private void PerformClassification(List <Bitmap>[] circles, List <Bitmap>[] triangles, List <Bitmap>[] rectangles, ImageViewModel viewModel)
{
var signData = new Dictionary <Bitmap[], Sign>();
for (int i = 0; i < circles[1].Count; i++)
{
var circle = circles[1][i];
var colorInfo = ColorInfo.Extract(circle);
signData.Add(new[] { circles[0][i], circles[1][i] }, new Sign(100, colorInfo[0], colorInfo[1], colorInfo[2], colorInfo[3]));
}
for (int i = 0; i < triangles[1].Count; i++)
{
var triangle = triangles[1][i];
var colorInfo = ColorInfo.Extract(triangle);
signData.Add(new[] { triangles[0][i], triangles[1][i] }, new Sign(50, colorInfo[0], colorInfo[1], colorInfo[2], colorInfo[3]));
}
for (int i = 0; i < rectangles[1].Count; i++)
{
var rectangle = rectangles[1][i];
var colorInfo = ColorInfo.Extract(rectangle);
signData.Add(new[] { rectangles[0][i], rectangles[1][i] }, new Sign(150, colorInfo[0], colorInfo[1], colorInfo[2], colorInfo[3]));
}
var containers = new[]
{
viewModel.WarningSigns, viewModel.ProhibitingSigns, viewModel.RegulatorySigns, viewModel.InformationSigns,
viewModel.TemporarySigns
};
var formatter = new BinaryFormatter();
Stream stream = new FileStream("..\\..\\..\\perceptrons.bin", FileMode.Open, FileAccess.Read, FileShare.None);
var perceptrons = (List <Perceptron>)formatter.Deserialize(stream);
stream.Close();
var tc = new PrebuildSignsClassifier();
tc.Teach();
var signsViewModel = (SignsGrid.DataContext) as SignViewModel;
signsViewModel.Signs.Clear();
foreach (var sign in signData)
{
var groupIndex = tc.FindClass(sign.Value) - 1;
var signsImageSource = BitmapConverter.GetBitmapSource(sign.Key[1]);
containers[groupIndex].Add(new ImageModel(signsImageSource));
var perceptron = perceptrons[groupIndex];
var histogram =
HistogramExtracter.Process(BilinearInterpolation.Resize(sign.Key[0], ResizeWidth, ResizeHeight));
var classificationResult = perceptron.Classify(histogram).ToList();
var classIndex = classificationResult.IndexOf(classificationResult.Max());
var description = SignDescription.Get(groupIndex, classIndex);
signsViewModel.Signs.Add(new SignModel(signsImageSource, description));
}
}
private void Teach_Click(object sender, RoutedEventArgs e)
{
var teachingData = new List <List <List <double[]> > >();
for (var i = 0; i < GroupsCount; i++)
{
var groupsContainer = new List <List <double[]> >();
for (var j = 0; j < ClassesCount; j++)
{
groupsContainer.Add(new List <double[]>());
}
teachingData.Add(groupsContainer);
}
var dialog = new FolderBrowserDialog
{
SelectedPath = Environment.GetFolderPath(Environment.SpecialFolder.MyPictures)
};
var result = dialog.ShowDialog(this.GetIWin32Window());
if (result == System.Windows.Forms.DialogResult.OK)
{
foreach (var path in Directory.EnumerateFiles(dialog.SelectedPath, "*.*", SearchOption.AllDirectories)
.Where(file => file.EndsWith(".processed.bmp", StringComparison.OrdinalIgnoreCase)))
{
var bitmap = BitmapConverter.GetBitmap(new BitmapImage(new Uri(path)));
var histogram = HistogramExtracter.Process(bitmap);
var tmp = path.Split('\\').ToList();
tmp.Reverse();
var currentGroup = int.Parse(tmp[2]) - 1;
var currentClass = int.Parse(tmp[1]) - 1;
teachingData[currentGroup][currentClass].Add(histogram);
}
}
var perceptrons = new List <Perceptron>();
perceptrons.AddRange((Enumerable.Repeat(new Perceptron(), GroupsCount)));
Parallel.ForEach(teachingData, groupData =>
{
var teachingSet = new Dictionary <double[], double[]>();
for (var i = 0; i < groupData.Count; i++)
{
var classData = groupData[i];
var expectedOutput = new double[groupData.Count];
expectedOutput[i] = 1;
foreach (var classInput in classData)
{
teachingSet.Add(classInput, expectedOutput);
}
}
var perceptron = new Perceptron();
perceptron.Reset();
perceptron.Teach(teachingSet);
var index = teachingData.IndexOf(groupData);
perceptrons[index] = perceptron;
});
var formatter = new BinaryFormatter();
Stream stream = new FileStream(dialog.SelectedPath + "\\perceptrons.bin", FileMode.Create, FileAccess.Write, FileShare.None);
formatter.Serialize(stream, perceptrons);
stream.Close();
}
