public mTextureClouds()
{
Pattern = new CloudsTexture();
Texture = Pattern;
}
public void custom_feature_test_haralick()
{
#region doc_feature_haralick
// Ensure results are reproducible
Accord.Math.Random.Generator.Seed = 0;
// The Bag-of-Visual-Words model converts images of arbitrary
// size into fixed-length feature vectors. In this example, we
// will be setting the codebook size to 3. This means all feature
// vectors that will be generated will have the same length of 3.
// By default, the BoW object will use the sparse SURF as the
// feature extractor and K-means as the clustering algorithm.
// In this example, we will use the Haralick feature extractor.
// Create a new Bag-of-Visual-Words (BoW) model using Haralick features
var bow = BagOfVisualWords.Create(new Haralick()
{
CellSize = 256, // divide images in cells of 256x256 pixels
Mode = HaralickMode.AverageWithRange,
}, new KMeans(3));
// Generate some training images. Haralick is best for classifying
// textures, so we will be generating examples of wood and clouds:
var woodenGenerator = new WoodTexture();
var cloudsGenerator = new CloudsTexture();
Bitmap[] images = new[]
{
woodenGenerator.Generate(512, 512).ToBitmap(),
woodenGenerator.Generate(512, 512).ToBitmap(),
woodenGenerator.Generate(512, 512).ToBitmap(),
cloudsGenerator.Generate(512, 512).ToBitmap(),
cloudsGenerator.Generate(512, 512).ToBitmap(),
cloudsGenerator.Generate(512, 512).ToBitmap()
};
// Compute the model
bow.Learn(images);
bow.ParallelOptions.MaxDegreeOfParallelism = 1;
// After this point, we will be able to translate
// images into double[] feature vectors using
double[][] features = bow.Transform(images);
#endregion
Assert.AreEqual(features.GetLength(), new[] { 6, 3 });
string str = features.ToCSharp();
double[][] expected = new double[][]
{
new double[] { 3, 0, 1 },
new double[] { 3, 0, 1 },
new double[] { 3, 0, 1 },
new double[] { 3, 1, 0 },
new double[] { 3, 1, 0 },
new double[] { 3, 1, 0 }
};
for (int i = 0; i < expected.Length; i++)
{
for (int j = 0; j < expected[i].Length; j++)
{
Assert.IsTrue(expected[i][j] == features[i][j]);
}
}
#region doc_classification_feature_haralick
// Now, the features can be used to train any classification
// algorithm as if they were the images themselves. For example,
// let's assume the first three images belong to a class and
// the second three to another class. We can train an SVM using
int[] labels = { -1, -1, -1, +1, +1, +1 };
// Create the SMO algorithm to learn a Linear kernel SVM
var teacher = new SequentialMinimalOptimization <Linear>()
{
Complexity = 100 // make a hard margin SVM
};
// Obtain a learned machine
var svm = teacher.Learn(features, labels);
// Use the machine to classify the features
bool[] output = svm.Decide(features);
// Compute the error between the expected and predicted labels
double error = new ZeroOneLoss(labels).Loss(output); // should be 0
#endregion
Assert.AreEqual(error, 0);
}
