/// <summary>
/// Extracts a random number of points, such that the probability of a point being extracted is equal
/// to <paramref name="percentage"/>
/// </summary>
/// <typeparam name="T">Underlying type of the image</typeparam>
/// <param name="image">The image</param>
/// <param name="percentage">The probability that a point will be extracted</param>
/// <param name="border">Border around the edge of the image to exclude</param>
/// <returns>A list of image points</returns>
public static IEnumerable <ImageDataPoint <T> > ExtractPoints <T>(this IMultichannelImage <T> image, double percentage, short border = 0)
{
int rows = image.Rows;
int columns = image.Columns;
int total = (int)((rows - 2 * border) * (columns - 2 * border) * percentage);
if (percentage <= .1)
{
for (int i = 0; i < total; i++)
{
short row = (short)ThreadsafeRandom.Next(border, rows - border);
short column = (short)ThreadsafeRandom.Next(border, rows - border);
yield return(new ImageDataPoint <T>(image, row, column, 0));
}
}
else
{
for (short r = border; r < rows - border; r++)
{
for (short c = border; c < columns - border; c++)
{
if (ThreadsafeRandom.NextDouble() < percentage)
{
yield return(new ImageDataPoint <T>(image, r, c, 0));
}
}
}
}
}
/// <summary>
/// Randomly samples the Gaussian distribution.
/// </summary>
/// <returns>A sample from the Gaussian distribution</returns>
public float Sample()
{
double x1, x2, w, y1, y2;
do
{
x1 = 2.0 * ThreadsafeRandom.NextDouble() - 1.0;
x2 = 2.0 * ThreadsafeRandom.NextDouble() - 1.0;
w = x1 * x1 + x2 * x2;
} while (w >= 1.0);
w = Math.Sqrt((-2.0 * Math.Log(w)) / w);
y1 = x1 * w;
y2 = x2 * w;
return((float)(y1 * _stddev + _mean));
}
