OpenCvSharp通过特征点匹配图⽚
现在的⼿游基本都是重复操作,⼀个动作要等好久，结束之后继续另⼀个动作.很⿇烦，所以动起了⾃⼰写⼀个游戏辅助的⼼思.
这个辅助本⾝没什么难度，就是通过不断的截图，然后从这个截图中出预先截好的能代表相应动作的按钮或者触发条件的⼩图.
到之后获取该⼦区域的左上⾓坐标，然后通过windows API调⽤⿏标或者键盘做操作就⾏了.
这⾥⾯最难的也就是图了，因为要精准图,⽽且最好能适应不同的分辨率下图，所以在模板匹配的基础上，就有了SIFT和SURF的特征点图⽅式.
在写的过程中查资料,⼤都是C++ 或者python的, 很少有原⽣的C#实现, 所以我就直接拿来翻译过来了(稍作改动).
SIFT算法
public static Bitmap MatchPicBySift(Bitmap imgSrc, Bitmap imgSub)
{
using (Mat matSrc = imgSrc.ToMat())
using (Mat matTo = imgSub.ToMat())
using (Mat matSrcRet = new Mat())
using (Mat matToRet = new Mat())
{
KeyPoint[] keyPointsSrc, keyPointsTo;
using (var sift = OpenCvSharp.XFeatures2D.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($"{keyPointsSrc[items[0].QueryIdx].Pt.X}, {keyPointsSrc[items[0].QueryIdx].Pt.Y}");
}
}
var outMat = new Mat();
// 算法RANSAC对匹配的结果做过滤
var pSrc = pointsSrc.ConvertAll(Point2fToPoint2d);
var pDst = pointsDst.ConvertAll(Point2fToPoint2d);
var outMask = new Mat();
// 如果原始的匹配结果为空, 则跳过过滤步骤
if (pSrc.Count > 0 && pDst.Count > 0)
Cv2.FindHomography(pSrc, pDst, HomographyMethods.Ransac, mask: outMask);
// 如果通过RANSAC处理后的匹配点⼤于10个，才应⽤过滤. 否则使⽤原始的匹配点结果(匹配点过少的时候通过RANSAC处理后，可能会得到0个匹配点的结果).
if (outMask.Rows > 10)
{
byte[] maskBytes = new byte[outMask.Rows * outMask.Cols];
outMask.GetArray(0, 0, maskBytes);
Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, matchesMask: maskBytes, flags: DrawMatchesFlags.NotDrawSinglePoints);
}
else
Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, flags: DrawMatchesFlags.NotDrawSinglePoints);
return OpenCvSharp.Extensions.BitmapConverter.ToBitmap(outMat);
}
}
}
SURF算法
public static Bitmap MatchPicBySurf(Bitmap imgSrc, Bitmap imgSub, double threshold = 400)
{
using (Mat matSrc = imgSrc.ToMat())
using (Mat matTo = imgSub.ToMat())
using (Mat matSrcRet = new Mat())
using (Mat matToRet = new Mat())
{
KeyPoint[] keyPointsSrc, keyPointsTo;
using (var surf = OpenCvSharp.XFeatures2D.SURF.Create(threshold,4,3,true,true))
{
surf.DetectAndCompute(matSrc, null, out keyPointsSrc, matSrcRet);
surf.DetectAndCompute(matTo, null, out keyPointsTo, matToRet);
}
using (var flnMatcher = new OpenCvSharp.FlannBasedMatcher())
{
var matches = flnMatcher.Match(matSrcRet, matToRet);
/
/求最⼩最⼤距离
double minDistance = 1000;//反向逼近
double maxDistance = 0;
for (int i = 0; i < matSrcRet.Rows; i++)
{
double distance = matches[i].Distance;
if (distance > maxDistance)
{
maxDistance = distance;
}
if (distance < minDistance)
{
minDistance = distance;
}
}
Console.WriteLine($"max distance : {maxDistance}");
Console.WriteLine($"min distance : {minDistance}");
var pointsSrc = new List<Point2f>();
var pointsDst = new List<Point2f>();
//筛选较好的匹配点
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);
//距离⼩于范围的压⼊新的DMatch
goodMatches.Add(matches[i]);
}
}
var outMat = new Mat();
/
/ 算法RANSAC对匹配的结果做过滤
var pSrc = pointsSrc.ConvertAll(Point2fToPoint2d);
var pDst = pointsDst.ConvertAll(Point2fToPoint2d);
var outMask = new Mat();
// 如果原始的匹配结果为空, 则跳过过滤步骤
if (pSrc.Count > 0 && pDst.Count > 0)
Cv2.FindHomography(pSrc, pDst, HomographyMethods.Ransac, mask: outMask);
// 如果通过RANSAC处理后的匹配点⼤于10个，才应⽤过滤. 否则使⽤原始的匹配点结果(匹配点过少的时候通过RANSAC处理后，可能会得到0个匹配点的结果).
if (outMask.Rows > 10)
{
byte[] maskBytes = new byte[outMask.Rows * outMask.Cols];
outMask.GetArray(0, 0, maskBytes);
Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, matchesMask: maskBytes, flags: DrawMatchesFlags.NotDrawSinglePoints);
}
writeline特点else
Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, flags: DrawMatchesFlags.NotDrawSinglePoints);
return OpenCvSharp.Extensions.BitmapConverter.ToBitmap(outMat);
}
}
}
模板匹配
public static System.Drawing.Point FindPicFromImage(Bitmap imgSrc, Bitmap imgSub, double threshold = 0.9)
{
OpenCvSharp.Mat srcMat = null;
OpenCvSharp.Mat dstMat = null;
OpenCvSharp.OutputArray outArray = null;
try
{
srcMat = imgSrc.ToMat();
dstMat = imgSub.ToMat();
outArray = OpenCvSharp.OutputArray.Create(srcMat);
OpenCvSharp.Cv2.MatchTemplate(srcMat, dstMat, outArray, plateMatchModes);
double minValue, maxValue;
OpenCvSharp.Point location, point;
OpenCvSharp.Cv2.MinMaxLoc(OpenCvSharp.InputArray.Create(outArray.GetMat()), out minValue, out maxValue, out location, out point); Console.WriteLine(maxValue);
if (maxValue >= threshold)
return new System.Drawing.Point(point.X, point.Y);
return System.Drawing.Point.Empty;
}
catch(Exception ex)
{
return System.Drawing.Point.Empty;
}
finally
{
if (srcMat != null)
srcMat.Dispose();
if (dstMat != null)
dstMat.Dispose();
if (outArray != null)
outArray.Dispose();
}
}
版权声明：本站内容均来自互联网，仅供演示用，请勿用于商业和其他非法用途。如果侵犯了您的权益请与我们联系QQ:729038198，我们将在24小时内删除。
发表评论