public ImageMLDataSet(IDownSample downsampler, bool findBounds, double hi, double lo)
{
if (((uint) lo) >= 0)
{
goto Label_005F;
}
Label_0018:
if ((((uint) lo) + ((uint) lo)) >= 0)
{
this.x4d5aabc7a55b12ba = -1;
this.x9b0739496f8b5475 = -1;
}
this.x20133758a5984793 = hi;
this.x8948c4575e007d39 = lo;
if (((uint) hi) <= uint.MaxValue)
{
return;
}
Label_005F:
this.x0677e4dbe212e9d2 = downsampler;
this.x103ca6537af9d723 = findBounds;
if ((((uint) lo) - ((uint) findBounds)) > uint.MaxValue)
{
return;
}
goto Label_0018;
}
/// <summary>
/// Construct this class with the specified downsampler.
/// </summary>
/// <param name="downsampler">The downsampler to use.</param>
/// <param name="findBounds">Should the bounds be found and clipped.</param>
/// <param name="hi">The high value to normalize to.</param>
/// <param name="lo">The low value to normalize to.</param>
public ImageMLDataSet(IDownSample downsampler,
bool findBounds, double hi, double lo)
{
this.downsampler = downsampler;
this.findBounds = findBounds;
height = -1;
width = -1;
this.hi = hi;
this.lo = lo;
}
/// <summary>
/// Construct this class with the specified downsampler.
/// </summary>
/// <param name="downsampler">The downsampler to use.</param>
/// <param name="findBounds">Should the bounds be found and clipped.</param>
/// <param name="hi">The high value to normalize to.</param>
/// <param name="lo">The low value to normalize to.</param>
public ImageMLDataSet(IDownSample downsampler,
bool findBounds, double hi, double lo)
{
this.downsampler = downsampler;
this.findBounds = findBounds;
height = -1;
width = -1;
this.hi = hi;
this.lo = lo;
}
/// <summary>
/// Downsample, and copy, the image contents into the data of this object.
/// Calling this method has no effect on the image, as the same image can be
/// downsampled multiple times to different resolutions.
/// </summary>
/// <param name="downsampler">The downsampler object to use.</param>
/// <param name="findBounds">Should the bounds be located and cropped.</param>
/// <param name="height">The height to downsample to.</param>
/// <param name="width">The width to downsample to.</param>
/// <param name="hi">The high value to normalize to.</param>
/// <param name="lo">The low value to normalize to.</param>
public void Downsample(IDownSample downsampler,
bool findBounds, int height, int width,
double hi, double lo)
{
if (findBounds)
{
downsampler.FindBounds();
}
double[] sample = downsampler.DownSample(this.Image, height,
width);
for (int i = 0; i < sample.Length; i++)
{
sample[i] = OutputFieldRangeMapped.Calculate(sample[i], 0,
255, hi, lo);
}
this.Data = sample;
}
/// <summary>
/// Downsample, and copy, the image contents into the data of this object.
/// Calling this method has no effect on the image, as the same image can be
/// downsampled multiple times to different resolutions.
/// </summary>
/// <param name="downsampler">The downsampler object to use.</param>
/// <param name="findBounds">Should the bounds be located and cropped.</param>
/// <param name="height">The height to downsample to.</param>
/// <param name="width">The width to downsample to.</param>
/// <param name="hi">The high value to normalize to.</param>
/// <param name="lo">The low value to normalize to.</param>
public void Downsample(IDownSample downsampler,
bool findBounds, int height, int width,
double hi, double lo)
{
if (findBounds)
{
downsampler.FindBounds();
}
double[] sample = downsampler.DownSample(this.Image, height,
width);
for (int i = 0; i < sample.Length; i++)
{
sample[i] = OutputFieldRangeMapped.Calculate(sample[i], 0,
255, hi, lo);
}
this.Data = sample;
}
/// <summary>
/// Downsample, and copy, the image contents into the data of this object.
/// Calling this method has no effect on the image, as the same image can be
/// downsampled multiple times to different resolutions.
/// </summary>
/// <param name="downsampler">The downsampler object to use.</param>
/// <param name="findBounds">Should the bounds be located and cropped.</param>
/// <param name="height">The height to downsample to.</param>
/// <param name="width">The width to downsample to.</param>
/// <param name="hi">The high value to normalize to.</param>
/// <param name="lo">The low value to normalize to.</param>
public void Downsample(IDownSample downsampler,
bool findBounds, int height, int width,
double hi, double lo)
{
if (findBounds)
{
downsampler.FindBounds();
}
double[] sample = downsampler.DownSample(Image, height,
width);
for (int i = 0; i < sample.Length; i++)
{
sample[i] = ((sample[i] - 0)
/ (255 - 0))
* (hi - lo) + lo;
}
Data = sample;
}
/// <summary>
/// Downsample, and copy, the image contents into the data of this object.
/// Calling this method has no effect on the image, as the same image can be
/// downsampled multiple times to different resolutions.
/// </summary>
/// <param name="downsampler">The downsampler object to use.</param>
/// <param name="findBounds">Should the bounds be located and cropped.</param>
/// <param name="height">The height to downsample to.</param>
/// <param name="width">The width to downsample to.</param>
/// <param name="hi">The high value to normalize to.</param>
/// <param name="lo">The low value to normalize to.</param>
public void Downsample(IDownSample downsampler,
bool findBounds, int height, int width,
double hi, double lo)
{
if (findBounds)
{
downsampler.FindBounds();
}
double[] sample = downsampler.DownSample(Image, height,
width);
for (int i = 0; i < sample.Length; i++)
{
sample[i] = ((sample[i] - 0)
/(255 - 0))
*(hi - lo) + lo;
}
_data = sample;
}
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 void Downsample(IDownSample downsampler, bool findBounds, int height, int width, double hi, double lo)
{
double[] numArray;
int num;
if (!findBounds)
{
goto Label_004B;
}
if (0 == 0)
{
goto Label_0033;
}
Label_0006:
if (num < numArray.Length)
{
goto Label_005F;
}
this.Data = numArray;
if ((((uint) width) - ((uint) height)) >= 0)
{
return;
}
Label_0033:
if (((uint) num) < 0)
{
goto Label_005F;
}
downsampler.FindBounds();
Label_004B:
numArray = downsampler.DownSample(this.Image, height, width);
num = 0;
goto Label_0006;
Label_005F:
numArray[num] = (((numArray[num] - 0.0) / 255.0) * (hi - lo)) + lo;
num++;
goto Label_0006;
}
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");
}
