opencv 目标检测深度学习人工智能计算机视觉机器学习 C# Onnx yolov8 水表读数检测

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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 input_tensor; List input_container; IDisposableReadOnlyCollection result_infer; DisposableNamedOnnxValue[] results_onnxvalue;

Tensor 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(y, x)[0] / 255f; input_tensor[0, 1, y, x] = resize_image.At(y, x)[1] / 255f; input_tensor[0, 2, y, x] = resize_image.At(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();

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(new[] { 1, 3, 640, 640 }); // 创建输入容器 input_container = new List();

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); } } } }

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 input_tensor;

List input_container;

IDisposableReadOnlyCollection result_infer;

DisposableNamedOnnxValue[] results_onnxvalue;

Tensor 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(y, x)[0] / 255f;

input_tensor[0, 1, y, x] = resize_image.At(y, x)[1] / 255f;

input_tensor[0, 2, y, x] = resize_image.At(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();

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(new[] { 1, 3, 640, 640 });

// 创建输入容器

input_container = new List();

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";

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);

}

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opencv 目标检测深度学习人工智能计算机视觉机器学习 C# Onnx yolov8 水表读数检测

魔百盒刷机魔百盒固件移动魔百盒机顶盒ROM 盒子ROM 魔百盒M101