public static void Classifiy(BackgroundWorker sender, ImageGrid g, ClassifyProcessParam p, int classifier, int NStep, string path, int blurRadius)
{
MainWindow.Log("Loading Image (" + p.WorldX + " " + p.WorldY + ")");
DateTime time = DateTime.Now;
ImageTile it = null;
int max = (int)Math.Pow(2, p.zoom);
int TileX = p.TileX - 1;
for (int x = p.WorldX - 1; x <= p.WorldX + 1; x++, TileX++)
{
int TileY = p.TileY - 1;
for (int y = p.WorldY - 1; y <= p.WorldY + 1; y++, TileY++)
{
if (x >= 0 && x < max && y >= 0 && y < max && TileX >= 0 && TileX < g.width && TileY >= 0 && TileY < g.height)
{
string id = g.id + "_" + p.zoom + "_" + y + "_" + x;
string filename = "tmp\\sat\\" + (p.zoom) + "_" + (y) + "_" + (x) + ".png";
if (x == p.WorldX && y == p.WorldY)
{
MainWindow.dispatcher.Invoke(() =>
{
if (imageTiles.ContainsKey(id))
{
it = imageTiles[id];
}
else
{
it = new ImageTile(MainWindow.Instance, g, filename, id, p.zoom, TileX, TileY, false);
}
});
}
else
{
MainWindow.dispatcher.Invoke(() =>
{
if (!imageTiles.ContainsKey(id))
{
ImageTile it_ = new ImageTile(MainWindow.Instance, g, filename, id, p.zoom, TileX, TileY, false);
g.AddTile(it_);
}
});
}
}
}
}
if (it == null)
{
return;
}
g.AddTile(it);
it.Status(new PixelColor(0, 255, 255, 100));
it.classified = false;
it.Lock();
MainWindow.Log("Classifying Image (" + p.WorldX + " " + p.WorldY + ")");
try
{
MainWindow.dispatcher.Invoke(() =>
{
foreach (Classes c in MainWindow.classesList)
{
g.AddOverlayClass(it, c, MainWindow.Instance);
if (c.classifiedPointsList.ContainsKey(it.id))
{
c.classifiedPointsList.Remove(it.id);
}
c.classifiedPointsList.Add(it.id, new FeaturePoint[256, 256]);
}
});
Parallel.For(0, 256, x =>
{
for (int y = 0; y < 256; y++)
{
FeaturePoint fp = FeaturePoint.GetOrAddFeaturePoint(x, y, it.id);
var output = Array.Empty <double>();
switch (classifier)
{
case 0:
dfprocess(MainWindow.Instance.DecisionForest, fp.GetFeatures(), ref output);
break;
case 1:
mlpprocess(MainWindow.Instance.NeuralNetwork, fp.GetFeatures(), ref output);
break;
case 2:
mlpeprocess(MainWindow.Instance.NeuralNetworkEnsemble, fp.GetFeatures(), ref output);
break;
default: break;
}
int predictedClass = 0;
for (int k = 1; k < output.Length; k++)
{
if (output[k] > output[predictedClass])
{
predictedClass = k;
}
}
MainWindow.GetClassByNum(predictedClass).classifiedPointsList[it.id][fp.y, fp.x] = fp;
}
});
it.Unlock();
it.classified = true;
float t = (float)(DateTime.Now - time).TotalSeconds;
averageClassificationTime = (averageClassificationTime * classificationDone + t) / (classificationDone + 1);
classificationDone++;
ReportProgress(sender);
classificationQueue--;
MainWindow.Log("Image (" + p.WorldX + " " + p.WorldY + ") Classification done in " + t + "s. Now waiting for postprocessing");
GC.Collect();
it.Status(new PixelColor(0, 255, 0, 100));
classificationDoneQueue.Enqueue(it.id);
}
catch (Exception e)
{
MainWindow.Log("ERROR when classifying Image (" + p.WorldX + " " + p.WorldY + "): " + e.Message);
it.Status(new PixelColor(0, 0, 255, 100));
if (it.classified)
{
classificationQueue++; classificationDone--;
}
it.classified = false;
it.Unlock();
errorClassificationRecoveryQueue.Enqueue(p);
GC.Collect();
}
}