C# Onnx 用于边缘检测的轻量级密集卷积神经网络LDC

效果

项目

代码

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Windows.Forms;namespace Onnx_Demo
{public partial class frmMain : Form{public frmMain(){InitializeComponent();}string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";string image_path = "";string startupPath;string model_path;DateTime dt1 = DateTime.Now;DateTime dt2 = DateTime.Now;int inpHeight = 360;int inpWidth = 640;int outHeight = 360;int outWidth = 640;Mat image;Mat result_image;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;StringBuilder sb = new StringBuilder();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 Bitmap(image_path);image = new Mat(image_path);}private void Form1_Load(object sender, EventArgs e){startupPath = Application.StartupPath + "\\model\\";model_path = startupPath + "LDC_640x360.onnx";// 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onnx_session = new InferenceSession(model_path, options);// 输入Tensorinput_tensor = new DenseTensor<float>(new[] { 1, 3, inpHeight, inpWidth });// 创建输入容器input_ontainer = new List<NamedOnnxValue>();}private void button2_Click(object sender, EventArgs e){if (image_path == ""){return;}textBox1.Text = "检测中，请稍等……";pictureBox2.Image = null;Application.DoEvents();//图片image = new Mat(image_path);Mat resize_image = new Mat();Cv2.Resize(image, resize_image, new OpenCvSharp.Size(inpWidth, inpHeight));// 输入Tensorfor (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];input_tensor[0, 1, y, x] = resize_image.At<Vec3b>(y, x)[1];input_tensor[0, 2, y, x] = resize_image.At<Vec3b>(y, x)[2];}}//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;Mat average_image = Mat.Zeros(image.Rows, image.Cols, MatType.CV_32FC1);Mat fuse_image = new Mat(image.Rows, image.Cols, MatType.CV_8UC1);results_onnxvalue = result_infer.ToArray();for (int i = 0; i < results_onnxvalue.Length; i++){result_tensors = results_onnxvalue[i].AsTensor<float>();result_array = result_tensors.ToArray();Mat result = new Mat(outHeight, outWidth, MatType.CV_32FC1, result_array);Mat TmpExp = new Mat();Cv2.Exp(-result, TmpExp);Mat mask = 1.0 / (1.0 + TmpExp);double min_value, max_value;Cv2.MinMaxLoc(mask, out min_value, out max_value);mask = (mask - min_value) * 255.0 / (max_value - min_value + 1e-12);mask.ConvertTo(mask, MatType.CV_8UC1);Cv2.BitwiseNot(mask, mask);Cv2.Resize(mask, mask, new OpenCvSharp.Size(image.Cols, image.Rows));Cv2.Accumulate(mask,average_image, mask);  //将所有图像叠加fuse_image = mask;}average_image = average_image / (float)results_onnxvalue.Length; //求出平均图像average_image.ConvertTo(average_image, MatType.CV_8UC1);result_image = average_image.Clone();if (!result_image.Empty()){//Cv2.ImShow("LDC-average_image", average_image);//Cv2.ImShow("LDC-fuse_image", fuse_image);pictureBox2.Image = new Bitmap(result_image.ToMemoryStream());sb.Clear();sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");textBox1.Text = sb.ToString();}else{textBox1.Text = "无信息";}}private void pictureBox2_DoubleClick(object sender, EventArgs e){Common.ShowNormalImg(pictureBox2.Image);}private void pictureBox1_DoubleClick(object sender, EventArgs e){Common.ShowNormalImg(pictureBox1.Image);}}
}