Tuple <List <Matrix>, List <Matrix> > ReadMNISTTrainingData()
{
String trainDir = "C:\\Users\\ivans_000\\Desktop\\MASTER\\Spring2019\\Deep_Learning\\Assignment2_Sangines\\Data\\Training1000\\";
//string trainDir = @"D:\csharp2018\DataSets\MNIST\MNISTBMP\Training1000";
//string trainDir = @"D:\csharp2016\DeepLearning\MNIST\data\TrainingAll60000";
//string trainDir = @"D:\csharp2018\DataSets\MNIST\MNISTBMP\data\TrainingAll60000";
int numFiles = 0;
int dataIndex = 0;
DirectoryInfo dinfo = new DirectoryInfo(trainDir);
List <Matrix> InputDataList = new List <Matrix>();
List <Matrix> OutputLabelsList = new List <Matrix>();
//count the number of files
foreach (FileInfo fi in dinfo.GetFiles())
{
numFiles++;
}
foreach (FileInfo fi in dinfo.GetFiles())
{
Matrix Img = new Matrix(28, 28);
String fname = fi.FullName;
Bitmap bmp = new Bitmap(Image.FromFile(fname));
if (ImageProc.IsGrayScale(bmp) == false) //make sure it is grayscale
{
ImageProc.ConvertToGray(bmp);
}
for (int i = 0; i < bmp.Width; i++)
{
for (int j = 0; j < bmp.Height; j++)
{ // rescale between 0 and 1
Img.D[i][j] = bmp.GetPixel(i, j).R / 255.0;
}
}
InputDataList.Add(Img);
Matrix outputLabel = new Matrix(10, 1);
String s1 = fi.Name;
Char output = s1[0];
int classLabel = (Convert.ToInt16(output) - 48); //will only work with numbers 0-9
outputLabel.D[classLabel][0] = 1; // others are 0 by default
OutputLabelsList.Add(outputLabel);
dataIndex++;
if ((dataIndex % 500) == 0)
{
Console.WriteLine("iter: " + dataIndex);
}
}
return(new Tuple <List <Matrix>, List <Matrix> >(InputDataList, OutputLabelsList));
}
Matrix ReadOneImage(string fname)
{
Matrix Img = new Matrix(28, 28);
Bitmap bmp = new Bitmap(Image.FromFile(fname));
if (ImageProc.IsGrayScale(bmp) == false) //make sure it is grayscale
{
ImageProc.ConvertToGray(bmp);
}
for (int i = 0; i < bmp.Width; i++)
{
for (int j = 0; j < bmp.Height; j++)
{ // rescale between 0 and 1
Img.D[i][j] = bmp.GetPixel(i, j).R / 255.0;
}
}
return(Img);
}
