public static Image <Rgb24> FrameSliceOf3DSpectrogram_DayOfYear(Image <Rgb24> bmp1, Image <Rgb24> titleBar, int year, int dayOfYear, TimeSpan xInterval, int herzValue, FileInfo sunriseSetData, int nyquistFreq)
{
Image <Rgb24> suntrack = SunAndMoon.AddSunTrackToImage(bmp1.Width, sunriseSetData, year, dayOfYear);
bmp1.Mutate(g =>
{
Pen pen = new Pen(Color.White, 1);
var stringFont = Drawing.Arial12;
//Font stringFont = Drawing.Tahoma9;
DateTime theDate = new DateTime(year, 1, 1).AddDays(dayOfYear - 1);
string dateString = $"{year} {DataTools.MonthNames[theDate.Month - 1]} {theDate.Day:d2}";
g.DrawText(dateString, stringFont, Color.Wheat, new PointF(10, 3));
});
TimeSpan xAxisPixelDuration = TimeSpan.FromSeconds(60);
var minuteOffset = TimeSpan.Zero;
double secondsDuration = xAxisPixelDuration.TotalSeconds * bmp1.Width;
TimeSpan fullDuration = TimeSpan.FromSeconds(secondsDuration);
// init frequency scale
int herzInterval = 1000;
int frameSize = bmp1.Height;
var freqScale = new DSP.FrequencyScale(nyquistFreq, frameSize, herzInterval);
SpectrogramTools.DrawGridLinesOnImage((Image <Rgb24>)bmp1, minuteOffset, fullDuration, xInterval, freqScale);
int trackHeight = 20;
int imageHt = bmp1.Height + trackHeight + trackHeight + trackHeight;
var xAxisTicInterval = TimeSpan.FromMinutes(60); // assume 60 pixels per hour
var timeScale24Hour = ImageTrack.DrawTimeTrack(fullDuration, minuteOffset, xAxisTicInterval, bmp1.Width, trackHeight, "hours");
var imageList = new List <Image <Rgb24> >
{
titleBar,
timeScale24Hour,
suntrack,
bmp1,
timeScale24Hour
};
var compositeBmp = ImageTools.CombineImagesVertically(imageList);
// trackHeight = compositeBmp.Height;
// Image<Rgb24> timeScale12Months = ImageTrack.DrawYearScaleVertical(40, trackHeight);
// Image<Rgb24> freqScale = DrawFreqScale_vertical(40, trackHeight, HerzValue, nyquistFreq);
imageList = new List <Image <Rgb24> >();
// imageList.Add(timeScale12Months);
imageList.Add(compositeBmp);
// imageList.Add(freqScale);
compositeBmp = ImageTools.CombineImagesInLine(imageList.ToArray());
return(compositeBmp);
}
public static Image <Rgb24> ConcatenateFourChannelImages(FileInfo[] imageFiles, DirectoryInfo imageDirectory, string fileSuffix, string date)
{
// get first image to find its dimensions
var image = (Image <Rgb24>)Image.Load(imageFiles[0].FullName);
var brush = Color.White;
Font stringFont = Drawing.Tahoma12;
//create spacer image
int width = 1;
int height = image.Height;
Image <Rgb24> spacerImage = new Image <Rgb24>(width, height);
spacerImage.Mutate(g =>
{
g.Clear(Color.DarkGray);
});
// init output list of images
var fourChannelList = new List <Image <Rgb24> >();
for (int channel = 0; channel < 4; channel++)
{
var imageList = new List <Image <Rgb24> >();
// Monitoring_Rosin_20120329T000000 + 0200_.merged.wav.channel_0__2Maps.png;
string fileMatch = $@"0000+0200_.merged.wav.channel_{channel}__{fileSuffix}";
foreach (FileInfo imageFile in imageFiles)
{
if (!imageFile.Name.EndsWith(fileMatch))
{
continue;
}
image = (Image <Rgb24>)Image.Load(imageFile.FullName);
imageList.Add(image);
imageList.Add(spacerImage);
}
imageList.Add(spacerImage);
imageList.Add(spacerImage);
var concatImage = ImageTools.CombineImagesInLine(imageList);
concatImage.Mutate(g =>
{
string chn = $"ch{channel + 1}";
g.DrawTextSafe(chn, stringFont, brush, new PointF(2, 40));
});
fourChannelList.Add(concatImage);
}
var combinedImage = ImageTools.CombineImagesVertically(fourChannelList);
return(combinedImage);
}
/// <summary>
/// Creates an image from the frequency/oscillations matrix.
/// The y-axis scale = frequency bins as per normal spectrogram.
/// The x-axis scale is oscillations per second.
/// </summary>
/// <param name="freqOscilMatrix">the input frequency/oscillations matrix</param>
/// <param name="framesPerSecond">to give the time scale</param>
/// <param name="freqBinWidth">to give the frequency scale</param>
/// <param name="sampleLength">to allow calculation of the oscillations scale</param>
/// <param name="algorithmName">the algorithm used to compute the oscillations.</param>
/// <returns>bitmap image</returns>
public static Image GetFreqVsOscillationsImage(double[,] freqOscilMatrix, double framesPerSecond, double freqBinWidth, int sampleLength, string algorithmName)
{
// remove the high cycles/sec end of the matrix because nothing really happens here.
int maxRows = freqOscilMatrix.GetLength(0) / 2;
freqOscilMatrix = MatrixTools.Submatrix(freqOscilMatrix, 0, 0, maxRows - 1, freqOscilMatrix.GetLength(1) - 1);
// get the OSC spectral index
var spectralIndex = MatrixTools.GetMaximumColumnValues(freqOscilMatrix);
// Convert spectrum index to oscillations per second
double oscillationBinWidth = framesPerSecond / sampleLength;
//draw an image
freqOscilMatrix = MatrixTools.MatrixRotate90Anticlockwise(freqOscilMatrix);
// each value is to be drawn as a 5 pixel x 5 pixel square
int xscale = 5;
int yscale = 5;
if (maxRows < 10)
{
xscale = 10;
}
//var image1 = ImageTools.DrawMatrixInColour(freqOscilMatrix, xPixelsPerCell: xscale, yPixelsPerCell: yscale);
//var image2 = ImageTools.DrawVectorInColour(DataTools.reverseArray(spectralIndex), cellWidth: xscale);
var image1 = ImageTools.DrawMatrixInGrayScale(freqOscilMatrix, xPixelsPerCell: xscale, yPixelsPerCell: yscale, reverse: true);
spectralIndex = DataTools.NormaliseByScalingMaxValueToOne(spectralIndex);
var image2 = ImageTools.DrawVectorInGrayScaleWithoutNormalisation(DataTools.reverseArray(spectralIndex), xscale, yscale, reverse: true);
var image = ImageTools.CombineImagesInLine(new[] { image1, image2 });
// place a grid line every 5 cycles per second.
double cycleInterval = 5.0;
double xTicInterval = cycleInterval / oscillationBinWidth * xscale;
// a tic every 1000 Hz.
int herzInterval = 1000;
double yTicInterval = herzInterval / freqBinWidth * yscale;
int xOffset = xscale / 2;
int yOffset = yscale / 2;
image = ImageTools.DrawYaxisScale(image, 10, herzInterval, yTicInterval, yOffset);
image = ImageTools.DrawXaxisScale(image, 15, cycleInterval, xTicInterval, 10, -xOffset);
var titleBar = DrawTitleBarOfOscillationSpectrogram(algorithmName, image.Width);
var imageList = new List <Image> {
titleBar, image
};
var compositeBmp = (Bitmap)ImageTools.CombineImagesVertically(imageList);
return(compositeBmp);
}
public static Image FrameSliceOf3DSpectrogram_DayOfYear(Image bmp1, Image titleBar, int year, int dayOfYear, TimeSpan xInterval, int herzValue, FileInfo sunriseSetData, int nyquistFreq)
{
Bitmap suntrack = SunAndMoon.AddSunTrackToImage(bmp1.Width, sunriseSetData, year, dayOfYear);
Graphics g = Graphics.FromImage(bmp1);
Pen pen = new Pen(Color.White);
Font stringFont = new Font("Arial", 12);
//Font stringFont = new Font("Tahoma", 9);
DateTime theDate = new DateTime(year, 1, 1).AddDays(dayOfYear - 1);
string dateString = string.Format("{0} {1} {2:d2}", year, DataTools.MonthNames[theDate.Month - 1], theDate.Day);
g.DrawString(dateString, stringFont, Brushes.Wheat, new PointF(10, 3));
TimeSpan xAxisPixelDuration = TimeSpan.FromSeconds(60);
var minuteOffset = TimeSpan.Zero;
double secondsDuration = xAxisPixelDuration.TotalSeconds * bmp1.Width;
TimeSpan fullDuration = TimeSpan.FromSeconds(secondsDuration);
// init frequency scale
int herzInterval = 1000;
int frameSize = bmp1.Height;
var freqScale = new DSP.FrequencyScale(nyquistFreq, frameSize, herzInterval);
SpectrogramTools.DrawGridLinesOnImage((Bitmap)bmp1, minuteOffset, fullDuration, xInterval, freqScale);
int trackHeight = 20;
int imageHt = bmp1.Height + trackHeight + trackHeight + trackHeight;
var xAxisTicInterval = TimeSpan.FromMinutes(60); // assume 60 pixels per hour
var timeScale24Hour = ImageTrack.DrawTimeTrack(fullDuration, minuteOffset, xAxisTicInterval, bmp1.Width, trackHeight, "hours");
var imageList = new List <Image>();
imageList.Add(titleBar);
imageList.Add(timeScale24Hour);
imageList.Add(suntrack);
imageList.Add(bmp1);
imageList.Add(timeScale24Hour);
Image compositeBmp = ImageTools.CombineImagesVertically(imageList.ToArray());
// trackHeight = compositeBmp.Height;
// Bitmap timeScale12Months = ImageTrack.DrawYearScaleVertical(40, trackHeight);
// Bitmap freqScale = DrawFreqScale_vertical(40, trackHeight, HerzValue, nyquistFreq);
imageList = new List <Image>();
// imageList.Add(timeScale12Months);
imageList.Add(compositeBmp);
// imageList.Add(freqScale);
compositeBmp = ImageTools.CombineImagesInLine(imageList.ToArray());
return(compositeBmp);
}
/// <summary>
/// Generates the FREQUENCY x OSCILLATIONS Graphs and csv
/// I have experimented with five methods to search for oscillations:
/// 1: string algorithmName = "Autocorr-FFT";
/// use this if want more detailed output - but not necessrily accurate!
/// 2: string algorithmName = "Autocorr-SVD-FFT";
/// use this if want only dominant oscillations
/// 3: string algorithmName = "Autocorr-Cwt";
/// a Wavelets option but could not get it to work well
/// 4: string algorithmName = "Autocorr-WPD";
/// another Wavelets option but could not get it to work well
/// 5: Discrete Cosine Transform
/// The DCT only works well when you know which periodicity you are looking for. e.g. Canetoad.
/// </summary>
public static Tuple <Image <Rgb24>, double[, ]> GenerateOscillationDataAndImages(FileInfo audioSegment, Dictionary <string, string> configDict, bool drawImage = false)
{
// set two oscillation detection parameters
double sensitivity = DefaultSensitivityThreshold;
if (configDict.ContainsKey(AnalysisKeys.OscilDetection2014SensitivityThreshold))
{
sensitivity = double.Parse(configDict[AnalysisKeys.OscilDetection2014SensitivityThreshold]);
}
// Sample length i.e. number of frames spanned to calculate oscillations per second
int sampleLength = DefaultSampleLength;
if (configDict.ContainsKey(AnalysisKeys.OscilDetection2014SampleLength))
{
sampleLength = int.Parse(configDict[AnalysisKeys.OscilDetection2014SampleLength]);
}
var sonoConfig = new SonogramConfig(configDict); // default values config
if (configDict.ContainsKey(AnalysisKeys.OscilDetection2014FrameSize))
{
sonoConfig.WindowSize = int.Parse(configDict[AnalysisKeys.OscilDetection2014FrameSize]);
}
var recordingSegment = new AudioRecording(audioSegment.FullName);
var spgm = GetSpectrogramMatrix(recordingSegment, DefaultFrameLength);
double framesPerSecond = DefaultResampleRate / (double)DefaultFrameLength;
double freqBinWidth = framesPerSecond;
// get the appropriate sampleLength (patch size) for short recordings
int framecount = spgm.GetLength(0);
sampleLength = AdjustSampleSize(framecount, sampleLength);
var algorithmName1 = "autocorr-svd-fft";
var freqOscilMatrix1 = GetFrequencyByOscillationsMatrix(spgm, sensitivity, sampleLength, algorithmName1);
var image1 = GetFreqVsOscillationsImage(freqOscilMatrix1, framesPerSecond, freqBinWidth, sampleLength, algorithmName1);
var algorithmName2 = "autocorr-fft";
var freqOscilMatrix2 = GetFrequencyByOscillationsMatrix(spgm, sensitivity, sampleLength, algorithmName2);
var image2 = GetFreqVsOscillationsImage(freqOscilMatrix2, framesPerSecond, freqBinWidth, sampleLength, algorithmName2);
//IMPORTANT NOTE: To generate an OSC spectral index matrix for making LDFC spectrograms, use the following line:
//var spectralIndex = MatrixTools.GetMaximumColumnValues(freqOscilMatrix2);
var compositeImage = ImageTools.CombineImagesInLine(image2, image1);
// Return (1) composite image of oscillations,
// (2) data matrix from only one algorithm,
return(Tuple.Create(compositeImage, freqOscilMatrix2));
}
public static Image ConcatenateFourChannelImages(FileInfo[] imageFiles, DirectoryInfo imageDirectory, string fileSuffix, string date)
{
// get first image to find its dimensions
Image image = Image.FromFile(imageFiles[0].FullName);
Brush brush = Brushes.White;
Font stringFont = new Font("Tahoma", 12);
//create spacer image
int width = 1;
int height = image.Height;
Bitmap spacerImage = new Bitmap(width, height);
Graphics g = Graphics.FromImage(spacerImage);
g.Clear(Color.DarkGray);
// init output list of images
var fourChannelList = new List <Image>();
for (int channel = 0; channel < 4; channel++)
{
var imageList = new List <Image>();
// Monitoring_Rosin_20120329T000000 + 0200_.merged.wav.channel_0__2Maps.png;
string fileMatch = string.Format(@"0000+0200_.merged.wav.channel_{0}__{1}", channel, fileSuffix);
foreach (FileInfo imageFile in imageFiles)
{
if (!imageFile.Name.EndsWith(fileMatch))
{
continue;
}
image = Image.FromFile(imageFile.FullName);
imageList.Add(image);
imageList.Add(spacerImage);
}
imageList.Add(spacerImage);
imageList.Add(spacerImage);
Image concatImage = ImageTools.CombineImagesInLine(imageList);
g = Graphics.FromImage(concatImage);
string chn = string.Format("ch{0}", channel + 1);
g.DrawString(chn, stringFont, brush, new PointF(2, 40));
fourChannelList.Add(concatImage);
}
Image combinedImage = ImageTools.CombineImagesVertically(fourChannelList);
return(combinedImage);
}
public void TestCombineImagesInLine()
{
var actual = ImageTools.CombineImagesInLine(
null,
Drawing.NewImage(10, 100, Color.Red),
Drawing.NewImage(100, 100, Color.Red),
Drawing.NewImage(20, 100, Color.Red),
null,
Drawing.NewImage(200, 100, Color.Red),
null);
Assert.That.ImageIsSize(330, 100, actual);
Assert.That.ImageRegionIsColor(actual.Bounds(), Color.Red, actual);
}
public static Image FrameSliceOf3DSpectrogram_ConstantFreq(Image bmp1, Image titleBar, TimeSpan xInterval, int herzValue, FileInfo sunriseSetData, int nyquistFreq)
{
SunAndMoon.AddSunRiseSetLinesToImage((Bitmap)bmp1, sunriseSetData, 0, 365, 1); // assume full year and 1px/day
var g = Graphics.FromImage(bmp1);
var pen = new Pen(Color.White);
var stringFont = new Font("Arial", 12);
var str = $"Freq = {herzValue} Hz";
g.DrawString(str, stringFont, Brushes.Wheat, new PointF(10, 7));
var xAxisPixelDuration = TimeSpan.FromSeconds(60);
var startOffset = TimeSpan.Zero;
double secondsDuration = xAxisPixelDuration.TotalSeconds * bmp1.Width;
var fullDuration = TimeSpan.FromSeconds(secondsDuration);
// init frequency scale
int herzInterval = 1000;
int frameSize = bmp1.Height;
var freqScale = new DSP.FrequencyScale(nyquistFreq, frameSize, herzInterval);
SpectrogramTools.DrawGridLinesOnImage((Bitmap)bmp1, startOffset, fullDuration, xInterval, freqScale);
int trackHeight = 20;
var xAxisTicInterval = TimeSpan.FromMinutes(60); // assume 60 pixels per hour
var timeScale24Hour = ImageTrack.DrawTimeTrack(fullDuration, startOffset, xAxisTicInterval, bmp1.Width, trackHeight, "hours");
var imageList = new List <Image> {
titleBar, timeScale24Hour, bmp1, timeScale24Hour
};
var compositeBmp = ImageTools.CombineImagesVertically(imageList.ToArray());
if (compositeBmp == null)
{
throw new ArgumentNullException(nameof(compositeBmp));
}
trackHeight = compositeBmp.Height;
Bitmap timeScale12Months = ImageTrack.DrawYearScaleVertical(40, trackHeight);
Bitmap freqScaleImage = DrawFreqScale_vertical(40, trackHeight, herzValue, nyquistFreq);
imageList = new List <Image> {
timeScale12Months, compositeBmp, freqScaleImage
};
compositeBmp = ImageTools.CombineImagesInLine(imageList.ToArray());
return(compositeBmp);
}
public void TestCombineImagesInLineDefaultFill()
{
var actual = ImageTools.CombineImagesInLine(
null,
Drawing.NewImage(100, 80, Color.Red),
Drawing.NewImage(100, 100, Color.Red),
Drawing.NewImage(100, 80, Color.Red),
null,
Drawing.NewImage(100, 100, Color.Red),
null);
Assert.That.ImageIsSize(400, 100, actual);
Assert.That.ImageRegionIsColor((0, 0, 400, 80).AsRect(), Color.Red, actual);
Assert.That.ImageRegionIsColor((0, 80, 100, 20).AsRect(), Color.Black, actual);
Assert.That.ImageRegionIsColor((100, 80, 100, 20).AsRect(), Color.Red, actual);
Assert.That.ImageRegionIsColor((200, 80, 100, 20).AsRect(), Color.Black, actual);
Assert.That.ImageRegionIsColor((300, 80, 100, 20).AsRect(), Color.Red, actual);
}
public static Image GetFreqVsOscillationsImage(double[,] freqOscilMatrix, double framesPerSecond, double freqBinWidth, int sampleLength, string algorithmName)
{
// remove the high cycles/sec end of the matrix because nothing really happens here.
freqOscilMatrix = MatrixTools.Submatrix(freqOscilMatrix, 0, 0, 30, freqOscilMatrix.GetLength(1) - 1);
// get the OSC spectral index
// double[] spectralIndex = ConvertMatrix2SpectralIndexBySummingFreqColumns(freqOscilMatrix, skipNrows: 0);
var spectralIndex = MatrixTools.GetMaximumColumnValues(freqOscilMatrix);
// Convert spectrum index to oscillations per second
double oscillationBinWidth = framesPerSecond / sampleLength;
//draw an image
freqOscilMatrix = MatrixTools.MatrixRotate90Anticlockwise(freqOscilMatrix);
int xscale = 5;
int yscale = 5;
var image1 = ImageTools.DrawMatrixInColour(freqOscilMatrix, xPixelsPerCell: xscale, yPixelsPerCell: yscale);
var image2 = ImageTools.DrawVectorInColour(DataTools.reverseArray(spectralIndex), cellWidth: xscale);
var image = ImageTools.CombineImagesInLine(new[] { image1, image2 });
// a tic every 5cpsec.
double cycleInterval = 5.0;
double xTicInterval = cycleInterval / oscillationBinWidth * xscale;
// a tic every 1000 Hz.
int herzInterval = 1000;
double yTicInterval = herzInterval / freqBinWidth * yscale;
int xOffset = xscale / 2;
int yOffset = yscale / 2;
image = ImageTools.DrawXandYaxes(image, 18, cycleInterval, xTicInterval, xOffset, herzInterval, yTicInterval, yOffset);
var titleBar = DrawTitleBarOfOscillationSpectrogram(algorithmName, image.Width);
var imageList = new List <Image> {
titleBar, image
};
var compositeBmp = (Bitmap)ImageTools.CombineImagesVertically(imageList);
return(compositeBmp);
}
/// <summary>
/// Can be used for visual checking and debugging purposes.
/// </summary>
public static void DrawNormalisedIndexMatrices(DirectoryInfo dir, string baseName, Dictionary <string, double[, ]> dictionary)
{
var list = new List <Image>();
foreach (string key in ContentSignatures.IndexNames)
{
var bmp = ImageTools.DrawReversedMatrixWithoutNormalisation(dictionary[key]);
// need to rotate spectrogram to get correct orientation.
bmp.RotateFlip(RotateFlipType.Rotate270FlipNone);
// draw grid lines and add axis scales
var xAxisPixelDuration = TimeSpan.FromSeconds(60);
var fullDuration = TimeSpan.FromTicks(xAxisPixelDuration.Ticks * bmp.Width);
var freqScale = new FrequencyScale(11025, 512, 1000);
SpectrogramTools.DrawGridLinesOnImage((Bitmap)bmp, TimeSpan.Zero, fullDuration, xAxisPixelDuration, freqScale);
const int trackHeight = 20;
var recordingStartDate = default(DateTimeOffset);
var timeBmp = ImageTrack.DrawTimeTrack(fullDuration, recordingStartDate, bmp.Width, trackHeight);
var array = new Image[2];
array[0] = bmp;
array[1] = timeBmp;
var image = ImageTools.CombineImagesVertically(array);
// add a header to the spectrogram
var header = new Bitmap(image.Width, 20);
Graphics g = Graphics.FromImage(header);
g.Clear(Color.LightGray);
g.SmoothingMode = SmoothingMode.AntiAlias;
g.InterpolationMode = InterpolationMode.HighQualityBicubic;
g.PixelOffsetMode = PixelOffsetMode.HighQuality;
g.DrawString(key, new Font("Tahoma", 9), Brushes.Black, 4, 4);
list.Add(ImageTools.CombineImagesVertically(new List <Image>(new[] { header, image })));
}
// save the image - the directory for the path must exist
var path = Path.Combine(dir.FullName, baseName + "__Towsey.Acoustic.GreyScaleImages.png");
var indexImage = ImageTools.CombineImagesInLine(list);
indexImage?.Save(path);
}
/// <summary>
/// Can be used for visual checking and debugging purposes.
/// </summary>
public static void DrawNormalisedIndexMatrices(DirectoryInfo dir, string baseName, Dictionary <string, double[, ]> dictionary)
{
var list = new List <Image <Rgb24> >();
foreach (string key in ContentSignatures.IndexNames)
{
var bmp = ImageTools.DrawReversedMatrixWithoutNormalisation(dictionary[key]);
// need to rotate spectrogram to get correct orientation.
bmp.RotateFlip(RotateFlipType.Rotate270FlipNone);
// draw grid lines and add axis scales
var xAxisPixelDuration = TimeSpan.FromSeconds(60);
var fullDuration = TimeSpan.FromTicks(xAxisPixelDuration.Ticks * bmp.Width);
var freqScale = new FrequencyScale(11025, 512, 1000);
SpectrogramTools.DrawGridLinesOnImage((Image <Rgb24>)bmp, TimeSpan.Zero, fullDuration, xAxisPixelDuration, freqScale);
const int trackHeight = 20;
var recordingStartDate = default(DateTimeOffset);
var timeBmp = ImageTrack.DrawTimeTrack(fullDuration, recordingStartDate, bmp.Width, trackHeight);
var image = ImageTools.CombineImagesVertically(bmp, timeBmp);
// add a header to the spectrogram
var header = Drawing.NewImage(image.Width, 20, Color.LightGray);
header.Mutate(g =>
{
g.DrawText(key, Drawing.Tahoma9, Color.Black, new PointF(4, 4));
list.Add(ImageTools.CombineImagesVertically(header, image));
});
}
// save the image - the directory for the path must exist
var path = Path.Combine(dir.FullName, baseName + "__Towsey.Acoustic.GreyScaleImages.png");
var indexImage = ImageTools.CombineImagesInLine(list);
indexImage?.Save(path);
}
// ##############################################################################################################
// ######################### ORIGINAL METHOD FOR STITCHING Gianna Pavan's DATA (10 minutes every 30 minutes)
/// <summary>
/// This method stitches together spectrogram images derived from consecutive shorter recordings over a 24 hour period.
/// Currently set for the recording protocol of Gianna Pavan (10 minutes every 30 minutes).
///
/// Call this method from Sandpit or where ever!
///
/// IMPORTANT NOTE: This method does NOT check to see if the images are in temporal order.
/// A SORT line should be inserted somewhere
/// </summary>
public static void StitchPartialSpectrograms()
{
//######################################################
// ********************* set the below parameters
var inputDirectory = new DirectoryInfo(@"Z:\Italy_GianniPavan\output4\Towsey.Acoustic");
string opFileStem = "Sassofratino_24hours_v3";
var outputDirectory = new DirectoryInfo(@"Z:\Italy_GianniPavan\output4\");
// a filter to select images to be stitched
string endString = "_000.2MAPS.png";
// recording protocol
int minutesBetweenRecordingStarts = 30;
TimeSpan minOffset = TimeSpan.Zero; // assume first recording in sequence started at midnight
// X-axis timescale
int pixelColumnsPerHour = 60;
int trackHeight = IndexDisplay.DefaultTrackHeight;
// ********************* set the above parameters
//######################################################
string[] fileEntries = Directory.GetFiles(inputDirectory.FullName);
var images = new List <Image>();
bool interpolateSpacer = true;
var imagePair = new Image[2];
TimeSpan xAxisTicInterval = TimeSpan.FromMinutes(pixelColumnsPerHour); // assume 60 pixels per hour
// loop through all files in the required directory
foreach (string path in fileEntries)
{
// filter files.
if (!path.EndsWith(endString))
{
continue;
}
var image = new Bitmap(path);
int spacerWidth = minutesBetweenRecordingStarts - image.Width;
if (interpolateSpacer)
{
var spacer = new Bitmap(spacerWidth, image.Height);
imagePair[0] = image;
imagePair[1] = spacer;
image = (Bitmap)ImageTools.CombineImagesInLine(imagePair);
}
images.Add(image);
}
var compositeBmp = ImageTools.CombineImagesInLine(images.ToArray());
var fullDuration = TimeSpan.FromMinutes(compositeBmp.Width);
var timeBmp = ImageTrack.DrawTimeTrack(fullDuration, minOffset, xAxisTicInterval, compositeBmp.Width, trackHeight, "hours");
var gr = Graphics.FromImage(compositeBmp);
int halfHeight = compositeBmp.Height / 2;
//add in the title bars
string title = $"24 hour FALSE-COLOUR SPECTROGRAM (scale: hours x kHz) (colour: R-G-B = BGN-AVG-CVR) {Meta.OrganizationTag} ";
var titleBmp = ImageTrack.DrawTitleTrack(compositeBmp.Width, trackHeight, title);
int offset = 0;
gr.DrawImage(titleBmp, 0, offset); //draw in the top time scale
title = $"24 hour FALSE-COLOUR SPECTROGRAM (scale: hours x kHz) (colour: R-G-B = ACI-ENT-EVN) {Meta.OrganizationTag} ";
titleBmp = ImageTrack.DrawTitleTrack(compositeBmp.Width, trackHeight, title);
offset = halfHeight;
gr.DrawImage(titleBmp, 0, offset); //draw in the top time scale
//add in the timescale tracks
offset = trackHeight;
gr.DrawImage(timeBmp, 0, offset); //draw in the top time scale
offset = compositeBmp.Height - trackHeight;
gr.DrawImage(timeBmp, 0, offset); //draw in the top time scale
offset = halfHeight - trackHeight;
gr.DrawImage(timeBmp, 0, offset); //draw in the top time scale
offset = halfHeight + trackHeight;
gr.DrawImage(timeBmp, 0, offset); //draw in the top time scale
compositeBmp.Save(Path.Combine(outputDirectory.FullName, opFileStem + ".png"));
}
public static void Execute(Arguments arguments)
{
var inputDirs = arguments.InputDataDirectories.Select(FileInfoExtensions.ToDirectoryInfo);
var output = arguments.OutputDirectory.ToDirectoryInfo();
string date = "# DATE AND TIME: " + DateTime.Now;
LoggedConsole.WriteLine("\n# DRAW an EASY IMAGE from consecutive days of SUMMARY INDICES in CSV files.");
LoggedConsole.WriteLine("# IT IS ASSUMED THAT THE CSV files are already concatenated into 24 hour files.");
LoggedConsole.WriteLine(date);
LoggedConsole.WriteLine("# Summary Index.csv files are in directories:");
foreach (DirectoryInfo dir in inputDirs)
{
LoggedConsole.WriteLine(" {0}", dir.FullName);
}
LoggedConsole.WriteLine("# Output directory: " + output);
if (arguments.StartDate == null)
{
LoggedConsole.WriteLine("# Start date = NULL (No argument provided). Will revise start date ....");
}
else
{
LoggedConsole.WriteLine("# Start date = " + arguments.StartDate.ToString());
}
if (arguments.EndDate == null)
{
LoggedConsole.WriteLine("# End date = NULL (No argument provided). Will revise end date ....");
}
else
{
LoggedConsole.WriteLine("# End date = " + arguments.EndDate.ToString());
}
LoggedConsole.WriteLine("# FILE FILTER = " + arguments.FileFilter);
LoggedConsole.WriteLine();
// PATTERN SEARCH FOR SUMMARY INDEX FILES.
//string pattern = "*__Towsey.Acoustic.Indices.csv";
FileInfo[] csvFiles = IndexMatrices.GetFilesInDirectories(inputDirs.ToArray(), arguments.FileFilter);
//LoggedConsole.WriteLine("# Subdirectories Count = " + subDirectories.Length);
LoggedConsole.WriteLine("# SummaryIndexFiles.csv Count = " + csvFiles.Length);
if (csvFiles.Length == 0)
{
LoggedConsole.WriteErrorLine("\n\nWARNING from method DrawEasyImage.Execute():");
LoggedConsole.WriteErrorLine(" No SUMMARY index files were found.");
LoggedConsole.WriteErrorLine(" RETURNING EMPTY HANDED!");
return;
}
// Sort the files by date and return as a dictionary: sortedDictionaryOfDatesAndFiles<DateTimeOffset, FileInfo>
//var sortedDictionaryOfDatesAndFiles = LDSpectrogramStitching.FilterFilesForDates(csvFiles, arguments.TimeSpanOffsetHint);
// calculate new start date if passed value = null.
DateTimeOffset?startDate = arguments.StartDate;
DateTimeOffset?endDate = arguments.EndDate;
TimeSpan totalTimespan = (DateTimeOffset)endDate - (DateTimeOffset)startDate;
int dayCount = totalTimespan.Days + 1; // assume last day has full 24 hours of recording available.
LoggedConsole.WriteLine("\n# Start date = " + startDate.ToString());
LoggedConsole.WriteLine("# End date = " + endDate.ToString());
LoggedConsole.WriteLine(string.Format("# Elapsed time = {0:f1} hours", dayCount * 24));
LoggedConsole.WriteLine("# Day count = " + dayCount + " (inclusive of start and end days)");
LoggedConsole.WriteLine("# Time Zone = " + arguments.TimeSpanOffsetHint.ToString());
// create top level output directory if it does not exist.
DirectoryInfo opDir = output;
if (!opDir.Exists)
{
opDir.Create();
}
// SET UP DEFAULT SITE LOCATION INFO -- DISCUSS IWTH ANTHONY
// The following location data is used only to draw the sunrise/sunset tracks on images.
double?latitude = null;
double?longitude = null;
var siteDescription = new SiteDescription();
siteDescription.SiteName = arguments.FileStemName;
siteDescription.Latitude = latitude;
siteDescription.Longitude = longitude;
// the following required if drawing the index images
FileInfo indexPropertiesConfig = null;
// require IndexGenerationData and indexPropertiesConfig for drawing
//indexGenerationData = IndexGenerationData.GetIndexGenerationData(csvFiles[0].Directory);
indexPropertiesConfig = arguments.IndexPropertiesConfig.ToFileInfo();
Dictionary <string, IndexProperties> listOfIndexProperties = IndexProperties.GetIndexProperties(indexPropertiesConfig);
Tuple <List <string>, List <double[]> > tuple = CsvTools.ReadCSVFile(csvFiles[0].FullName);
var names = tuple.Item1;
// default EASY indices
int redID = 3; // backgroundNoise
int grnID = 5; // avSNROfActiveframes
int bluID = 7; // events per second
string rep = @"bgn-avsnr-evn";
// ACI Ht Hpeaks EASY indices
if (false)
{
redID = 11; // ACI
grnID = 12; // Ht
//bluID = 13; // HavgSp
//bluID = 14; // Hvariance
//bluID = 15; // Hpeaks
bluID = 16; // Hcov
//bluID = 7; // SPT
rep = @"aci-ht-hcov";
//rep = @"aci-ht-spt";
}
// LF, MF, HF
if (true)
{
redID = 10; // LF
grnID = 9; // MF
bluID = 8; // HF
rep = @"lf-mf-hf";
}
IndexProperties redIndexProps = listOfIndexProperties[names[redID]];
IndexProperties grnIndexProps = listOfIndexProperties[names[grnID]];
IndexProperties bluIndexProps = listOfIndexProperties[names[bluID]];
int dayPixelHeight = 4;
int rowCount = (dayPixelHeight * dayCount) + 35; // +30 for grid lines
int colCount = 1440;
var bitmap = new Image <Rgb24>(colCount, rowCount);
var colour = Color.Yellow;
int currentRow = 0;
var oneDay = TimeSpan.FromHours(24);
int graphWidth = colCount;
int trackHeight = 20;
var stringFont = Drawing.Arial8;
string[] monthNames = { "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec" };
// for drawing the y-axis scale
int scaleWidth = trackHeight + 7;
var yAxisScale = new Image <Rgb24>(scaleWidth, rowCount + (2 * trackHeight));
yAxisScale.Mutate(g =>
{
g.Clear(Color.Black);
// loop over days
for (int d = 0; d < dayCount; d++)
{
var thisday = ((DateTimeOffset)startDate).AddDays(d);
if (thisday.Day == 1)
{
int nextRow = currentRow + 1;
for (int c = 0; c < colCount; c++)
{
bitmap[c, currentRow] = Color.Gray;
bitmap[c, nextRow] = Color.Gray;
}
for (int c = 0; c < scaleWidth; c++)
{
yAxisScale[c, currentRow + trackHeight] = Color.Gray;
yAxisScale[c, nextRow + trackHeight] = Color.Gray;
}
string month = monthNames[thisday.Month - 1];
if (thisday.Month == 1) // January
{
g.DrawText(thisday.Year.ToString(), stringFont, Color.White,
new PointF(0, nextRow + trackHeight + 1)); //draw time
g.DrawText(month, stringFont, Color.White,
new PointF(1, nextRow + trackHeight + 11)); //draw time
}
else
{
g.DrawText(month, stringFont, Color.White,
new PointF(1, nextRow + trackHeight + 1)); //draw time
}
currentRow += 2;
}
// get the exact date and time
LoggedConsole.WriteLine($"READING DAY {d + 1} of {dayCount}: {thisday.ToString()}");
// CREATE DAY LEVEL OUTPUT DIRECTORY for this day
string dateString = $"{thisday.Year}{thisday.Month:D2}{thisday.Day:D2}";
tuple = CsvTools.ReadCSVFile(csvFiles[d].FullName);
var arrays = tuple.Item2;
var redArray = arrays[redID];
var grnArray = arrays[grnID];
var bluArray = arrays[bluID];
// NormaliseMatrixValues the indices
redArray = DataTools.NormaliseInZeroOne(redArray, redIndexProps.NormMin, redIndexProps.NormMax);
grnArray = DataTools.NormaliseInZeroOne(grnArray, grnIndexProps.NormMin, grnIndexProps.NormMax);
bluArray = DataTools.NormaliseInZeroOne(bluArray, bluIndexProps.NormMin, bluIndexProps.NormMax);
for (int c = 0; c < colCount; c++)
{
for (int r = 0; r < dayPixelHeight; r++)
{
//transformedValue = Math.Sqrt(redArray[c]);
var transformedValue = redArray[c] * redArray[c];
int redVal = (int)Math.Round(transformedValue * 255);
if (redVal < 0)
{
redVal = 0;
}
else if (redVal > 255)
{
redVal = 255;
}
//transformedValue = Math.Sqrt(grnArray[c]);
transformedValue = grnArray[c] * grnArray[c]; // square the value
int grnVal = (int)Math.Round(transformedValue * 255);
if (grnVal < 0)
{
grnVal = 0;
}
else if (grnVal > 255)
{
grnVal = 255;
}
//transformedValue = Math.Sqrt(bluArray[c]);
transformedValue = bluArray[c] * bluArray[c]; // square the value
int bluVal = (int)Math.Round(transformedValue * 255);
if (bluVal < 0)
{
bluVal = 0;
}
else if (bluVal > 255)
{
bluVal = 255;
}
bitmap[c, currentRow + r] = Color.FromRgb((byte)redVal, (byte)grnVal, (byte)bluVal);
}
} // over all columns
currentRow += dayPixelHeight;
if (thisday.Day % 7 == 0)
{
for (int c = 0; c < colCount; c++)
{
bitmap[c, currentRow] = Color.Gray;
}
currentRow++;
}
} // over days
});
// draw on civil dawn and dusk lines
int startdayOfYear = ((DateTimeOffset)startDate).DayOfYear;
int endDayOfYear = ((DateTimeOffset)endDate).DayOfYear;
SunAndMoon.AddSunRiseSetLinesToImage(bitmap, arguments.BrisbaneSunriseDatafile.ToFileInfo(), startdayOfYear, endDayOfYear, dayPixelHeight);
// add the time scales
Image <Rgb24> timeBmp1 = ImageTrack.DrawTimeRelativeTrack(oneDay, graphWidth, trackHeight);
var imageList = new [] { timeBmp1, bitmap, timeBmp1 };
Image <Rgb24> compositeBmp1 = (Image <Rgb24>)ImageTools.CombineImagesVertically(imageList);
imageList = new [] { yAxisScale, compositeBmp1 };
Image <Rgb24> compositeBmp2 = (Image <Rgb24>)ImageTools.CombineImagesInLine(imageList);
// indices used for image
string indicesDescription = $"{redIndexProps.Name}|{grnIndexProps.Name}|{bluIndexProps.Name}";
string startString = $"{startDate.Value.Year}/{startDate.Value.Month}/{startDate.Value.Day}";
string endString = $"{endDate.Value.Year}/{endDate.Value.Month}/{endDate.Value.Day}";
string title = $"EASY: {arguments.FileStemName} From {startString} to {endString} Indices: {indicesDescription}";
Image <Rgb24> titleBar = ImageTrack.DrawTitleTrack(compositeBmp2.Width, trackHeight, title);
imageList = new [] { titleBar, compositeBmp2 };
compositeBmp2 = (Image <Rgb24>)ImageTools.CombineImagesVertically(imageList);
var outputFileName = Path.Combine(opDir.FullName, arguments.FileStemName + "." + rep + ".EASY.png");
compositeBmp2.Save(outputFileName);
} // Execute()
public override AnalysisResult2 Analyze <T>(AnalysisSettings analysisSettings, SegmentSettings <T> segmentSettings)
{
var configuration = (StandardizedFeatureExtractionConfig)analysisSettings.Configuration;
var audioFile = segmentSettings.SegmentAudioFile;
var recording = new AudioRecording(audioFile.FullName);
// Configurations non-specific for bands
TimeSpan indexCalculationDuration = configuration.IndexCalculationDurationTimeSpan;
TimeSpan bgNoiseNeighbourhood = configuration.BgNoiseBuffer;
// Bands
List <StandardizedFeatureExtractionConfig.BandsProperties> bandsList = configuration.Bands;
// Check if there are identical bands
CheckForIdenticalBands(bandsList);
// Estimate total number of subsegments
double segmentDurationSeconds = segmentSettings.AnalysisIdealSegmentDuration.TotalSeconds;
double subsegmentDuration = indexCalculationDuration.TotalSeconds;
int subsegmentCount = (int)Math.Round(segmentDurationSeconds / subsegmentDuration);
int totalSubsegmentCount = subsegmentCount * bandsList.Count;
// Store results of all subsegments
var analysisResults = new AnalysisResult2(analysisSettings, segmentSettings, recording.Duration);
analysisResults.AnalysisIdentifier = this.Identifier;
var trackScores = new List <Plot>(totalSubsegmentCount);
var tracks = new List <SpectralTrack>(totalSubsegmentCount);
analysisResults.SummaryIndices = new SummaryIndexBase[totalSubsegmentCount];
analysisResults.SpectralIndices = new SpectralIndexBase[totalSubsegmentCount];
// Create list to store images, one for each band. They are later combined into one image.
var list = new List <Image <Rgb24> >();
string imagePath = segmentSettings.SegmentImageFile.FullName;
int maxImageWidth = 0;
int bandCount = 0;
foreach (var band in bandsList)
{
Log.DebugFormat("Starting band {0}/{1}", bandCount + 1, bandsList.Count);
// Calculate spectral indices
// get a fresh copy of the ICC config
var config = (IndexCalculateConfig)((ICloneable)configuration).Clone();
// Add values specific for band from custom configuration file to config
config.MinBandWidth = band.Bandwidth.Min;
config.MaxBandWidth = band.Bandwidth.Max;
config.FrameLength = band.FftWindow;
if (band.MelScale != 0)
{
config.FrequencyScale = FreqScaleType.Mel;
config.MelScale = band.MelScale;
}
else
{
config.FrequencyScale = FreqScaleType.Linear;
}
// Calculate indices for each subsegment and for each band
IndexCalculateResult[] subsegmentResults = AcousticIndices.CalculateIndicesInSubsegments(
recording,
segmentSettings.SegmentStartOffset,
segmentSettings.AnalysisIdealSegmentDuration,
indexCalculationDuration,
config.IndexProperties,
segmentSettings.Segment.SourceMetadata.SampleRate,
config);
int columnsAmplitudeSpectrogram = subsegmentResults[0].AmplitudeSpectrogram.GetLength(1);
double[,] amplitudeSpectrogramSegment = new double[0, columnsAmplitudeSpectrogram];
for (int i = 0; i < subsegmentResults.Length; i++)
{
var indexCalculateResult = subsegmentResults[i];
indexCalculateResult.SummaryIndexValues.FileName = segmentSettings.Segment.SourceMetadata.Identifier;
indexCalculateResult.SpectralIndexValues.FileName = segmentSettings.Segment.SourceMetadata.Identifier;
analysisResults.SummaryIndices[bandCount + (i * bandsList.Count)] = indexCalculateResult.SummaryIndexValues;
analysisResults.SpectralIndices[bandCount + (i * bandsList.Count)] = indexCalculateResult.SpectralIndexValues;
trackScores.AddRange(indexCalculateResult.TrackScores);
if (indexCalculateResult.Tracks != null)
{
tracks.AddRange(indexCalculateResult.Tracks);
}
if (analysisSettings.AnalysisImageSaveBehavior.ShouldSave())
{
// Add amplitude spectrograms of each subsegment together to get amplitude spectrogram of one segment
double[,] amplitudeSpectrogramSubsegment = indexCalculateResult.AmplitudeSpectrogram;
amplitudeSpectrogramSegment = MatrixTools.ConcatenateMatrixRows(
amplitudeSpectrogramSegment,
amplitudeSpectrogramSubsegment);
}
}
if (analysisSettings.AnalysisImageSaveBehavior.ShouldSave())
{
// Create image of amplitude spectrogram
var image = ImageTools.DrawReversedMatrix(MatrixTools.MatrixRotate90Anticlockwise(amplitudeSpectrogramSegment));
// Label information
string minBandWidth = band.Bandwidth.Min.ToString();
string maxBandWidth = band.Bandwidth.Max.ToString();
string fftWindow = band.FftWindow.ToString();
string mel;
string melScale;
if (band.MelScale != 0)
{
mel = "Mel";
melScale = band.MelScale.ToString();
}
else
{
mel = "Standard";
melScale = 0.ToString();
}
// Create label
string segmentSeparator = "_";
string[] segments = { minBandWidth, maxBandWidth, fftWindow, mel, melScale };
string labelText = segments.Aggregate(string.Empty, (aggregate, item) => aggregate + segmentSeparator + item);
var stringFont = Drawing.Arial14;
int width = 250;
int height = image.Height;
var label = new Image <Rgb24>(width, height);
label.Mutate(g1 =>
{
g1.Clear(Color.Gray);
g1.DrawText(labelText, stringFont, Color.Black, new PointF(4, 30));
g1.DrawLine(new Pen(Color.Black, 1), 0, 0, width, 0); //draw upper boundary
g1.DrawLine(new Pen(Color.Black, 1), 0, 1, width, 1); //draw upper boundary
});
var labelledImage = ImageTools.CombineImagesInLine(label, image);
// Add labeled image to list
list.Add(labelledImage);
// Update maximal width of image
if (image.Width > maxImageWidth)
{
maxImageWidth = image.Width;
}
}
bandCount += 1;
Log.InfoFormat("Completed band {0}/{1}", bandCount, bandsList.Count);
}
if (analysisSettings.AnalysisDataSaveBehavior)
{
this.WriteSummaryIndicesFile(segmentSettings.SegmentSummaryIndicesFile, analysisResults.SummaryIndices);
analysisResults.SummaryIndicesFile = segmentSettings.SegmentSummaryIndicesFile;
analysisResults.SpectraIndicesFiles =
this.WriteSpectrumIndicesFiles(
segmentSettings.SegmentSpectrumIndicesDirectory,
Path.GetFileNameWithoutExtension(segmentSettings.SegmentAudioFile.Name),
analysisResults.SpectralIndices);
}
if (analysisSettings.AnalysisImageSaveBehavior.ShouldSave())
{
var finalImage = ImageTools.CombineImagesVertically(list, maxImageWidth);
finalImage.Save(imagePath);
analysisResults.ImageFile = new FileInfo(imagePath);
LoggedConsole.WriteLine("See {0} for spectrogram pictures", imagePath);
}
return(analysisResults);
}
/// <summary>
/// Generates the FREQUENCY x OSCILLATIONS Graphs and csv
/// </summary>
public static Tuple <Image, double[, ], double[]> GenerateOscillationDataAndImages(FileInfo audioSegment, Dictionary <string, string> configDict, bool drawImage = false)
{
// set two oscillation detection parameters
double sensitivity = DefaultSensitivityThreshold;
if (configDict.ContainsKey(AnalysisKeys.OscilDetection2014SensitivityThreshold))
{
sensitivity = double.Parse(configDict[AnalysisKeys.OscilDetection2014SensitivityThreshold]);
}
// Sample length i.e. number of frames spanned to calculate oscillations per second
int sampleLength = DefaultSampleLength;
if (configDict.ContainsKey(AnalysisKeys.OscilDetection2014SampleLength))
{
sampleLength = int.Parse(configDict[AnalysisKeys.OscilDetection2014SampleLength]);
}
SonogramConfig sonoConfig = new SonogramConfig(configDict); // default values config
if (configDict.ContainsKey(AnalysisKeys.OscilDetection2014FrameSize))
{
sonoConfig.WindowSize = int.Parse(configDict[AnalysisKeys.OscilDetection2014FrameSize]);
}
var recordingSegment = new AudioRecording(audioSegment.FullName);
BaseSonogram sonogram = new AmplitudeSonogram(sonoConfig, recordingSegment.WavReader);
// remove the DC bin if it has not already been removed.
// Assume test of divisible by 2 is good enough.
int binCount = sonogram.Data.GetLength(1);
if (!binCount.IsEven())
{
sonogram.Data = MatrixTools.Submatrix(sonogram.Data, 0, 1, sonogram.FrameCount - 1, binCount - 1);
}
//LoggedConsole.WriteLine("Oscillation Detection: Sample rate = {0}", sonogram.SampleRate);
//LoggedConsole.WriteLine("Oscillation Detection: FramesPerSecond = {0}", sonogram.FramesPerSecond);
// Do LOCAL CONRAST Normalisation first. LCN over frequency bins is better and faster than standard noise removal.
double neighbourhoodSeconds = 0.25;
int neighbourhoodFrames = (int)(sonogram.FramesPerSecond * neighbourhoodSeconds);
double lcnContrastLevel = 0.5; // was previously 0.1
LoggedConsole.WriteLine("LCN: FramesPerSecond (Prior to LCN) = {0}", sonogram.FramesPerSecond);
LoggedConsole.WriteLine("LCN: Neighbourhood of {0} seconds = {1} frames", neighbourhoodSeconds, neighbourhoodFrames);
sonogram.Data = NoiseRemoval_Briggs.NoiseReduction_byLCNDivision(sonogram.Data, neighbourhoodFrames, lcnContrastLevel);
string algorithmName1 = "autocorr-svd-fft";
double[,] freqOscilMatrix1 = GetFrequencyByOscillationsMatrix(sonogram.Data, sensitivity, sampleLength, algorithmName1);
//get the max spectral index - this reduces the matrix to an array
double[] spectralIndex1 = ConvertMatrix2SpectralIndexBySummingFreqColumns(freqOscilMatrix1, 0);
Image compositeImage = null;
if (drawImage)
{
string algorithmName2 = "autocorr-fft";
double[,] freqOscilMatrix2 = GetFrequencyByOscillationsMatrix(sonogram.Data, sensitivity, sampleLength, algorithmName2);
var image1 = GetFreqVsOscillationsImage(freqOscilMatrix1, sonogram.FramesPerSecond, sonogram.FBinWidth, sampleLength, algorithmName1);
var image2 = GetFreqVsOscillationsImage(freqOscilMatrix2, sonogram.FramesPerSecond, sonogram.FBinWidth, sampleLength, algorithmName2);
compositeImage = ImageTools.CombineImagesInLine(new[] { image1, image2 });
}
// Return (1) composite image of oscillations, (2) data matrix from only one algorithm,
// and (3) spectrum of oscillation values for accumulation into data from a multi-hour recording.
return(Tuple.Create(compositeImage, freqOscilMatrix1, spectralIndex1));
}
public static void Main(Arguments arguments)
{
// 1. set up the necessary files
FileInfo sourceRecording = arguments.Source;
FileInfo configFile = arguments.Config.ToFileInfo();
DirectoryInfo opDir = arguments.Output;
opDir.Create();
if (arguments.StartOffset.HasValue ^ arguments.EndOffset.HasValue)
{
throw new InvalidStartOrEndException("If StartOffset or EndOffset is specified, then both must be specified");
}
var offsetsProvided = arguments.StartOffset.HasValue && arguments.EndOffset.HasValue;
// set default offsets - only use defaults if not provided in argments list
TimeSpan?startOffset = null;
TimeSpan?endOffset = null;
if (offsetsProvided)
{
startOffset = TimeSpan.FromSeconds(arguments.StartOffset.Value);
endOffset = TimeSpan.FromSeconds(arguments.EndOffset.Value);
}
const string Title = "# MAKE A SONOGRAM FROM AUDIO RECORDING and do OscillationsGeneric activity.";
string date = "# DATE AND TIME: " + DateTime.Now;
LoggedConsole.WriteLine(Title);
LoggedConsole.WriteLine(date);
LoggedConsole.WriteLine("# Input audio file: " + sourceRecording.Name);
string sourceName = Path.GetFileNameWithoutExtension(sourceRecording.FullName);
// 2. get the config dictionary
Config configuration = ConfigFile.Deserialize(configFile);
// below three lines are examples of retrieving info from Config config
// string analysisIdentifier = configuration[AnalysisKeys.AnalysisName];
// bool saveIntermediateWavFiles = (bool?)configuration[AnalysisKeys.SaveIntermediateWavFiles] ?? false;
// scoreThreshold = (double?)configuration[AnalysisKeys.EventThreshold] ?? scoreThreshold;
// Resample rate must be 2 X the desired Nyquist. Default is that of recording.
var resampleRate = configuration.GetIntOrNull(AnalysisKeys.ResampleRate) ?? AppConfigHelper.DefaultTargetSampleRate;
var configDict = new Dictionary <string, string>(configuration.ToDictionary());
// #NOISE REDUCTION PARAMETERS
//string noisereduce = configDict[ConfigKeys.Mfcc.Key_NoiseReductionType];
configDict[AnalysisKeys.NoiseDoReduction] = "false";
configDict[AnalysisKeys.NoiseReductionType] = "NONE";
configDict[AnalysisKeys.AddAxes] = configuration[AnalysisKeys.AddAxes] ?? "true";
configDict[AnalysisKeys.AddSegmentationTrack] = configuration[AnalysisKeys.AddSegmentationTrack] ?? "true";
configDict[ConfigKeys.Recording.Key_RecordingCallName] = sourceRecording.FullName;
configDict[ConfigKeys.Recording.Key_RecordingFileName] = sourceRecording.Name;
configDict[AnalysisKeys.AddTimeScale] = configuration[AnalysisKeys.AddTimeScale] ?? "true";
configDict[AnalysisKeys.AddAxes] = configuration[AnalysisKeys.AddAxes] ?? "true";
configDict[AnalysisKeys.AddSegmentationTrack] = configuration[AnalysisKeys.AddSegmentationTrack] ?? "true";
// ####################################################################
// print out the sonogram parameters
LoggedConsole.WriteLine("\nPARAMETERS");
foreach (KeyValuePair <string, string> kvp in configDict)
{
LoggedConsole.WriteLine("{0} = {1}", kvp.Key, kvp.Value);
}
LoggedConsole.WriteLine("Sample Length for detecting oscillations = {0}", SampleLength);
// 3: GET RECORDING
FileInfo tempAudioSegment = new FileInfo(Path.Combine(opDir.FullName, "tempWavFile.wav"));
// delete the temp audio file if it already exists.
if (File.Exists(tempAudioSegment.FullName))
{
File.Delete(tempAudioSegment.FullName);
}
// This line creates a temporary version of the source file downsampled as per entry in the config file
MasterAudioUtility.SegmentToWav(sourceRecording, tempAudioSegment, new AudioUtilityRequest()
{
TargetSampleRate = resampleRate
});
// 1) get amplitude spectrogram
AudioRecording recordingSegment = new AudioRecording(tempAudioSegment.FullName);
SonogramConfig sonoConfig = new SonogramConfig(configDict); // default values config
BaseSonogram sonogram = new AmplitudeSonogram(sonoConfig, recordingSegment.WavReader);
Console.WriteLine("FramesPerSecond = {0}", sonogram.FramesPerSecond);
// remove the DC bin
sonogram.Data = MatrixTools.Submatrix(sonogram.Data, 0, 1, sonogram.FrameCount - 1, sonogram.Configuration.FreqBinCount);
// ###############################################################
// DO LocalContrastNormalisation
//int fieldSize = 9;
//sonogram.Data = LocalContrastNormalisation.ComputeLCN(sonogram.Data, fieldSize);
// LocalContrastNormalisation over frequency bins is better and faster.
int neighbourhood = 15;
double contrastLevel = 0.5;
sonogram.Data = NoiseRemoval_Briggs.NoiseReduction_byLCNDivision(sonogram.Data, neighbourhood, contrastLevel);
// ###############################################################
// lowering the sensitivity threshold increases the number of hits.
if (configDict.ContainsKey(AnalysisKeys.OscilDetection2014SensitivityThreshold))
{
Oscillations2014.DefaultSensitivityThreshold = double.Parse(configDict[AnalysisKeys.OscilDetection2014SensitivityThreshold]);
}
if (configDict.ContainsKey(AnalysisKeys.OscilDetection2014SampleLength))
{
Oscillations2014.DefaultSampleLength = int.Parse(configDict[AnalysisKeys.OscilDetection2014SensitivityThreshold]);
}
var list1 = new List <Image>();
//var result = Oscillations2014.GetFreqVsOscillationsDataAndImage(sonogram, 64, "Autocorr-FFT");
//list1.Add(result.FreqOscillationImage);
var result = Oscillations2014.GetFreqVsOscillationsDataAndImage(sonogram, "Autocorr-FFT");
list1.Add(result.FreqOscillationImage);
result = Oscillations2014.GetFreqVsOscillationsDataAndImage(sonogram, "Autocorr-SVD-FFT");
list1.Add(result.FreqOscillationImage);
result = Oscillations2014.GetFreqVsOscillationsDataAndImage(sonogram, "Autocorr-WPD");
list1.Add(result.FreqOscillationImage);
Image compositeOscImage1 = ImageTools.CombineImagesInLine(list1.ToArray());
// ###############################################################
// init the sonogram image stack
var sonogramList = new List <Image>();
var image = sonogram.GetImageFullyAnnotated("AMPLITUDE SPECTROGRAM");
sonogramList.Add(image);
//string testPath = @"C:\SensorNetworks\Output\Sonograms\amplitudeSonogram.png";
//image.Save(testPath, ImageFormat.Png);
Image envelopeImage = ImageTrack.DrawWaveEnvelopeTrack(recordingSegment, image.Width);
sonogramList.Add(envelopeImage);
// 2) now draw the standard decibel spectrogram
sonogram = new SpectrogramStandard(sonoConfig, recordingSegment.WavReader);
// ###############################################################
list1 = new List <Image>();
//result = Oscillations2014.GetFreqVsOscillationsDataAndImage(sonogram, 64, "Autocorr-FFT");
//list1.Add(result.FreqOscillationImage);
result = Oscillations2014.GetFreqVsOscillationsDataAndImage(sonogram, "Autocorr-FFT");
list1.Add(result.FreqOscillationImage);
result = Oscillations2014.GetFreqVsOscillationsDataAndImage(sonogram, "Autocorr-SVD-FFT");
list1.Add(result.FreqOscillationImage);
result = Oscillations2014.GetFreqVsOscillationsDataAndImage(sonogram, "Autocorr-WPD");
list1.Add(result.FreqOscillationImage);
Image compositeOscImage2 = ImageTools.CombineImagesInLine(list1.ToArray());
// ###############################################################
//image = sonogram.GetImageFullyAnnotated("DECIBEL SPECTROGRAM");
//list.Add(image);
// combine eight images
list1 = new List <Image>();
list1.Add(compositeOscImage1);
list1.Add(compositeOscImage2);
Image compositeOscImage3 = ImageTools.CombineImagesVertically(list1.ToArray());
string imagePath3 = Path.Combine(opDir.FullName, sourceName + "_freqOscilMatrix.png");
compositeOscImage3.Save(imagePath3, ImageFormat.Png);
Image segmentationImage = ImageTrack.DrawSegmentationTrack(
sonogram,
EndpointDetectionConfiguration.K1Threshold,
EndpointDetectionConfiguration.K2Threshold,
image.Width);
sonogramList.Add(segmentationImage);
// 3) now draw the noise reduced decibel spectrogram
sonoConfig.NoiseReductionType = NoiseReductionType.Standard;
sonoConfig.NoiseReductionParameter = configuration.GetDoubleOrNull(AnalysisKeys.NoiseBgThreshold) ?? 3.0;
sonogram = new SpectrogramStandard(sonoConfig, recordingSegment.WavReader);
image = sonogram.GetImageFullyAnnotated("NOISE-REDUCED DECIBEL SPECTROGRAM");
sonogramList.Add(image);
// ###############################################################
// deriving osscilation graph from this noise reduced spectrogram did not work well
//Oscillations2014.SaveFreqVsOscillationsDataAndImage(sonogram, sampleLength, algorithmName, opDir);
// ###############################################################
Image compositeSonogram = ImageTools.CombineImagesVertically(sonogramList);
string imagePath2 = Path.Combine(opDir.FullName, sourceName + ".png");
compositeSonogram.Save(imagePath2, ImageFormat.Png);
LoggedConsole.WriteLine("\n##### FINISHED FILE ###################################################\n");
}
} // method DrawAggregatedSpectrograms()
public static Image DrawGrayScaleSpectrograms(Arguments arguments, string fileStem, TimeSpan dataScale, Dictionary <string, double[, ]> spectra = null)
{
int sampleRate = 22050;
int frameWidth = 512;
//double backgroundFilter = 0.0; // 0.0 means small values are removed.
double backgroundFilter = 0.75; // 0.75 means small values are accentuated.
string analysisType = AcousticIndices.TowseyAcoustic;
string[] keys = LDSpectrogramRGB.GetArrayOfAvailableKeys();
//LoggedConsole.WriteLine("# Spectrogram Config file: " + arguments.SpectrogramConfigPath);
//LoggedConsole.WriteLine("# Index Properties Config file: " + arguments.IndexPropertiesConfig);
var inputDirectory = arguments.InputDataDirectory;
Dictionary <string, IndexProperties> indexProperties = IndexProperties.GetIndexProperties(arguments.IndexPropertiesConfig.ToFileInfo());
if (spectra == null)
{
//C:\SensorNetworks\Output\BIRD50\Training\ID0001\Towsey.Acoustic\ID0001__Towsey.Acoustic.ACI
spectra = IndexMatrices.ReadSpectralIndices(inputDirectory.ToDirectoryInfo(), fileStem, analysisType, keys);
}
// note: the spectra are oriented as per visual orientation, i.e. xAxis = time frames
//int frameCount = spectra[keys[0]].GetLength(1);
var cs1 = new LDSpectrogramRGB(minuteOffset: TimeSpan.Zero, xScale: dataScale, sampleRate: sampleRate, frameWidth: frameWidth, colourMap: null)
{
FileName = fileStem,
BackgroundFilter = backgroundFilter,
IndexCalculationDuration = dataScale,
};
cs1.SetSpectralIndexProperties(indexProperties); // set the relevant dictionary of index properties
cs1.SpectrogramMatrices = spectra;
if (cs1.GetCountOfSpectrogramMatrices() == 0)
{
LoggedConsole.WriteLine("WARNING: " + fileStem + ": No spectrogram matrices in the dictionary. Spectrogram files do not exist?");
return(null);
}
List <Image> list = new List <Image>();
Font stringFont = new Font("Arial", 14);
foreach (string key in keys)
{
var image = cs1.DrawGreyscaleSpectrogramOfIndex(key);
int width = 70;
int height = image.Height;
var label = new Bitmap(width, height);
var g1 = Graphics.FromImage(label);
g1.Clear(Color.Gray);
g1.DrawString(key, stringFont, Brushes.Black, new PointF(4, 30));
g1.DrawLine(new Pen(Color.Black), 0, 0, width, 0); //draw upper boundary
g1.DrawLine(new Pen(Color.Black), 0, 1, width, 1); //draw upper boundary
Image[] imagearray = { label, image };
var labelledImage = ImageTools.CombineImagesInLine(imagearray);
list.Add(labelledImage);
} //foreach key
var combinedImage = ImageTools.CombineImagesVertically(list.ToArray());
return(combinedImage);
} // method DrawGrayScaleSpectrograms()
} // method DrawAggregatedSpectrograms()
public static Image <Rgb24> DrawGrayScaleSpectrograms(Arguments arguments, string fileStem, TimeSpan dataScale, Dictionary <string, double[, ]> spectra = null)
{
// default values
int sampleRate = 22050;
int frameWidth = 512;
//double backgroundFilter = 0.0; // 0.0 means small values are removed.
double backgroundFilter = 0.75; // 0.75 means small values are accentuated.
string analysisType = AcousticIndices.TowseyAcoustic;
string[] keys = LDSpectrogramRGB.GetArrayOfAvailableKeys();
var inputDirectory = arguments.InputDataDirectory;
Dictionary <string, IndexProperties> indexProperties = IndexProperties.GetIndexProperties(arguments.IndexPropertiesConfig.ToFileInfo());
if (spectra == null)
{
spectra = IndexMatrices.ReadSpectralIndices(inputDirectory.ToDirectoryInfo(), fileStem, analysisType, keys);
}
// note: the spectra are oriented as per visual orientation, i.e. xAxis = time frames
//int frameCount = spectra[keys[0]].GetLength(1);
var cs1 = new LDSpectrogramRGB(minuteOffset: TimeSpan.Zero, xScale: dataScale, sampleRate: sampleRate, frameWidth: frameWidth, colorMap: null)
{
FileName = fileStem,
BackgroundFilter = backgroundFilter,
IndexCalculationDuration = dataScale,
};
cs1.SetSpectralIndexProperties(indexProperties); // set the relevant dictionary of index properties
cs1.SpectrogramMatrices = spectra;
if (cs1.GetCountOfSpectrogramMatrices() == 0)
{
LoggedConsole.WriteLine("WARNING: " + fileStem + ": No spectrogram matrices in the dictionary. Spectrogram files do not exist?");
return(null);
}
var list = new List <Image <Rgb24> >();
var stringFont = Drawing.Arial14;
foreach (string key in keys)
{
var image = cs1.DrawGreyscaleSpectrogramOfIndex(key);
int width = 70;
int height = image.Height;
var label = new Image <Rgb24>(width, height);
label.Mutate(g1 =>
{
g1.Clear(Color.Gray);
g1.DrawText(key, stringFont, Color.Black, new PointF(4, 30));
g1.DrawLine(new Pen(Color.Black, 1), 0, 0, width, 0); //draw upper boundary
g1.DrawLine(new Pen(Color.Black, 1), 0, 1, width, 1); //draw upper boundary
});
var imagearray = new[] { label, image };
var labelledImage = ImageTools.CombineImagesInLine(imagearray);
list.Add(labelledImage);
} //foreach key
var combinedImage = ImageTools.CombineImagesVertically(list);
return(combinedImage);
} // method DrawGrayScaleSpectrograms()
public static void ConcatenateDays()
{
DirectoryInfo parentDir = new DirectoryInfo(@"C:\SensorNetworks\Output\Frommolt");
DirectoryInfo dataDir = new DirectoryInfo(parentDir + @"\AnalysisOutput\mono");
var imageDirectory = new DirectoryInfo(parentDir + @"\ConcatImageOutput");
//string indexPropertiesConfig = @"C:\Work\GitHub\audio-analysis\AudioAnalysis\AnalysisConfigFiles\IndexPropertiesConfigHiRes.yml";
DateTimeOffset?startDate = new DateTimeOffset(2012, 03, 29, 0, 0, 0, TimeSpan.Zero);
DateTimeOffset?endDate = new DateTimeOffset(2012, 06, 20, 0, 0, 0, TimeSpan.Zero);
var timeSpanOffsetHint = new TimeSpan(01, 0, 0);
//string fileSuffix = @"2Maps.png";
//string fileSuffix = @"ACI-ENT-EVN.png";
// WARNING: POW was removed in December 2018
string fileSuffix = @"BGN-POW-EVN.png";
TimeSpan totalTimespan = (DateTimeOffset)endDate - (DateTimeOffset)startDate;
int dayCount = totalTimespan.Days + 1; // assume last day has full 24 hours of recording available.
bool verbose = true;
if (verbose)
{
LoggedConsole.WriteLine("\n# Start date = " + startDate.ToString());
LoggedConsole.WriteLine("# End date = " + endDate.ToString());
LoggedConsole.WriteLine(string.Format("# Elapsed time = {0:f1} hours", dayCount * 24));
LoggedConsole.WriteLine("# Day count = " + dayCount + " (inclusive of start and end days)");
LoggedConsole.WriteLine("# Time Zone = " + timeSpanOffsetHint.ToString());
}
//string dirMatch = "Monitoring_Rosin_2012*T*+0200_.merged.wav.channel_0.wav";
string stem = "Monitoring_Rosin_2012????T??0000+0200_.merged.wav.channel_";
string dirMatch = stem + "?.wav";
DirectoryInfo[] subDirectories = dataDir.GetDirectories(dirMatch, SearchOption.AllDirectories);
string format = "yyyyMMdd";
string startDay = ((DateTimeOffset)startDate).ToString(format);
//string fileMatch = stem + "?__" + fileSuffix;
//FileInfo[] files = IndexMatrices.GetFilesInDirectories(subDirectories, fileMatch);
// Sort the files by date and return as a dictionary: sortedDictionaryOfDatesAndFiles<DateTimeOffset, FileInfo>
//var sortedDictionaryOfDatesAndFiles = FileDateHelpers.FilterFilesForDates(files, timeSpanOffsetHint);
//following needed if a day is missing.
int defaultDayWidth = 20;
int defaultDayHeight = 300;
Brush brush = Brushes.White;
Font stringFont = new Font("Tahoma", 12);
var list = new List <Image>();
// loop over days
for (int d = 0; d < dayCount; d++)
{
Console.WriteLine(string.Format("Day {0} of {1} days", d, dayCount));
var thisday = ((DateTimeOffset)startDate).AddDays(d);
string date = thisday.ToString(format);
stem = "Monitoring_Rosin_" + date + "T??0000+0200_.merged.wav.channel_";
string fileMatch = stem + "?__" + fileSuffix;
FileInfo[] files = IndexMatrices.GetFilesInDirectories(subDirectories, fileMatch);
if (files.Length == 0)
{
Bitmap gapImage = new Bitmap(defaultDayWidth, defaultDayHeight);
Graphics g5 = Graphics.FromImage(gapImage);
g5.Clear(Color.Gray);
g5.DrawString("Day", stringFont, brush, new PointF(2, 5));
g5.DrawString("missing", stringFont, brush, new PointF(2, 35));
list.Add(gapImage);
continue;
}
// Sort the files by date and return as a dictionary: sortedDictionaryOfDatesAndFiles<DateTimeOffset, FileInfo>
//var sortedDictionaryOfDatesAndFiles = FileDateHelpers.FilterFilesForDates(files, timeSpanOffsetHint);
Image image = ConcatenateFourChannelImages(files, imageDirectory, fileSuffix, date);
defaultDayHeight = image.Height;
list.Add(image);
}
Image combinedImage = ImageTools.CombineImagesInLine(list);
Bitmap labelImage1 = new Bitmap(combinedImage.Width, 24);
Graphics g1 = Graphics.FromImage(labelImage1);
g1.Clear(Color.Black);
g1.DrawString(fileSuffix, stringFont, brush, new PointF(2, 2));
//labelImage1.Save(Path.Combine(imageDirectory.FullName, suffix1));
Graphics g = Graphics.FromImage(combinedImage);
g.DrawImage(labelImage1, 0, 0);
string fileName = string.Format(startDay + "." + fileSuffix);
combinedImage.Save(Path.Combine(imageDirectory.FullName, fileName));
}