public static void TestHaarWaveletDecomposition()
{
Console.WriteLine();
Console.WriteLine("The Standard 2D HaarWaveletDecomposition method (Tensor)");
var haar = new StandardHaarWaveletDecomposition();
double[][] mat = Get2DTestData();
haar.DecomposeImageInPlace(mat);
var result = new Matrix(mat);
result.PrintPretty();
Console.WriteLine();
Console.WriteLine("The Non Standard 2D HaarWaveletDecomposition method (JPEG 2000)");
var haarNonStandard = new NonStandardHaarWaveletDecomposition();
mat = Get2DTestData();
haarNonStandard.DecomposeImageInPlace(mat);
var resultNonStandard = new Matrix(mat);
resultNonStandard.PrintPretty();
}
public void ShouldDecomposeAsExpected()
{
var wd = new StandardHaarWaveletDecomposition();
var floats = new[] { 8f, 4, 1, 3 };
wd.DecomposeImageInPlace(floats, 1, 4, 2d); // Let's use 2 as norm, to reconstruct the result more easily
CollectionAssert.AreEqual(new[] { 4, 2, 2, -1 }, floats);
}
public void StandardDecompositionTest()
{
const int rows = 128;
const int cols = 32;
float[][] frames = new float[rows][];
float[] concatenated = new float[rows * cols];
for (int i = 0; i < rows; i++)
{
frames[i] = TestUtilities.GenerateRandomSingleArray(cols, i);
Buffer.BlockCopy(frames[i], 0, concatenated, sizeof(float) * i * cols, sizeof(float) * cols);
}
AssertAreSame(rows, cols, frames, concatenated);
var waveletDecomposition = new StandardHaarWaveletDecomposition();
waveletDecomposition.DecomposeImageInPlace(concatenated, rows, cols, 1d);
DecomposeImageLocal(frames);
AssertAreSame(rows, cols, frames, concatenated);
}
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.Haar:
Haar.Haar2D(image, height, width);
dwtMatrix = new Matrix(image);
break;
case WaveletMethod.HaarTransformTensor: // This is using the tensor product layout
dwtMatrix = WaveletUtils.HaarWaveletTransform2D(image);
break;
case WaveletMethod.HaarWaveletDecompositionTensor: // This is using the tensor product layout
var haar = new StandardHaarWaveletDecomposition();
haar.DecomposeImageInPlace(image);
dwtMatrix = new Matrix(image);
break;
case WaveletMethod.NonStandardHaarWaveletDecomposition: // JPEG 2000
var haarNonStandard = new NonStandardHaarWaveletDecomposition();
haarNonStandard.DecomposeImageInPlace(image);
dwtMatrix = new Matrix(image);
break;
case WaveletMethod.HaarCSharp:
ForwardWaveletTransform.Transform2D(image, false, 2);
dwtMatrix = new Matrix(image);
break;
case WaveletMethod.HaarWaveletCompress:
int lastHeight = 0;
int lastWidth = 0;
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));
// increase all values
const int mul = 50;
double[][] haarImageNormalized5k = dwtMatrix.MatrixData.Select(i => i.Select(j => j * mul).ToArray()).ToArray();
// convert to byte values (0 - 255)
// duplicate the octave/ matlab method uint8
var 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];
}
}
}
var uint8Matrix = new Matrix(uint8);
return(uint8Matrix);
}
