/// <summary>
/// Main constructor for windows form
/// </summary>
public MainWindow()
{
InitializeComponent();
//Add properties for openFileDialog
openFileDialog.DefaultExt = ".tif";
openFileDialog.Title = "Open Image";
openFileDialog.Filter = "JPEG files (*.jpg)| *.jpg|TIFF files (*.tif)| *.tif|" +
" All files | *.*";
//Close button Yes and No, which user click for assert recognition
labelCorrect.Visible = false;
buttonCorrectYes.Visible = false;
buttonCorrectYes.Visible = false;
this.BackColor = Color.White;
//Read ready SVM and bagOfContourFragments
multiSVM = BinarySave.ReadBinary(true);
bagOfContourFragments = BinarySave.ReadBinary();
}
/// <summary>
/// This methods only for admin, and this recompute bagOfContourFragments and svm
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void buttonCompute_Click(object sender, EventArgs e)
{
//Accord.Math.Random.Generator.Seed = 1;
DirectoryInfo path = new DirectoryInfo(Path.Combine(Application.StartupPath, "Resources/Res"));
///Create dictionary for train images
originalTrainImages = new Dictionary <int, Bitmap>();
int j = 0;
int k = 0;
foreach (DirectoryInfo classFolder in path.EnumerateDirectories())
{
///Add name of folder
string name = classFolder.Name;
///Upload all files in aarray
FileInfo[] files = GetFilesByExtensions(classFolder, ".jpg", ".tif").ToArray();
//Shuffle objects in array
Vector.Shuffle(files);
//For each image complite some easy operations
for (int i = 0; i < files.Length; i++)
{
//Uploat only train images
//70%
if ((i / (double)files.Length) < 0.7)
{
//Add file
FileInfo file = files[i];
//Create image from file
Bitmap image = (Bitmap)Bitmap.FromFile(file.FullName);
//Use detector
CannyEdgeDetector filterCanny = new CannyEdgeDetector();
//Apply changes
filterCanny.ApplyInPlace(image);
//Add some information of image
string shortName = file.Name;
int imageKey = j;
//Add image to dictionary
originalTrainImages.Add(j, image);
//Save correct key of class for image
outputsResult[j] = k;
j++;
}
}
//Change key of folder
k++;
}
//Create teacher for svm, using Histogram Intersection
var teacher = new MulticlassSupportVectorLearning <HistogramIntersection>()
{
//Add leaner params
Learner = (param) => new SequentialMinimalOptimization <HistogramIntersection>()
{
//Create kernel with optimal params
Kernel = new HistogramIntersection(0.25, 1),
}
};
//Create KMeans algr
var kmodes = new KModes <byte>(numberOfContour, new Hamming());
//Create detector
var detector = new FastRetinaKeypointDetector();
//Create bagOfContourFragments
bagOfContourFragments = new BagOfVisualWords(numberOfContour);
//Learned bagOfContourFragments
bagOfContourFragments.Learn(originalTrainImages.Values.ToArray());
//For each iamge add inputs info
for (int i = 0; i < originalTrainImages.Count; i++)
{
Bitmap image = originalTrainImages[i] as Bitmap;
inputsInfo[i] = (bagOfContourFragments as ITransform <Bitmap, double[]>).Transform(image);
}
//Save condition of bagOfContourFragments
BinarySave.WriteBinary(bagOfContourFragments);
//Teach svm
multiSVM = teacher.Learn(inputsInfo, outputsResult);
//Save condition of svm
BinarySave.WriteBinary(multiSVM);
}
