public ImageTrainDataSet BalanceData()
{
Random rand = new Random();
ImageTrainDataSet ret = new ImageTrainDataSet();
ret.AddRange(this);
TrainDataInfo[] infos = this.GetInfo(this.GetNumOutput());
int maxCount = infos.Max(x => x.count);
for (int i = 0; i < infos.Length; i++)
{
for (int j = infos[i].count; j < maxCount; j++)
{
//Random _self for that index
while (true)
{
int index = rand.Next(this.Count);
if (this[index].labelIndex == i)
{
ret.Add(this[index].Clone());
break;
}
}
}
}
return(ret);
}
public ImageTrainDataSet SplitTestData(int num)
{
Random rand = new Random();
//List<ImageTrainData> ret = _self.GetRange(0, num);
//_self.RemoveRange(0, num);
int numOutput = this.GetNumOutput();
ImageTrainDataSet ret = new ImageTrainDataSet();
while (ret.Count() < num)
{
for (int i = 0; i < numOutput; i++)
{
while (true)
{
int index = rand.Next(this.Count);
if (this[index].labelIndex == i)
{
ret.Add(this[index]);
this.RemoveAt(index);
break;
}
}
}
}
return(ret);
}
public static ImageTrainDataSet GetAllTrainImageData(String folder, String[] subFolders, String imageFilter = "*.png", String imageFilterOut = "mask.png")
{
ImageTrainDataSet ret = new ImageTrainDataSet();
for (int i = 0; i < subFolders.Length; i++)
{
String folderName = folder + @"\" + subFolders[i];
ImageFileData[] images = GetImagesFromDir(folderName, imageFilter, imageFilterOut);
foreach (ImageFileData img in images)
{
ret.Add(
new ImageTrainData()
{
label = subFolders[i],
labelIndex = i,
fileName = img.fileName,
}
);
}
}
return(ret);
}