} /* MakePredictions */
public static void MakePredictions(PicesFeatureVector featureVector,
ref PicesPrediction model1Prediction1,
ref PicesPrediction model1Prediction2,
ref PicesPrediction model2Prediction1,
ref PicesPrediction model2Prediction2,
PicesRunLog runLog
)
{
if (model1Prediction1 == null)
{
model1Prediction1 = new PicesPrediction(null, 0, 0.0f);
}
if (model1Prediction2 == null)
{
model1Prediction2 = new PicesPrediction(null, 0, 0.0f);
}
if (model2Prediction1 == null)
{
model2Prediction1 = new PicesPrediction(null, 0, 0.0f);
}
if (model2Prediction2 == null)
{
model2Prediction2 = new PicesPrediction(null, 0, 0.0f);
}
if (model1 != null)
{
model1.PredictClass(featureVector, model1Prediction1, model1Prediction2);
}
if (model2 != null)
{
model2.PredictClass(featureVector, model2Prediction1, model2Prediction2);
}
} /* MakePredictions */
private PicesPredictionList PredictClasses(PicesFeatureVector fv,
PicesPrediction pred1,
PicesPrediction pred2,
String imageFileName,
String imageFileRootName
)
{
PicesFeatureVector fvToUse = fv;
PicesPredictionList probPredList = null;
if (fvToUse.FeatureDataMissing)
{
fvToUse = null;
// Feature data is missing. we need to compute from original image.
PicesDataBaseImage pdbi = DbConn().ImageLoad(imageFileRootName);
if (pdbi != null)
{
PicesRaster pr = DbConn().ImageFullSizeFind(imageFileRootName);
if (pr == null)
{
ErrorMsgsAdd("Failed to Load FullSize image [" + imageFileRootName + "]");
}
else
{
fvToUse = new PicesFeatureVector(pr, imageFileName, null, runLog);
// Since we had to compute the FeatureDatya from the raster image we now need to
// get the instrument data that matches it.
PicesInstrumentData id = DbConn().InstrumentDataGetByScanLine(pdbi.SipperFileName, pdbi.TopLeftRow);
if (id != null)
{
fvToUse.AddInstrumentData(id);
}
DbConn().FeatureDataInsertRow(pdbi.SipperFileName, fvToUse);
pr.Dispose();
}
pr = null;
}
}
if (fvToUse != null)
{
classifier.PredictClass(fvToUse, pred1, pred2);
probPredList = classifier.PredictProbabilities(fvToUse);
}
else
{
pred1.MLClass = PicesClassList.GetUnKnownClassStatic(); pred1.Probability = 1.0f;
pred2.MLClass = PicesClassList.GetUnKnownClassStatic(); pred2.Probability = 0.0f;
}
return(probPredList);
} /* PredictClasses */
} /* LoadData */
private void PerformPredictions()
{
PicesPrediction p1 = new PicesPrediction(null, 0, 0.0f);
PicesPrediction p2 = new PicesPrediction(null, 0, 0.0f);
foreach (PicesFeatureVector fv in groundTruth)
{
PicesClass knownClass = fv.MLClass;
if (cancelProcessing)
{
break;
}
trainingModel.PredictClass(fv, p1, p2);
PicesClass predClass = p1.MLClass;
confusionMatrix.MakePrediction(knownClass, predClass, p1.Probability, fv, trainingModel);
numGroundTruthImagesProcessed++;
}
} /* PerformPredictions*/
private void ClassInitialization()
{
sizeDistributionDown = new SizeDistribution2(0.1f, 1.2f, 10.0f);
sizeDistributionUp = new SizeDistribution2(0.1f, 1.2f, 10.0f);
depthDistribution_1 = new SizeDistribution(500, 1);
depthDistribution_1Down = new SizeDistribution(500, 1);
depthDistribution_1Up = new SizeDistribution(500, 1);
depthDistribution_10 = new SizeDistribution(50, 10);
classCounts = new ClassStatSortedList();
numImagesClassified = 0;
pred1 = new PicesPrediction(null, 0, 0.0f);
pred2 = new PicesPrediction(null, 0, 0.0f);
if (classifier != null)
{
classesInClassifier = classifier.MLClasses();
}
}
} /* MakePredictions */
public static void MakePredictions(PicesRaster raster,
ref PicesPrediction model1Prediction1,
ref PicesPrediction model1Prediction2,
ref PicesPrediction model2Prediction1,
ref PicesPrediction model2Prediction2,
PicesRunLog runLog
)
{
String imageFileName = raster.FileName;
ParsedImageFileName pifn = PicesFeatureVector.ParseImageFileName(imageFileName);
String sipperFileName = pifn.sipperFileName;
uint scanLineNum = pifn.scanLineNum;
PicesFeatureVector fv = new PicesFeatureVector(raster, imageFileName, null, runLog);
{
PicesDataBase dbConn = PicesDataBase.GetGlobalDatabaseManagerNewInstance(runLog);
PicesInstrumentData pid = dbConn.InstrumentDataGetByScanLine(sipperFileName, scanLineNum);
if (pid != null)
{
fv.AddInstrumentData(pid);
}
pid = null;
dbConn.Close();
dbConn = null;
}
MakePredictions(fv,
ref model1Prediction1,
ref model1Prediction2,
ref model2Prediction1,
ref model2Prediction2,
runLog
);
fv = null;
}
} /* 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 */
} /* GradeAgainstCrossValidationRun */
void CrossValidate(PicesFeatureVectorList testExamples,
PicesFeatureVectorList trainingExamples,
int foldNum
)
{
msgQueue.AddMsg("\n\n" + "Starting Fold[" + (foldNum + 1).ToString() + "] of Fold[" + numOfFolds + "]" + "\n");
if (trainingModel != null)
{
trainingModel = null;
}
msgQueue.AddMsg("Building Classifier");
try
{
trainingModel = new TrainingModel2(runLog, curSelModel);
trainingModel.BuildTrainingModel(trainingExamples);
}
catch (Exception e)
{
msgQueue.AddMsg("\n\n\n" + "Exception occur building training model" + "\n" + e.ToString() + "\n\n");
MessageBox.Show(this, "Exception Occurred Building Training Model." + "\n" + e.ToString(), "CrossValidate", MessageBoxButtons.OK);
trainingModel = null;
return;
}
if (cancelProcessing)
{
trainingModel = null;
return;
}
if (!trainingModel.Valid)
{
MessageBox.Show(this, "Training Model is Invalid.", "Ground Thruthing", MessageBoxButtons.OK);
}
else
{
msgQueue.AddMsg("\n" + "Predictions for fold[" + (foldNum + 1).ToString() + "]");
PicesClass knownClass = null;
foreach (PicesFeatureVector example in testExamples)
{
if (cancelProcessing)
{
break;
}
knownClass = example.MLClass;
PicesPrediction prediction = trainingModel.PredictClass(example);
if ((otherClass != null) && (prediction.MLClass == noAgreementClass))
{
prediction.MLClass = otherClass;
}
/**@todo Add code for "NoAgreement:" to be treeted as a Other Class when there is "NoAgreement". */
confusionMatrix.MakePrediction(knownClass, prediction.MLClass, prediction.Probability, example, trainingModel);
}
}
trainingModel.CleanUp();
trainingModel = null;
} /* CrossValidate */
} /* PerformAllOperations */
private void MakePredictions()
{
pred1ClassName.Text = "";
pred2ClassName.Text = "";
BlobDetector bd = new BlobDetector(raster, 20);
BlobList blobs = bd.ExtractBlobs();
if ((blobs == null) || (blobs.Count < 1))
{
return;
}
Blob largestBlob = blobs.LocateLargestBlob();
if (largestBlob == null)
{
return;
}
byte[][] largestRaster = bd.ExtractedBlobImage(largestBlob);
PicesRunLog runLog = new PicesRunLog();
PicesRaster picesRaster = new PicesRaster(largestRaster);
//PicesPredictionList trainLibrary1Predictions = null;
//PicesPredictionList trainLibrary2Predictions = null;
//ActiveTrainingLibraries.MakePredictions (picesRaster,
// instrumentData,
// imageFileName,
// ref trainLibrary1Predictions,
// ref trainLibrary2Predictions,
// runLog
// );
picesRaster.FileName = imageFileName;
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(picesRaster,
ref model1Prediction1,
ref model1Prediction2,
ref model2Prediction1,
ref model2Prediction2,
runLog
);
if (model1Prediction1 != null)
{
TrainLib1Name.Text = ActiveTrainingLibraries.Model1Name;
pred1Prob.Text = model1Prediction1.Probability.ToString("p");
pred1ClassName.Text = model1Prediction1.ClassName;
this.Text = model1Prediction1.ClassName;
if (model1Prediction2 != null)
{
pred2Prob.Text = model1Prediction2.Probability.ToString("p");
pred2ClassName.Text = model1Prediction2.ClassName;
}
}
if (model2Prediction1 != null)
{
TrainLib2Name.Text = ActiveTrainingLibraries.Model2Name;
lib2Pred1Prob.Text = model2Prediction1.Probability.ToString("p");
lib2Pred1ClassName.Text = model2Prediction1.ClassName;
if (model2Prediction2 != null)
{
lib2Pred2Prob.Text = model2Prediction2.Probability.ToString("p");
lib2Pred2ClassName.Text = model2Prediction2.ClassName;
}
}
picesRaster = null;
blobs.Dispose();
largestBlob.Dispose();
bd.Dispose();
bd = null;
} /* MakePredictions */
