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[DB] Source reorg

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This commit is contained in:
emeric
2016-06-04 18:17:30 +02:00
parent 147c0d7526
commit f2b635d263
14 changed files with 44 additions and 1037 deletions
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src/database/DatabaseClassifier.cpp
-878
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@@ -1,878 +0,0 @@
/*
* Copyright (C) 2016 Emeric Poupon
*
* This file is part of LMS.
*
* LMS is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* LMS is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with LMS. If not, see <http://www.gnu.org/licenses/>.
*/
//#define BOOST_SPIRIT_THREADSAFE
#include <boost/property_tree/json_parser.hpp>
#include "logger/Logger.hpp"
#include "feature/FeatureExtractor.hpp"
#include "knnl/neural_net_headers.hpp"
#include "DatabaseClassifier.hpp"
namespace Database {
Classifier::Classifier(Wt::Dbo::SqlConnectionPool& connectionPool)
: _db(connectionPool)
{}
void
Classifier::processTrackUpdate(bool added, Track::id_type trackId, std::string mbid, boost::filesystem::path path)
{
if (!added)
return;
if (mbid.empty())
{
// TODO compute from file
LMS_LOG(DBUPDATER, INFO) << "File '" << path << "' has no MBID: skipping feature extraction";
return;
}
boost::property_tree::ptree pt;
if (::Feature::Extractor::getLowLevel(pt, mbid))
{
std::ostringstream oss;
boost::property_tree::write_json(oss, pt);
{
Wt::Dbo::Transaction transaction(_db.getSession());
Track::pointer track = Database::Track::getById(_db.getSession(), trackId);
Feature::create( _db.getSession(), track, "low_level", oss.str());
}
}
pt.clear();
if (::Feature::Extractor::getHighLevel(pt, mbid))
{
std::ostringstream oss;
boost::property_tree::write_json(oss, pt);
{
Wt::Dbo::Transaction transaction(_db.getSession());
Track::pointer track = Database::Track::getById(_db.getSession(), trackId);
Feature::create( _db.getSession(), track, "high_level", oss.str());
}
}
}
typedef std::vector<double> entry_t;
typedef std::vector<entry_t> Entries;
static bool entryAddData(entry_t& entry, const boost::property_tree::ptree& pt, std::size_t nbDimensions)
{
if (pt.empty() && nbDimensions == 1)
{
double value = std::stod(pt.data());
entry.push_back(value);
return true;
}
if (pt.size() == nbDimensions)
{
for (auto it : pt)
{
double value = std::stod(it.second.data());
entry.push_back(value);
}
return true;
}
LMS_LOG(DBUPDATER, DEBUG) << "Bad entry";
return false;
}
struct FeatureDesc
{
std::string lowLevelName;
std::size_t nbDimensions;
double coeff;
};
static std::vector<FeatureDesc> features =
{
// { "lowlevel.average_loudness", 1, 1.0 },
// { "lowlevel.barkbands.dmean", 27, 1.0 },
// { "lowlevel.barkbands.dmean2", 27, 1.0 },
// { "lowlevel.barkbands.dvar", 27, 1.0 },
// { "lowlevel.barkbands.dvar2", 27, 1.0 },
// { "lowlevel.barkbands.max", 27, 1.0 },
// { "lowlevel.barkbands.mean", 27, 1.0 },
// { "lowlevel.barkbands.median", 27, 1.0 },
// { "lowlevel.barkbands.min", 27, 1.0 },
// { "lowlevel.barkbands.var", 27, 1.0 },
// { "lowlevel.barkbands_crest.dmean", 1, 1.0 },
// { "lowlevel.barkbands_crest.dmean2", 1, 1.0 },
// { "lowlevel.barkbands_crest.dvar", 1, 1.0 },
// { "lowlevel.barkbands_crest.dvar2", 1, 1.0 },
// { "lowlevel.barkbands_crest.max", 1, 1.0 },
// { "lowlevel.barkbands_crest.mean", 1, 1.0 },
// { "lowlevel.barkbands_crest.median", 1, 1.0 },
// { "lowlevel.barkbands_crest.min", 1, 1.0 },
// { "lowlevel.barkbands_crest.var", 1, 1.0 },
// { "lowlevel.barkbands_flatness_db.dmean", 1, 1.0 },
// { "lowlevel.barkbands_flatness_db.dmean2", 1, 1.0 },
// { "lowlevel.barkbands_flatness_db.dvar", 1, 1.0 },
// { "lowlevel.barkbands_flatness_db.dvar2", 1, 1.0 },
// { "lowlevel.barkbands_flatness_db.max", 1, 1.0 },
// { "lowlevel.barkbands_flatness_db.mean", 1, 1.0 },
// { "lowlevel.barkbands_flatness_db.median", 1, 1.0 },
// { "lowlevel.barkbands_flatness_db.min", 1, 1.0 },
// { "lowlevel.barkbands_flatness_db.var", 1, 1.0 },
// { "lowlevel.barkbands_kurtosis.dmean", 1, 1.0 },
// { "lowlevel.barkbands_kurtosis.dmean2", 1, 1.0 },
// { "lowlevel.barkbands_kurtosis.dvar", 1, 1.0 },
// { "lowlevel.barkbands_kurtosis.dvar2", 1, 1.0 },
// { "lowlevel.barkbands_kurtosis.max", 1, 1.0 },
// { "lowlevel.barkbands_kurtosis.mean", 1, 1.0 },
// { "lowlevel.barkbands_kurtosis.median", 1, 1.0 },
// { "lowlevel.barkbands_kurtosis.min", 1, 1.0 },
// { "lowlevel.barkbands_kurtosis.var", 1, 1.0 },
// { "lowlevel.barkbands_skewness.dmean", 1, 1.0 },
// { "lowlevel.barkbands_skewness.dmean2", 1, 1.0 },
// { "lowlevel.barkbands_skewness.dvar", 1, 1.0 },
// { "lowlevel.barkbands_skewness.dvar2", 1, 1.0 },
// { "lowlevel.barkbands_skewness.max", 1, 1.0 },
// { "lowlevel.barkbands_skewness.mean", 1, 1.0 },
// { "lowlevel.barkbands_skewness.median", 1, 1.0 },
// { "lowlevel.barkbands_skewness.min", 1, 1.0 },
// { "lowlevel.barkbands_skewness.var", 1, 1.0 },
// { "lowlevel.barkbands_spread.dmean", 1, 1.0 },
// { "lowlevel.barkbands_spread.dmean2", 1, 1.0 },
// { "lowlevel.barkbands_spread.dvar", 1, 1.0 },
// { "lowlevel.barkbands_spread.dvar2", 1, 1.0 },
// { "lowlevel.barkbands_spread.max", 1, 1.0 },
// { "lowlevel.barkbands_spread.mean", 1, 1.0 },
// { "lowlevel.barkbands_spread.median", 1, 1.0 },
// { "lowlevel.barkbands_spread.min", 1, 1.0 },
// { "lowlevel.barkbands_spread.var", 1, 1.0 },
// { "lowlevel.dissonance.dmean", 1, 1.0 },
// { "lowlevel.dissonance.dmean2", 1, 1.0 },
// { "lowlevel.dissonance.dvar", 1, 1.0 },
// { "lowlevel.dissonance.dvar2", 1, 1.0 },
// { "lowlevel.dissonance.max", 1, 1.0 },
// { "lowlevel.dissonance.mean", 1, 1.0 },
// { "lowlevel.dissonance.median", 1, 1.0 },
// { "lowlevel.dissonance.min", 1, 1.0 },
// { "lowlevel.dissonance.var", 1, 1.0 },
// { "lowlevel.dynamic_complexity", 1, 1.0 },
// { "lowlevel.erbbands.dmean", 40, 1.0 },
// { "lowlevel.erbbands.dmean2", 40, 1.0 },
// { "lowlevel.erbbands.dvar", 40, 1.0 },
// { "lowlevel.erbbands.dvar2", 40, 1.0 },
// { "lowlevel.erbbands.max", 40, 1.0 },
// { "lowlevel.erbbands.mean", 40, 1.0 },
// { "lowlevel.erbbands.median", 40, 1.0 },
// { "lowlevel.erbbands.min", 40, 1.0 },
// { "lowlevel.erbbands.var", 40, 1.0 },
// { "lowlevel.erbbands_crest.dmean", 1, 1.0 },
// { "lowlevel.erbbands_crest.dmean2", 1, 1.0 },
// { "lowlevel.erbbands_crest.dvar", 1, 1.0 },
// { "lowlevel.erbbands_crest.dvar2", 1, 1.0 },
// { "lowlevel.erbbands_crest.max", 1, 1.0 },
// { "lowlevel.erbbands_crest.mean", 1, 1.0 },
// { "lowlevel.erbbands_crest.median", 1, 1.0 },
// { "lowlevel.erbbands_crest.min", 1, 1.0 },
// { "lowlevel.erbbands_crest.var", 1, 1.0 },
// { "lowlevel.erbbands_flatness_db.dmean", 1, 1.0 },
// { "lowlevel.erbbands_flatness_db.dmean2", 1, 1.0 },
// { "lowlevel.erbbands_flatness_db.dvar", 1, 1.0 },
// { "lowlevel.erbbands_flatness_db.dvar2", 1, 1.0 },
// { "lowlevel.erbbands_flatness_db.max", 1, 1.0 },
// { "lowlevel.erbbands_flatness_db.mean", 1, 1.0 },
// { "lowlevel.erbbands_flatness_db.median", 1, 1.0 },
// { "lowlevel.erbbands_flatness_db.min", 1, 1.0 },
// { "lowlevel.erbbands_flatness_db.var", 1, 1.0 },
// { "lowlevel.erbbands_kurtosis.dmean", 1, 1.0 },
// { "lowlevel.erbbands_kurtosis.dmean2", 1, 1.0 },
// { "lowlevel.erbbands_kurtosis.dvar", 1, 1.0 },
// { "lowlevel.erbbands_kurtosis.dvar2", 1, 1.0 },
// { "lowlevel.erbbands_kurtosis.max", 1, 1.0 },
// { "lowlevel.erbbands_kurtosis.mean", 1, 1.0 },
// { "lowlevel.erbbands_kurtosis.median", 1, 1.0 },
// { "lowlevel.erbbands_kurtosis.min", 1, 1.0 },
// { "lowlevel.erbbands_kurtosis.var", 1, 1.0 },
// { "lowlevel.erbbands_skewness.dmean", 1, 1.0 },
// { "lowlevel.erbbands_skewness.dmean2", 1, 1.0 },
// { "lowlevel.erbbands_skewness.dvar", 1, 1.0 },
// { "lowlevel.erbbands_skewness.dvar2", 1, 1.0 },
// { "lowlevel.erbbands_skewness.max", 1, 1.0 },
// { "lowlevel.erbbands_skewness.mean", 1, 1.0 },
// { "lowlevel.erbbands_skewness.median", 1, 1.0 },
// { "lowlevel.erbbands_skewness.min", 1, 1.0 },
// { "lowlevel.erbbands_skewness.var", 1, 1.0 },
// { "lowlevel.erbbands_spread.dmean", 1, 1.0 },
// { "lowlevel.erbbands_spread.dmean2", 1, 1.0 },
// { "lowlevel.erbbands_spread.dvar", 1, 1.0 },
// { "lowlevel.erbbands_spread.dvar2", 1, 1.0 },
// { "lowlevel.erbbands_spread.max", 1, 1.0 },
// { "lowlevel.erbbands_spread.mean", 1, 1.0 },
// { "lowlevel.erbbands_spread.median", 1, 1.0 },
// { "lowlevel.erbbands_spread.min", 1, 1.0 },
// { "lowlevel.erbbands_spread.var", 1, 1.0 },
// { "lowlevel.gfcc.mean", 13, 1.0 },
// { "lowlevel.hfc.dmean", 1, 1.0 },
// { "lowlevel.hfc.dmean2", 1, 1.0 },
// { "lowlevel.hfc.dvar", 1, 1.0 },
// { "lowlevel.hfc.dvar2", 1, 1.0 },
// { "lowlevel.hfc.max", 1, 1.0 },
// { "lowlevel.hfc.mean", 1, 1.0 },
// { "lowlevel.hfc.median", 1, 1.0 },
// { "lowlevel.hfc.min", 1, 1.0 },
// { "lowlevel.hfc.var", 1, 1.0 },
// { "lowlevel.melbands.dmean", 40, 1.0 },
// { "lowlevel.melbands.dmean2", 40, 1.0 },
// { "lowlevel.melbands.dvar", 40, 1.0 },
// { "lowlevel.melbands.dvar2", 40, 1.0 },
// { "lowlevel.melbands.max", 40, 1.0 },
// { "lowlevel.melbands.mean", 40, 1.0 },
// { "lowlevel.melbands.median", 40, 1.0 },
// { "lowlevel.melbands.min", 40, 1.0 },
// { "lowlevel.melbands.var", 40, 1.0 },
// { "lowlevel.melbands_crest.dmean", 1, 1.0 },
// { "lowlevel.melbands_crest.dmean2", 1, 1.0 },
// { "lowlevel.melbands_crest.dvar", 1, 1.0 },
// { "lowlevel.melbands_crest.dvar2", 1, 1.0 },
// { "lowlevel.melbands_crest.max", 1, 1.0 },
// { "lowlevel.melbands_crest.mean", 1, 1.0 },
// { "lowlevel.melbands_crest.median", 1, 1.0 },
// { "lowlevel.melbands_crest.min", 1, 1.0 },
// { "lowlevel.melbands_crest.var", 1, 1.0 },
// { "lowlevel.melbands_flatness_db.dmean", 1, 1.0 },
// { "lowlevel.melbands_flatness_db.dmean2", 1, 1.0 },
// { "lowlevel.melbands_flatness_db.dvar", 1, 1.0 },
// { "lowlevel.melbands_flatness_db.dvar2", 1, 1.0 },
// { "lowlevel.melbands_flatness_db.max", 1, 1.0 },
// { "lowlevel.melbands_flatness_db.mean", 1, 1.0 },
// { "lowlevel.melbands_flatness_db.median", 1, 1.0 },
// { "lowlevel.melbands_flatness_db.min", 1, 1.0 },
// { "lowlevel.melbands_flatness_db.var", 1, 1.0 },
// { "lowlevel.melbands_kurtosis.dmean", 1, 1.0 },
// { "lowlevel.melbands_kurtosis.dmean2", 1, 1.0 },
// { "lowlevel.melbands_kurtosis.dvar", 1, 1.0 },
// { "lowlevel.melbands_kurtosis.dvar2", 1, 1.0 },
// { "lowlevel.melbands_kurtosis.max", 1, 1.0 },
// { "lowlevel.melbands_kurtosis.mean", 1, 1.0 },
// { "lowlevel.melbands_kurtosis.median", 1, 1.0 },
// { "lowlevel.melbands_kurtosis.min", 1, 1.0 },
// { "lowlevel.melbands_kurtosis.var", 1, 1.0 },
// { "lowlevel.melbands_spread.dmean", 1, 1.0 },
// { "lowlevel.melbands_spread.dmean2", 1, 1.0 },
// { "lowlevel.melbands_spread.dvar", 1, 1.0 },
// { "lowlevel.melbands_spread.dvar2", 1, 1.0 },
// { "lowlevel.melbands_spread.max", 1, 1.0 },
// { "lowlevel.melbands_spread.mean", 1, 1.0 },
// { "lowlevel.melbands_spread.median", 1, 1.0 },
// { "lowlevel.melbands_spread.min", 1, 1.0 },
// { "lowlevel.melbands_spread.var", 1, 1.0 },
// { "lowlevel.mfcc.mean", 13, 1.0 },
// { "lowlevel.pitch_salience.dmean", 1, 1.0 },
// { "lowlevel.pitch_salience.dmean2", 1, 1.0 },
// { "lowlevel.pitch_salience.dvar", 1, 1.0 },
// { "lowlevel.pitch_salience.dvar2", 1, 1.0 },
// { "lowlevel.pitch_salience.max", 1, 1.0 },
// { "lowlevel.pitch_salience.mean", 1, 1.0 },
// { "lowlevel.pitch_salience.median", 1, 1.0 },
// { "lowlevel.pitch_salience.min", 1, 1.0 },
// { "lowlevel.pitch_salience.var", 1, 1.0 },
// { "lowlevel.silence_rate_20dB.dmean", 1, 1.0 },
// { "lowlevel.silence_rate_20dB.dmean2", 1, 1.0 },
// { "lowlevel.silence_rate_20dB.dvar", 1, 1.0 },
// { "lowlevel.silence_rate_20dB.dvar2", 1, 1.0 },
// { "lowlevel.silence_rate_20dB.max", 1, 1.0 },
// { "lowlevel.silence_rate_20dB.mean", 1, 1.0 },
// { "lowlevel.silence_rate_20dB.median", 1, 1.0 },
// { "lowlevel.silence_rate_20dB.min", 1, 1.0 },
// { "lowlevel.silence_rate_20dB.var", 1, 1.0 },
// { "lowlevel.silence_rate_30dB.dmean", 1, 1.0 },
// { "lowlevel.silence_rate_30dB.dmean2", 1, 1.0 },
// { "lowlevel.silence_rate_30dB.dvar", 1, 1.0 },
// { "lowlevel.silence_rate_30dB.dvar2", 1, 1.0 },
// { "lowlevel.silence_rate_30dB.max", 1, 1.0 },
// { "lowlevel.silence_rate_30dB.mean", 1, 1.0 },
// { "lowlevel.silence_rate_30dB.median", 1, 1.0 },
// { "lowlevel.silence_rate_30dB.min", 1, 1.0 },
// { "lowlevel.silence_rate_30dB.var", 1, 1.0 },
// { "lowlevel.silence_rate_60dB.dmean", 1, 1.0 },
// { "lowlevel.silence_rate_60dB.dmean2", 1, 1.0 },
// { "lowlevel.silence_rate_60dB.dvar", 1, 1.0 },
// { "lowlevel.silence_rate_60dB.dvar2", 1, 1.0 },
// { "lowlevel.silence_rate_60dB.max", 1, 1.0 },
// { "lowlevel.silence_rate_60dB.mean", 1, 1.0 },
// { "lowlevel.silence_rate_60dB.median", 1, 1.0 },
// { "lowlevel.silence_rate_60dB.min", 1, 1.0 },
// { "lowlevel.silence_rate_60dB.var", 1, 1.0 },
// { "lowlevel.spectral_centroid.dmean", 1, 1.0 },
// { "lowlevel.spectral_centroid.dmean2", 1, 1.0 },
// { "lowlevel.spectral_centroid.dvar", 1, 1.0 },
// { "lowlevel.spectral_centroid.dvar2", 1, 1.0 },
// { "lowlevel.spectral_centroid.max", 1, 1.0 },
{ "lowlevel.spectral_centroid.mean", 1, 1.0 },
// { "lowlevel.spectral_centroid.median", 1, 1.0 },
// { "lowlevel.spectral_centroid.min", 1, 1.0 },
{ "lowlevel.spectral_centroid.var", 1, 1.0 },
// { "lowlevel.spectral_complexity.dmean", 1, 1.0 },
// { "lowlevel.spectral_complexity.dmean2", 1, 1.0 },
// { "lowlevel.spectral_complexity.dvar", 1, 1.0 },
// { "lowlevel.spectral_complexity.dvar2", 1, 1.0 },
// { "lowlevel.spectral_complexity.max", 1, 1.0 },
{ "lowlevel.spectral_complexity.mean", 1, 1.0 },
// { "lowlevel.spectral_complexity.median", 1, 1.0 },
// { "lowlevel.spectral_complexity.min", 1, 1.0 },
{ "lowlevel.spectral_complexity.var", 1, 1.0 },
// { "lowlevel.spectral_decrease.dmean", 1, 1.0 },
// { "lowlevel.spectral_decrease.dmean2", 1, 1.0 },
// { "lowlevel.spectral_decrease.dvar", 1, 1.0 },
// { "lowlevel.spectral_decrease.dvar2", 1, 1.0 },
// { "lowlevel.spectral_decrease.max", 1, 1.0 },
{ "lowlevel.spectral_decrease.mean", 1, 1.0 },
// { "lowlevel.spectral_decrease.median", 1, 1.0 },
// { "lowlevel.spectral_decrease.min", 1, 1.0 },
{ "lowlevel.spectral_decrease.var", 1, 1.0 },
// { "lowlevel.spectral_energy.dmean", 1, 1.0 },
// { "lowlevel.spectral_energy.dmean2", 1, 1.0 },
// { "lowlevel.spectral_energy.dvar", 1, 1.0 },
// { "lowlevel.spectral_energy.dvar2", 1, 1.0 },
// { "lowlevel.spectral_energy.max", 1, 1.0 },
{ "lowlevel.spectral_energy.mean", 1, 1.0 },
// { "lowlevel.spectral_energy.median", 1, 1.0 },
// { "lowlevel.spectral_energy.min", 1, 1.0 },
{ "lowlevel.spectral_energy.var", 1, 1.0 },
// { "lowlevel.spectral_energyband_low.dmean", 1, 1.0 },
// { "lowlevel.spectral_energyband_low.dmean2", 1, 1.0 },
// { "lowlevel.spectral_energyband_low.dvar", 1, 1.0 },
// { "lowlevel.spectral_energyband_low.dvar2", 1, 1.0 },
// { "lowlevel.spectral_energyband_low.max", 1, 1.0 },
{ "lowlevel.spectral_energyband_low.mean", 1, 1.0 },
// { "lowlevel.spectral_energyband_low.median", 1, 1.0 },
// { "lowlevel.spectral_energyband_low.min", 1, 1.0 },
{ "lowlevel.spectral_energyband_low.var", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_high.dmean", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_high.dmean2", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_high.dvar", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_high.dvar2", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_high.max", 1, 1.0 },
{ "lowlevel.spectral_energyband_middle_high.mean", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_high.median", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_high.min", 1, 1.0 },
{ "lowlevel.spectral_energyband_middle_high.var", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_low.dmean", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_low.dmean2", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_low.dvar", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_low.dvar2", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_low.max", 1, 1.0 },
{ "lowlevel.spectral_energyband_middle_low.mean", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_low.median", 1, 1.0 },
// { "lowlevel.spectral_energyband_middle_low.min", 1, 1.0 },
{ "lowlevel.spectral_energyband_middle_low.var", 1, 1.0 },
// { "lowlevel.spectral_entropy.dmean", 1, 1.0 },
// { "lowlevel.spectral_entropy.dmean2", 1, 1.0 },
// { "lowlevel.spectral_entropy.dvar", 1, 1.0 },
// { "lowlevel.spectral_entropy.dvar2", 1, 1.0 },
// { "lowlevel.spectral_entropy.max", 1, 1.0 },
{ "lowlevel.spectral_entropy.mean", 1, 1.0 },
// { "lowlevel.spectral_entropy.median", 1, 1.0 },
// { "lowlevel.spectral_entropy.min", 1, 1.0 },
{ "lowlevel.spectral_entropy.var", 1, 1.0 },
// { "lowlevel.spectral_flux.dmean", 1, 1.0 },
// { "lowlevel.spectral_flux.dmean2", 1, 1.0 },
// { "lowlevel.spectral_flux.dvar", 1, 1.0 },
// { "lowlevel.spectral_flux.dvar2", 1, 1.0 },
// { "lowlevel.spectral_flux.max", 1, 1.0 },
// { "lowlevel.spectral_flux.mean", 1, 1.0 },
// { "lowlevel.spectral_flux.median", 1, 1.0 },
// { "lowlevel.spectral_flux.min", 1, 1.0 },
// { "lowlevel.spectral_flux.var", 1, 1.0 },
// { "lowlevel.spectral_kurtosis.dmean", 1, 1.0 },
// { "lowlevel.spectral_kurtosis.dmean2", 1, 1.0 },
// { "lowlevel.spectral_kurtosis.dvar", 1, 1.0 },
// { "lowlevel.spectral_kurtosis.dvar2", 1, 1.0 },
// { "lowlevel.spectral_kurtosis.max", 1, 1.0 },
// { "lowlevel.spectral_kurtosis.mean", 1, 1.0 },
// { "lowlevel.spectral_kurtosis.median", 1, 1.0 },
// { "lowlevel.spectral_kurtosis.min", 1, 1.0 },
// { "lowlevel.spectral_kurtosis.var", 1, 1.0 },
// { "lowlevel.spectral_rms.dmean", 1, 1.0 },
// { "lowlevel.spectral_rms.dmean2", 1, 1.0 },
// { "lowlevel.spectral_rms.dvar", 1, 1.0 },
// { "lowlevel.spectral_rms.dvar2", 1, 1.0 },
// { "lowlevel.spectral_rms.max", 1, 1.0 },
{ "lowlevel.spectral_rms.mean", 1, 1.0 },
// { "lowlevel.spectral_rms.median", 1, 1.0 },
// { "lowlevel.spectral_rms.min", 1, 1.0 },
{ "lowlevel.spectral_rms.var", 1, 1.0 },
// { "lowlevel.spectral_rolloff.dmean", 1, 1.0 },
// { "lowlevel.spectral_rolloff.dmean2", 1, 1.0 },
// { "lowlevel.spectral_rolloff.dvar", 1, 1.0 },
// { "lowlevel.spectral_rolloff.dvar2", 1, 1.0 },
// { "lowlevel.spectral_rolloff.max", 1, 1.0 },
// { "lowlevel.spectral_rolloff.mean", 1, 1.0 },
// { "lowlevel.spectral_rolloff.median", 1, 1.0 },
// { "lowlevel.spectral_rolloff.min", 1, 1.0 },
// { "lowlevel.spectral_rolloff.var", 1, 1.0 },
// { "lowlevel.spectral_skewness.dmean", 1, 1.0 },
// { "lowlevel.spectral_skewness.dmean2", 1, 1.0 },
// { "lowlevel.spectral_skewness.dvar", 1, 1.0 },
// { "lowlevel.spectral_skewness.dvar2", 1, 1.0 },
// { "lowlevel.spectral_skewness.max", 1, 1.0 },
// { "lowlevel.spectral_skewness.mean", 1, 1.0 },
// { "lowlevel.spectral_skewness.median", 1, 1.0 },
// { "lowlevel.spectral_skewness.min", 1, 1.0 },
// { "lowlevel.spectral_skewness.var", 1, 1.0 },
// { "lowlevel.spectral_spread.dmean", 1, 1.0 },
// { "lowlevel.spectral_spread.dmean2", 1, 1.0 },
// { "lowlevel.spectral_spread.dvar", 1, 1.0 },
// { "lowlevel.spectral_spread.dvar2", 1, 1.0 },
// { "lowlevel.spectral_spread.max", 1, 1.0 },
{ "lowlevel.spectral_spread.mean", 1, 1.0 },
// { "lowlevel.spectral_spread.median", 1, 1.0 },
// { "lowlevel.spectral_spread.min", 1, 1.0 },
{ "lowlevel.spectral_spread.var", 1, 1.0 },
// { "lowlevel.spectral_strongpeak.dmean", 1, 1.0 },
// { "lowlevel.spectral_strongpeak.dmean2", 1, 1.0 },
// { "lowlevel.spectral_strongpeak.dvar", 1, 1.0 },
// { "lowlevel.spectral_strongpeak.dvar2", 1, 1.0 },
// { "lowlevel.spectral_strongpeak.max", 1, 1.0 },
{ "lowlevel.spectral_strongpeak.mean", 1, 1.0 },
// { "lowlevel.spectral_strongpeak.median", 1, 1.0 },
// { "lowlevel.spectral_strongpeak.min", 1, 1.0 },
{ "lowlevel.spectral_strongpeak.var", 1, 1.0 },
// { "lowlevel.zerocrossingrate.dmean", 1, 1.0 },
// { "lowlevel.zerocrossingrate.dmean2", 1, 1.0 },
// { "lowlevel.zerocrossingrate.dvar", 1, 1.0 },
// { "lowlevel.zerocrossingrate.dvar2", 1, 1.0 },
// { "lowlevel.zerocrossingrate.max", 1, 1.0 },
{ "lowlevel.zerocrossingrate.mean", 1, 1.0 },
// { "lowlevel.zerocrossingrate.median", 1, 1.0 },
// { "lowlevel.zerocrossingrate.min", 1, 1.0 },
{ "lowlevel.zerocrossingrate.var", 1, 1.0 },
// { "rhythm.beats_count", 1, 1.0 },
// { "rhythm.beats_loudness.dmean", 1, 1.0 },
// { "rhythm.beats_loudness.dmean2", 1, 1.0 },
// { "rhythm.beats_loudness.dvar", 1, 1.0 },
// { "rhythm.beats_loudness.dvar2", 1, 1.0 },
// { "rhythm.beats_loudness.max", 1, 1.0 },
{ "rhythm.beats_loudness.mean", 1, 1.0 },
// { "rhythm.beats_loudness.median", 1, 1.0 },
// { "rhythm.beats_loudness.min", 1, 1.0 },
{ "rhythm.beats_loudness.var", 1, 1.0 },
// { "rhythm.bpm", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_bpm.dmean", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_bpm.dmean2", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_bpm.dvar", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_bpm.dvar2", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_bpm.max", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_bpm.mean", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_bpm.median", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_bpm.min", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_bpm.var", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_spread.dmean", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_spread.dmean2", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_spread.dvar", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_spread.dvar2", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_spread.max", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_spread.mean", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_spread.median", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_spread.min", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_spread.var", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_weight.dmean", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_weight.dmean2", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_weight.dvar", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_weight.dvar2", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_weight.max", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_weight.mean", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_weight.median", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_weight.min", 1, 1.0 },
// { "rhythm.bpm_histogram_first_peak_weight.var", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_bpm.dmean", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_bpm.dmean2", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_bpm.dvar", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_bpm.dvar2", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_bpm.max", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_bpm.mean", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_bpm.median", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_bpm.min", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_bpm.var", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_spread.dmean", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_spread.dmean2", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_spread.dvar", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_spread.dvar2", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_spread.max", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_spread.mean", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_spread.median", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_spread.min", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_spread.var", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_weight.dmean", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_weight.dmean2", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_weight.dvar", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_weight.dvar2", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_weight.max", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_weight.mean", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_weight.median", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_weight.min", 1, 1.0 },
// { "rhythm.bpm_histogram_second_peak_weight.var", 1, 1.0 },
// { "rhythm.danceability", 1, 1.0},
// { "rhythm.onset_rate", 1, 1.0},
//
// { "tonal.chords_changes_rate", 1, 1.0 },
// { "tonal.chords_histogram", 24, 1.0 },
// { "tonal.chords_number_rate", 1, 1.0 },
// { "tonal.hpcp_entropy.dmean", 1, 1.0 },
// { "tonal.hpcp_entropy.dmean2", 1, 1.0 },
// { "tonal.hpcp_entropy.dvar", 1, 1.0 },
// { "tonal.hpcp_entropy.dvar2", 1, 1.0 },
// { "tonal.hpcp_entropy.max", 1, 1.0 },
// { "tonal.hpcp_entropy.mean", 1, 1.0 },
// { "tonal.hpcp_entropy.median", 1, 1.0 },
// { "tonal.hpcp_entropy.min", 1, 1.0 },
// { "tonal.hpcp_entropy.var", 1, 1.0 },
// { "tonal.tuning_frequency", 1, 1.0 },
};
static bool entryConstructFromJSON(entry_t& entry, const std::string& jsonData)
{
std::istringstream iss(jsonData);
boost::property_tree::ptree pt;
boost::property_tree::json_parser::read_json(iss, pt);
for (auto& elem : features)
{
auto child = pt.get_child_optional(elem.lowLevelName);
if (!child)
{
LMS_LOG(DBUPDATER, DEBUG) << "Cannot get '" << elem.lowLevelName << "'";
return false;
}
if (!entryAddData(entry, *child, elem.nbDimensions))
return false;
}
return true;
}
static entry_t computeWeightCoeffs(const Entries& entries)
{
std::vector<FeatureDesc> expandedFeatures;
std::vector<double> userCoeffs;
for (auto& feature : features)
{
for (std::size_t i = 0; i < feature.nbDimensions; ++i)
{
expandedFeatures.push_back( { feature.lowLevelName + std::to_string(i), 1, feature.coeff / (double)feature.nbDimensions} );
}
}
entry_t coeffs;
::neural_net::Ranges<Entries> ranges (*entries.begin());
ranges( entries );
entry_t::const_iterator pos_max = ranges.get_max().begin();
entry_t::const_iterator pos_min = ranges.get_min().begin();
for (std::size_t i = 0; pos_max != ranges.get_max().end(); ++pos_max, ++pos_min, ++i)
{
coeffs.push_back( expandedFeatures.at(i).coeff *
::operators::inverse (
( *pos_max - *pos_min ) * ( *pos_max - *pos_min )
) ); // weight for i-th axis
std::cout << "Feature = " << expandedFeatures.at(i).lowLevelName << std::endl;
std::cout << "min = " << *pos_min << ", max = " << *pos_max << std::endl;
std::cout << "Coeff = " << coeffs.back() << std::endl;
}
return coeffs;
}
template <class T, size_t ROW, size_t COL>
using Matrix = std::array<std::array<T, COL>, ROW>;
void
Classifier::processDatabaseUpdate(Updater::Stats stats)
{
LMS_LOG(DBUPDATER, DEBUG) << "Database complete Called!";
// typedef ::neural_net::Cauchy_function < entry_t::value_type, entry_t::value_type, ::boost::int32_t> CauchyFunction;
typedef ::neural_net::Gauss_function < entry_t::value_type, entry_t::value_type, ::boost::int32_t > GaussFunction;
// typedef ::distance::Euclidean_distance_function < entry_t > EuclideanDistFunction;
typedef ::distance::Weighted_euclidean_distance_function < entry_t, entry_t > WeightedEuclideanDistFunction;
typedef ::neural_net::Basic_neuron < GaussFunction, WeightedEuclideanDistFunction > KohonenNeuron;
typedef ::neural_net::Rectangular_container < KohonenNeuron > KohonenNetwork;
// typedef ::neural_net::Hexagonal_topology < ::boost::int32_t > HexagonalTopology;
typedef ::neural_net::Max_topology < ::boost::int32_t > MaxTopology;
typedef GaussFunction GaussFunctionSpace;
typedef ::neural_net::Gauss_function < ::boost::int32_t, entry_t::value_type, ::boost::int32_t > GaussFunctionNet;
// typedef ::neural_net::Constant_function < entry_t::value_type, entry_t::value_type > ConstFuncSpace;
typedef ::neural_net::Classic_training_weight
<
entry_t,
::boost::int32_t,
GaussFunctionNet,
GaussFunctionSpace,
MaxTopology,
WeightedEuclideanDistFunction,
::boost::int32_t
> ClassicWeight;
typedef ::neural_net::Wtm_classical_training_functional
<
entry_t,
double,
::boost::int32_t,
::boost::int32_t,
ClassicWeight
> WtmTrainingFunc;
typedef ::neural_net::Wtm_training_algorithm
<
KohonenNetwork,
entry_t,
Entries::iterator,
WtmTrainingFunc,
::boost::int32_t
> WtmTrainingAlg;
Entries entries;
LMS_LOG(DBUPDATER, DEBUG) << "Getting track ids";
std::vector<Track::id_type> trackIdsAll = Database::Track::getAllIds(_db.getSession());
::std::random_shuffle ( trackIdsAll.begin(), trackIdsAll.end() );
LMS_LOG(DBUPDATER, DEBUG) << "Getting JSON data";
std::vector<Track::id_type> trackIds;
for (Track::id_type trackId : trackIdsAll)
{
std::string jsonData;
{
Wt::Dbo::Transaction transaction(_db.getSession());
std::vector<Feature::pointer> features = Feature::getByTrack(_db.getSession(), trackId, "low_level");
if (features.empty())
{
LMS_LOG(DBUPDATER, DEBUG) << "No JSON data for track " << trackId;;
continue;
}
jsonData = features.front()->getValue();
}
entry_t entry;
if (entryConstructFromJSON(entry, jsonData))
{
trackIds.push_back(trackId);
entries.push_back(entry);
}
else
{
LMS_LOG(DBUPDATER, ERROR) << "Skipping track " << trackId;
continue;
}
// if (entries.size() > 100)
// break;
}
std::cout << "Feature vector dimension = " << entries.front().size() << std::endl;
if (entries.empty())
return;
// TODO compute rows / columns from the DBB
const std::size_t nbRows = 32;
const std::size_t nbColumns = 32;
// CauchyFunction cauchyFunc(2.0, 1);
GaussFunction gaussFunc(2.0, 1 );
LMS_LOG(DBUPDATER, DEBUG) << "Computing coeffs";
entry_t coeffs = computeWeightCoeffs(entries);
LMS_LOG(DBUPDATER, DEBUG) << "Initializing distfunc";
WeightedEuclideanDistFunction weightedEuclideanDistFunc (&coeffs);
// prepare randomization policy
::neural_net::Internal_randomize internalRandomize;
KohonenNetwork network;
// generate networks initialized by data
LMS_LOG(DBUPDATER, DEBUG) << "Generating network...";
::neural_net::generate_kohonen_network(nbRows, nbColumns, gaussFunc, weightedEuclideanDistFunc, entries, network, internalRandomize);
::std::cout << "Network weights:" << ::std::endl;
::neural_net::print_network_weights ( ::std::cout, network );
::std::cout << ::std::endl;
GaussFunctionNet gaussFuncNetwork(10, 1);
GaussFunctionSpace gaussFuncSpace(10, 1);
MaxTopology maxTopology;
// EuclideanDistFunction euclideanDistFunc;
ClassicWeight classicWeight(gaussFuncNetwork, gaussFuncSpace, maxTopology, weightedEuclideanDistFunc);
WtmTrainingFunc trainingFunc(classicWeight, 0.3);
WtmTrainingAlg wtmTrainAlg( trainingFunc );
LMS_LOG(DBUPDATER, DEBUG) << "Training...";
// tricky training
std::size_t nbPass = 20;
for (std::size_t i = 0; i < nbPass; ++i )
{
LMS_LOG(DBUPDATER, DEBUG) << "Training pass " << i << " / " << nbPass;
// train network using data
auto entries_copy = entries;
::std::random_shuffle ( entries_copy.begin(), entries_copy.end() );
wtmTrainAlg(entries_copy.begin(), entries_copy.end(), &network);
// decrease sigma parameter in network will make training proces more sharpen with each epoch,
// but it have to be done slowly :-)
wtmTrainAlg.training_functional.generalized_training_weight.network_function.sigma *= 2.0/3.0;
// wtmTrainAlg.training_functional.generalized_training_weight.network_function.sigma *= 9.0/10.0;
// shuffle data
// ::std::random_shuffle ( entries.begin(), entries.end() );
}
LMS_LOG(DBUPDATER, DEBUG) << "Training DONE...";
::std::cout << "Network weights:" << ::std::endl;
::neural_net::print_network_weights ( ::std::cout, network );
::std::cout << ::std::endl;
struct ClusterEntry
{
Track::id_type trackId;
entry_t entry;
double distance;
};
Matrix<std::vector<ClusterEntry>, nbRows, nbColumns> trackClusters;
for (std::size_t id = 0; id < entries.size(); ++id)
{
std::cout << "Entry " << id << " : " << std::endl;
// Display input vector
for (auto& value : entries[id])
{
std::cout << value << " ";
}
std::cout << std::endl;
auto entry = entries[id];
std::pair<int, int> coordinates = {0,0};
double maxValue = 0.0;
for (std::size_t i = 0; i < network.objects.size(); ++i)
{
for (std::size_t j = 0; j < network.objects[0].size(); ++j)
{
double value = network.objects[i][j]( entry);
if (value > maxValue)
{
coordinates = {i, j};
maxValue = value;
}
}
}
/* ::std::cout << "Network for entry " << id << std::endl;
::neural_net::print_network ( ::std::cout, network, entry );
::std::cout << ::std::endl;
std::cout << "Max is in {" << coordinates.first << ", " << coordinates.second << "}" << std::endl;
*/
trackClusters[coordinates.first][coordinates.second].push_back({trackIds[id], entry, maxValue});
}
// Create clusters
LMS_LOG(DBUPDATER, DEBUG) << "Erasing old clusters";
{
Wt::Dbo::Transaction transaction(_db.getSession());
Cluster::removeByType(_db.getSession(), "similarity");
}
LMS_LOG(DBUPDATER, DEBUG) << "Creating new cluster...";
for (std::size_t i = 0; i < nbRows; i++)
{
for (std::size_t j = 0; j < nbColumns; j++)
{
LMS_LOG(DBUPDATER, DEBUG) << "Creating cluster " << i << " " << j;
Wt::Dbo::Transaction transaction(_db.getSession());
Cluster::pointer cluster = Cluster::create(_db.getSession(), "similarity", "cluster_" + std::to_string(i) + "_" + std::to_string(j));
for (auto track : trackClusters[i][j])
{
Track::pointer t = Database::Track::getById(_db.getSession(), track.trackId);
cluster.modify()->addTrack(t);
}
}
}
for (std::size_t i = 0; i < nbRows; i++)
{
for (std::size_t j = 0; j < nbColumns; j++)
{
std::cout << "Cluster [" << i << "," << j << "] - ";
// Display the neuron for this cluster
for (auto& value : network.objects[i][j].weights)
std::cout << value << " ";
std::cout << std::endl;
for (auto track : trackClusters[i][j])
{
Wt::Dbo::Transaction transaction(_db.getSession());
std::cout << "- " << track.distance;
Track::pointer t = Database::Track::getById(_db.getSession(), track.trackId);
std::cout << " - " << track.trackId << " - " << t->getArtist()->getName() << " - " << t->getName() << " - (" ;
// Display input vector
for (auto& value : track.entry)
std::cout << value << " ";
std::cout << ")" << std::endl;
}
}
}
}
} // namespace Database
src/database/DatabaseClassifier.hpp
-55
View File
@@ -1,55 +0,0 @@
/*
* Copyright (C) 2016 Emeric Poupon
*
* This file is part of LMS.
*
* LMS is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* LMS is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with LMS. If not, see <http://www.gnu.org/licenses/>.
*/
#pragma once
#include <Wt/Dbo/Dbo>
#include "DatabaseHandler.hpp"
#include "DatabaseUpdater.hpp"
namespace Database {
class ClusterClassifier
{
public:
ClusterClassifier();
private:
};
class Classifier
{
public:
Classifier(Wt::Dbo::SqlConnectionPool& connectionPool);
void processTrackUpdate(bool added, Track::id_type trackId, std::string mbid, boost::filesystem::path p);
void processDatabaseUpdate(Updater::Stats stats);
private:
Database::Handler _db;
};
} // namespace Database
src/database/cluster/DatabaseSimilarityCluster.hpp
View File
src/database/tagger/GenreTagger.cpp
-40
View File
@@ -1,40 +0,0 @@
/*
* Copyright (C) 2016 Emeric Poupon
*
* This file is part of LMS.
*
* LMS is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* LMS is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with LMS. If not, see <http://www.gnu.org/licenses/>.
*/
#include "GenreTagger.hpp"
namespace Database {
void
GenreTagger::processTrackUpdate(bool added, Track::id_type trackId, std::string mbid, boost::filesystem::path p)
{
}
void
GenreTagger::processDatabaseUpdate(Updater::Stats stats)
{
// TODO: if disabled, delete all the genre tags
// TODO: if enabled, reconstruct all the genre tags from tracks
}
} // namespace Database
src/database/tagger/GenreTagger.hpp
-37
View File
@@ -1,37 +0,0 @@
/*
* Copyright (C) 2016 Emeric Poupon
*
* This file is part of LMS.
*
* LMS is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* LMS is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with LMS. If not, see <http://www.gnu.org/licenses/>.
*/
#pragma once
#include "database/DatabaseUpdater.hpp"
namespace Database {
class GenreTagger
{
public:
void processTrackUpdate(bool added, Track::id_type trackId, std::string mbid, boost::filesystem::path p);
void processDatabaseUpdate(Updater::Stats stats);
private:
};
} // namespace Database
src/database/DatabaseFeatureExtractor.cpp → src/database/updater/DatabaseFeatureExtractor.cpp
+2 -2
View File
@@ -21,8 +21,8 @@
#include "feature/FeatureExtractor.hpp"
#include "feature/FeatureStore.hpp"
#include "database/Setting.hpp"
#include "Setting.hpp"
#include "DatabaseFeatureExtractor.hpp"
namespace Database {
@@ -35,7 +35,7 @@ static std::string getMBID(Track::id_type trackId)
}
void
FeatureExtractor::processDatabaseUpdate(Updater::Stats stats)
FeatureExtractor::handleFilesUpdated(void)
{
bool fetchHighLevel = Setting::getBool(UpdaterDboSession(), "tags_highlevel_acousticbrainz");
bool fetchLowLevel = Setting::getBool(UpdaterDboSession(), "tags_similarity_acousticbrainz");
src/database/DatabaseFeatureExtractor.hpp → src/database/updater/DatabaseFeatureExtractor.hpp
+3 -3
View File
@@ -19,14 +19,14 @@
#pragma once
#include "database/DatabaseUpdater.hpp"
#include "DatabaseUpdater.hpp"
namespace Database {
class FeatureExtractor
class FeatureExtractor : public UpdaterEventHandler
{
public:
void processDatabaseUpdate(Updater::Stats stats);
void handleFilesUpdated(void);
};
src/database/cluster/DatabaseHighLevelCluster.cpp → src/database/updater/DatabaseHighLevelCluster.cpp
+1 -1
View File
@@ -165,7 +165,7 @@ static std::list<std::string> getClustersFromFeature(Feature::Type& feature, dou
}
void
HighLevelCluster::processDatabaseUpdate(Updater::Stats stats)
HighLevelCluster::handleFilesUpdated(void)
{
bool createTags = Setting::getBool(UpdaterDboSession(), "tags_highlevel_acousticbrainz", false);
double minProb = Setting::getInt(UpdaterDboSession(), "tags_highlevel_acousticbrainz_min_probability", false) / 100.;
src/database/cluster/DatabaseHighLevelCluster.hpp → src/database/updater/DatabaseHighLevelCluster.hpp
+3 -3
View File
@@ -19,14 +19,14 @@
#pragma once
#include "database/DatabaseUpdater.hpp"
#include "DatabaseUpdater.hpp"
namespace Database {
class HighLevelCluster
class HighLevelCluster : public UpdaterEventHandler
{
public:
void processDatabaseUpdate(Updater::Stats stats);
void handleFilesUpdated(void);
};
src/database/DatabaseUpdater.cpp → src/database/updater/DatabaseUpdater.cpp
+3 -2
View File
@@ -28,8 +28,9 @@
#include "utils/Utils.hpp"
#include "utils/Path.hpp"
#include "Setting.hpp"
#include "Types.hpp"
#include "database/Setting.hpp"
#include "database/Types.hpp"
#include "DatabaseUpdater.hpp"
namespace {
src/database/DatabaseUpdater.hpp → src/database/updater/DatabaseUpdater.hpp
+22 -1
View File
@@ -20,6 +20,7 @@
#pragma once
#include <mutex>
#include <list>
#include <boost/asio/deadline_timer.hpp>
@@ -32,6 +33,9 @@
namespace Database {
class UpdaterEventHandler;
class Updater
{
public:
@@ -60,7 +64,7 @@ class Updater
std::size_t nbChanges() const { return nbAdded + nbRemoved + nbModified;}
};
// Emitted when the whole database has been scanned
// Emitted when the whole database has been scanned (and all the event handlers have been called)
Wt::Signal<Stats>& scanComplete() { return _sigScanComplete; }
// Emitted when a track changed
@@ -76,6 +80,9 @@ class Updater
Database::Handler& getDb(void) { return *_db; }
bool quitRequested(void) const { return !_running;}
void registerEventHandler(std::shared_ptr<UpdaterEventHandler> handler) { _eventHandlers.push_back(handler); }
private:
Updater();
@@ -136,6 +143,8 @@ class Updater
MetaData::TagLibParser _metadataParser;
std::list<std::shared_ptr<UpdaterEventHandler> > _eventHandlers;
}; // class Updater
// Helper to get the updater session data
@@ -149,5 +158,17 @@ static inline bool UpdaterQuitRequested()
return Updater::instance().quitRequested();
}
class UpdaterEventHandler
{
public:
// called when all the files have been scanned by the updater
virtual void handleFilesUpdated(void) = 0;
private:
};
} // Database