private void BtnCorrelation_OnClick(object sender, RoutedEventArgs e)
{
// Step 2: Match feature points using a correlation measure
CorrelationMatching matcher = new CorrelationMatching(9);
IntPoint[][] matches = matcher.Match(img1, img2, harrisPoints1, harrisPoints2);
// Get the two sets of points
correlationPoints1 = matches[0];
correlationPoints2 = matches[1];
// Concatenate the two images in a single image (just to show on screen)
Concatenate concat = new Concatenate(img1);
Bitmap img3 = concat.Apply(img2);
// Show the marked correlations in the concatenated image
PairsMarker pairs = new PairsMarker(
correlationPoints1, // Add image1's width to the X points to show the markings correctly
correlationPoints2.Apply(p => new IntPoint(p.X + img1.Width, p.Y)));
PictureBox.Source = pairs.Apply(img3);
}
private void btnCorrelation_Click(object sender, EventArgs e)
{
if (surfPoints1 == null)
{
MessageBox.Show("Please, click SURF button first! :-)");
return;
}
// Step 2: Match feature points using a k-NN
KNearestNeighborMatching matcher = new KNearestNeighborMatching(5);
IntPoint[][] matches = matcher.Match(surfPoints1, surfPoints2);
// Get the two sets of points
correlationPoints1 = matches[0];
correlationPoints2 = matches[1];
// Concatenate the two images in a single image (just to show on screen)
Concatenate concat = new Concatenate(img1);
Bitmap img3 = concat.Apply(img2);
// Show the marked correlations in the concatenated image
PairsMarker pairs = new PairsMarker(
correlationPoints1, // Add image1's width to the X points to show the markings correctly
correlationPoints2.Apply(p => new IntPoint(p.X + img1.Width, p.Y)));
pictureBox.Image = pairs.Apply(img3);
}
private void btnRansac_Click(object sender, EventArgs e)
{
if (correlationPoints1 == null)
{
MessageBox.Show("Please, click Nearest Neighbor button first! :-)");
return;
}
if (correlationPoints1.Length < 4 || correlationPoints2.Length < 4)
{
MessageBox.Show("Insufficient points to attempt a fit.");
return;
}
// Step 3: Create the homography matrix using a robust estimator
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99);
homography = ransac.Estimate(correlationPoints1, correlationPoints2);
// Plot RANSAC results against correlation results
IntPoint[] inliers1 = correlationPoints1.Submatrix(ransac.Inliers);
IntPoint[] inliers2 = correlationPoints2.Submatrix(ransac.Inliers);
// Concatenate the two images in a single image (just to show on screen)
Concatenate concat = new Concatenate(img1);
Bitmap img3 = concat.Apply(img2);
// Show the marked correlations in the concatenated image
PairsMarker pairs = new PairsMarker(
inliers1, // Add image1's width to the X points to show the markings correctly
inliers2.Apply(p => new IntPoint(p.X + img1.Width, p.Y)));
pictureBox.Image = pairs.Apply(img3);
}
/// <summary>
/// Matches detected keypoints.
/// </summary>
private void MatchKeypoints()
{
// matching keypoints step needs at least two images
if (input_images.Count < 2) return;
// using RANSAC homography estimator
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99);
CorrelationMatching matcher = new CorrelationMatching(9);
Bitmap mergedImage = new Bitmap(input_images.Count * input_images[0].Width, input_images[0].Height); //it is assumed the images are of same size!
Graphics graphics = Graphics.FromImage(mergedImage);
IntPoint[][] matches;
int cumulativeWidth = 0;
int keypoints_cntr = 0;
// draw all images
for (int i = 0; i < input_images.Count; i++) {
graphics.DrawImage(input_images[i], cumulativeWidth, 0, input_images[i].Width, input_images[i].Height);
cumulativeWidth += input_images[i].Width;
}
// iterate through all images
for (int i = 0; i < input_images.Count; i++)
{
if (i != input_images.Count - 1)
{
// match detected keypoints with maximum cross-correlation algorithm
matches = matcher.Match(new Bitmap(input_images[i]), new Bitmap(input_images[i + 1]), keypoints[keypoints_cntr].ToArray(), keypoints[keypoints_cntr + 1].ToArray());
IntPoint[] correlationPoints1 = matches[0];
IntPoint[] correlationPoints2 = matches[1];
// Plot RANSAC results against correlation results
homography = ransac.Estimate(correlationPoints1, correlationPoints2);
// take only inliers - good matches
IntPoint[] inliers1 = correlationPoints1.Submatrix(ransac.Inliers);
IntPoint[] inliers2 = correlationPoints2.Submatrix(ransac.Inliers);
PairsMarker pairs = new PairsMarker(correlationPoints1, correlationPoints2.Apply(p => new IntPoint(p.X + input_images[i].Width, p.Y)));
// draw all matching pairs
for (int j = 0; j < pairs.Points1.Count(); j++) {
if (i % 2 == 0) graphics.DrawLine(new Pen(Color.Red, 1.5f), new System.Drawing.Point(correlationPoints1[j].X + i * input_images[i].Width, correlationPoints1[j].Y), new System.Drawing.Point(correlationPoints2[j].X + (i + 1) * input_images[i].Width, correlationPoints2[j].Y));
}
// draw inliers
for (int j = 0; j < inliers1.Count(); j++) {
if (i % 2 == 0) graphics.DrawLine(new Pen(Color.GreenYellow, 1.5f), new System.Drawing.Point(inliers1[j].X + i * input_images[i].Width, inliers1[j].Y), new System.Drawing.Point(inliers2[j].X + (i + 1) * input_images[i].Width, inliers2[j].Y));
else graphics.DrawLine(new Pen(Color.Blue, 1.5f), new System.Drawing.Point(inliers1[j].X + i * input_images[i].Width, inliers1[j].Y), new System.Drawing.Point(inliers2[j].X + (i + 1) * input_images[i].Width, inliers2[j].Y));
}
keypoints_cntr += 2;
}
}
ShowThumbnail(mergedImage, true, PanoramaPhase.MatchKeypoints);
}
private Bitmap CompareAndDrawImage(Bitmap modelImage, Bitmap observedImage, SurfSettings setting)
{
Stopwatch watch1 = new Stopwatch();
Stopwatch watch2 = new Stopwatch();
Bitmap returnBitmap;
watch2.Start();
watch1.Reset(); watch1.Start();
double hessianThreshold = setting.HessianThresh.HasValue ? setting.HessianThresh.Value : 500;
float hessianThreshold2 = (float)hessianThreshold / 1000000;
Debug.WriteLine("hessianThreshold2: {0}", hessianThreshold2);
SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector(hessianThreshold2);
List<SpeededUpRobustFeaturePoint> surfPoints1 = surf.ProcessImage(modelImage);
List<SpeededUpRobustFeaturePoint> surfPoints2 = surf.ProcessImage(observedImage);
Debug.WriteLine("Surf points count: {0}", surfPoints1.Count);
Debug.WriteLine("Surf points count: {0}", surfPoints2.Count);
//long memoryFootprint = MemorySize.GetBlobSizeinKb(surfPoints2);
//Debug.WriteLine("Surf extractor: {0} kb", memoryFootprint);
watch1.Stop();
Debug.WriteLine("Surf Detection tooked {0} ms", watch1.ElapsedMilliseconds);
watch1.Reset(); watch1.Start();
// Show the marked points in the original images
Bitmap img1mark = new FeaturesMarker(surfPoints1, 2).Apply(modelImage);
Bitmap img2mark = new FeaturesMarker(surfPoints2, 2).Apply(observedImage);
// Concatenate the two images together in a single image (just to show on screen)
Concatenate concatenate = new Concatenate(img1mark);
returnBitmap = concatenate.Apply(img2mark);
watch1.Stop();
Debug.WriteLine("Surf point plotting tooked {0} ms", watch1.ElapsedMilliseconds);
//watch1.Reset(); watch1.Start();
//List<IntPoint>[] coretionalMatches = getMatches(surfPoints1, surfPoints2);
//watch1.Stop();
//Debug.WriteLine("Correctional Match tooked {0} ms", watch1.ElapsedMilliseconds);
//// Get the two sets of points
//IntPoint[] correlationPoints11 = coretionalMatches[0].ToArray();
//IntPoint[] correlationPoints22 = coretionalMatches[1].ToArray();
//Debug.WriteLine("Correclation points count: {0}", correlationPoints11.Length);
//Debug.WriteLine("Correclation points count: {0}", correlationPoints22.Length);
Debug.WriteLine("Threshold: {0}", setting.UniquenessThreshold.Value);
watch1.Reset(); watch1.Start();
// Step 2: Match feature points using a k-NN
KNearestNeighborMatching matcher = new KNearestNeighborMatching(2);
matcher.Threshold = setting.UniquenessThreshold.Value;
IntPoint[][] matches = matcher.Match(surfPoints1, surfPoints2);
watch1.Stop();
Debug.WriteLine("Knn Match tooked {0} ms", watch1.ElapsedMilliseconds);
// Get the two sets of points
IntPoint[] correlationPoints1 = matches[0];
IntPoint[] correlationPoints2 = matches[1];
Debug.WriteLine("Knn points count: {0}", correlationPoints1.Length);
Debug.WriteLine("Knn points count: {0}", correlationPoints2.Length);
//watch1.Reset(); watch1.Start();
//// Show the marked correlations in the concatenated image
//PairsMarker pairs = new PairsMarker(
// correlationPoints1, // Add image1's width to the X points to show the markings correctly
// correlationPoints2.Apply(p => new IntPoint(p.X + modelImage.Width, p.Y)), Color.Blue);
//returnBitmap = pairs.Apply(returnBitmap);
//watch1.Stop();
//Debug.WriteLine("Match pair marking tooked {0} ms", watch1.ElapsedMilliseconds);
if (correlationPoints1.Length < 4 || correlationPoints2.Length < 4)
{
MessageBox.Show("Insufficient points to attempt a fit.");
return null;
}
watch1.Reset(); watch1.Start();
// Step 3: Create the homography matrix using a robust estimator
//RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99);
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99);
MatrixH homography = ransac.Estimate(correlationPoints1, correlationPoints2);
watch1.Stop();
Debug.WriteLine("Ransac tooked {0} ms", watch1.ElapsedMilliseconds);
watch1.Reset(); watch1.Start();
// Plot RANSAC results against correlation results
IntPoint[] inliers1 = correlationPoints1.Submatrix(ransac.Inliers);
IntPoint[] inliers2 = correlationPoints2.Submatrix(ransac.Inliers);
watch1.Stop();
Debug.WriteLine("Ransac SubMatrix {0} ms", watch1.ElapsedMilliseconds);
Debug.WriteLine("Ransac points count: {0}", inliers1.Length);
Debug.WriteLine("Ransac points count: {0}", inliers2.Length);
watch1.Reset(); watch1.Start();
PairsMarker inlierPairs = new PairsMarker(
inliers1, // Add image1's width to the X points to show the markings correctly
inliers2.Apply(p => new IntPoint(p.X + modelImage.Width, p.Y)), Color.Red);
returnBitmap = inlierPairs.Apply(returnBitmap);
watch1.Stop();
Debug.WriteLine("Ransac plotting tooked {0} ms", watch1.ElapsedMilliseconds);
watch2.Stop();
return returnBitmap;
}
private void BtnRansac_OnClick(object sender, RoutedEventArgs e)
{
// Step 3: Create the homography matrix using a robust estimator
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99);
homography = ransac.Estimate(correlationPoints1, correlationPoints2);
// Plot RANSAC results against correlation results
IntPoint[] inliers1 = correlationPoints1.Submatrix(ransac.Inliers);
IntPoint[] inliers2 = correlationPoints2.Submatrix(ransac.Inliers);
// Concatenate the two images in a single image (just to show on screen)
Concatenate concat = new Concatenate(img1);
Bitmap img3 = concat.Apply(img2);
// Show the marked correlations in the concatenated image
PairsMarker pairs = new PairsMarker(
inliers1, // Add image1's width to the X points to show the markings correctly
inliers2.Apply(p => new IntPoint(p.X + img1.Width, p.Y)));
PictureBox.Source = pairs.Apply(img3);
}
private void button2_Click(object sender, EventArgs e)
{
// Step 3: Create the homography matrix using a robust estimator
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99);
homography = ransac.Estimate(correlationPoints1, correlationPoints2);
// Plot RANSAC results against correlation results
IntPoint[] inliers1 = correlationPoints1.Submatrix(ransac.Inliers);
IntPoint[] inliers2 = correlationPoints2.Submatrix(ransac.Inliers);
// Concatenate the two images in a single image (just to show on screen)
Concatenate concat = new Concatenate(img1);
Bitmap img3 = concat.Apply(img2);
// Show the marked correlations in the concatenated image
PairsMarker pairs = new PairsMarker(
inliers1, // Add image1's width to the X points to show the markings correctly
inliers2.Apply(p => new IntPoint(p.X + img1.Width, p.Y)));
pictureBox1.Image = pairs.Apply(img3);
numA.Value = (decimal)homography.Elements[0];
numB.Value = (decimal)homography.Elements[1];
numC.Value = (decimal)homography.Elements[2];
numD.Value = (decimal)homography.Elements[3];
numE.Value = (decimal)homography.Elements[4];
numF.Value = (decimal)homography.Elements[5];
numG.Value = (decimal)homography.Elements[6];
numH.Value = (decimal)homography.Elements[7];
}
