internal static extern IntPtr cveCudaFastFeatureDetectorCreate(
int threshold,
[MarshalAs(CvInvoke.BoolMarshalType)]
bool nonmaxSupression,
FastDetector.DetectorType type,
int maxPoints,
ref IntPtr feature2D,
ref IntPtr feature2DAsync);
public bool testFAST(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage)
{
bool isFound = false;
HomographyMatrix homography = null;
FastDetector fastCPU = new FastDetector(10, true);
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix<int> indices;
BriefDescriptorExtractor descriptor = new BriefDescriptorExtractor();
Matrix<byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
//extract features from the object image
modelKeyPoints = fastCPU.DetectKeyPointsRaw(modelImage, null);
Matrix<Byte> modelDescriptors = descriptor.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// extract features from the observed image
observedKeyPoints = fastCPU.DetectKeyPointsRaw(observedImage, null);
Matrix<Byte> observedDescriptors = descriptor.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher<Byte> matcher = new BruteForceMatcher<Byte>(DistanceType.L2);
matcher.Add(modelDescriptors);
indices = new Matrix<int>(observedDescriptors.Rows, k);
using (Matrix<float> dist = new Matrix<float>(observedDescriptors.Rows, k))
{
matcher.KnnMatch(observedDescriptors, indices, dist, k, null);
mask = new Matrix<byte>(dist.Rows, 1);
mask.SetValue(255);
Features2DToolbox.VoteForUniqueness(dist, uniquenessThreshold, mask);
}
int nonZeroCount = CvInvoke.cvCountNonZero(mask);
if (nonZeroCount >= 4)
{
nonZeroCount = Features2DToolbox.VoteForSizeAndOrientation(modelKeyPoints, observedKeyPoints, indices, mask, 1.5, 20);
if (nonZeroCount >= 4)
homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(
modelKeyPoints, observedKeyPoints, indices, mask, 2);
}
//Draw the matched keypoints
Image<Bgr, Byte> result = Features2DToolbox.DrawMatches(modelImage, modelKeyPoints, observedImage, observedKeyPoints,
indices, new Bgr(255, 255, 255), new Bgr(255, 255, 255), mask, Features2DToolbox.KeypointDrawType.DEFAULT);
#region draw the projected 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);
if (CvInvoke.cvCountNonZero(mask) >= 10)
isFound = true;
result.DrawPolyline(Array.ConvertAll<PointF, Point>(pts, Point.Round), true, new Bgr(Color.LightGreen), 5);
}
#endregion
return isFound;
}
/// <summary>
/// Create a fast detector with the specific parameters
/// </summary>
/// <param name="threshold">Threshold on difference between intensity of center pixel and pixels on circle around
/// this pixel. Use 10 for default.</param>
/// <param name="nonmaxSupression">Specifiy if non-maximum supression should be used.</param>
/// <param name="maxNKeypoints">The maximum number of keypoints to be extracted.</param>
/// <param name="type">The detector type</param>
public CudaFastFeatureDetector(int threshold = 10, bool nonmaxSupression = true, FastDetector.DetectorType type = DetectorType.Type9_16, int maxNKeypoints = 5000)
{
_ptr = CudaInvoke.cveCudaFastFeatureDetectorCreate(threshold, nonmaxSupression, type, maxNKeypoints, ref _feature2D, ref _feature2DAsyncPtr);
}
public void TestFreak()
{
FastDetector fast = new FastDetector(10, true);
Freak freak = new Freak(true, true, 22.0f, 4);
//ParamDef[] parameters = freak.GetParams();
//int nOctaves = freak.GetInt("nbOctave");
EmguAssert.IsTrue(TestFeature2DTracker(fast, freak), "Unable to find homography matrix");
}
public void TestFAST()
{
FastDetector fast = new FastDetector(10, true);
//GridAdaptedFeatureDetector fastGrid = new GridAdaptedFeatureDetector(fast, 2000, 4, 4);
BriefDescriptorExtractor brief = new BriefDescriptorExtractor(32);
//ParamDef[] parameters = fastGrid.GetParams();
EmguAssert.IsTrue(TestFeature2DTracker(fast, brief), "Unable to find homography matrix");
}
public void TestBruteForceHammingDistance()
{
if (CudaInvoke.HasCuda)
{
Image<Gray, byte> box = new Image<Gray, byte>("box.png");
FastDetector fast = new FastDetector(100, true);
BriefDescriptorExtractor brief = new BriefDescriptorExtractor(32);
#region extract features from the object image
Stopwatch stopwatch = Stopwatch.StartNew();
VectorOfKeyPoint modelKeypoints = new VectorOfKeyPoint();
fast.DetectRaw(box, modelKeypoints);
Mat modelDescriptors = new Mat();
brief.Compute(box, modelKeypoints, modelDescriptors);
stopwatch.Stop();
Trace.WriteLine(String.Format("Time to extract feature from model: {0} milli-sec", stopwatch.ElapsedMilliseconds));
#endregion
Image<Gray, Byte> observedImage = new Image<Gray, byte>("box_in_scene.png");
#region extract features from the observed image
stopwatch.Reset(); stopwatch.Start();
VectorOfKeyPoint observedKeypoints = new VectorOfKeyPoint();
fast.DetectRaw(observedImage, observedKeypoints);
Mat observedDescriptors = new Mat();
brief.Compute(observedImage, observedKeypoints, observedDescriptors);
stopwatch.Stop();
Trace.WriteLine(String.Format("Time to extract feature from image: {0} milli-sec", stopwatch.ElapsedMilliseconds));
#endregion
Mat homography = null;
using (GpuMat<Byte> gpuModelDescriptors = new GpuMat<byte>(modelDescriptors)) //initialization of GPU code might took longer time.
{
stopwatch.Reset(); stopwatch.Start();
CudaBFMatcher hammingMatcher = new CudaBFMatcher(DistanceType.Hamming);
//BFMatcher hammingMatcher = new BFMatcher(BFMatcher.DistanceType.Hamming, modelDescriptors);
int k = 2;
Matrix<int> trainIdx = new Matrix<int>(observedKeypoints.Size, k);
Matrix<float> distance = new Matrix<float>(trainIdx.Size);
using (GpuMat<Byte> gpuObservedDescriptors = new GpuMat<byte>(observedDescriptors))
//using (GpuMat<int> gpuTrainIdx = new GpuMat<int>(trainIdx.Rows, trainIdx.Cols, 1, true))
//using (GpuMat<float> gpuDistance = new GpuMat<float>(distance.Rows, distance.Cols, 1, true))
using (VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch())
{
Stopwatch w2 = Stopwatch.StartNew();
//hammingMatcher.KnnMatch(gpuObservedDescriptors, gpuModelDescriptors, matches, k);
hammingMatcher.KnnMatch(gpuObservedDescriptors, gpuModelDescriptors, matches, k, null, true);
w2.Stop();
Trace.WriteLine(String.Format("Time for feature matching (excluding data transfer): {0} milli-sec",
w2.ElapsedMilliseconds));
//gpuTrainIdx.Download(trainIdx);
//gpuDistance.Download(distance);
Mat mask = new Mat(distance.Rows, 1, DepthType.Cv8U, 1);
mask.SetTo(new MCvScalar(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);
nonZeroCount = CvInvoke.CountNonZero(mask);
}
stopwatch.Stop();
Trace.WriteLine(String.Format("Time for feature matching (including data transfer): {0} milli-sec",
stopwatch.ElapsedMilliseconds));
}
}
if (homography != null)
{
Rectangle rect = box.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)};
PointF[] points = CvInvoke.PerspectiveTransform(pts, homography);
//homography.ProjectPoints(points);
//Merge the object image and the observed image into one big image for display
Image<Gray, Byte> res = box.ConcateVertical(observedImage);
for (int i = 0; i < points.Length; i++)
points[i].Y += box.Height;
res.DrawPolyline(Array.ConvertAll<PointF, Point>(points, Point.Round), true, new Gray(255.0), 5);
//ImageViewer.Show(res);
}
}
}
public void DetectFeatures()
{
//const int numFeatures = 15;
//const double quality = 5000;
//const double minDistance = 50;
//const int blockSize = 11;
// note, the histogram is a property of the cloud itself and is updated when the cloud is created
// load depth image into something that emgucv likes
Image<Gray, Byte> depthImage = new Image<Gray, byte>(depthX, depthY);
// have to convert this to gray via luminosity
byte[] bytes = colorizedDepth.Select(x => (byte)(0.21*x.r + 0.71*x.g + 0.07*x.b)).ToArray();
depthImage.Bytes = bytes;
// detect features of depth image using the fast detector
// I don't really feel like implementing a Harris detector
FastDetector fast = new FastDetector(10, true);
var keyPoints = fast.DetectKeyPoints(depthImage, null); // no mask because I don't know what that is
List<CloudPoint> cloudFeatures = new List<CloudPoint>();
foreach (var p in keyPoints)
{
cloudFeatures.Add(findCloudPoint((int)p.Point.X, (int)p.Point.Y));
}
FeatureTree = KdTree<CloudPoint>.Construct(4, cloudFeatures, x => x.ColorLocation());
}