public void RemoveInliersFromKeypointsAndDescriptors(VectorOfDMatch inliers, ref VectorOfKeyPoint keypointsQueryImageInOut, ref Mat descriptorsQueryImageInOut)
{
List <int> inliersKeypointsPositions = new List <int>();
for (int inliersIndex = 0; inliersIndex < inliers.Size; ++inliersIndex)
{
MDMatch match = inliers[inliersIndex];
inliersKeypointsPositions.Add(match.QueryIdx);
}
inliersKeypointsPositions.Sort();
VectorOfKeyPoint keypointsQueryImageBackup = null;
keypointsQueryImageBackup = keypointsQueryImageInOut;
keypointsQueryImageInOut = new VectorOfKeyPoint();
Mat filteredDescriptors = new Mat();
for (int rowIndex = 0; rowIndex < descriptorsQueryImageInOut.Rows; ++rowIndex)
{
if (!inliersKeypointsPositions.Exists(i => i == rowIndex))
{
keypointsQueryImageInOut.Push(new MKeyPoint[] { keypointsQueryImageBackup[rowIndex] });
Matrix <float> matrix = new Matrix <float>(descriptorsQueryImageInOut.Size);
descriptorsQueryImageInOut.ConvertTo(matrix, Emgu.CV.CvEnum.DepthType.Cv32F);
var linha = matrix.GetRow(rowIndex).Mat;
filteredDescriptors.PushBack(linha);
}
}
filteredDescriptors.CopyTo(descriptorsQueryImageInOut);
}
/// <summary>
/// Get the item in the specific index
/// </summary>
/// <param name="index">The index</param>
/// <returns>The item in the specific index</returns>
public MDMatch this[int index]
{
get
{
MDMatch result = new MDMatch();
VectorOfDMatchGetItem(_ptr, index, ref result);
return(result);
}
}
/// <summary>
/// Convert the standard vector to an array of DMatch
/// </summary>
/// <returns>An array of DMatch</returns>
public MDMatch[] ToArray()
{
MDMatch[] res = new MDMatch[Size];
if (res.Length > 0)
{
GCHandle handle = GCHandle.Alloc(res, GCHandleType.Pinned);
CvInvoke.VectorOfDMatchCopyData(_ptr, handle.AddrOfPinnedObject());
handle.Free();
}
return(res);
}
public String GetLabelFromMatches(VectorOfVectorOfDMatch vDMatch, Mat uniqueMask)
{
Dictionary <String, int> labelCount = new Dictionary <string, int>();
for (int i = 0; i < vDMatch.Size; i++)
{
// Not need to use uniqueMask because we have multiple train feature for one class
//if (uniqueMask.GetData(i)[0] == 0) continue;
VectorOfDMatch vMatch = vDMatch[i];
for (int j = 0; j < vMatch.Size; j++)
{
MDMatch dmatch = vMatch[j];
//sb.Append("\n\t" + JsonConvert.SerializeObject(dmatch) + " " + );
String label = GetLabel(dmatch.ImgIdx);
if (labelCount.ContainsKey(label))
{
labelCount[label] += 1;
}
else
{
labelCount[label] = 1;
}
}
}
String ret = labelCount.Keys.Aggregate((i, j) => labelCount[i] >= labelCount[j] ? i : j);
// Scan to find best imgIndex
Dictionary <int, int> imgIndexCount = new Dictionary <int, int>();
for (int i = 0; i < vDMatch.Size; i++)
{
VectorOfDMatch vMatch = vDMatch[i];
for (int j = 0; j < vMatch.Size; j++)
{
MDMatch dmatch = vMatch[j];
if (GetLabel(dmatch.ImgIdx) == ret)
{
if (imgIndexCount.ContainsKey(dmatch.ImgIdx))
{
imgIndexCount[dmatch.ImgIdx] += 1;
}
else
{
imgIndexCount[dmatch.ImgIdx] = 1;
}
}
}
}
int imgIndex = imgIndexCount.Keys.Aggregate((i, j) => imgIndexCount[i] >= imgIndexCount[j] ? i : j);
Console.WriteLine("imgIndex=" + imgIndex);
lastMatchFeatureData = this[imgIndex];
return(ret);
}
/// <summary>
/// Convert the standard vector to arrays of int
/// </summary>
/// <returns>Arrays of int</returns>
public MDMatch[][] ToArrayOfArray()
{
int size = Size;
MDMatch[][] res = new MDMatch[size][];
for (int i = 0; i < size; i++)
{
using (VectorOfDMatch v = this[i])
{
res[i] = v.ToArray();
}
}
return res;
}
/// <summary>
/// Convert the standard vector to arrays of int
/// </summary>
/// <returns>Arrays of int</returns>
public MDMatch[][] ToArrayOfArray()
{
int size = Size;
MDMatch[][] res = new MDMatch[size][];
for (int i = 0; i < size; i++)
{
using (GpuMat v = this[i])
{
res[i] = v.ToArray();
}
}
return(res);
}
public VectorOfKeyPoint GetInliersKeypoints()
{
if (_inliersKeyPoints.Size == 0)
{
for (int i = 0; i < _inliers.Size; ++i)
{
MDMatch match = _inliers[i];
if (match.QueryIdx < _keypointsEvalImag.Size)
{
_inliersKeyPoints.Push(new MKeyPoint[] { _keypointsEvalImag[match.QueryIdx] });
}
}
}
return(_inliersKeyPoints);
}
public String MatchesToString(VectorOfVectorOfDMatch vDMatch, String indent = "")
{
StringBuilder sb = new StringBuilder();
sb.Append(indent + "[VectorOfVectorOfDMatch Size=" + vDMatch.Size);
for (int i = 0; i < vDMatch.Size; i++)
{
VectorOfDMatch vMatch = vDMatch[i];
for (int j = 0; j < vMatch.Size; j++)
{
MDMatch dmatch = vMatch[j];
sb.Append("\n\t" + JsonConvert.SerializeObject(dmatch) + " " + GetLabel(dmatch.ImgIdx));
}
//sb.Append("\n" + ToString(vDMatch[i], indent + "\t"));
}
sb.Append("\n" + indent + "]");
return(sb.ToString());
}
private void FindMinMaxDistInMatches(MDMatch[][] matchesArray, ref float ms_MAX_DIST, ref float ms_MIN_DIST)
{
for (int i = 0; i < matchesArray.Length; i++)
{
if (matchesArray[i].Length == 0)
{
continue;
}
MDMatch first = matchesArray[i][0];
float dist1 = matchesArray[i][0].Distance;
if (ms_MAX_DIST < dist1)
{
ms_MAX_DIST = dist1;
}
if (ms_MIN_DIST > dist1)
{
ms_MIN_DIST = dist1;
}
}
}
public void TestThreeViewsCorrespondences()
{
MKeyPoint[] kps1 = new MKeyPoint[]
{
new MKeyPoint()
{
Point = new PointF(0, 0)
},
new MKeyPoint()
{
Point = new PointF(5, 2)
},
new MKeyPoint()
{
Point = new PointF(5, 7)
},
new MKeyPoint()
{
Point = new PointF(2, 1)
},
new MKeyPoint()
{
Point = new PointF(-4, 2)
},
};
MKeyPoint[] kps2a = new MKeyPoint[]
{
new MKeyPoint()
{
Point = new PointF(0, 2)
},
new MKeyPoint()
{
Point = new PointF(9, 0)
},
new MKeyPoint()
{
Point = new PointF(9, -4)
},
new MKeyPoint()
{
Point = new PointF(6, 1)
},
new MKeyPoint()
{
Point = new PointF(-3, -3)
},
};
MKeyPoint[] kps2b = new MKeyPoint[]
{
new MKeyPoint()
{
Point = new PointF(0, 2)
},
new MKeyPoint()
{
Point = new PointF(9, 0)
},
new MKeyPoint()
{
Point = new PointF(9, -4)
},
new MKeyPoint()
{
Point = new PointF(6, 1)
},
new MKeyPoint()
{
Point = new PointF(-3, -3)
},
};
MKeyPoint[] kps3 = new MKeyPoint[]
{
new MKeyPoint()
{
Point = new PointF(0, 4)
},
new MKeyPoint()
{
Point = new PointF(11, 2)
},
new MKeyPoint()
{
Point = new PointF(11, -6)
},
new MKeyPoint()
{
Point = new PointF(8, 1)
},
new MKeyPoint()
{
Point = new PointF(-7, 0)
},
};
MDMatch[] matches12 = new MDMatch[4]
{
new MDMatch {
QueryIdx = 1, TrainIdx = 0
},
new MDMatch {
QueryIdx = 0, TrainIdx = 3
},
new MDMatch {
QueryIdx = 2, TrainIdx = 1
},
new MDMatch {
QueryIdx = 4, TrainIdx = 2
},
};
MDMatch[] matches23 = new MDMatch[4]
{
new MDMatch {
QueryIdx = 0, TrainIdx = 4
},
new MDMatch {
QueryIdx = 1, TrainIdx = 2
},
new MDMatch {
QueryIdx = 4, TrainIdx = 0
},
new MDMatch {
QueryIdx = 2, TrainIdx = 1
},
};
MatchingResult match12 = new MatchingResult()
{
LeftKps = kps1,
RightKps = kps2a,
Matches = new Emgu.CV.Util.VectorOfDMatch(matches12)
};
MatchingResult match23 = new MatchingResult()
{
LeftKps = kps2b,
RightKps = kps3,
Matches = new Emgu.CV.Util.VectorOfDMatch(matches23)
};
List <Correspondences.MatchPair> correspondences = Correspondences.FindCorrespondences12to23(match12, match23);
Assert.AreEqual(3, correspondences.Count);
// They are sorted same as matches23
var c = correspondences[0];
Assert.AreEqual(1, c.Match12.QueryIdx);
Assert.AreEqual(0, c.Match12.TrainIdx);
Assert.AreEqual(0, c.Match23.QueryIdx);
Assert.AreEqual(4, c.Match23.TrainIdx);
Assert.AreEqual(kps1[1].Point, c.Kp1.Point);
Assert.AreEqual(kps2a[0].Point, c.Kp2.Point);
Assert.AreEqual(kps3[4].Point, c.Kp3.Point);
c = correspondences[1];
Assert.AreEqual(2, c.Match12.QueryIdx);
Assert.AreEqual(1, c.Match12.TrainIdx);
Assert.AreEqual(1, c.Match23.QueryIdx);
Assert.AreEqual(2, c.Match23.TrainIdx);
c = correspondences[2];
Assert.AreEqual(4, c.Match12.QueryIdx);
Assert.AreEqual(2, c.Match12.TrainIdx);
Assert.AreEqual(2, c.Match23.QueryIdx);
Assert.AreEqual(1, c.Match23.TrainIdx);
}
internal static extern void VectorOfDMatchGetItem(IntPtr vec, int index, ref MDMatch element);
public static String ToString(MDMatch dMatch, String indent = "")
{
return(indent + JsonConvert.SerializeObject(dMatch));
}
void NewORBDetector()
{
float ms_MIN_RATIO = 100;
float ms_MAX_DIST = 100;
(Image <Bgr, byte> Image, VectorOfKeyPoint Keypoints, Mat Descriptors)_imgModel = (new Image <Bgr, byte>(@"C:\Images\ImgModel.jpg").Resize(0.2, Inter.Area), new VectorOfKeyPoint(), new Mat());
(Image <Bgr, byte> Image, VectorOfKeyPoint Keypoints, Mat Descriptors)_imgTest = (new Image <Bgr, byte>(@"C:\Images\ImgTest.jpg").Resize(0.2, Inter.Area), new VectorOfKeyPoint(), new Mat());
Mat imgKeypointsModel = new Mat();
Mat imgKeypointsTest = new Mat();
Mat imgMatches = new Mat();
Mat imgWarped = new Mat();
VectorOfVectorOfDMatch matches = new VectorOfVectorOfDMatch();
VectorOfVectorOfDMatch filteredMatches = new VectorOfVectorOfDMatch();
List <MDMatch[]> filteredMatchesList = new List <MDMatch[]>();
ORBDetector _ORB = new ORBDetector();
BFMatcher _BFMatcher = new BFMatcher(DistanceType.Hamming2);
_ORB.DetectAndCompute(_imgModel.Image, null, _imgModel.Keypoints, _imgModel.Descriptors, false);
_ORB.DetectAndCompute(_imgTest.Image, null, _imgTest.Keypoints, _imgTest.Descriptors, false);
_BFMatcher.Add(_imgModel.Descriptors);
_BFMatcher.KnnMatch(_imgTest.Descriptors, matches, k: 2, mask: null);
MDMatch[][] matchesArray = matches.ToArrayOfArray();
//Apply ratio test
for (int i = 0; i < matchesArray.Length; i++)
{
MDMatch first = matchesArray[i][0];
float dist1 = matchesArray[i][0].Distance;
float dist2 = matchesArray[i][1].Distance;
if (dist1 < ms_MIN_RATIO * dist2)
{
filteredMatchesList.Add(matchesArray[i]);
}
}
//Filter by threshold
MDMatch[][] defCopy = new MDMatch[filteredMatchesList.Count][];
filteredMatchesList.CopyTo(defCopy);
filteredMatchesList = new List <MDMatch[]>();
foreach (var item in defCopy)
{
if (item[0].Distance < ms_MAX_DIST)
{
filteredMatchesList.Add(item);
}
}
filteredMatches = new VectorOfVectorOfDMatch(filteredMatchesList.ToArray());
Mat homography = Features2DToolbox.GetHomographyMatrixFromMatchedFeatures(_imgModel.Keypoints, _imgTest.Keypoints, filteredMatches, null, 10);
CvInvoke.WarpPerspective(_imgTest.Image, imgWarped, homography, _imgTest.Image.Size);
Features2DToolbox.DrawKeypoints(_imgModel.Image, _imgModel.Keypoints, imgKeypointsModel, new Bgr(0, 0, 255));
Features2DToolbox.DrawKeypoints(_imgTest.Image, _imgTest.Keypoints, imgKeypointsTest, new Bgr(0, 0, 255));
Features2DToolbox.DrawMatches(_imgModel.Image, _imgModel.Keypoints, _imgTest.Image, _imgTest.Keypoints, filteredMatches, imgMatches, new MCvScalar(0, 255, 0), new MCvScalar(0, 0, 255), null, Features2DToolbox.KeypointDrawType.Default);
Task.Factory.StartNew(() => Emgu.CV.UI.ImageViewer.Show(imgKeypointsModel, "Keypoints Model"));
Task.Factory.StartNew(() => Emgu.CV.UI.ImageViewer.Show(imgKeypointsTest, "Keypoints Test"));
Task.Factory.StartNew(() => Emgu.CV.UI.ImageViewer.Show(imgMatches, "Matches"));
Task.Factory.StartNew(() => Emgu.CV.UI.ImageViewer.Show(imgWarped, "Warp"));
}
