public void VisualizeResults()
{
foreach (var path in Directory.GetFiles(Path.Combine(OutputPath, "predicted"), "*.npy"))
{
var info = new FileInfo(path);
var predicted = np.load(info.FullName).AsGeneric <float>();
using (var stream = File.Create(Path.Combine(OutputPath, "Predicted", info.Name.Replace(".npy", ".png"))))
{
new ImageConverter().ToPng(predicted, stream, HeightToGrayscale.Unit8());
}
var original = np.load(Path.Combine(OutputPath, "test_y", info.Name)).AsGeneric <float>();
}
}
public void ExtractTrainingData()
{
int count = 0;
foreach (var fullCode in TileCodes)
{
var full = np.load(Path.Combine(OutputPath, $"{fullCode}.npy")).AsGeneric <float>();
var fullRange = full.Range();
foreach (var tile in GetYTiles(full))
{
var tileRange = tile.Range();
if (count % 11 < 2 && tileRange.Delta > 1)
{
// Don't use tiles with a very small range
var set = count % 7 == 0 ? "test" : "train";
var range = count % 2 == 0 ? fullRange : tileRange;
Normalize(tile, range);
np.save(Path.Combine(OutputPath, $"{set}_y", $"sample-{count:00000}.npy"), tile);
using (var stream = File.Create(Path.Combine(OutputPath, $"{set}_y", $"sample-{count:00000}.png")))
{
new ImageConverter().ToPng(tile, stream, HeightToGrayscale.Unit8());
}
var x =
tile
.Downscale(Scale);
if (count % 4 < 2)
{
Downsample(x, 255);
}
else
{
Downsample(x, 65535);
}
np.save(Path.Combine(OutputPath, $"{set}_x", $"sample-{count:00000}.npy"), x);
using (var stream = File.Create(Path.Combine(OutputPath, $"{set}_x", $"sample-{count:00000}.png")))
{
new ImageConverter().ToPng(x, stream, HeightToGrayscale.Unit8());
}
}
count++;
}
}
}
