/// <summary>
/// Given an original image and a transformed one, we get back one that
/// has the same dimensions the original and if we transform it we get back image, i.e:
/// <code> UnTransform(orig, Transform(orig)) = orig </code>
/// </summary>
/// <param name="orig">The original image.</param>
/// <param name="image">The transformed image.</param>
/// <returns>An image satisfying the above equation.</returns>
public Vector <double> UnTransform(Vector <double> orig, Vector <double> image)
{
Vector <double> ret = DenseVector.Create(orig.Count(), 0.0);
orig.CopyTo(ret);
// Now ret is a copy of orig, so simply overwrite the relevant pixels
int center_row = inputCoordinates_.RowCount / 2;
int center_col = inputCoordinates_.ColumnCount / 2;
int topleft_row = center_row - cropSize_ / 2;
int topleft_col = center_col - cropSize_ / 2;
for (int channel = 0; channel < inputCoordinates_.ChannelCount; channel++)
{
for (int i = 0; i < cropSize_; i++)
{
for (int j = 0; j < cropSize_; j++)
{
int input_idx = inputCoordinates_.GetIndex(channel, topleft_row + i, topleft_col + j);
int output_idx = outputCoordinates_.GetIndex(channel, i, j);
if (input_idx >= 0 && input_idx < inputDimension_ && output_idx >= 0 && output_idx < outputDimension_)
{
ret[input_idx] = image[output_idx];
}
}
}
}
return(ret);
}
public List <double[]> Augment(double[] datum)
{
List <double[]> newdatums = new List <double[]>();
for (int i = 0; i < _how_many; i++)
{
var newdatum = new double[datum.Length];
// Sample epsilon
double eps = (_typ == RANDTYPE.UNIFORM) ?
(_random.NextDouble() * 2.0 - 1.0) :
Utils.URand.NextGaussian(_random);
for (int c = 0; c < _coords.ChannelCount; c++)
{
for (int x = 0; x < _coords.RowCount; x++)
{
for (int y = 0; y < _coords.ColumnCount; y++)
{
int xnew = (int)(x + eps * _xoffset);
int ynew = (int)(y + eps * _yoffset);
if (xnew < 0 || xnew >= _coords.RowCount)
{
xnew = x;
}
if (ynew < 0 || ynew >= _coords.ColumnCount)
{
ynew = y;
}
int idx = _coords.GetIndex(c, x, y);
int newidx = _coords.GetIndex(c, xnew, ynew);
newdatum[newidx] = datum[idx];
}
}
}
// Utils.UDraw.DisplayImageAndPause(Utils.UArray.ToRGBArray(datum, 1.0, 0.0), 32, 32, true);
// Utils.UDraw.DisplayImageAndPause(Utils.UArray.ToRGBArray(newdatum, 1.0, 0.0), 32, 32, true);
newdatums.Add(newdatum);
}
return(newdatums);
}
public List <double[]> Augment(double[] datum)
{
List <double[]> newdatums = new List <double[]>();
// How many to generate
for (int i = 0; i < _how_many; i++)
{
// Allocate data
var newdatum = new double[datum.Length];
// Sample epsilon
double eps = (_typ == RANDTYPE.UNIFORM) ?
(_random.NextDouble() * 2.0 - 1.0) * _max_eps:
Utils.URand.NextGaussian(_random) * _max_eps;
// Add constant epsilon to all channels.
for (int c = 0; c < _coords.ChannelCount; c++)
{
for (int x = 0; x < _coords.RowCount; x++)
{
for (int y = 0; y < _coords.ColumnCount; y++)
{
int idx = _coords.GetIndex(c, x, y);
newdatum[idx] = Utils.UMath.Clamp(datum[idx] + eps, Utils.RobustnessOptions.MinValue, Utils.RobustnessOptions.MaxValue);
}
}
}
newdatums.Add(newdatum);
}
return(newdatums);
}
public double[] ChannelAverages(double[] datum)
{
double[] rets = new double[_coords.ChannelCount];
for (int c = 0; c < _coords.ChannelCount; c++)
{
for (int x = 0; x < _coords.RowCount; x++)
{
for (int y = 0; y < _coords.ColumnCount; y++)
{
int idx = _coords.GetIndex(c, x, y);
rets[c] += datum[idx];
}
}
}
return(rets);
}