static void Run()
{
SURFDetector surfParam = new SURFDetector(350, false);
Image<Gray, Byte> modelImage = new Image<Gray, byte>("box.png");
//extract features from the object image
ImageFeature[] modelFeatures = surfParam.DetectFeatures(modelImage, null);
//Create a Feature Tracker
Features2DTracker tracker = new Features2DTracker(modelFeatures);
Image<Gray, Byte> observedImage = new Image<Gray, byte>("box_in_scene.png");
Stopwatch watch = Stopwatch.StartNew();
// extract features from the observed image
ImageFeature[] imageFeatures = surfParam.DetectFeatures(observedImage, null);
Features2DTracker.MatchedImageFeature[] matchedFeatures = tracker.MatchFeature(imageFeatures, 2, 20);
matchedFeatures = Features2DTracker.VoteForUniqueness(matchedFeatures, 0.8);
matchedFeatures = Features2DTracker.VoteForSizeAndOrientation(matchedFeatures, 1.5, 20);
HomographyMatrix homography = Features2DTracker.GetHomographyMatrixFromMatchedFeatures(matchedFeatures);
watch.Stop();
//Merge the object image and the observed image into one image for display
Image<Gray, Byte> res = modelImage.ConcateVertical(observedImage);
#region draw lines between the matched features
foreach (Features2DTracker.MatchedImageFeature matchedFeature in matchedFeatures)
{
PointF p = matchedFeature.ObservedFeature.KeyPoint.Point;
p.Y += modelImage.Height;
res.Draw(new LineSegment2DF(matchedFeature.SimilarFeatures[0].Feature.KeyPoint.Point, p), new Gray(0), 1);
}
#endregion
#region draw the project region on the image
if (homography != null)
{ //draw a rectangle along the projected model
Rectangle rect = modelImage.ROI;
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)};
homography.ProjectPoints(pts);
for (int i = 0; i < pts.Length; i++)
pts[i].Y += modelImage.Height;
res.DrawPolyline(Array.ConvertAll<PointF, Point>(pts, Point.Round), true, new Gray(255.0), 5);
}
#endregion
ImageViewer.Show(res, String.Format("Matched in {0} milliseconds", watch.ElapsedMilliseconds));
}
public SignDetector(Image<Bgr, Byte> stopSignModel)
{
_detector2 = new SURFDetector(500, false);
using (Image<Gray, Byte> redMask = GetColorPixelMask(stopSignModel))
{
try
{
_tracker2 = new Features2DTracker<float>(_detector2.DetectFeatures(redMask, null));
}
catch { }
}
_octagonStorage2 = new MemStorage();
_octagon2 = new Contour<Point>(_octagonStorage2);
_octagon2.PushMulti(new Point[] {
//hexagon
new Point(1, 0),
new Point(2, 0),
new Point(3, 1),
new Point(2, 2),
new Point(1, 2),
new Point(0, 1)},
//octagon
//new Point(1, 0),
//new Point(2, 0),
//new Point(3, 1),
//new Point(3, 2),
//new Point(2, 3),
//new Point(1, 3),
//new Point(0, 2),
//new Point(0, 1)},
Emgu.CV.CvEnum.BACK_OR_FRONT.FRONT);
}
private void CreateSurfaceTracker()
{
surfaceParameters = new SURFDetector(500, false);
using (Image<Bgr, Byte> stopSignModel = new Image<Bgr, Byte>(Properties.Resources.SignModel))
using (Image<Gray, Byte> redMask = GetRedPixelMask(stopSignModel))
{
featureTracker = new Features2DTracker(surfaceParameters.DetectFeatures(redMask, null));
}
}
public ImageDetector(Image<Gray, Byte> imgModel)
{
_detector = new SURFDetector(500, false);
ImageFeature<float>[] features = _detector.DetectFeatures(imgModel, null);
if (features.Length == 0)
throw new Exception("No image feature has been found in the image model");
_tracker = new Features2DTracker<float>(features);
_octagonStorage = new MemStorage();
_octagon = new Contour<Point>(_octagonStorage);
_octagon.PushMulti(new Point[] {
new Point(1, 0),
new Point(2, 0),
new Point(3, 1),
new Point(3, 2),
new Point(2, 3),
new Point(1, 3),
new Point(0, 2),
new Point(0, 1)},
Emgu.CV.CvEnum.BACK_OR_FRONT.FRONT);
}
public StopSignDetector(Image<Bgr, Byte> stopSignModel)
{
_detector = new SURFDetector(500, false);
using (Image<Gray, Byte> redMask = GetRedPixelMask(stopSignModel))
{
ImageFeature<float>[] temp = _detector.DetectFeatures(redMask, null);
_tracker = new Features2DTracker<float>(temp);
}
_octagonStorage = new MemStorage();
_octagon = new Contour<Point>(_octagonStorage);
_octagon.PushMulti(new Point[] {
new Point(1, 0),
new Point(2, 0),
new Point(3, 1),
new Point(3, 2),
new Point(2, 3),
new Point(1, 3),
new Point(0, 2),
new Point(0, 1)},
Emgu.CV.CvEnum.BACK_OR_FRONT.FRONT
);
}
public List<ImageRecord> QueryImage(string queryImagePath, SurfSettings surfSetting = null)
{
List<ImageRecord> rtnImageList = new List<ImageRecord>();
var observerFeatureSets = SurfRepository.GetSurfRecordList();
#region Surf Dectator Region
double hessianThresh = 500;
double uniquenessThreshold = 0.8;
int minGoodMatchPercent = 50;
if (surfSetting != null)
{
hessianThresh = surfSetting.HessianThresh.Value;
uniquenessThreshold = surfSetting.UniquenessThreshold.Value;
minGoodMatchPercent = surfSetting.GoodMatchThreshold.Value;
}
SURFDetector surfDectector = new SURFDetector(hessianThresh, false);
#endregion
using (Image<Gray, byte> modelImage = new Image<Gray, byte>(queryImagePath))
{
ImageFeature<float>[] modelFeatures = surfDectector.DetectFeatures(modelImage, null);
if (modelFeatures.Length < 4) throw new InvalidOperationException("Model image didn't have any significant features to detect");
Features2DTracker<float> tracker = new Features2DTracker<float>(modelFeatures);
foreach (var surfRecord in observerFeatureSets)
{
string queryImageName = System.IO.Path.GetFileName(queryImagePath);
string modelImageName = surfRecord.ImageName;
Features2DTracker<float>.MatchedImageFeature[] matchedFeatures = tracker.MatchFeature(surfRecord.observerFeatures, 2);
Features2DTracker<float>.MatchedImageFeature[] uniqueFeatures = Features2DTracker<float>.VoteForUniqueness(matchedFeatures, uniquenessThreshold);
Features2DTracker<float>.MatchedImageFeature[] uniqueRotOriFeatures = Features2DTracker<float>.VoteForSizeAndOrientation(uniqueFeatures, 1.5, 20);
int goodMatchCount = 0;
goodMatchCount = uniqueRotOriFeatures.Length;
bool isMatch = false;
double totalnumberOfModelFeature = modelFeatures.Length;
double matchPercentage = ((totalnumberOfModelFeature - (double)goodMatchCount) / totalnumberOfModelFeature);
matchPercentage = (1 - matchPercentage) * 100;
matchPercentage = Math.Round(matchPercentage);
if (matchPercentage >= minGoodMatchPercent)
{
HomographyMatrix homography =
Features2DTracker<float>.GetHomographyMatrixFromMatchedFeatures(uniqueRotOriFeatures);
if (homography != null)
{
isMatch = homography.IsValid(5);
if (isMatch)
{
surfRecord.Distance = matchPercentage;
rtnImageList.Add((ImageRecord)surfRecord);
}
}
}
//bool isMatch = false;
//if (uniqueFeatures.Length > 4)
//{
// HomographyMatrix homography =
// Features2DTracker<float>.GetHomographyMatrixFromMatchedFeatures(uniqueRotOriFeatures);
// if (homography != null)
// {
// isMatch = homography.IsValid(5);
// }
//}
//if (isMatch)
//{
// surfRecord.Distance = goodMatchCount;
// rtnImageList.Add((ImageRecord)surfRecord);
//}
//int goodMatchCount = 0;
//foreach (Features2DTracker<float>.MatchedImageFeature ms in matchedFeatures)
//{
// if (ms.SimilarFeatures[0].Distance < uniquenessThreshold)
// goodMatchCount++;
//}
//double totalnumberOfModelFeature = modelFeatures.Length;
//double matchPercentage = ((totalnumberOfModelFeature - (double)goodMatchCount) / totalnumberOfModelFeature);
//matchPercentage = (1 - matchPercentage) * 100;
//matchPercentage = Math.Round(matchPercentage);
//if (matchPercentage >= minGoodMatchPercent)
//{
// surfRecord.Distance = matchPercentage;
// rtnImageList.Add((ImageRecord)surfRecord);
//}
}
}
rtnImageList = rtnImageList.OrderByDescending(x => x.Distance).ToList();
return rtnImageList;
}