public void processClassification()
{
List <int[]> positions = aw.getClass().getLastMatches();
List <string> labels = aw.getClass().getLastLabels();
List <string> lines = new List <string>();
if (positions.Count > 0)
{
lines.Add("\t\t Classification of found colonies : ");
}
for (int i = 0; i < positions.Count; i++)
{
lines.Add("\t\t\t Colony " + i + " classified as: " + labels[i] + " in map position : " + positions[i][0] + " " + positions[i][1]);
}
file.AppendClassData(lines);
}
// In each step, colony tracking is performed by comparing the current taken image with the
// first one (serving as background) and the last one in the look for changes
public void newStep(object param)
{
while (analysisInProgress)
{
Thread.Sleep(1000);
}
analysisInProgress = true;
object[] objects = (object[])param;
System.Drawing.Bitmap bmp = (System.Drawing.Bitmap)objects[0];
CaptureWindow cw = (CaptureWindow)objects[1];
Tracking_Images.Add(bmp);
Tracking_Images[Tracking_Images.Count - 1].Tag = DateTime.Now.ToString("hh:mm:ss");
// Firstly we will find the best matching repositioning to the images by selecting the one that minimizes the edges difference
// This is needed because of the Sprout's image acquisition impresition, which leads to minor mismatches between the same object's captures
int[] positions = Matching_Robinson.RobinsonRepositioning(background, bmp);
// Once the reposition is calculated, a difference between the taken image and the background is performed
// For this difference, Manhattan difference computation has been used. However, Eucliedean distance computation and
// Pearson's correlation index difference computation functions are included in the code and ready to be used.
Map resultDifference = DifferenceComputation.getManhattan(background, bmp, positions);
// A cluster search is computed
List <Cluster> frameBlobs = FindObjects(resultDifference, DateTime.Now.ToString("hh:mm:ss"));
// If this is the first tracking step made, the found clusters are set as base ones,
// otherwise a tracking algorithm trying to match current clusters with already monitored ones is performed
if (track.isEmpty())
{
track.firstScan(frameBlobs, step); events = new int[0];
}
else
{
events = track.assignBlobs(frameBlobs, step);
}
events = checkBounds(events, track.getLast(), bmp);
if (aw.getPicker().classAnalysis&& !aw.getClass().hasError())
{
aw.getClass().newStep(track.getLast(), bmp);
App.Current.Dispatcher.BeginInvoke(System.Windows.Threading.DispatcherPriority.ApplicationIdle,
new Action(() => cw.processClassification()));
}
step++;
App.Current.Dispatcher.BeginInvoke(System.Windows.Threading.DispatcherPriority.ApplicationIdle,
new Action(() => cw.processEvents()));
analysisInProgress = false;
}