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Tensor
            input_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Tensor
            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];
                    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);
        }
    }
}

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