public static void FindMatch(Mat modelImage, Mat observedImage, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, VectorOfVectorOfDMatch matches, out Mat mask, out Mat homography, out long score)
{
int k = 2;
double uniquenessThreshold = 0.80;
Stopwatch watch;
homography = null;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
using (UMat uModelImage = modelImage.GetUMat(AccessType.Read))
using (UMat uObservedImage = observedImage.GetUMat(AccessType.Read))
{
// KAZE featureDetector = new KAZE();
SURF featureDetector = new SURF(100);
// SIFT featureDetector = new SIFT();
Mat modelDescriptors = new Mat();
//进行检测和计算，把opencv中的两部分和到一起了，分开用也可以
featureDetector.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
watch = Stopwatch.StartNew();
Mat observedDescriptors = new Mat();
featureDetector.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
// KdTree for faster results / less accuracy
using (var ip = new Emgu.CV.Flann.KdTreeIndexParams())
using (var sp = new SearchParams())
// using (DescriptorMatcher matcher = new FlannBasedMatcher(ip, sp))//开始进行匹配
using (BFMatcher matcher = new BFMatcher(DistanceType.L2))
{
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, k, null);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);//去除重复的匹配
// Calculate score based on matches size
// ---------------------------------------------->
score = 0;
for (int i = 0; i < matches.Size; i++)
{
if (mask.GetData(i)[0] == 0)
{
continue;
}
foreach (var e in matches[i].ToArray())
{
++score;
}
}
// <----------------------------------------------
int nonZeroCount = CvInvoke.CountNonZero(mask);//用于寻找模板在图中的位置
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, matches, mask, 2);
}
}
}
watch.Stop();
}
matchTime = watch.ElapsedMilliseconds;
}
public FeatureResult GetFeature(Bitmap bmpSrc)
{
Mat matTest = CVUtil.BitmapToMat(bmpSrc);
matTest = ProcessImage(matTest);
/*Bitmap bmp = Utils.cropImage(bmpSrc, cropRect);
* Mat matTest = CVUtil.BitmapToMat(bmp);
* if (colorIndex != -1)
* {
* matTest = matTest.Split()[colorIndex];
* }*/
Mat observedDescriptors = new Mat();
VectorOfKeyPoint observedKeyPoints = new VectorOfKeyPoint();
featureDetector.DetectAndCompute(matTest, mask, observedKeyPoints, observedDescriptors, false);
int k = 2;
double uniquenessThreshold = 0.80;
//Mat homography = null;
// Bruteforce, slower but more accurate
// You can use KDTree for faster matching with slight loss in accuracy
using (Emgu.CV.Flann.KdTreeIndexParams ip = new Emgu.CV.Flann.KdTreeIndexParams())
using (Emgu.CV.Flann.SearchParams sp = new SearchParams())
using (DescriptorMatcher matcher = new FlannBasedMatcher(ip, sp))
{
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
foreach (SimpleFeatureData sd in this)
{
matcher.Add(sd.descriptors);
//break;
}
matcher.KnnMatch(observedDescriptors, matches, k, null);
lastMatches = matches;
lastObserved = matTest;
lastObservedKeyPoint = observedKeyPoints;
//Mat mat = new Mat();
//Features2DToolbox.DrawKeypoints(matTest, observedKeyPoints, mat, new Bgr(Color.Blue));
//FormOpenCV.lstMat.Add(mat);
//Console.WriteLine(CVUtil.ToString(observedDescriptors));
//Console.WriteLine(CVUtil.ToString(observedKeyPoints));
//Console.WriteLine(CVUtil.ToString(matches));
//Console.WriteLine(MatchesToString(matches));
Mat uniqueMask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
uniqueMask.SetTo(new MCvScalar(255));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, uniqueMask);
int nonZeroCount = CvInvoke.CountNonZero(uniqueMask);
if (nonZeroCount > 4)
{
//Console.WriteLine(CVUtil.ToString(uniqueMask));
String retLabel = GetLabelFromMatches(matches, uniqueMask);
SimpleFeatureData mfd = lastMatchFeatureData;
try
{
//int nonZeroCount2 = Features2DToolbox.VoteForSizeAndOrientation(mfd.keyPoints, observedKeyPoints, matches, uniqueMask, 1.5, 20);
//Console.WriteLine("nonZeroCount2=" + nonZeroCount2);
if (nonZeroCount > 4)
{
Mat homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(mfd.keyPoints, observedKeyPoints, matches, uniqueMask, 2);
/*Console.WriteLine(CVUtil.ToString(homography));
* Rectangle rect = CVUtil.GetRect(mfd.keyPoints);
* PointF[] src = {
* new PointF(rect.X,rect.Y),new PointF(rect.X,rect.Y + rect.Height-1),
* new PointF(rect.X + rect.Width-1,rect.Y + rect.Height-1),new PointF(rect.X + rect.Width-1,rect.Y)
* };
* PointF[] points = CvInvoke.PerspectiveTransform(src, homography);
* foreach (var p in points)
* {
* Console.WriteLine(p.ToString());
* }
* Point[] ap = Array.ConvertAll(points,
* new Converter<PointF, Point>(CVUtil.PointFToPoint));
* Mat testImage = matTest.Clone();
* CvInvoke.Polylines(testImage, ap, true, new MCvScalar(255, 0, 0));
*/
//CvInvoke.Rectangle(testImage, new Rectangle(0, 0, 100, 100), new MCvScalar(255, 255, 0));
//CvInvoke.Circle(testImage, new Point(100, 100), 50, new MCvScalar(255, 255, 0), -1);
//lstMat.Add(testImage);
FeatureResult ret = new FeatureResult();
ret.keyPoint = observedKeyPoints;
ret.label = retLabel;
ret.homography = homography;
ret.matchFeatureData = mfd;
return(ret);
}
}catch (Exception ex)
{
}
}
return(null);
}
}
