public static int[,] ParsePicture(string path, PictureBox pictBox = null)
{
int[,] ret = null;
if (!File.Exists(path))
{
throw new FileNotFoundException();
}
Image img = Image.FromFile(path);
Bitmap bitmap = img as Bitmap;
if (bitmap != null)
{
List <MyLine> hLines = new List <MyLine>();
List <MyLine> vLines = new List <MyLine>();
LoadPicture(ref bitmap, hLines, vLines);
if (m_learnWindow != null)
{
DisconnectPictureBox();
}
m_learnWindow = new LearnWindow(bitmap, hLines, vLines, pictBox);
//m_learnWindow.ShowDialog();
ret = m_learnWindow.RecognizeDigits();
bitmap.Dispose();
}
return(ret);
}
private void butTeach_Click(object sender, EventArgs e)
{
if (txtInputFolder.Text == "" || txtOutputFolder.Text == "" || numLearningRate.Value == 0)
{
MessageBox.Show("Incorrect parameter(s)", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
int[] sizes = new int[] { LearnWindow.DIGIT_PATTERN_WIDTH *LearnWindow.DIGIT_PATTERN_HEIGHT, (int)numHidNeurons.Value, 10 };
lock (m_cancelLock)
{
m_cancel = false;
}
PrintLine("Loading images...");
progressBar1.Show();
butTeach.Enabled = false;
butOk.Enabled = false;
butCancel.Enabled = false;
butStop.Enabled = true;
progressBar1.Minimum = 0;
progressBar1.Style = ProgressBarStyle.Continuous;
progressBar1.Value = 0;
NeuralNetwork net = new NeuralNetwork(sizes);
var data = new List <Tuple <MyVector, MyVector> >();
DirectoryInfo dir = new DirectoryInfo(txtInputFolder.Text);
//DirectoryInfo dir = new DirectoryInfo(@"c:\Users\Luky\Downloads\t10k-images");
List <FileInfo> files = new List <FileInfo>(dir.GetFiles("*.png", SearchOption.AllDirectories));
files.AddRange(dir.GetFiles("*.jpg", SearchOption.AllDirectories));
files.AddRange(dir.GetFiles("*.bmp", SearchOption.AllDirectories));
files.AddRange(dir.GetFiles("*.jpeg", SearchOption.AllDirectories));
progressBar1.Maximum = files.Count;
foreach (var file in files)
{
using (Image temp = Image.FromFile(file.FullName))
{
var bitmap = temp as Bitmap;
if (bitmap != null)
{
if (IsCancelled())
{
return;
}
if (bitmap.Width != LearnWindow.DIGIT_PATTERN_WIDTH ||
bitmap.Height != LearnWindow.DIGIT_PATTERN_HEIGHT)
{
continue;
}
MyVector input = LearnWindow.BitmapToInputVector(bitmap);
int output = int.Parse(file.Name.Substring(0, 1));
data.Add(new Tuple <MyVector, MyVector>(input, MyVector.UnitVector(10, output)));
Debug.Print("Picture num: {0}/{1} loaded.", data.Count, files.Count);
progressBar1.Value++;
//PrintLine(String.Format("Picture num: {0}/{1} loaded.", data.Count, files.Count)); // Very slow!!! TODO: Better loging console
}
}
}
PrintLine("Network learning started...");
progressBar1.Style = ProgressBarStyle.Marquee;
net.StochasticGradientDescent(data.ToArray(), (int)numOfEpochs.Value, (int)numBatchSize.Value, (double)numLearningRate.Value, IsCancelled, PrintLine, txtOutputFolder.Text, (double)numLambda.Value, null, false, false, false, true);
progressBar1.Hide();
butTeach.Enabled = true;
butStop.Enabled = false;
butOk.Enabled = true;
butCancel.Enabled = true;
}
