現(xiàn)在的手游基本都是重復(fù)操作,一個動作要等好久,結(jié)束之后繼續(xù)另一個動作.很麻煩,所以動起了自己寫一個游戲輔助的心思.
專注于為中小企業(yè)提供成都網(wǎng)站設(shè)計、做網(wǎng)站服務(wù),電腦端+手機端+微信端的三站合一,更高效的管理,為中小企業(yè)灌南免費做網(wǎng)站提供優(yōu)質(zhì)的服務(wù)。我們立足成都，凝聚了一批互聯(lián)網(wǎng)行業(yè)人才，有力地推動了成百上千家企業(yè)的穩(wěn)健成長，幫助中小企業(yè)通過網(wǎng)站建設(shè)實現(xiàn)規(guī)模擴充和轉(zhuǎn)變。
這個輔助本身沒什么難度,就是通過不斷的截圖,然后從這個截圖中找出預(yù)先截好的能代表相應(yīng)動作的按鈕或者觸發(fā)條件的小圖.
找到之后獲取該子區(qū)域的左上角坐標,然后通過windows API調(diào)用鼠標或者鍵盤做操作就行了.
這里面最難的也就是找圖了,因為要精準找圖,而且最好能適應(yīng)不同的分辨率下找圖,所以在模板匹配的基礎(chǔ)上,就有了SIFT和SURF的特征點找圖方式.
在寫的過程中查找資料,大都是C++ 或者python的, 很少有原生的C#實現(xiàn), 所以我就直接拿來翻譯過來了(稍作改動).
SIFT算法
重慶分公司
重慶分公司
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();
var pointsDst = new List();
var goodMatches = new List();
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($"{keyPointsSrc[items[0].QueryIdx].Pt.X}, {keyPointsSrc[items[0].QueryIdx].Pt.Y}");
}
}
var outMat = new Mat();
// 算法RANSAC對匹配的結(jié)果做過濾
var pSrc = pointsSrc.ConvertAll(Point2fToPoint2d);
var pDst = pointsDst.ConvertAll(Point2fToPoint2d);
var outMask = new Mat();
// 如果原始的匹配結(jié)果為空, 則跳過過濾步驟
if (pSrc.Count > 0 && pDst.Count > 0)
Cv2.FindHomography(pSrc, pDst, HomographyMethods.Ransac, mask: outMask);
// 如果通過RANSAC處理后的匹配點大于10個,才應(yīng)用過濾. 否則使用原始的匹配點結(jié)果(匹配點過少的時候通過RANSAC處理后,可能會得到0個匹配點的結(jié)果).
if (outMask.Rows > 10)
{
byte[] maskBytes = new byte[outMask.Rows * outMask.Cols];
outMask.GetArray(0, 0, maskBytes);
Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, matchesMask: maskBytes, flags: DrawMatchesFlags.NotDrawSinglePoints);
}
else
Cv2.DrawMatches(matSrc, keyPointsSrc, matTo, keyPointsTo, goodMatches, outMat, flags: DrawMatchesFlags.NotDrawSinglePoints);
return OpenCvSharp.Extensions.BitmapConverter.ToBitmap(outMat);
}
}
}
SURF算法
public static Bitmap MatchPicBySurf(Bitmap imgSrc, Bitmap imgSub, double threshold = 400)
{
using (Mat matSrc = imgSrc.ToMat())
using (Mat matTo = imgSub.ToMat())
using (Mat matSrcRet = new Mat())
using (Mat matToRet = new Mat())
{
KeyPoint[] keyPointsSrc, keyPointsTo;
using (var surf = OpenCvSharp.XFeatures2D.SURF.Create(threshold,4,3,true,true))
{
surf.DetectAndCompute(matSrc, null, out keyPointsSrc, matSrcRet);
surf.DetectAndCompute(matTo, null, out keyPointsTo, matToRet);
}
using (var flnMatcher = new OpenCvSharp.FlannBasedMatcher())
{
var matches = flnMatcher.Match(matSrcRet, matToRet);
//求最小最大距離
double minDistance = 1000;//反向逼近
double maxDistance = 0;
for (int i = 0; i < matSrcRet.Rows; i++)
{
double distance = matches[i].Distance;
if (distance > maxDistance)
{
maxDistance = distance;
}
if (distance < minDistance)
{
minDistance = distance;
}
}
Console.WriteLine($"max distance : {maxDistance}");
Console.WriteLine($"min distance : {minDistance}");
var pointsSrc = new List();
var pointsDst = new List();
//篩選較好的匹配點
var goodMatches = new List();
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處理后的匹配點大于10個,才應(yīng)用過濾. 否則使用原始的匹配點結(jié)果(匹配點過少的時候通過RANSAC處理后,可能會得到0個匹配點的結(jié)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);
}
}
}
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, Common.templateMatchModes);
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();
}
}
總結(jié)
以上所述是小編給大家介紹的C#中OpenCvSharp 通過特征點匹配圖片,希望對大家有所幫助，如果大家有任何疑問請給我留言，小編會及時回復(fù)大家的。在此也非常感謝大家對創(chuàng)新互聯(lián)網(wǎng)站的支持！
如果你覺得本文對你有幫助，歡迎轉(zhuǎn)載，煩請注明出處，謝謝！
