public Bitmap DrawDataToGrid(IList <Tuple <Vector <double>, Color, string> > data, int imageSize, Color bg, int cols)
{
Contract.Requires(data.Count > 0);
int count = data.Count;
int rows = (count + cols - 1) / cols;
Size size = new Size(1 + cols * (imageSize + 1), 1 + rows * (imageSize + 1));
Bitmap img = new Bitmap(size.Width, size.Height, PixelFormat.Format24bppRgb);
var font = new Font(FontFamily.GenericSansSerif, 5);
var drawingImg = new Bitmap(imageSize, imageSize, PixelFormat.Format24bppRgb);
var ic = new NetInputConvertor();
using (Graphics g = Graphics.FromImage(img), imgG = Graphics.FromImage(drawingImg)) {
g.Clear(bg);
int row = 0;
int col = 0;
foreach (var item in data)
{
Contract.Assert(row < rows && col < cols);
var bb = new Rectangle(1 + col * (imageSize + 1), 1 + row * (imageSize + 1), imageSize, imageSize);
imgG.Clear(item.Item2);
ic.VectorToImage(item.Item1, drawingImg);
g.DrawImageUnscaled(drawingImg, bb);
if (!string.IsNullOrWhiteSpace(item.Item3))
{
g.DrawString(item.Item3, font, Brushes.White, bb.X + 1, bb.Y + 1);
g.DrawString(item.Item3, font, Brushes.Black, bb.Location);
}
col += 1;
if (col >= cols)
{
col = 0;
row += 1;
}
}
}
return(img);
}
private void prepareInOut(MausrDb db)
{
var ic = new NetInputConvertor();
var dbInputs = db.SymbolDrawings.Where(x => x.Approved == true).ToList();
var baseInputsAndDrawings = dbInputs
.Select(sd => new { SymbolId = sd.Symbol.SymbolId, RawDrawing = sd.GetRawDrawing() })
.ToList();
var inputsAndDrawings = baseInputsAndDrawings.ToList();
var dataProc = new RawDataProcessor();
if (trainSettings.GenerateExtraInputsByRotation != 0)
{
inputsAndDrawings.AddRange(baseInputsAndDrawings
.Select(x => new {
SymbolId = x.SymbolId,
RawDrawing = dataProc.Rotate(x.RawDrawing, trainSettings.GenerateExtraInputsByRotation)
}));
inputsAndDrawings.AddRange(baseInputsAndDrawings
.Select(x => new {
SymbolId = x.SymbolId,
RawDrawing = dataProc.Rotate(x.RawDrawing, -trainSettings.GenerateExtraInputsByRotation)
}));
}
if (trainSettings.NormalizeInput)
{
foreach (var iad in inputsAndDrawings)
{
dataProc.NormalizeInPlace(iad.RawDrawing);
}
}
ic.Shuffle(inputsAndDrawings);
var rasterizer = new Rasterizer()
{
ImageSize = trainSettings.InputImgSizePx,
DrawPoints = false,
ExtraMargin = true,
PenSizePerc = trainSettings.PenThicknessPerc / 100f
};
int testSamplesCount = 1 + (trainSettings.TestDataSetSizePerc * inputsAndDrawings.Count / 100);
{
var testSet = inputsAndDrawings.Take(testSamplesCount).ToList();
var testDrawings = testSet.Select(x => x.RawDrawing).ToList();
testInputs = ic.CreateInputsMatrix(testDrawings, rasterizer, false);
testOutIds = testSet.Select(x => x.SymbolId).ToArray();
testOutNeuronIndices = ic.CreateOutIndicesFromIds(testOutIds, network);
}
{
var trainSet = inputsAndDrawings.Skip(testSamplesCount).ToList();
var trainDrawings = trainSet.Select(x => x.RawDrawing).ToList();
trainInputs = ic.CreateInputsMatrix(trainDrawings, rasterizer, false);
trainOutIds = trainSet.Select(x => x.SymbolId).ToArray();
trainOutNeuronIndices = ic.CreateOutIndicesFromIds(trainOutIds, network);
}
}
