/// <summary>
/// Calculates OA grade prediction.
/// </summary>
/// <param name="mod">Loaded model.</param>
/// <param name="features">LBP features.</param>
/// <param name="volume">Data array.</param>
/// <returns>Returns string containing the OA grade</returns>
public static string Predict(Model mod, ref int[,] features, ref Rendering.renderPipeLine volume)
{
// Default variables
int threshold = 50;
int[] size = { 400, 30 };
//int threshold = 5;
//int[] size = { 10, 3 };
// Load default model
string state = LoadModel(ref mod);
// Surface extraction
Processing.SurfaceExtraction(ref volume, threshold, size, out int[,] surfacecoordinates, out byte[,,] surface);
// Mean and std images
Processing.MeanAndStd(surface, out double[,] meanImage, out double[,] stdImage);
//
// LBP features
//
LBPLibrary.Functions.Save(@"C:\Users\sarytky\Desktop\trials\mean.png", meanImage, true);
LBPLibrary.Functions.Save(@"C:\Users\sarytky\Desktop\trials\std.png", stdImage, true);
features = LBP(meanImage.Add(stdImage));
// PCA
double[,] dataAdjust = Processing.SubtractMean(features.ToDouble());
double[,] PCA = dataAdjust.Dot(mod.eigenVectors.ToDouble());
// Regression
double[] grade = PCA.Dot(mod.weights).Add(1.5);
//double sum = CompareGrades(grade);
return("OA grade: " + grade[0].ToString("####.##", CultureInfo.InvariantCulture));
//return "Sum of differences between pretrained model and actual grade: " + sum.ToString("###.###", CultureInfo.InvariantCulture);
}
/// <summary>
/// Scans the input mask slice by slice and selects the largest binary component of each slice.
/// Return cleaned mask as vtkImageData
/// </summary>
/// <param name="input"></param>
/// <param name="extent"></param>
/// <param name="threshold"></param>
/// <param name="axes"></param>
/// <returns></returns>
public static vtkImageData FalsePositiveSuppresion(vtkImageData input, int[] extent, double threshold, int axis, double scale = 1.0)
{
//Slice extractor
vtkExtractVOI slicer = vtkExtractVOI.New();
//Permuter
vtkImagePermute permuter = vtkImagePermute.New();
//List of outputs
List <byte[, , ]> outputs = new List <byte[, , ]>();
//List of output orientations
List <int[]> orientations = new List <int[]>();
//vtkImageData size
int[] full_extent = input.GetExtent();
//Set range for slices
int start = 0, stop = 0;
int[] size = new int[2];
int[] outextent = new int[4];
if (axis == 0)
{
start = extent[0];
stop = extent[1];
size = new int[] { extent[3] - extent[2] + 1, extent[5] - extent[4] + 1 };
outextent = new int[] { extent[2], extent[3] + 1, extent[4], extent[5] + 1 };
}
if (axis == 1)
{
start = extent[2];
stop = extent[3];
size = new int[] { extent[1] - extent[0] + 1, extent[5] - extent[4] + 1 };
outextent = new int[] { extent[0], extent[1] + 1, extent[4], extent[5] + 1 };
}
if (axis == 2)
{
start = extent[4];
stop = extent[5];
size = new int[] { extent[1] - extent[0] + 1, extent[3] - extent[2] + 1 };
outextent = new int[] { extent[0], extent[1] + 1, extent[2], extent[3] + 1 };
}
//Temporary array for output
byte[,,] output = new byte[full_extent[1] + 1, full_extent[3] + 1, full_extent[5] + 1];
int[] outsize = new int[] { size[0], size[1], stop - start + 1 };
//Loop over current axis
for (int k = start; k < stop; k++)
{
byte[] bytedata = new byte[size[0] * size[1]];
//Select slice
if (axis == 0)
{
slicer.Dispose();
slicer = vtkExtractVOI.New();
slicer.SetInput(input);
slicer.SetVOI(k, k, extent[2], extent[3], extent[4], extent[5]);
slicer.Update();
permuter.Dispose();
permuter = vtkImagePermute.New();
permuter.SetInput(slicer.GetOutput());
permuter.SetFilteredAxes(1, 2, 0);
permuter.Update();
}
if (axis == 1)
{
slicer.Dispose();
slicer = vtkExtractVOI.New();
slicer.SetInput(input);
slicer.SetVOI(extent[0], extent[1], k, k, extent[4], extent[5]);
slicer.Update();
permuter.Dispose();
permuter = vtkImagePermute.New();
permuter.SetInput(slicer.GetOutput());
permuter.SetFilteredAxes(0, 2, 1);
permuter.Update();
}
if (axis == 2)
{
slicer.Dispose();
slicer = vtkExtractVOI.New();
slicer.SetInput(input);
slicer.SetVOI(extent[0], extent[1], extent[2], extent[3], k, k);
slicer.Update();
permuter.Dispose();
permuter = vtkImagePermute.New();
permuter.SetInput(slicer.GetOutput());
permuter.SetFilteredAxes(0, 1, 2);
permuter.Update();
}
//Convert data to byte
bytedata = DataTypes.vtkToByte(permuter.GetOutput());
slicer.Dispose();
permuter.Dispose();
//convert data to Mat
Mat image = new Mat(size[1], size[0], MatType.CV_8UC1, bytedata);
//Get largest binary object
Mat bw = Processing.LargestBWObject(image, 0.7 * 255.0);
//Set slice to byte array
if (bw.Sum().Val0 > 0)
{
output = DataTypes.setByteSlice(output, bw, outextent, axis, k);
}
}
return(DataTypes.byteToVTK(output));
}
