public static void RecognizeImage()
{
var size = 1000;
var parasitizedPath = @"Parasitized\";
var unparasitizedPath = @"Uninfected\";
var converter = new PictureConverter();
var testParasitizedImageInput = converter.Convert(@"Images\Parasitized.png");
var testUnparasitizedImageInput = converter.Convert(@"Images\Uninfected.png");
var topology = new Topology(testParasitizedImageInput.Length, 1, 0.1, testParasitizedImageInput.Length / 2);
var neuralNetwork = new NeuralNetwork(topology);
double[,] parasitizedInputs = GetData(parasitizedPath, converter, testParasitizedImageInput, size);
neuralNetwork.Learn(new double[] { 1 }, parasitizedInputs, 1);
double[,] unparasitizedInputs = GetData(unparasitizedPath, converter, testUnparasitizedImageInput, size);
neuralNetwork.Learn(new double[] { 0 }, unparasitizedInputs, 1);
var par = neuralNetwork.Predict(testParasitizedImageInput.Select(t => (double)t).ToArray());
var unpar = neuralNetwork.Predict(testUnparasitizedImageInput.Select(t => (double)t).ToArray());
Console.WriteLine(1 + Math.Round(par.Output, 2));
Console.WriteLine(0 + Math.Round(unpar.Output, 2));
}
public static void ConverterPicture()
{
var converter = new PictureConverter();
var inputs = converter.Convert(@"Images\Uninfected.png");
converter.Save("f:\\image.png", inputs);
}
private static double[,] GetData(string parasitizedPath, PictureConverter converter, double[] testImageInput, int size)
{
var images = Directory.GetFiles(parasitizedPath);
var result = new double[size, testImageInput.Length];
for (int i = 0; i < size; i++)
{
var image = converter.Convert(images[i]);
for (int j = 0; j < image.Length; j++)
{
result[i, j] = image[j];
}
}
return(result);
}
