private void ProcessImage(object sender, RoutedEventArgs e)
{
if (loadedImage == null)
{
MessageBox.Show("Image needs to be loaded first");
return;
}
processedImage = loadedImage.Clone();
Emgu.CV.Features2D.Feature2D detector = (Emgu.CV.Features2D.Feature2D)Parameter.ValueFor("Feature Detector", parametersInput.Parameters, parametersInput.Values);
MKeyPoint[] kps = detector.Detect(loadedImage.Mat);
// TODO: open cv probably has some function to draw features automatically
foreach (var kp in kps)
{
DrawCricle(processedImage, new Bgr(Color.Wheat), new System.Drawing.Point((int)kp.Point.X, (int)kp.Point.Y), new System.Drawing.Size(10, 10));
}
Emgu.CV.Util.VectorOfKeyPoint vectorOfKp = new Emgu.CV.Util.VectorOfKeyPoint(kps);
var desc = new Emgu.CV.XFeatures2D.BriefDescriptorExtractor(32);
Mat descriptors = new Mat();
desc.Compute(loadedImage, vectorOfKp, descriptors);
imageViewer.Source = ImageLoader.ImageSourceForBitmap(processedImage.Bitmap);
}
private void LoadImage(object sender, RoutedEventArgs e)
{
loadedImage = ImageLoader.FromFile();
if (loadedImage != null)
{
imageViewer.Source = ImageLoader.ImageSourceForBitmap(loadedImage.Bitmap);
}
}
public static void DrawCricles(ImageViewer view, Mat image, MKeyPoint[] points)
{
var processedImage = image.Clone();
foreach (var kp in points)
{
DrawCricle(processedImage, new Bgr(Color.Wheat), new System.Drawing.Point((int)kp.Point.X, (int)kp.Point.Y), new System.Drawing.Size(10, 10));
}
view.Source = ImageLoader.ImageSourceForBitmap(processedImage.Bitmap);
}
public static void DrawFeatures(Mat left, Mat right, MatchingResult match, double takeBest, ImageViewer macthedView)
{
Mat matchesImage = new Mat();
VectorOfVectorOfDMatch matches2 = new VectorOfVectorOfDMatch();
VectorOfKeyPoint vectorOfKp2 = new VectorOfKeyPoint(match.LeftKps);
VectorOfKeyPoint vectorOfKp1 = new VectorOfKeyPoint(match.RightKps);
matches2.Push(new VectorOfDMatch(match.Matches.ToArray().OrderBy((x) => x.Distance).Take((int)(match.Matches.Size * takeBest)).ToArray()));
// Features2DToolbox.DrawMatches(left, vectorOfKp1, right, vectorOfKp2, matches2, matchesImage, new Bgr(Color.Red).MCvScalar, new Bgr(Color.Blue).MCvScalar);
Features2DToolbox.DrawMatches(right, vectorOfKp1, left, vectorOfKp2, matches2, matchesImage, new Bgr(Color.Red).MCvScalar, new Bgr(Color.Blue).MCvScalar);
macthedView.Source = ImageLoader.ImageSourceForBitmap(matchesImage.Bitmap);
}
private void UdpateFrame(int n)
{
if (Frames == null || n >= Frames.Count - Step || n < 0)
{
isRunning = false;
nextFrameTimer.Stop();
return;
}
Dispatcher.BeginInvoke((Action)(() =>
{
currentFrame = n;
try
{
var frame = frames[n];
var frame2 = frames[n + Step];
frame = Undistort(frame);
frame2 = Undistort(frame2);
var mat = frame.ToImage <Bgr, byte>();
var mat2 = frame2.ToImage <Bgr, byte>();
double maxDistance = MaxDistance(frame);
Func <int, int, MatchingResult> matcher = (i1, i2) =>
{
if (!features.TryGetValue(i1, out var features1))
{
MatchImagePair.FindFeatures(frames[i1], Detector, Descriptor, out MKeyPoint[] kps1, out Mat desc1);
features1 = new MatchingResult()
{
LeftKps = kps1,
LeftDescriptors = desc1
};
}
if (!features.TryGetValue(i2, out var features2))
{
MatchImagePair.FindFeatures(frames[i2], Detector, Descriptor, out MKeyPoint[] kps2, out Mat desc2);
features2 = new MatchingResult()
{
LeftKps = kps2,
LeftDescriptors = desc2
};
}
return(MatchImagePair.Match(features1.LeftKps, features1.LeftDescriptors, features2.LeftKps, features2.LeftDescriptors, DistanceType, maxDistance));
};
OdometerFrame odometerFrame = scaler.NextFrame(n, n + Step, matcher);
// OdometerFrame odometerFrame = FindTransformation.GetOdometerFrame(mat.Mat, mat2.Mat, Detector, Descriptor, DistanceType, maxDistance, K);
if (odometerFrame != null)
{
videoViewer.Source = ImageLoader.ImageSourceForBitmap(frame.Bitmap);
recursive = true;
frameProgression.Value = n;
recursive = false;
frameCurrentLabel.Content = n;
totalRotation = odometerFrame.RotationMatrix.Multiply(totalRotation);
var rotationEuler = RotationConverter.MatrixToEulerXYZ(totalRotation);
totalTranslation = totalTranslation + odometerFrame.Translation;
infoComputed.Text = FormatInfo(odometerFrame.Translation, odometerFrame.Rotation, "Comp Diff");
infoComputedCumulative.Text = FormatInfo(totalTranslation, rotationEuler, "Comp Cumulative");
infoK.Text = FormatInfoK(odometerFrame);
MatchDrawer.DrawFeatures(mat.Mat, mat2.Mat, odometerFrame.Match, TakeBest, matchedView);
}
}
catch (Exception e)
{
infoComputed.Text = "Error!";
}
if (isRunning)
{
nextFrameTimer.Start();
}
}));
}