public Task <MatrixH> Adjust(Bitmap baseBitmap, Size baseOriginalSize, Bitmap targetBitmap, Size targetOriginalSize)
{
return(Task.Run(() =>
{
var basePoints = keypointDetector.Transform(baseBitmap).Select(p => new IntPoint((int)Math.Round(p.X), (int)Math.Round(p.Y))).ToArray();
var targetPoints = keypointDetector.Transform(targetBitmap).Select(p => new IntPoint((int)Math.Round(p.X), (int)Math.Round(p.Y))).ToArray();
var matches = new CorrelationMatching(9, 50, baseBitmap, targetBitmap).Match(basePoints, targetPoints);
var baseMatchPoints = matches[0].Select(p => new IntPoint(p.X * baseOriginalSize.Width / baseBitmap.Width, p.Y * baseOriginalSize.Height / baseBitmap.Height)).ToArray();
var targetMatchPoints = matches[1].Select(p => new IntPoint(p.X * targetOriginalSize.Width / targetBitmap.Width, p.Y * targetOriginalSize.Height / targetBitmap.Height)).ToArray();
var tasks = Enumerable.Range(0, 20).AsParallel().Select(a =>
{
RansacHomographyEstimator ransac = new RansacHomographyEstimator(0.001, 0.99);
ransac.Ransac.MaxEvaluations = 1000;
ransac.Ransac.MaxSamplings = 100;
var homography = ransac.Estimate(baseMatchPoints, targetMatchPoints);
if (ransac.Inliers.Length > 0)
{
var directions =
Accord.Math.Matrix.Get(baseMatchPoints, ransac.Inliers).Zip(
Accord.Math.Matrix.Get(targetMatchPoints, ransac.Inliers),
(point1, point2) => Math.Atan2(point2.Y - point1.Y, point2.X - point1.X) + Math.PI * 2).ToArray();
var avgDirection = directions.Average();
var variance = directions.Average(d => Math.Pow(d - avgDirection, 2));
return new
{
homography,
ransac.Inliers.Length,
variance
};
}
else
{
return null;
}
}).Where(h => h != null).OrderByDescending(h => h.Length).ThenBy(h => h.variance).ToArray();
return tasks.FirstOrDefault()?.homography;
}));
}
private void btnFreak_Click(object sender, EventArgs e)
{
// Step 1: Detect feature points using FREAK Features Detector
FastRetinaKeypointDetector freak = new FastRetinaKeypointDetector();
keyPoints1 = freak.Transform(img1);
keyPoints2 = freak.Transform(img2);
// Show the marked points in the original images
// TODO: The following construct can be simplified
Bitmap img1mark = new PointsMarker(keyPoints1.Select(x => (IFeaturePoint)x).ToList()).Apply(img1);
Bitmap img2mark = new PointsMarker(keyPoints2.Select(x => (IFeaturePoint)x).ToList()).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);
}
public void ExampleTest()
{
Bitmap lena = Accord.Imaging.Image.Clone(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:
IList <FastRetinaKeypoint> points = freak.Transform(lena).ToList();
// 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 process1()
{
var bitmap1 = (Bitmap)sourcebox1.Image;
var bitmap2 = (Bitmap)sourcebox2.Image;
var hash1 = ImagePhash.ComputeDigest(bitmap1.ToLuminanceImage());
var hash2 = ImagePhash.ComputeDigest(bitmap2.ToLuminanceImage());
var score = ImagePhash.GetCrossCorrelation(hash1, hash2);
Console.WriteLine("score: {0}", score);
//threshold value
var thres = new Threshold(110);
Grayscale filter = new Grayscale(0.2125, 0.7154, 0.0721);
// apply the filter to the model
Bitmap grey1 = filter.Apply(bitmap1);
thres.ApplyInPlace(grey1);
// Apply the filter to the observed image
Bitmap grey2 = filter.Apply(bitmap2);
thres.ApplyInPlace(grey2);
int modelPoints = 0, matchingPoints = 0;
var skewChecker = new DocumentSkewChecker();
var angle1 = skewChecker.GetSkewAngle(grey1);
var rotationFilter1 = new RotateBicubic(-angle1);
rotationFilter1.FillColor = Color.White;
grey1 = rotationFilter1.Apply(grey1);
var angle2 = skewChecker.GetSkewAngle(grey2);
var rotationFilter2 = new RotateBicubic(-angle2);
rotationFilter2.FillColor = Color.White;
grey2 = rotationFilter2.Apply(grey2);
//CorrelationMatching matcher = new CorrelationMatching(5, grey1, grey2);
//var results = matcher.GetHashCode();
var detector = new FastCornersDetector(15);
var freak = new FastRetinaKeypointDetector(detector);
FastRetinaKeypoint[] features1 = freak.Transform(grey1).ToArray();
modelPoints = features1.Count();
Console.WriteLine("count: {0}", modelPoints);
FastRetinaKeypoint[] features2 = freak.Transform(grey2).ToArray();
Console.WriteLine("count: {0}", features2.Count());
KNearestNeighborMatching matcher = new KNearestNeighborMatching(7);
//var length = 0;
IntPoint[][] results = matcher.Match(features1, features2);
matchingPoints = results[0].Count(); // similarity of image1 to image2
////matchingPoints = results[1].Count(); // similarity of image2 to image1
Console.WriteLine("matched points: {0}", matchingPoints);
sourcebox1.Image = bitmap1;
sourcebox2.Image = bitmap2;
var marker1 = new FeaturesMarker(features1, 30);
var marker2 = new FeaturesMarker(features2, 30);
double similPercent = 0;
if (matchingPoints <= 0)
{
similPercent = 0.0f;
}
similPercent = (matchingPoints * 100d) / (double)modelPoints;
Console.WriteLine("score: {0}", similPercent);
simil1.Text = similPercent.ToString("##.##") + "%";
simil2.Text = (score * 100.00d).ToString("##.##") + "%";
angle_text.Text = angle2.ToString("##.##") + "°";
resultbox1.Image = marker1.Apply(grey1);
resultbox2.Image = marker2.Apply(grey2);
}
public void ProcessImageTest()
{
Bitmap lena = Accord.Imaging.Image.Clone(Resources.lena512);
FastRetinaKeypointDetector target = new FastRetinaKeypointDetector();
IList <FastRetinaKeypoint> actual = target.Transform(lena).ToList();
string code;
Assert.AreEqual(1283, actual.Count);
int i = 0;
Assert.AreEqual(223, actual[i].X);
Assert.AreEqual(45, actual[i].Y);
Assert.AreEqual(80.122163710144434, actual[i].Orientation);
code = actual[i].ToBinary();
Assert.AreEqual("00101100101000101100000010111000110111111010111011110000011011110111111111101001011000111111010110100110101001000010100000001100100000111110110100000010100010010000100000110000100010110000000101110001100100101100011010111101100100110101111110000100010100111101100001010101110010000001100111111010100001111000111001101000011101111011110111001101111010101101001111110101010100000100100100101100101010011010011000000000001010000110101010011000111001111011111101110101110011100001001010110010100100001000101001010011", code);
code = actual[i].ToHex();
Assert.AreEqual("3445031dfb750ff6fe97c6af65251430c1b74091100cd1808e4963bdc9fa21ca1baa13985fe17116eebdb357cbaf0a9234956500145619e7fdae73484d0951ca", code);
code = actual[i].ToBase64();
Assert.AreEqual("NEUDHft1D/b+l8avZSUUMMG3QJEQDNGAjkljvcn6IcobqhOYX+FxFu69s1fLrwqSNJVlABRWGef9rnNITQlRyg==", code);
i = 124;
Assert.AreEqual(141.0, actual[i].X);
Assert.AreEqual(184.0, actual[i].Y);
Assert.AreEqual(158.74949449286677, actual[i].Orientation);
code = actual[i].ToBinary();
Assert.AreEqual("11111000010001010000011000000010000100100010010101000010100000011110101010110111001110010110111110010101100001000100010100001000110010000001010000010000000001010011010110000010101110110000001000010000100000010101011010111011110000001011101110001011100001110010000001000000001011000010100010001001100001101000010100001000000001001010110100000000001010011101001010000111011010011000110010000100110000100010011001010000001010010110001000010000111000011000000000100100110000100001001000010010100100000000100001000001", code);
code = actual[i].ToHex();
Assert.AreEqual("1fa2604048a4428157ed9cf6a921a210132808a0ac41dd4008816add03ddd1e1040234149161a11020b500944be196312143640a944608870124434848091082", code);
code = actual[i].ToBase64();
Assert.AreEqual("H6JgQEikQoFX7Zz2qSGiEBMoCKCsQd1ACIFq3QPd0eEEAjQUkWGhECC1AJRL4ZYxIUNkCpRGCIcBJENISAkQgg==", code);
i = 763;
Assert.AreEqual(104, actual[i].X);
Assert.AreEqual(332, actual[i].Y);
Assert.AreEqual(-174.28940686250036, actual[i].Orientation);
code = actual[i].ToBinary();
Assert.AreEqual("01010001111011001100011000010110101011111010010001000101001111110111111101101101111000101101010111010110111010001110001011011100111000010000010111100000010010010101010001010001110100010101111011000001101100111001001100001000101100010111011110111100111101110100011000100101001010010011010000111110000100000000000011001000000011111011010011011001001010011101010011111101001001010111000001010100011011110111010000010101100000010110110101111100101111111000100011111001100011100000110011011011100000001010100101011101", code);
code = actual[i].ToHex();
Assert.AreEqual("8a376368f525a2fcfeb647ab6b17473b87a007922a8a8b7a83cdc9108dee3def62a4942c7c080013f02d9b942bbfa40e2af62ea881b63efd119f7130db0195ba", code);
code = actual[i].ToBase64();
Assert.AreEqual("ijdjaPUlovz+tkeraxdHO4egB5Iqiot6g83JEI3uPe9ipJQsfAgAE/Atm5Qrv6QOKvYuqIG2Pv0Rn3Ew2wGVug==", code);
i = 1042;
Assert.AreEqual(116, actual[i].X);
Assert.AreEqual(410, actual[i].Y);
Assert.AreEqual(-86.11209043916692, actual[i].Orientation, 1e-10);
code = actual[i].ToBinary();
Assert.AreEqual("11110111010011000010101001011011010101100111010011011101001111010110001010110000111001010111101011001011001001101101100011011011110111110000010111111000000000010001001000010110101010010001100011011110101000100101100010101100000100111011101100010110001000111000100001110100001000110001110011111011000001100000111001001000011111111011010101001101110000010101001110111101011100001100110110000100111010100010000001000000011110000110111010011000111100111011111110110101110011100000001001110010100100001000101001110011", code);
code = actual[i].ToHex();
Assert.AreEqual("ef3254da6a2ebbbc460da75ed3641bdbfba01f80486895187b451a35c8dd68c4112ec438df607012feadb283cabd0eb3215704021e7619cffdad73404e0951ce", code);
code = actual[i].ToBase64();
Assert.AreEqual("7zJU2mouu7xGDade02Qb2/ugH4BIaJUYe0UaNcjdaMQRLsQ432BwEv6tsoPKvQ6zIVcEAh52Gc/9rXNATglRzg==", code);
i = 1282;
Assert.AreEqual(425.0, actual[i].X);
Assert.AreEqual(488.0, actual[i].Y);
Assert.AreEqual(-70.722526916337571, actual[i].Orientation);
code = actual[i].ToBinary();
Assert.AreEqual("10111011111111011101111000011111111101110111111011111111111111111111111111111011011110111111111111111111011011101001111101101110111101111111101111111110111011101111111010110011100100111110101101111111111110111111111110111111111111111111011011101101110111111111111010110101110111000001010101111111100111011000111111110111011100111111111111111101111110101111111111110111010101000111011100111111101011011011111100011011111011111111101111111001111011111011111111110111111111110001001110111110100110101111111001010111", code);
code = actual[i].ToHex();
Assert.AreEqual("ddbf7bf8ef7effffffdfdeffff76f976efdf7f777fcdc9d7fedffffdff6fb7fb7fad3ba8feb9f1efceffbf5fffef2aeefcb5fdd8f7df9ff7fdefffc87d597fea", code);
}