public override void ViewDidLoad()
{
base.ViewDidLoad();
ButtonText = "Match";
base.OnButtonClick +=
delegate
{
long processingTime;
Size frameSize = FrameSize;
using (Mat modelImage = CvInvoke.Imread("box.png", Emgu.CV.CvEnum.ImreadModes.Grayscale))
using (Mat observedImage = CvInvoke.Imread("box_in_scene.png", Emgu.CV.CvEnum.ImreadModes.Grayscale))
using (Emgu.CV.Features2D.KAZE kaze = new Emgu.CV.Features2D.KAZE())
using (Mat image = DrawMatches.Draw(modelImage, observedImage, kaze, out processingTime))
using (Mat resized = new Mat())
{
double dx = ((double)frameSize.Width) / image.Width;
double dy = ((double)frameSize.Height) / image.Height;
double min = Math.Min(dx, dy);
CvInvoke.Resize(image, resized, Size.Empty, min, min);
//image.Resize(frameSize.Width, frameSize.Height, Emgu.CV.CvEnum.Inter.Nearest, true)
MessageText = String.Format("Matching Time: {0} milliseconds.", processingTime);
SetImage(resized);
}
};
}
//public static Highlighter[] HighlightMatches(Rectangle[] Matches)
//{
// var result = new List<Highlighter>();
// foreach (var m in Matches)
// {
// result.Add(new Highlighter(m, System.Drawing.Color.Red));
// }
// return result.ToArray();
//}
//public static void HighlightMatches(Rectangle[] Matches, TimeSpan Duration)
//{
// foreach (var Match in Matches)
// {
// Task.Factory.StartNew(() =>
// {
// var h2 = new Highlighter(Match, System.Drawing.Color.Red);
// System.Threading.Thread.Sleep(Duration);
// h2.remove();
// });
// }
//}
//public static void HighlightMatch(Rectangle Match, bool Blocking, Color Color, TimeSpan Duration)
//{
// if (!Blocking)
// {
// Task.Factory.StartNew(() =>
// {
// var h2 = new Highlighter(Match, System.Drawing.Color.Red);
// System.Threading.Thread.Sleep(Duration);
// h2.remove();
// System.Windows.Forms.Application.DoEvents();
// });
// return;
// }
// var h = new Highlighter(Match, System.Drawing.Color.Red);
// System.Threading.Thread.Sleep(Duration);
// h.remove();
// System.Windows.Forms.Application.DoEvents();
//}
//https://www.meridium.se/sv/blogg/imagematching-using-opencv/
public static void FindMatch(Mat modelImage, Mat observedImage, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, VectorOfVectorOfDMatch matches, out Mat mask, out Mat homography, out long score)
{
int k = 2;
double uniquenessThreshold = 0.80;
homography = null;
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
using (UMat uModelImage = modelImage.GetUMat(AccessType.Read))
using (UMat uObservedImage = observedImage.GetUMat(AccessType.Read))
{
var featureDetector = new Emgu.CV.Features2D.KAZE();
Mat modelDescriptors = new Mat();
featureDetector.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
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 Emgu.CV.Flann.SearchParams())
using (var matcher = new Emgu.CV.Features2D.FlannBasedMatcher(ip, sp))
{
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, k, null);
mask = new Mat(matches.Size, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(255));
Emgu.CV.Features2D.Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);
// 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);
if (nonZeroCount >= 4)
{
nonZeroCount = Emgu.CV.Features2D.Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Emgu.CV.Features2D.Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints, observedKeyPoints, matches, mask, 2);
}
}
}
}
}
