C# Onnx yolov8n forklift detection

目录

效果

模型

项目

代码

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---

C# Onnx yolov8n forklift detection

效果

模型

Model Properties

---

date：2023-12-25T16:22:05.530078

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: 'forklift', 1: 'person'}

---

Inputs

---

name：images

tensor：Float[1, 3, 640, 640]

---

Outputs

---

name：output0

tensor：Float[1, 6, 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.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;

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 = "";

//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());

textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";

}

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/yolov8n-forklift-detection.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);

}

}

}

}

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.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;

        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 = "";

            //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());
                textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
            }
            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/yolov8n-forklift-detection.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);
            }
        }
    }
}

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