public static OdometerFrame GetOdometerFrame(Mat left, Mat right, Feature2D detector, Feature2D descriptor, DistanceType distanceType, double maxDistance, Image <Arthmetic, double> K, double takeBest = 1.0)
{
var match = MatchImagePair.Match(left, right, detector, descriptor, distanceType, maxDistance);
var lps = match.LeftPointsList.Take((int)(match.LeftPoints.Size * takeBest));
var rps = match.RightPointsList.Take((int)(match.RightPoints.Size * takeBest));
var lps_n = lps.ToList();
var rps_n = rps.ToList();
var H = EstimateHomography(lps_n, rps_n, K);
if (IsPureRotation(H))
{
OdometerFrame odometerFrame = new OdometerFrame();
odometerFrame.Rotation = RotationConverter.MatrixToEulerXYZ(H);
odometerFrame.RotationMatrix = RotationConverter.EulerXYZToMatrix(odometerFrame.Rotation);
odometerFrame.MatK = K;
odometerFrame.Match = match;
odometerFrame.Translation = new Image <Arthmetic, double>(1, 3);
return(odometerFrame);
}
else
{
if (!FindTwoViewsMatrices(lps_n, rps_n, K, out var F, out var E, out var R, out var t, out var X))
{
return(null);
}
OdometerFrame odometerFrame = new OdometerFrame();
odometerFrame.Rotation = RotationConverter.MatrixToEulerXYZ(R);
odometerFrame.RotationMatrix = R;
odometerFrame.MatK = K;
odometerFrame.Match = match;
Image <Arthmetic, double> C = R.T().Multiply(t).Mul(-1);
odometerFrame.Translation = C.Mul(1.0 / C.Norm);
return(odometerFrame);
}
}
public OdometerFrame NextFrame(int left, int right, Func <int, int, MatchingResult> matcher)
{
MatchingResult match23 = matcher(left, right);
if (match23.Matches.Size < MinimumCorrespondencesNeeded)
{
// Track of points is lost, at least temporarly. Let's put handling lost-track case ou of scope for now.
lastGoodMatch = null;
isContinuous = false;
return(null);
}
OdometerFrame frame = new OdometerFrame()
{
MatK = K,
Match = match23,
Rotation = new Image <Arthmetic, double>(1, 3),
RotationMatrix = RotationConverter.EulerXYZToMatrix(new Image <Arthmetic, double>(1, 3)),
Translation = new Image <Arthmetic, double>(1, 3)
};
// 1) Determine if transformation between next frames has high enough baseline to be accurate
// 1a) For now lets determine it by finding if lone rotation is good enough
var H = FindTransformation.EstimateHomography(match23.LeftPointsList, match23.RightPointsList, K);
if (FindTransformation.IsPureRotation(H, RotationTreshold1, RotationTreshold2))
{
// 1c) If not then transformation is described only by rotation
// 1b) Find rotation and rotate all points in current set
isContinuous = false;
frame.Rotation = RotationConverter.MatrixToEulerXYZ(H);
frame.RotationMatrix = RotationConverter.EulerXYZToMatrix(frame.Rotation);
if (last3dPoints != null && R12 != null)
{
last3dPoints = Utils.PutRTo4x4(frame.RotationMatrix).Multiply(last3dPoints);
R12 = frame.RotationMatrix.Multiply(R12);
}
else
{
R12 = frame.RotationMatrix;
}
// 1c) Skip frame and wait for next one (but save matches)
return(frame);
}
// 2) We have legit frames
if (!FindTransformation.FindTwoViewsMatrices(match23.LeftPointsList, match23.RightPointsList, K,
out var F23, out var E23, out var R23, out var t23, out var X23))
{
// 3a) Or not
isContinuous = false;
return(null);
}
frame.Rotation = RotationConverter.MatrixToEulerXYZ(R23);
frame.RotationMatrix = R23;
frame.Translation = t23;
// 3) Find same points between old frame and current one
if (lastGoodMatch == null)
{
last3dPoints = X23;
isContinuous = true;
}
else
{
#region NonContinousCase
//if (!isContinuous) // This doesn't work well. Lets put it out of scope and just reset scale
// {
// Find correspondences between last right and new left
//var match12 = lastGoodMatch;
//var match34 = match23;
//var match23_ = matcher(lastGoodRightImage, left); // TODO: make use of already found feature points
//var correspondences23to34 = Correspondences.FindCorrespondences12to23(match23_, match34);
//// Now extend each correspondence to 4 points - find if point on 2 is matched to some point on 1
//var correspondences13to34 = new List<Correspondences.MatchPair>();
//foreach(var c in correspondences23to34)
//{
// var m23 = c.Match12;
// for (int i = 0; i < match12.Matches.Size; ++i)
// {
// if(match12.Matches[i].TrainIdx == m23.QueryIdx)
// {
// correspondences13to34.Add(new Correspondences.MatchPair()
// {
// Kp1 = match12.LeftKps[match12.Matches[i].QueryIdx],
// Kp2 = c.Kp2,
// Kp3 = c.Kp3
// });
// }
// }
//}
//if (correspondences13to34.Count >= MinimumCorrespondencesNeeded)
//{
// var t13 = R12.Multiply(c12).Mul(-1);
// FindBestScale(R12, t13, R23, t23, K, correspondences13to34, MinimumCorrespondencesNeeded, out double scale, out double confidence, out List<int> inliers);
// t23 = t23.Mul(scale);
// frame.Translation = t23;
// FindTransformation.TriangulateChieral(match23.LeftPointsList, match23.RightPointsList, K, R23, t23, out last3dPoints);
// isContinuous = true;
//}
//else
//{
// isContinuous = false;
//}
// }
#endregion
if (isContinuous)
{
var correspondences = Correspondences.FindCorrespondences12to23(lastGoodMatch, match23);
if (correspondences.Count >= MinimumCorrespondencesNeeded)
{
// Normalize to |t| = 1
t12 = t12.Mul(1.0 / t12.Norm);
t23 = t23.Mul(1.0 / t23.Norm);
FindBestScale(R12, t12, R23, t23, K, correspondences, MinimumCorrespondencesNeeded, out double scale, out double confidence, out List <int> inliers);
t23 = t23.Mul(scale);
frame.Translation = t23;
FindTransformation.TriangulateChieral(match23.LeftPointsList, match23.RightPointsList, K, R23, t23, out last3dPoints);
}
else
{
isContinuous = false;
}
}
}
lastGoodMatch = match23;
lastGoodLeftImage = left;
lastGoodRightImage = right;
R12 = R23;
t12 = t23;
c12 = R23.T().Multiply(t23).Mul(-1);
return(frame);
}