public static void TestJWave() {
double[][] mat = Get2DTestData();
WaveletInterface wavelet = null;
wavelet = new Haar02();
TransformInterface bWave = null;
//bWave = new FastWaveletTransform(wavelet);
//bWave = new WaveletPacketTransform(wavelet);
bWave = new DiscreteWaveletTransform(wavelet);
Transform t = new Transform(bWave); // perform all steps
Console.Write("\n\nThe 2D JWave Haar02 Dwt method: ");
Console.Write("\n");
double[][] dwtArray = t.forward(mat);
Matrix dwtMatrix = new Matrix(dwtArray);
dwtMatrix.Print();
Console.Write("\n\nThe 2D JWave Haar02 Inverse Dwt method: ");
Console.Write("\n");
double[][] idwtArray = t.reverse(dwtArray);
Matrix idwtMatrix = new Matrix(idwtArray);
idwtMatrix.Print();
}
public static Matrix GetWaveletTransformedMatrix(double[][] image, WaveletMethod waveletMethod)
{
int width = image[0].Length;
int height = image.Length;
Matrix dwtMatrix = null;
Stopwatch stopWatch = Stopwatch.StartNew();
long startS = stopWatch.ElapsedTicks;
switch(waveletMethod) {
case WaveletMethod.Dwt:
Wavelets.Dwt dwt = new Wavelets.Dwt(8);
Matrix imageMatrix = new Matrix(image);
dwtMatrix = dwt.Transform(imageMatrix);
break;
case WaveletMethod.Haar:
Haar.Haar2d(image, height, width);
dwtMatrix = new Matrix(image);
break;
case WaveletMethod.HaarTransformTensor: // This is using the tensor product layout
dwtMatrix = HaarWaveletTransform(image);
break;
case WaveletMethod.HaarWaveletDecompositionTensor: // This is using the tensor product layout
StandardHaarWaveletDecomposition haar = new StandardHaarWaveletDecomposition();
haar.DecomposeImageInPlace(image);
dwtMatrix = new Matrix(image);
break;
case WaveletMethod.HaarWaveletDecomposition:
StandardHaarWaveletDecomposition haarNew = new StandardHaarWaveletDecomposition(false);
haarNew.DecomposeImageInPlace(image);
dwtMatrix = new Matrix(image);
break;
case WaveletMethod.NonStandardHaarWaveletDecomposition:
NonStandardHaarWaveletDecomposition haarNonStandard = new NonStandardHaarWaveletDecomposition();
haarNonStandard.DecomposeImageInPlace(image);
dwtMatrix = new Matrix(image);
break;
case WaveletMethod.JWaveTensor: // This is using the tensor product layout
WaveletInterface wavelet = null;
wavelet = new Haar02();
//wavelet = new Daub02();
TransformInterface bWave = null;
bWave = new FastWaveletTransform(wavelet);
//bWave = new WaveletPacketTransform(wavelet);
//bWave = new DiscreteWaveletTransform(wavelet);
Transform t = new Transform(bWave); // perform all steps
double[][] dwtArray = t.forward(image);
dwtMatrix = new Matrix(dwtArray);
break;
case WaveletMethod.HaarWaveletCompress:
int lastHeight = 0;
int lastWidth = 0;
Compress.WaveletCompress.HaarTransform2D(image, 10000, out lastHeight, out lastWidth);
dwtMatrix = new Matrix(image);
break;
default:
break;
}
long endS = stopWatch.ElapsedTicks;
Console.WriteLine("WaveletMethod: {0} Time in ticks: {1}", Enum.GetName(typeof(WaveletMethod), waveletMethod), (endS - startS));
//dwtMatrix.WriteCSV("HaarImageNormalized.csv", ";");
// increase all values
double[][] haarImageNormalized5k = dwtMatrix.MatrixData.Select(i => i.Select(j => j*5000).ToArray()).ToArray();
//Matrix haarImageNormalized5kMatrix = new Matrix(haarImageNormalized5k);
//haarImageNormalized5kMatrix.WriteCSV("HaarImageNormalized5k.csv", ";");
// convert to byte values (0 - 255)
// duplicate the octave/ matlab method uint8
double[][] uint8 = new double[haarImageNormalized5k.Length][];
for (int i = 0; i < haarImageNormalized5k.Length; i++) {
uint8[i] = new double[haarImageNormalized5k.Length];
for (int j = 0; j < haarImageNormalized5k[i].Length; j++) {
double v = haarImageNormalized5k[i][j];
if (v > 255) {
uint8[i][j] = 255;
} else if (v < 0) {
uint8[i][j] = 0;
} else {
uint8[i][j] = (byte) haarImageNormalized5k[i][j];
}
}
}
Matrix uint8Matrix = new Matrix(uint8);
//uint8Matrix.WriteCSV("Uint8HaarImageNormalized5k.csv", ";");
return uint8Matrix;
}
