public static int FindMatch2(Mat modelImage, TemplateContainer.ImageData template, out long matchTime, out VectorOfKeyPoint modelKeyPoints, out VectorOfKeyPoint observedKeyPoints, VectorOfVectorOfDMatch matches, out Mat mask, out Mat homography)
{
Stopwatch watch;
homography = null;
BriefDescriptorExtractor descriptor = new BriefDescriptorExtractor();
modelKeyPoints = new VectorOfKeyPoint();
observedKeyPoints = new VectorOfKeyPoint();
using (UMat uModelImage = modelImage.GetUMat(AccessType.Read))
//using (UMat uObservedImage = template.image.Mat.GetUMat(AccessType.Read))
{
//extract features from the object image
Mat modelDescriptors = new Mat();
featureDetector.DetectAndCompute(uModelImage, null, modelKeyPoints, modelDescriptors, false);
watch = Stopwatch.StartNew();
// extract features from the observed image
//featureDetector.DetectAndCompute(uObservedImage, null, observedKeyPoints, observedDescriptors, false);
observedKeyPoints = template.keyPointsSurf;
observedDescriptors = template.descriptorSurf;
// Bruteforce, slower but more accurate
// You can use KDTree for faster matching with slight loss in accuracy
using (Emgu.CV.Flann.LinearIndexParams ip = new Emgu.CV.Flann.LinearIndexParams())
using (Emgu.CV.Flann.SearchParams sp = new SearchParams())
using (DescriptorMatcher matcher = new 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));
Features2DToolbox.VoteForUniqueness(matches, uniquenessThreshold, mask);
nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= 10)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints,
matches, mask, 1.8, 18);
if (nonZeroCount >= 12)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeyPoints,
observedKeyPoints, matches, mask, 2);
}
}
}
watch.Stop();
}
matchTime = watch.ElapsedMilliseconds;
return(nonZeroCount);
}
/// <summary>
/// Draw the model image and observed image, the matched features and homography projection.
/// </summary>
/// <param name="modelImage">The model image</param>
/// <param name="observedImage">The observed image</param>
/// <param name="matchTime">The output total time for computing the homography matrix.</param>
/// <returns>The model image and observed image, the matched features and homography projection.</returns>
// public static Mat Draw(Mat modelImage, Mat observedImage, out long matchTime)
// {
// using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
// {
// Mat mask;
// FindMatch(modelImage, observedImage, out matchTime, out modelKeyPoints, out observedKeyPoints, matches,
// out mask, out homography);
// //Draw the matched keypoint
// Mat result = new Mat();
// Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
// matches, result, new MCvScalar(255, 255, 255), new MCvScalar(255, 255, 255), mask);
// #region draw the projected region on the image
// if (homography != null)
// {
// //draw a rectangle along the projected model
// Rectangle rect = new Rectangle(Point.Empty, modelImage.Size);
// PointF[] pts = new PointF[]
// {
// new PointF(rect.Left, rect.Bottom),
// new PointF(rect.Right, rect.Bottom),
// new PointF(rect.Right, rect.Top),
// new PointF(rect.Left, rect.Top)
// };
// pts = CvInvoke.PerspectiveTransform(pts, homography);
//#if NETFX_CORE
// Point[] points = Extensions.ConvertAll<PointF, Point>(pts, Point.Round);
//#else
// Point[] points = Array.ConvertAll<PointF, Point>(pts, Point.Round);
//#endif
// using (VectorOfPoint vp = new VectorOfPoint(points))
// {
// CvInvoke.Polylines(result, vp, true, new MCvScalar(255, 0, 0, 255), 5);
// }
// }
// #endregion
// return result;
// }
// }
public static int MatchResult2(Mat modelImage, TemplateContainer.ImageData observedImage, out long matchTime)
{
//Mat homography;
//VectorOfKeyPoint modelKeyPoints;
//VectorOfKeyPoint observedKeyPoints;
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
{
Mat mask;
int nonZeroCount = FindMatch2(modelImage, observedImage, out matchTime, out modelKeyPoints, out observedKeyPoints, matches,
out mask, out homography);
bool result;
if (homography != null)
{
return(nonZeroCount);
}
else
{
return(0);
}
#region draw the projected region on the image
//if (homography != null)
//{
// //draw a rectangle along the projected model
// Rectangle rect = new Rectangle(Point.Empty, modelImage.Size);
// PointF[] pts = new PointF[]
// {
// new PointF(rect.Left, rect.Bottom),
// new PointF(rect.Right, rect.Bottom),
// new PointF(rect.Right, rect.Top),
// new PointF(rect.Left, rect.Top)
// };
// pts = CvInvoke.PerspectiveTransform(pts, homography);
#if NETFX_CORE
Point[] points = Extensions.ConvertAll <PointF, Point>(pts, Point.Round);
#else
//Point[] points = Array.ConvertAll<PointF, Point>(pts, Point.Round);
#endif
// using (VectorOfPoint vp = new VectorOfPoint(points))
// {
// CvInvoke.Polylines(result, vp, true, new MCvScalar(255, 0, 0, 255), 5);
// }
//}
#endregion
return(0);
}
}
