private void PredictImages(object sender, RoutedEventArgs e)
{
imageInfo.Text = "Predicting";
if (bow == null)
{
MessageBox.Show("No BoW model!");
return;
}
if (svmIm == null)
{
MessageBox.Show("No SVM model!");
return;
}
Bitmap[] trainIms = new Bitmap[imagesEdited.Count];
ushort z = 0;
foreach (BitmapImage b in imagesEdited)
{
trainIms[z++] = UtilFn.BitmapImage2Bitmap(b);
}
double[][] features = bow.Transform(trainIms);
output = svmIm.Decide(features);
if (output != null)
{
if (output[j] && !cvs.Children.Contains(rectSel2))
{
cvs.Children.Add(rectSel2);
}
else if (!output[j] && cvs.Children.Contains(rectSel2))
{
cvs.Children.Remove(rectSel2);
}
}
imageInfo.Text = "Done";
btnCorrect.IsEnabled = true;
}
public void learn_new()
{
#region doc_learn
// 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 10. This means all feature
// vectors that will be generated will have the same length of 10.
// By default, the BoW object will use the sparse SURF as the
// feature extractor and K-means as the clustering algorithm.
// Create a new Bag-of-Visual-Words (BoW) model
BagOfVisualWords bow = new BagOfVisualWords(10);
// Note: the BoW model can also be created using
// var bow = BagOfVisualWords.Create(10);
// Get some training images
Bitmap[] images = GetImages();
// Compute the model
bow.Learn(images);
// After this point, we will be able to translate
// images into double[] feature vectors using
double[][] features = bow.Transform(images);
#endregion
var kmeans = bow.Clustering as KMeans;
Assert.AreEqual(64, kmeans.Clusters.NumberOfInputs);
Assert.AreEqual(10, kmeans.Clusters.NumberOfOutputs);
Assert.AreEqual(10, kmeans.Clusters.NumberOfClasses);
string str = kmeans.Clusters.Proportions.ToCSharp();
double[] expectedProportions = new double[] { 0.0960793804453049, 0.0767182962245886, 0.103823814133591, 0.0738141335914811, 0.0997095837366893, 0.0815585672797677, 0.0788964181994192, 0.090513068731849, 0.117376573088093, 0.181510164569216 };
Assert.IsTrue(kmeans.Clusters.Proportions.IsEqual(expectedProportions, 1e-10));
Assert.IsTrue(kmeans.Clusters.Covariances.All(x => x == null));
Assert.AreEqual(features.GetLength(), new[] { 6, 10 });
str = features.ToCSharp();
double[][] expected = new double[][]
{
new double[] { 47, 44, 42, 4, 23, 22, 28, 53, 50, 96 },
new double[] { 26, 91, 71, 49, 99, 70, 59, 28, 155, 79 },
new double[] { 71, 34, 51, 33, 53, 25, 44, 64, 32, 145 },
new double[] { 49, 41, 31, 24, 54, 19, 41, 63, 66, 72 },
new double[] { 137, 16, 92, 115, 39, 75, 24, 92, 41, 88 },
new double[] { 67, 91, 142, 80, 144, 126, 130, 74, 141, 270 }
};
for (int i = 0; i < features.Length; i++)
{
for (int j = 0; j < features[i].Length; j++)
{
Assert.IsTrue(expected[i].Contains(features[i][j]));
}
}
#region doc_classification
// 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 = 10000 // 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);
#endregion
Assert.IsTrue(new ZeroOneLoss(labels).IsBinary);
Assert.AreEqual(error, 0);
}
public void learn_new()
{
#region doc_learn
// 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 10. This means all feature
// vectors that will be generated will have the same length of 10.
// By default, the BoW object will use the sparse SURF as the
// feature extractor and K-means as the clustering algorithm.
// Create a new Bag-of-Visual-Words (BoW) model
BagOfVisualWords bow = new BagOfVisualWords(10);
// Note: the BoW model can also be created using
// var bow = BagOfVisualWords.Create(10);
// Ensure results are reproducible
bow.ParallelOptions.MaxDegreeOfParallelism = 1;
// Get some training images
Bitmap[] images = GetImages();
// Compute the model
bow.Learn(images);
// 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, 10 });
string str = features.ToCSharp();
double[][] expected = new double[][]
{
new double[] { 4, 28, 24, 68, 51, 97, 60, 35, 18, 24 },
new double[] { 53, 111, 89, 70, 24, 80, 130, 46, 50, 74 },
new double[] { 31, 29, 57, 102, 63, 142, 40, 18, 37, 33 },
new double[] { 24, 52, 57, 78, 56, 69, 65, 22, 21, 16 },
new double[] { 124, 35, 33, 145, 90, 83, 31, 4, 95, 79 },
new double[] { 97, 110, 127, 131, 71, 264, 139, 58, 116, 152 }
};
for (int i = 0; i < features.Length; i++)
{
for (int j = 0; j < features[i].Length; j++)
{
Assert.IsTrue(expected[i].Contains(features[i][j]));
}
}
#region doc_classification
// 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 = 10000 // 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);
#endregion
Assert.IsTrue(new ZeroOneLoss(labels).IsBinary);
Assert.AreEqual(error, 0);
}
