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);
}
public DetectorResult Process(Image<Bgr, byte> rawFrame, Image<Gray, byte> grayFrame)
{
var surfParam = new SurfDetector(900, true);
var modelImage = new Image<Gray, byte>("iphone\\signin.jpg");
var modelFeatures = surfParam.DetectFeatures(modelImage, null);
var tracker = new Features2DTracker(modelFeatures);
var imageFeatures = surfParam.DetectFeatures(grayFrame, null);
var homographyMatrix = tracker.Detect(imageFeatures, 100.0);
Image<Bgr, Byte> processedImage = modelImage.Convert<Bgr, Byte>().ConcateVertical(rawFrame);
if (homographyMatrix != null)
{
var rect = modelImage.ROI;
var pts = new[]
{
new PointF(rect.Left, rect.Bottom),
new PointF(rect.Right, rect.Bottom),
new PointF(rect.Right, rect.Top),
new PointF(rect.Left, rect.Top)
};
homographyMatrix.ProjectPoints(pts);
for (int i = 0; i < pts.Length; i++)
pts[i].Y += modelImage.Height;
processedImage.DrawPolyline(Array.ConvertAll(pts, Point.Round), true, new Bgr(Color.DarkOrange), 1);
}
return new DetectorResult(){RawImage = rawFrame, ProcessedImage = processedImage};
}
/// <summary>
/// Detect image features from the given image
/// </summary>
/// <param name="image">The image to detect features from</param>
/// <param name="mask">The optional mask, can be null if not needed</param>
/// <returns>The Image features detected from the given image</returns>
public ImageFeature[] DetectFeatures(Image <Gray, Byte> image, Image <Gray, byte> mask)
{
using (VectorOfKeyPoint kpts = DetectKeyPointsRaw(image, mask))
using (Matrix <float> desc = ComputeDescriptorsRaw(image, mask, kpts))
{
return(Features2DTracker.ConvertToImageFeature(kpts, desc));
}
}
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));
}
}
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));
}
/// <summary>
/// Compute the descriptor given the image and the point location
/// </summary>
/// <param name="image">The image where the descriptor will be computed from</param>
/// <param name="mask">The optional mask, can be null if not needed</param>
/// <param name="keyPoints">The keypoint where the descriptor will be computed from</param>
/// <returns>The image features founded on the keypoint location</returns>
public ImageFeature[] ComputeDescriptors(Image <Gray, Byte> image, Image <Gray, byte> mask, MKeyPoint[] keyPoints)
{
int sizeOfdescriptor = _surfParams.Extended ? 128 : 64;
using (VectorOfKeyPoint pts = new VectorOfKeyPoint())
{
pts.Push(keyPoints);
using (Matrix <float> descriptors = ComputeDescriptorsRaw(image, mask, pts))
return(Features2DTracker.ConvertToImageFeature(pts, descriptors));
}
}
/*
* /// <summary>
* /// Compute the descriptor given the bgr image and the point location, using oppponent color (CGIV 2008 "Color Descriptors for Object Category Recognition").
* /// </summary>
* /// <param name="image">The image where the descriptor will be computed from</param>
* /// <param name="keyPoints">The keypoint where the descriptor will be computed from</param>
* /// <returns>The descriptors founded on the keypoint location</returns>
* public Matrix<float> ComputeDescriptorsRaw(Image<Bgr, Byte> image, VectorOfKeyPoint keyPoints)
* {
* int count = keyPoints.Size;
* if (count == 0) return null;
* Matrix<float> descriptors = new Matrix<float>(count, DescriptorSize * 3, 1);
* CvSIFTDetectorComputeDescriptorsBGR(_ptr, image, keyPoints, descriptors);
* return descriptors;
* }*/
/// <summary>
/// Compute the descriptor given the image and the point location
/// </summary>
/// <param name="image">The image where the descriptor will be computed from</param>
/// <param name="mask">The optional mask, can be null if not needed</param>
/// <param name="keyPoints">The keypoint where the descriptor will be computed from</param>
/// <returns>The descriptors founded on the keypoint location</returns>
public ImageFeature[] ComputeDescriptors(Image <Gray, Byte> image, Image <Gray, byte> mask, MKeyPoint[] keyPoints)
{
if (keyPoints.Length == 0)
{
return(new ImageFeature[0]);
}
using (VectorOfKeyPoint kpts = new VectorOfKeyPoint())
{
kpts.Push(keyPoints);
using (Matrix <float> descriptor = ComputeDescriptorsRaw(image, mask, kpts))
{
return(Features2DTracker.ConvertToImageFeature(kpts, descriptor));
}
}
}
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;
}
private void InitializeModels(string modelImageDirPath)
{
string []files = Directory.GetFiles(modelFilesDirectory);
progressBar.Minimum = 0;
progressBar.Maximum = files.Length * 2;
progressBar.Value = 0;
foreach (string file in files)
{
if (initRequired)
{
Image<Gray, Byte> modelImage = new Image<Gray, byte>(file);
ImageFeature[] modelFeatures = surfParam.DetectFeatures(modelImage, null);
Features2DTracker tracker = new Features2DTracker(modelFeatures);
modelFeatureTracker.Add(tracker);
modelImageFilePath.Add(file);
}
progressBar.Increment(1);
}
initRequired = false;
}
