public void meanShift()
{
string basePath = Path.Combine(NUnit.Framework.TestContext.CurrentContext.TestDirectory, "kmeans");
Directory.CreateDirectory(basePath);
#region doc_meanshift
// Load a test image (shown in a picture box below)
var sampleImages = new TestImages(path: basePath);
Bitmap image = sampleImages.GetImage("airplane.png");
// ImageBox.Show("Original", image).Hold();
// Create converters to convert between Bitmap images and double[] arrays
var imageToArray = new ImageToArray(min: -1, max: +1);
var arrayToImage = new ArrayToImage(image.Width, image.Height, min: -1, max: +1);
// Transform the image into an array of pixel values
double[][] pixels; imageToArray.Convert(image, out pixels);
// Create a MeanShift algorithm using given bandwidth
// and a Gaussian density kernel as kernel function.
MeanShift meanShift = new MeanShift()
{
Kernel = new GaussianKernel(3),
Bandwidth = 0.06,
// We will compute the mean-shift algorithm until the means
// change less than 0.05 between two iterations of the algorithm
Tolerance = 0.05,
MaxIterations = 10
};
// Learn the clusters from the data
var clusters = meanShift.Learn(pixels);
// Use clusters to decide class labels
int[] labels = clusters.Decide(pixels);
// Replace every pixel with its corresponding centroid
double[][] replaced = pixels.Apply((x, i) => clusters.Modes[labels[i]]);
// Retrieve the resulting image (shown in a picture box)
Bitmap result; arrayToImage.Convert(replaced, out result);
// ImageBox.Show("Mean-Shift clustering", result).Hold();
#endregion
}
public void kmeans()
{
string basePath = Path.Combine(NUnit.Framework.TestContext.CurrentContext.TestDirectory, "kmeans");
Directory.CreateDirectory(basePath);
#region doc_kmeans
// Load a test image (shown in a picture box below)
var sampleImages = new TestImages(path: basePath);
Bitmap image = sampleImages.GetImage("airplane.png");
// ImageBox.Show("Original", image).Hold();
// Create converters to convert between Bitmap images and double[] arrays
var imageToArray = new ImageToArray(min: -1, max: +1);
var arrayToImage = new ArrayToImage(image.Width, image.Height, min: -1, max: +1);
// Transform the image into an array of pixel values
double[][] pixels; imageToArray.Convert(image, out pixels);
// Create a K-Means algorithm using given k and a
// square Euclidean distance as distance metric.
KMeans kmeans = new KMeans(k: 5)
{
Distance = new SquareEuclidean(),
// We will compute the K-Means algorithm until cluster centroids
// change less than 0.5 between two iterations of the algorithm
Tolerance = 0.05
};
// Learn the clusters from the data
var clusters = kmeans.Learn(pixels);
// Use clusters to decide class labels
int[] labels = clusters.Decide(pixels);
// Replace every pixel with its corresponding centroid
double[][] replaced = pixels.Apply((x, i) => clusters.Centroids[labels[i]]);
// Retrieve the resulting image (shown in a picture box)
Bitmap result; arrayToImage.Convert(replaced, out result);
// ImageBox.Show("k-Means clustering", result).Hold();
#endregion
}