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;
}
/// <summary>
/// Start matching period
/// Based on Automatic Panoramic Image Stitching using Invariant Features. M Brown et. al. IJCV 2007
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void buttonImageMatch_Click(object sender, EventArgs e)
{
if (imgStitchList.Count >= 2)
{
// Calculate SIFT features
// Get SIFT from srcImg and dstImg
SIFTDetector sift = new SIFTDetector();
List<ImageFeature<float>[]> features = new List<ImageFeature<float>[]>();
for (var i = 0; i < imgStitchList.Count; ++i)
{
Image<Gray, byte> grayImg = new Image<Gray, byte>(imgStitchList[i].ToBitmap());
features.Add(sift.DetectFeatures(grayImg, null));
Console.WriteLine(String.Format("Img No.{0}, Features: {1}", i, features[i].Length));
}
// Get matching pictures <dstId, srcId>, then generate an order
// We assume that the images are in horizontal order
hMatList.Clear();
int sumWidth = 0;
for (var dstId = 0; dstId + 1 < imgStitchList.Count; ++dstId)
{
int srcId = dstId + 1;
ImageFeature<float>[] dstFeat = features[dstId];
ImageFeature<float>[] srcFeat = features[srcId];
int matchCount = 0;
List<PointF> dstPntList = new List<PointF>();
List<PointF> srcPntList = new List<PointF>();
for (var i = 0; i < dstFeat.Length; ++i)
{
// Calculate distance: Brute Force
double[] dist = new double[srcFeat.Length];
for (var j = 0; j < srcFeat.Length; ++j)
{
dist[j] = getEuclidDistance(dstFeat[i].Descriptor, srcFeat[j].Descriptor);
}
// Get 2-min distance points
int minIndex;
double minDist, minDist2;
if (dist[0] < dist[1])
{
minIndex = 0;
minDist = dist[0];
minDist2 = dist[1];
}
else
{
minIndex = 1;
minDist = dist[1];
minDist2 = dist[0];
}
for (var j = 2; j < srcFeat.Length; ++j)
{
if (dist[j] < minDist)
{
minIndex = j;
minDist2 = minDist;
minDist = dist[j];
}
else if (dist[j] < minDist2)
{
minDist2 = dist[j];
}
}
// If the distance is small enough.
if (minDist / minDist2 < SIFT_FEAT_DIST_THRESHOLD)
{
PointF pi = dstFeat[i].KeyPoint.Point;
PointF pj = srcFeat[minIndex].KeyPoint.Point;
matchGraph.DrawLine(matchPen, new PointF(pi.X + sumWidth, pi.Y), new PointF(pj.X + sumWidth + imgStitchList[dstId].Width, pj.Y));
//rawMatchPairs.Add(new Tuple<int, int>(i, minIndex));
dstPntList.Add(pi);
srcPntList.Add(pj);
++matchCount;
}
}
Console.WriteLine(String.Format("Dst:[{0}] <-> Src:[{1}], {2} matching pairs.", dstId, srcId, matchCount));
if (matchCount >= RANSAC_INIT_SET_SIZE)
{
// Use RANSAC (Ramdom Sample Consensus) as a filter
HomographyMatrix hMat = CameraCalibration.FindHomography(srcPntList.ToArray(), dstPntList.ToArray(),
Emgu.CV.CvEnum.HOMOGRAPHY_METHOD.RANSAC, RANSAC_REPROJ_THRESHOLD);
if (hMat != null)
{
for (var r = 0; r < 3; ++r)
{
for (var c = 0; c < 3; ++c)
Console.Write(hMat[r, c] + ", ");
Console.Write("\n");
}
hMatList.Add(hMat);
// Show inliers
for (var j = 0; j < srcPntList.Count; ++j)
{
// Calculate distance
Matrix<double> srcPntMat, dstProjMat;
double dstProjX, dstProjY;
double dist;
srcPntMat = new Matrix<double>(3, 1);
srcPntMat[0, 0] = srcPntList[j].X;
srcPntMat[1, 0] = srcPntList[j].Y;
srcPntMat[2, 0] = 1;
dstProjMat = hMat.Mul(srcPntMat);
dstProjX = dstProjMat[0, 0] / dstProjMat[2, 0];
dstProjY = dstProjMat[1, 0] / dstProjMat[2, 0];
dist = Math.Sqrt(Math.Pow(dstProjX - dstPntList[j].X, 2) + Math.Pow(dstProjY - dstPntList[j].Y, 2));
if (dist <= RANSAC_REPROJ_THRESHOLD)
{
Console.WriteLine(String.Format("Valid point pair({0},{1}) <-> ({2},{3})", srcPntMat[0, 0], srcPntMat[1, 0], dstProjX, dstProjY));
matchGraph.DrawLine(inlierPen, new Point((int)dstProjX + sumWidth, (int)dstProjY),
new Point((int)srcPntList[j].X + sumWidth + imgStitchList[dstId].Width, (int)srcPntList[j].Y));
}
Console.WriteLine("No." + j + " distance: " + dist);
}
}
}
sumWidth += imgStitchList[dstId].Width;
}
}
}
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;
}
