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
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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'}
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Inputs
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name：images
tensor：Float[1, 3, 640, 640]
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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<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_det = ir.Outputs[0])
            using (Tensor output_proto = ir.Outputs[1])
            {
                det_result_array = output_det.GetData<float>().ToArray();
                proto_result_array = output_proto.GetData<float>().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<string> str = new List<string>();
            StreamReader sr = new StreamReader(classer_path);
            string line;
            while ((line = sr.ReadLine()) != null)
            {
                str.Add(line);
            }
            class_names = str.ToArray();

        }
    }
}

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<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_det = ir.Outputs[0])using (Tensor output_proto = ir.Outputs[1]){det_result_array = output_det.GetData<float>().ToArray();proto_result_array = output_proto.GetData<float>().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<string> str = new List<string>();StreamReader sr = new StreamReader(classer_path);string line;while ((line = sr.ReadLine()) != null){str.Add(line);}class_names = str.ToArray();}}
}

数据集

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