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 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());
}
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());
}
