public void DecomposeWithUnsafeCode()
{
using (var originalImage = new Bitmap(OriginalFilename))
{
var transform = new ForwardWaveletTransform(1);
var channels = new UnsafeColorChannels(originalImage.Width, originalImage.Height);
var imageProcessor = new ImageProcessor(channels, transform);
imageProcessor.ApplyTransform(originalImage);
VerifyAsImage(originalImage);
}
}
public static void TestHaarInputOutput(string imageInPath)
{
var image = ReadImageGrayscale(imageInPath);
#region Wavelet Compress Methods
// Test HaarWaveletTransform
var inputMatrix = new Matrix(image);
inputMatrix.WriteCSV("haar-before.csv");
HaarWaveletTransform.HaarTransform2D(image, inputMatrix.Rows, inputMatrix.Columns);
Matrix haarMatrixInverse = inputMatrix.Copy();
HaarWaveletTransform.InverseHaarTransform2D(haarMatrixInverse.MatrixData, haarMatrixInverse.Rows, haarMatrixInverse.Columns);
haarMatrixInverse.WriteCSV("haar-after.csv");
haarMatrixInverse.DrawMatrixImage("haar-transform-forward-and-backward.png", -1, -1, false);
// Test Wavelet Compress and Decompress in one step
const int compLevels = 8;
const int compTreshold = 150;
Matrix haarMatrixCompDecomp = haarMatrixInverse.Copy();
WaveletComDec.CompressDecompress2D(haarMatrixCompDecomp.MatrixData, compLevels, compTreshold);
haarMatrixCompDecomp.DrawMatrixImage("haar-compress-and-decompress-combined.png", -1, -1, false);
// Test Compress and Decompress in two steps
int lastHeight = 0;
int lastWidth = 0;
Matrix haarMatrixComp = haarMatrixInverse.Copy();
WaveletCompress.Compress2D(haarMatrixComp.MatrixData, compLevels, compTreshold, out lastHeight, out lastWidth);
WaveletDecompress.Decompress2D(haarMatrixComp.MatrixData, compLevels, lastHeight, lastWidth);
haarMatrixComp.DrawMatrixImage("haar-compress-and-decompress.png", -1, -1, false);
#endregion
#region Test using HaarCSharp
// Test HaarCSharp using iterations
const int haarCSharpIterations = 3;
Matrix haarMatrixCSharp = haarMatrixInverse.Copy();
ForwardWaveletTransform.Transform2D(haarMatrixCSharp.MatrixData, false, haarCSharpIterations);
haarMatrixCSharp.DrawMatrixImage("haar-forward.png", -1, -1, false);
InverseWaveletTransform.Transform2D(haarMatrixCSharp.MatrixData, false, haarCSharpIterations);
haarMatrixCSharp.DrawMatrixImage("haar-inverse.png", -1, -1, false);
// Test HaarCSharp using all levels and only 1 iteration
Matrix haarMatrixCSharpAll = haarMatrixInverse.Copy();
ForwardWaveletTransform.Transform2D(haarMatrixCSharpAll.MatrixData, true, 1);
haarMatrixCSharpAll.DrawMatrixImage("haar-forward-all.png", -1, -1, false);
InverseWaveletTransform.Transform2D(haarMatrixCSharpAll.MatrixData, true, 1);
haarMatrixCSharpAll.DrawMatrixImage("haar-inverse-all.png", -1, -1, false);
#endregion
}
public static void TestHaarCSharp2D()
{
Console.WriteLine();
Console.WriteLine("The HaarCSharp2D");
double[][] mat = Get2DTestData();
ForwardWaveletTransform.Transform2D(mat);
var result = new Matrix(mat);
result.PrintPretty();
Assert.That(mat, Is.EqualTo(Get2DResultData()).AsCollection.Within(0.001), "fail at [0]");
InverseWaveletTransform.Transform2D(mat);
result.PrintPretty();
Assert.That(mat, Is.EqualTo(Get2DTestData()).AsCollection.Within(0.001), "fail at [0]");
}
public static void TestHaarCSharp1D()
{
Console.WriteLine();
Console.WriteLine("The HaarCSharp1D");
double[] data = Get1DTestData();
ForwardWaveletTransform.Transform1D(data, true);
IOUtils.Print(Console.Out, data);
// check if it's the same as
double[] result = Get1DResultData();
Assert.That(data, Is.EqualTo(result).AsCollection.Within(0.001), "fail at [0]");
InverseWaveletTransform.Transform1D(data, true);
IOUtils.Print(Console.Out, data);
Assert.That(data, Is.EqualTo(Get1DTestData()).AsCollection.Within(0.001), "fail at [0]");
}
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);
}
