• C# Onnx Yolov8 Seg 分割

    目录

    效果

    模型信息

    项目

    代码

    下载 

    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(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(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_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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  • 原文地址：https://blog.csdn.net/lw112190/article/details/132800530