public static void teste()
{
Console.WriteLine("\n\nA imagem deve estar na pasta debug, com o nome 'char.bmp' (já deve ter uma lá)");
var net2 = SerializationExtensions.FromJSON(File.ReadAllText("best"));
byte[] imgbytes = null;
var newImage = MnistReader.ScaleImage(new Bitmap(@"char.bmp"), 24, 24);
var x = new Volume(newImage.Width, newImage.Height, 1, 0.0);
for (var i = 0; i < newImage.Width; i++)
{
for (var j = 0; j < newImage.Width; j++)
{
x.Weights[j + i * newImage.Width] = (newImage.GetPixel(j, i).B + newImage.GetPixel(j, i).R + newImage.GetPixel(j, i).G) / 3;
}
}
if (File.Exists("oi.txt"))
{
File.Delete("oi.txt");
}
for (int i = 0; i < x.Weights.Length; i++)
{
using (StreamWriter file = new StreamWriter(@"oi.txt", true))
file.WriteLine(x.Weights[i].ToString());
}
net2.Forward(x);
var yhat = net2.GetPrediction();
Console.WriteLine("label prevista = {0}", yhat);
}
public static void segment_n_rec()
{
var image = new Bitmap(@"a.bmp");
image = image.Clone(new Rectangle(0, 0, image.Width, image.Height), PixelFormat.Format8bppIndexed);
byte[] imgbytes = new byte[image.Height * image.Width];
#region parallel grey image conv
var tasks = new Task[Environment.ProcessorCount];
int oi = -1;
for (var ai = 0; ai < image.Width; ai++)
{
for (int taskNumber = 0; taskNumber < Environment.ProcessorCount; taskNumber++)
{
tasks[taskNumber] = Task.Factory.StartNew(() =>
{
int j = Interlocked.Increment(ref oi);
while (j < image.Width)
{
imgbytes[j + ai * image.Width] = (byte)(((int)image.GetPixel(j, ai).B + (int)image.GetPixel(j, ai).R + (int)image.GetPixel(j, ai).G) / 3);
j = Interlocked.Increment(ref oi);
}
});
}
Task.WaitAll(tasks);
}
#endregion
new OtsuThreshold().ApplyInPlace(image);
// check threshold value
var net2 = SerializationExtensions.FromJSON(File.ReadAllText("best"));
var newImage = MnistReader.ScaleImage(new Bitmap(@"C:\Users\leoni.win7-PC\Desktop\Nova pasta\a.bmp"), 24, 24);
var x = new Volume(newImage.Width, newImage.Height, 1, 0.0);
for (var i = 0; i < newImage.Width; i++)
{
for (var j = 0; j < newImage.Width; j++)
{
x.Weights[j + i * newImage.Width] = (newImage.GetPixel(j, i).B + newImage.GetPixel(j, i).R + newImage.GetPixel(j, i).G) / 3;
}
}
if (File.Exists("oi.txt"))
{
File.Delete("oi.txt");
}
for (int i = 0; i < x.Weights.Length; i++)
{
using (StreamWriter file = new StreamWriter(@"oi.txt", true))
file.WriteLine(x.Weights[i].ToString());
}
net2.Forward(x);
var yhat = net2.GetPrediction();
}
public static List <byte[]> LoadImages(string folder, bool train)
{
var result = new List <byte[]>();
int train_count = 750, test_count = 250;
DirectoryInfo root = new DirectoryInfo(folder);
foreach (DirectoryInfo dr in root.GetDirectories())
{
var files = dr.GetFiles();
for (int i = (train ? 0 : train_count); i < (train ? train_count : test_count + train_count); i++)
{
byte[] imgbytes = new byte[24 * 24];
var newImage = MnistReader.ScaleImage(new Bitmap(files[i].FullName), 24, 24);
for (var ai = 0; ai < newImage.Width; ai++)
{
for (var j = 0; j < newImage.Width; j++)
{
imgbytes[j + ai * newImage.Width] = (byte)(((int)newImage.GetPixel(j, ai).B + (int)newImage.GetPixel(j, ai).R + (int)newImage.GetPixel(j, ai).G) / 3);
}
}
//var tasks = new Task[Environment.ProcessorCount];
//int oi = -1;
//for (var ai = 0; ai < newImage.Width; ai++)
//{
// for (int taskNumber = 0; taskNumber < Environment.ProcessorCount; taskNumber++)
// tasks[taskNumber] = Task.Factory.StartNew(() =>
// {
// int j = Interlocked.Increment(ref oi);
// while (j < newImage.Width)
// {
// imgbytes[j + ai * newImage.Width]= (byte)(((int)newImage.GetPixel(j, ai).B + (int)newImage.GetPixel(j, ai).R + (int)newImage.GetPixel(j, ai).G)/ 3);
// j = Interlocked.Increment(ref oi);
// }
// });
// Task.WaitAll(tasks);
//}
//if (File.Exists("oi.txt")) { File.Delete("oi.txt"); }
//for (int asd = 0; asd < imgbytes.Length; asd++)
// using (StreamWriter file = new StreamWriter(@"oi.txt", true))
// file.WriteLine(imgbytes[asd].ToString());
result.Add(imgbytes);
}
}
return(result);
}
private const string folder = @"..\..\..\char\"; // para que saia da pasta 'debug' encontre os dados de treino
private void MnistTrain()
{
// carrega os chars
Console.WriteLine("carregando datasets...");
if (File.Exists("dataset_training.dat") || File.Exists("dataset_testing.dat"))
{ // se já tiver os datasets tratados, carrega eles
training = useful.ReadObject <List <MnistEntry> >(File.ReadAllBytes("dataset_training.dat"));
testing = useful.ReadObject <List <MnistEntry> >(File.ReadAllBytes("dataset_testing.dat"));
}
else
{ // caso contrário, carrega a partir das imagens
training = MnistReader.Load(folder, true);
testing = MnistReader.Load(folder, false);
File.WriteAllBytes("dataset_training.dat", useful.WriteObject <List <MnistEntry> >(training));
File.WriteAllBytes("dataset_testing.dat", useful.WriteObject <List <MnistEntry> >(testing));
}
Random rnd = new Random();
training = training.OrderBy(x => rnd.Next()).ToList();
testing = testing.OrderBy(x => rnd.Next()).ToList();
if (training.Count == 0 || testing.Count == 0)
{
Console.WriteLine("ajuste o diretório dos arquivos de treino/teste.");
Console.ReadKey();
return;
}
// cria uma CNN simples
net = new Net();
net.AddLayer(new InputLayer(24, 24, 1)); //tamanho que eu escalei as imagens
net.AddLayer(new ConvLayer(5, 5, 20)
{
Stride = 1, Pad = 2, Activation = Activation.Relu
});
net.AddLayer(new PoolLayer(2, 2)
{
Stride = 2
});
net.AddLayer(new ConvLayer(5, 5, 35)
{
Stride = 1, Pad = 2, Activation = Activation.Relu
});
net.AddLayer(new PoolLayer(3, 3)
{
Stride = 3
});
net.AddLayer(new FullyConnLayer(50));
net.AddLayer(new SoftmaxLayer(33));
// este é o meu otimizador
// ver depois http://int8.io/comparison-of-optimization-techniques-stochastic-gradient-descent-momentum-adagrad-and-adadelta/#AdaDelta
trainer = new AdadeltaTrainer(net)
{
BatchSize = 20,
L2Decay = 0.001,
};
Console.WriteLine("CNN treinando... aperte uma tecla para parar");
// o passo
do
{
var sample = SampleTrainingInstance();
// // só pra ver no matlab a imagem (no matlab eu uso o script imag.m)
//if (File.Exists("oi.txt")) { File.Delete("oi.txt"); }
//for (int i = 0; i < sample.Volume.Weights.Length; i++)
// using (StreamWriter file = new StreamWriter(@"oi.txt", true))
// file.WriteLine(((sample.Volume.Weights[i] * 255.0)).ToString());
Step(sample);
} while (!Console.KeyAvailable);
}
