//Func<int, int, double> window
public static void SaveWaveletImage(string imageInPath, string imageOutPath, WaveletMethod waveletMethod)
{
// Read Image
Image img = Image.FromFile(imageInPath);
Bitmap bmp = new Bitmap(img);
double[][] image = new double[bmp.Height][];
for (int i = 0; i < bmp.Height; i++)
{
image[i] = new double[bmp.Width];
for (int j = 0; j < bmp.Width; j++)
{
//image[i][j] = bmp.GetPixel(j, i).ToArgb();
image[i][j] = bmp.GetPixel(j, i).B; // use only blue channel
}
}
// Normalize the pixel values to the range 0..1.0. It does this by dividing all pixel values by the max value.
double max = image.Max((b) => b.Max((v) => (v)));
double[][] imageNormalized = image.Select(i => i.Select(j => j / max).ToArray()).ToArray();
//Matrix normalizedMatrix = new Matrix(imageNormalized);
//normalizedMatrix.WriteCSV("ImageNormalized.csv", ";");
Matrix bitmap = GetWaveletTransformedMatrix(imageNormalized, waveletMethod);
bitmap.DrawMatrixImage(imageOutPath, -1, -1, false);
img.Dispose();
bmp.Dispose();
bitmap = null;
}
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;
}
//Func<int, int, double> window
public static void SaveWaveletImage(string imageInPath, string imageOutPath, WaveletMethod waveletMethod) {
// Read Image
Image img = Image.FromFile(imageInPath);
Bitmap bmp = new Bitmap(img);
double[][] image = new double[bmp.Height][];
for (int i = 0; i < bmp.Height; i++)
{
image[i] = new double[bmp.Width];
for (int j = 0; j < bmp.Width; j++) {
//image[i][j] = bmp.GetPixel(j, i).ToArgb();
image[i][j] = bmp.GetPixel(j, i).B; // use only blue channel
}
}
// Normalize the pixel values to the range 0..1.0. It does this by dividing all pixel values by the max value.
double max = image.Max((b) => b.Max((v) => (v)));
double[][] imageNormalized = image.Select(i => i.Select(j => j/max).ToArray()).ToArray();
//Matrix normalizedMatrix = new Matrix(imageNormalized);
//normalizedMatrix.WriteCSV("ImageNormalized.csv", ";");
Matrix bitmap = GetWaveletTransformedMatrix(imageNormalized, waveletMethod);
bitmap.DrawMatrixImage(imageOutPath, -1, -1, false);
img.Dispose();
bmp.Dispose();
bitmap = null;
}
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);
}
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);
}
