private void FaceRecognition_Click(object sender, EventArgs e)
{
var Dir = Directory.GetFiles(PathToLoadFolder).ToList <string>();
var Pictures = from Pic in Dir
where Pic.Substring(Pic.Length - 3, 3) == "jpg"
select Pic;
var Tab = new int[Pictures.Count()];
int RowCount = 3;
int ColCount = 3;
int counter = 0;
int? min = null;
string PersonRecognized = "Blad programu";
int[, ][] Shades = new int[RowCount, ColCount][];
Bitmap[,] BitmapMatrix = new Bitmap[RowCount, ColCount];
DividePicture(imageAfter, BitmapMatrix);
ArrayHistogramValues(BitmapMatrix, Shades, RowCount, ColCount);
Bitmap bitmap;
AdvancedSimilarity[] Base = new AdvancedSimilarity[Pictures.Count()];
foreach (var item in Pictures)
{
bitmap = (Bitmap)Image.FromFile(item);
Base[counter] = new AdvancedSimilarity(RowCount, ColCount);
Base[counter].Name = item.Substring(PathToLoadFolder.Length + 1, item.Length - PathToLoadFolder.Length - 5);
DividePicture(bitmap, Base[counter].BitmapParts);
ArrayHistogramValues(Base[counter].BitmapParts, Base[counter].Shades, RowCount, ColCount);
var calc = ComparePicturesShades(Shades, Base[counter].Shades, RowCount, ColCount);
Tab[counter] = SimilarityScale(calc, RowCount, ColCount);
if (min == null)
{
min = Tab[counter];
}
else if (Tab[counter] < min)
{
min = Tab[counter];
PersonRecognized = Base[counter].Name;
}
counter++;
}
Debug.WriteLine($"Person from image {PersonRecognized} is recognised");
}
private void CompareAndSaveResults_Click(object sender, EventArgs e)
{
var SelectedFolder = Directory.GetFiles(PathToLoadFolder);
int Files = SelectedFolder.Length;
AdvancedSimilarity[] Results = new AdvancedSimilarity[Files];
string Name;
int Index;
int RowCount = 3;
int ColCount = 3;
for (int i = 0; i < Files; i++)
{
Results[i] = new AdvancedSimilarity(RowCount, ColCount);
}
foreach (var Item in SelectedFolder)
{
Name = Item.Substring(PathToLoadFolder.Length + 1, Item.Length - PathToLoadFolder.Length - 5);
Index = Int32.Parse(Name) - 1;
Results[Index].Name = Name;
DividePicture((Bitmap)Bitmap.FromFile(Item), Results[Index].BitmapParts);
ArrayHistogramValues(Results[Index].BitmapParts, Results[Index].Shades, RowCount, ColCount);
}
for (int i = 0; i < Files; i++)
{
Debug.WriteLine(Results[i].Name);
}
float[,] CrossValidation = new float[Results.Length, Results.Length];
int[, ][] Sum = new int[RowCount, ColCount][];
for (int i = 0; i < Results.Length; i++)
{
for (int j = 0; j < Results.Length; j++)
{
Sum = ComparePicturesShades(Results[i].Shades, Results[j].Shades, RowCount, ColCount);
CrossValidation[i, j] = ((float)(SimilarityScale(Sum, RowCount, ColCount)) / 77760) * 100; // hard coded 77760 (workes only with pic 320x243)
/*CrossValidation[i, j] = SimilarityScale(Sum, RowCount, ColCount);*/
}
}
string Text;
Text = "Index\t";
File.AppendAllText(PathForCSV, Text, Encoding.UTF8);
Text = null;
for (int i = 1; i <= Results.Length; i++)
{
Text += i.ToString() + ",\t\t";
}
Text += Environment.NewLine;
File.AppendAllText(PathForCSV, Text, Encoding.UTF8);
for (int i = 0; i < Results.Length; i++)
{
Text = (i + 1).ToString() + ",\t";
for (int j = 0; j < Results.Length; j++)
{
if (CrossValidation[i, j].ToString().Length < 7)
{
Text += CrossValidation[i, j].ToString() + ",\t\t";
}
else
{
Text += CrossValidation[i, j].ToString() + ",\t";
}
}
Text += Environment.NewLine;
File.AppendAllText(PathForCSV, Text, Encoding.UTF8);
}
Debug.WriteLine("Koniec");
}
