/// <summary>
/// Returns the image mean for the ImageSet.
/// </summary>
/// <param name="log">Specifies the Log used to output status.</param>
/// <param name="rgAbort">Specifies a set of wait handles for aborting the operation.</param>
/// <param name="bQueryOnly">Specifies whether or not to only query for the mean and not calculate if missing.</param>
/// <returns>The SimpleDatum with the image mean is returned.</returns>
public SimpleDatum GetImageMean(Log log, WaitHandle[] rgAbort, bool bQueryOnly)
{
if (m_imgMean != null || bQueryOnly)
{
return(m_imgMean);
}
int nLoadedCount = GetLoadedCount();
int nTotalCount = GetTotalCount();
if (nLoadedCount < nTotalCount)
{
double dfPct = (double)nLoadedCount / (double)nTotalCount;
if (log != null)
{
log.WriteLine("WARNING: Cannot create the image mean until all images have loaded - the data is currently " + dfPct.ToString("P") + " loaded.");
}
return(null);
}
if (OnCalculateImageMean != null)
{
CalculateImageMeanArgs args = new CalculateImageMeanArgs(m_rgImages);
OnCalculateImageMean(this, args);
if (args.Cancelled)
{
return(null);
}
m_imgMean = args.ImageMean;
return(m_imgMean);
}
RawImageMean imgMean = m_factory.GetRawImageMean();
if (m_imgMean != null)
{
m_imgMean = m_factory.LoadDatum(imgMean);
}
else
{
log.WriteLine("Calculating mean...");
m_imgMean = SimpleDatum.CalculateMean(log, m_rgImages, rgAbort);
m_factory.PutRawImageMean(m_imgMean, true);
}
m_imgMean.SetLabel(0);
return(m_imgMean);
}
public void Create(DatasetConfiguration config, IXDatasetCreatorProgress progress)
{
string strCsvFile = Properties.Settings.Default.CsvFile;
string strDsName = config.Name;
string strTrainingSrc = config.Name + ".training";
string strTestingSrc = config.Name + ".testing";
m_bCancel = false;
m_iprogress = progress;
m_factory.DeleteSources(strTrainingSrc, strTestingSrc);
Log log = new Log("CSV Dataset Creator");
log.OnWriteLine += new EventHandler <LogArg>(log_OnWriteLine);
try
{
//-----------------------------------------
// Load the schema that defines the layout
// of the CSV file.
//-----------------------------------------
m_schema = loadSchema(config.Settings);
//-----------------------------------------
// Load and parse the CSV file.
//-----------------------------------------
DataConfigSetting dsCsvFile = config.Settings.Find("CSV File");
strCsvFile = dsCsvFile.Value.ToString();
if (strCsvFile.Length == 0)
{
throw new Exception("CSV data file name not specified!");
}
log.WriteLine("Loading the data file...");
if (m_bCancel)
{
return;
}
m_parser.Load(strCsvFile, m_schema);
//-----------------------------------------
// Split the data into training and testing
// sets.
//-----------------------------------------
List <DataItem> rgTraining = new List <DataItem>();
List <DataItem> rgTesting = new List <DataItem>();
DataConfigSetting dsPctTesting = config.Settings.Find("Testing Percentage");
double dfVal = (double)dsPctTesting.Value;
Random random = new Random();
for (int i = 0; i < m_parser.Data.Count; i++)
{
if (random.NextDouble() > dfVal)
{
rgTraining.Add(m_parser.Data[i]);
}
else
{
rgTesting.Add(m_parser.Data[i]);
}
}
Properties.Settings.Default.TestingPct = dfVal;
//-----------------------------------------
// Create the training data source.
//-----------------------------------------
int nCellHorizCount = 0;
List <int> rgDim = getImageDim(m_parser, m_schema, out nCellHorizCount);
int nTrainSrcId = m_factory.AddSource(strTrainingSrc, rgDim[0], rgDim[1], rgDim[2], false, 0);
m_factory.Open(nTrainSrcId, 500, Database.FORCE_LOAD.FROM_FILE); // use file based data.
log.WriteLine("Deleting existing data from '" + m_factory.OpenSource.Name + "'.");
m_factory.DeleteSourceData();
if (!loadData(log, m_factory, m_parser, rgTraining, rgDim, true, true))
{
return;
}
m_factory.UpdateSourceCounts();
updateLabels(m_factory);
log.WriteLine("Creating the image mean...");
SimpleDatum dMean = SimpleDatum.CalculateMean(log, m_rgImages.ToArray(), new WaitHandle[] { new ManualResetEvent(false) });
m_factory.PutRawImageMean(dMean, true);
m_rgImages.Clear();
m_factory.Close();
//-----------------------------------------
// Create the testing data source.
//-----------------------------------------
int nTestSrcId = m_factory.AddSource(strTestingSrc, rgDim[0], rgDim[1], rgDim[2], false, 0);
m_factory.Open(nTestSrcId, 500, Database.FORCE_LOAD.FROM_FILE); // use file based data.
log.WriteLine("Deleting existing data from '" + m_factory.OpenSource.Name + "'.");
m_factory.DeleteSourceData();
if (!loadData(log, m_factory, m_parser, rgTesting, rgDim, false, false))
{
return;
}
m_factory.UpdateSourceCounts();
updateLabels(m_factory);
m_factory.Close();
//-----------------------------------------
// Crate the data set.
//-----------------------------------------
log.WriteLine("Done loading training and testing data.");
int nDatasetID = 0;
using (DNNEntities entities = EntitiesConnection.CreateEntities())
{
List <Source> rgSrcTraining = entities.Sources.Where(p => p.Name == strTrainingSrc).ToList();
List <Source> rgSrcTesting = entities.Sources.Where(p => p.Name == strTestingSrc).ToList();
if (rgSrcTraining.Count == 0)
{
throw new Exception("Could not find the training source '" + strTrainingSrc + "'.");
}
if (rgSrcTesting.Count == 0)
{
throw new Exception("Could not find the tesing source '" + strTestingSrc + "'.");
}
DataConfigSetting dsName = config.Settings.Find("Output Dataset Name");
int nSrcTestingCount = rgSrcTesting[0].ImageCount.GetValueOrDefault();
int nSrcTrainingCount = rgSrcTraining[0].ImageCount.GetValueOrDefault();
int nSrcTotalCount = nSrcTestingCount + nSrcTrainingCount;
double dfTestingPct = (nSrcTrainingCount == 0) ? 0.0 : nSrcTestingCount / (double)nSrcTotalCount;
Dataset ds = new Dataset();
ds.ImageHeight = rgSrcTraining[0].ImageHeight;
ds.ImageWidth = rgSrcTraining[0].ImageWidth;
ds.Name = strDsName;
ds.ImageEncoded = rgSrcTesting[0].ImageEncoded;
ds.ImageChannels = rgSrcTesting[0].ImageChannels;
ds.TestingPercent = (decimal)dfTestingPct;
ds.TestingSourceID = rgSrcTesting[0].ID;
ds.TestingTotal = rgSrcTesting[0].ImageCount;
ds.TrainingSourceID = rgSrcTraining[0].ID;
ds.TrainingTotal = rgSrcTraining[0].ImageCount;
ds.DatasetCreatorID = config.ID;
ds.DatasetGroupID = 0;
ds.ModelGroupID = 0;
entities.Datasets.Add(ds);
entities.SaveChanges();
nDatasetID = ds.ID;
}
m_factory.SetDatasetParameter(nDatasetID, "PixelSize", m_schema.CellSize.ToString());
m_factory.SetDatasetParameter(nDatasetID, "AttributeCount", m_parser.DataDescriptions.Count.ToString());
m_factory.SetDatasetParameter(nDatasetID, "AttributeCountHoriz", nCellHorizCount.ToString());
m_factory.SetDatasetParameter(nDatasetID, "AttributeCountVert", nCellHorizCount.ToString());
}
catch (Exception excpt)
{
log.WriteLine("ERROR: " + excpt.Message);
}
finally
{
Properties.Settings.Default.CsvFile = strCsvFile;
Properties.Settings.Default.Save();
if (m_bCancel)
{
log.WriteLine("ABORTED converting CSV data files.");
}
else
{
log.WriteLine("Done converting CSV data files.");
}
if (m_bCancel)
{
m_iprogress.OnCompleted(new CreateProgressArgs(1, 1, "ABORTED!", null, true));
}
else
{
m_iprogress.OnCompleted(new CreateProgressArgs(1, "COMPLETED."));
}
}
}
private ImageSet loadImageset(string strType, SourceDescriptor src, WaitHandle[] rgAbort, ref SimpleDatum imgMean, out int nLastImageIdx, int nPadW = 0, int nPadH = 0, Log log = null, IMAGEDB_LOAD_METHOD loadMethod = IMAGEDB_LOAD_METHOD.LOAD_ALL, int nImageDbLoadLimit = 0, int nImageDbLoadLimitStartIdx = 0, bool bLoadNext = false)
{
try
{
RawImageMean imgMeanRaw = null;
m_factory.Open(src);
nLastImageIdx = nImageDbLoadLimitStartIdx;
if (loadMethod != IMAGEDB_LOAD_METHOD.LOAD_ALL)
{
if (imgMean == null)
{
imgMeanRaw = m_factory.GetRawImageMean();
if (imgMeanRaw == null)
{
if (log != null)
{
log.WriteLine("WARNING: No image mean exists in the database, changing image database load from " + loadMethod.ToString() + " to " + IMAGEDB_LOAD_METHOD.LOAD_ALL.ToString());
}
loadMethod = IMAGEDB_LOAD_METHOD.LOAD_ALL;
}
}
}
int nCount = src.ImageCount;
if (nCount == 0)
{
throw new Exception("Could not find any images with " + strType + " Source = '" + src.Name + "'.");
}
if (log != null)
{
log.WriteLine("Loading '" + src.Name + "' - " + nCount.ToString("N0") + " images.");
}
ImageSet imgset = new ImageSet(m_factory, src, loadMethod, nImageDbLoadLimit);
if (OnCalculateImageMean != null)
{
imgset.OnCalculateImageMean += OnCalculateImageMean;
}
if (loadMethod != IMAGEDB_LOAD_METHOD.LOAD_ON_DEMAND)
{
bool bDataIsReal = src.IsRealData;
int nBatchSize = 20000;
Stopwatch sw = new Stopwatch();
int nImageSize = src.ImageHeight * src.ImageWidth;
if (nImageSize > 60000)
{
nBatchSize = 5000;
}
else if (nBatchSize > 20000)
{
nBatchSize = 7500;
}
else if (nImageSize > 3000)
{
nBatchSize = 10000;
}
if (nImageDbLoadLimit <= 0)
{
nImageDbLoadLimit = nCount;
}
List <int> rgIdx = getIndexList(nImageDbLoadLimitStartIdx, nImageDbLoadLimit);
int nIdx = 0;
sw.Start();
while (nIdx < rgIdx.Count)
{
int nImageIdx = rgIdx[nIdx];
int nImageCount = Math.Min(rgIdx.Count - nIdx, nBatchSize);
List <RawImage> rgImg = m_factory.GetRawImagesAt(nImageIdx, nImageCount);
for (int j = 0; j < rgImg.Count; j++)
{
SimpleDatum sd1 = m_factory.LoadDatum(rgImg[j], nPadW, nPadH);
imgset.Add(nIdx + j, sd1);
if (sw.Elapsed.TotalMilliseconds > 1000)
{
if (log != null)
{
double dfPct = (double)(nIdx + j) / (double)nCount;
log.Progress = dfPct;
log.WriteLine("image loading at " + dfPct.ToString("P") + "...");
}
sw.Restart();
if (EventWaitHandle.WaitAny(rgAbort, 0) != EventWaitHandle.WaitTimeout)
{
return(null);
}
}
}
nIdx += rgImg.Count;
if (loadMethod == IMAGEDB_LOAD_METHOD.LOAD_ALL && rgImg.Count == 0 && nIdx < nCount)
{
log.WriteLine("WARNING: Loaded " + nIdx.ToString("N0") + " images, yet " + (nCount - nIdx).ToString("N0") + " images are unaccounted for. You may need to reindex the dataset.");
break;
}
}
if (log != null)
{
log.Progress = 0;
}
if (rgIdx.Count > 0)
{
nLastImageIdx = rgIdx[rgIdx.Count - 1] + 1;
}
}
else if (bLoadNext)
{
nLastImageIdx += nImageDbLoadLimit;
}
if (imgMean == null)
{
if (imgMeanRaw == null)
{
imgMeanRaw = m_factory.GetRawImageMean();
}
if (imgMeanRaw != null)
{
imgMean = m_factory.LoadDatum(imgMeanRaw, nPadW, nPadH);
}
else
{
if (log != null)
{
log.WriteLine("Calculating mean...");
}
imgMean = imgset.GetImageMean(log, rgAbort);
m_factory.PutRawImageMean(imgMean, true);
}
}
if (imgMean != null)
{
imgset.SetImageMean(imgMean);
}
imgset.CompleteLoad(nLastImageIdx);
return(imgset);
}
finally
{
m_factory.Close();
}
}
public void Create(DatasetConfiguration config, IXDatasetCreatorProgress progress)
{
string strTrainingBatchFile1 = Properties.Settings.Default.TrainingDataFile1;
string strTrainingBatchFile2 = Properties.Settings.Default.TrainingDataFile2;
string strTrainingBatchFile3 = Properties.Settings.Default.TrainingDataFile3;
string strTrainingBatchFile4 = Properties.Settings.Default.TrainingDataFile4;
string strTrainingBatchFile5 = Properties.Settings.Default.TrainingDataFile5;
string strTestingBatchFile = Properties.Settings.Default.TestingDataFile;
string strDsName = config.Name;
string strTrainingSrc = config.Name + ".training";
string strTestingSrc = config.Name + ".testing";
int nIdx = 0;
int nTotal = 50000;
m_bCancel = false;
m_iprogress = progress;
m_factory.DeleteSources(strTrainingSrc, strTestingSrc);
Log log = new Log("CIFAR Dataset Creator");
log.OnWriteLine += new EventHandler <LogArg>(log_OnWriteLine);
try
{
DataConfigSetting dsTrainingDataFile1 = config.Settings.Find("Training Data File 1");
DataConfigSetting dsTrainingDataFile2 = config.Settings.Find("Training Data File 2");
DataConfigSetting dsTrainingDataFile3 = config.Settings.Find("Training Data File 3");
DataConfigSetting dsTrainingDataFile4 = config.Settings.Find("Training Data File 4");
DataConfigSetting dsTrainingDataFile5 = config.Settings.Find("Training Data File 5");
DataConfigSetting dsTestingDataFile = config.Settings.Find("Testing Data File");
strTrainingBatchFile1 = dsTrainingDataFile1.Value.ToString();
if (strTrainingBatchFile1.Length == 0)
{
throw new Exception("Training data file #1 name not specified!");
}
strTrainingBatchFile2 = dsTrainingDataFile2.Value.ToString();
if (strTrainingBatchFile2.Length == 0)
{
throw new Exception("Training data file #2 name not specified!");
}
strTrainingBatchFile3 = dsTrainingDataFile3.Value.ToString();
if (strTrainingBatchFile3.Length == 0)
{
throw new Exception("Training data file #3 name not specified!");
}
strTrainingBatchFile4 = dsTrainingDataFile4.Value.ToString();
if (strTrainingBatchFile4.Length == 0)
{
throw new Exception("Training data file #4 name not specified!");
}
strTrainingBatchFile5 = dsTrainingDataFile5.Value.ToString();
if (strTrainingBatchFile5.Length == 0)
{
throw new Exception("Training data file #5 name not specified!");
}
strTestingBatchFile = dsTestingDataFile.Value.ToString();
if (strTestingBatchFile.Length == 0)
{
throw new Exception("Testing data file name not specified!");
}
log.WriteLine("Loading the data files...");
if (m_bCancel)
{
return;
}
int nTrainSrcId = m_factory.AddSource(strTrainingSrc, 3, 32, 32, false, 0);
m_factory.Open(nTrainSrcId, 500, Database.FORCE_LOAD.FROM_FILE); // use file based data.
log.WriteLine("Deleting existing data from '" + m_factory.OpenSource.Name + "'.");
m_factory.DeleteSourceData();
if (!loadFile(log, dsTrainingDataFile1.Name, strTrainingBatchFile1, m_factory, nTotal, true, ref nIdx))
{
return;
}
if (!loadFile(log, dsTrainingDataFile2.Name, strTrainingBatchFile2, m_factory, nTotal, true, ref nIdx))
{
return;
}
if (!loadFile(log, dsTrainingDataFile3.Name, strTrainingBatchFile3, m_factory, nTotal, true, ref nIdx))
{
return;
}
if (!loadFile(log, dsTrainingDataFile4.Name, strTrainingBatchFile4, m_factory, nTotal, true, ref nIdx))
{
return;
}
if (!loadFile(log, dsTrainingDataFile5.Name, strTrainingBatchFile5, m_factory, nTotal, true, ref nIdx))
{
return;
}
m_factory.UpdateSourceCounts();
updateLabels(m_factory);
log.WriteLine("Creating the image mean...");
SimpleDatum dMean = SimpleDatum.CalculateMean(log, m_rgImages.ToArray(), new WaitHandle[] { new ManualResetEvent(false) });
m_factory.PutRawImageMean(dMean, true);
m_rgImages.Clear();
m_factory.Close();
int nTestSrcId = m_factory.AddSource(strTestingSrc, 3, 32, 32, false, 0);
m_factory.Open(nTestSrcId, 500, Database.FORCE_LOAD.FROM_FILE); // use file based data.
log.WriteLine("Deleting existing data from '" + m_factory.OpenSource.Name + "'.");
m_factory.DeleteSourceData();
nIdx = 0;
nTotal = 10000;
if (!loadFile(log, dsTestingDataFile.Name, strTestingBatchFile, m_factory, nTotal, false, ref nIdx))
{
return;
}
m_factory.CopyImageMean(strTrainingSrc, strTestingSrc);
m_factory.UpdateSourceCounts();
updateLabels(m_factory);
m_factory.Close();
log.WriteLine("Done loading training and testing data.");
using (DNNEntities entities = EntitiesConnection.CreateEntities())
{
List <Source> rgSrcTraining = entities.Sources.Where(p => p.Name == strTrainingSrc).ToList();
List <Source> rgSrcTesting = entities.Sources.Where(p => p.Name == strTestingSrc).ToList();
if (rgSrcTraining.Count == 0)
{
throw new Exception("Could not find the training source '" + strTrainingSrc + "'.");
}
if (rgSrcTesting.Count == 0)
{
throw new Exception("Could not find the tesing source '" + strTestingSrc + "'.");
}
DataConfigSetting dsName = config.Settings.Find("Output Dataset Name");
int nSrcTestingCount = rgSrcTesting[0].ImageCount.GetValueOrDefault();
int nSrcTrainingCount = rgSrcTraining[0].ImageCount.GetValueOrDefault();
int nSrcTotalCount = nSrcTestingCount + nSrcTrainingCount;
double dfTestingPct = (nSrcTrainingCount == 0) ? 0.0 : nSrcTestingCount / (double)nSrcTotalCount;
Dataset ds = new Dataset();
ds.ImageHeight = rgSrcTraining[0].ImageHeight;
ds.ImageWidth = rgSrcTraining[0].ImageWidth;
ds.Name = strDsName;
ds.ImageEncoded = rgSrcTesting[0].ImageEncoded;
ds.ImageChannels = rgSrcTesting[0].ImageChannels;
ds.TestingPercent = (decimal)dfTestingPct;
ds.TestingSourceID = rgSrcTesting[0].ID;
ds.TestingTotal = rgSrcTesting[0].ImageCount;
ds.TrainingSourceID = rgSrcTraining[0].ID;
ds.TrainingTotal = rgSrcTraining[0].ImageCount;
ds.DatasetCreatorID = config.ID;
ds.DatasetGroupID = 0;
ds.ModelGroupID = 0;
entities.Datasets.Add(ds);
entities.SaveChanges();
}
}
catch (Exception excpt)
{
log.WriteLine("ERROR: " + excpt.Message);
}
finally
{
Properties.Settings.Default.TrainingDataFile1 = strTrainingBatchFile1;
Properties.Settings.Default.TrainingDataFile2 = strTrainingBatchFile2;
Properties.Settings.Default.TrainingDataFile3 = strTrainingBatchFile3;
Properties.Settings.Default.TrainingDataFile4 = strTrainingBatchFile4;
Properties.Settings.Default.TrainingDataFile5 = strTrainingBatchFile5;
Properties.Settings.Default.TestingDataFile = strTestingBatchFile;
Properties.Settings.Default.Save();
if (m_bCancel)
{
log.WriteLine("ABORTED converting CIFAR data files.");
}
else
{
log.WriteLine("Done converting CIFAR data files.");
}
if (m_bCancel)
{
m_iprogress.OnCompleted(new CreateProgressArgs(nIdx, nTotal, "ABORTED!", null, true));
}
else
{
m_iprogress.OnCompleted(new CreateProgressArgs(1, "COMPLETED."));
}
}
}
public uint ConvertData(string strImageFile, string strLabelFile, string strDBPath, string strDBPathMean, bool bCreateImgMean, bool bGetItemCountOnly = false, int nChannels = 1)
{
string strExt;
List <SimpleDatum> rgImg = new List <SimpleDatum>();
strExt = Path.GetExtension(strImageFile).ToLower();
if (strExt == ".gz")
{
m_log.WriteLine("Unpacking '" + strImageFile + "'...");
strImageFile = expandFile(strImageFile);
}
strExt = Path.GetExtension(strLabelFile).ToLower();
if (strExt == ".gz")
{
m_log.WriteLine("Unpacking '" + strLabelFile + "'...");
strLabelFile = expandFile(strLabelFile);
}
BinaryFile image_file = new BinaryFile(strImageFile);
BinaryFile label_file = new BinaryFile(strLabelFile);
try
{
uint magicImg = image_file.ReadUInt32();
uint magicLbl = label_file.ReadUInt32();
if (magicImg != 2051)
{
if (m_log != null)
{
m_log.FAIL("Incorrect image file magic.");
}
if (OnLoadError != null)
{
OnLoadError(this, new LoadErrorArgs("Incorrect image file magic."));
}
}
if (magicLbl != 2049)
{
if (m_log != null)
{
m_log.FAIL("Incorrect label file magic.");
}
if (OnLoadError != null)
{
OnLoadError(this, new LoadErrorArgs("Incorrect label file magic."));
}
}
uint num_items = image_file.ReadUInt32();
uint num_labels = label_file.ReadUInt32();
if (num_items != num_labels)
{
if (m_log != null)
{
m_log.FAIL("The number of items must equal the number of labels.");
}
throw new Exception("The number of items must equal the number of labels." + Environment.NewLine + " Label File: '" + strLabelFile + Environment.NewLine + " Image File: '" + strImageFile + "'.");
}
if (bGetItemCountOnly)
{
return(num_items);
}
uint rows = image_file.ReadUInt32();
uint cols = image_file.ReadUInt32();
int nSrcId = m_factory.AddSource(strDBPath, nChannels, (int)cols, (int)rows, false, 0, true);
m_factory.Open(nSrcId, 500, Database.FORCE_LOAD.FROM_FILE); // use file based data.
m_factory.DeleteSourceData();
// Storing to db
byte[] rgLabel;
byte[] rgPixels;
Datum datum = new Datum(false, nChannels, (int)cols, (int)rows);
if (m_log != null)
{
m_log.WriteHeader("LOADING " + strDBPath + " items.");
m_log.WriteLine("A total of " + num_items.ToString() + " items.");
m_log.WriteLine("Rows: " + rows.ToString() + " Cols: " + cols.ToString());
}
if (OnLoadStart != null)
{
OnLoadStart(this, new LoadStartArgs((int)num_items));
}
for (int item_id = 0; item_id < num_items; item_id++)
{
rgPixels = image_file.ReadBytes((int)(rows * cols));
rgLabel = label_file.ReadBytes(1);
List <byte> rgData = new List <byte>(rgPixels);
if (nChannels == 3)
{
rgData.AddRange(new List <byte>(rgPixels));
rgData.AddRange(new List <byte>(rgPixels));
}
datum.SetData(rgData, (int)rgLabel[0]);
if (m_bmpTargetOverlay != null)
{
datum = createTargetOverlay(datum);
}
m_factory.PutRawImageCache(item_id, datum);
if (bCreateImgMean)
{
rgImg.Add(new SimpleDatum(datum));
}
if ((item_id % 1000) == 0)
{
if (m_log != null)
{
m_log.WriteLine("Loaded " + item_id.ToString("N") + " items...");
m_log.Progress = (double)item_id / (double)num_items;
}
if (OnLoadProgress != null)
{
LoadArgs args = new LoadArgs(item_id);
OnLoadProgress(this, args);
if (args.Cancel)
{
break;
}
}
}
}
m_factory.ClearImageCache(true);
m_factory.UpdateSourceCounts();
if (bCreateImgMean)
{
if (strDBPath != strDBPathMean)
{
m_factory.CopyImageMean(strDBPathMean, strDBPath);
}
else
{
m_log.WriteLine("Creating image mean...");
SimpleDatum dMean = SimpleDatum.CalculateMean(m_log, rgImg.ToArray(), new WaitHandle[] { new ManualResetEvent(false) });
m_factory.PutRawImageMean(dMean, true);
}
}
if (OnLoadProgress != null)
{
LoadArgs args = new LoadArgs((int)num_items);
OnLoadProgress(this, args);
}
return(num_items);
}
finally
{
image_file.Dispose();
label_file.Dispose();
}
}
