mirror of
https://github.com/babalae/better-genshin-impact.git
synced 2026-03-15 07:43:20 +08:00
329 lines
14 KiB
C#
329 lines
14 KiB
C#
using System.Diagnostics;
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using System.IO;
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using System.Runtime.InteropServices;
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using System.Runtime.Serialization;
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using System.Runtime.Serialization.Formatters.Binary;
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using System.Xml;
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using OpenCvSharp;
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namespace BetterGenshinImpact.Test.Simple.AllMap;
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public class KeyPointMatchTest
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{
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public static void Test()
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{
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// var trainMat = new Mat(@"E:\HuiTask\更好的原神\地图匹配\比较\叠图\无法匹配2.png", ImreadModes.Color);
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var trainMat = new Mat(@"E:\HuiTask\更好的原神\地图匹配\比较\小地图\Clip_20240323_152015.png", ImreadModes.Grayscale);
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// tar = tar.Resize(new Size(tar.Width * 2, tar.Height * 2), 0, 0, InterpolationFlags.Nearest);
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// var queryMat = new Mat(@"E:\HuiTask\更好的原神\地图匹配\combined_image_lim[quick].png", ImreadModes.Color);
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var queryMat = new Mat(@"E:\HuiTask\更好的原神\地图匹配\combined_image_2048_lim[quick].png", ImreadModes.Grayscale);
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var res = MatchPicBySurf(trainMat, queryMat);
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Cv2.ImWrite(@"E:\HuiTask\更好的原神\地图匹配\s1.png", res);
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}
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/*public static Mat MatchPicBySift(Mat matSrc, Mat matTo)
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{
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Stopwatch sw = new Stopwatch();
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sw.Start();
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Mat matSrcRet = new Mat();
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using Mat matToRet = new Mat();
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KeyPoint[] keyPointsSrc, keyPointsTo;
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using (var sift = SIFT.Create())
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{
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var kpPath = @"E:\HuiTask\更好的原神\地图匹配\sift.kp";
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var kpMatPath = @"E:\HuiTask\更好的原神\地图匹配\sift.mat";
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if (File.Exists(kpPath) && File.Exists(kpMatPath))
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{
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keyPointsSrc = (KeyPoint[])DeserializeObject(File.ReadAllBytes(kpPath));
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GCHandle pinnedArray = GCHandle.Alloc(DeserializeObject(File.ReadAllBytes(kpMatPath)), GCHandleType.Pinned);
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IntPtr pointer = pinnedArray.AddrOfPinnedObject();
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matSrcRet = new Mat(166767, 128, MatType.CV_32FC1, pointer);
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}
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else
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{
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sift.DetectAndCompute(matSrc, null, out keyPointsSrc, matSrcRet);
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byte[] arr = new byte[matSrcRet.Step(0) * matSrcRet.Rows]; // matSrcRet.Total() * matSrcRet.ElemSize()
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Marshal.Copy(matSrcRet.Data, arr, 0, arr.Length);
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File.WriteAllBytes(kpMatPath, SerializeObject(arr));
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File.WriteAllBytes(kpPath, SerializeObject(keyPointsSrc));
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}
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sw.Stop();
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Debug.WriteLine($"大地图kp耗时：{sw.ElapsedMilliseconds}ms.");
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sw.Restart();
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sift.DetectAndCompute(matTo, null, out keyPointsTo, matToRet);
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sw.Stop();
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Debug.WriteLine($"模板kp耗时：{sw.ElapsedMilliseconds}ms.");
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sw.Restart();
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}
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using (var bfMatcher = new OpenCvSharp.BFMatcher())
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{
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var matches = bfMatcher.KnnMatch(matSrcRet, matToRet, k: 2);
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var pointsSrc = new List<Point2f>();
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var pointsDst = new List<Point2f>();
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var goodMatches = new List<DMatch>();
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foreach (DMatch[] items in matches.Where(x => x.Length > 1))
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{
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if (items[0].Distance < 0.5 * items[1].Distance)
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{
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pointsSrc.Add(keyPointsSrc[items[0].QueryIdx].Pt);
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pointsDst.Add(keyPointsTo[items[0].TrainIdx].Pt);
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goodMatches.Add(items[0]);
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Debug.WriteLine($"{keyPointsSrc[items[0].QueryIdx].Pt.X}, {keyPointsSrc[items[0].QueryIdx].Pt.Y}");
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}
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}
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sw.Stop();
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Debug.WriteLine($"bfMatcher耗时：{sw.ElapsedMilliseconds}ms.");
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sw.Restart();
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var outMat = new Mat();
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// algorithm RANSAC Filter the matched results
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var pSrc = pointsSrc.ConvertAll(Point2fToPoint2d);
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var pDst = pointsDst.ConvertAll(Point2fToPoint2d);
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var outMask = new Mat();
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// If the original matching result is null, Skip the filtering step
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if (pSrc.Count > 0 && pDst.Count > 0)
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Cv2.FindHomography(pSrc, pDst, HomographyMethods.Ransac, mask: outMask);
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sw.Stop();
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Debug.WriteLine($"FindHomography耗时：{sw.ElapsedMilliseconds}ms.");
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sw.Restart();
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// If passed RANSAC After processing, the matching points are more than 10.,Only filters are used. Otherwise, use the original matching point result(When the matching point is too small, it passes through RANSAC After treatment,It is possible to get the result of 0 matching points.).
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if (outMask.Rows > 10)
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{
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byte[] maskBytes = new byte[outMask.Rows * outMask.Cols];
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outMask.GetArray(out maskBytes);
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Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, matchesMask: maskBytes, flags: DrawMatchesFlags.NotDrawSinglePoints);
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}
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else
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Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, flags: DrawMatchesFlags.NotDrawSinglePoints);
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sw.Stop();
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Debug.WriteLine($"绘图耗时：{sw.ElapsedMilliseconds}ms.");
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sw.Restart();
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return outMat;
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}
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}*/
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//This method may be missed, you may read a lot of blogs, but none of them wrote
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private static Point2d Point2fToPoint2d(Point2f input)
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{
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Point2d p2 = new Point2d(input.X, input.Y);
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return p2;
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}
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public static Mat MatchPicBySurf(Mat matSrc, Mat matTo, double threshold = 400)
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{
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Stopwatch sw = new Stopwatch();
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sw.Start();
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using Mat matSrcRet = new Mat();
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using Mat matToRet = new Mat();
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KeyPoint[] keyPointsSrc, keyPointsTo;
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using (var surf = OpenCvSharp.XFeatures2D.SURF.Create(threshold, 4, 3, false, true))
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{
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surf.DetectAndCompute(matSrc, null, out keyPointsSrc, matSrcRet);
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sw.Stop();
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Debug.WriteLine($"模板kp耗时：{sw.ElapsedMilliseconds}ms.");
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sw.Restart();
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surf.DetectAndCompute(matTo, null, out keyPointsTo, matToRet);
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sw.Stop();
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Debug.WriteLine($"大地图kp耗时：{sw.ElapsedMilliseconds}ms.");
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sw.Restart();
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}
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using (var flnMatcher = new FlannBasedMatcher())
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{
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var matches = flnMatcher.Match(matSrcRet, matToRet);
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//Finding the Minimum and Maximum Distance
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double minDistance = 1000; //Backward approximation
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double maxDistance = 0;
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for (int i = 0; i < matSrcRet.Rows; i++)
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{
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double distance = matches[i].Distance;
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if (distance > maxDistance)
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{
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maxDistance = distance;
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}
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if (distance < minDistance)
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{
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minDistance = distance;
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}
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}
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Debug.WriteLine($"max distance : {maxDistance}");
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Debug.WriteLine($"min distance : {minDistance}");
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var pointsSrc = new List<Point2f>();
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var pointsDst = new List<Point2f>();
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//Screening better matching points
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var goodMatches = new List<DMatch>();
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for (int i = 0; i < matSrcRet.Rows; i++)
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{
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double distance = matches[i].Distance;
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if (distance < Math.Max(minDistance * 2, 0.02))
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{
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pointsSrc.Add(keyPointsSrc[matches[i].QueryIdx].Pt);
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pointsDst.Add(keyPointsTo[matches[i].TrainIdx].Pt);
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//Compression of new ones with distances less than ranges DMatch
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goodMatches.Add(matches[i]);
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}
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}
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sw.Stop();
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Debug.WriteLine($"flnMatcher耗时：{sw.ElapsedMilliseconds}ms.");
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sw.Restart();
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var outMat = new Mat();
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// algorithm RANSAC Filter the matched results
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var pSrc = pointsSrc.ConvertAll(Point2fToPoint2d);
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var pDst = pointsDst.ConvertAll(Point2fToPoint2d);
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var outMask = new Mat();
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// If the original matching result is null, Skip the filtering step
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if (pSrc.Count > 0 && pDst.Count > 0)
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{
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var hMat = Cv2.FindHomography(pSrc, pDst, HomographyMethods.Ransac, mask: outMask);
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var objCorners = new Point2f[4];
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objCorners[0] = new Point2f(0, 0);
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objCorners[1] = new Point2f(0, matSrc.Rows);
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objCorners[2] = new Point2f(matSrc.Cols, matSrc.Rows);
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objCorners[3] = new Point2f(matSrc.Cols, 0);
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var sceneCorners = Cv2.PerspectiveTransform(objCorners, hMat);
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// for (int i = 0; i < 4; i++)
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// {
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// /* 作用和perspectiveTransform一样
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// double x = obj_corners[i].x;
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// double y = obj_corners[i].y;
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// double Z = 1. / (H.At<double>(2, 0) * x + H.At<double>(2, 1) * y + H.At<double>(2, 2));
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// double X = (H.At<double>(0, 0) * x + H.At<double>(0, 1) * y + H.At<double>(0, 2)) * Z;
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// double Y = (H.At<double>(1, 0) * x + H.At<double>(1, 1) * y + H.At<double>(1, 2)) * Z;
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// scene_corners[i] = new Point(cv.Round(X) + img_1.Cols, cv.Round(Y));*/
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// sceneCorners[i].X += matSrc.Cols;
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// }
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Cv2.Line(matTo, sceneCorners[0].ToPoint(), sceneCorners[1].ToPoint(), Scalar.Red, 2);
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Cv2.Line(matTo, sceneCorners[1].ToPoint(), sceneCorners[2].ToPoint(), Scalar.Red, 2);
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Cv2.Line(matTo, sceneCorners[2].ToPoint(), sceneCorners[3].ToPoint(), Scalar.Red, 2);
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Cv2.Line(matTo, sceneCorners[3].ToPoint(), sceneCorners[0].ToPoint(), Scalar.Red, 2);
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}
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sw.Stop();
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Debug.WriteLine($"FindHomography耗时：{sw.ElapsedMilliseconds}ms.");
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sw.Restart();
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// If passed RANSAC After processing, the matching points are more than 10.,Only filters are used. Otherwise, use the original matching point result(When the matching point is too small, it passes through RANSAC After treatment,It's possible to get the result of 0 matching points.).
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if (outMask.Rows > 10)
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{
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Debug.WriteLine($"使用了Ransac的结果");
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byte[] maskBytes = new byte[outMask.Rows * outMask.Cols];
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outMask.GetArray(out maskBytes);
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Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, matchesMask: maskBytes, flags: DrawMatchesFlags.NotDrawSinglePoints);
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}
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else
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Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, flags: DrawMatchesFlags.NotDrawSinglePoints);
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sw.Stop();
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Debug.WriteLine($"绘图耗时：{sw.ElapsedMilliseconds}ms.");
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return outMat;
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}
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}
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/// <summary>
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/// 序列化
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/// </summary>
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/// <param name="obj"></param>
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/// <returns></returns>
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private static byte[] Serialize(object obj)
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{
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using var memoryStream = new MemoryStream();
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DataContractSerializer ser = new DataContractSerializer(typeof(object));
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ser.WriteObject(memoryStream, obj);
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var data = memoryStream.ToArray();
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return data;
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}
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/// <summary>
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/// 反序列化
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/// </summary>
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/// <typeparam name="T"></typeparam>
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/// <param name="data"></param>
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/// <returns></returns>
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private static T Deserialize<T>(byte[] data)
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{
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using var memoryStream = new MemoryStream(data);
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XmlDictionaryReader reader = XmlDictionaryReader.CreateTextReader(memoryStream, new XmlDictionaryReaderQuotas());
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DataContractSerializer ser = new DataContractSerializer(typeof(T));
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var result = (T)ser.ReadObject(reader, true);
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return result;
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}
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static bool WriteRawImage(Mat image, string filename)
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{
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using (FileStream file = new FileStream(filename, FileMode.Create))
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{
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if (file == null || !file.CanWrite)
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return false;
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BinaryWriter writer = new BinaryWriter(file);
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writer.Write(image.Rows);
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writer.Write(image.Cols);
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int depth = image.Depth();
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int type = image.Type();
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int channels = image.Channels();
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writer.Write(depth);
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writer.Write(type);
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writer.Write(channels);
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int sizeInBytes = (int)image.Step() * image.Rows;
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writer.Write(sizeInBytes);
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byte[] arr = new byte[sizeInBytes];
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Marshal.Copy(image.Data, arr, 0, arr.Length);
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writer.Write(arr, 0, sizeInBytes);
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}
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return true;
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}
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static bool ReadRawImage(out Mat image, string filename)
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{
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int rows, cols, data, depth, type, channels;
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image = null;
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using (FileStream file = new FileStream(filename, FileMode.Open))
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{
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if (file == null || !file.CanRead)
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return false;
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try
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{
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BinaryReader reader = new BinaryReader(file);
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rows = reader.ReadInt32();
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cols = reader.ReadInt32();
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depth = reader.ReadInt32();
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type = reader.ReadInt32();
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channels = reader.ReadInt32();
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data = reader.ReadInt32();
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image = new Mat(rows, cols, (MatType)type);
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// reader.Read(image.Data, 0, data);
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}
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catch (Exception)
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{
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return false;
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}
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}
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return true;
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}
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}
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