private static Bitmap DrawSample(ShapeModelSample sample)
{
return ImageHelpers.DrawShape(
sample.ShapeModel.GridWidth,
sample.ShapeModel.GridHeight,
sample.ShapePartLocations,
sample.ShapePartOrientations);
}
public ShapeModelSample[] Sample(double[][] traits, bool withNoise, bool ignoreLocationPrior)
{
Debug.Assert(traits[0].Length == this.TraitCount);
int sampleCount = traits.Length;
ShapeModelSample[] result = new ShapeModelSample[sampleCount];
double[] shapeLocationMean = this.ShapeLocationMean.Sample();
double[] shapeLocationPrecision = this.ShapeLocationPrecision.Sample();
double[][][] shapePartOffsetMeanWeights = GetMean(this.ShapePartOffsetWeights); //this.ShapePartOffsetWeights.Sample();
double[][][] shapePartLogScaleMeanWeights = GetMean(this.ShapePartLogScaleWeights);//this.ShapePartScaleWeights.Sample();
double[][] shapePartAngleMeanWeights = GetMean(this.ShapePartAngleWeights);//this.ShapePartAngleWeights.Sample();
double[][] shapePartOffsetPrecisions = GetMean(this.ShapePartOffsetPrecisions);//this.ShapePartOffsetPrecisions.Sample();
double[][] shapePartLogScalePrecisions = GetMean(this.ShapePartLogScalePrecisions);//this.ShapePartScalePrecisions.Sample();
double[] shapePartAnglePrecisions = GetMean(this.ShapePartAnglePrecisions);//this.ShapePartAnglePrecisions.Sample();
for (int sampleIndex = 0; sampleIndex < sampleCount; ++sampleIndex)
{
// Sample locations from priors
double locationX, locationY;
if (ignoreLocationPrior)
{
locationX = 0.5;
locationY = 0.5;
}
else
{
locationX = Gaussian.Sample(shapeLocationMean[0], shapeLocationPrecision[0]);
locationY = Gaussian.Sample(shapeLocationMean[1], shapeLocationPrecision[1]);
}
// Sample shape part locations
result[sampleIndex] = new ShapeModelSample() { ShapeModel = this, ShapePartLocations = new Vector[this.ShapePartCount], ShapePartOrientations = new PositiveDefiniteMatrix[this.ShapePartCount] };
for (int shapePartIndex = 0; shapePartIndex < this.ShapePartCount; ++shapePartIndex)
{
Vector traitVector = Vector.FromArray(traits[sampleIndex]);
double meanOffsetX = Vector.InnerProduct(traitVector, Vector.FromArray(shapePartOffsetMeanWeights[shapePartIndex][0]));
double meanOffsetY = Vector.InnerProduct(traitVector, Vector.FromArray(shapePartOffsetMeanWeights[shapePartIndex][1]));
double meanLogScaleX = Vector.InnerProduct(traitVector, Vector.FromArray(shapePartLogScaleMeanWeights[shapePartIndex][0]));
double meanLogScaleY = Vector.InnerProduct(traitVector, Vector.FromArray(shapePartLogScaleMeanWeights[shapePartIndex][1]));
double meanAngle = Vector.InnerProduct(traitVector, Vector.FromArray(shapePartAngleMeanWeights[shapePartIndex]));
double x = locationX + Gaussian.FromMeanAndPrecision(meanOffsetX, shapePartOffsetPrecisions[shapePartIndex][0]).Sample();
double y = locationY + Gaussian.FromMeanAndPrecision(meanOffsetY, shapePartOffsetPrecisions[shapePartIndex][1]).Sample();
double logScaleX = Gaussian.FromMeanAndPrecision(meanLogScaleX, shapePartLogScalePrecisions[shapePartIndex][0]).Sample();
double logScaleY = Gaussian.FromMeanAndPrecision(meanLogScaleY, shapePartLogScalePrecisions[shapePartIndex][1]).Sample();
double angle = Gaussian.FromMeanAndPrecision(meanAngle, shapePartAnglePrecisions[shapePartIndex]).Sample();
// Truncate samples to match the model constraints (not entirely correct)
angle = Math.Min(Math.PI / 4 - 0.01, angle);
angle = Math.Max(-Math.PI / 4 + 0.01, angle);
PositiveDefiniteMatrix orientation = ShapeFactors.MatrixFromAngleScale(logScaleX, logScaleY, angle);
result[sampleIndex].ShapePartLocations[shapePartIndex] = Vector.FromArray(x, y);
result[sampleIndex].ShapePartOrientations[shapePartIndex] = orientation;
}
var labels = new bool[this.GridWidth, this.GridHeight];
var noisyLabels = new bool[this.GridWidth, this.GridHeight];
for (int i = 0; i < this.GridWidth; ++i)
{
for (int j = 0; j < this.GridHeight; ++j)
{
labels[i, j] = false;
for (int k = 0; k < this.ShapePartCount; ++k)
{
labels[i, j] |= ShapeFactors.LabelFromShape(
Vector.FromArray((i + 0.5) / this.GridWidth, (j + 0.5) / this.GridHeight),
result[sampleIndex].ShapePartLocations[k][0],
result[sampleIndex].ShapePartLocations[k][1],
result[sampleIndex].ShapePartOrientations[k]);
}
noisyLabels[i, j] = Rand.Double() >= this.ObservationNoiseProb ? labels[i, j] : !labels[i, j];
}
}
result[sampleIndex].Labels = withNoise ? noisyLabels : labels;
}
return result;
}
