C# OnnxRuntime DIS高精度图像二类分割

说明

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

github地址：

https://github.com/xuebinqin/DIS

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<float> input_tensor;
 Tensor<float> mask_tensor;
 List<NamedOnnxValue> input_container;

 IDisposableReadOnlyCollection<DisposableNamedOnnxValue> 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 = ;
 pictureBox2.Image = ;
 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_container = new List<NamedOnnxValue>();

 // 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_container = new List<NamedOnnxValue>();

 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 = ;
 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<float>(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_container.Add(NamedOnnxValue.CreateFromTensor("input", 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();

float[] pred = results_onnxvalue[0].AsTensor<float>().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 != )
 {
 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);
 }
 }
}