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：inputtensor：Float[1, 3, 480, 640]---------------------------------------------------------------Outputs-------------------------name：outputtensor：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 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); } }}