public SimpleSegmentator(Config configuration, AnalyzablePage analyzablePage)
{
GeneticConfiguration = configuration;
_analyzablePage = analyzablePage;
_hystogram = Functions.FeautureScaling(Hystogram.GetHystogram(_analyzablePage.Original));
_width = _analyzablePage.Width;
}
public AnalyzablePage GetSegmentation()
{
if (_solution != null)
{
return(_solution);
}
Solve();
_solution = Solution(BestOrganism);
return(_solution);
}
private AnalyzablePage Solution(Organism organism)
{
try
{
var triggerValue = organism.Factors[0].Value;
var minHeight = organism.Factors[1].Value;
var height = _hystogram.GetLength(0);
var page = new AnalyzablePage(height, _width);
int start = 0;
var isLine = false;
for (var y = 0; y < height; y++)
{
var trigger = (_hystogram[y] > triggerValue);
if (trigger)
{
if (!isLine)
{
start = y;
isLine = true;
}
}
else
{
if (isLine)
{
int end = y;
var lineHeight = end - start + 1;
if (lineHeight > minHeight)
{
page.AddLine(new AnalyzableLine(start, lineHeight));
}
isLine = false;
}
}
}
return(page);
}
catch (Exception e)
{
throw new Exception("Cant generate solution", e);
}
}
private void AnalyzePage()
{
AnalyzablePage page;
Cursor = Cursors.WaitCursor;
Enabled = false;
try { page = new AnalyzablePage(Page); }
catch (Exception exception)
{
Enabled = true;
MessageBox.Show(exception.Message, @"Ошибка", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
Status.Text = @"Автоматическое построение разметки...";
page.SegmentatePage();
Status.Text = "";
Enabled = true;
}
private void OwlFormLoad(object sender, EventArgs e)
{
var bitmap = new Bitmap(100, 100);
try
{
bitmap = new Bitmap(@"Data/IMAGE.BMP");
}
catch (ArgumentException)
{
Application.Exit();
}
var page = new AnalyzablePage(bitmap);
var ranges = page.Ranges();
var points = page.Summ;
var factors = new List <GrayCode>();
var str = new StreamWriter("Ranges.dat");
foreach (double t in ranges)
{
str.WriteLine(t);
}
str.Close();
var str1 = new StreamWriter("Points.dat");
foreach (double t in points)
{
str1.WriteLine(t);
}
str1.Close(); /*
* ranges = Repositories.Functions.FeautureScaling(ranges);
* points = Repositories.Functions.FeautureScaling(points);
* factors.Add(new GrayCode(0,-1,1));
* factors.Add(new GrayCode(0, 0, 1));
* factors.Add(new GrayCode(0, 0, 1)); */
//var geneticAlgorithm = new GeneticAlgorithm(points, ranges, factors);
//page.Lines = geneticAlgorithm.Solve();
page.RenderLines(imageOutput);
}
protected override double Fitness(Organism organism)
{
try
{
AnalyzablePage segmentation = Solution(organism);
int heightsRange = segmentation.HeightRange();
int distanceRange = segmentation.DistanceRange();
//double densityRange = segmentation.DensityRange();
var summ = (segmentation.Lines.Count > 2) ? segmentation.Lines.Sum(line => line.Height) : 0;
double fitness = 1 /
((double)(1 + heightsRange) * (1 + distanceRange) * (1 + Math.Abs(2 * segmentation.Height - summ)));
if (!((fitness >= 0) && (fitness <= 1)))
{
throw new ArgumentOutOfRangeException();
}
return(fitness);
}
catch (Exception e)
{
throw new Exception("Cant calculate fitness", e);
}
}