private void Guess()
{
if (!CheckEnvironment())
{
return;
}
int id = nn.Guess(pictureBox2.Image, true);
EntityNetwork.Feature feature = nn.GetFeature(id);
if (feature != null)
{
label2.Text = feature.description + " (" + feature.name + ")";
pictureBox4.Image = feature.image;
}
else
{
label2.Text = "Не могу распознать";
pictureBox4.Image = null;
}
if (debugger != null)
{
debugger.UpdateNetworkView();
}
}
private List <EntityNetwork.Feature> ExtractDataGrid()
{
List <EntityNetwork.Feature> items = new List <EntityNetwork.Feature>();
DataGridViewTextBoxCell textCell;
DataGridViewImageCell imageCell;
DataGridView grid = dataGridView1;
int rows = grid.Rows.Count;
for (int i = 0; i < rows; i++)
{
textCell = (DataGridViewTextBoxCell)grid.Rows[i].Cells[0]; // name
string name = (string)textCell.Value;
if (name == null)
{
continue;
}
if (name.Trim() == "")
{
continue;
}
EntityNetwork.Feature item = new EntityNetwork.Feature();
textCell = (DataGridViewTextBoxCell)grid.Rows[i].Cells[1]; // description
if (textCell.Value != null)
{
item.description = ((string)textCell.Value).Trim();
}
item.name = name.Trim();
imageCell = (DataGridViewImageCell)grid.Rows[i].Cells[2]; // image
item.image = (Image)imageCell.Value;
textCell = (DataGridViewTextBoxCell)grid.Rows[i].Cells[3]; // entities
if (textCell.Value != null)
{
item.entities = ((string)textCell.Value).Trim();
}
items.Add(item);
}
return(items);
}
private void Guess()
{
int id = nn.Guess(pictureBox2.Image, true);
EntityNetwork.Feature feature = nn.GetFeature(id);
if (feature != null)
{
label2.Text = feature.description + " (" + feature.name + ")";
pictureBox4.Image = feature.image;
}
else
{
label2.Text = "I can't recognize";
pictureBox4.Image = null;
}
}
/// <summary>
/// The main worker, which extracts the features from the original image, tries to find out from the neural network what this feature is,
/// and if the neural network recognizes it, it takes the entities from the feature and adds it to the `insertionNode`.
/// Entities from the fixture are placed in the center of the window under test.
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void backgroundWorkerML_DoWork(object sender, DoWorkEventArgs e)
{
while (!backgroundWorkerML.CancellationPending)
{
// Pick a random sub-image (window)
Rectangle rect = new Rectangle();
rect.Width = nn.GetWindowSize();
rect.Height = nn.GetWindowSize();
bool zigzag = true;
if (zigzag)
{
rect.X = windowsPos.X;
rect.Y = windowsPos.Y;
windowsPos.X += 1;
if (windowsPos.X >= (ML_sourceBitmap.Width - rect.Width))
{
windowsPos.X = 0;
windowsPos.Y += 1;
if (windowsPos.Y >= (ML_sourceBitmap.Height - rect.Height))
{
Console.WriteLine("Zigzag scan complete");
return;
}
}
}
else
{
rect.X = rnd.Next(0, ML_sourceBitmap.Width - rect.Width - 1);
rect.Y = rnd.Next(0, ML_sourceBitmap.Height - rect.Height - 1);
}
Bitmap subImage = ML_sourceBitmap.Clone(rect, ML_sourceBitmap.PixelFormat);
// Ask the neural network what it is
int id = nn.Guess(subImage, false);
EntityNetwork.Feature feature = nn.GetFeature(id);
if (feature != null)
{
// If the neural network has detected the feature, get a list of the feature entities and center them in the sub-image window.
if (feature.entities != null)
{
XmlSerializer ser = new XmlSerializer(typeof(List <Entity>));
using (StringReader textReader = new StringReader(feature.entities))
{
PointF center = entityBox1.ImageToLambda(rect.X + rect.Width / 2, rect.Y + rect.Height / 2);
List <Entity> entities = (List <Entity>)ser.Deserialize(textReader);
EntityAligner.CenterFeatureEntities(center, entities);
entityBox1.root.Children.AddRange(entities);
Console.WriteLine("Found " + feature.name);
//entityBox1.Invalidate();
}
}
}
}
}
