private void button1_Click(object sender, EventArgs e)
{
using (OpenFileDialog dlg = new OpenFileDialog())
{
dlg.Title = "Open Image";
dlg.Filter = "Image files (*.bmp, *.jpg, *.jpeg, *.jpe, *.jfif, *.png) | *.bmp; *.jpg; *.jpeg; *.jpe; *.jfif; *.png";
if (dlg.ShowDialog() == DialogResult.OK)
{
var bitMap = new Bitmap(dlg.FileName);
NeuralForm n = new NeuralForm();
n.BitMap = bitMap;
n.Show();
//
var network = new BasicNetwork();
FileInfo networkFile = new FileInfo(NETWORK_PATH);
if (System.IO.File.Exists(NETWORK_PATH))
{
network = (BasicNetwork)(Encog.Persist.EncogDirectoryPersistence.LoadObject(networkFile));
}
int hCounter = 0; int Wcounter = 0;
int hMax = bitMap.Height / SIZE;
int wMax = bitMap.Width / SIZE;
var outputBitmap = new Bitmap(bitMap.Width, bitMap.Height);
while (hMax > hCounter)
{
Encog.ML.Data.Image.ImageMLDataSet testingSet = new Encog.ML.Data.Image.ImageMLDataSet(new Encog.Util.DownSample.RGBDownsample(), false, 1, -1);
var _25x25bitmap = new Bitmap(SIZE, SIZE);
for (int i = 0; i < SIZE; i++)
{
for (int j = 0; j < SIZE; j++)
{
var px = bitMap.GetPixel(Wcounter * SIZE + j, hCounter * SIZE + i);
_25x25bitmap.SetPixel(i, j, px);
}
}
ImageMLData data = new ImageMLData(_25x25bitmap);
testingSet.Add(data, null);
testingSet.Downsample(SIZE, SIZE);
bool isVege = false;
foreach (IMLDataPair pair in testingSet)
{
IMLData output = network.Compute(pair.Input);
if (output[1] > .05)
{
isVege = true;
}
}
for (int i = 0; i < SIZE; i++)
{
for (int j = 0; j < SIZE; j++)
{
var px = _25x25bitmap.GetPixel(i, j);
if (!isVege)
{
outputBitmap.SetPixel(Wcounter * SIZE + j, hCounter * SIZE + i, px);
}
else
{
outputBitmap.SetPixel(Wcounter * SIZE + j, hCounter * SIZE + i, Color.FromArgb(px.R, 0, px.B));
}
}
}
Wcounter++;
if (wMax <= Wcounter)
{
Wcounter = 0;
hCounter++;
}
}
NeuralForm n2 = new NeuralForm();
n2.BitMap = outputBitmap;
n2.Show();
}
}
}
private void ProcessCreateTraining()
{
String strWidth = GetArg("width");
String strHeight = GetArg("height");
String strType = GetArg("type");
downsampleHeight = int.Parse(strWidth);
downsampleWidth = int.Parse(strHeight);
if (strType.Equals("RGB"))
{
downsample = new RGBDownsample();
}
else
{
downsample = new SimpleIntensityDownsample();
}
training = new ImageMLDataSet(downsample, false, 1, -1);
app.WriteLine("Training set created");
}
public static void Run()
{
FileInfo networkFile = new FileInfo(@"D:\Imagery\network\network.eg");
var network = new BasicNetwork();
network.AddLayer(new BasicLayer(null, true, SIZE * SIZE * 3));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), true, NERUONCOUNT));
network.AddLayer(new BasicLayer(new ActivationSigmoid(), false, 2));
network.Structure.FinalizeStructure();
network.Reset();
if (System.IO.File.Exists(@"D:\Imagery\network\network.eg"))
{
network = (BasicNetwork)(Encog.Persist.EncogDirectoryPersistence.LoadObject(networkFile));
}
Encog.ML.Data.Image.ImageMLDataSet trainingSet = new Encog.ML.Data.Image.ImageMLDataSet(new RGBDownsample(), false, 1, -1);
Random rnd = new Random();
//take 1000,, take 5000 from nothing and scramble them
List <string> fileEntries = Directory.GetFiles(@"D:\Imagery\_Vege").OrderBy(x => rnd.Next()).Take(1000).ToList();
fileEntries.AddRange(Directory.GetFiles(@"D:\Imagery\_Nothing").OrderBy(x => rnd.Next()).Take(5000).ToArray());
fileEntries = fileEntries.OrderBy(x => rnd.Next()).Take(6000).ToList();
foreach (var file in fileEntries)
{
var bitmap = new System.Drawing.Bitmap(file);
ImageMLData data = new ImageMLData(bitmap);
if (file.Contains("_Nothing"))
{
BasicMLData ideal = new BasicMLData(Nothing);
trainingSet.Add(data, ideal);
}
else
{
BasicMLData ideal = new BasicMLData(Vegetation);
trainingSet.Add(data, ideal);
}
}
trainingSet.Downsample(SIZE, SIZE);
IMLTrain train = new Backpropagation(network, trainingSet, .001, 0.02)
{
};
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine(@"Epoch #" + epoch + @" Error: " + train.Error);
epoch++;
} while (epoch < 50);
train.FinishTraining();
Encog.Persist.EncogDirectoryPersistence.SaveObject(networkFile, (BasicNetwork)network);
Encog.ML.Data.Image.ImageMLDataSet testingSet = new Encog.ML.Data.Image.ImageMLDataSet(new RGBDownsample(), false, 1, -1);
fileEntries = Directory.GetFiles(@"D:\Imagery\_VegeTest").ToList();
foreach (var file in fileEntries)
{
ImageMLData data = new ImageMLData(new System.Drawing.Bitmap(file));
BasicMLData ideal = new BasicMLData(Vegetation);
testingSet.Add(data, ideal);
}
fileEntries = Directory.GetFiles(@"D:\Imagery\_NothingTest").ToList();
foreach (var file in fileEntries)
{
ImageMLData data = new ImageMLData(new System.Drawing.Bitmap(file));
BasicMLData ideal = new BasicMLData(Nothing);
testingSet.Add(data, ideal);
}
testingSet.Downsample(SIZE, SIZE);
Console.WriteLine(@"Neural Network Results:");
foreach (IMLDataPair pair in testingSet)
{
IMLData output = network.Compute(pair.Input);
Console.WriteLine(@", actual (" + output[0] + @"," + output[1] + @"),ideal (" + pair.Ideal[0] + @"," + pair.Ideal[1] + ")");
}
EncogFramework.Instance.Shutdown();
}