private void AppExit_Click(object sender, RoutedEventArgs e)
{
PrebuildSignsClassifier tc = new PrebuildSignsClassifier();
List<Sign> result = tc.Teach();
int num = tc.FindClass(new Sign(20,1, 27, 1,57));
Application.Current.Shutdown();
}
private void PerformClassification(List<Bitmap> circles, List<Bitmap> triangles, List<Bitmap> rectangles, ImageViewModel viewModel)
{
var signData = new Dictionary<Bitmap, Sign>();
foreach (var circle in circles)
{
var colorInfo = ColorInfo.Extract(circle);
signData.Add(circle, new Sign(100, colorInfo[0], colorInfo[1], colorInfo[2], colorInfo[3]));
}
foreach (var triangle in triangles)
{
var colorInfo = ColorInfo.Extract(triangle);
signData.Add(triangle, new Sign(50, colorInfo[0], colorInfo[1], colorInfo[2], colorInfo[3]));
}
foreach (var rectangle in rectangles)
{
var colorInfo = ColorInfo.Extract(rectangle);
signData.Add(rectangle, new Sign(150, colorInfo[0], colorInfo[1], colorInfo[2], colorInfo[3]));
}
var containers = new[]
{
null, viewModel.WarningSigns, viewModel.ProhibitingSigns, viewModel.RegulatorySigns, viewModel.InformationSigns,
viewModel.TemporarySigns
};
var tc = new PrebuildSignsClassifier();
tc.Teach();
foreach (var sign in signData)
{
var classIndex = tc.FindClass(sign.Value);
var resizedImage = BilinearInterpolation.Resize(sign.Key, 150, 150);
containers[classIndex].Add(new ImageModel(BitmapConverter.GetBitmapSource(resizedImage)));
}
}
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));
}
}
