public List<Keypoint> usingSift(Bitmap image)
{
SIFTDetector sift = new SIFTDetector();
Image<Gray, Byte> modelImage = new Image<Gray, byte>(new Bitmap(image));
VectorOfKeyPoint modelKeyPoints = sift.DetectKeyPointsRaw(modelImage, null);
MKeyPoint[] keypoints = modelKeyPoints.ToArray();
Keypoint key;
List<Keypoint> keypointsList = new List<Keypoint>();
foreach (MKeyPoint keypoint in keypoints)
{
key = new Keypoint(keypoint.Point.X, keypoint.Point.Y, keypoint.Size);
keypointsList.Add(key);
}
return keypointsList;
}
public bool testSIFT(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage)
{
bool isFound = false;
HomographyMatrix homography = null;
SIFTDetector siftCPU = new SIFTDetector();
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix<int> indices;
Matrix<byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
//extract features from the object image
modelKeyPoints = siftCPU.DetectKeyPointsRaw(modelImage, null);
Matrix<float> modelDescriptors = siftCPU.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// extract features from the observed image
observedKeyPoints = siftCPU.DetectKeyPointsRaw(observedImage, null);
Matrix<float> observedDescriptors = siftCPU.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher<float> matcher = new BruteForceMatcher<float>(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;
}
public static Image<Bgr, Byte> SIFT(Image<Gray, Byte> modelImage, Image<Gray, byte> observedImage)
{
bool isFound = false;
long matchTime;
Stopwatch watch;
HomographyMatrix homography = null;
SIFTDetector siftCPU = new SIFTDetector();
VectorOfKeyPoint modelKeyPoints;
VectorOfKeyPoint observedKeyPoints;
Matrix<int> indices;
Matrix<byte> mask;
int k = 2;
double uniquenessThreshold = 0.8;
watch = Stopwatch.StartNew();
//extract features from the object image
modelKeyPoints = siftCPU.DetectKeyPointsRaw(modelImage, null);
Matrix<float> modelDescriptors = siftCPU.ComputeDescriptorsRaw(modelImage, null, modelKeyPoints);
// extract features from the observed image
observedKeyPoints = siftCPU.DetectKeyPointsRaw(observedImage, null);
Matrix<float> observedDescriptors = siftCPU.ComputeDescriptorsRaw(observedImage, null, observedKeyPoints);
BruteForceMatcher<float> matcher = new BruteForceMatcher<float>(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);
}
watch.Stop();
//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
matchTime = watch.ElapsedMilliseconds;
_richTextBox1.Clear();
_richTextBox1.AppendText("objek ditemukan: " + isFound + "\n");
_richTextBox1.AppendText("waktu pendeteksian SIFT: " + matchTime + "ms\n");
_richTextBox1.AppendText("fitur mentah pada model yang terdeteksi: " + modelKeyPoints.Size + "\n");
_richTextBox1.AppendText("match yang ditemukan: " + CvInvoke.cvCountNonZero(mask).ToString());
return result;
}