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C# Onnx Yolov8 Seg 分割_yolo c# 用onnxruntime

作者：weixin_40725706 | 2024-06-03 21:28:54

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yolo c# 用onnxruntime

目录

效果

模型信息

项目

代码

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效果

模型信息

Model Properties
-------------------------
date：2023-09-07T17:11:46.798385
description：Ultralytics YOLOv8n-seg model trained on coco.yaml
author：Ultralytics
task：segment
license：AGPL-3.0 https://ultralytics.com/license
version：8.0.172
stride：32
batch：1
imgsz：[640, 640]
names：{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
---------------------------------------------------------------

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

Outputs
-------------------------
name：output0
tensor：Float[1, 116, 8400]
name：output1
tensor：Float[1, 32, 160, 160]
---------------------------------------------------------------

项目

代码

// 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[] det_result_array = new float[8400 * 116];
float[] proto_result_array = new float[32 * 160 * 160];
float[] factors = new float[4];
factors[0] = factors[1] = (float)(max_image_length / 640.0);
factors[2] = image.Rows;
factors[3] = image.Cols;

// 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
// input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
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_ontainer.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));

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

  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.ComponentModel;
  7. using System.Data;
  8. using System.Drawing;
  9. using System.Linq;
  10. using System.Text;
  11. using System.Windows.Forms;
  12. using static System.Net.Mime.MediaTypeNames;
  13.  
  14. namespace Onnx_Yolov8_Demo
  15. {
  16.     public partial class Form1 : Form
  17.     {
  18.         public Form1()
  19.         {
  20.             InitializeComponent();
  21.         }
  22.  
  23.         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
  24.         string image_path = "";
  25.         string startupPath;
  26.         string classer_path;
  27.         DateTime dt1 = DateTime.Now;
  28.         DateTime dt2 = DateTime.Now;
  29.         string model_path;
  30.         Mat image;
  31.         SegmentationResult result_pro;
  32.         Mat result_image;
  33.  
  34.         SessionOptions options;
  35.         InferenceSession onnx_session;
  36.         Tensor<float> input_tensor;
  37.         List<NamedOnnxValue> input_ontainer;
  38.         IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
  39.         DisposableNamedOnnxValue[] results_onnxvalue;
  40.  
  41.         Tensor<float> result_tensors_det;
  42.         Tensor<float> result_tensors_proto;
  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.             // 配置图片数据
  65.             image = new Mat(image_path);
  66.             int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
  67.             Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
  68.             Rect roi = new Rect(0, 0, image.Cols, image.Rows);
  69.             image.CopyTo(new Mat(max_image, roi));
  70.  
  71.             float[] det_result_array = new float[8400 * 116];
  72.             float[] proto_result_array = new float[32 * 160 * 160];
  73.             float[] factors = new float[4];
  74.             factors[0] = factors[1] = (float)(max_image_length / 640.0);
  75.             factors[2] = image.Rows;
  76.             factors[3] = image.Cols;
  77.  
  78.             // 将图片转为RGB通道
  79.             Mat image_rgb = new Mat();
  80.             Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);
  81.             Mat resize_image = new Mat();
  82.             Cv2.Resize(image_rgb, resize_image, new OpenCvSharp.Size(640, 640));
  83.  
  84.             // 输入Tensor
  85.             // input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
  86.             for (int y = 0; y < resize_image.Height; y++)
  87.             {
  88.                 for (int x = 0; x < resize_image.Width; x++)
  89.                 {
  90.                     input_tensor[0, 0, y, x] = resize_image.At<Vec3b>(y, x)[0] / 255f;
  91.                     input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1] / 255f;
  92.                     input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2] / 255f;
  93.                 }
  94.             }
  95.  
  96.             //将 input_tensor 放入一个输入参数的容器，并指定名称
  97.             input_ontainer.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));
  98.  
  99.             dt1 = DateTime.Now;
  100.             //运行 Inference 并获取结果
  101.             result_infer = onnx_session.Run(input_ontainer);
  102.  
  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_det = results_onnxvalue[0].AsTensor<float>();
  110.             result_tensors_proto = results_onnxvalue[1].AsTensor<float>();
  111.  
  112.             det_result_array = result_tensors_det.ToArray();
  113.             proto_result_array = result_tensors_proto.ToArray();
  114.  
  115.             resize_image.Dispose();
  116.             image_rgb.Dispose();
  117.  
  118.             result_pro = new SegmentationResult(classer_path, factors);
  119.             result_image = result_pro.draw_result(result_pro.process_result(det_result_array, proto_result_array), image.Clone());
  120.  
  121.             if (!result_image.Empty())
  122.             {
  123.                 pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());
  124.                 textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
  125.             }
  126.             else
  127.             {
  128.                 textBox1.Text = "无信息";
  129.             }
  130.         }
  131.  
  132.         private void Form1_Load(object sender, EventArgs e)
  133.         {
  134.             startupPath = System.Windows.Forms.Application.StartupPath;
  135.             model_path = startupPath + "\\yolov8n-seg.onnx";
  136.             classer_path = startupPath + "\\yolov8-detect-lable.txt";
  137.  
  138.             // 创建输出会话，用于输出模型读取信息
  139.             options = new SessionOptions();
  140.             options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
  141.             // 设置为CPU上运行
  142.             options.AppendExecutionProvider_CPU(0);
  143.  
  144.             // 创建推理模型类，读取本地模型文件
  145.             onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径
  146.  
  147.             // 输入Tensor
  148.             input_tensor = new DenseTensor<float>(new[] { 1, 3, 640, 640 });
  149.  
  150.             // 创建输入容器
  151.             input_ontainer = new List<NamedOnnxValue>();
  152.  
  153.         }
  154.     }
  155. }

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完整Demo下载

exe程序下载

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