} /* LoadPlanktonImage */
private void AddImageToTraningLibraries()
{
if (!ActiveTrainingLibraries.ModelsAreAvailable())
{
MessageBox.Show("There are no selected Training Models to add Image to.");
return;
}
if (image.ValidatedClass == null)
{
MessageBox.Show("No Validated Class Selected.");
return;
}
TrainingModel2 tm1 = ActiveTrainingLibraries.Model1();
if (tm1 == null)
{
tm1 = ActiveTrainingLibraries.Model2();
}
if (tm1 == null)
{
MessageBox.Show("No active training library specified.");
return;
}
if (!tm1.IncludesClass(image.ValidatedClass))
{
MessageBox.Show("Training Model[" + tm1.ModelName + "] does not include a definition for class[" + image.ValidatedClass.Name + "].", "ImageViewer.AddImageToTraningLibraries");
}
else
{
try
{
tm1.AddImageToTrainingLibray(image.ImageFileName, raster, image.ValidatedClass, true);
}
catch (Exception e)
{
MessageBox.Show("Error adding image to TrainingModel2[" + tm1.ModelName + "]" + "\n\n" + e.ToString(), "ImageViewer.AddImageToTraningLibraries");
}
}
} /* AddImageToTraningLibraries */
} /* UpdatedataFields*/
private void MakePredictions()
{
if (!ActiveTrainingLibraries.ModelsAreAvailable())
{
// Since there are NO training models loaded; there is no point doing the work for a prediction.
return;
}
featureVector = dbConn.FeatureDataRecLoad(image);
if (featureVector == null)
{
featureVector = new PicesFeatureVector(raster, image.ImageFileName, null, runLog);
// Since we had to compute the FeatureDatya from the raster we now need to
// get the instrument data that matches it.
if (instrumentData != null)
{
featureVector.AddInstrumentData(instrumentData);
}
dbConn.FeatureDataInsertRow(image.SipperFileName, featureVector);
}
float esd = 0.0f;
float eBv = 0.0f;
float filledArea = image.PixelCount;
float cropWidth = 3800.0f; // (3900.0f - 200.0f);
float flowRate2 = 0.5f;
if (featureVector != null)
{
filledArea = featureVector.FilledArea;
}
if (sipperFile != null)
{
scanRate = sipperFile.ScanRate;
}
esd = (float)(2.0 * Math.Sqrt(filledArea * (0.096 / cropWidth) * 1000.0 * (flowRate1 / sipperFile.ScanRate) * 1000.0 / 3.1415926));
eBv = (float)((4.0 / 3.0) * Math.PI * Math.Pow(Math.Sqrt(filledArea * (chamberWidth / cropWidth) * 1000 * (flowRate1 / scanRate) * 1000.0 / Math.PI), 3));
PicesPrediction model1Prediction1 = new PicesPrediction(null, 0, 0.0f);
PicesPrediction model1Prediction2 = new PicesPrediction(null, 0, 0.0f);
PicesPrediction model2Prediction1 = new PicesPrediction(null, 0, 0.0f);
PicesPrediction model2Prediction2 = new PicesPrediction(null, 0, 0.0f);
ActiveTrainingLibraries.MakePredictions(featureVector,
ref model1Prediction1,
ref model1Prediction2,
ref model2Prediction1,
ref model2Prediction2,
runLog
);
if (model1Prediction1 != null)
{
Lib1Pred1Class.Text = model1Prediction1.ClassName;
Lib1Pred1Prob.Text = model1Prediction1.Probability.ToString("##0.00%");
Lib1Pred1Votes.Text = model1Prediction1.Votes.ToString("#,##0");
}
if (model1Prediction2 != null)
{
Lib1Pred2Class.Text = model1Prediction2.ClassName;
Lib1Pred2Prob.Text = model1Prediction2.Probability.ToString("##0.00%");
Lib1Pred2Votes.Text = model1Prediction2.Votes.ToString("#,##0");
}
if (model2Prediction1 != null)
{
Lib2Pred1Class.Text = model2Prediction1.ClassName;
Lib2Pred1Prob.Text = model2Prediction1.Probability.ToString("##0.00%");
Lib2Pred1Votes.Text = model2Prediction1.Votes.ToString("#,##0");
}
if (model1Prediction2 != null)
{
Lib2Pred2Class.Text = model2Prediction2.ClassName;
Lib2Pred2Prob.Text = model2Prediction2.Probability.ToString("##0.00%");
Lib2Pred2Votes.Text = model2Prediction2.Votes.ToString("#,##0");
}
if (featureVector != null)
{
AreaMMSquare.Text = featureVector.AreaMMSquare.ToString("#,##0.000");
}
ESD.Text = esd.ToString("#,##0.00");
EBv.Text = eBv.ToString("##0.0000");
return;
} /* MakePredictions */
