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.Get(ransac.Inliers);
IntPoint[] inliers2 = correlationPoints2.Get(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);
}
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 knn()
{
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)));
img3 = img3.Clone(new Rectangle(0, 0, img3.Width, img3.Height), PixelFormat.Format24bppRgb);
var pic = pairs.Apply(img3);
pictureBox1.Image = pic;
}
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];
}
protected void drawFastHarrisFeaturesCorrelations(List <Bitmap> imgs)
{
List <IntPoint[]> harrisPoints = new List <IntPoint[]>();
MatrixH homography;
//Calculate all the Harris Points
HarrisCornersDetector harris = new HarrisCornersDetector(0.03f, 10000f);
foreach (Bitmap img in imgs)
{
harrisPoints.Add(harris.ProcessImage(img).ToArray());
}
//Map them and draw them!
Bitmap harrisImg = imgs[0];
for (int i = 0; i < imgs.Count - 1; i++)
{
//Correlate the Harris pts between imgs
CorrelationMatching matcher = new CorrelationMatching(5, imgs[i], imgs[i + 1]);
IntPoint[][] matches = matcher.Match(harrisPoints[i], harrisPoints[i + 1]);
//Create the homography matrix using ransac
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.025, 0.99);
homography = ransac.Estimate(matches[0], matches[1]);
Concatenate concat = new Concatenate(harrisImg);
Bitmap img = concat.Apply(imgs[i + 1]);
Color color = Color.White;
if (i % 3 == 1)
{
color = Color.OrangeRed;
}
if (i % 3 == 2)
{
color = Color.Blue;
}
PairsMarker pairs = new PairsMarker(matches[0].Apply(p => new IntPoint(p.X + harrisImg.Width - imgs[0].Width, p.Y)), matches[1].Apply(p => new IntPoint(p.X + harrisImg.Width, p.Y)), color);
harrisImg = pairs.Apply(img);
}
showImage(harrisImg);
}
/// <summary>
/// </summary>
/// <param name="sender">
/// </param>
/// <param name="e">
/// </param>
private void BtnCorrelation_OnClick(object sender, RoutedEventArgs e)
{
// Step 2: Match feature points using a correlation measure
var matcher = new CorrelationMatching(9, this.img1, this.img2);
var matches = matcher.Match(this.harrisPoints1, this.harrisPoints2);
// Get the two sets of points
this.correlationPoints1 = matches[0];
this.correlationPoints2 = matches[1];
// Concatenate the two images in a single image (just to show on screen)
var concat = new Concatenate(this.img1);
var img3 = concat.Apply(this.img2);
// Show the marked correlations in the concatenated image
var pairs = new PairsMarker(this.correlationPoints1, // Add image1's width to the X points to show the markings correctly
this.correlationPoints2.Apply(p => new IntPoint(p.X + this.img1.Width, p.Y)));
this.PictureBox.Source = (ImageSource)pairs.Apply(img3);
}
private void btnCorrelation_Click(object sender, EventArgs 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.Image = pairs.Apply(img3);
}
// Robust homography estimation
private void RansacRobustHomographer()
{
// 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)));
_processImage3 = pairs.Apply(img3);
}
/// <summary>
/// </summary>
/// <param name="sender">
/// </param>
/// <param name="e">
/// </param>
private void BtnRansac_OnClick(object sender, RoutedEventArgs e)
{
// Step 3: Create the homography matrix using a robust estimator
var ransac = new RansacHomographyEstimator(0.001, 0.99);
this.homography = ransac.Estimate(this.correlationPoints1, this.correlationPoints2);
// Plot RANSAC results against correlation results
var inliers1 = this.correlationPoints1.Submatrix(ransac.Inliers);
var inliers2 = this.correlationPoints2.Submatrix(ransac.Inliers);
// Concatenate the two images in a single image (just to show on screen)
var concat = new Concatenate(this.img1);
var img3 = concat.Apply(this.img2);
// Show the marked correlations in the concatenated image
var pairs = new PairsMarker(
inliers1, // Add image1's width to the X points to show the markings correctly
inliers2.Apply(p => new IntPoint(p.X + this.img1.Width, p.Y)));
this.PictureBox.Source = (ImageSource)pairs.Apply(img3);
}
protected void drawFreakFeaturesCorrelations(List <Bitmap> imgs)
{
List <FastRetinaKeypoint[]> freakPoints = new List <FastRetinaKeypoint[]>();
//Calculate all the FREAK Points
FastRetinaKeypointDetector freak = new FastRetinaKeypointDetector();
foreach (Bitmap img in imgs)
{
freakPoints.Add(freak.ProcessImage(img).ToArray());
}
//Map them and draw them!
Bitmap img2 = imgs[0];
for (int i = 0; i < imgs.Count - 1; i++)
{
KNearestNeighborMatching matcher = new KNearestNeighborMatching(200);
matcher.Threshold = 0.015;
IntPoint[][] matches = matcher.Match(freakPoints[i], freakPoints[i + 1]);
Concatenate concat = new Concatenate(img2);
Bitmap img3 = concat.Apply(imgs[i + 1]);
Color color = Color.White;
if (i % 3 == 1)
{
color = Color.OrangeRed;
}
if (i % 3 == 2)
{
color = Color.Blue;
}
PairsMarker pairs = new PairsMarker(matches[0].Apply(p => new IntPoint(p.X + img2.Width - imgs[0].Width, p.Y)), matches[1].Apply(p => new IntPoint(p.X + img2.Width, p.Y)), color);
img2 = pairs.Apply(img3);
}
showImage(img2);
}
protected void drawSurfFeaturesCorrelations(List <Bitmap> imgs)
{
List <SpeededUpRobustFeaturePoint[]> surfPoints = new List <SpeededUpRobustFeaturePoint[]>();
//Calculate all the Surf Points
SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector();
foreach (Bitmap img in imgs)
{
surfPoints.Add(surf.ProcessImage(img).ToArray());
}
//Map them and draw them!
Bitmap surfImg = imgs[0];
for (int i = 0; i < imgs.Count - 1; i++)
{
KNearestNeighborMatching matcher = new KNearestNeighborMatching(5);
matcher.Threshold = 0.005;
IntPoint[][] matches = matcher.Match(surfPoints[i], surfPoints[i + 1]);
Concatenate concat = new Concatenate(surfImg);
Bitmap img = concat.Apply(imgs[i + 1]);
Color color = Color.White;
if (i % 3 == 1)
{
color = Color.OrangeRed;
}
if (i % 3 == 2)
{
color = Color.Blue;
}
PairsMarker pairs = new PairsMarker(matches[0].Apply(p => new IntPoint(p.X + surfImg.Width - imgs[0].Width, p.Y)), matches[1].Apply(p => new IntPoint(p.X + surfImg.Width, p.Y)), color);
surfImg = pairs.Apply(img);
}
showImage(surfImg);
}
// Correlation Matching
private void Correlator()
{
// 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)));
_processImage2 = pairs.Apply(img3);
}
public static Geometry AutoAlign(Bitmap needle, Bitmap haystack, double retryThreshold = 1, int retryLimit = 10)
{
IntPoint[] harrisPoints1;
IntPoint[] harrisPoints2;
IntPoint[] correlationPoints1;
IntPoint[] correlationPoints2;
MatrixH homography;
var mi1 = new MagickImage(needle); mi1.Equalize(); needle = mi1.ToBitmap();
var mi2 = new MagickImage(haystack); mi2.Equalize(); haystack = mi2.ToBitmap();
HarrisCornersDetector harris = new HarrisCornersDetector(0.04f, 20000f);
harrisPoints1 = harris.ProcessImage(needle).ToArray();
harrisPoints2 = harris.ProcessImage(haystack).ToArray();
CorrelationMatching matcher = new CorrelationMatching(9, needle, haystack);
IntPoint[][] matches = matcher.Match(harrisPoints1, harrisPoints2);
correlationPoints1 = matches[0];
correlationPoints2 = matches[1];
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.999);
homography = ransac.Estimate(correlationPoints1, correlationPoints2);
IntPoint[] inliers1 = correlationPoints1.Get(ransac.Inliers);
IntPoint[] inliers2 = correlationPoints2.Get(ransac.Inliers);
Concatenate concat = new Concatenate(needle);
Bitmap img3 = concat.Apply(haystack);
PairsMarker pairs = new PairsMarker(
inliers1,
inliers2.Apply(p => new IntPoint(p.X + needle.Width, p.Y)));
var Image = pairs.Apply(img3);
Image.Save(@"C:\AutoAlignDebug.png");
var pointCount = inliers1.Length;
int[] xList1 = new int[pointCount];
int[] yList1 = new int[pointCount];
int[] xList2 = new int[pointCount];
int[] yList2 = new int[pointCount];
for (int n = 0; n < pointCount; n++)
{
xList1[n] = inliers1[n].X;
yList1[n] = inliers1[n].Y;
xList2[n] = inliers2[n].X;
yList2[n] = inliers2[n].Y;
}
var f = new double[8] {
xList1.Min(), yList1.Min(), xList1.Max(), yList1.Max(), xList2.Min(), yList2.Min(), xList2.Max(), yList2.Max()
};
double distFromX1 = f[0] / needle.Width;
double distFromX2 = f[2] / needle.Width;
double leftRatio = f[0] / (f[2] - f[0]);
double rightRatio = (needle.Width - f[2]) / (f[2] - f[0]);
double distFromY1 = f[1] / needle.Height;
double distFromY2 = f[3] / needle.Height;
double topRatio = f[1] / (f[3] - f[1]);
double bottomRatio = (needle.Height - f[3]) / (f[3] - f[1]);
double leftDist = (f[6] - f[4]) * leftRatio;
double rightDist = (f[6] - f[4]) * rightRatio;
double topDist = (f[7] - f[5]) * topRatio;
double bottomDist = (f[7] - f[5]) * bottomRatio;
double x = f[4] - leftDist;
double y = f[5] - topDist;
double width = leftDist + (f[6] - f[4]) + rightDist;
double height = topDist + (f[7] - f[5]) + bottomDist;
mi1.Resize(new MagickGeometry((int)Math.Round(width), (int)Math.Round(height))
{
IgnoreAspectRatio = true
});
var mg = new MagickGeometry((int)Math.Round(x), (int)Math.Round(y), (int)Math.Round(width), (int)Math.Round(height))
{
IgnoreAspectRatio = true
};
mi2.Extent(mg, Gravity.Northwest, MagickColor.FromRgba(0, 0, 0, 0));
double delta = mi1.Compare(mi2, ErrorMetric.NormalizedCrossCorrelation);
Geometry outGeo = new Geometry(x, y, width, height);
if (delta < retryThreshold && retryLimit > 0)
{
retryLimit--;
outGeo = AutoAlign(needle, haystack, delta, retryLimit);
}
return(outGeo);
}
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);
}
