public void TestWithAdequateMinutiaeSet()
{
int[,] mask = Segmentator.Segmetator(img, windowSize, weight, threshold);
double[,] binaryImage = img; //
//---------------------------------------
double sigma = 1.4d;
double[,] smoothing = LocalBinarizationCanny.Smoothing(binaryImage, sigma);
double[,] sobel = LocalBinarizationCanny.Sobel(smoothing);
double[,] nonMax = LocalBinarizationCanny.NonMaximumSupperession(sobel);
nonMax = GlobalBinarization.Binarization(nonMax, 60);
nonMax = LocalBinarizationCanny.Inv(nonMax);
int sizeWin = 16;
binaryImage = LocalBinarizationCanny.LocalBinarization(binaryImage, nonMax, sizeWin, 1.3d);
//---------------------------------------
binaryImage = Thining.ThinPicture(binaryImage);
//---------------------------------------
List <Minutia> minutiae = MinutiaeDetection.FindMinutiae(binaryImage);
for (int i = 0; i < minutiae.Count; i++)
{
if (mask[minutiae[i].Y, minutiae[i].X] == 0)
{
minutiae.Remove(minutiae[i]);
i--;
}
}
minutiae = MinutiaeDetection.FindBigMinutiae(minutiae);
var response = MCC.MCCMethod(minutiae, img.GetLength(0), img.GetLength(1));
SaveResponse(response, minutiae);
}
public void ExperimentMethod()
{
double[,] img1 = ImageHelper.LoadImage(Resources._104_6);
int[,] resultImg1;
resultImg1 = Segmentator.Segmetator(img1, windowSize, weight, threshold).Select2D(x => x > 0 ? 255 : 0);
ImageHelper.SaveIntArray(resultImg1, Path.GetTempPath() + "Segm_104_6" + ".png");
}
public void TestMcc()
{
int[,] maskOfSegmentation2D = Segmentator.Segmetator(img, windowSize, weight, threshold);
var thining = Thining.ThinPicture(GlobalBinarization.Binarization(img, board));
List <Minutia> minutiaList = MinutiaeDetection.FindMinutiae(thining);
List <Minutia> validMinutiae = new List <Minutia>();
foreach (Minutia minutia in minutiaList)
{
if (maskOfSegmentation2D[minutia.Y, minutia.X] == 1) // coordinates swap - ok
{
validMinutiae.Add(minutia);
}
}
var response = MCC.MCCMethod(validMinutiae, thining.GetLength(0), thining.GetLength(1));
SaveResponse(response, validMinutiae);
}
public float[] SegmentImage(float[] image, int rows, int columns, out int[,] mask, bool useCuda = false)
{
if (useCuda)
{
var maskRows = (int)Math.Ceiling((double)rows / Constants.SegmentationWindowSize);
var maskColumns = (int)Math.Ceiling((double)columns / Constants.SegmentationWindowSize);
var maskLinearized = new int[maskRows * maskColumns];
var sw = new Stopwatch();
sw.Start();
CUDASegmentator(image, columns, rows,
(float)Constants.SegmentationWeight, Constants.SegmentationWindowSize, maskLinearized, maskColumns, maskRows);
sw.Stop();
mask = maskLinearized.Make2D(maskRows, maskColumns);
Segmentator.PostProcessing(mask, Constants.SegmentationThreshold);
var bigMask = Segmentator.GetBigMask(mask, rows, columns,
Constants.SegmentationWindowSize);
// we discard border regions in order to not catch minutiae there
var oldMask = mask;
mask = oldMask.Select2D((x, row, column) =>
{
if (x == 0)
{
return(0);
}
if (row > 0)
{
if (oldMask[row - 1, column] == 0)
{
return(0);
}
if (column > 0)
{
if (oldMask[row - 1, column - 1] == 0)
{
return(0);
}
}
if (column < maskColumns - 1)
{
if (oldMask[row - 1, column + 1] == 0)
{
return(0);
}
}
if (row < maskRows - 1)
{
if (oldMask[row + 1, column] == 0)
{
return(0);
}
if (column > 0)
{
if (oldMask[row + 1, column - 1] == 0)
{
return(0);
}
}
if (column < maskColumns - 1)
{
if (oldMask[row + 1, column + 1] == 0)
{
return(0);
}
}
}
}
if (column > 0)
{
if (oldMask[row, column - 1] == 0)
{
return(0);
}
}
if (column < maskColumns - 1)
{
if (oldMask[row, column + 1] == 0)
{
return(0);
}
}
return(1);
});
return(Segmentator.ColorImage(image, rows, columns, bigMask));
}
mask = Segmentator.Segmetator(image.Make2D(rows, columns).Select2D(x => (double)x),
Constants.SegmentationWindowSize, Constants.SegmentationWeight,
Constants.SegmentationThreshold);
return(Segmentator.ColorImage(image.Make2D(rows, columns).Select2D(x => (double)x),
Segmentator.GetBigMask(mask, image.GetLength(0), image.GetLength(1),
Constants.SegmentationWindowSize)).Make1D().Select(x => (float)x).ToArray());
}
public static double[,] SegmentImage(double[,] image, int rows, int columns, out int[,] mask)
{
mask = Segmentator.Segmetator(image, Constants.SegmentationWindowSize, Constants.SegmentationWeight, Constants.SegmentationThreshold);
return(Segmentator.ColorImage(image, Segmentator.GetBigMask(mask, image.GetLength(0), image.GetLength(1), Constants.SegmentationWindowSize)));
}