C# Onnx实现特征匹配DeDoDe检测

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;
using System.Numerics;
 
namespace Onnx_Demo
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }
 
        string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
        string image_path = "";
        string image_path2 = "";
 
        DateTime dt1 = DateTime.Now;
        DateTime dt2 = DateTime.Now;
 
        int inpWidth;
        int inpHeight;
 
        float[] mean =new float[] { 0.485f, 0.456f, 0.406f };
        float[] std = new float[] { 0.229f, 0.224f, 0.225f };
 
        Mat image;
        Mat image2;
 
        string model_path = "";
 
        SessionOptions options;
        InferenceSession onnx_session;
        Tensor<float> input_tensor;
        Tensor<float> mask_tensor;
        List<NamedOnnxValue> input_ontainer;
 
        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 = null;
            pictureBox2.Image = null;
            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_ontainer = 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/dedode_end2end_1024.onnx";
 
            inpHeight = 256;
            inpWidth = 256;
 
            onnx_session = new InferenceSession(model_path, options);
 
            // 创建输入容器
            input_ontainer = new List<NamedOnnxValue>();
 
            image_path = "test_img/im_A.jpg";
            pictureBox1.Image = new Bitmap(image_path);
 
            image_path2 = "test_img/im_B.jpg";
            pictureBox3.Image = new Bitmap(image_path2);
 
        }
 
        private unsafe void button2_Click(object sender, EventArgs e)
        {
            if (image_path == "")
            {
                return;
            }
            textBox1.Text = "检测中，请稍等……";
            pictureBox2.Image = null;
            System.Windows.Forms.Application.DoEvents();
 
            image = new Mat(image_path);
            image2 = new Mat(image_path2);
 
            float[] input_tensor_data = new float[2 * 3 * inpWidth * inpHeight];
 
            //preprocess
            Mat dstimg = new Mat();
            Cv2.CvtColor(image, dstimg, ColorConversionCodes.BGR2RGB);
            Cv2.Resize(dstimg, dstimg, new OpenCvSharp.Size(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*)(dstimg.Ptr(i).ToPointer()))[j * 3 + c];
                        input_tensor_data[c * inpWidth * inpHeight + i * inpWidth + j] = (float)((pix / 255.0 - mean[c]) / std[c]);
                    }
                }
            }
 
            Cv2.CvtColor(image2, dstimg, ColorConversionCodes.BGR2RGB);
            Cv2.Resize(dstimg, dstimg, new OpenCvSharp.Size(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*)(dstimg.Ptr(i).ToPointer()))[j * 3 + c];
                        input_tensor_data[(3+c )* inpWidth * inpHeight + i * inpWidth + j] = (float)((pix / 255.0 - mean[c]) / std[c]);
                    }
                }
            }
 
            input_tensor = new DenseTensor<float>(input_tensor_data, new[] { 2, 3, inpHeight, inpWidth });
 
            //将 input_tensor 放入一个输入参数的容器，并指定名称
            input_ontainer.Add(NamedOnnxValue.CreateFromTensor("images", input_tensor));
 
            dt1 = DateTime.Now;
            //运行 Inference 并获取结果
            result_infer = onnx_session.Run(input_ontainer);
            dt2 = DateTime.Now;
 
            //Postprocessing
            //将输出结果转为DisposableNamedOnnxValue数组
            results_onnxvalue = result_infer.ToArray();
 
            float[] matches_A = results_onnxvalue[0].AsTensor<float>().ToArray();
            float[] matches_B = results_onnxvalue[1].AsTensor<float>().ToArray();
            int num_points = results_onnxvalue[0].AsTensor<float>().Dimensions[0];
 
            List<KeyPoint> points_A = new List<KeyPoint>();
            List<KeyPoint> points_B = new List<KeyPoint>();
 
            KeyPoint temp;
            for (int i = 0; i < num_points; i++)
            {
                temp = new KeyPoint();
                temp.Pt.X = (float)((matches_A[i * 2] + 1) * 0.5 * image.Cols);
                temp.Pt.Y = (float)((matches_A[i * 2 + 1] + 1) * 0.5 * image.Rows);
                temp.Size = 1f;
                points_A.Add(temp);
            }
 
            num_points = results_onnxvalue[1].AsTensor<float>().Dimensions[0];
            for (int i = 0; i < num_points; i++)
            {
                temp = new KeyPoint();
                temp.Pt.X = (float)((matches_B[i * 2] + 1) * 0.5 * image2.Cols);
                temp.Pt.Y = (float)((matches_B[i * 2 + 1] + 1) * 0.5 * image2.Rows);
                temp.Size = 1f;
                points_B.Add(temp);
            }
 
            //匹配结果放在matches里面
            num_points = points_A.Count();
            List<DMatch> matches=new List<DMatch>();
            for (int i = 0; i < num_points; i++)
            {
                matches.Add(new DMatch(i, i, 0f));
            }
 
            //按照匹配关系将图画出来，背景图为match_img
            Mat match_img = new Mat();
            Cv2.DrawMatches(image, points_A, image2, points_B, matches, match_img);
 
            pictureBox2.Image = new System.Drawing.Bitmap(match_img.ToMemoryStream());
            textBox1.Text = "推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms";
 
        }
 
        private void pictureBox2_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox2.Image);
        }
 
        private void button3_Click(object sender, EventArgs e)
        {
            OpenFileDialog ofd = new OpenFileDialog();
            ofd.Filter = fileFilter;
            if (ofd.ShowDialog() != DialogResult.OK) return;
 
            pictureBox3.Image = null;
            pictureBox2.Image = null;
            textBox1.Text = "";
 
            image_path2 = ofd.FileName;
            pictureBox3.Image = new System.Drawing.Bitmap(image_path2);
            image2 = new Mat(image_path2);
        }
 
        private void pictureBox3_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox3.Image);
        }
 
        private void pictureBox1_DoubleClick(object sender, EventArgs e)
        {
            Common.ShowNormalImg(pictureBox1.Image);
        }
    }
}