|
private Match ( |
||
| image1 | ||
| image2 | ||
| points1 | IEnumerable |
|
| points2 | IEnumerable |
|
| return | AForge.IntPoint[][] | |
private void btnCorrelation_Click(object sender, EventArgs e)
{
if (harrisPoints1 == null)
{
MessageBox.Show("Please, click Harris button first! :-)");
return;
}
// 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);
}
/// <summary>
/// Maximum cross-correlation feature point matching algorithm.
/// </summary>
/// <param name="image1">First image.</param>
/// <param name="image2">Second image.</param>
/// <param name="points1">Points from the first image.</param>
/// <param name="points2">Points from the second image.</param>
/// <param name="windowSize">The size of the correlation window.</param>
/// <param name="maxDistance">The maximum distance to consider points as correlated.</param>
/// <returns>Matched point-pairs.</returns>
public static Point[][] Match(Gray<byte>[,] image1, Gray<byte>[,] image2, Point[] points1, Point[] points2, int windowSize, int maxDistance)
{
Point[][] matches = null;
using (var uImg1 = image1.Lock())
using(var uImg2 = image2.Lock())
{
var correlationMatching = new CorrelationMatching(windowSize, maxDistance, uImg1.AsBitmap(), uImg2.AsBitmap());
matches = correlationMatching.Match(points1.ToPoints(), points2.ToPoints()).ToPoints();
}
return matches;
}
public void MatchTest()
{
int windowSize = 3;
#pragma warning disable 0618
CorrelationMatching target = new CorrelationMatching(windowSize);
#pragma warning restore 0618
Bitmap image1 = Accord.Imaging.Image.Clone(Properties.Resources.image1);
Bitmap image2 = Accord.Imaging.Image.Clone(Properties.Resources.image1);
IntPoint[] points1 =
{
new IntPoint( 3, 3),
new IntPoint(14, 3),
new IntPoint( 3, 14),
new IntPoint(14, 14),
};
IntPoint[] points2 =
{
new IntPoint( 3, 3),
new IntPoint(14, 3),
new IntPoint( 3, 14),
new IntPoint(14, 14),
};
IntPoint[][] expected =
{
new IntPoint[]
{
new IntPoint( 3, 3),
new IntPoint(14, 3),
new IntPoint( 3, 14),
new IntPoint(14, 14)
},
new IntPoint[]
{
new IntPoint( 3, 3),
new IntPoint(14, 3),
new IntPoint( 3, 14),
new IntPoint(14, 14)
},
};
#pragma warning disable 0618
IntPoint[][] actual = target.Match(image1, image2, points1, points2);
#pragma warning restore 0618
Assert.IsTrue(actual.IsEqual(expected));
}
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);
}
public void MatchTest()
{
int windowSize = 3;
CorrelationMatching target = new CorrelationMatching(windowSize);
Bitmap image1 = Properties.Resources.image1;
Bitmap image2 = Properties.Resources.image1;
IntPoint[] points1 =
{
new IntPoint( 3, 3),
new IntPoint(14, 3),
new IntPoint( 3, 14),
new IntPoint(14, 14),
};
IntPoint[] points2 =
{
new IntPoint( 3, 3),
new IntPoint(14, 3),
new IntPoint( 3, 14),
new IntPoint(14, 14),
};
IntPoint[][] expected =
{
new IntPoint[]
{
new IntPoint( 3, 3),
new IntPoint(14, 3),
new IntPoint( 3, 14),
new IntPoint(14, 14)
},
new IntPoint[]
{
new IntPoint( 3, 3),
new IntPoint(14, 3),
new IntPoint( 3, 14),
new IntPoint(14, 14)
},
};
IntPoint[][] actual = target.Match(image1, image2, points1, points2);
Assert.IsTrue(actual.IsEqual(expected));
}
public void MatchTest2()
{
for (int windowSize = 1; windowSize <= 15; windowSize += 2)
{
CorrelationMatching target = new CorrelationMatching(windowSize);
Bitmap image1 = Properties.Resources.image1;
Bitmap image2 = Properties.Resources.image1;
Assert.AreEqual(16, image1.Height);
Assert.AreEqual(16, image2.Height);
Assert.AreEqual(16, image1.Width);
Assert.AreEqual(16, image2.Width);
// will test every possible point in the image
// (and also some points outside just to make sure)
List<IntPoint> points = new List<IntPoint>();
for (int i = -5; i < 20; i++)
for (int j = -5; j < 20; j++)
points.Add(new IntPoint(i, j));
// Assert that no exception if thrown
IntPoint[][] actual = target.Match(image1, image2, points.ToArray(), points.ToArray());
Assert.IsNotNull(actual);
Assert.AreEqual(2, actual.Length);
var p1 = actual[0]; var p2 = actual[1];
Assert.AreEqual(p1.Length, p2.Length);
for (int i = 0; i < p1.Length; i++)
{
// As the images are the same, assert that
// each point correlates with itself.
Assert.AreEqual(p1[i], p2[i]);
// Also assert we have no bogus values
Assert.IsTrue(p1[i].X >= 0 && p1[i].X < 16);
Assert.IsTrue(p1[i].Y >= 0 && p1[i].Y < 16);
}
}
}
/// <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 bool GenerateKeypointMatches(Bitmap target, Bitmap frame, Rectangle prevTarget)
{
HarrisCornersDetector harrisDetector = new HarrisCornersDetector(HarrisCornerMeasure.Harris, 1000f, 1f, 2);
targetKeyPoints=harrisDetector.ProcessImage(target).ToArray();
frameKeyPoints=harrisDetector.ProcessImage(frame).ToArray();
//Console.WriteLine("tr={0} fr={1}", targetKeyPoints.Length, frameKeyPoints.Length);
if (targetKeyPoints.Length==0||frameKeyPoints.Length==0)
{
return false;
}
CorrelationMatching matcher=new CorrelationMatching(15);
IntPoint[][] matches=matcher.Match(target, frame, targetKeyPoints, frameKeyPoints);
targetMacthes=matches[0];
frameMatches=matches[1];
if (targetMacthes.Length<4||frameMatches.Length<4)
{
return false;
}
RansacHomographyEstimator ransac=new RansacHomographyEstimator(0.1, 0.50);
MatrixH estiment = new MatrixH();
try
{
estiment = ransac.Estimate(targetMacthes, frameMatches);
}
catch
{
return false;
}
IntPoint[] targetGoodMatch=targetMacthes.Submatrix(ransac.Inliers);
IntPoint[] frameGoodMatch=frameMatches.Submatrix(ransac.Inliers);
CalculatePosChange(targetGoodMatch, frameGoodMatch, prevTarget);
return true;
}
// PANORAMIC STICHING CODE
// Accord.NET and AForge.NET frameworks and code examples provided from
// http://www.codeproject.com/KB/recipes/automatic_panoramas.aspx
private void panoramicStitchingToolStripMenuItem_Click(object sender, EventArgs e)
{
// Save a copy of the current image, ask user to open another image to merge with
Bitmap img2 = img;
openToolStripMenuItem_Click(sender, e);
// Check whether the current loaded image is different
// (If the user cancelled the open image operation, the current image in the viewer
// is still the same image object)
if (img2 != img)
{
Bitmap img1 = img;
AForge.IntPoint[] harrisPoints1;
AForge.IntPoint[] harrisPoints2;
AForge.IntPoint[] correlationPoints1;
AForge.IntPoint[] correlationPoints2;
MatrixH homography;
// Use Harris Corners Detector to find points of interest
HarrisCornersDetector harris = new HarrisCornersDetector(0.04f, 1000f);
harrisPoints1 = harris.ProcessImage(img1).ToArray();
harrisPoints2 = harris.ProcessImage(img2).ToArray();
// This check fixes an out of bounds exception generated by matcher.Match() below when a
// monocolour image (0 harris points) is stitched with a non-monocolour image
if (harrisPoints1.Length == 0 || harrisPoints2.Length == 0)
{
MessageBox.Show("Panoramic stitching cannot continue because at least one of the images does not contain any Harris points.", "Warning");
}
else
{
// Match detected points using correlation
CorrelationMatching matcher = new CorrelationMatching(9);
AForge.IntPoint[][] matches = matcher.Match(img1, img2, harrisPoints1, harrisPoints2);
// Separate the two arrays
correlationPoints1 = matches[0];
correlationPoints2 = matches[1];
// Find homography matrix using RANSAC algorithm
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99);
// This check is to handle the ransac.Estimate() function
// which throws an exception if the array parameters do not contain at least 4 elements
if (correlationPoints1.Length < 4 || correlationPoints2.Length < 4)
{
MessageBox.Show("Panoramic stitching cannot continue because at least one of the images does not contain at least 4 correlation points.", "Warning");
}
else
{
homography = ransac.Estimate(correlationPoints1, correlationPoints2);
// Merge the images
Blend blend = new Blend(homography, img1);
img = blend.Apply(img2);
//save the image properly and resize main form
origImg.Dispose();
origImg = new Bitmap(img);
pictureBox.Image = img;
mainForm.ActiveForm.Width = img.Width + widthPad;
mainForm.ActiveForm.Height = img.Height + heightPad;
}
}
}
}