//Relies less on neighbor pixels and more on RANSAC
protected void fastHarrisRansacBlend(List <Bitmap> imgs)
{
List <IntPoint[]> harrisPoints = new List <IntPoint[]>();
MatrixH homography;
//Calculate all the Harris Points
HarrisCornersDetector harris = new HarrisCornersDetector(0.03f, 10000f);
for (int i = 0; i < imgs.Count; i++)
{
harrisPoints.Add(harris.ProcessImage(imgs[i]).ToArray());
}
Bitmap final = imgs[0];
for (int i = 1; i < imgs.Count; i++)
{
IntPoint[] harrisFinal = harris.ProcessImage(final).ToArray();
//Correlate the Harris pts between imgs
CorrelationMatching matcher = new CorrelationMatching(5, final, imgs[i]);
IntPoint[][] matches = matcher.Match(harrisFinal, harrisPoints[i]);
//Create the homography matrix using ransac
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.025, 0.99);
homography = ransac.Estimate(matches[0], matches[1]);
Blend blend = new Blend(homography, final);
blend.Gradient = true;
final = blend.Apply(imgs[i]);
}
showImage(final);
}
/*
* Detects feature points using Harris Corners Detection.
* Corners are a good feature to match on photos because they are stable and have
* large variations in their neighborhood.
*/
private void HarrisCornersDetectorRecursive()
{
HarrisCornersDetector harris = new HarrisCornersDetector(0.04f, 1000f);
harrisPoints1 = harris.ProcessImage(images[0]).ToArray();
harrisPoints2 = harris.ProcessImage(images[1]).ToArray();
}
protected void fastHarrisRansacBlendStraight(List <Bitmap> imgs)
{
List <IntPoint[]> harrisPoints = new List <IntPoint[]>();
MatrixH homography;
//Calculate all the Harris Points
HarrisCornersDetector harris = new HarrisCornersDetector(0.03f, 10000f);
for (int i = 0; i < imgs.Count; i++)
{
harrisPoints.Add(harris.ProcessImage(imgs[i]).ToArray());
}
Bitmap final = imgs[0];
for (int i = 1; i < imgs.Count; i++)
{
//Convert my frames to grayscale so I can find and adjust the normal vectors
AForge.Imaging.Filters.GrayscaleBT709 grayscale = new AForge.Imaging.Filters.GrayscaleBT709();
AForge.Imaging.DocumentSkewChecker skew = new AForge.Imaging.DocumentSkewChecker();
double finalAngle = skew.GetSkewAngle(grayscale.Apply(final));
double imgAngle = skew.GetSkewAngle(grayscale.Apply(imgs[i]));
//Less than 5% to account for human error with rotations and wobbles
if (Math.Abs(finalAngle - imgAngle) < 5)
{
AForge.Imaging.Filters.RotateBilinear rotate = new AForge.Imaging.Filters.RotateBilinear(finalAngle - imgAngle);
rotate.FillColor = Color.FromArgb(0, 255, 255, 255);
imgs[i] = rotate.Apply(imgs[i]);
//Update harris
harrisPoints[i] = harris.ProcessImage(imgs[i]).ToArray();
}
IntPoint[] harrisFinal = harris.ProcessImage(final).ToArray();
//Correlate the Harris pts between imgs
CorrelationMatching matcher = new CorrelationMatching(5, final, imgs[i]);
IntPoint[][] matches = matcher.Match(harrisFinal, harrisPoints[i]);
//Create the homography matrix using ransac
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.025, 0.99);
homography = ransac.Estimate(matches[0], matches[1]);
Blend blend = new Blend(homography, final);
blend.Gradient = true;
final = blend.Apply(imgs[i]);
}
showImage(final);
}
public void ProcessImageTest()
{
UnmanagedImage image = UnmanagedImage.FromManagedImage(Accord.Imaging.Image.Clone(Properties.Resources.image1));
HarrisCornersDetector target = new HarrisCornersDetector(0.04f, 1000f, 1.4);
target.Suppression = 1;
List <IntPoint> actual = target.ProcessImage(image);
/*
* PointsMarker marker = new PointsMarker(actual.ToArray());
* marker.Width = 1;
* marker.MarkerColor = Color.FromArgb(128, 128, 128);
* var markers = marker.Apply(image);
* ImageBox.Show(markers.ToManagedImage(), PictureBoxSizeMode.Zoom);
*/
/*
* Assert.AreEqual(4, actual.Count);
* Assert.IsTrue(actual.Contains(new IntPoint(3, 3)));
* Assert.IsTrue(actual.Contains(new IntPoint(14, 3)));
* Assert.IsTrue(actual.Contains(new IntPoint(3, 14)));
* Assert.IsTrue(actual.Contains(new IntPoint(14, 14)));
*/
Assert.AreEqual(4, actual.Count);
Assert.IsTrue(actual.Contains(new IntPoint(3, 3)));
Assert.IsTrue(actual.Contains(new IntPoint(12, 3)));
Assert.IsTrue(actual.Contains(new IntPoint(3, 12)));
Assert.IsTrue(actual.Contains(new IntPoint(12, 12)));
}
/// <summary>
/// </summary>
/// <param name="sender">
/// </param>
/// <param name="e">
/// </param>
private void BtnHarris_OnClick(object sender, RoutedEventArgs e)
{
// Step 1: Detect feature points using Harris Corners Detector
var harris = new HarrisCornersDetector(0.04f, 1000f);
this.harrisPoints1 = harris.ProcessImage(this.img1).ToArray();
this.harrisPoints2 = harris.ProcessImage(this.img2).ToArray();
// Show the marked points in the original images
var img1mark = new PointsMarker(this.harrisPoints1).Apply(this.img1);
var img2mark = new PointsMarker(this.harrisPoints2).Apply(this.img2);
// Concatenate the two images together in a single image (just to show on screen)
var concatenate = new Concatenate(img1mark);
this.PictureBox.Source = (ImageSource)concatenate.Apply(img2mark);
}
// Interest point detection
private void InterestPtDetector()
{
// Step 1: Detect feature points using Harris Corners Detector
HarrisCornersDetector harris = new HarrisCornersDetector(0.04f, 1000f);
harrisPoints1 = harris.ProcessImage(_img1).ToArray();
harrisPoints2 = harris.ProcessImage(_img2).ToArray();
// Show the marked points in the original images
Bitmap img1mark = new PointsMarker(harrisPoints1).Apply(_img1);
Bitmap img2mark = new PointsMarker(harrisPoints2).Apply(_img2);
// Concatenate the two images together in a single image (just to show on screen)
Concatenate concatenate = new Concatenate(img1mark);
_processImage1 = concatenate.Apply(img2mark);
}
private void btnHarris_Click(object sender, EventArgs e)
{
// Step 1: Detect feature points using Harris Corners Detector
HarrisCornersDetector harris = new HarrisCornersDetector(0.04f, 1000f);
harrisPoints1 = harris.ProcessImage(img1).ToArray();
harrisPoints2 = harris.ProcessImage(img2).ToArray();
// Show the marked points in the original images
Bitmap img1mark = new PointsMarker(harrisPoints1).Apply(img1);
Bitmap img2mark = new PointsMarker(harrisPoints2).Apply(img2);
// Concatenate the two images together in a single image (just to show on screen)
Concatenate concatenate = new Concatenate(img1mark);
pictureBox.Image = concatenate.Apply(img2mark);
}
/// <summary>
/// Harris Corners Detector.
/// <para>Accord.NET internal call. Please see: <see cref="Accord.Imaging.HarrisCornersDetector"/> for details.</para>
/// </summary>
/// <typeparam name="TDepth">Channel type.</typeparam>
/// <param name="im">Image.</param>
/// <param name="measure">Corners measures.</param>
/// <param name="threshold">Harris threshold.</param>
/// <param name="sigma">Gaussian smoothing sigma.</param>
/// <param name="suppression">Non-maximum suppression window radius.</param>
/// <returns>Interest point locations.</returns>
public static List <IntPoint> HarrisCorners <TDepth>(this Image <Gray, TDepth> im, HarrisCornerMeasure measure = HarrisCornerMeasure.Harris, float threshold = 20000f, double sigma = 1.2, int suppression = 3)
where TDepth : struct
{
HarrisCornersDetector harris = new HarrisCornersDetector(measure, threshold, sigma, suppression);
var points = harris.ProcessImage(im.ToAForgeImage(copyAlways: false, failIfCannotCast: true));
return(points);
}
/// <summary>
/// Поиск ключевых точек
/// </summary>
/// <param name="sigma">Сигма</param>
/// <param name="k">k</param>
/// <param name="threshold">Граница</param>
/// <param name="img">Изображение</param>
/// <returns>Лист ключевых точек</returns>
public List <IntPoint> KeyPoint(double sigma, float k, float threshold, Bitmap img)
{
HarrisCornersDetector harris = new HarrisCornersDetector(k)
{
Threshold = threshold,
Sigma = sigma
};
var OriginCorner = harris.ProcessImage(img);
return(OriginCorner);
}
public void GetCorners(int threshold, int sigma)
{
// create corners detector's instance
HarrisCornersDetector hcd = new HarrisCornersDetector(HarrisCornerMeasure.Noble, threshold, sigma);
// Apply the filter and return the points
List <IntPoint> corners = hcd.ProcessImage(AForge.Imaging.Image.FromFile(this.CurrentImage));
if (ImageComplete != null)
{
ImageComplete(corners);
}
}
/// <summary>
/// Harris Corners Detector.
/// <para>Accord.NET internal call. Please see: <see cref="Accord.Imaging.HarrisCornersDetector"/> for details.</para>
/// </summary>
/// <param name="im">Image.</param>
/// <param name="measure">Corners measures.</param>
/// <param name="threshold">Harris threshold.</param>
/// <param name="sigma">Gaussian smoothing sigma.</param>
/// <param name="suppression">Non-maximum suppression window radius.</param>
/// <returns>Interest point locations.</returns>
public static List <IntPoint> HarrisCorners <TDepth>(this Gray <byte>[,] im, HarrisCornerMeasure measure = HarrisCornerMeasure.Harris, float threshold = 20000f, double sigma = 1.2, int suppression = 3)
{
HarrisCornersDetector harris = new HarrisCornersDetector(measure, threshold, sigma, suppression);
List <IntPoint> points;
using (var uImg = im.Lock())
{
points = harris.ProcessImage(uImg.AsAForgeImage());
}
return(points);
}
private void MakeCorners_Click(object sender, EventArgs e)
{
CornerListView.Items.Clear();
// Zaciągnięcie pliku do bitmapy
Bitmap image = Accord.Imaging.Image.FromFile("Samples/" + ImageList.Text);
float kinput = float.Parse(k_Input.Text);
float thresholdinput = float.Parse(Threshold_input.Text);
float sigmainput = float.Parse(sigma_Input.Text);
// Rozpoznawanie wierzchołków Harrisa (Lepiej się sprawdziło niż np. Suzan)
HarrisCornersDetector target = new HarrisCornersDetector(kinput, thresholdinput, sigmainput);
target.Suppression = 1;
// Wyznaczenie wierzchołków do listy punktów
List <Accord.IntPoint> actual = target.ProcessImage(image);
for (int i = 0, n = actual.Count; i < n; i++)
{
// Listowanie punktów wierzchołków
Console.WriteLine("Punkt: [" + actual[i].X + " , " + actual[i].Y + "]");
CornerListView.Items.Add("Punkt " + (i + 1) + " : [" + actual[i].X + " , " + actual[i].Y + "]");
try
{
// Generowanie białych kwadratów w miejscu wierzchołków
for (int w = 0; w <= 10; w++)
{
for (int h = 0; h <= 10; h++)
{
image.SetPixel(actual[i].X + (w - 5), actual[i].Y + (h - 5), Color.White);
}
}
}
catch
{
Console.WriteLine("Piksel za burtą!");
}
}
Preview_1.Image = image;
}
protected void drawHarrisFeatures(List <Bitmap> imgs)
{
List <IntPoint[]> harrisPoints = new List <IntPoint[]>();
//Calculate all the Harris Points
HarrisCornersDetector harris = new HarrisCornersDetector(0.03f, 10000f);
foreach (Bitmap img in imgs)
{
harrisPoints.Add(harris.ProcessImage(img).ToArray());
}
//Draw & Show all the harris points
for (int i = 0; i < imgs.Count; i++)
{
showImage(new PointsMarker(harrisPoints[i]).Apply(imgs[i]));
}
}
//---------------------------------------Модуль для обработки Accord (ключевых точек)---------------------------------------//
void CornerFound(params object[] list)
{
if (list.Length != 1)
{
double sigma = (double)list[3];
double k = (double)list[1];
int threshold = (int)list[2];
// Create a new Harris Corners Detector using the given parameters
HarrisCornersDetector harris = new HarrisCornersDetector((float)k)
{
Threshold = (float)threshold,
Sigma = sigma
};
// Create a new AForge's Corner Marker Filter
CornersMarker corners = new CornersMarker(harris, Color.White);
// input.RotateFlip(RotateFlipType.Rotate180FlipX);
var OriginCorner = harris.ProcessImage((Bitmap)list[0]); // ОРИГИНАЛ
var CornerRrev = corners.Apply((Bitmap)list[0]);
var Picture = GrayImg.ResizeImg(pictureBox1.Height, pictureBox1.Width); // подгонка для первоначального показа
Bitmap endpick = new Bitmap(CornerRrev, new Size(Picture.Item2, Picture.Item1));
pictureBox1.Image = endpick;
CreateTable(OriginCorner);// построение таблицы key-точек
}
else
{
double sigma = (double)numSigma.Value;
float k = (float)numK.Value;
float threshold = (float)numThreshold.Value;
// Create a new Harris Corners Detector using the given parameters
HarrisCornersDetector harris = new HarrisCornersDetector(k)
{
Threshold = threshold,
Sigma = sigma
};
// Create a new AForge's Corner Marker Filter
CornersMarker corners = new CornersMarker(harris, Color.White);
// input.RotateFlip(RotateFlipType.Rotate180FlipX);
var OriginCorner = harris.ProcessImage((Bitmap)list[0]); // ОРИГИНАЛ
var CornerRrev = corners.Apply((Bitmap)list[0]);
var Picture = GrayImg.ResizeImg(pictureBox1.Height, pictureBox1.Width); // подгонка для первоначального показа
Bitmap endpick = new Bitmap(CornerRrev, new Size(Picture.Item2, Picture.Item1));
pictureBox1.Image = endpick;
CreateTable(OriginCorner);// построение таблицы key-точек
}
}
public void ProcessImageTest2()
{
UnmanagedImage image = UnmanagedImage.FromManagedImage(Accord.Imaging.Image.Clone(Properties.Resources.sample_black));
HarrisCornersDetector target = new HarrisCornersDetector(HarrisCornerMeasure.Noble, 700f, 1.4, 1);
List <IntPoint> actual = target.ProcessImage(image);
/*
* PointsMarker marker = new PointsMarker(actual.ToArray());
* marker.Width = 3;
* marker.MarkerColor = Color.FromArgb(255, 0, 0);
* var markers = marker.Apply(image);
* ImageBox.Show(markers.ToManagedImage(), PictureBoxSizeMode.Zoom);
*/
Assert.AreEqual(actual.Count, 42);
}
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);
}
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);
}
//Finds the center coordinate of the part
//Utilizes Harris Corner Detection from the Accord.Net Library
//Reference Material:http://accord-framework.net/docs/html/T_Accord_Imaging_HarrisCornersDetector.htm
public Bitmap findCenter(Bitmap inImage)
{
//Creating Harris Corener Dection method
HarrisCornersDetector cornerDetector = new HarrisCornersDetector(.04f, 100);
//List of IntPoints containing the found corners
List <Accord.IntPoint> cornerArray = cornerDetector.ProcessImage(inImage);
//Creating a temporary bitmap to apply graphics to(Grpahics doesn't accept canny images)
Bitmap tempBitmap = new Bitmap(inImage.Width, inImage.Height);
Graphics g = Graphics.FromImage(tempBitmap);
//Redrawing original image on temporary bitmap
g.DrawImage(inImage, 0, 0);
Pen aquaPen = new Pen(Color.Aqua, 2.0f);
//Draws a circle at each found corner
foreach (Accord.IntPoint p in cornerArray)
{
g.DrawEllipse(aquaPen, p.X - 1, p.Y + 1, 2, 2);
}
//Finds edges of the part by looking for the minimum and maximum x and y values from found corners
//IMPORTANT: This does not exculde outlier corners sometimes found in images. These are ussaly eliminated by increasing gama or the light in the overall picture. An outlier remover may be necessary based on conditions
if (cornerArray.Count > 1)
{
int topEdge = cornerArray[0].Y;
int bottomEdge = cornerArray[cornerArray.Count - 1].Y;
int leftEdge = cornerArray[0].X;
int rightEdge = cornerArray[cornerArray.Count - 1].X;
for (int i = 0; i < cornerArray.Count - 1; i++)
{
if (topEdge > cornerArray[i].Y)
{
topEdge = cornerArray[i].Y;
}
if (bottomEdge < cornerArray[i].Y)
{
bottomEdge = cornerArray[i].Y;
}
if (leftEdge > cornerArray[i].X)
{
leftEdge = cornerArray[i].X;
}
if (rightEdge < cornerArray[i].X)
{
rightEdge = cornerArray[i].X;
}
}
//Storing the X and Y dimensions of the part
xDimensionPixels = Math.Abs(rightEdge - leftEdge);
yDimensionPixels = Math.Abs(topEdge - bottomEdge);
//Finding center of the part based on the avearge of the previously found edges
centerPoint.X = ((int)((leftEdge + rightEdge) / 2.0));
centerPoint.Y = (int)((topEdge + bottomEdge) / 2.0);
//Draws a circle at the center point of the image
g.DrawEllipse(aquaPen, centerPoint.X - 10f, centerPoint.Y - 10f, 20f, 20f);
}
//Draws the center and corners if desired by the users
if (centerCheckbox.Checked)
{
cannyImagePanel.Image = tempBitmap;
}
return(tempBitmap);
}