devbox
小小林熬夜学编程
这个屌丝很懒，什么也没留下！
关注作者
热门标签
热门文章
当前位置:   article > 正文

C# Onnx yolov8 水表读数检测_yolo水表读书

作者：小小林熬夜学编程 | 2024-06-10 08:51:40

赞

踩

yolo水表读书

目录

效果

模型信息

项目

代码

训练数据

下载

C# Onnx yolov8 水表读数检测

效果

模型信息

Model Properties
-------------------------
date：2024-01-31T10:18:10.141465
author：Ultralytics
task：detect
license：AGPL-3.0 https://ultralytics.com/license
version：8.0.172
stride：32
batch：1
imgsz：[640, 640]
names：{0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'counter', 11: 'liter'}
---------------------------------------------------------------

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

Outputs
-------------------------
name：output0
tensor：Float[1, 16, 8400]
---------------------------------------------------------------

项目

代码

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Drawing.Imaging;
using System.Linq;
using System.Text;
using System.Windows.Forms;

namespace Onnx_Yolov8_Demo
{
    public partial class Form1 : Form
    {
        public Form1()
        {
            InitializeComponent();
        }

        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string startupPath;
        string classer_path;
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
        string model_path;
        Mat image;
        DetectionResult result_pro;
        Mat result_image;
        Result result;

        SessionOptions options;
        InferenceSession onnx_session;
        Tensor<float> input_tensor;
        List<NamedOnnxValue> input_container;
        IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
        DisposableNamedOnnxValue[] results_onnxvalue;

        Tensor<float> result_tensors;

        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 = "";
            image = new Mat(image_path);
            pictureBox2.Image = null;
        }

        private void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }

            button2.Enabled = false;
            pictureBox2.Image = null;
            textBox1.Text = "";
            sb.Clear();

            //图片缩放
            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[] result_array = new float[8400 * 84];
            float[] factors = new float[2];
            factors[0] = factors[1] = (float)(max_image_length / 640.0);

            // 将图片转为RGB通道
            Mat image_rgb = new Mat();
            Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);
            Mat resize_image = new Mat();
            Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(640, 640));

            // 输入Tensor
            for (int y = 0; y < resize_image.Height; y++)
            {
                for (int x = 0; x < resize_image.Width; x++)
                {
                    input_tensor[0, 0, y, x] = resize_image.At<Vec3b>(y, x)[0] / 255f;
                    input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;
                    input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;
                }
            }

            //将 input_tensor 放入一个输入参数的容器，并指定名称
            input_container.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));

            dt1 = DateTime.Now;
            //运行 Inference 并获取结果
            result_infer = onnx_session.Run(input_container);
            dt2 = DateTime.Now;

            // 将输出结果转为DisposableNamedOnnxValue数组
            results_onnxvalue = result_infer.ToArray();

            // 读取第一个节点输出并转为Tensor数据
            result_tensors = results_onnxvalue[0].AsTensor<float>();

            result_array = result_tensors.ToArray();

            resize_image.Dispose();
            image_rgb.Dispose();

            result_pro = new DetectionResult(classer_path, factors);
            result = result_pro.process_result(result_array);
            result_image = result_pro.draw_result(result, image.Clone());

            if (!result_image.Empty())
            {
                pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
                sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
                sb.AppendLine("--------------------------------------------");

                for (int i = 0; i < result.length; i++)
                {
                    sb.AppendLine(result.classes[i] + "-" + result.scores[i].ToString("F2"));
                }

                textBox1.Text = sb.ToString();
            }
            else
            {
                textBox1.Text = "无信息";
            }

            button2.Enabled = true;
        }

        private void Form1_Load(object sender, EventArgs e)
        {
            startupPath = System.Windows.Forms.Application.StartupPath;

            model_path = "model/last.onnx";
            classer_path = "model/lable.txt";

            // 创建输出会话，用于输出模型读取信息
            options = new SessionOptions();
            options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
            options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行

            // 创建推理模型类，读取本地模型文件
            onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径

            // 输入Tensor
            input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
            // 创建输入容器
            input_container = new List<NamedOnnxValue>();

            image_path = "test_img/1.jpg";
            pictureBox1.Image = new Bitmap(image_path);
            image = new Mat(image_path);

        }

        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }

        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }

        SaveFileDialog sdf = new SaveFileDialog();
        private void button3_Click(object sender, EventArgs e)
        {
            if (pictureBox2.Image == null)
            {
                return;
            }
            Bitmap output = new Bitmap(pictureBox2.Image);
            sdf.Title = "保存";
            sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";
            if (sdf.ShowDialog() == DialogResult.OK)
            {
                switch (sdf.FilterIndex)
                {
                    case 1:
                        {
                            output.Save(sdf.FileName, ImageFormat.Jpeg);
                            break;
                        }
                    case 2:
                        {
                            output.Save(sdf.FileName, ImageFormat.Png);
                            break;
                        }
                    case 3:
                        {
                            output.Save(sdf.FileName, ImageFormat.Bmp);
                            break;
                        }
                    case 4:
                        {
                            output.Save(sdf.FileName, ImageFormat.Emf);
                            break;
                        }
                    case 5:
                        {
                            output.Save(sdf.FileName, ImageFormat.Exif);
                            break;
                        }
                    case 6:
                        {
                            output.Save(sdf.FileName, ImageFormat.Gif);
                            break;
                        }
                    case 7:
                        {
                            output.Save(sdf.FileName, ImageFormat.Icon);
                            break;
                        }

                    case 8:
                        {
                            output.Save(sdf.FileName, ImageFormat.Tiff);
                            break;
                        }
                    case 9:
                        {
                            output.Save(sdf.FileName, ImageFormat.Wmf);
                            break;
                        }
                }
                MessageBox.Show("保存成功，位置：" + sdf.FileName);
            }
        }
    }
}

  1. using Microsoft.ML.OnnxRuntime;
  2. using Microsoft.ML.OnnxRuntime.Tensors;
  3. using OpenCvSharp;
  4. using System;
  5. using System.Collections.Generic;
  6. using System.Drawing;
  7. using System.Drawing.Imaging;
  8. using System.Linq;
  9. using System.Text;
  10. using System.Windows.Forms;
  11.  
  12. namespace Onnx_Yolov8_Demo
  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 classer_path;
  25.         DateTime dt1 = DateTime.Now;
  26.         DateTime dt2 = DateTime.Now;
  27.         string model_path;
  28.         Mat image;
  29.         DetectionResult result_pro;
  30.         Mat result_image;
  31.         Result result;
  32.  
  33.         SessionOptions options;
  34.         InferenceSession onnx_session;
  35.         Tensor<float> input_tensor;
  36.         List<NamedOnnxValue> input_container;
  37.         IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
  38.         DisposableNamedOnnxValue[] results_onnxvalue;
  39.  
  40.         Tensor<float> result_tensors;
  41.  
  42.         StringBuilder sb = new StringBuilder();
  43.  
  44.         private void button1_Click(object sender, EventArgs e)
  45.         {
  46.             OpenFileDialog ofd = new OpenFileDialog();
  47.             ofd.Filter = fileFilter;
  48.             if (ofd.ShowDialog() != DialogResult.OK) return;
  49.             pictureBox1.Image = null;
  50.             image_path = ofd.FileName;
  51.             pictureBox1.Image = new Bitmap(image_path);
  52.             textBox1.Text = "";
  53.             image = new Mat(image_path);
  54.             pictureBox2.Image = null;
  55.         }
  56.  
  57.         private void button2_Click(object sender, EventArgs e)
  58.         {
  59.             if (image_path == "")
  60.             {
  61.                 return;
  62.             }
  63.  
  64.             button2.Enabled = false;
  65.             pictureBox2.Image = null;
  66.             textBox1.Text = "";
  67.             sb.Clear();
  68.  
  69.             //图片缩放
  70.             image = new Mat(image_path);
  71.             int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
  72.             Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
  73.             Rect roi = new Rect(0, 0, image.Cols, image.Rows);
  74.             image.CopyTo(new Mat(max_image, roi));
  75.  
  76.             float[] result_array = new float[8400 * 84];
  77.             float[] factors = new float[2];
  78.             factors[0] = factors[1] = (float)(max_image_length / 640.0);
  79.  
  80.             // 将图片转为RGB通道
  81.             Mat image_rgb = new Mat();
  82.             Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);
  83.             Mat resize_image = new Mat();
  84.             Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(640, 640));
  85.  
  86.             // 输入Tensor
  87.             for (int y = 0; y < resize_image.Height; y++)
  88.             {
  89.                 for (int x = 0; x < resize_image.Width; x++)
  90.                 {
  91.                     input_tensor[0, 0, y, x] = resize_image.At<Vec3b>(y, x)[0] / 255f;
  92.                     input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;
  93.                     input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;
  94.                 }
  95.             }
  96.  
  97.             //将 input_tensor 放入一个输入参数的容器，并指定名称
  98.             input_container.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));
  99.  
  100.             dt1 = DateTime.Now;
  101.             //运行 Inference 并获取结果
  102.             result_infer = onnx_session.Run(input_container);
  103.             dt2 = DateTime.Now;
  104.  
  105.             // 将输出结果转为DisposableNamedOnnxValue数组
  106.             results_onnxvalue = result_infer.ToArray();
  107.  
  108.             // 读取第一个节点输出并转为Tensor数据
  109.             result_tensors = results_onnxvalue[0].AsTensor<float>();
  110.  
  111.             result_array = result_tensors.ToArray();
  112.  
  113.             resize_image.Dispose();
  114.             image_rgb.Dispose();
  115.  
  116.             result_pro = new DetectionResult(classer_path, factors);
  117.             result = result_pro.process_result(result_array);
  118.             result_image = result_pro.draw_result(result, image.Clone());
  119.  
  120.             if (!result_image.Empty())
  121.             {
  122.                 pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
  123.                 sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
  124.                 sb.AppendLine("--------------------------------------------");
  125.  
  126.                 for (int i = 0; i < result.length; i++)
  127.                 {
  128.                     sb.AppendLine(result.classes[i] + "-" + result.scores[i].ToString("F2"));
  129.                 }
  130.  
  131.                 textBox1.Text = sb.ToString();
  132.             }
  133.             else
  134.             {
  135.                 textBox1.Text = "无信息";
  136.             }
  137.  
  138.             button2.Enabled = true;
  139.         }
  140.  
  141.         private void Form1_Load(object sender, EventArgs e)
  142.         {
  143.             startupPath = System.Windows.Forms.Application.StartupPath;
  144.  
  145.             model_path = "model/last.onnx";
  146.             classer_path = "model/lable.txt";
  147.  
  148.             // 创建输出会话，用于输出模型读取信息
  149.             options = new SessionOptions();
  150.             options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
  151.             options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行
  152.  
  153.             // 创建推理模型类，读取本地模型文件
  154.             onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径
  155.  
  156.             // 输入Tensor
  157.             input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
  158.             // 创建输入容器
  159.             input_container = new List<NamedOnnxValue>();
  160.  
  161.             image_path = "test_img/1.jpg";
  162.             pictureBox1.Image = new Bitmap(image_path);
  163.             image = new Mat(image_path);
  164.  
  165.         }
  166.  
  167.         private void pictureBox1_DoubleClick(object sender, EventArgs e)
  168.         {
  169.             Common.ShowNormalImg(pictureBox1.Image);
  170.         }
  171.  
  172.         private void pictureBox2_DoubleClick(object sender, EventArgs e)
  173.         {
  174.             Common.ShowNormalImg(pictureBox2.Image);
  175.         }
  176.  
  177.         SaveFileDialog sdf = new SaveFileDialog();
  178.         private void button3_Click(object sender, EventArgs e)
  179.         {
  180.             if (pictureBox2.Image == null)
  181.             {
  182.                 return;
  183.             }
  184.             Bitmap output = new Bitmap(pictureBox2.Image);
  185.             sdf.Title = "保存";
  186.             sdf.Filter = "Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf";
  187.             if (sdf.ShowDialog() == DialogResult.OK)
  188.             {
  189.                 switch (sdf.FilterIndex)
  190.                 {
  191.                     case 1:
  192.                         {
  193.                             output.Save(sdf.FileName, ImageFormat.Jpeg);
  194.                             break;
  195.                         }
  196.                     case 2:
  197.                         {
  198.                             output.Save(sdf.FileName, ImageFormat.Png);
  199.                             break;
  200.                         }
  201.                     case 3:
  202.                         {
  203.                             output.Save(sdf.FileName, ImageFormat.Bmp);
  204.                             break;
  205.                         }
  206.                     case 4:
  207.                         {
  208.                             output.Save(sdf.FileName, ImageFormat.Emf);
  209.                             break;
  210.                         }
  211.                     case 5:
  212.                         {
  213.                             output.Save(sdf.FileName, ImageFormat.Exif);
  214.                             break;
  215.                         }
  216.                     case 6:
  217.                         {
  218.                             output.Save(sdf.FileName, ImageFormat.Gif);
  219.                             break;
  220.                         }
  221.                     case 7:
  222.                         {
  223.                             output.Save(sdf.FileName, ImageFormat.Icon);
  224.                             break;
  225.                         }
  226.  
  227.                     case 8:
  228.                         {
  229.                             output.Save(sdf.FileName, ImageFormat.Tiff);
  230.                             break;
  231.                         }
  232.                     case 9:
  233.                         {
  234.                             output.Save(sdf.FileName, ImageFormat.Wmf);
  235.                             break;
  236.                         }
  237.                 }
  238.                 MessageBox.Show("保存成功，位置：" + sdf.FileName);
  239.             }
  240.         }
  241.     }
  242. }

训练数据

下载

训练数据下载

源码下载

声明：本文内容由网友自发贡献，不代表【wpsshop博客】立场，版权归原作者所有，本站不承担相应法律责任。如您发现有侵权的内容，请联系我们。转载请注明出处：https://www.wpsshop.cn/w/小小林熬夜学编程/article/detail/698023
推荐阅读
相关标签

Copyright © 2003-2013 www.wpsshop.cn 版权所有，并保留所有权利。

  

闽ICP备14008679号