public static NeuroNet LoadNeuronet(string filepath, object NeuroDataSupportClass)
{
NeuroNet neuroNet = FileWork.LoadNeuroNet(filepath);
neuroNet.neuroData.SupportClass = NeuroDataSupportClass;
return(neuroNet);
}
public NeuroNet neuroNet;// = NeuroNet.CreateNeuroNet(new int[] {400, 15, 1 }, new NeuroData(1000, 0.3f));
public NeuroWork(int offset, int number_of_passes, float edukationK)
{
neuroNet = NeuroNet.CreateNeuroNet(new int[] { offset *offset, offset, 1 }, new NeuroData(number_of_passes, edukationK));
this.offset = offset;
PreShow = new Bitmap(offset, offset);
start_signals = new int[offset * offset];
}
public static void SaveNeuroNet(NeuroNet neuroNet, string filePath)
{
//Random random = new Random();
using (StreamWriter writer = new StreamWriter(filePath))
{
NeuroData.SaveNeuroData(neuroNet.neuroData, writer);
writer.WriteLine(neuroNet.neuroLayer.Length);
for (int i = 0; i < neuroNet.neuroLayer.Length; i++)
{
writer.WriteLine(neuroNet.neuroLayer[i].Neurons.Length);
}
for (int i = 0; i < neuroNet.neuroLayer.Length; i++)
{
for (int j = 0; j < neuroNet.neuroLayer[i].Neurons.Length; j++)
{
for (int k = 0; k < neuroNet.neuroLayer[i].Neurons[j].weights.Length; k++)
{
writer.WriteLine(neuroNet.neuroLayer[i].Neurons[j].weights[k]);
}
}
if (i < neuroNet.neuroLayer.Length - 1)
{
for (int k = 0; k < neuroNet.neuroLayer[i + 1].Neurons.Length; k++)
{
writer.WriteLine(neuroNet.neuroLayer[i].AdditionalNeuron.weights[k]);
}
}
}
}
}
public void StartWriteText()
{
string NeuroNetFile = "";
NeuroNet neuroNet;
OpenFileDialog NeuroNetDialog = new OpenFileDialog
{
Title = "Выбор нейросети",
Filter = "Neuronet files|*.NN|All files|*.*"
};
if (NeuroNetDialog.ShowDialog() == DialogResult.OK)
{
NeuroNetFile = NeuroNetDialog.FileName;
}
else
{
return;
}
neuroNet = NeuroNet.LoadNeuronet(NeuroNetFile);
NormalizePhoto();
FindAllLines();
DebugLines();
FindAllLetters();
PreparationWriteTextPass();
int k = 0;
while (WriteTextPass(neuroNet) && k < 200000)
{
k++;
}
Debug.Log("Расшифровка изображения завершена");
SaveFileDialog SaveTextDialog = new SaveFileDialog
{
Title = "SaveTextDialog",
Filter = "Text|*.txt|All files|*.*"
};
if (SaveTextDialog.ShowDialog() == DialogResult.OK)
{
FileWork.SaveText(final_massage, SaveTextDialog.FileName);
}
//FileWork.SaveText(final_massage, @"C:\Users\Admin\Desktop\Letter\ForComplete\text.txt");
Debug.Log("Текст скопирован в буфер обмена");
Clipboard.SetText(final_massage);
}
void StartLearningThread()
{
Debug.Log("начало обучения");
Bitmap LetterImage;
char letter;
int count = 100000;
float[] start_signals;
float[] ideal_signals;
//neuroNet = NeuroNet.CreateNeuroNet(new int[] {offset * offset, 42, 255 }, new NeuroData(1, 0.0002f));
int load_neuroNet = 10800;//34500;
neuroNet = NeuroNet.LoadNeuronet(@"C:\Users\Admin\Desktop\Letter\forNeuronet\NN" + load_neuroNet + ".NN");
Debug.SetMaxValueProgressBar(count);
for (int j = load_neuroNet + 1; j < count; j++)
{
neuroNet.ZeroingErrorOfEra();
for (int i = 0; i < 1030; i++)
{
StreamReader TextReader = new StreamReader(@"C:\Users\Admin\Desktop\ForText\" + i + ".txt");
LetterImage = new Bitmap(@"C:\Users\Admin\Desktop\ForText\" + i + ".jpg");
letter = TextReader.ReadLine()[0];
start_signals = GetNormalisedStartSignalsFromImage(LetterImage);
ideal_signals = GetNormalisedIdealSignals(letter);
neuroNet.Pass(start_signals, ideal_signals);
}
if (j % 50 == 0)
{
if (j != 0)
{
neuroNet.SaveNeuronet(@"C:\Users\Admin\Desktop\Letter\forNeuronet\NN" + j + ".NN");
Debug.Log("завершено на " + (j * 100f / count) + "%");
}
else
{
neuroNet.SaveNeuronet(@"C:\Users\Admin\Desktop\Letter\forNeuronet\NN" + 0 + ".NN");
}
Debug.Log("error of era = " + neuroNet.ShowErrorOfEra());
//neuroNet.edukationK *= 0.99f;
}
//Debug.Log("error of era = " + neuroNet.ShowErrorOfEra());
Debug.SetCurrentValueProgressBar(j);
}
neuroNet.SaveNeuronet(@"C:\Users\Admin\Desktop\Letter\forNeuronet\NN" + count + ".NN");
Debug.Log("обучение завершено");
}
public static NeuroNet CreateNeuroNet(int[] layers, NeuroData neuroData)
{
NeuroNet neuroNet = new NeuroNet();
neuroNet.neuroData = neuroData;
neuroNet.neuroLayer = new NeuroLayer[layers.Length];
neuroNet.SetData();
for (int i = 0; i < layers.Length; i++)
{
if (i < layers.Length - 1)
{
neuroNet.neuroLayer[i] = new NeuroLayer(layers[i], layers[i + 1]);
}
else
{
neuroNet.neuroLayer[i] = new NeuroLayer(layers[i], 0);
}
}
neuroNet.FullZeroingNeurons();
return(neuroNet);
}
public static NeuroNet LoadNeuroNet(string filePath)
{
NeuroNet neuroNet;
using (StreamReader reader = new StreamReader(filePath))
{
NeuroData neuroData = NeuroData.LoadNeuroData(reader);
int number_of_layers = Convert.ToInt32(reader.ReadLine());
int[] layers = new int[number_of_layers];
for (int i = 0; i < number_of_layers; i++)
{
layers[i] = Convert.ToInt32(reader.ReadLine());
}
neuroNet = NeuroNet.CreateNeuroNet(layers, neuroData);
for (int i = 0; i < neuroNet.neuroLayer.Length; i++)
{
for (int j = 0; j < neuroNet.neuroLayer[i].Neurons.Length; j++)
{
for (int k = 0; k < neuroNet.neuroLayer[i].Neurons[j].weights.Length; k++)
{
neuroNet.neuroLayer[i].Neurons[j].weights[k] = Convert.ToSingle(reader.ReadLine());
}
}
if (i < neuroNet.neuroLayer.Length - 1)
{
for (int k = 0; k < neuroNet.neuroLayer[i + 1].Neurons.Length; k++)
{
neuroNet.neuroLayer[i].AdditionalNeuron.weights[k] = Convert.ToSingle(reader.ReadLine());
}
}
}
}
return(neuroNet);
}
bool WriteTextPass(NeuroNet ReadTextNeuroNet)
{
if (current_line_WriteTextPass >= LineList.Count)
{
//Debug.Log("current_line_WriteTextPass >= LineList.Count");
return(false);
}
if (LineList[current_line_WriteTextPass].LetterList.Count == 0)
{
//Debug.Log("LineList[current_line_WriteTextPass].LetterList.Count == 0");
current_column_WriteTextPass = 0;
current_line_WriteTextPass++;
final_massage += "\n ";
//FileWork.SaveText("\n", @"C:\Users\Admin\Desktop\Letter\ForComplete\text.txt");
return(true);
}
int min_y = LineList[current_line_WriteTextPass].up_y;
int min_x = LineList[current_line_WriteTextPass].LetterList[current_column_WriteTextPass].left_x;
ClearStartSignals();
int[,] start_signals_pre = new int[offset, offset * 5];
for (int y = 0; y < start_signals_pre.GetLength(1); y++)
{
for (int x = 0; x < start_signals_pre.GetLength(0); x++)
{
start_signals_pre[x, y] = 255;
}
}
for (int i = 0; i < start_signals.Length; i++)
{
start_signals[i] = 1;
}
for (int y = LineList[current_line_WriteTextPass].up_y; y < LineList[current_line_WriteTextPass].down_y; y++)
{
for (int x = LineList[current_line_WriteTextPass].LetterList[current_column_WriteTextPass].left_x; x < LineList[current_line_WriteTextPass].LetterList[current_column_WriteTextPass].right_x; x++)
{
if (x - min_x < offset && y - min_y < offset)
{
Color color = NormalizeImage.GetPixel(x, y);
start_signals_pre[(x - min_x), (y - min_y)] = color.R;
//NormalizeImage.SetPixel(x, y, Color.White);
start_signals[(x - min_x) + (y - min_y) * offset] = color.R;
}
else
{
if (x - min_x < start_signals_pre.GetLength(0) && y - min_y < start_signals_pre.GetLength(1))
{
Color color = NormalizeImage.GetPixel(x, y);
start_signals_pre[(x - min_x), (y - min_y)] = color.R;
}
}
}
}
bool free_line = true;
int start_line = 0;
for (int y = 0; y < start_signals_pre.GetLength(1); y++)
{
for (int x = 0; x < start_signals_pre.GetLength(0); x++)
{
if (start_signals_pre[x, y] == 0)
{
free_line = false;
}
}
if (free_line)
{
start_line++;
}
else
{
break;
}
}
int current_start_signal = 0;
for (int y = start_line; y < start_signals_pre.GetLength(1); y++)
{
for (int x = 0; x < start_signals_pre.GetLength(0); x++)
{
if (x < offset && y - start_line < offset)
{
Color color = Color.FromArgb(255, start_signals_pre[x, y], start_signals_pre[x, y], start_signals_pre[x, y]);
start_signals[current_start_signal] = start_signals_pre[x, y];
current_start_signal++;
}
}
}
float[] start_signals_float = GetNormarisedStartSignals(start_signals);
float[] output_signals = ReadTextNeuroNet.PassOutput(start_signals_float);
float max_value = 0;
int letter = 0;
for (int i = 0; i < output_signals.Length; i++)
{
if (output_signals[i] > max_value)
{
max_value = output_signals[i];
letter = i;
}
}
//Debug.Log("letter = " + (char)letter + " (" + letter + ")");
if (letter > 30)
{
final_massage += (char)(letter);
string mes = "";
mes += (char)(letter);
//FileWork.SaveText(mes, @"C:\Users\Admin\Desktop\Letter\ForComplete\text.txt");
}
else
{
final_massage += "?";
//FileWork.SaveText("&", @"C:\Users\Admin\Desktop\Letter\ForComplete\text.txt");
}
current_column_WriteTextPass++;
if (LineList[current_line_WriteTextPass].LetterList.Count <= current_column_WriteTextPass)
{
current_column_WriteTextPass = 0;
current_line_WriteTextPass++;
final_massage += "\n ";
//FileWork.SaveText("\n", @"C:\Users\Admin\Desktop\Letter\ForComplete\text.txt");
}
return(true);
}