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 btnSurf_Click(object sender, EventArgs e)
{
// Step 1: Detect feature points using Surf Corners Detector
SpeededUpRobustFeaturesDetector surf = new SpeededUpRobustFeaturesDetector();
surfPoints1 = surf.ProcessImage(img1).ToArray();
surfPoints2 = surf.ProcessImage(img2).ToArray();
// Show the marked points in the original images
Bitmap img1mark = new FeaturesMarker(surfPoints1).Apply(img1);
Bitmap img2mark = new FeaturesMarker(surfPoints2).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);
}
private void button1_Click(object sender, EventArgs e)
{
// Open a image
Bitmap lenna = Properties.Resources.lena512;
float threshold = (float)numThreshold.Value;
int octaves = (int)numOctaves.Value;
int initial = (int)numInitial.Value;
// Create a new SURF Features Detector using the given parameters
SpeededUpRobustFeaturesDetector surf =
new SpeededUpRobustFeaturesDetector(threshold, octaves, initial);
List<SpeededUpRobustFeaturePoint> points = surf.ProcessImage(lenna);
// Create a new AForge's Corner Marker Filter
FeaturesMarker features = new FeaturesMarker(points);
// Apply the filter and display it on a picturebox
pictureBox1.Image = features.Apply(lenna);
}
private void button1_Click(object sender, EventArgs e)
{
// Open a image
Bitmap lenna = Surf.Properties.Resources.lena512;
// Create a new SURF Features Detector using the given parameters
SpeededUpRobustFeaturesDetector surf =
new SpeededUpRobustFeaturesDetector(0.0002f, 5, 2);
var points = surf.ProcessImage(lenna);
// Get the SURF Features Descriptor from the detector
SurfDescriptor descriptor = surf.GetDescriptor();
descriptor.Describe(points);
// Create a new AForge's Corner Marker Filter
FeaturesMarker features = new FeaturesMarker(points.ToArray());
// Apply the filter and display it on a picturebox
pictureBox1.Image = features.Apply(lenna);
}
public void ProcessImageTest4()
{
Bitmap[] bitmaps =
{
Resources.flower01,
Resources.flower03,
Resources.flower06,
Resources.flower07,
Resources.flower09,
Resources.flower10,
};
var surf = new SpeededUpRobustFeaturesDetector();
int current = 0;
foreach (Bitmap img in bitmaps)
{
List<SpeededUpRobustFeaturePoint> expected;
List<SpeededUpRobustFeaturePoint> actual;
// Create OpenSURF detector by Chris Evans
{
// Create Integral Image
OpenSURFcs.IntegralImage iimg = OpenSURFcs.IntegralImage.FromImage(img);
// Extract the interest points
var pts = OpenSURFcs.FastHessian.getIpoints(0.0002f, 5, 2, iimg);
// Describe the interest points
OpenSURFcs.SurfDescriptor.DecribeInterestPoints(pts, false, false, iimg);
expected = new List<SpeededUpRobustFeaturePoint>();
foreach (var p in pts)
expected.Add(new SpeededUpRobustFeaturePoint(p.x, p.y, p.scale,
p.laplacian, p.orientation, p.response));
}
// Create Accord.NET SURF detector (based on OpenSURF by Chris Evans)
{
actual = surf.ProcessImage(img);
}
var img1 = new FeaturesMarker(actual).Apply(img);
var img2 = new FeaturesMarker(expected).Apply(img);
// ImageBox.Show(new Concatenate(img1).Apply(img2), PictureBoxSizeMode.Zoom);
current++;
for (int i = 0; i < expected.Count; i++)
{
var e = expected[i];
var a = actual[i];
Assert.AreEqual(e, a);
}
}
}
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;
}
