internal IEnumerable <Fingerprint> CreateOriginalFingerprintsFromFrames(IEnumerable <Frame> frames, FingerprintConfiguration configuration)
{
var fingerprints = new ConcurrentBag <Fingerprint>();
var images = frames.ToList();
if (!images.Any())
{
return(Enumerable.Empty <Fingerprint>());
}
var length = images.First().Length;
Parallel.ForEach(images, () => new ushort[length], (frame, loop, cachedIndexes) =>
{
float[] rowCols = configuration.OriginalPointSaveTransform != null ? frame.GetImageRowColsCopy() : frame.ImageRowCols;
waveletDecomposition.DecomposeImageInPlace(rowCols, frame.Rows, frame.Cols, configuration.HaarWaveletNorm);
RangeUtils.PopulateIndexes(length, cachedIndexes);
var image = fingerprintDescriptor.ExtractTopWavelets(rowCols, configuration.TopWavelets, cachedIndexes);
if (!image.IsSilence())
{
fingerprints.Add(new Fingerprint(image, frame.StartsAt, frame.SequenceNumber));
}
return(cachedIndexes);
},
cachedIndexes => { });
return(fingerprints.ToList());
}
public Image GetWaveletTransformedImage(float[][] image, IWaveletDecomposition wavelet, double haarWaveletNorm)
{
int width = image[0].Length;
int height = image.Length;
float[] transformedImages = new float[width * height];
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
transformedImages[i * width + j] = image[i][j];
}
}
wavelet.DecomposeImageInPlace(transformedImages, height, width, haarWaveletNorm);
Bitmap transformed = new Bitmap(width, height, PixelFormat.Format16bppRgb565);
for (int i = 0; i < transformed.Height; i++)
{
for (int j = 0; j < transformed.Width; j++)
{
transformed.SetPixel(j, i, Color.FromArgb((int)image[i][j]));
}
}
return(transformed);
}
private List <Fingerprint> CreateFingerprintsFromLogSpectrum(IEnumerable <SpectralImage> spectralImages, FingerprintConfiguration configuration)
{
var fingerprints = new ConcurrentBag <Fingerprint>();
Parallel.ForEach(spectralImages, spectralImage =>
{
waveletDecomposition.DecomposeImageInPlace(spectralImage.Image);
bool[] image = fingerprintDescriptor.ExtractTopWavelets(spectralImage.Image, configuration.TopWavelets);
if (!IsSilence(image))
{
fingerprints.Add(new Fingerprint(image, spectralImage.StartsAt, spectralImage.SequenceNumber));
}
});
return(fingerprints.ToList());
}
public Image GetWaveletsImages(List <SpectralImage> spectralImages, int imagesPerRow, double haarWaveletNorm)
{
foreach (var spectralImage in spectralImages)
{
waveletDecomposition.DecomposeImageInPlace(spectralImage.Image, spectralImage.Rows, spectralImage.Cols, haarWaveletNorm);
}
int width = spectralImages[0].Rows;
int height = spectralImages[0].Cols;
int fingersCount = spectralImages.Count;
int rowCount = (int)System.Math.Ceiling((float)fingersCount / imagesPerRow);
int imageWidth = (imagesPerRow * (width + PixelsBetweenImages)) + PixelsBetweenImages;
int imageHeight = (rowCount * (height + PixelsBetweenImages)) + PixelsBetweenImages;
Bitmap image = new Bitmap(imageWidth, imageHeight, PixelFormat.Format16bppRgb565);
SetBackground(image, Color.White);
int verticalOffset = PixelsBetweenImages;
int horizontalOffset = PixelsBetweenImages;
int count = 0;
foreach (float[][] spectralImage in spectralImages.Select(Transform2D))
{
for (int i = 0; i < width /*128*/; i++)
{
for (int j = 0; j < height /*32*/; j++)
{
Color color = ValueToBlackWhiteColor(spectralImage[i][j]);
image.SetPixel(i + horizontalOffset, j + verticalOffset, color);
}
}
count++;
if (count % imagesPerRow == 0)
{
verticalOffset += height + PixelsBetweenImages;
horizontalOffset = PixelsBetweenImages;
}
else
{
horizontalOffset += width + PixelsBetweenImages;
}
}
return(image);
}
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);
Buffer.BlockCopy(frames[i], 0, concatenated, sizeof(float) * i * Cols, sizeof(float) * Cols);
}
AssertAreSame(Rows, Cols, frames, concatenated);
waveletDecomposition.DecomposeImageInPlace(concatenated, Rows, Cols, 1d);
DecomposeImageLocal(frames);
AssertAreSame(Rows, Cols, frames, concatenated);
}
public Image GetWaveletTransformedImage(double[][] image, IWaveletDecomposition wavelet)
{
int width = image[0].Length;
int height = image.Length;
wavelet.DecomposeImageInPlace(image);
Bitmap transformed = new Bitmap(width, height, PixelFormat.Format16bppRgb565);
for (int i = 0; i < transformed.Height; i++)
{
for (int j = 0; j < transformed.Width; j++)
{
transformed.SetPixel(j, i, Color.FromArgb((int)image[i][j]));
}
}
return(transformed);
}
private List <Fingerprint> CreateFingerprintsFromLogSpectrum(IEnumerable <SpectralImage> spectralImages, FingerprintConfiguration configuration)
{
var fingerprints = new List <Fingerprint>();
foreach (var spectralImage in spectralImages)
{
waveletDecomposition.DecomposeImageInPlace(spectralImage.Image);
bool[] image = fingerprintDescriptor.ExtractTopWavelets(spectralImage.Image, configuration.TopWavelets);
if (!IsSilence(image))
{
fingerprints.Add(new Fingerprint {
Signature = image, Timestamp = spectralImage.Timestamp, SequenceNumber = spectralImage.SequenceNumber
});
}
}
return(fingerprints);
}
public List <Fingerprint> CreateFingerprintsFromLogSpectrum(IEnumerable <SpectralImage> spectralImages, FingerprintConfiguration configuration)
{
var fingerprints = new ConcurrentBag <Fingerprint>();
var spectrumLength = configuration.SpectrogramConfig.ImageLength * configuration.SpectrogramConfig.LogBins;
Parallel.ForEach(spectralImages, () => new ushort[spectrumLength], (spectralImage, loop, cachedIndexes) =>
{
waveletDecomposition.DecomposeImageInPlace(spectralImage.Image, spectralImage.Rows, spectralImage.Cols, configuration.HaarWaveletNorm);
RangeUtils.PopulateIndexes(spectrumLength, cachedIndexes);
var image = fingerprintDescriptor.ExtractTopWavelets(spectralImage.Image, configuration.TopWavelets, cachedIndexes);
if (!image.IsSilence())
{
fingerprints.Add(new Fingerprint(image, spectralImage.StartsAt, spectralImage.SequenceNumber));
}
return(cachedIndexes);
},
cachedIndexes => { });
return(fingerprints.ToList());
}
public void StandardDecompositionTest()
{
const int Rows = 128;
const int Cols = 32;
float[][] frames = new float[Rows][];
float[][] framesLocal = new float[Rows][];
for (int i = 0; i < Rows; i++)
{
frames[i] = TestUtilities.GenerateRandomDoubleArray(Cols);
framesLocal[i] = new float[Cols];
frames[i].CopyTo(framesLocal[i], 0);
}
for (int i = 0; i < Rows; i++)
{
for (int j = 0; j < Cols; j++)
{
Assert.AreEqual(true, (frames[i][j] - framesLocal[i][j]) < 0.00001);
}
}
waveletDecomposition.DecomposeImageInPlace(frames);
decimal sumFrames = frames.Sum(target => target.Sum(d => (decimal)Math.Abs(d)));
decimal sumFrameLocal = framesLocal.Sum(target => target.Sum(d => (decimal)Math.Abs(d)));
Assert.AreEqual(true, Math.Abs(sumFrames - sumFrameLocal) > (decimal)0.1);
DecomposeImageLocal(framesLocal);
for (int i = 0; i < Rows; i++)
{
for (var j = 0; j < Cols; j++)
{
Assert.AreEqual(true, (frames[i][j] - framesLocal[i][j]) < 0.001);
}
}
}
internal IEnumerable <Fingerprint> CreateOriginalFingerprintsFromFrames(IEnumerable <Frame> frames, FingerprintConfiguration configuration)
{
var normalized = configuration.FrameNormalizationTransform.Normalize(frames);
var blurred = configuration.GaussianBlurConfiguration.GaussianFilter switch
{
GaussianFilter.None => normalized,
_ => BlurFrames(normalized, configuration.GaussianBlurConfiguration)
};
var images = blurred.ToList();
if (!images.Any())
{
return(Enumerable.Empty <Fingerprint>());
}
var fingerprints = new ConcurrentBag <Fingerprint>();
var length = images.First().Length;
var saveTransform = configuration.OriginalPointSaveTransform;
Parallel.ForEach(images, () => new ushort[length], (frame, loop, cachedIndexes) =>
{
byte[] originalPoint = saveTransform(frame);
float[] rowCols = frame.ImageRowCols;
waveletDecomposition.DecomposeImageInPlace(rowCols, frame.Rows, frame.Cols, configuration.HaarWaveletNorm);
RangeUtils.PopulateIndexes(length, cachedIndexes);
var image = fingerprintDescriptor.ExtractTopWavelets(rowCols, configuration.TopWavelets, cachedIndexes);
if (!image.IsSilence())
{
fingerprints.Add(new Fingerprint(image, frame.StartsAt, frame.SequenceNumber, originalPoint));
}
return(cachedIndexes);
},
cachedIndexes => { });
return(fingerprints.ToList());
}
/// <summary>
/// Get wavelet decomposition on an image
/// </summary>
/// <param name = "wavelet">Wavelet used</param>
/// <param name = "image">Image to be decomposed</param>
/// <returns>New image</returns>
public static Image GetWaveletTransformation(IWaveletDecomposition wavelet, float[][] image)
{
int width = image[0].Length;
int height = image.Length;
wavelet.DecomposeImageInPlace(image);
Bitmap transformed = new Bitmap(width, height, PixelFormat.Format16bppRgb565);
for (int i = 0; i < transformed.Height; i++)
for (int j = 0; j < transformed.Width; j++)
transformed.SetPixel(j, i, Color.FromArgb((int)image[i][j]));
return transformed;
}
