public static void GetGlobalOtsuThreshold(double[,] inputMatrix, out byte[,] opByteMatrix, out double opThreshold, out Image <Rgb24> histogramImage)
{
if (inputMatrix == null)
{
throw new ArgumentNullException(nameof(inputMatrix));
}
var normMatrix = MatrixTools.NormaliseInZeroOne(inputMatrix, out var min, out var max);
var byteMatrix = MatrixTools.ConvertMatrixOfDouble2Byte(normMatrix);
GetOtsuThreshold(byteMatrix, out opByteMatrix, out var threshold, out histogramImage);
opThreshold = threshold / (double)byte.MaxValue;
opThreshold = min + (opThreshold * (max - min));
}
// ################################# STATIC METHODS BELOW HERE ###############################
public static Image DrawSpectrogramAnnotated(double[,] data, SpectrogramSettings config, SpectrogramAttributes attributes)
{
// normalise the data between 0 and 95th percentiles
int binCount = 100;
double min;
double max;
DataTools.MinMax(data, out min, out max);
double binWidth = (max - min) / binCount;
var histogram = Histogram.Histo(data, binCount, min, max, binWidth);
int percentile = 95;
int binId = Histogram.GetPercentileBin(histogram, percentile);
double upperBound = min + (binId * percentile);
var normedMatrix = MatrixTools.NormaliseInZeroOne(data, min, upperBound);
/*
* int minPercentile = 5;
* int minBinId = Histogram.GetPercentileBin(histogram, minPercentile);
* double lowerBound = min + (minBinId * minPercentile);
* int maxPercentile = 95;
* int maxBinId = Histogram.GetPercentileBin(histogram, maxPercentile);
* double upperBound = min + (maxBinId * maxPercentile);
* var normedMatrix = MatrixTools.NormaliseInZeroOne(data, lowerBound, upperBound);
*/
int nyquist = attributes.NyquistFrequency;
int frameSize = config.WindowSize;
// assuming linear frequency scale
int finalBinCount = frameSize / 2;
var scaleType = FreqScaleType.Linear;
// if doing mel scale then
if (config.DoMelScale)
{
finalBinCount = 256; //128; //512; // 256; // 100; // 40; // 200; //
scaleType = FreqScaleType.Mel;
}
var freqScale = new FrequencyScale(scaleType, nyquist, frameSize, finalBinCount, hertzGridInterval: 1000);
var image = SpectrogramTools.GetImage(normedMatrix, nyquist, config.DoMelScale);
var annotatedImage = SpectrogramTools.GetImageFullyAnnotated(image, config.SourceFileName + ": " + scaleType.ToString(), freqScale.GridLineLocations, attributes.Duration);
return(annotatedImage);
}
/// <summary>
/// Used to normalise a spectrogram in 0,1.
/// </summary>
/// <param name="matrix">the spectrogram data.</param>
/// <param name="truncateMin">set all values above to 1.0.</param>
/// <param name="truncateMax">set all values below to zero.</param>
/// <param name="backgroundFilterCoeff">used to de-emphisize the background.</param>
/// <returns>a normalised matrix of spectrogram data.</returns>
public static double[,] NormaliseSpectrogramMatrix(double[,] matrix, double truncateMin, double truncateMax, double backgroundFilterCoeff)
{
double[,] m = MatrixTools.NormaliseInZeroOne(matrix, truncateMin, truncateMax);
m = MatrixTools.FilterBackgroundValues(m, backgroundFilterCoeff); // to de-demphasize the background small values
return(m);
}
//############################################################################################################################################################
//############################################################################################################################################################
public static double[,] NormaliseSpectrogramMatrix(IndexProperties indexProperties, double[,] matrix, double backgroundFilterCoeff)
{
matrix = MatrixTools.NormaliseInZeroOne(matrix, indexProperties.NormMin, indexProperties.NormMax);
matrix = MatrixTools.FilterBackgroundValues(matrix, backgroundFilterCoeff); // to de-demphasize the background small values
return(matrix);
}
