public mTextureWood(double RingParameter)
{
Rings = RingParameter;
Pattern = new WoodTexture(Rings);
Texture = Pattern;
}
public void apply_test()
{
#region doc_apply
// Ensure results can be reproduced:
Accord.Math.Random.Generator.Seed = 0;
// Let's generate a square image containing a wooden texture:
Bitmap wood = new WoodTexture().Generate(512, 512).ToBitmap();
// Show the image or save it to disk so we can visualize it later:
// ImageBox.Show(wood); // visualizes it
// wood.Save("wood.png"); // saves to disk
// Create a new Haralick extractor:
Haralick haralick = new Haralick()
{
Mode = HaralickMode.AverageWithRange,
CellSize = 0 // use the entire image
};
// Extract the descriptors from the texture image
List <double[]> descriptors = haralick.ProcessImage(wood);
// If desired, we can obtain the GLCM for the image:
GrayLevelCooccurrenceMatrix glcm = haralick.Matrix;
// Since we specified CellSize = 0 when we created the Haralick object
// above, the Haralick extractor should have been computed considering
// the entire image and therefore it will return just one descriptor:
double[] descriptor = descriptors[0]; // should be similar to
// {
// 0.000452798780435224, 0.000176452252541844, 2185.7835571369, 1055.78890910489,
// 1819136356.0869, 31272466.1144943, 162.811942113822, 0.0294747289650559,
// 0.0600645989906271, 0.014234218481406, 325.617817549069, 0.0288571168872522,
// 124520.583178736, 1051.39892825528, 6.04466262360143, 0.0166538281740083,
// 9.9387182389777, 0.188948901162149, 4.21006097507467E-05, 1.50197212765552E-05,
// 4.51859838204172, 0.250994550822876, -0.127589342042208, 0.0356360581349288,
// 0.858214424241827, 0.0572031826003976
// }
#endregion
string str = descriptor.ToCSharp();
double[] expected = new double[] { 0.000452798780435224, 0.000176452252541844, 2185.7835571369, 1055.78890910489, 1819136356.0869, 31272466.1144943, 162.811942113822, 0.0294747289650559, 0.0600645989906271, 0.014234218481406, 325.617817549069, 0.0288571168872522, 124520.583178736, 1051.39892825528, 6.04466262360143, 0.0166538281740083, 9.9387182389777, 0.188948901162149, 4.21006097507467E-05, 1.50197212765552E-05, 4.51859838204172, 0.250994550822876, -0.127589342042208, 0.0356360581349288, 0.858214424241827, 0.0572031826003976 };
Assert.IsTrue(expected.IsEqual(descriptor, rtol: 1e-6));
Assert.AreEqual(1, descriptors.Count);
Assert.AreEqual(new HashSet <CooccurrenceDegree>
{
CooccurrenceDegree.Degree0,
CooccurrenceDegree.Degree45,
CooccurrenceDegree.Degree90,
CooccurrenceDegree.Degree135
}, haralick.Degrees);
}
private static ObjectId AddMaterial(string nameWood)
{
//
Database db = Application.DocumentManager.MdiActiveDocument.Database;
TransactionManager tm = db.TransactionManager;
Transaction trans = tm.StartTransaction();
Editor ed = Application.DocumentManager.MdiActiveDocument.Editor;
ObjectId materialId = ObjectId.Null;
try
{
DBDictionary materialDict;
ObjectId materialDictId = db.MaterialDictionaryId;
materialDict = trans.GetObject(materialDictId, OpenMode.ForWrite) as DBDictionary;
Material material = new Material();
material.Name = nameWood;
WoodTexture woodTexture = new WoodTexture();
MaterialColor matColor, woodColor1, woodColor2;
EntityColor entColor = new EntityColor(128, 0, 128);
matColor = new MaterialColor(Method.Override, 1.0, entColor);
EntityColor entColor2 = new EntityColor(128, 128, 0);
woodColor1 = new MaterialColor(Method.Override, 1.0, entColor2);
EntityColor entColor3 = new EntityColor(0, 128, 128);
woodColor2 = new MaterialColor(Method.Override, 1.0, entColor3);
woodTexture.Color1 = woodColor1;
woodTexture.Color2 = woodColor2;
MaterialMap materialMap = new MaterialMap(Source.Procedural, woodTexture, 0, null);
material.Diffuse = new MaterialDiffuseComponent(matColor, materialMap);
MaterialColor inheritColor = new MaterialColor(Method.Inherit, 1.0, entColor);
material.Ambient = matColor;
material.Specular = new MaterialSpecularComponent(matColor, new MaterialMap(), 0.5);
material.Mode = Mode.Realistic;
materialDict.SetAt(nameWood, material);
materialId = material.ObjectId;
trans.Commit();
}
catch (System.Exception e)
{
ed.WriteMessage(e.Message);
trans.Abort();
}
return(materialId);
}
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);
}
