• C# Onnx DIS高精度图像二类分割

    目录

    介绍

    效果

    模型信息

    项目

    代码

    下载

    介绍

    github地址：https://github.com/xuebinqin/DIS

    This is the repo for our new project Highly Accurate Dichotomous Image Segmentation

    对应的paper是ECCV2022的一篇文章《Highly Accurate Dichotomous Image Segmentation》， 跟BASNet和U2-Net都是出自同一个作者写的。 

    效果

    模型信息

    Inputs
    -------------------------
    name：input
    tensor：Float[1, 3, 480, 640]
    ---------------------------------------------------------------

    Outputs
    -------------------------
    name：output
    tensor：Float[1, 1, 480, 640]
    ---------------------------------------------------------------

    项目

    VS2022

    .net framework 4.8

    OpenCvSharp 4.8

    Microsoft.ML.OnnxRuntime 1.16.2

    代码

    using Microsoft.ML.OnnxRuntime.Tensors;
    using Microsoft.ML.OnnxRuntime;
    using OpenCvSharp;
    using System;
    using System.Collections.Generic;
    using System.Windows.Forms;
    using System.Linq;
    using System.Drawing;
    using static System.Net.Mime.MediaTypeNames;

    namespace Onnx_Demo
    {
        public partial class frmMain : Form
        {
            public frmMain()
            {
                InitializeComponent();
            }

            string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
            string image_path = "";

            DateTime dt1 = DateTime.Now;
            DateTime dt2 = DateTime.Now;

            int inpWidth;
            int inpHeight;

            int outHeight, outWidth;

            Mat image;

            string model_path = "";

            SessionOptions options;
            InferenceSession onnx_session;
            Tensor input_tensor;
            Tensor mask_tensor;
            List input_ontainer;

            IDisposableReadOnlyCollection result_infer;
            DisposableNamedOnnxValue[] results_onnxvalue;

            private void button1_Click(object sender, EventArgs e)
            {
                OpenFileDialog ofd = new OpenFileDialog();
                ofd.Filter = fileFilter;
                if (ofd.ShowDialog() != DialogResult.OK) return;

                pictureBox1.Image = null;
                pictureBox2.Image = null;
                textBox1.Text = "";

                image_path = ofd.FileName;
                pictureBox1.Image = new System.Drawing.Bitmap(image_path);
                image = new Mat(image_path);
            }

            private void Form1_Load(object sender, EventArgs e)
            {
                // 创建输入容器
                input_ontainer = new List();

                // 创建输出会话
                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
                model_path = "model/isnet_general_use_480x640.onnx";

                inpHeight = 480;
                inpWidth = 640;

                outHeight = 480;
                outWidth = 640;

                onnx_session = new InferenceSession(model_path, options);

                // 创建输入容器
                input_ontainer = new List();

                image_path = "test_img/bike.jpg";
                pictureBox1.Image = new Bitmap(image_path);

            }

            private unsafe void button2_Click(object sender, EventArgs e)
            {
                if (image_path == "")
                {
                    return;
                }
                textBox1.Text = "检测中，请稍等……";
                pictureBox2.Image = null;
                System.Windows.Forms.Application.DoEvents();

                image = new Mat(image_path);

                Mat resize_image = new Mat();
                Cv2.Resize(image, resize_image, new OpenCvSharp.Size(inpWidth, inpHeight));

                float[] input_tensor_data = new float[1 * 3 * inpWidth * inpHeight];

                for (int c = 0; c < 3; c++)
                {
                    for (int i = 0; i < inpHeight; i++)
                    {
                        for (int j = 0; j < inpWidth; j++)
                        {
                            float pix = ((byte*)(resize_image.Ptr(i).ToPointer()))[j * 3 + 2 - c];
                            input_tensor_data[c * inpHeight * inpWidth + i * inpWidth + j] = (float)(pix / 255.0 - 0.5);
                        }
                    }
                }

                input_tensor = new DenseTensor(input_tensor_data, new[] { 1, 3, inpHeight, inpWidth });

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

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

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

                float[] pred = results_onnxvalue[0].AsTensor().ToArray();

                Mat mask = new Mat(outHeight, outWidth, MatType.CV_32FC1, pred);
                double min_value, max_value;
                Cv2.MinMaxLoc(mask, out min_value, out max_value);

                mask = (mask - min_value) / (max_value - min_value);

                mask *= 255;
                mask.ConvertTo(mask, MatType.CV_8UC1);

                Cv2.Resize(mask, mask, new OpenCvSharp.Size(image.Cols, image.Rows));

                Mat result_image = mask.Clone();

                if (pictureBox2.Image != null)
                {
                    pictureBox2.Image.Dispose();
                }

                pictureBox2.Image = new System.Drawing.Bitmap(result_image.ToMemoryStream());
                textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";

                mask.Dispose();
                image.Dispose();
                resize_image.Dispose();
                result_image.Dispose();
            }

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

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

    1. using Microsoft.ML.OnnxRuntime.Tensors;
    2. using Microsoft.ML.OnnxRuntime;
    3. using OpenCvSharp;
    4. using System;
    5. using System.Collections.Generic;
    6. using System.Windows.Forms;
    7. using System.Linq;
    8. using System.Drawing;
    9. using static System.Net.Mime.MediaTypeNames;
    10.  
    11. namespace Onnx_Demo
    12. {
    13.     public partial class frmMain : Form
    14.     {
    15.         public frmMain()
    16.         {
    17.             InitializeComponent();
    18.         }
    19.  
    20.         string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
    21.         string image_path = "";
    22.  
    23.         DateTime dt1 = DateTime.Now;
    24.         DateTime dt2 = DateTime.Now;
    25.  
    26.         int inpWidth;
    27.         int inpHeight;
    28.  
    29.         int outHeight, outWidth;
    30.  
    31.         Mat image;
    32.  
    33.         string model_path = "";
    34.  
    35.         SessionOptions options;
    36.         InferenceSession onnx_session;
    37.         Tensor<float> input_tensor;
    38.         Tensor<float> mask_tensor;
    39.         List input_ontainer;
    40.  
    41.         IDisposableReadOnlyCollection result_infer;
    42.         DisposableNamedOnnxValue[] results_onnxvalue;
    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.  
    50.             pictureBox1.Image = null;
    51.             pictureBox2.Image = null;
    52.             textBox1.Text = "";
    53.  
    54.             image_path = ofd.FileName;
    55.             pictureBox1.Image = new System.Drawing.Bitmap(image_path);
    56.             image = new Mat(image_path);
    57.         }
    58.  
    59.         private void Form1_Load(object sender, EventArgs e)
    60.         {
    61.             // 创建输入容器
    62.             input_ontainer = new List();
    63.  
    64.             // 创建输出会话
    65.             options = new SessionOptions();
    66.             options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
    67.             options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行
    68.  
    69.             // 创建推理模型类，读取本地模型文件
    70.             model_path = "model/isnet_general_use_480x640.onnx";
    71.  
    72.             inpHeight = 480;
    73.             inpWidth = 640;
    74.  
    75.             outHeight = 480;
    76.             outWidth = 640;
    77.  
    78.             onnx_session = new InferenceSession(model_path, options);
    79.  
    80.             // 创建输入容器
    81.             input_ontainer = new List();
    82.  
    83.             image_path = "test_img/bike.jpg";
    84.             pictureBox1.Image = new Bitmap(image_path);
    85.  
    86.         }
    87.  
    88.         private unsafe void button2_Click(object sender, EventArgs e)
    89.         {
    90.             if (image_path == "")
    91.             {
    92.                 return;
    93.             }
    94.             textBox1.Text = "检测中，请稍等……";
    95.             pictureBox2.Image = null;
    96.             System.Windows.Forms.Application.DoEvents();
    97.  
    98.             image = new Mat(image_path);
    99.  
    100.             Mat resize_image = new Mat();
    101.             Cv2.Resize(image, resize_image, new OpenCvSharp.Size(inpWidth, inpHeight));
    102.  
    103.             float[] input_tensor_data = new float[1 * 3 * inpWidth * inpHeight];
    104.  
    105.             for (int c = 0; c < 3; c++)
    106.             {
    107.                 for (int i = 0; i < inpHeight; i++)
    108.                 {
    109.                     for (int j = 0; j < inpWidth; j++)
    110.                     {
    111.                         float pix = ((byte*)(resize_image.Ptr(i).ToPointer()))[j * 3 + 2 - c];
    112.                         input_tensor_data[c * inpHeight * inpWidth + i * inpWidth + j] = (float)(pix / 255.0 - 0.5);
    113.                     }
    114.                 }
    115.             }
    116.  
    117.             input_tensor = new DenseTensor<float>(input_tensor_data, new[] { 1, 3, inpHeight, inpWidth });
    118.  
    119.             //将 input_tensor 放入一个输入参数的容器，并指定名称
    120.             input_ontainer.Add(NamedOnnxValue.CreateFromTensor("input", input_tensor));
    121.  
    122.             dt1 = DateTime.Now;
    123.             //运行 Inference 并获取结果
    124.             result_infer = onnx_session.Run(input_ontainer);
    125.             dt2 = DateTime.Now;
    126.  
    127.             //将输出结果转为DisposableNamedOnnxValue数组
    128.             results_onnxvalue = result_infer.ToArray();
    129.  
    130.             float[] pred = results_onnxvalue[0].AsTensor<float>().ToArray();
    131.  
    132.             Mat mask = new Mat(outHeight, outWidth, MatType.CV_32FC1, pred);
    133.             double min_value, max_value;
    134.             Cv2.MinMaxLoc(mask, out min_value, out max_value);
    135.  
    136.             mask = (mask - min_value) / (max_value - min_value);
    137.  
    138.             mask *= 255;
    139.             mask.ConvertTo(mask, MatType.CV_8UC1);
    140.  
    141.             Cv2.Resize(mask, mask, new OpenCvSharp.Size(image.Cols, image.Rows));
    142.  
    143.             Mat result_image = mask.Clone();
    144.  
    145.             if (pictureBox2.Image != null)
    146.             {
    147.                 pictureBox2.Image.Dispose();
    148.             }
    149.  
    150.             pictureBox2.Image = new System.Drawing.Bitmap(result_image.ToMemoryStream());
    151.             textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
    152.  
    153.             mask.Dispose();
    154.             image.Dispose();
    155.             resize_image.Dispose();
    156.             result_image.Dispose();
    157.         }
    158.  
    159.         private void pictureBox2_DoubleClick(object sender, EventArgs e)
    160.         {
    161.             Common.ShowNormalImg(pictureBox2.Image);
    162.         }
    163.  
    164.         private void pictureBox1_DoubleClick(object sender, EventArgs e)
    165.         {
    166.             Common.ShowNormalImg(pictureBox1.Image);
    167.         }
    168.     }
    169. }

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  • 原文地址：https://blog.csdn.net/lw112190/article/details/134510487