public static void createBoW()
{
string[] files = Directory.GetFiles ("testset");
Dictionary<string, Bitmap> testImages = new Dictionary<string, Bitmap>();
for (int i = 0; i < files.Length; i++) {
string name = Path.GetFileNameWithoutExtension (files [i]);
testImages.Add(name, (Bitmap)Bitmap.FromFile(files[i]));
}
int numberOfWords = 6; // number of cluster centers: typically >>100
// Create a Binary-Split clustering algorithm
BinarySplit binarySplit = new BinarySplit(numberOfWords);
// Create bag-of-words (BoW) with the given algorithm
BagOfVisualWords surfBow = new BagOfVisualWords(binarySplit);
// Compute the BoW codebook using training images only
Bitmap[] bmps = new Bitmap[testImages.Count];
testImages.Values.CopyTo(bmps, 0);
surfBow.Compute(bmps);
surfBow.Save ("bagOfWords");
}
public void BinarySplitConstructorTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Declare some observations
double[][] observations =
{
new double[] { -5, -2, -1 },
new double[] { -5, -5, -6 },
new double[] { 2, 1, 1 },
new double[] { 1, 1, 2 },
new double[] { 1, 2, 2 },
new double[] { 3, 1, 2 },
new double[] { 11, 5, 4 },
new double[] { 15, 5, 6 },
new double[] { 10, 5, 6 },
};
double[][] orig = observations.MemberwiseClone();
// Create a new binary split with 3 clusters
BinarySplit binarySplit = new BinarySplit(3);
// Compute the algorithm, retrieving an integer array
// containing the labels for each of the observations
int[] labels = binarySplit.Compute(observations);
// As a result, the first two observations should belong to the
// same cluster (thus having the same label). The same should
// happen to the next four observations and to the last three.
Assert.AreEqual(labels[0], labels[1]);
Assert.AreEqual(labels[2], labels[3]);
Assert.AreEqual(labels[2], labels[4]);
Assert.AreEqual(labels[2], labels[5]);
Assert.AreEqual(labels[6], labels[7]);
Assert.AreEqual(labels[6], labels[8]);
Assert.AreNotEqual(labels[0], labels[2]);
Assert.AreNotEqual(labels[2], labels[6]);
Assert.AreNotEqual(labels[0], labels[6]);
int[] labels2 = binarySplit.Clusters.Nearest(observations);
Assert.IsTrue(labels.IsEqual(labels2));
// the data must not have changed!
Assert.IsTrue(orig.IsEqual(observations));
}
/// <summary>
/// This methods computes the Bag-of-Visual-Words with the training images.
/// </summary>
///
private void btnBagOfWords_Click(object sender, EventArgs e)
{
int numberOfWords = (int)numWords.Value;
// Create a Binary-Split clustering algorithm
BinarySplit binarySplit = new BinarySplit(numberOfWords);
Stopwatch sw1 = Stopwatch.StartNew();
IBagOfWords<Bitmap> bow;
if (rbSurf.Checked)
{
// Create bag-of-words (BoW) with the given algorithm
var surfBow = new BagOfVisualWords(binarySplit);
// Compute the BoW codebook using training images only
surfBow.Compute(originalTrainImages.Values.ToArray());
bow = surfBow;
}
else
{
// Alternative creation using the FREAK detector
// Create a Binary-Split clustering algorithm
var kmodes = new KModes<byte>(numberOfWords, Distance.BitwiseHamming);
var detector = new FastRetinaKeypointDetector();
// Create bag-of-words (BoW) with the given algorithm
var freakBow = new BagOfVisualWords<FastRetinaKeypoint, byte[]>(detector, kmodes);
// Compute the BoW codebook using training images only
freakBow.Compute(originalTrainImages.Values.ToArray());
bow = freakBow;
}
sw1.Stop();
Stopwatch sw2 = Stopwatch.StartNew();
// Extract features for all images
foreach (ListViewItem item in listView1.Items)
{
// Get item image
Bitmap image = originalImages[item.ImageKey] as Bitmap;
// Process image
double[] featureVector = bow.GetFeatureVector(image);
string featureString = featureVector.ToString(DefaultArrayFormatProvider.InvariantCulture);
if (item.SubItems.Count == 2)
item.SubItems[1].Text = featureString;
else item.SubItems.Add(featureString);
int classLabel = (item.Tag as Tuple<double[], int>).Item2;
item.Tag = Tuple.Create(featureVector, classLabel);
}
sw2.Stop();
lbStatus.Text = "BoW constructed in " + sw1.Elapsed + "s. Features extracted in " + sw2.Elapsed + "s.";
btnSampleRunAnalysis.Enabled = true;
}
public void binary_split_new_method()
{
#region doc_sample1
// Use a fixed seed for reproducibility
Accord.Math.Random.Generator.Seed = 0;
// Declare some data to be clustered
double[][] input =
{
new double[] { -5, -2, -1 },
new double[] { -5, -5, -6 },
new double[] { 2, 1, 1 },
new double[] { 1, 1, 2 },
new double[] { 1, 2, 2 },
new double[] { 3, 1, 2 },
new double[] { 11, 5, 4 },
new double[] { 15, 5, 6 },
new double[] { 10, 5, 6 },
};
// Create a new binary split with 3 clusters
BinarySplit binarySplit = new BinarySplit(3);
// Learn a data partitioning using the Binary Split algorithm
KMeansClusterCollection clustering = binarySplit.Learn(input);
// Predict group labels for each point
int[] output = clustering.Decide(input);
// As a result, the first two observations should belong to the
// same cluster (thus having the same label). The same should
// happen to the next four observations and to the last three.
#endregion
Assert.AreEqual(output[0], output[1]);
Assert.AreEqual(output[2], output[3]);
Assert.AreEqual(output[2], output[4]);
Assert.AreEqual(output[2], output[5]);
Assert.AreEqual(output[6], output[7]);
Assert.AreEqual(output[6], output[8]);
Assert.AreNotEqual(output[0], output[2]);
Assert.AreNotEqual(output[2], output[6]);
Assert.AreNotEqual(output[0], output[6]);
int[] labels2 = binarySplit.Clusters.Nearest(input);
Assert.IsTrue(output.IsEqual(labels2));
}
public void BinarySplitConstructorTest3()
{
// Create a new algorithm
BinarySplit binarySplit = new BinarySplit(3);
Assert.IsNotNull(binarySplit.Clusters);
Assert.IsNotNull(binarySplit.Distance);
Assert.IsNotNull(binarySplit.Clusters.Centroids);
Assert.IsNotNull(binarySplit.Clusters.Count);
Assert.IsNotNull(binarySplit.Clusters.Covariances);
Assert.IsNotNull(binarySplit.Clusters.Proportions);
}
/// <summary>
/// This methods computes the Bag-of-Visual-Words with the training images.
/// </summary>
///
private void btnBagOfWords_Click(object sender, EventArgs e)
{
int numberOfWords = (int)numWords.Value;
// Create a Binary-Split clustering algorithm
BinarySplit binarySplit = new BinarySplit(numberOfWords);
// Create bag-of-words (BoW) with the given algorithm
BagOfVisualWords bow = new BagOfVisualWords(binarySplit);
if (cbExtended.Checked)
bow.Detector.ComputeDescriptors = SpeededUpRobustFeatureDescriptorType.Extended;
Stopwatch sw1 = Stopwatch.StartNew();
// Compute the BoW codebook using training images only
var points = bow.Compute(originalTrainImages.Values.ToArray());
sw1.Stop();
Stopwatch sw2 = Stopwatch.StartNew();
// Extract features for all images
foreach (ListViewItem item in listView1.Items)
{
// Get item image
Bitmap image = originalImages[item.ImageKey] as Bitmap;
// Process image
double[] featureVector = bow.GetFeatureVector(image);
string featureString = featureVector.ToString(DefaultArrayFormatProvider.InvariantCulture);
if (item.SubItems.Count == 2)
item.SubItems[1].Text = featureString;
else item.SubItems.Add(featureString);
int classLabel = (item.Tag as Tuple<double[], int>).Item2;
item.Tag = Tuple.Create(featureVector, classLabel);
}
sw2.Stop();
lbStatus.Text = "BoW constructed in " + sw1.Elapsed + "s. Features extracted in " + sw2.Elapsed + "s.";
btnSampleRunAnalysis.Enabled = true;
}
private void setBinaryCluster(System.IO.StreamReader sr)
{
BinarySplit bSplit = new BinarySplit(k);
KMeansClusterCollection kmeansColl = bSplit.Clusters;
for (int i = 0; i < k; i++)
{
double[] mns = (from s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
sr.ReadLine();
double p = System.Convert.ToDouble(sr.ReadLine());
KMeansCluster kc = new KMeansCluster(kmeansColl, i);
kc.Mean = mns;
kc.Proportion = p;
}
clusterCollection = kmeansColl;
model = bSplit;
}
public void buildModel()
{
if (inputMatrix == null) getMatrix();
switch (cType)
{
case clusterType.KMEANS:
KMeans kmeans = new KMeans(k);
kmeans.Compute(inputMatrix, precision);
clusterCollection = kmeans.Clusters;
model = kmeans;
break;
case clusterType.BINARY:
BinarySplit bSplit = new BinarySplit(k);
bSplit.Compute(inputMatrix, precision);
clusterCollection = bSplit.Clusters;
model = bSplit;
//Console.WriteLine("BinarySplit");
break;
case clusterType.GAUSSIANMIXTURE:
GaussianMixtureModel gModel = new GaussianMixtureModel(k);
gModel.Compute(inputMatrix, precision);
clusterCollection = gModel.Gaussians;
model = gModel;
break;
default:
break;
}
lbl = new List<string>();
for (int i = 0; i < k; i++)
{
lbl.Add(i.ToString());
}
}