} /* 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 */
private void ImportImage(PicesDataBase threadConn,
String fileName
)
{
String rootName = OSservices.GetRootName(fileName);
String picesRootName = sipperFileName + "_" + rootName;
PicesDataBaseImage dbi = threadConn.ImageLoad(picesRootName);
if (dbi != null)
{
return;
}
PicesRaster r = new PicesRaster(fileName);
r = r.Padded(2);
PicesFeatureVector fv = new PicesFeatureVector(r, picesRootName, null, runLog);
fv.ExampleFileName = picesRootName;
int imageId = 0;
bool successful = false;
uint centroidRow = (uint)(0.5f + fv.CentroidRow);
uint centroidCol = (uint)(0.5f + fv.CentroidCol);
if ((centroidRow < 0) || (centroidRow > r.Height))
{
centroidRow = (uint)(r.Height / 2);
}
if ((centroidCol < 0) || (centroidCol > r.Width))
{
centroidCol = (uint)(r.Width / 2);
}
threadConn.ImageInsert(r,
picesRootName,
sipperFileName,
0, 0, 0,
(uint)r.Height, (uint)r.Width,
(uint)fv.OrigSize,
3,
extractionLogEntryId.LogEntryId, extractionLogEntryId.LogEntryId,
centroidRow, centroidCol,
unknownClass, 1.0f, null, 0.0f, null,
0.0f, // depth,
0.0f, // Image Size
null,
ref imageId,
ref successful
);
if (successful)
{
threadConn.FeatureDataInsertRow(sipperFileName, fv);
}
else
{
RunLogAddMsg("RootName[" + rootName + "] Failed: " + threadConn.LastErrorDesc() + "\n");
}
} /* ImportImage */
