/// <summary>
/// Computes the descriptors for a set of keypoints in an image.
/// </summary>
/// <param name="extractor">The descriptor extractor used to compute keypoint descriptors.</param>
/// <param name="image">The image from which to extract keypoint descriptors.</param>
/// <param name="keyPoints">
/// The keypoints for which to extract descriptors. Keypoints for which a
/// descriptor cannot be computed are removed.
/// </param>
/// <returns>The array of descriptors computed for the specified set of keypoints.</returns>
public static Mat Compute(this IDescriptorExtractor extractor, Arr image, KeyPointCollection keyPoints)
{
if (extractor == null)
{
throw new ArgumentNullException("extractor");
}
if (keyPoints == null)
{
throw new ArgumentNullException("keyPoints");
}
var descriptors = new Mat(keyPoints.Count, extractor.DescriptorSize, extractor.DescriptorType);
extractor.Compute(image, keyPoints, descriptors);
if (descriptors.Rows != keyPoints.Count)
{
descriptors = descriptors.GetSubRect(new Rect(0, 0, descriptors.Cols, keyPoints.Count));
}
return(descriptors);
}
public static bool TestFeature2DTracker(IFeatureDetector keyPointDetector, IDescriptorExtractor descriptorGenerator)
{
//for (int k = 0; k < 1; k++)
{
Feature2D feature2D = null;
if (keyPointDetector == descriptorGenerator)
{
feature2D = keyPointDetector as Feature2D;
}
Image <Gray, Byte> modelImage = EmguAssert.LoadImage <Gray, byte>("box.png");
//Image<Gray, Byte> modelImage = new Image<Gray, byte>("stop.jpg");
//modelImage = modelImage.Resize(400, 400, true);
//modelImage._EqualizeHist();
#region extract features from the object image
Stopwatch stopwatch = Stopwatch.StartNew();
VectorOfKeyPoint modelKeypoints = new VectorOfKeyPoint();
Mat modelDescriptors = new Mat();
if (feature2D != null)
{
feature2D.DetectAndCompute(modelImage, null, modelKeypoints, modelDescriptors, false);
}
else
{
keyPointDetector.DetectRaw(modelImage, modelKeypoints);
descriptorGenerator.Compute(modelImage, modelKeypoints, modelDescriptors);
}
stopwatch.Stop();
EmguAssert.WriteLine(String.Format("Time to extract feature from model: {0} milli-sec", stopwatch.ElapsedMilliseconds));
#endregion
//Image<Gray, Byte> observedImage = new Image<Gray, byte>("traffic.jpg");
Image <Gray, Byte> observedImage = EmguAssert.LoadImage <Gray, byte>("box_in_scene.png");
//Image<Gray, Byte> observedImage = modelImage.Rotate(45, new Gray(0.0));
//image = image.Resize(400, 400, true);
//observedImage._EqualizeHist();
#region extract features from the observed image
stopwatch.Reset();
stopwatch.Start();
VectorOfKeyPoint observedKeypoints = new VectorOfKeyPoint();
using (Mat observedDescriptors = new Mat())
{
if (feature2D != null)
{
feature2D.DetectAndCompute(observedImage, null, observedKeypoints, observedDescriptors, false);
}
else
{
keyPointDetector.DetectRaw(observedImage, observedKeypoints);
descriptorGenerator.Compute(observedImage, observedKeypoints, observedDescriptors);
}
stopwatch.Stop();
EmguAssert.WriteLine(String.Format("Time to extract feature from image: {0} milli-sec", stopwatch.ElapsedMilliseconds));
#endregion
//Merge the object image and the observed image into one big image for display
Image <Gray, Byte> res = modelImage.ConcateVertical(observedImage);
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)
};
HomographyMatrix homography = null;
stopwatch.Reset();
stopwatch.Start();
int k = 2;
DistanceType dt = modelDescriptors.Depth == CvEnum.DepthType.Cv8U ? DistanceType.Hamming : DistanceType.L2;
//using (Matrix<int> indices = new Matrix<int>(observedDescriptors.Rows, k))
//using (Matrix<float> dist = new Matrix<float>(observedDescriptors.Rows, k))
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
using (BruteForceMatcher matcher = new BruteForceMatcher(dt))
{
matcher.Add(modelDescriptors);
matcher.KnnMatch(observedDescriptors, matches, k, null);
Matrix <byte> mask = new Matrix <byte>(matches.Size, 1);
mask.SetValue(255);
Features2DToolbox.VoteForUniqueness(matches, 0.8, mask);
int nonZeroCount = CvInvoke.CountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeypoints, observedKeypoints, matches, mask, 1.5, 20);
if (nonZeroCount >= 4)
{
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(modelKeypoints, observedKeypoints, matches, mask, 2);
}
}
}
stopwatch.Stop();
EmguAssert.WriteLine(String.Format("Time for feature matching: {0} milli-sec", stopwatch.ElapsedMilliseconds));
bool success = false;
if (homography != null)
{
PointF[] points = pts.Clone() as PointF[];
homography.ProjectPoints(points);
for (int i = 0; i < points.Length; i++)
{
points[i].Y += modelImage.Height;
}
res.DrawPolyline(
#if NETFX_CORE
Extensions.
#else
Array.
#endif
ConvertAll <PointF, Point>(points, Point.Round), true, new Gray(255.0), 5);
success = true;
}
//Emgu.CV.UI.ImageViewer.Show(res);
return(success);
}
/*
* stopwatch.Reset(); stopwatch.Start();
* //set the initial region to be the whole image
* using (Image<Gray, Single> priorMask = new Image<Gray, float>(observedImage.Size))
* {
* priorMask.SetValue(1.0);
* homography = tracker.CamShiftTrack(
* observedFeatures,
* (RectangleF)observedImage.ROI,
* priorMask);
* }
* Trace.WriteLine(String.Format("Time for feature tracking: {0} milli-sec", stopwatch.ElapsedMilliseconds));
*
* if (homography != null) //set the initial tracking window to be the whole image
* {
* PointF[] points = pts.Clone() as PointF[];
* homography.ProjectPoints(points);
*
* for (int i = 0; i < points.Length; i++)
* points[i].Y += modelImage.Height;
* res.DrawPolyline(Array.ConvertAll<PointF, Point>(points, Point.Round), true, new Gray(255.0), 5);
* return true;
* }
* else
* {
* return false;
* }*/
}
}