/// <summary>
/// Features from Accelerated Segment Test (FAST) corners detector.
/// <para>Accord.NET internal call. Please see: <see cref="Accord.Imaging.FastCornersDetector"/> for details.</para>
/// </summary>
/// <param name="im">Image.</param>
/// <param name="threshold">The suppression threshold. Decreasing this value increases the number of points detected by the algorithm.</param>
/// <returns>Interest point locations.</returns>
public static List<IntPoint> CornerFeaturesDetector(this Image<Gray, byte> im, int threshold = 20)
{
FastCornersDetector fast = new FastCornersDetector(threshold);
var points = fast.ProcessImage(im.ToAForgeImage(copyAlways: false, failIfCannotCast: true));
return points;
}
public List <Rg.Point3d> GetFastCorners()
{
Ai.FastCornersDetector corners = new Ai.FastCornersDetector();
if (Threshold >= 0)
{
corners.Threshold = Threshold;
}
return(corners.ProcessImage(bitmap).ToRhinoPoints(bitmap.Height));
}
/// <summary>
/// Features from Accelerated Segment Test (FAST) corners detector.
/// <para>Accord.NET internal call. Please see: <see cref="Accord.Imaging.FastCornersDetector"/> for details.</para>
/// </summary>
/// <param name="im">Image.</param>
/// <param name="threshold">The suppression threshold. Decreasing this value increases the number of points detected by the algorithm.</param>
/// <returns>Interest point locations.</returns>
public static List<Point> CornerFeaturesDetector(this Gray<byte>[,] im, int threshold = 20)
{
FastCornersDetector fast = new FastCornersDetector(threshold);
List<Point> points;
using (var uImg = im.Lock())
{
points = fast.ProcessImage(uImg.AsAForgeImage())
.Select(x => x.ToPoint())
.ToList();
}
return points;
}
public void ExampleTest()
{
Bitmap lena = Resources.lena512;
// The freak detector can be used with any other corners detection
// algorithm. The default corners detection method used is the FAST
// corners detection. So, let's start creating this detector first:
//
var detector = new FastCornersDetector(60);
// Now that we have a corners detector, we can pass it to the FREAK
// feature extraction algorithm. Please note that if we leave this
// parameter empty, FAST will be used by default.
//
var freak = new FastRetinaKeypointDetector(detector);
// Now, all we have to do is to process our image:
List<FastRetinaKeypoint> points = freak.ProcessImage(lena);
// Afterwards, we should obtain 83 feature points. We can inspect
// the feature points visually using the FeaturesMarker class as
//
FeaturesMarker marker = new FeaturesMarker(points, scale: 20);
// And showing it on screen with
// ImageBox.Show(marker.Apply(lena));
// We can also inspect the feature vectors (descriptors) associated
// with each feature point. In order to get a descriptor vector for
// any given point, we can use
//
byte[] feature = points[42].Descriptor;
// By default, feature vectors will have 64 bytes in length. We can also
// display those vectors in more readable formats such as HEX or base64
//
string hex = points[42].ToHex();
string b64 = points[42].ToBase64();
// The above base64 result should be:
//
// "3W8M/ev///ffbr/+v3f34vz//7X+f0609v//+++/1+jfq/e83/X5/+6ft3//b4uaPZf7ePb3n/P93/rIbZlf+g=="
//
Assert.AreEqual(83, points.Count);
Assert.AreEqual(64, feature.Length);
Assert.AreEqual("3W8M/ev///ffbr/+v3f34vz//7X+f0609v//+++/1+jfq/e83/X5/+6ft3//b4uaPZf7ePb3n/P93/rIbZlf+g==", b64);
}
private void button1_Click(object sender, EventArgs e)
{
// Open a image
Bitmap lenna = Fast.Properties.Resources.lena512;
// Create a new SURF Features Detector using the given parameters
FastCornersDetector fast = new FastCornersDetector();
fast.Threshold = (int)numThreshold.Value;
fast.Suppress = true;
// Create a new AForge's Corner Marker Filter
CornersMarker corners = new CornersMarker(fast, Color.White);
// Apply the filter and display it on a picturebox
pictureBox1.Image = corners.Apply(lenna);
}
public void ProcessImageTest2()
{
UnmanagedImage image = UnmanagedImage.FromManagedImage(Accord.Imaging.Image.Clone(Properties.Resources.lena512));
FastCornersDetector target = new FastCornersDetector();
target.Suppress = true;
target.Threshold = 40;
List<IntPoint> actual = target.ProcessImage(image);
Assert.AreEqual(324, actual.Count);
Assert.AreEqual(506, actual[0].X);
Assert.AreEqual(4, actual[0].Y);
Assert.AreEqual(152, actual[6].X);
Assert.AreEqual(75, actual[6].Y);
Assert.AreEqual(416, actual[11].X);
Assert.AreEqual(115, actual[11].Y);
Assert.AreEqual(140, actual[65].X);
Assert.AreEqual(246, actual[65].Y);
Assert.AreEqual(133, actual[73].X);
Assert.AreEqual(253, actual[73].Y);
}
public void ProcessImageTest()
{
UnmanagedImage image = UnmanagedImage.FromManagedImage(Accord.Imaging.Image.Clone(Properties.Resources.sample_black));
FastCornersDetector target = new FastCornersDetector();
target.Suppress = false;
target.Threshold = 20;
List<IntPoint> actual = target.ProcessImage(image);
Assert.AreEqual(237, actual.Count);
Assert.AreEqual(404, actual[0].X);
Assert.AreEqual(35, actual[0].Y);
Assert.AreEqual(407, actual[6].X);
Assert.AreEqual(36, actual[6].Y);
Assert.AreEqual(407, actual[11].X);
Assert.AreEqual(38, actual[11].Y);
Assert.AreEqual(55, actual[65].X);
Assert.AreEqual(135, actual[65].Y);
Assert.AreEqual(103, actual[73].X);
Assert.AreEqual(137, actual[73].Y);
}
public void MatchTest3()
{
FastCornersDetector fast = new FastCornersDetector(threshold: 10);
FastRetinaKeypointDetector freak = new FastRetinaKeypointDetector(fast);
var keyPoints1 = freak.ProcessImage(Properties.Resources.old).ToArray();
var keyPoints2 = freak.ProcessImage(Properties.Resources.flower01).ToArray();
var matcher = new KNearestNeighborMatching<byte[]>(5, Distance.BitwiseHamming);
{ // direct
IntPoint[][] matches = matcher.Match(keyPoints1, keyPoints2);
Assert.AreEqual(2, matches.Length);
Assert.AreEqual(143, matches[0].Length);
Assert.AreEqual(143, matches[1].Length);
Assert.AreEqual(532, matches[0][0].X);
Assert.AreEqual(159, matches[0][0].Y);
Assert.AreEqual(keyPoints2[0].ToIntPoint(), matches[1][0]);
}
{ // reverse
IntPoint[][] matches = matcher.Match(keyPoints2, keyPoints1);
Assert.AreEqual(2, matches.Length);
Assert.AreEqual(143, matches[0].Length);
Assert.AreEqual(143, matches[1].Length);
Assert.AreEqual(keyPoints2[0].ToIntPoint(), matches[0][0]);
Assert.AreEqual(532, matches[1][0].X);
Assert.AreEqual(159, matches[1][0].Y);
}
}
public void SerializeTest2()
{
Accord.Math.Tools.SetupGenerator(0);
FastCornersDetector fast = new FastCornersDetector();
FastRetinaKeypointDetector freak = new FastRetinaKeypointDetector(fast);
var kmodes = new KModes<byte[]>(5, Distance.BitwiseHamming);
var bow = new BagOfVisualWords<FastRetinaKeypoint, byte[]>(freak, kmodes);
bow.Compute(images);
double[][] expected = new double[images.Length][];
for (int i = 0; i < expected.Length; i++)
expected[i] = bow.GetFeatureVector(images[i]);
MemoryStream stream = new MemoryStream();
BinaryFormatter fmt = new BinaryFormatter();
fmt.Serialize(stream, bow);
stream.Seek(0, SeekOrigin.Begin);
bow = (BagOfVisualWords<FastRetinaKeypoint, byte[]>)fmt.Deserialize(stream);
double[][] actual = new double[expected.Length][];
for (int i = 0; i < actual.Length; i++)
actual[i] = bow.GetFeatureVector(images[i]);
Assert.IsTrue(expected.IsEqual(actual));
}
public void ProcessImageTest()
{
UnmanagedImage image = UnmanagedImage.FromManagedImage(Properties.Resources.sample_black);
FastCornersDetector target = new FastCornersDetector();
target.Suppress = false;
target.Threshold = 20;
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(237, actual.Count);
Assert.AreEqual(404, actual[0].X);
Assert.AreEqual(35, actual[0].Y);
Assert.AreEqual(407, actual[6].X);
Assert.AreEqual(36, actual[6].Y);
Assert.AreEqual(407, actual[11].X);
Assert.AreEqual(38, actual[11].Y);
Assert.AreEqual(55, actual[65].X);
Assert.AreEqual(135, actual[65].Y);
Assert.AreEqual(103, actual[73].X);
Assert.AreEqual(137, actual[73].Y);
}
public void ProcessImageTest2()
{
UnmanagedImage image = UnmanagedImage.FromManagedImage(Properties.Resources.lena512);
FastCornersDetector target = new FastCornersDetector();
target.Suppress = true;
target.Threshold = 40;
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(324, actual.Count);
Assert.AreEqual(506, actual[0].X);
Assert.AreEqual(4, actual[0].Y);
Assert.AreEqual(152, actual[6].X);
Assert.AreEqual(75, actual[6].Y);
Assert.AreEqual(416, actual[11].X);
Assert.AreEqual(115, actual[11].Y);
Assert.AreEqual(140, actual[65].X);
Assert.AreEqual(246, actual[65].Y);
Assert.AreEqual(133, actual[73].X);
Assert.AreEqual(253, actual[73].Y);
}
