private void Learn_Click(object sender, RoutedEventArgs e)
{
var downsample = new Downsampler();
var training = new ImageMLDataSet(downsample, true, 1, -1);
for (var i = 0; i < Images.Count; ++i)
{
var ideal = new BasicMLData(DIGITS_COUNT);
for (int j = 0; j < DIGITS_COUNT; ++j)
{
if (j == i)
{
ideal[j] = 1;
}
else
{
ideal[j] = -1;
}
}
foreach (var img in Images[i])
{
MemoryStream stream = new MemoryStream();
BitmapEncoder encoder = new BmpBitmapEncoder();
encoder.Frames.Add(BitmapFrame.Create(img));
encoder.Save(stream);
var bitmap = new Drawing.Bitmap(stream);
var data = new ImageMLData(bitmap);
training.Add(data, ideal);
}
}
training.Downsample(DIGIT_HEIGHT, DIGIT_WIDTH);
network = EncogUtility.SimpleFeedForward(training.InputSize, 35, 0, training.IdealSize, true);
double strategyError = 0.01;
int strategyCycles = 2000;
var train = new ResilientPropagation(network, training);
//train.AddStrategy(new ResetStrategy(strategyError, strategyCycles));
EncogUtility.TrainDialog(train, network, training);
EncogDirectoryPersistence.SaveObject(new FileInfo("network.eg"), network);
}
private void RecCanvas_MouseUp(object sender, MouseButtonEventArgs e)
{
recCanvas_drawing = false;
var size = new Size(RecCanvas.ActualWidth, RecCanvas.ActualHeight);
// Measure and arrange the surface
// VERY IMPORTANT
RecCanvas.Measure(size);
RecCanvas.Arrange(new Rect(size));
var renderBitmap = new RenderTargetBitmap((int)size.Width, (int)size.Height, 96d, 96d, PixelFormats.Pbgra32);
renderBitmap.Render(RecCanvas);
MemoryStream stream = new MemoryStream();
BitmapEncoder encoder = new BmpBitmapEncoder();
encoder.Frames.Add(BitmapFrame.Create(renderBitmap));
encoder.Save(stream);
var bitmap = new Drawing.Bitmap(stream);
var downsample = new Downsampler();
var result = downsample.DownSample(bitmap, DIGIT_HEIGHT, DIGIT_WIDTH);
var image = new Drawing.Bitmap(DIGIT_WIDTH, DIGIT_HEIGHT);
for (var i = 0; i < DIGIT_HEIGHT; ++i)
{
for (var j = 0; j < DIGIT_WIDTH; ++j)
{
image.SetPixel(j, i, Drawing.Color.FromArgb(
255,
(int)result[i * DIGIT_WIDTH + j],
(int)result[i * DIGIT_WIDTH + j],
(int)result[i * DIGIT_WIDTH + j]
));
}
}
RecComp.Source = Imaging.CreateBitmapSourceFromHBitmap(
image.GetHbitmap(),
IntPtr.Zero,
Int32Rect.Empty,
BitmapSizeOptions.FromEmptyOptions());
if (network == null)
{
return;
}
var input = new ImageMLData(bitmap);
input.Downsample(downsample, false, DIGIT_HEIGHT, DIGIT_WIDTH, 1, -1);
int winner = network.Winner(input);
RecDigit.Text = winner.ToString();
var data = network.Compute(input);
for (var i = 0; i < DIGITS_COUNT; ++i)
{
Results[i] = new ResultObject
{
Char = i.ToString(),
Result = (data[i] + 1) / 2
};
}
}
