image_path = ofd.FileName;
  48.             pictureBox1.Image = new Bitmap(image_path);
  49.             textBox1.Text = "";
  50.             src = new Mat(image_path);
  51.             pictureBox2.Image = null;
  52.         }
  53.  
  54.         unsafe private void button2_Click(object sender, EventArgs e)
  55.         {
  56.             if (pictureBox1.Image == null)
  57.             {
  58.                 return;
  59.             }
  60.  
  61.             pictureBox2.Image = null;
  62.             textBox1.Text = "";
  63.             sb.Clear();
  64.  
  65.             src = new Mat(image_path);
  66.  
  67.             Model rawModel = OVCore.Shared.ReadModel(model_path);
  68.             PrePostProcessor pp = rawModel.CreatePrePostProcessor();
  69.             PreProcessInputInfo inputInfo = pp.Inputs.Primary;
  70.  
  71.             inputInfo.TensorInfo.Layout = Sdcb.OpenVINO.Layout.NHWC;
  72.             inputInfo.ModelInfo.Layout = Sdcb.OpenVINO.Layout.NCHW;
  73.  
  74.             Model m = pp.BuildModel();
  75.             CompiledModel cm = OVCore.Shared.CompileModel(m, "CPU");
  76.             InferRequest ir = cm.CreateInferRequest();
  77.  
  78.             Shape inputShape = m.Inputs[0].Shape;
  79.  
  80.             float[] factors = new float[4];
  81.             factors[0] = 1f * src.Width / inputShape[2];
  82.             factors[1] = 1f * src.Height / inputShape[1];
  83.             factors[2] = src.Rows;
  84.             factors[3] = src.Cols;
  85.  
  86.             result_pro = new SegmentationResult(class_names, factors,0.3f,0.5f);
  87.  
  88.             Stopwatch stopwatch = new Stopwatch();
  89.             Mat resized = src.Resize(new OpenCvSharp.Size(inputShape[2], inputShape[1]));
  90.             Mat f32 = new Mat();
  91.             resized.ConvertTo(f32, MatType.CV_32FC3, 1.0 / 255);
  92.  
  93.             using (Tensor input = Tensor.FromRaw(
  94.                  new ReadOnlySpan<byte>((void*)f32.Data, (int)((long)f32.DataEnd - (long)f32.DataStart)),
  95.                 new Shape(1, f32.Rows, f32.Cols, 3),
  96.                 ov_element_type_e.F32))
  97.             {
  98.                 ir.Inputs.Primary = input;
  99.             }
  100.             double preprocessTime = stopwatch.Elapsed.TotalMilliseconds;
  101.             stopwatch.Restart();
  102.  
  103.             ir.Run();
  104.             double inferTime = stopwatch.Elapsed.TotalMilliseconds;
  105.             stopwatch.Restart();
  106.  
  107.             using (Tensor output_det = ir.Outputs[0])
  108.             using (Tensor output_proto = ir.Outputs[1])
  109.             {
  110.                 det_result_array = output_det.GetData<float>().ToArray();
  111.                 proto_result_array = output_proto.GetData<float>().ToArray();
  112.  
  113.                 seg_result = result_pro.process_result(det_result_array, proto_result_array);
  114.  
  115.                 double postprocessTime = stopwatch.Elapsed.TotalMilliseconds;
  116.                 stopwatch.Stop();
  117.  
  118.                 double totalTime = preprocessTime + inferTime + postprocessTime;
  119.  
  120.                 result_image = src.Clone();
  121.                 Mat masked_img = new Mat();
  122.  
  123.                 // 将识别结果绘制到图片上
  124.                 for (int i = 0; i < seg_result.length; i++)
  125.                 {
  126.                     Cv2.Rectangle(result_image, seg_result.rects[i], new Scalar(0, 0, 255), 2, LineTypes.Link8);
  127.                     Cv2.Rectangle(result_image, new OpenCvSharp.Point(seg_result.rects[i].TopLeft.X, seg_result.rects[i].TopLeft.Y - 20),
  128.                         new OpenCvSharp.Point(seg_result.rects[i].BottomRight.X, seg_result.rects[i].TopLeft.Y), new Scalar(0, 255, 255), -1);
  129.                     Cv2.PutText(result_image, seg_result.classes[i] + "-" + seg_result.scores[i].ToString("0.00"),
  130.                         new OpenCvSharp.Point(seg_result.rects[i].X, seg_result.rects[i].Y - 5),
  131.                         HersheyFonts.HersheySimplex, 0.6, new Scalar(0, 0, 0), 1);
  132.                     Cv2.AddWeighted(result_image, 0.5, seg_result.masks[i], 0.5, 0, masked_img);
  133.  
  134.                     sb.AppendLine($"{seg_result.classes[i]}:{seg_result.scores[i]:P0}");
  135.                 }
  136.  
  137.                 if (seg_result.length > 0)
  138.                 {
  139.                     if (pictureBox2.Image != null)
  140.                     {
  141.                         pictureBox2.Image.Dispose();
  142.                     }
  143.                     pictureBox2.Image = new Bitmap(masked_img.ToMemoryStream());
  144.                     sb.AppendLine($"Preprocess: {preprocessTime:F2}ms");
  145.                     sb.AppendLine($"Infer: {inferTime:F2}ms");
  146.                     sb.AppendLine($"Postprocess: {postprocessTime:F2}ms");
  147.                     sb.AppendLine($"Total: {totalTime:F2}ms");
  148.                     textBox1.Text = sb.ToString();
  149.                 }
  150.                 else
  151.                 {
  152.                     textBox1.Text = "无信息";
  153.                 }
  154.  
  155.                 masked_img.Dispose();
  156.                 result_image.Dispose();
  157.             }
  158.         }
  159.  
  160.         private void Form1_Load(object sender, EventArgs e)
  161.         {
  162.             image_path = "1.jpg";
  163.             pictureBox1.Image = new Bitmap(image_path);
  164.  
  165.             startupPath = Application.StartupPath;
  166.  
  167.             model_path = startupPath + "\\crack_m_best.onnx";
  168.             classer_path = startupPath + "\\lable.txt";
  169.  
  170.             List<string> str = new List<string>();
  171.             StreamReader sr = new StreamReader(classer_path);
  172.             string line;
  173.             while ((line = sr.ReadLine()) != null)
  174.             {
  175.                 str.Add(line);
  176.             }
  177.             class_names = str.ToArray();
  178.  
  179.         }
  180.     }
  181. }

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