C# OpenCvSharp實(shí)現(xiàn)通過特征點(diǎn)匹配圖片

更新時間：2023年11月09日 10:37:01 作者：天天代碼碼天天

這篇文章主要為大家詳細(xì)介紹了C#如何結(jié)合OpenCVSharp4實(shí)現(xiàn)通過特征點(diǎn)匹配圖片,文中的示例代碼簡潔易懂,具有一定的學(xué)習(xí)價值,需要的小伙伴可以參考下

SIFT匹配

SURF匹配

項(xiàng)目

代碼

using OpenCvSharp;
using OpenCvSharp.Extensions;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using System.Text.RegularExpressions;
using System.Windows.Forms;
using static System.Net.Mime.MediaTypeNames;
 
namespace OpenCvSharp_Demo
{
    public partial class frmMain : Form
    {
        public frmMain()
        {
            InitializeComponent();
        }
 
        private void Form1_Load(object sender, EventArgs e)
        {
 
        }
 
        private void button2_Click(object sender, EventArgs e)
        {
 
            Mat matSrc = new Mat("1.jpg");
            Mat matTo = new Mat("2.jpg");
 
            var outMat = MatchPicBySift(matSrc, matTo);
 
            pictureBox2.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(outMat);
 
        }
 
        private void button1_Click(object sender, EventArgs e)
        {
            Mat matSrc = new Mat("1.jpg");
            Mat matTo = new Mat("2.jpg");
 
            var outMat = MatchPicBySurf(matSrc, matTo, 10);
 
            pictureBox2.Image = OpenCvSharp.Extensions.BitmapConverter.ToBitmap(outMat);
        }
 
        public Point2d Point2fToPoint2d(Point2f point) => new Point2d((double)point.X, (double)point.Y);
 
        public Mat MatchPicBySift(Mat matSrc, Mat matTo)
        {
            using (Mat matSrcRet = new Mat())
            using (Mat matToRet = new Mat())
            {
                KeyPoint[] keyPointsSrc, keyPointsTo;
                using (var sift = OpenCvSharp.Features2D.SIFT.Create())
                {
                    sift.DetectAndCompute(matSrc, null, out keyPointsSrc, matSrcRet);
                    sift.DetectAndCompute(matTo, null, out keyPointsTo, matToRet);
                }
                using (var bfMatcher = new OpenCvSharp.BFMatcher())
                {
                    var matches = bfMatcher.KnnMatch(matSrcRet, matToRet, k: 2);
 
                    var pointsSrc = new List<Point2f>();
                    var pointsDst = new List<Point2f>();
                    var goodMatches = new List<DMatch>();
                    foreach (DMatch[] items in matches.Where(x => x.Length > 1))
                    {
                        if (items[0].Distance < 0.5 * items[1].Distance)
                        {
                            pointsSrc.Add(keyPointsSrc[items[0].QueryIdx].Pt);
                            pointsDst.Add(keyPointsTo[items[0].TrainIdx].Pt);
                            goodMatches.Add(items[0]);
                            Console.WriteLine($"{keyPointsSrc[items[0].QueryIdx].Pt.X}, {keyPointsSrc[items[0].QueryIdx].Pt.Y}");
                        }
                    }
 
                    var outMat = new Mat();
 
                    // 算法RANSAC對匹配的結(jié)果做過濾
                    var pSrc = pointsSrc.ConvertAll(Point2fToPoint2d);
                    var pDst = pointsDst.ConvertAll(Point2fToPoint2d);
                    var outMask = new Mat();
                    // 如果原始的匹配結(jié)果為空, 則跳過過濾步驟
                    if (pSrc.Count > 0 && pDst.Count > 0)
                        Cv2.FindHomography(pSrc, pDst, HomographyMethods.Ransac, mask: outMask);
                    // 如果通過RANSAC處理后的匹配點(diǎn)大于10個,才應(yīng)用過濾. 否則使用原始的匹配點(diǎn)結(jié)果(匹配點(diǎn)過少的時候通過RANSAC處理后,可能會得到0個匹配點(diǎn)的結(jié)果).
                    if (outMask.Rows > 10)
                    {
                        byte[] maskBytes = new byte[outMask.Rows * outMask.Cols];
                        outMask.GetArray(out maskBytes);
                        Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, matchesMask: maskBytes, flags: DrawMatchesFlags.NotDrawSinglePoints);
                    }
                    else
                        Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, flags: DrawMatchesFlags.NotDrawSinglePoints);
                    return outMat;
                }
            }
        }
 
        public Mat MatchPicBySurf(Mat matSrc, Mat matTo, double threshold = 400)
        {
            using (Mat matSrcRet = new Mat())
            using (Mat matToRet = new Mat())
            {
                KeyPoint[] keyPointsSrc, keyPointsTo;
                using (var surf = OpenCvSharp.XFeatures2D.SURF.Create(threshold, 4, 3, true, true))
                {
                    surf.DetectAndCompute(matSrc, null, out keyPointsSrc, matSrcRet);
                    surf.DetectAndCompute(matTo, null, out keyPointsTo, matToRet);
                }
 
                using (var flnMatcher = new OpenCvSharp.FlannBasedMatcher())
                {
                    var matches = flnMatcher.Match(matSrcRet, matToRet);
                    //求最小最大距離
                    double minDistance = 1000;//反向逼近
                    double maxDistance = 0;
                    for (int i = 0; i < matSrcRet.Rows; i++)
                    {
                        double distance = matches[i].Distance;
                        if (distance > maxDistance)
                        {
                            maxDistance = distance;
                        }
                        if (distance < minDistance)
                        {
                            minDistance = distance;
                        }
                    }
                    Console.WriteLine($"max distance : {maxDistance}");
                    Console.WriteLine($"min distance : {minDistance}");
 
                    var pointsSrc = new List<Point2f>();
                    var pointsDst = new List<Point2f>();
                    //篩選較好的匹配點(diǎn)
                    var goodMatches = new List<DMatch>();
                    for (int i = 0; i < matSrcRet.Rows; i++)
                    {
                        double distance = matches[i].Distance;
                        if (distance < Math.Max(minDistance * 2, 0.02))
                        {
                            pointsSrc.Add(keyPointsSrc[matches[i].QueryIdx].Pt);
                            pointsDst.Add(keyPointsTo[matches[i].TrainIdx].Pt);
                            //距離小于范圍的壓入新的DMatch
                            goodMatches.Add(matches[i]);
                        }
                    }
 
                    var outMat = new Mat();
 
                    // 算法RANSAC對匹配的結(jié)果做過濾
                    var pSrc = pointsSrc.ConvertAll(Point2fToPoint2d);
                    var pDst = pointsDst.ConvertAll(Point2fToPoint2d);
                    var outMask = new Mat();
                    // 如果原始的匹配結(jié)果為空, 則跳過過濾步驟
                    if (pSrc.Count > 0 && pDst.Count > 0)
                        Cv2.FindHomography(pSrc, pDst, HomographyMethods.Ransac, mask: outMask);
                    // 如果通過RANSAC處理后的匹配點(diǎn)大于10個,才應(yīng)用過濾. 否則使用原始的匹配點(diǎn)結(jié)果(匹配點(diǎn)過少的時候通過RANSAC處理后,可能會得到0個匹配點(diǎn)的結(jié)果).
                    if (outMask.Rows > 10)
                    {
                        byte[] maskBytes = new byte[outMask.Rows * outMask.Cols];
                        outMask.GetArray(out maskBytes);
                        Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, matchesMask: maskBytes, flags: DrawMatchesFlags.NotDrawSinglePoints);
                    }
                    else
                        Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, flags: DrawMatchesFlags.NotDrawSinglePoints);
                    return outMat;
                }
            }
        }
 
    }
}

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