} /* ExploreMatrixCell */
public void MakePrediction(PicesClass knownClass,
PicesClass predictedClass,
double predProb,
PicesFeatureVector fv,
TrainingModel2 trainModel
)
{
bool partOfOriginalClassifier = ClassInClassifierClasses(knownClass);
if (!partOfOriginalClassifier)
{
String notPartClassName = "(V)" + knownClass.Name;
knownClass = PicesClassList.GetUniqueClass(notPartClassName, "");
}
int x = classes.LookUpIndex(knownClass);
if (x < 0)
{
classes.Add(knownClass);
}
x = classes.LookUpIndex(predictedClass);
if (x < 0)
{
classes.Add(predictedClass);
}
Prediction p = new Prediction(knownClass, predictedClass, predProb, fv, trainModel, partOfOriginalClassifier);
predictions.Add(p);
} /* featureVector */
} /* MakePredictions */
public static void MakePredictions(PicesFeatureVector featureVector,
ref PicesPredictionList model1Predictions,
ref PicesPredictionList model2Predictions,
PicesRunLog runLog
)
{
model1Predictions = null;
model2Predictions = null;
if ((!ModelsAreAvailable()) || (featureVector == null))
{
return;
}
if (model1 != null)
{
model1Predictions = model1.PredictProbabilities(featureVector);
}
if (model2 != null)
{
model2Predictions = model2.PredictProbabilities(featureVector);
}
return;
} /* MakePredictions */
} /* 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 */
public PredictionBreakDownDisplay(PicesDataBase _dbConn,
PicesDataBaseImage _image,
PicesRaster _raster,
TrainingModel2 _trainingModel,
PicesInstrumentData _instrumentData, /*!< Instrument data that goes with image, if null will read from database. */
PicesRunLog _runLog
)
{
if (_runLog == null)
{
runLog = new PicesRunLog();
}
else
{
runLog = _runLog;
}
if (_image == null)
{
return;
}
fileName = _image.ImageFileName;
instrumentData = _instrumentData;
trainingModel = _trainingModel;
PicesRaster raster = _raster;
if (_dbConn != null)
{
if (raster == null)
{
raster = _dbConn.ImageFullSizeFind(fileName);
}
if (instrumentData == null)
{
instrumentData = _dbConn.InstrumentDataGetByScanLine(_image.SipperFileName, _image.TopLeftRow);
}
dbFeatureVector = _dbConn.FeatureDataRecLoad(fileName);
}
if (raster == null)
{
return;
}
//raster = raster.ConnectedComponent ();
featureCalcImages = new PicesRasterList();
featureVector = new PicesFeatureVector(raster, fileName, featureCalcImages, runLog);
if (instrumentData != null)
{
featureVector.AddInstrumentData(instrumentData);
}
InitializeComponent();
}
} /* LoadImageGrid */
private void UpdateDetailProbabilityDisplay()
{
if ((trainingModel == null) || (trainingModel.ModelType != TrainingModel2.ModelTypes.Dual))
{
return;
}
PicesPredictionList class1Results = null;
PicesPredictionList class2Results = null;
String classifier1Desc = null;
String classifier2Desc = null;
PicesFeatureVector fvToUse = dbFeatureVector;
if (fvToUse == null)
{
fvToUse = featureVector;
}
trainingModel.ProbabilitiesByClassDual(fvToUse,
ref classifier1Desc,
ref classifier2Desc,
ref class1Results,
ref class2Results
);
this.Classifier1Name.Text = classifier1Desc;
this.Classifier2Name.Text = classifier2Desc;
if (class1Results != null)
{
Classifier1Probabilities.Rows.Clear();
foreach (PicesPrediction p in class1Results)
{
Object[] data = new Object[3];
data[0] = p.ClassName;
data[1] = p.Probability;
data[2] = p.Votes;
Classifier1Probabilities.Rows.Add(data);
}
}
if (class2Results != null)
{
Classifier2Probabilities.Rows.Clear();
foreach (PicesPrediction p in class2Results)
{
Object[] data = new Object[3];
data[0] = p.ClassName;
data[1] = p.Probability;
data[2] = p.Votes;
Classifier2Probabilities.Rows.Add(data);
}
}
} /* UpdateDetailProbabilityDisplay */
/// <summary>
/// Using supplied 'Raster' data it will call each currently active model and return there predictions.
/// </summary>
/// <param name="raster">Raster of image to predict. .</param>
/// <param name="instrumentData">Instrument data that was recorded with the 'raster' .</param>
/// <param name="imageFileName">Name of the image file.</param>
/// <param name="model1Predictions">The model1 predictions.</param>
/// <param name="model2Predictions">The model2 predictions.</param>
/// <param name="runLog">The run log.</param>
public static void MakePredictions(PicesRaster raster,
InstrumentData instrumentData,
String imageFileName,
ref PicesPredictionList model1Predictions,
ref PicesPredictionList model2Predictions,
PicesRunLog runLog
)
{
model1Predictions = null;
model2Predictions = null;
if (!ModelsAreAvailable())
{
return;
}
ParsedImageFileName pifn = PicesFeatureVector.ParseImageFileName(imageFileName);
String sipperFileName = pifn.sipperFileName;
uint scanLineNum = pifn.scanLineNum;
PicesFeatureVector fv = new PicesFeatureVector(raster, imageFileName, null, runLog);
if (instrumentData != null)
{
fv.AddInstrumentData(instrumentData.Depth(),
instrumentData.Salinity(),
instrumentData.Oxygen(),
instrumentData.Fluorescence()
);
}
else
{
PicesDataBase dbConn = PicesDataBase.GetGlobalDatabaseManagerNewInstance(runLog);
PicesInstrumentData pid = dbConn.InstrumentDataGetByScanLine(sipperFileName, scanLineNum);
if (pid != null)
{
fv.AddInstrumentData(pid);
}
pid = null;
dbConn.Close();
dbConn = null;
}
if (model1 != null)
{
model1Predictions = model1.PredictProbabilities(fv);
}
if (model2 != null)
{
model2Predictions = model2.PredictProbabilities(fv);
}
fv = null;
return;
} /* MakePredictions */
} /* CreateFeatureDataFileThread */
void MakeSureDepthFieldIsIncluded(PicesFeatureVectorList data)
{
PicesDataBase dbConn = null;
PicesRunLog runLog = new PicesRunLog(dialogMsgQueue);
data.SortByImageFileName();
String curSipperFileName = "";
float curSipperFileDepth = 0.0f;
foreach (PicesFeatureVector fv in data)
{
if (fv.Depth == 0.0f)
{
if (dbConn == null)
{
dbConn = PicesDataBase.GetGlobalDatabaseManagerNewInstance(runLog);
}
uint sacnLineNum = 0;
uint scanCol = 0;
String nextSipperFileName = "";
PicesFeatureVector.ParseImageFileName(fv.ExampleFileName, ref nextSipperFileName, ref sacnLineNum, ref scanCol);
if (nextSipperFileName != curSipperFileName)
{
curSipperFileDepth = 0.0f;
curSipperFileName = nextSipperFileName;
PicesSipperFile sf = dbConn.SipperFileRecLoad(curSipperFileName);
if (sf != null)
{
curSipperFileDepth = sf.Depth;
}
}
PicesInstrumentData id = dbConn.InstrumentDataGetByScanLine(curSipperFileName, sacnLineNum);
if ((id == null) || (id.Depth == 0.0))
{
fv.Depth = curSipperFileDepth;
}
else
{
fv.Depth = id.Depth;
}
}
}
if (dbConn != null)
{
dbConn.Close();
dbConn = null;
GC.Collect();
}
} /* MakeSureDepthFieldIsIncluded */
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 */
public PredictionBreakDownDisplayDual(PicesDataBase _dbConn,
String _fileName,
TrainingModel2 _trainingModel,
PicesInstrumentData _instrumentData, /**< Instrument data that goes with image, if null will read from dtabase. */
PicesRunLog _runLog
)
{
if (_runLog == null)
{
runLog = new PicesRunLog();
}
else
{
runLog = _runLog;
}
fileName = _fileName;
instrumentData = _instrumentData;
trainingModel = _trainingModel;
PicesDataBase dbConn = PicesDataBase.GetGlobalDatabaseManagerNewInstance(runLog);
PicesRaster raster = dbConn.ImageFullSizeFind(fileName);
if (raster == null)
{
return;
}
if (_dbConn != null)
{
dbFeatureVector = _dbConn.FeatureDataRecLoad(_fileName);
if (instrumentData == null)
{
PicesDataBaseImage image = dbConn.ImageLoad(fileName);
if (image != null)
{
instrumentData = dbConn.InstrumentDataGetByScanLine(image.SipperFileName, image.TopLeftRow);
}
}
}
featureVector = new PicesFeatureVector(raster, fileName, null, runLog);
if (instrumentData != null)
{
featureVector.AddInstrumentData(instrumentData);
}
dbConn.Close();
dbConn = null;
GC.Collect();
InitializeComponent();
}
} /* StartThread */
private PicesFeatureVector GetNextFeatureVector()
{
PicesFeatureVector nextFv = null;
lock (featureVectorQueue)
{
if (featureVectorQueue.Count > 0)
{
nextFv = featureVectorQueue.Dequeue();
}
}
return(nextFv);
} /* GetNextFeatureVector*/
Prediction(PicesClass _knownClass,
PicesClass _predClass,
double _prob,
PicesFeatureVector _fv,
TrainingModel2 _trainModel,
bool _partOfClassifier
)
{
knowClass = _knownClass;
predClass = _predClass;
prob = _prob;
fv = _fv;
trainModel = _trainModel;
partOfClassifier = _partOfClassifier;
}
} /* ProcessDirectory */
private void ProcessOneImage(String fullImageFileName)
{
String rootName = OSservices.GetRootName(fullImageFileName);
PicesDataBaseImage i = dbConn.ImageLoad(rootName);
if (i != null)
{
runStatsImagesInDataBase++;
return;
}
String sipperFileName = null;
uint scanLine = 0;
uint scanCol = 0;
PicesFeatureVector.ParseImageFileName(fullImageFileName, ref sipperFileName, ref scanLine, ref scanCol);
if (String.IsNullOrEmpty(sipperFileName) || (scanLine < 1))
{
// We are not going to be able to locate this image in the Database.
runStatsImagesNotInDataBase++;
reconcilingRunLog.Writeln(fullImageFileName + "\t" + "Name format will not let me derive needed info.");
return;
}
String sqlStr = "call ImagesLocateClosestImage(" + "\"" + sipperFileName + "\"" + ", " + scanLine.ToString() + ", " + scanCol.ToString() + ")";
String[][] results = dbConn.QueryStatement(sqlStr, null);
if ((results == null) || (results.Length < 1))
{
runStatsImagesNotInDataBase++;
reconcilingRunLog.Writeln(fullImageFileName + "\t" + "Could not find an appropriate image in the Database");
RemoveImage(fullImageFileName);
return;
}
String correctImageFileName = results[0][1];
String newFullName = OSservices.AddSlash(OSservices.GetPathPartOfFile(fullImageFileName)) +
correctImageFileName + "." +
OSservices.GetFileExtension(fullImageFileName);
OSservices.RenameFile(fullImageFileName, newFullName);
runStatsImagesFixed++;
} /* ProcessOneImage */
} /* GetNextFeatureVector*/
private void ThreadProcess()
{
threadRunning = true;
PicesDataBase.ThreadInit();
if ((classifier == null) && (!String.IsNullOrEmpty(classifierName)))
{
PicesRunLog log = new PicesRunLog();
classifier = new TrainingModel2(log, classifierName);
classifier.LoadExistingModelOtherwiseBuild(null);
classifierWeOwnIt = true;
}
while (!cancelFlag)
{
PicesFeatureVector fv = GetNextFeatureVector();
if (fv != null)
{
ProcessOneFeatureVector(fv);
}
else
{
if (threadTerminate)
{
break;
}
Thread.Sleep(10);
}
}
if (dbConn != null)
{
dbConn.Close();
dbConn = null;
GC.Collect();
}
PicesDataBase.ThreadEnd();
threadRunning = false;
threadDone = true;
} /* ThreadProcess */
} /* PredictClasses */
PicesClass AssignVallidatedClassAsPredictedClass(PicesFeatureVector fv)
{
PicesClass validatedClass = fv.MLClass;
if ((validatedClass == null) || (classesInClassifier == null))
{
return(validatedClass);
}
if (classesInClassifier.LookUpIndex(validatedClass) >= 0)
{
return(validatedClass);
}
// Lets see if the Validated Class is a decedent of one of the classes in the Classifier.
PicesClass candidate = null;
int numGenerations = 99999;
foreach (PicesClass pc in classesInClassifier)
{
int zed = pc.NumGenerations(validatedClass);
if (zed >= 0)
{
if (zed < numGenerations)
{
candidate = pc;
numGenerations = zed;
}
}
}
if (candidate != null)
{
return(candidate);
}
return(validatedClass);
} /* AssignVallidatedClassAsPredictedClass */
} /* 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;
}
} /* GradeAgainstGroundTruth */
private void GradeAgainstCrossValidationRun()
{
if (curSelModel == null)
{
curSelModel = new PicesTrainingConfigManaged(configFileName, runLog);
}
if (!curSelModel.Valid())
{
MessageBox.Show("Configuration File[" + configFileName + "] is not valid");
return;
}
bool changesMade = false;
PicesFeatureVectorList data = curSelModel.LoadFeatureDataFromTrainingLibraries(changesMade);
if (cancelProcessing)
{
data = null;
return;
}
if (data == null)
{
MessageBox.Show("No data was loaded from Configuration File[" + configFileName + "] is not valid");
return;
}
PicesFeatureVectorList examples = data.StratifyAmoungstClasses(numOfFolds);
int imageCount = examples.Count;
int numImagesPerFold = (imageCount + numOfFolds - 1) / numOfFolds;
int firstInGroup = 0;
int foldNum;
for (foldNum = 0; foldNum < numOfFolds; foldNum++)
{
int lastInGroup;
// If We are doing the last Fold Make sure that we are including all the examples
// that have not been tested.
if (foldNum == (numOfFolds - 1))
{
lastInGroup = imageCount;
}
else
{
lastInGroup = firstInGroup + numImagesPerFold - 1;
}
PicesFeatureVectorList trainingImages = new PicesFeatureVectorList();
PicesFeatureVectorList testImages = new PicesFeatureVectorList();
for (int x = 0; (x < imageCount) && (!cancelProcessing); x++)
{
PicesFeatureVector newImage = new PicesFeatureVector(examples[x]);
if ((x >= firstInGroup) && (x <= lastInGroup))
{
testImages.Add(newImage);
}
else
{
trainingImages.Add(newImage);
}
}
if (cancelProcessing)
{
break;
}
CrossValidate(testImages, trainingImages, foldNum);
firstInGroup = firstInGroup + numImagesPerFold;
}
if (!cancelProcessing)
{
if (trainingModel != null)
{
trainingModel = null;
}
msgQueue.AddMsg("\n" + "Building Final Classifier for User Query");
trainingModel = new TrainingModel2(runLog, curSelModel);
trainingModel.BuildTrainingModel(data);
}
if ((!cancelProcessing) && (trainingModel != null) && (!trainingModel.Valid))
{
MessageBox.Show("Error Loading Training Model; refer to dialog box");
cancelProcessing = true;
}
else if (!cancelProcessing)
{
confusionMatrix.SetTrainingModel(trainingModel);
}
// We no longer need the Training Model or Ground Truth loaded
BlockStart();
data = null;
BlockEnd();
} /* GradeAgainstCrossValidationRun */
} /* 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 */
public FeatureDataDisplay(String imageFileName,
PicesFeatureVector calcFV,
PicesFeatureVector dbFV
)
{
InitializeComponent();
int numFeatures = 0;
PicesFeatureVector descFV = null;
if (calcFV != null)
{
numFeatures = calcFV.NumFeatures;
descFV = calcFV;
}
if ((dbFV != null) && (dbFV.NumFeatures < numFeatures))
{
numFeatures = dbFV.NumFeatures;
descFV = dbFV;
}
if ((descFV == null) || (numFeatures < 1))
{
return;
}
for (int x = 0; x < numFeatures; x++)
{
ListViewItem lvi = new ListViewItem(descFV.FeatureName(x));
double fvValueCalc = 0.0;
double fvValueDb = 0.0;
if (calcFV != null)
{
fvValueCalc = calcFV.FeatureValue(x);
}
if (dbFV != null)
{
fvValueDb = dbFV.FeatureValue(x);
}
double delta = fvValueCalc - fvValueDb;
String formatStr = "f6";
if (Math.Max(Math.Abs(fvValueCalc), Math.Abs(fvValueDb)) < 1.0)
{
formatStr = "e4";
}
lvi.SubItems.Add(fvValueCalc.ToString(formatStr));
lvi.SubItems.Add(fvValueDb.ToString(formatStr));
if (delta != 0)
{
lvi.SubItems.Add(delta.ToString(formatStr));
lvi.SubItems[lvi.SubItems.Count - 1].ForeColor = Color.Red;
}
FeatureDataList.Items.Add(lvi);
}
}
} /* ThreadProcess */
private void ProcessOneFeatureVector(PicesFeatureVector fv)
{
int x = midPoint.CompareTo(fv.CtdDateTime);
PicesPredictionList probPredList = null;
bool downCast = (x > 0);
if (reClassify)
{
String imageFileName = fv.ExampleFileName;
String imageFileRootName = OSservices.GetRootName(imageFileName);
if ((fv.Validated) && (validatedTrumps))
{
PicesClass validatedClass = AssignVallidatedClassAsPredictedClass(fv);
probPredList = PredictClasses(fv, pred1, pred2, imageFileName, imageFileRootName);
if (validatedClass != null)
{
if (pred1.MLClass != validatedClass)
{
pred2.MLClass = pred1.MLClass;
}
pred2.Probability = 0.0f;
pred1.MLClass = validatedClass;
pred1.Probability = 1.0f;
}
}
else
{
probPredList = PredictClasses(fv, pred1, pred2, imageFileName, imageFileRootName);
}
String imageRootName = OSservices.GetRootName(fv.ExampleFileName);
lock (predictionUpdateQueue)
{
if (pred1.MLClass.ClassId < 0)
{
pred1.MLClass = UpdateClassId(pred1.MLClass);
}
if (pred2.MLClass.ClassId < 0)
{
pred2.MLClass = UpdateClassId(pred2.MLClass);
}
predictionUpdateQueue.Enqueue(new PredictionResult(imageRootName,
pred1.MLClass.ClassId,
pred1.Probability,
pred2.MLClass.ClassId,
pred2.Probability,
probPredList
)
);
}
}
else
{
if ((fv.Validated) && (validatedTrumps))
{
pred1.MLClass = fv.MLClass;
pred1.Probability = 1.0f;
}
else
{
pred1.MLClass = fv.MLClass;
pred1.Probability = fv.Probability;
}
}
String className = pred1.ClassName;
bool noiseClass = ((className.Length >= 5) &&
(className.Substring(0, 5).Equals("Noise", StringComparison.InvariantCultureIgnoreCase))
);
if (noiseClass)
{
}
else
{
lock (classCounts)
{
classCounts.AddOne(pred1.MLClass);
depthDistribution_1.Increment(pred1.MLClass, (int)fv.Depth);
if (downCast)
{
depthDistribution_1Down.Increment(pred1.MLClass, (int)fv.Depth);
sizeDistributionDown.Increment(pred1.MLClass, fv.AreaMMSquare);
}
else
{
depthDistribution_1Up.Increment(pred1.MLClass, (int)fv.Depth);
sizeDistributionUp.Increment(pred1.MLClass, fv.AreaMMSquare);
}
depthDistribution_10.Increment(pred1.MLClass, (int)fv.Depth);
numImagesClassified++;
}
}
} /* ProcessOneFeatureVector */
} /* DialogTimer_Tick */
private void CreateTuningFeatureFiles()
{
tuningThreadRunning = true;
PicesDataBase.ThreadInit();
AddMsgToDialogMsgQueue(DateTime.Now.ToShortTimeString() + " Started");
AddMsgToDialogMsgQueue(DateTime.Now.ToShortTimeString() + " Creating Destination Directory");
String destinationDirectory = OSservices.AddSlash(DestinationDirectory.Text) + OSservices.GetRootName(modelName);
OSservices.CreateDirectoryPath(destinationDirectory);
AddMsgToDialogMsgQueue(DateTime.Now.ToShortTimeString() + " Loading feature data for Model[" + modelName + "]");
config = new PicesTrainingConfigManaged(modelName, runLog);
if (!config.Valid())
{
AddMsgToDialogMsgQueue("\n\n");
AddMsgToDialogMsgQueue("Model[" + modelName + "] is invalid.");
tuningThreadFailed = true;
tuningThreadDone = true;
tuningThreadRunning = false;
return;
}
bool changesMade = false;
PicesFeatureVectorList data = config.LoadFeatureDataFromTrainingLibraries(changesMade);
if (data == null)
{
AddMsgToDialogMsgQueue("\n\n");
AddMsgToDialogMsgQueue("Model[" + modelName + "] Could not load Feature Data.");
tuningThreadFailed = true;
tuningThreadDone = true;
tuningThreadRunning = false;
return;
}
if (tuningThreadCancelReq)
{
AddMsgToDialogMsgQueue("\n\n");
AddMsgToDialogMsgQueue("Process Canceled.");
tuningThreadDone = true;
tuningThreadRunning = false;
return;
}
AddMsgToDialogMsgQueue(DateTime.Now.ToShortTimeString() + " Feature Data Loaded.");
AddMsgToDialogMsgQueue(DateTime.Now.ToShortTimeString() + " Checking that Depth Data Exists.");
MakeSureDepthFieldIsIncluded(data);
AddMsgToDialogMsgQueue(DateTime.Now.ToShortTimeString() + " Splitting into separate files.");
PicesFeatureVectorList trainData = new PicesFeatureVectorList();
PicesFeatureVectorList testData = new PicesFeatureVectorList();
PicesFeatureVectorList validationData = new PicesFeatureVectorList();
PicesClassList classes = data.ExtractListOfClasses();
if (classes == null)
{
AddMsgToDialogMsgQueue("\n\n");
AddMsgToDialogMsgQueue("Could not extract list of classes from data.");
tuningThreadFailed = true;
tuningThreadDone = true;
tuningThreadRunning = false;
return;
}
foreach (PicesClass c in classes)
{
int trainCount = 0;
int testCount = 0;
int validationCount = 0;
PicesFeatureVectorList examplesForClass = data.ExtractExamplesForAGivenClass(c);
int n = examplesForClass.Count;
AddMsgToDialogMsgQueue(DateTime.Now.ToShortTimeString() + " Processing Class[" + c.Name + "] Count[" + n + "]");
trainCount = (int)((float)n * trainingDataPercentage / 100.0f + 0.5f);
if (trainingDataMaxImagesPerClass > 0)
{
if (trainCount > trainingDataMaxImagesPerClass)
{
trainCount = trainingDataMaxImagesPerClass;
}
}
if (validationDataPercentage == 0.0f)
{
testCount = n - trainCount;
}
else
{
testCount = (int)((float)n * testDataPercentage / 100.0f + 0.5f);
}
validationCount = n - (trainCount + testCount);
examplesForClass.RandomizeOrder();
int validationStart = trainCount + testCount;
int testStart = trainCount;
for (int idx = 0; idx < n; idx++)
{
PicesFeatureVector fv = examplesForClass[idx];
if (idx >= validationStart)
{
validationData.Add(fv);
}
else if (idx >= testStart)
{
testData.Add(fv);
}
else
{
trainData.Add(fv);
}
}
if (tuningThreadCancelReq)
{
AddMsgToDialogMsgQueue("\n\n");
AddMsgToDialogMsgQueue("Process Canceled.");
break;
}
}
if (!tuningThreadCancelReq)
{
AddMsgToDialogMsgQueue("\n\n" + DateTime.Now.ToShortTimeString() + " Stratifying Data");
PicesFeatureVectorList trainDataStratified = trainData.StratifyAmoungstClasses(numOfFolds);
PicesFeatureVectorList testDataStratified = testData.StratifyAmoungstClasses(numOfFolds);
PicesFeatureVectorList validationDataStratified = validationData.StratifyAmoungstClasses(numOfFolds);
String rootName = OSservices.AddSlash(destinationDirectory) + OSservices.GetRootName(modelName);
if ((trainDataStratified.Count > 0) && (!tuningThreadCancelReq))
{
trainDataFileName = rootName + "_Train.data";
AddMsgToDialogMsgQueue("Saving Training Data[" + trainDataFileName + "]");
trainDataStratified.Save(trainDataFileName, "", runLog);
}
if ((testDataStratified.Count > 0) && (!tuningThreadCancelReq))
{
testDataFileName = rootName + "_Test.data";
AddMsgToDialogMsgQueue("Saving Test Data[" + testDataFileName + "]");
testDataStratified.Save(testDataFileName, "", runLog);
}
if ((validationDataStratified.Count > 0) && (!tuningThreadCancelReq))
{
validationDataFileName = rootName + "_Validation.data";
AddMsgToDialogMsgQueue("Saving Validation Data[" + validationDataFileName + "]");
validationDataStratified.Save(validationDataFileName, "", runLog);
}
}
AddMsgToDialogMsgQueue("\n\n");
if (tuningThreadCancelReq)
{
AddMsgToDialogMsgQueue("Process *** CANCELED ***.");
}
else
{
AddMsgToDialogMsgQueue("Process Completed.");
}
PicesDataBase.ThreadEnd();
tuningThreadDone = true;
tuningThreadRunning = false;
} /* CreateTuningFeatureFiles */
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 */
