C# U2Net 抠图

2024-06-24码农

介绍

github地址：

https://github.com/xuebinqin/U-2-Net

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

效果(u2net.onnx)

效果(u2net_human_seg.onnx)

模型信息

u2net.onnx

Inputs ------------------------- name：input_image tensor：Float[1, 3, 320, 320] --------------------------------------------------------------- Outputs ------------------------- name：output_image tensor：Float[1, 1, 320, 320] name：2016 tensor：Float[1, 1, 320, 320] name：2017 tensor：Float[1, 1, 320, 320] name：2018 tensor：Float[1, 1, 320, 320] name：2019 tensor：Float[1, 1, 320, 320] name：2020 tensor：Float[1, 1, 320, 320] name：2021 tensor：Float[1, 1, 320, 320] ---------------------------------------------------------------

u2net_human_seg.onnx

项目

VS2022

.net framework 4.8

OpenCvSharp 4.8

Microsoft.ML.OnnxRuntime 1.16.2

代码

using Microsoft.ML.OnnxRuntime; using Microsoft.ML.OnnxRuntime.Tensors; using OpenCvSharp; using System; using System.Collections.Generic; using System.Drawing; using System.Drawing.Imaging; using System.Linq; using System.Threading.Tasks; using System.Windows.Forms; namespace U2Net { public partial class frmMain : Form { public frmMain() { InitializeComponent(); } string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png"; string image_path = ""; string startupPath; DateTime dt1 = DateTime.Now; DateTime dt2 = DateTime.Now; string model_path; Mat image; Mat result_image; int modelSize = 512; SessionOptions options; InferenceSession onnx_session; Tensor<float> input_tensor; List<NamedOnnxValue> input_ontainer; IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer; DisposableNamedOnnxValue[] results_onnxvalue; Tensor<float> result_tensors; float[] result_array; private void button1_Click(object sender, EventArgs e) { OpenFileDialog ofd = new OpenFileDialog(); ofd.Filter = fileFilter; if (ofd.ShowDialog() != DialogResult.OK) return; pictureBox1.Image = null; image_path = ofd.FileName; pictureBox1.Image = new Bitmap(image_path); textBox1.Text = ""; image = new Mat(image_path); pictureBox2.Image = null; } private void button2_Click(object sender, EventArgs e) { if (image_path == "") { return; } textBox1.Text = ""; pictureBox2.Image = null; int oldwidth = image.Cols; int oldheight = image.Rows; //缩放图片大小 int maxEdge = Math.Max(image.Rows, image.Cols); float ratio = 1.0f * modelSize / maxEdge; int newHeight = (int)(image.Rows * ratio); int newWidth = (int)(image.Cols * ratio); Mat resize_image = image.Resize(new OpenCvSharp.Size(newWidth, newHeight)); int width = resize_image.Cols; int height = resize_image.Rows; if (width != modelSize || height != modelSize) { resize_image = resize_image.CopyMakeBorder(0, modelSize - newHeight, 0, modelSize - newWidth, BorderTypes.Constant, new Scalar(255, 255, 255)); } Cv2.CvtColor(resize_image, resize_image, ColorConversionCodes.BGR2RGB); 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 - 0.485f) / 0.229f; input_tensor[0, 1, y, x] = (resize_image.At<Vec3b>(y, x)[1] / 255f - 0.456f) / 0.224f; input_tensor[0, 2, y, x] = (resize_image.At<Vec3b>(y, x)[2] / 255f - 0.406f) / 0.225f; } } //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_image", 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(); //读取第一个节点输出并转为Tensor数据 result_tensors = results_onnxvalue[0].AsTensor<float>(); result_array = result_tensors.ToArray(); //黑白色反转 //for (int i = 0; i < result_array.Length; i++) //{ // result_array[i] = 1 - result_array[i]; //} float maxVal = result_array.Max(); float minVal = result_array.Min(); for (int i = 0; i < result_array.Length; i++) { result_array[i] = (result_array[i] - minVal) / (maxVal - minVal) * 255; } result_image = new Mat(modelSize, modelSize, MatType.CV_32F, result_array); Cv2.CvtColor(result_image, result_image, ColorConversionCodes.RGB2BGR); //还原图像大小 if (width != modelSize || height != modelSize) { Rect rect = new Rect(0, 0, width, height); result_image = result_image.Clone(rect); } result_image = result_image.Resize(new OpenCvSharp.Size(oldwidth, oldheight)); pictureBox2.Image = new Bitmap(result_image.ToMemoryStream()); textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms"; } private void Form1_Load(object sender, EventArgs e) { startupPath = Application.StartupPath; //model_path = startupPath + "\\model\\u2net.onnx"; //model_path = startupPath + "\\model\\u2netp.onnx"; model_path = startupPath + "\\model\\u2net_human_seg.onnx"; modelSize = 320; //创建输出会话，用于输出模型读取信息 options = new SessionOptions(); options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO; //设置为CPU上运行 options.AppendExecutionProvider_CPU(0); //创建推理模型类，读取本地模型文件 onnx_session = new InferenceSession(model_path, options); //创建输入容器 input_ontainer = new List<NamedOnnxValue>(); //输入Tensor input_tensor = new DenseTensor<float>(new[] { 1, 3, modelSize, modelSize }); } private void button3_Click(object sender, EventArgs e) { if (pictureBox2.Image == null) { return; } Bitmap output = new Bitmap(pictureBox2.Image); var sdf = new SaveFileDialog(); sdf.Title = "保存"; sdf.Filter = "Images (*.bmp)|*.bmp|Images (*.emf)|*.emf|Images (*.exif)|*.exif|Images (*.gif)|*.gif|Images (*.ico)|*.ico|Images (*.jpg)|*.jpg|Images (*.png)|*.png|Images (*.tiff)|*.tiff|Images (*.wmf)|*.wmf"; if (sdf.ShowDialog() == DialogResult.OK) { switch (sdf.FilterIndex) { case 1: { output.Save(sdf.FileName, ImageFormat.Bmp); break; } case 2: { output.Save(sdf.FileName, ImageFormat.Emf); break; } case 3: { output.Save(sdf.FileName, ImageFormat.Exif); break; } case 4: { output.Save(sdf.FileName, ImageFormat.Gif); break; } case 5: { output.Save(sdf.FileName, ImageFormat.Icon); break; } case 6: { output.Save(sdf.FileName, ImageFormat.Jpeg); break; } case 7: { output.Save(sdf.FileName, ImageFormat.Png); break; } case 8: { output.Save(sdf.FileName, ImageFormat.Tiff); break; } case 9: { output.Save(sdf.FileName, ImageFormat.Wmf); break; } } MessageBox.Show("保存成功，位置：" + sdf.FileName); } } } }