public RealtimeAudioSamplesAggregator(IStride stride, int minSize)
{
Stride = stride;
MinSize = minSize;
buffer = new float[minSize];
}
private void RunTest(string[] parameters)
{
string action = parameters[0];
switch (action)
{
case "Insert":
string folderWithSongs = parameters[1];
var stride = utils.ToStride(parameters[2], parameters[3], parameters[4]);
DeleteAll();
var sb = TestRunnerWriter.StartInsert();
Insert(folderWithSongs, stride, sb);
TestRunnerWriter.SaveInsertDataToFolder(sb, pathToResultsFolder, stride);
lastInsertStride = stride;
break;
case "Run":
string folderWithPositives = parameters[1];
string folderWithNegatives = parameters[2];
var queryStride = utils.ToStride(parameters[3], parameters[4], parameters[5]);
int seconds = int.Parse(parameters[6]);
var startAts = ToStartAts(parameters[7]);
RunTestScenario(folderWithPositives, folderWithNegatives, queryStride, seconds, startAts);
break;
}
}
/// <summary>
/// Create fingerprints out of down sampled samples
/// </summary>
/// <param name = "samples">Down sampled to 5512 samples </param>
/// <param name = "track">Track</param>
/// <param name = "stride">Stride</param>
/// <param name = "hashTables">Number of hash tables</param>
/// <param name = "hashKeys">Number of hash keys</param>
public void CreateInsertFingerprints(
float[] samples,
Track track,
IStride stride,
int hashTables,
int hashKeys)
{
if (track == null)
{
return; /*track is not eligible*/
}
/*Create fingerprints that will be used as initial fingerprints to be queried*/
List<bool[]> fingerprints = workUnitBuilder.BuildWorkUnit()
.On(samples)
.WithCustomConfiguration(config => config.Stride = stride)
.GetFingerprintsUsingService(service)
.Result;
storage.InsertTrack(track); /*Insert track into the storage*/
/*Get signature's hash signature, and associate it to a specific track*/
List<HashSignature> creationalsignatures = GetSignatures(fingerprints, track, hashTables, hashKeys);
foreach (HashSignature hash in creationalsignatures)
{
storage.InsertHash(hash, HashType.Creational);
/*Set this hashes as also the query hashes*/
storage.InsertHash(hash, HashType.Query);
}
}
private void Insert(string folderWithSongs, IStride stride, StringBuilder sb)
{
var allFiles = AllFiles(folderWithSongs);
int inserted = 0;
var stopWatch = new Stopwatch();
stopWatch.Start();
Parallel.ForEach(
allFiles,
file =>
{
OnTestRunnerEvent(
OngoingActionEvent,
new TestRunnerOngoingEventArgs
{
Message = string.Format(
"Inserting tracks {0} out of {1}. Track {2}",
Interlocked.Increment(ref inserted),
allFiles.Count,
System.IO.Path.GetFileNameWithoutExtension(file))
});
var track = InsertTrack(file);
var hashes = fcb.BuildFingerprintCommand()
.From(file)
.WithFingerprintConfig(config => { config.Stride = stride; })
.UsingServices(audioService)
.Hash()
.Result;
modelService.InsertHashDataForTrack(hashes, track);
});
stopWatch.Stop();
sb.AppendLine(string.Format("{0},{1}", inserted, stopWatch.ElapsedMilliseconds / 1000));
}
public List<bool[]> CreateFingerprintsFromLogSpectrum(
double[][] logarithmizedSpectrum, IStride stride, int fingerprintLength, int overlap, int topWavelets)
{
DbgTimer t = new DbgTimer();
t.Start ();
// Cut the logaritmic spectrogram into smaller spectrograms with one stride between each
List<double[][]> spectralImages = SpectrumService.CutLogarithmizedSpectrum(logarithmizedSpectrum, stride, fingerprintLength, overlap);
// Then apply the wavelet transform on them to later reduce the resolution
// do this in place
WaveletService.ApplyWaveletTransformInPlace(spectralImages);
// Then for each of the wavelet reduce the resolution by only keeping the top wavelets
// and ignore the magnitude of the top wavelets.
// Instead, we can simply keep the sign of it (+/-).
// This information is enough to keep the extract perceptual characteristics of a song.
List<bool[]> fingerprints = new List<bool[]>();
foreach (var spectralImage in spectralImages)
{
bool[] image = FingerprintDescriptor.ExtractTopWavelets(spectralImage, topWavelets);
fingerprints.Add(image);
}
Dbg.WriteLine ("Created {1} Fingerprints from Log Spectrum - Execution Time: {0} ms", t.Stop().TotalMilliseconds, fingerprints.Count);
return fingerprints;
}
/// <summary>
/// Create fingerprints according to the Google's researchers algorithm
/// </summary>
/// <param name = "spectrum">Spectrogram of the song</param>
/// <param name = "stride">Stride between 2 consecutive fingerprints</param>
/// <returns>Fingerprint signatures</returns>
public List <bool[]> CreateFingerprints(float[][] spectrum, IStride stride)
{
int fingerprintLength = FingerprintLength;
int overlap = Overlap;
int logbins = LogBins;
int start = stride.GetFirstStride() / overlap;
List <bool[]> fingerprints = new List <bool[]>();
int width = spectrum.GetLength(0);
while (start + fingerprintLength < width)
{
float[][] frames = new float[fingerprintLength][];
for (int i = 0; i < fingerprintLength; i++)
{
frames[i] = new float[logbins];
Array.Copy(spectrum[start + i], frames[i], logbins);
}
start += fingerprintLength + stride.GetStride() / overlap;
WaveletDecomposition.DecomposeImageInPlace(frames); /*Compute wavelets*/
bool[] image = ExtractTopWavelets(frames);
fingerprints.Add(image);
}
return(fingerprints);
}
private Task <QueryResult> BuildQuery(IStride queryStride, int seconds, string positive, int startAt)
{
return(qcb.BuildQueryCommand()
.From(positive, seconds, startAt)
.WithFingerprintConfig(fingerprintConfig => { fingerprintConfig.Stride = queryStride; })
.UsingServices(modelService, audioService)
.Query());
}
public RepositoryGateway()
{
_storage = new RamStorage(NUMBER_OF_HASH_TABLES); /*Number of LSH Tables, used for storage purposes*/
_permutations = new LocalPermutations(PATH_TO_PERMUTATIONS, SEPARATOR); /*Permutations*/
_repository = new Repository(_storage, _permutations);
_proxy = new BassProxy(); /*audio proxy used in reading the file*/
_createStride = new IncrementalStaticStride(STRIDE_SIZE_INCREMENTAL, SAMPLES_IN_FINGERPRINT);
_queryStride = new IncrementalRandomStride(STRIDE_SIZE_INCREMENTAL, SAMPLES_IN_FINGERPRINT, SAMPLES_IN_FINGERPRINT);
}
public Image GetWaveletsImages(
double[][] spectrum,
IStride strideBetweenConsecutiveImages,
int fingerprintLength,
int overlap,
int imagesPerRow)
{
List <double[][]> spectralImages = spectrumService.CutLogarithmizedSpectrum(
spectrum, strideBetweenConsecutiveImages, fingerprintLength, overlap);
waveletService.ApplyWaveletTransformInPlace(spectralImages);
int width = spectralImages[0].GetLength(0);
int height = spectralImages[0][0].Length;
int fingersCount = spectralImages.Count;
int rowCount = (int)Math.Ceiling((float)fingersCount / imagesPerRow);
int imageWidth = (rowCount == 1 ? width + 2 * SpaceBetweenImages : ((imagesPerRow * (width + SpaceBetweenImages)) + SpaceBetweenImages));
int imageHeight = (rowCount * (height + SpaceBetweenImages)) + SpaceBetweenImages;
Bitmap image = new Bitmap(imageWidth, imageHeight, PixelFormat.Format16bppRgb565);
SetBackground(image, Color.White);
int verticalOffset = SpaceBetweenImages;
int horizontalOffset = SpaceBetweenImages;
int count = 0;
foreach (double[][] spectralImage in spectralImages)
{
double average = spectralImage.Average(col => col.Average(v => Math.Abs(v)));
for (int i = 0; i < width /*128*/; i++)
{
for (int j = 0; j < height /*32*/; j++)
{
Color color = ValueToBlackWhiteColor(spectralImage[i][j], average);
image.SetPixel(i + horizontalOffset, j + verticalOffset, color);
}
}
count++;
if (count % imagesPerRow == 0)
{
verticalOffset += height + SpaceBetweenImages;
horizontalOffset = SpaceBetweenImages;
}
else
{
horizontalOffset += width + SpaceBetweenImages;
}
}
return(image);
}
public static Dictionary<Int32, QueryStats> QueryOneSongMinHashFast(
string pathToSong,
IStride queryStride,
IAudioService proxy,
DaoGateway dalManager,
int seconds,
int lshHashTables,
int lshGroupsPerKey,
int thresholdTables,
int topWavelets,
ref long queryTime)
{
///*Fingerprint service*/
//fingerprintService service = new fingerprintService {TopWavelets = topWavelets};
//Stopwatch stopWatch = new Stopwatch();
//stopWatch.Start();
//int startIndex = -1;
//Dictionary<Int32, QueryStats> stats = new Dictionary<Int32, QueryStats>();
//int lenOfQuery = service.SampleRate*seconds;
//double[] samples = service.GetSamplesFromSong(proxy, pathToSong);
//int startOfQuery = Random.Next(0, samples.Length - lenOfQuery);
//double[] querySamples = new double[lenOfQuery];
//Array.Copy(samples, startOfQuery, querySamples, 0, lenOfQuery);
//startIndex = startOfQuery/service.SampleRate;
//MinHash minHash = new MinHash(dalManager);
//IStride stride = queryStride;
//int index = stride.FirstStrideSize();
//while (index + service.SamplesPerFingerprint < querySamples.Length)
//{
// Fingerprint f = service.CreateFingerprintFromSamplesArray(querySamples, index);
// if (f == null) continue;
// index += service.SamplesPerFingerprint + stride.StrideSize();
// int[] bin = minHash.ComputeMinHashSignature(f); /*Compute Min Hash on randomly selected fingerprints*/
// Dictionary<int, long> hashes = minHash.GroupMinHashToLSHBuckets(bin, lshHashTables, lshGroupsPerKey); /*Find all candidates by querying the database*/
// long[] hashbuckets = hashes.Values.ToArray();
// var candidates = dalManager.ReadFingerprintsByHashBucketLSH(hashbuckets, thresholdTables);
// if (candidates != null && candidates.Count > 0)
// {
// var query = (from candidate in candidates
// select candidate.Value.Item1).Distinct();
// foreach (var item in query)
// {
// stats.Add(item, new QueryStats(0, 0, 0, startIndex, startIndex + seconds, 0));
// }
// break;
// }
//}
//stopWatch.Stop();
//queryTime = stopWatch.ElapsedMilliseconds; /*Set the query Time parameter*/
return null;
}
public ResultEntry Validate(
ResultEntry result,
IStride validationStride,
string pathToAudioFile,
IModelService modelService,
IAudioService audioService,
int topWavelets,
int thresholdVotes)
{
double startAt = result.TrackStartsAt, length = result.QueryLength - result.TrackStartsAt;
if (startAt + result.Track.Length < result.QueryLength)
{
length = result.Track.Length;
}
var newResult = queryCommandBuilder.BuildQueryCommand()
.From(pathToAudioFile, length, startAt).WithConfigs(
config =>
{
config.Stride = validationStride;
config.TopWavelets = topWavelets;
},
queryConfig =>
{
queryConfig.ThresholdVotes = thresholdVotes;
})
.UsingServices(modelService, audioService)
.Query()
.Result;
if (!newResult.ContainsMatches)
{
return(result);
}
var newEntry = newResult.BestMatch;
if (newEntry.Confidence > result.Confidence)
{
return(new ResultEntry(
newEntry.Track,
newEntry.QueryMatchStartsAt,
newEntry.QueryMatchLength,
newEntry.TrackMatchStartsAt,
newEntry.TrackStartsAt + result.TrackStartsAt,
newEntry.Confidence,
newEntry.HammingSimilaritySum,
newEntry.QueryLength,
newEntry.BestMatch));
}
return(result);
}
public Image GetLogSpectralImages(
double[][] spectrum,
IStride strideBetweenConsecutiveImages,
int fingerprintLength,
int overlap,
int imagesPerRow)
{
List <double[][]> spectralImages = spectrumService.CutLogarithmizedSpectrum(
spectrum, strideBetweenConsecutiveImages, fingerprintLength, overlap);
return(GetLogSpectralImages(spectralImages, imagesPerRow));
}
public Image GetLogSpectralImages(
float[][] spectrum,
IStride strideBetweenConsecutiveImages,
int fingerprintLength,
int overlap,
int imagesPerRow)
{
List<float[][]> spetralImages = spectrumService.CutLogarithmizedSpectrum(
spectrum, strideBetweenConsecutiveImages, fingerprintLength, overlap);
int width = spetralImages[0].GetLength(0);
int height = spetralImages[0][0].Length;
int fingersCount = spetralImages.Count;
int rowCount = (int)Math.Ceiling((float)fingersCount / imagesPerRow);
int imageWidth = (imagesPerRow * (width + SpaceBetweenImages)) + SpaceBetweenImages;
int imageHeight = (rowCount * (height + SpaceBetweenImages)) + SpaceBetweenImages;
Bitmap image = new Bitmap(imageWidth, imageHeight, PixelFormat.Format16bppRgb565);
SetBackground(image, Color.White);
int verticalOffset = SpaceBetweenImages;
int horizontalOffset = SpaceBetweenImages;
int count = 0;
foreach (float[][] spectralImage in spetralImages)
{
double average = spectralImage.Average(col => col.Average(v => Math.Abs(v)));
for (int i = 0; i < width /*128*/; i++)
{
for (int j = 0; j < height /*32*/; j++)
{
Color color = ValueToBlackWhiteColor(spectralImage[i][j], average);
image.SetPixel(i + horizontalOffset, j + verticalOffset, color);
}
}
count++;
if (count % imagesPerRow == 0)
{
verticalOffset += height + SpaceBetweenImages;
horizontalOffset = SpaceBetweenImages;
}
else
{
horizontalOffset += width + SpaceBetweenImages;
}
}
return image;
}
private List<bool[]> CreateFingerprintsFromLogSpectrum(float[][] logarithmizedSpectrum, IStride stride, int fingerprintLength, int overlap, int topWavelets)
{
List<float[][]> spectralImages = spectrumService.CutLogarithmizedSpectrum(logarithmizedSpectrum, stride, fingerprintLength, overlap);
waveletService.ApplyWaveletTransformInPlace(spectralImages);
List<bool[]> fingerprints = new List<bool[]>();
foreach (var spectralImage in spectralImages)
{
bool[] image = fingerprintDescriptor.ExtractTopWavelets(spectralImage, topWavelets);
fingerprints.Add(image);
}
return fingerprints;
}
private List<bool[]> CreateFingerprintsFromLogSpectrum(float[][] logarithmizedSpectrum, IStride stride, int fingerprintLength, int overlap, int topWavelets)
{
List<float[][]> spectralImages = spectrumService.CutLogarithmizedSpectrum(logarithmizedSpectrum, stride, fingerprintLength, overlap);
waveletDecomposition.DecomposeImagesInPlace(spectralImages);
var fingerprints = new List<bool[]>();
foreach (var spectralImage in spectralImages)
{
bool[] image = fingerprintDescriptor.ExtractTopWavelets(spectralImage, topWavelets);
if (!IsSilence(image))
{
fingerprints.Add(image);
}
}
return fingerprints;
}
public void ExtractCandidatesUsingSamples(float[] samples)
{
int recognized = 0, verified = 0;
IStride samplesToSkip = queryStride;
queryCommandBuilder.BuildQueryCommand()
.From(samples)
.WithConfigs(
fingerprintConfig =>
{
fingerprintConfig.Stride = samplesToSkip;
fingerprintConfig.NumberOfLSHTables = hashTables;
fingerprintConfig.NumberOfMinHashesPerTable = hashKeys;
},
queryConfig =>
{
queryConfig.ThresholdVotes = threshold;
})
.UsingServices(modelService, audioService)
.Query()
.ContinueWith(
t =>
{
var queryResult = t.Result;
if (!queryResult.IsSuccessful)
{
AddGridLine(new object[] { string.Empty, "No candidates!", false, -1, -1 }, Color.Red);
return;
}
TrackData recognizedTrack = queryResult.BestMatch.Track;
recognized++;
verified++;
AddGridLine(
new object[]
{
"Uknown Song",
recognizedTrack.Title + "-" + recognizedTrack.Artist,
true, -1, -1
},
Color.Empty);
_tbResults.Text = ((float)recognized / verified).ToString(CultureInfo.InvariantCulture);
},
TaskScheduler.FromCurrentSynchronizationContext());
}
public ResultEntry Validate(
ResultEntry result,
IStride validationStride,
string pathToAudioFile,
IModelService modelService,
IAudioService audioService)
{
double startAt = result.TrackStartsAt, length = result.QueryLength - result.TrackStartsAt;
if (startAt + result.Track.Length < result.QueryLength)
{
length = result.Track.Length;
}
var newResult = queryCommandBuilder.BuildQueryCommand()
.From(pathToAudioFile, length, startAt)
.WithFingerprintConfig(config => config.Stride = validationStride)
.UsingServices(modelService, audioService)
.Query()
.Result;
if (!newResult.ContainsMatches)
{
return result;
}
var newEntry = newResult.BestMatch;
if (newEntry.Confidence > result.Confidence)
{
return new ResultEntry(
newEntry.Track,
newEntry.QueryMatchStartsAt,
newEntry.QueryMatchLength,
newEntry.TrackMatchStartsAt,
newEntry.TrackStartsAt + result.TrackStartsAt,
newEntry.Confidence,
newEntry.HammingSimilaritySum,
newEntry.QueryLength,
newEntry.BestMatch);
}
return result;
}
public WinQueryResults(
int secondsToAnalyze,
int startSecond,
int hashTables,
int hashKeys,
int threshold,
IStride queryStride,
ITagService tagService,
IModelService modelService,
IAudioService audioService,
IQueryCommandBuilder queryCommandBuilder)
{
InitializeComponent();
Icon = Resources.Sound;
this.secondsToAnalyze = secondsToAnalyze;
this.startSecond = startSecond;
this.queryStride = queryStride;
this.hashTables = hashTables;
this.hashKeys = hashKeys;
this.threshold = threshold;
this.tagService = tagService;
this.modelService = modelService;
this.audioService = audioService;
this.queryCommandBuilder = queryCommandBuilder;
// ReSharper disable PossibleNullReferenceException
_dgvResults.Columns.Add(ColSongName, "Initial Song");
_dgvResults.Columns[ColSongName].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColResultName, "Result Song");
_dgvResults.Columns[ColResultName].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColResult, "ISRC Match");
_dgvResults.Columns[ColResult].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColHammingAvg, "Hamming Distance");
_dgvResults.Columns[ColHammingAvg].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColNumberOfCandidates, "Number of candidates");
_dgvResults.Columns[ColNumberOfCandidates].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColISRC, "Result ISRC");
_dgvResults.Columns[ColISRC].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
// ReSharper restore PossibleNullReferenceException
_btnExport.Enabled = false;
}
public WinQueryResults(
int secondsToAnalyze,
int startSecond,
int hashTables,
int hashKeys,
int threshold,
IStride queryStride,
ITagService tagService,
IModelService modelService,
IAudioService audioService,
IQueryCommandBuilder queryCommandBuilder)
{
InitializeComponent();
Icon = Resources.Sound;
this.secondsToAnalyze = secondsToAnalyze;
this.startSecond = startSecond;
this.queryStride = queryStride;
this.hashTables = hashTables;
this.hashKeys = hashKeys;
this.threshold = threshold;
this.tagService = tagService;
this.modelService = modelService;
this.audioService = audioService;
this.queryCommandBuilder = queryCommandBuilder;
// ReSharper disable PossibleNullReferenceException
_dgvResults.Columns.Add(ColSongName, "Initial Song");
_dgvResults.Columns[ColSongName].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColResultName, "Result Song");
_dgvResults.Columns[ColResultName].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColResult, "ISRC Match");
_dgvResults.Columns[ColResult].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColHammingAvg, "Hamming Distance");
_dgvResults.Columns[ColHammingAvg].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColNumberOfCandidates, "Number of candidates");
_dgvResults.Columns[ColNumberOfCandidates].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColISRC, "Result ISRC");
_dgvResults.Columns[ColISRC].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
// ReSharper restore PossibleNullReferenceException
_btnExport.Enabled = false;
}
/// <summary>
/// Create fingerprints out of down sampled samples
/// </summary>
/// <param name = "samples">Down sampled to 5512 samples </param>
/// <param name = "track">Track</param>
/// <param name = "stride">Stride</param>
/// <param name = "hashTables">Number of hash tables</param>
/// <param name = "hashKeys">Number of hash keys</param>
public void CreateInsertFingerprints(float[] samples,
Track track,
IStride stride,
int hashTables,
int hashKeys)
{
if (track == null) return; /*track is not eligible*/
/*Create fingerprints that will be used as initial fingerprints to be queried*/
List<bool[]> dbFingers = _manager.CreateFingerprints(samples, stride);
_storage.InsertTrack(track); /*Insert track into the storage*/
/*Get fingerprint's hash signature, and associate it to a specific track*/
List<HashSignature> creationalsignatures = GetSignatures(dbFingers, track, hashTables, hashKeys);
foreach (HashSignature hash in creationalsignatures)
{
_storage.InsertHash(hash, HashType.Creational);
/*Set this hashes as also the query hashes*/
_storage.InsertHash(hash, HashType.Query);
}
return;
}
private TestRunnerEventArgs GetTestRunnerEventArgsForFinishedTestIteration(IStride queryStride, int seconds, List <int> startAts, FScore fscore, HammingDistanceResultStatistics statistics, int iteration, Stopwatch stopwatch, int verified)
{
return(new TestRunnerEventArgs
{
FScore = fscore,
RowWithDetails =
new object[]
{
GetInsertMetadata(), queryStride.ToString(), seconds, startAts[iteration],
fscore.Precision, fscore.Recall, fscore.F1,
statistics.TruePositiveInfo,
statistics.TruePositivePercentileInfo,
statistics.FalseNegativesInfo,
statistics.FalseNegativesPercentileInfo,
statistics.FalsePositivesInfo,
statistics.FalsePositivesPercentileInfo,
(double)stopwatch.ElapsedMilliseconds / 1000
},
Verified = verified
});
}
/// <summary>
/// Create fingerprints out of down sampled samples
/// </summary>
/// <param name = "samples">Down sampled to 5512 samples</param>
/// <param name = "track">Track</param>
/// <param name = "stride">Stride</param>
/// <param name = "hashTables">Number of hash tables</param>
/// <param name = "hashKeys">Number of hash keys</param>
public void CreateInsertFingerprints(float[] samples, Track track, IStride stride, int hashTables, int hashKeys)
{
if (track == null)
{
return; /*track is not eligible*/
}
/*Create fingerprints that will be used as initial fingerprints to be queried*/
List<bool[]> fingerprints = fingerprintUnitBuilder.BuildFingerprints()
.On(samples)
.WithCustomConfiguration(config => config.Stride = stride)
.RunAlgorithm()
.Result;
storage.InsertTrack(track); /*Insert track into the storage*/
/*Get signature's hash signature, and associate it to a specific track*/
IEnumerable<HashSignature> creationalsignatures = GetSignatures(fingerprints, track, hashTables, hashKeys);
foreach (HashSignature hash in creationalsignatures)
{
storage.InsertHash(hash);
}
}
public List<float[][]> CutLogarithmizedSpectrum(float[][] logarithmizedSpectrum, IStride strideBetweenConsecutiveImages, int fingerprintImageLength, int overlap)
{
int start = strideBetweenConsecutiveImages.FirstStrideSize / overlap;
int logarithmicBins = logarithmizedSpectrum[0].Length;
List<float[][]> spectralImages = new List<float[][]>();
int width = logarithmizedSpectrum.GetLength(0);
while (start + fingerprintImageLength < width)
{
float[][] spectralImage = AllocateMemoryForFingerprintImage(fingerprintImageLength, logarithmicBins);
for (int i = 0; i < fingerprintImageLength; i++)
{
Array.Copy(logarithmizedSpectrum[start + i], spectralImage[i], logarithmicBins);
}
start += fingerprintImageLength + (strideBetweenConsecutiveImages.StrideSize / overlap);
spectralImages.Add(spectralImage);
}
return spectralImages;
}
/// <summary>
/// Cut logarithmized spetrum to spectral images
/// </summary>
/// <param name="logarithmizedSpectrum">Logarithmized spectrum of the initial signal</param>
/// <param name="strideBetweenConsecutiveImages">Stride between consecutive images (static 928ms db, random 46ms query)</param>
/// <param name="fingerprintImageLength">Length of 1 fingerprint image (w parameter equal to 128, which alogside overlap leads to 128*64 = 8192 = 1.48s)</param>
/// <param name="overlap">Overlap between consecutive spectral images, taken previously (64 ~ 11.6ms)</param>
/// <returns>List of logarithmic images</returns>
public List <double[][]> CutLogarithmizedSpectrum(
double[][] logarithmizedSpectrum, IStride strideBetweenConsecutiveImages, int fingerprintImageLength, int overlap)
{
int start = strideBetweenConsecutiveImages.FirstStrideSize / overlap;
int logarithmicBins = logarithmizedSpectrum[0].Length;
List <double[][]> spectralImages = new List <double[][]>();
int width = logarithmizedSpectrum.GetLength(0);
while (start + fingerprintImageLength < width)
{
double[][] spectralImage = this.AllocateMemoryForFingerprintImage(fingerprintImageLength, logarithmicBins);
for (int i = 0; i < fingerprintImageLength; i++)
{
Array.Copy(logarithmizedSpectrum[start + i], spectralImage[i], logarithmicBins);
}
start += fingerprintImageLength + (strideBetweenConsecutiveImages.StrideSize / overlap);
spectralImages.Add(spectralImage);
}
// Make sure at least the input spectrum is a part of the output list
if (spectralImages.Count == 0)
{
double[][] spectralImage = this.AllocateMemoryForFingerprintImage(fingerprintImageLength, logarithmicBins);
int rowCount = logarithmizedSpectrum.Length;
int columnCount = logarithmizedSpectrum[0].Length;
for (int i = 0; i < rowCount; i++)
{
for (int j = 0; j < columnCount; j++)
{
spectralImage[i][j] = logarithmizedSpectrum[i][j];
}
}
spectralImages.Add(spectralImage);
}
return(spectralImages);
}
public RealtimeQueryConfiguration(int thresholdVotes,
IRealtimeResultEntryFilter resultEntryFilter,
Action <ResultEntry> successCallback,
Action <ResultEntry> didNotPassFilterCallback,
Action <Hashes> queryFingerprintsCallback,
Action <Exception, Hashes> errorCallback,
Action restoredAfterErrorCallback,
IEnumerable <Hashes> downtimeHashes,
IStride stride,
double permittedGap,
double downtimeCapturePeriod,
int millisecondsDelay,
IDictionary <string, string> metaFieldFilters)
{
QueryConfiguration = new DefaultQueryConfiguration
{
ThresholdVotes = thresholdVotes,
FingerprintConfiguration = new DefaultFingerprintConfiguration
{
SpectrogramConfig = new DefaultSpectrogramConfig
{
Stride = stride
}
},
YesMetaFieldsFilters = metaFieldFilters,
PermittedGap = permittedGap
};
ResultEntryFilter = resultEntryFilter;
SuccessCallback = successCallback;
DidNotPassFilterCallback = didNotPassFilterCallback;
QueryFingerprintsCallback = queryFingerprintsCallback;
ErrorCallback = errorCallback;
RestoredAfterErrorCallback = restoredAfterErrorCallback;
DowntimeHashes = downtimeHashes;
DowntimeCapturePeriod = downtimeCapturePeriod;
MillisecondsDelay = millisecondsDelay;
}
public QueryResults(
int secondsToAnalyze,
int startSecond,
int hashTables,
int hashKeys,
int threshold,
IStride queryStride,
ITagService tagService,
IModelService modelService,
IAudioService audioService,
IQueryCommandBuilder queryCommandBuilder)
{
this.secondsToAnalyze = secondsToAnalyze;
this.startSecond = startSecond;
this.queryStride = queryStride;
this.hashTables = hashTables;
this.hashKeys = hashKeys;
this.threshold = threshold;
this.tagService = tagService;
this.modelService = modelService;
this.audioService = audioService;
this.queryCommandBuilder = queryCommandBuilder;
}
public WinQueryResults(
int secondsToAnalyze,
int startSecond,
int hashTables,
int hashKeys,
int threshold,
IStride queryStride,
ITagService tagService,
IModelService modelService,
IFingerprintQueryBuilder fingerprintQueryBuilder)
{
InitializeComponent();
Icon = Resources.Sound;
this.secondsToAnalyze = secondsToAnalyze;
this.startSecond = startSecond;
this.queryStride = queryStride;
this.hashTables = hashTables;
this.hashKeys = hashKeys;
this.threshold = threshold;
this.tagService = tagService;
this.modelService = modelService;
this.fingerprintQueryBuilder = fingerprintQueryBuilder;
// ReSharper disable PossibleNullReferenceException
_dgvResults.Columns.Add(ColSongName, "Initial Song");
_dgvResults.Columns[ColSongName].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColResultName, "Result Song");
_dgvResults.Columns[ColResultName].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColResult, "Result");
_dgvResults.Columns[ColResult].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColHammingAvg, "Hamming Distance");
_dgvResults.Columns[ColHammingAvg].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(ColElapsedTime, "Elapsed Time");
_dgvResults.Columns[ColElapsedTime].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
// ReSharper restore PossibleNullReferenceException
_btnExport.Enabled = false;
}
private void Insert(string folderWithSongs, IStride stride, StringBuilder sb)
{
var allFiles = AllFiles(folderWithSongs);
int inserted = 0;
var stopWatch = new Stopwatch();
stopWatch.Start();
Parallel.ForEach(
allFiles,
file =>
{
OnTestRunnerEvent(
OngoingActionEvent,
new TestRunnerOngoingEventArgs
{
Message = $"Inserting tracks {Interlocked.Increment(ref inserted)} out of {allFiles.Count}. Track {System.IO.Path.GetFileNameWithoutExtension(file)}"
});
var track = GetTrack(file);
var hashes = fcb.BuildFingerprintCommand()
.From(file)
.WithFingerprintConfig(
config =>
{
config.Stride = stride;
return(config);
})
.UsingServices(audioService)
.Hash()
.Result;
modelService.Insert(track, hashes);
});
stopWatch.Stop();
sb.AppendLine($"{inserted},{stopWatch.ElapsedMilliseconds / 1000}");
}
/// <summary>
/// Cut logarithmized spetrum to spectral images
/// </summary>
/// <param name="logarithmizedSpectrum">Logarithmized spectrum of the initial signal</param>
/// <param name="strideBetweenConsecutiveImages">Stride between consecutive images (static 928ms db, random 46ms query)</param>
/// <param name="fingerprintImageLength">Length of 1 fingerprint image (w parameter equal to 128, which alogside overlap leads to 128*64 = 8192 = 1.48s)</param>
/// <param name="overlap">Overlap between consecutive spectral images, taken previously (64 ~ 11.6ms)</param>
/// <returns>List of logarithmic images</returns>
public List<double[][]> CutLogarithmizedSpectrum(
double[][] logarithmizedSpectrum, IStride strideBetweenConsecutiveImages, int fingerprintImageLength, int overlap)
{
int start = strideBetweenConsecutiveImages.FirstStrideSize / overlap;
int logarithmicBins = logarithmizedSpectrum[0].Length;
List<double[][]> spectralImages = new List<double[][]>();
int width = logarithmizedSpectrum.GetLength(0);
while (start + fingerprintImageLength < width)
{
double[][] spectralImage = this.AllocateMemoryForFingerprintImage(fingerprintImageLength, logarithmicBins);
for (int i = 0; i < fingerprintImageLength; i++)
{
Array.Copy(logarithmizedSpectrum[start + i], spectralImage[i], logarithmicBins);
}
start += fingerprintImageLength + (strideBetweenConsecutiveImages.StrideSize / overlap);
spectralImages.Add(spectralImage);
}
// Make sure at least the input spectrum is a part of the output list
if (spectralImages.Count == 0) {
double[][] spectralImage = this.AllocateMemoryForFingerprintImage(fingerprintImageLength, logarithmicBins);
int rowCount = logarithmizedSpectrum.Length;
int columnCount = logarithmizedSpectrum[0].Length;
for (int i = 0; i < rowCount; i++) {
for (int j = 0; j < columnCount; j++) {
spectralImage[i][j] = logarithmizedSpectrum[i][j];
}
}
spectralImages.Add(spectralImage);
}
return spectralImages;
}
/// <summary>
/// Public constructor for NN algorithm
/// </summary>
/// <param name = "connectionString">Connection string</param>
/// <param name = "secondsToAnalyze">Number of fingerprints to analyze</param>
/// <param name = "startSeconds">Starting seconds</param>
/// <param name = "stride">Query stride</param>
/// <param name = "topWavelets">Number of top wavelets</param>
/// <param name = "fileList">File list to analyze</param>
/// <param name = "pathToEnsemble">Path to ensemble</param>
public WinQueryResults(string connectionString, int secondsToAnalyze, int startSeconds, IStride stride, int topWavelets, List<string> fileList, string pathToEnsemble)
: this(connectionString, secondsToAnalyze, startSeconds, stride, topWavelets, fileList)
{
_ensemble = NNEnsemble.Load(pathToEnsemble);
_dgvResults.Columns.Add(COL_HIT, "Number of hits");
_dgvResults.Columns[COL_HIT].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
Action action = ExtractCandidatesWithNeuralHasher;
action.BeginInvoke((result) => action.EndInvoke(result), action);
}
private void RunTestScenario(string folderWithPositives, string folderWithNegatives, IStride queryStride, int seconds, List <int> startAts)
{
int iterations = startAts.Count;
var positives = AllFiles(folderWithPositives);
var negatives = AllFiles(folderWithNegatives);
for (int iteration = 0; iteration < iterations; ++iteration)
{
OnTestRunnerEvent(OngoingActionEvent, new TestRunnerOngoingEventArgs
{
Message = $"Iteration {iteration + 1} out of {iterations} with {queryStride}, query seconds {seconds}"
});
var stopwatch = new Stopwatch();
stopwatch.Start();
int trueNegatives = 0, truePositives = 0, falseNegatives = 0, falsePositives = 0, verified = 0;
var truePositiveHammingDistance = new ConcurrentBag <int>();
var falseNegativesHammingDistance = new ConcurrentBag <int>();
var falsePositivesHammingDistance = new ConcurrentBag <int>();
var sb = TestRunnerWriter.StartTestIteration();
int currentIteration = iteration;
int startAt = startAts[currentIteration];
Parallel.ForEach(
positives,
positive =>
{
Interlocked.Increment(ref verified);
var tags = GetTagsFromFile(positive);
var actualTrack = GetActualTrack(tags);
var queryResult = BuildQuery(queryStride, seconds, positive, startAt).Result;
if (!queryResult.ContainsMatches)
{
Interlocked.Increment(ref falseNegatives);
var notFoundLine = GetNotFoundLine(tags);
AppendLine(sb, notFoundLine);
OnTestRunnerEvent(
PositiveNotFoundEvent,
GetTestRunnerEventArgs(
truePositives,
trueNegatives,
falsePositives,
falseNegatives,
notFoundLine,
verified));
return;
}
var recognizedTrack = queryResult.BestMatch.Track;
bool isSuccessful = recognizedTrack.Id.Equals(actualTrack.Id);
if (isSuccessful)
{
Interlocked.Increment(ref truePositives);
truePositiveHammingDistance.Add((int)queryResult.BestMatch.Score);
}
else
{
Interlocked.Increment(ref falsePositives);
falseNegativesHammingDistance.Add((int)queryResult.BestMatch.Score);
}
var foundLine = GetFoundLine(ToTrackString(actualTrack.Artist, actualTrack.Title), recognizedTrack, isSuccessful, queryResult);
AppendLine(sb, foundLine);
OnTestRunnerEvent(PositiveFoundEvent, GetTestRunnerEventArgs(truePositives, trueNegatives, falsePositives, falseNegatives, foundLine, verified));
});
Parallel.ForEach(
negatives,
negative =>
{
Interlocked.Increment(ref verified);
var tags = GetTagsFromFile(negative);
var queryResult = BuildQuery(queryStride, seconds, negative, startAt).Result;
if (!queryResult.ContainsMatches)
{
Interlocked.Increment(ref trueNegatives);
var notFoundLine = GetNotFoundLine(tags);
AppendLine(sb, notFoundLine);
OnTestRunnerEvent(
NegativeNotFoundEvent,
GetTestRunnerEventArgs(
truePositives,
trueNegatives,
falsePositives,
falseNegatives,
notFoundLine,
verified));
return;
}
var recognizedTrack = queryResult.BestMatch.Track;
falsePositivesHammingDistance.Add((int)queryResult.BestMatch.Score);
Interlocked.Increment(ref falsePositives);
var foundLine = GetFoundLine(ToTrackString(tags), recognizedTrack, false, queryResult);
AppendLine(sb, foundLine);
OnTestRunnerEvent(
NegativeFoundEvent,
GetTestRunnerEventArgs(truePositives, trueNegatives, falsePositives, falseNegatives, foundLine, verified));
});
stopwatch.Stop();
var fscore = new FScore(truePositives, trueNegatives, falsePositives, falseNegatives);
var stats = HammingDistanceResultStatistics.From(
truePositiveHammingDistance,
falseNegativesHammingDistance,
falsePositivesHammingDistance,
testRunnerConfig.Percentiles);
TestRunnerWriter.FinishTestIteration(sb, fscore, stats, stopwatch.ElapsedMilliseconds);
TestRunnerWriter.SaveTestIterationToFolder(sb, pathToResultsFolder, queryStride, GetInsertMetadata(), seconds, startAt);
var finishedTestIteration = GetTestRunnerEventArgsForFinishedTestIteration(queryStride, seconds, startAts, fscore, stats, iteration, stopwatch, verified);
OnTestRunnerEvent(TestIterationFinishedEvent, finishedTestIteration);
TestRunnerWriter.AppendLine(suite, finishedTestIteration.RowWithDetails);
}
}
public Image GetLogSpectralImages(
double[][] spectrum,
IStride strideBetweenConsecutiveImages,
int fingerprintLength,
int overlap,
int imagesPerRow)
{
List<double[][]> spectralImages = spectrumService.CutLogarithmizedSpectrum(
spectrum, strideBetweenConsecutiveImages, fingerprintLength, overlap);
return GetLogSpectralImages(spectralImages, imagesPerRow);
}
/// <summary>
/// Public constructor for LSH + Min Hash algorithm
/// </summary>
/// <param name = "connectionString">Connection string</param>
/// <param name = "secondsToAnalyze">Number of fingerprints to analyze</param>
/// <param name = "stride">Stride</param>
/// <param name = "fileList">File list</param>
/// <param name = "hashTables">Min hash hash tables</param>
/// <param name = "hashKeys">Min hash hash keys</param>
/// <param name = "startSecond">Starting second of analysis</param>
/// <param name = "thresholdTables">Number of threshold tables</param>
/// <param name = "topWavelets">Number of top wavelets to consider</param>
public WinQueryResults(string connectionString, int secondsToAnalyze, int startSecond, IStride stride, List<string> fileList, int hashTables, int hashKeys, int thresholdTables, int topWavelets)
: this(connectionString, secondsToAnalyze, startSecond, stride, topWavelets, fileList)
{
_hashTables = hashTables;
_hashKeys = hashKeys;
_threshold = thresholdTables;
_dgvResults.Columns.Add(COL_HAMMING_AVG_BY_TRACK, "Hamming Avg. By Track");
// ReSharper disable PossibleNullReferenceException
_dgvResults.Columns[COL_HAMMING_AVG_BY_TRACK].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_MIN_HAMMING, "Min. Hamming");
_dgvResults.Columns[COL_MIN_HAMMING].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_SORT_VALUE, "Sort Value");
_dgvResults.Columns[COL_SORT_VALUE].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_TOTAL_TABLES, "Total Table Votes");
_dgvResults.Columns[COL_TOTAL_TABLES].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_TOTAL_TRACK_VOTES, "Total Track Votes");
_dgvResults.Columns[COL_TOTAL_TRACK_VOTES].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_START_QUERY_INDEX, "Query Index Start");
_dgvResults.Columns[COL_START_QUERY_INDEX].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_TOTAL_FINGERPRINTS, "Total Fingerprints");
_dgvResults.Columns[COL_TOTAL_FINGERPRINTS].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_SIMILARITY, "Min Similarity");
_dgvResults.Columns[COL_SIMILARITY].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_ELAPSED_TIME, "Elapsed Time");
_dgvResults.Columns[COL_ELAPSED_TIME].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
// ReSharper restore PossibleNullReferenceException
_btnExport.Enabled = false;
_nudTotal.Value = _fileList.Count;
Action action = ExtractCandidatesWithMinHashAlgorithm; /*Extract candidates using MinHash + LSH algorithm*/
action.BeginInvoke(
(result) =>
{
try
{
action.EndInvoke(result);
}
catch (ThreadAbortException)
{
/*Recognition aborted*/
return;
}
catch (Exception e)
{
MessageBox.Show(Resources.RecognitionEndedWithAnError + e.Message + Resources.LineFeed + e.StackTrace, Resources.Error, MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
MessageBox.Show(Resources.RecognitionEnded, Resources.RecognitionEnded, MessageBoxButtons.OK, MessageBoxIcon.Information);
Invoke(new Action(
() =>
{
_btnStop.Enabled = true;
_btnExport.Enabled = true;
}));
}, action);
}
private void RunTestScenario(string folderWithPositives, string folderWithNegatives, IStride queryStride, int seconds, List<int> startAts)
{
int iterations = startAts.Count;
var positives = AllFiles(folderWithPositives);
var negatives = AllFiles(folderWithNegatives);
for (int iteration = 0; iteration < iterations; ++iteration)
{
OnTestRunnerEvent(
OngoingActionEvent,
new TestRunnerOngoingEventArgs
{
Message =
string.Format(
"Iteration {0} out of {1} with {2}, query seconds {3}",
iteration + 1,
iterations,
queryStride,
seconds)
});
var stopwatch = new Stopwatch();
stopwatch.Start();
int trueNegatives = 0, truePositives = 0, falseNegatives = 0, falsePositives = 0, verified = 0;
var truePositiveHammingDistance = new ConcurrentBag<int>();
var falseNegativesHammingDistance = new ConcurrentBag<int>();
var falsePositivesHammingDistance = new ConcurrentBag<int>();
var sb = TestRunnerWriter.StartTestIteration();
int currentIteration = iteration;
int startAt = startAts[currentIteration];
Parallel.ForEach(
positives,
positive =>
{
Interlocked.Increment(ref verified);
var tags = GetTagsFromFile(positive);
var actualTrack = GetActualTrack(tags);
var queryResult = BuildQuery(queryStride, seconds, positive, startAt).Result;
if (!queryResult.ContainsMatches)
{
Interlocked.Increment(ref falseNegatives);
var notFoundLine = GetNotFoundLine(tags);
AppendLine(sb, notFoundLine);
OnTestRunnerEvent(
PositiveNotFoundEvent,
GetTestRunnerEventArgs(
truePositives,
trueNegatives,
falsePositives,
falseNegatives,
notFoundLine,
verified));
return;
}
var recognizedTrack = queryResult.BestMatch.Track;
bool isSuccessful = recognizedTrack.TrackReference.Equals(actualTrack.TrackReference);
if (isSuccessful)
{
Interlocked.Increment(ref truePositives);
truePositiveHammingDistance.Add(queryResult.BestMatch.HammingSimilaritySum);
}
else
{
Interlocked.Increment(ref falsePositives);
falseNegativesHammingDistance.Add(queryResult.BestMatch.HammingSimilaritySum);
}
var foundLine = GetFoundLine(ToTrackString(actualTrack), recognizedTrack, isSuccessful, queryResult);
AppendLine(sb, foundLine);
OnTestRunnerEvent(PositiveFoundEvent, GetTestRunnerEventArgs(truePositives, trueNegatives, falsePositives, falseNegatives, foundLine, verified));
});
Parallel.ForEach(
negatives,
negative =>
{
Interlocked.Increment(ref verified);
var tags = GetTagsFromFile(negative);
var queryResult = BuildQuery(queryStride, seconds, negative, startAt).Result;
if (!queryResult.ContainsMatches)
{
Interlocked.Increment(ref trueNegatives);
var notFoundLine = GetNotFoundLine(tags);
AppendLine(sb, notFoundLine);
OnTestRunnerEvent(
NegativeNotFoundEvent,
GetTestRunnerEventArgs(
truePositives,
trueNegatives,
falsePositives,
falseNegatives,
notFoundLine,
verified));
return;
}
var recognizedTrack = queryResult.BestMatch.Track;
falsePositivesHammingDistance.Add(queryResult.BestMatch.HammingSimilaritySum);
Interlocked.Increment(ref falsePositives);
var foundLine = GetFoundLine(ToTrackString(tags), recognizedTrack, false, queryResult);
AppendLine(sb, foundLine);
OnTestRunnerEvent(
NegativeFoundEvent,
GetTestRunnerEventArgs(truePositives, trueNegatives, falsePositives, falseNegatives, foundLine, verified));
});
stopwatch.Stop();
var fscore = new FScore(truePositives, trueNegatives, falsePositives, falseNegatives);
var stats = HammingDistanceResultStatistics.From(
truePositiveHammingDistance,
falseNegativesHammingDistance,
falsePositivesHammingDistance,
testRunnerConfig.Percentiles);
TestRunnerWriter.FinishTestIteration(sb, fscore, stats, stopwatch.ElapsedMilliseconds);
TestRunnerWriter.SaveTestIterationToFolder(sb, pathToResultsFolder, queryStride, GetInsertMetadata(), seconds, startAt);
var finishedTestIteration = GetTestRunnerEventArgsForFinishedTestIteration(queryStride, seconds, startAts, fscore, stats, iteration, stopwatch, verified);
OnTestRunnerEvent(TestIterationFinishedEvent, finishedTestIteration);
TestRunnerWriter.AppendLine(suite, finishedTestIteration.RowWithDetails);
}
}
/// <summary>
/// Create fingerprints/min-hash signatures/LSH buckets and insert them in underlying storage
/// </summary>
/// <param name = "files">Files to be processed</param>
/// <param name = "audioProxy">Audio proxy used in processing</param>
/// <param name = "queryStride">Stride between 2 consecutive fingerprints on query</param>
/// <param name = "creationalStride">Stride between 2 consecutive fingerprints on creation</param>
/// <param name = "mintracklen">Minimum track length</param>
/// <param name = "maxtracklen">Maximum track length</param>
/// <param name = "milliSecondsToProcess">Number of milliseconds to process</param>
/// <param name = "startMillisecond">Start processing at a specific millisecond</param>
/// <param name = "hashTables">Number of hash-tables used in LSH decomposition</param>
/// <param name = "hashKeys">Number of Min Hash keys per LSH table</param>
/// <param name = "trackProcessed">Invoked once the track is processed</param>
/// <returns>List of processed tracks</returns>
public List <Track> ProcessTracks(List <string> files, BassProxy audioProxy, IStride queryStride, IStride creationalStride,
int mintracklen, int maxtracklen,
int milliSecondsToProcess, int startMillisecond, int hashTables, int hashKeys, Action <Track> trackProcessed)
{
List <Track> tracks = new List <Track>();
_threadCounts++;
foreach (string file in files) //filter files that can be processed
{
if (_aborted)
{
break;
}
Track track = GetTrack(mintracklen, maxtracklen, file, audioProxy);
if (track == null)
{
continue; /*track is not eligible because of min/max parameters*/
}
//create spectrogram of the file
float[][] logSpectrum = null;
try
{
//try creating the spectrum from a file
logSpectrum = _manager.CreateLogSpectrogram(audioProxy, track.Path, milliSecondsToProcess, startMillisecond);
}
catch (Exception ex)
{
if (ex is ThreadAbortException)
{
throw;
}
/*the file might be corrupted or missing*/
continue; /*Continue processing even if creation of the spectrogram failed*/
}
_storage.InsertTrack(track); /*Insert track into the storage*/
/*Create fingerprints that will be used as initial fingerprints to be queried*/
List <bool[]> dbFingers = _manager.CreateFingerprints(logSpectrum, creationalStride);
/*Get fingerprint's hash signature, and associate it to a specific track*/
List <HashSignature> creationalsignatures = GetSignatures(dbFingers, track, hashTables, hashKeys);
foreach (HashSignature hash in creationalsignatures)
{
_storage.InsertHash(hash, HashType.Creational);
/*Set this hashes as also the query hashes*/
_storage.InsertHash(hash, HashType.Query);
}
/*Create fingerprints for query*/
List <bool[]> queryFingers = _manager.CreateFingerprints(logSpectrum, queryStride); /*Create fingerprints*/
List <HashSignature> querysignatures = GetSignatures(queryFingers, track, hashTables, hashKeys);
// ***** PIN TODO: CHANGE THIS
object[][] arr = new object[querysignatures.Count][];
int count = 0;
foreach (HashSignature hash in querysignatures)
{
_storage.InsertHash(hash, HashType.Query); /*Insert hash-buckets into hash-tables*/
String signatureText = "{";
for (int s = 0; s < hash.Signature.Length; s++)
{
signatureText += hash.Signature[s];
signatureText += " ";
}
signatureText += "}";
arr[count++] = new object[5] {
hash.Id,
hash.Track.Title,
hash.Track.Artist,
hash.Track.TrackLength,
signatureText
};
}
String filenameToSave = String.Format("C:\\{0}-hash-buckets.txt", System.IO.Path.GetFileNameWithoutExtension(file));
CSVWriter csv = new CSVWriter(filenameToSave);
csv.Write(arr);
// ***** end PIN
if (trackProcessed != null)
{
trackProcessed.Invoke(track);
}
tracks.Add(track);
}
_threadCounts--;
return(tracks);
}
private Task<QueryResult> BuildQuery(IStride queryStride, int seconds, string positive, int startAt)
{
return qcb.BuildQueryCommand()
.From(positive, seconds, startAt)
.WithFingerprintConfig(fingerprintConfig => { fingerprintConfig.Stride = queryStride; })
.UsingServices(modelService, audioService)
.Query();
}
/// <summary>
/// Get fingerprint similarity of one song
/// </summary>
/// <param name = "manager">Fingerprint manager used in file decomposition</param>
/// <param name = "dbstride">Database creation stride</param>
/// <param name = "queryStride">Query stride</param>
/// <param name = "numberOfItemsToCompare">Number of subsequent elements to compare with</param>
/// <param name = "proxy">Proxy</param>
/// <param name = "path">Path to first file</param>
/// <param name = "results">Results object to be filled with the corresponding data</param>
private static void GetFingerprintSimilarity(FingerprintManager manager, IStride dbstride, IStride queryStride, int numberOfItemsToCompare, IAudio proxy, string path, DumpResults results)
{
int startindex = 0;
int count = 0;
double sum = 0;
List<bool[]> list = manager.CreateFingerprints(proxy, path, dbstride);
List<bool[]> listToCompare = manager.CreateFingerprints(proxy, path, queryStride);
count = list.Count;
int toCompare = listToCompare.Count;
double max = double.MinValue;
for (int i = 0; i < count; i++)
{
for (int j = 0; j < toCompare; j++)
{
double value = MinHash.CalculateSimilarity(list[i], listToCompare[j]);
if (value > max)
max = value;
sum += value;
}
}
results.Results.SumJaqFingerprintsSimilarity = sum;
results.Results.AverageJaqFingerprintSimilarity = sum/(count*toCompare);
results.Results.MaxJaqFingerprintSimilarity = max;
}
public RepositoryGateway()
{
_storage = new RamStorage(NUMBER_OF_HASH_TABLES); /*Number of LSH Tables, used for storage purposes*/
_permutations = new LocalPermutations(PATH_TO_PERMUTATIONS, SEPARATOR); /*Permutations*/
_repository = new Repository(_storage, _permutations);
_proxy = new BassProxy(); /*audio proxy used in reading the file*/
_createStride = new IncrementalStaticStride(STRIDE_SIZE_INCREMENTAL, SAMPLES_IN_FINGERPRINT);
_queryStride = new IncrementalRandomStride(STRIDE_SIZE_INCREMENTAL, SAMPLES_IN_FINGERPRINT, SAMPLES_IN_FINGERPRINT);
}
public static void SaveTestIterationToFolder(StringBuilder sb, string resultsFolder, IStride queryStride, string insertMetadata, int queryLength, int startAt)
{
string filename = string.Format("results_{0}_{1}_q{2}s_at{3}s.csv", insertMetadata, queryStride, queryLength, startAt);
string absolutePath = Path.Combine(resultsFolder, filename);
Write(sb, absolutePath);
}
public static void SaveTestIterationToFolder(StringBuilder sb, string resultsFolder, IStride queryStride, string insertMetadata, int queryLength, int startAt)
{
string filename = string.Format("results_{0}_{1}_q{2}s_at{3}s.csv", insertMetadata, queryStride, queryLength, startAt);
string absolutePath = Path.Combine(resultsFolder, filename);
Write(sb, absolutePath);
}
public Image GetLogSpectralImages(
double[][] spectrum,
IStride strideBetweenConsecutiveImages,
int fingerprintLength,
int overlap,
int imagesPerRow)
{
List<double[][]> spectralImages = spectrumService.CutLogarithmizedSpectrum(
spectrum, strideBetweenConsecutiveImages, fingerprintLength, overlap);
int blockSizeX = 4;
int blockSizeY = 4;
int width = spectralImages[0].GetLength(0);
int height = spectralImages[0][0].Length;
int fingersCount = spectralImages.Count;
int rowCount = (int)Math.Ceiling((float)fingersCount / imagesPerRow);
int imageWidth = (blockSizeX * imagesPerRow * (width + SpaceBetweenImages)) + SpaceBetweenImages;
int imageHeight = (blockSizeY * rowCount * (height + SpaceBetweenImages)) + SpaceBetweenImages;
//Bitmap image = new Bitmap(imageWidth, imageHeight, PixelFormat.Format16bppRgb565);
Bitmap image = new Bitmap(imageWidth, imageHeight);
Graphics graphics = Graphics.FromImage(image);
SetBackground(image, Color.White);
int verticalOffset = SpaceBetweenImages;
int horizontalOffset = SpaceBetweenImages;
int count = 0;
foreach (double[][] spectralImage in spectralImages)
{
double maxValue = spectralImage.Max((b) => b.Max((v) => Math.Abs(v)));
maxValue = 20 * Math.Log10(maxValue);
//double average = spectralImage.Average(col => col.Average(v => Math.Abs(v)));
for (int i = 0; i < width /*128*/; i++)
{
for (int j = 0; j < height /*32*/; j++)
{
//Color color = ValueToBlackWhiteColor(spectralImage[i][j], average);
//image.SetPixel(i + horizontalOffset, j + verticalOffset, color);
double val = 20 * Math.Log10(spectralImage[i][j]);
Color color = Color.White;
if (!double.IsNaN(val) && !double.IsInfinity(val)) {
color = ValueToBlackWhiteColor(val, maxValue);
}
Brush brush = new SolidBrush(color);
//graphics.FillRectangle(brush, i + horizontalOffset, j + verticalOffset, (int)deltaX + 1, 1);
//image.SetPixel(i + horizontalOffset, j + verticalOffset, color);
graphics.FillRectangle(brush, (i + horizontalOffset)*blockSizeX, (height - j + verticalOffset + 1)*blockSizeY, blockSizeX, blockSizeY);
}
}
count++;
if (count % imagesPerRow == 0)
{
verticalOffset += height + SpaceBetweenImages;
horizontalOffset = SpaceBetweenImages;
}
else
{
horizontalOffset += width + SpaceBetweenImages;
}
}
image = ColorUtils.Colorize(image, 255, ColorUtils.ColorPaletteType.MATLAB);
return image;
}
/// <summary>
/// Get hash similarity of one song
/// </summary>
/// <param name = "manager">Fingerprint manager</param>
/// <param name = "dbstride">Database stride between fingerprints</param>
/// <param name = "queryStride">Query stride between fingerprints</param>
/// <param name = "numberOfFingerprintsToAnalyze">Number of fingerprints to analyze</param>
/// <param name = "hashTables">Number of hash tables in the LSH transformation</param>
/// <param name = "hashKeys">Number of hash keys per table in the LSH transformation</param>
/// <param name = "proxy">Audio proxy</param>
/// <param name = "path">Path to analyzed file</param>
/// <param name = "results">Results object to be filled with the appropriate data</param>
private static void GetHashSimilarity(FingerprintManager manager, IStride dbstride, IStride queryStride, int numberOfFingerprintsToAnalyze, int hashTables, int hashKeys, IAudio proxy, string path, DumpResults results)
{
double sum = 0;
int hashesCount = 0;
int startindex = 0;
List<bool[]> listDb = manager.CreateFingerprints(proxy, path, dbstride);
List<bool[]> listQuery = manager.CreateFingerprints(proxy, path, queryStride);
IPermutations perms = new DbPermutations(ConfigurationManager.ConnectionStrings["FingerprintConnectionString"].ConnectionString);
MinHash minHash = new MinHash(perms);
List<int[]> minHashDb = listDb.Select(minHash.ComputeMinHashSignature).ToList();
List<int[]> minHashQuery = listQuery.Select(minHash.ComputeMinHashSignature).ToList();
/*Calculate Min Hash signature similarity by comparing 2 consecutive signatures*/
int countDb = minHashDb.Count;
int countQuery = minHashQuery.Count;
int minHashSignatureLen = minHashDb[0].Length;
int similarMinHashValues = 0;
for (int i = 0; i < countDb; i++)
{
for (int j = 0; j < countQuery; j++)
{
for (int k = 0; k < minHashSignatureLen; k++)
if (minHashDb[i][k] == minHashQuery[j][k])
similarMinHashValues++;
}
}
results.Results.SumIdenticalMinHash = similarMinHashValues;
results.Results.AverageIdenticalMinHash = (double) similarMinHashValues/(countDb*countQuery*minHashSignatureLen);
/*Group min hash signatures into LSH Buckets*/
List<Dictionary<int, long>> lshBucketsDb =
minHashDb.Select(item => minHash.GroupMinHashToLSHBuckets(item, hashTables, hashKeys)).ToList();
List<Dictionary<int, long>> lshBucketsQuery =
minHashQuery.Select(item => minHash.GroupMinHashToLSHBuckets(item, hashTables, hashKeys)).ToList();
int countSignatures = lshBucketsDb.Count;
sum = 0;
foreach (Dictionary<int, long> a in lshBucketsDb)
{
Dictionary<int, long>.ValueCollection aValues = a.Values;
foreach (Dictionary<int, long> b in lshBucketsQuery)
{
Dictionary<int, long>.ValueCollection bValues = b.Values;
hashesCount += aValues.Intersect(bValues).Count();
}
}
results.Results.SumJaqLSHBucketSimilarity = -1;
results.Results.AverageJaqLSHBucketSimilarity = -1;
results.Results.TotalIdenticalLSHBuckets = hashesCount;
}
public void ExtractCandidatesWithMinHashAlgorithm(List <string> fileList)
{
int recognized = 0, verified = 0;
IStride samplesToSkip = queryStride;
Action <object[], Color> actionInterface = AddGridLine;
ParallelOptions parallelOptions = new ParallelOptions
{
MaxDegreeOfParallelism = 4,
CancellationToken = cancellationTokenSource.Token
};
Task.Factory.StartNew(() =>
{
Parallel.For(
0,
fileList.Count,
parallelOptions,
(index, loopState) =>
{
if (stopQuerying)
{
cancellationTokenSource.Cancel();
loopState.Stop();
}
string pathToFile = fileList[index]; /*Path to song to recognize*/
TagInfo tags = tagService.GetTagInfo(pathToFile); // Get Tags from file
if (tags == null || tags.IsEmpty)
{
Invoke(actionInterface, new object[] { string.Empty, pathToFile }, Color.Red);
return;
}
string artist = string.IsNullOrEmpty(tags.Artist)
? Path.GetFileNameWithoutExtension(pathToFile)
: tags.Artist; // Artist
string title = string.IsNullOrEmpty(tags.Title)
? Path.GetFileNameWithoutExtension(pathToFile)
: tags.Title; // Title
string isrc = tags.ISRC;
double duration = tags.Duration; // Duration
// Check whether the duration is ok
if (duration < MinTrackLength || duration > MaxTrackLength || secondsToAnalyze > duration)
{
// Duration too small
Invoke(actionInterface, new object[] { "BAD DURATION", pathToFile }, Color.Red);
return;
}
TrackData actualTrack = null;
if (!string.IsNullOrEmpty(isrc))
{
actualTrack = modelService.ReadTrackByISRC(isrc);
}
else if (!string.IsNullOrEmpty(tags.Artist) && !string.IsNullOrEmpty(tags.Title))
{
actualTrack = modelService.ReadTrackByArtistAndTitleName(tags.Artist, tags.Title).FirstOrDefault();
}
var queryResult =
queryCommandBuilder.BuildQueryCommand()
.From(pathToFile, secondsToAnalyze, startSecond)
.WithConfigs(
fingerprintConfig =>
{
fingerprintConfig.Stride = samplesToSkip;
fingerprintConfig.NumberOfLSHTables = hashTables;
fingerprintConfig.NumberOfMinHashesPerTable = hashKeys;
},
queryConfig =>
{
queryConfig.ThresholdVotes = threshold;
})
.UsingServices(modelService, audioService)
.Query()
.Result;
if (cancellationTokenSource.IsCancellationRequested)
{
return;
}
if (!queryResult.IsSuccessful)
{
Invoke(
actionInterface,
new object[]
{
title + "-" + artist, "No match found!", false, -1, -1, "No match found!"
},
Color.Red);
if (actualTrack != null)
{
verified++;
}
return;
}
verified++;
TrackData recognizedTrack = queryResult.BestMatch.Track;
bool isSuccessful = actualTrack == null || recognizedTrack.TrackReference.Equals(actualTrack.TrackReference);
if (isSuccessful)
{
recognized++;
}
Invoke(
actionInterface,
new object[]
{
title + "-" + artist, recognizedTrack.Title + "-" + recognizedTrack.Artist,
isSuccessful, queryResult.BestMatch.Similarity, queryResult.AnalyzedCandidatesCount,
recognizedTrack.ISRC
},
Color.Empty);
Invoke(new Action(
() =>
{
_tbResults.Text =
((float)recognized / verified).ToString(CultureInfo.InvariantCulture);
}));
});
}).ContinueWith(task =>
{
MessageBox.Show("Finished!", "Finished query!");
});
}
private void Insert(string folderWithSongs, IStride stride, StringBuilder sb)
{
var allFiles = AllFiles(folderWithSongs);
int inserted = 0;
var stopWatch = new Stopwatch();
stopWatch.Start();
Parallel.ForEach(
allFiles,
file =>
{
OnTestRunnerEvent(
OngoingActionEvent,
new TestRunnerOngoingEventArgs
{
Message = string.Format(
"Inserting tracks {0} out of {1}. Track {2}",
Interlocked.Increment(ref inserted),
allFiles.Count,
System.IO.Path.GetFileNameWithoutExtension(file))
});
var track = InsertTrack(file);
var hashes = fcb.BuildFingerprintCommand()
.From(file)
.WithFingerprintConfig(config => { config.Stride = stride; })
.UsingServices(audioService)
.Hash()
.Result;
modelService.InsertHashDataForTrack(hashes, track);
});
stopWatch.Stop();
sb.AppendLine(string.Format("{0},{1}", inserted, stopWatch.ElapsedMilliseconds / 1000));
}
private List <bool[]> CreateFingerprintsFromLogSpectrum(float[][] logarithmizedSpectrum, IStride stride, int fingerprintLength, int overlap, int topWavelets)
{
List <float[][]> spectralImages = spectrumService.CutLogarithmizedSpectrum(logarithmizedSpectrum, stride, fingerprintLength, overlap);
waveletDecomposition.DecomposeImagesInPlace(spectralImages);
var fingerprints = new List <bool[]>();
foreach (var spectralImage in spectralImages)
{
bool[] image = fingerprintDescriptor.ExtractTopWavelets(spectralImage, topWavelets);
if (!IsSilence(image))
{
fingerprints.Add(image);
}
}
return(fingerprints);
}
/// <summary>
/// Get fingerprint similarity between 2 different songs.
/// </summary>
/// <param name = "manager">Fingerprint manager used in file decomposition</param>
/// <param name = "stride">Stride object parameter</param>
/// <param name = "proxy">Proxy to the audio object</param>
/// <param name = "path">Path to first file</param>
/// <param name = "differentPath">Path to different file</param>
/// <param name = "results">Results object to be filled with the corresponding data</param>
private static void GetFingerprintSimilarity(FingerprintManager manager, IStride stride, IAudio proxy, string path, string differentPath, DumpResults results)
{
int startindex = 0;
int count = 0;
double sum = 0;
List<bool[]> imglista = manager.CreateFingerprints(proxy, path, stride);
List<bool[]> imglistb = manager.CreateFingerprints(proxy, differentPath, stride);
count = imglista.Count > imglistb.Count ? imglistb.Count : imglista.Count;
double max = double.MinValue;
for (int i = 0; i < count; i++)
{
int j = i;
double value = MinHash.CalculateSimilarity(imglista[i], imglistb[j]);
if (value > max)
max = value;
sum += value;
}
results.SumJaqFingerprintSimilarityBetweenDiffertSongs = sum;
results.AverageJaqFingerprintsSimilarityBetweenDifferentSongs = sum/count;
results.MaxJaqFingerprintsSimilarityBetweenDifferentSongs = max;
}
private TestRunnerEventArgs GetTestRunnerEventArgsForFinishedTestIteration(IStride queryStride, int seconds, List<int> startAts, FScore fscore, HammingDistanceResultStatistics statistics, int iteration, Stopwatch stopwatch, int verified)
{
return new TestRunnerEventArgs
{
FScore = fscore,
RowWithDetails =
new object[]
{
this.GetInsertMetadata(), queryStride.ToString(), seconds, startAts[iteration],
fscore.Precision, fscore.Recall, fscore.F1,
statistics.TruePositiveInfo,
statistics.TruePositivePercentileInfo,
statistics.FalseNegativesInfo,
statistics.FalseNegativesPercentileInfo,
statistics.FalsePositivesInfo,
statistics.FalsePositivesPercentileInfo,
(double)stopwatch.ElapsedMilliseconds / 1000
},
Verified = verified
};
}
public static void SaveInsertDataToFolder(StringBuilder sb, string resultsFolder, IStride insertStride)
{
string filename = string.Format("insert_{0}.csv", insertStride);
string absolutePath = Path.Combine(resultsFolder, filename);
Write(sb, absolutePath);
}
private void RunTest(string[] parameters)
{
string action = parameters[0];
switch (action)
{
case "Insert":
string folderWithSongs = parameters[1];
var stride = utils.ToStride(parameters[2], parameters[3], parameters[4]);
DeleteAll();
var sb = TestRunnerWriter.StartInsert();
Insert(folderWithSongs, stride, sb);
TestRunnerWriter.SaveInsertDataToFolder(sb, pathToResultsFolder, stride);
lastInsertStride = stride;
break;
case "Run":
string folderWithPositives = parameters[1];
string folderWithNegatives = parameters[2];
var queryStride = utils.ToStride(parameters[3], parameters[4], parameters[5]);
int seconds = int.Parse(parameters[6]);
var startAts = ToStartAts(parameters[7]);
RunTestScenario(folderWithPositives, folderWithNegatives, queryStride, seconds, startAts);
break;
}
}
public List <float[][]> CutLogarithmizedSpectrum(float[][] logarithmizedSpectrum, IStride strideBetweenConsecutiveImages, int fingerprintImageLength, int overlap)
{
int index = (int)((float)strideBetweenConsecutiveImages.FirstStride / overlap);
int numberOfLogBins = logarithmizedSpectrum[0].Length;
var spectralImages = new List <float[][]>();
int width = logarithmizedSpectrum.GetLength(0);
while (index + fingerprintImageLength <= width)
{
float[][] spectralImage = AllocateMemoryForFingerprintImage(fingerprintImageLength, numberOfLogBins);
for (int i = 0; i < fingerprintImageLength; i++)
{
Array.Copy(logarithmizedSpectrum[index + i], spectralImage[i], numberOfLogBins);
}
index += fingerprintImageLength + (int)((float)strideBetweenConsecutiveImages.GetNextStride() / overlap);
spectralImages.Add(spectralImage);
}
return(spectralImages);
}
/// <summary>
/// Create fingerprints gathered from one specific song
/// </summary>
/// <param name = "proxy">Proxy used in reading the audio file</param>
/// <param name = "filename">Filename</param>
/// <param name = "stride">Stride used in fingerprint creation</param>
/// <returns>List of fingerprint signatures</returns>
public List <bool[]> CreateFingerprints(IWaveformPlayer proxy, string filename, IStride stride)
{
return(CreateFingerprints(proxy, filename, stride, 0, 0));
}
/// <summary>
/// Protected constructor of WinQueryResults class
/// </summary>
/// <param name = "connectionString">Connection string used for the underlying data source</param>
/// <param name = "secondsToAnalyze">Number of consequent fingerprints to analyze</param>
/// <param name = "startSecond">Starting seconds</param>
/// <param name = "stride">Stride used in the query</param>
/// <param name = "topWavelets">Number of top wavelets to analyze</param>
/// <param name = "fileList">List of all files to be recognized</param>
protected WinQueryResults(string connectionString, int secondsToAnalyze, int startSecond,
IStride stride, int topWavelets, List<string> fileList)
{
InitializeComponent(); /*Initialize Designer Components*/
Icon = Resources.Sound;
_connectionString = connectionString;
_topWavelets = topWavelets;
_dalManager = new DaoGateway(ConfigurationManager.ConnectionStrings["FingerprintConnectionString"].ConnectionString);
_permStorage = new DbPermutations(ConfigurationManager.ConnectionStrings["FingerprintConnectionString"].ConnectionString);
_manager = new FingerprintManager {TopWavelets = topWavelets};
_dalManager.SetConnectionString(_connectionString); /*Set connection string for DAL manager*/
_secondsToAnalyze = secondsToAnalyze; /*Number of fingerprints to analyze from each song*/
_startSecond = startSecond;
_fileList = fileList; /*List of files to analyze*/
_dgvResults.Columns.Add(COL_SONG_NAME, "Initial Song");
_dgvResults.Columns[COL_SONG_NAME].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_RESULT_NAME, "Result Song");
_dgvResults.Columns[COL_RESULT_NAME].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_POSITION, "Position");
_dgvResults.Columns[COL_POSITION].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_RESULT, "Result");
_dgvResults.Columns[COL_RESULT].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_dgvResults.Columns.Add(COL_HAMMING_AVG, "Hamming Avg.");
_dgvResults.Columns[COL_HAMMING_AVG].AutoSizeMode = DataGridViewAutoSizeColumnMode.Fill;
_queryStride = stride;
}
private void InsertInDatabase(int start, int end)
{
int topWavelets = (int)_nudTopWav.Value;
IStride stride = null;
Invoke(
new Action(
() =>
{
stride = WinUtils.GetStride(
(StrideType)_cmbStrideType.SelectedIndex,
(int)_nudStride.Value,
0,
new DefaultFingerprintConfiguration().SamplesPerFingerprint);
}),
null);
Action actionInterface = () =>
{
_pbTotalSongs.PerformStep();
_nudProcessed.Value = processed;
_nudLeft.Value = left;
_nudBadFiles.Value = badFiles;
_nudDetectedDuplicates.Value = duplicates;
};
Action <object[], Color> actionAddItems = (parameters, color) =>
{
int index = _dgvFillDatabase.Rows.Add(parameters);
_dgvFillDatabase.FirstDisplayedScrollingRowIndex = index;
if (color != Color.Empty)
{
_dgvFillDatabase.Rows[index].DefaultCellStyle.BackColor = color;
}
};
for (int i = start; i < end; i++)
{
// Process the corresponding files
if (stopFlag)
{
return;
}
TagInfo tags = tagService.GetTagInfo(fileList[i]); // Get Tags from file
if (tags == null || tags.IsEmpty)
{
badFiles++;
processed++;
left--;
Invoke(actionAddItems, new object[] { "TAGS ARE NULL", fileList[i], 0, 0 }, Color.Red);
Invoke(actionInterface);
continue;
}
string isrc = tags.ISRC;
string artist = tags.Artist; // Artist
string title = tags.Title; // Title
int releaseYear = tags.Year;
string album = tags.Album;
double duration = tags.Duration; // Duration
// Check whether the duration is OK
if (duration < MinTrackLength || duration > MaxTrackLength)
{
// Duration too small
badFiles++;
processed++;
left--;
Invoke(actionAddItems, new object[] { "Bad duration", fileList[i], 0, 0 }, Color.Red);
Invoke(actionInterface);
continue;
}
// Check whether the tags are properly defined
if (string.IsNullOrEmpty(isrc) && (string.IsNullOrEmpty(artist) || string.IsNullOrEmpty(title)))
{
badFiles++;
processed++;
left--;
Invoke(
actionAddItems,
new object[] { "ISRC Tag is missing. Skipping file...", fileList[i], 0, 0 },
Color.Red);
Invoke(actionInterface);
continue;
}
IModelReference trackReference;
try
{
lock (this)
{
// Check if this file is already in the database
if (IsDuplicateFile(isrc, artist, title))
{
duplicates++; // There is such file in the database
processed++;
left--;
Invoke(actionInterface);
continue;
}
var track = new TrackData(isrc, artist, title, album, releaseYear, (int)duration);
trackReference = modelService.InsertTrack(track); // Insert new Track in the database
}
}
catch (Exception e)
{
// catch any exception and abort the insertion
processed++;
left--;
badFiles++;
MessageBox.Show(e.Message, Resources.Error, MessageBoxButtons.OK, MessageBoxIcon.Error);
Invoke(actionInterface);
return;
}
int count;
try
{
var hashDatas = fingerprintCommandBuilder
.BuildFingerprintCommand()
.From(fileList[i])
.WithFingerprintConfig(
config =>
{
config.TopWavelets = topWavelets;
config.Stride = stride;
})
.UsingServices(audioService)
.Hash()
.Result; // Create SubFingerprints
modelService.InsertHashDataForTrack(hashDatas, trackReference);
count = hashDatas.Count;
}
catch (Exception e)
{
// catch any exception and abort the insertion
MessageBox.Show(e.Message, Resources.Error, MessageBoxButtons.OK, MessageBoxIcon.Error);
badFiles++;
processed++;
left--;
Invoke(actionInterface);
return;
}
Invoke(actionAddItems, new object[] { artist, title, isrc, duration, count }, Color.Empty);
left--;
processed++;
Invoke(actionInterface);
}
}
public static void SaveInsertDataToFolder(StringBuilder sb, string resultsFolder, IStride insertStride)
{
string filename = string.Format("insert_{0}.csv", insertStride);
string absolutePath = Path.Combine(resultsFolder, filename);
Write(sb, absolutePath);
}
