/// <summary>
/// Project object to zip stream
/// </summary>
/// <param name="cnnProject">The CNNProject</param>
/// <param name="zip">The zip stream</param>
/// <param name="fileName">Name of the file within the zip archive</param>
public static void ProjectToZip(CNNProject cnnProject, ZipOutputStream zip, string fileName)
{
byte[] buffer;
using (MemoryStream stream = new MemoryStream())
{
new SoapFormatter().Serialize(stream, cnnProject);
buffer = stream.GetBuffer();
}
PutZipEntry(zip, buffer, fileName);
}
/// <summary>
/// Opens a project from file
/// </summary>
public void Open()
{
try
{
// stops the training, if it is still running
CNNProject.ImgDetectionNN.StopTraining = true;
CNNProject.ImgDetectionNN.StopTrainingSilently = true;
OpenFileDialog dialog = new OpenFileDialog();
dialog.SupportMultiDottedExtensions = true;
dialog.Filter = this.DefaultProjectFileFilter;
dialog.DefaultExt = this.DefaultProjectFileExt;
dialog.FileName = "*." + this.DefaultProjectFileExt;
if (dialog.ShowDialog() == DialogResult.OK)
{
Stream fileStream = File.OpenRead(dialog.FileName);
ZipInputStream zip = new ZipInputStream(fileStream);
// temp vars
List<TmpImageListItem> tmpImageList = new List<TmpImageListItem>();
CNNProject tmpCNNProject = null;
INeuralNetwork tmpNetwork = null;
ZipEntry entry;
while ((entry = zip.GetNextEntry()) != null)
{
ZipFileEntry zipFileEntry = new ZipFileEntry(zip, entry);
if (zipFileEntry.IsImage)
{
tmpImageList.Add(new TmpImageListItem(zipFileEntry.GetImage(), zipFileEntry.FileNameWithoutExtension, zipFileEntry.DirectoryName));
}
else if (zipFileEntry.FileName == this._internZipProjectFileName)
{
tmpCNNProject = zipFileEntry.GetProject();
}
else if (zipFileEntry.FileName == this.DefaultNetworkFileName)
{
tmpNetwork = zipFileEntry.GetNetwork();
}
}
zip.Close();
fileStream.Close();
if (tmpCNNProject != null && tmpNetwork != null)
{
// now we have everything together, so we can rebuild the project
_cnnProject = tmpCNNProject;
_cnnProject.ResetNonSerializableAttributes();
_cnnProject.ImgDetectionNN.Network = tmpNetwork;
// adds images
foreach (TmpImageListItem item in tmpImageList)
{
if (item.Directory == "Matching")
{
_cnnProject.Matching.Images.Add(item.Name, item.Image);
}
else if (item.Directory == "NotMatching")
{
_cnnProject.NotMatching.Images.Add(item.Name, item.Image);
}
}
// fires the event
if (ProjectChanged != null)
{
ProjectChanged(this, new EventArgs());
}
}
else
{
MessageBox.Show("This zip file does not contains all necessary items!",
"Zip Project File Error",
MessageBoxButtons.OK,
MessageBoxIcon.Error);
}
}
}
catch (NotImplementedException ex)
{
StaticClasses.ShowException(ex);
}
}
public CNNProjectHolder()
{
_cnnProject = new CNNProject();
}
/// <summary>
/// Routine to train the network
/// </summary>
/// <param name="cnnProject">The CNN project.</param>
/// <param name="numberOfRounds">The number of training rounds.</param>
public void TrainPattern(CNNProject cnnProject, long numberOfTrainingRounds)
{
// 1. preparation - both project's imageLists generalized images to the shuffled trainingItem-list
#region 1. preparation
List<TrainingItem> trainingItemList = new List<TrainingItem>();
#region fills the trainingItemList
ImageList imgList;
bool matching;
for (int i = 0; i < 2; i++)
{
// at first we include the matching images
if (i == 0)
{
imgList = cnnProject.Matching;
matching = true;
}
// and then the not matching images
else
{
imgList = cnnProject.Matching;
matching = false;
}
foreach (Image img in imgList.Images)
{
// generalizes the image
Image generalizedImg = ImageHandling.GeneralizeImage(img);
trainingItemList.Add(new TrainingItem(generalizedImg, matching));
}
}
#endregion
// filled list gets shuffled
// (maybe this optimizes the result)
StaticClasses.Shuffle<TrainingItem>(trainingItemList);
#endregion
// 2. build of training data items and add it to the helper
#region 2. trainingItem
// used later on to create the training thread
NetworkHelper helper = new NetworkHelper(_network);
foreach (TrainingItem trainingItem in trainingItemList)
{
Image img = trainingItem.Image;
ArrayList arryListInput;
#region fills arryListInput
// Converts an image of any size to a pattern that can be feed to the network.
ImageProcessingHelper imgHelper = new ImageProcessingHelper();
//note: this is a (monochrome) collection of 0's and 1's !!
arryListInput = imgHelper.ArrayListFromImage(img);
if (img.Width * img.Height != _network.InputLayer.Count)
{
throw new InvalidInputException("The number of pixels in the input image doesn't match the number of input layer neurons!", null);
}
#region Debugging
/*
// Convert an arrayList by rounding each value to a pattern of 0s and 1s
PatternProcessingHelper patHelper = new PatternProcessingHelper();
String tmpPatern = patHelper.PatternFromArraylist(tmpImgList);
Debug.WriteLine("Added : " + tmpPatern);
*/
#endregion
#endregion
ArrayList arryListOutput;
#region fills arryListOutput
arryListOutput = new ArrayList();
// true is going to be a single 1, false a single 0
arryListOutput.Add(trainingItem.Matching ? 1 : 0);
#endregion
// a training data item is used for a single training round
TrainingData trainingDataItem = new TrainingData(arryListInput, arryListOutput);
// this could be also used; one training round directly
//_network.TrainNetwork(trainingDataItem);
helper.AddTrainingData(trainingDataItem);
}
#endregion
// Let's go!
_trainStart = DateTime.Now;
// 3. training
#region 3. training
// ShowProgress delegate
helper.TrainingProgress += new NetworkHelper.TrainingProgressEventHandler(ShowProgress);
// Start training
// --- here we are going to wait --
helper.Train(numberOfTrainingRounds, true); // <--
// releasing
helper.TrainingProgress -= new NetworkHelper.TrainingProgressEventHandler(ShowProgress);
// show message box
if (StopTrainingSilently == false)
{
MessageBox.Show("Training of the neuronal network completed at " + DateTime.Now,
"Training Completed",
MessageBoxButtons.OK,
MessageBoxIcon.Information);
}
#endregion
}
