annotate vamp-sdk/hostext/PluginSummarisingAdapter.cpp @ 182:3fcac0f3afdc

* More fixes to continuous time averaging

/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */

/*
    Vamp

    An API for audio analysis and feature extraction plugins.

    Centre for Digital Music, Queen Mary, University of London.
    Copyright 2006-2008 Chris Cannam and QMUL.
  
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    Except as contained in this notice, the names of the Centre for
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*/

#include "PluginSummarisingAdapter.h"

#include <map>
#include <cmath>

namespace Vamp {

namespace HostExt {

class PluginSummarisingAdapter::Impl
{
public:
    Impl(Plugin *plugin, float inputSampleRate);
    ~Impl();

    FeatureSet process(const float *const *inputBuffers, RealTime timestamp);
    FeatureSet getRemainingFeatures();

    void setSummarySegmentBoundaries(const SegmentBoundaries &);

    FeatureList getSummaryForOutput(int output,
                                    SummaryType type,
                                    AveragingMethod avg);

    FeatureSet getSummaryForAllOutputs(SummaryType type,
                                       AveragingMethod avg);

protected:
    Plugin *m_plugin;
    float m_inputSampleRate;

    SegmentBoundaries m_boundaries;
    
    typedef std::vector<float> ValueList;
    typedef std::map<int, ValueList> BinValueMap;
    typedef std::vector<RealTime> DurationList;
    
    struct OutputAccumulator {
        int count;
        BinValueMap values; // bin number -> values ordered by time
        DurationList durations;
        OutputAccumulator() : count(0), values(), durations() { }
    };

    typedef std::map<int, OutputAccumulator> OutputAccumulatorMap;
    OutputAccumulatorMap m_accumulators; // output number -> accumulator

    typedef std::map<int, RealTime> OutputTimestampMap;
    OutputTimestampMap m_prevTimestamps; // output number -> timestamp

    struct OutputBinSummary {

        int count;

        // extents
        float minimum;
        float maximum;
        float sum;

        // sample-average results
        float median;
        float mode;
        float variance;

        // continuous-time average results
        float median_c;
        float mode_c;
        float mean_c;
        float variance_c;
    };

    typedef std::map<int, OutputBinSummary> OutputSummary;
    typedef std::map<RealTime, OutputSummary> SummarySegmentMap;
    typedef std::map<int, SummarySegmentMap> OutputSummarySegmentMap;

    OutputSummarySegmentMap m_summaries;

    RealTime m_lastTimestamp;
    RealTime m_prevDuration;

    void accumulate(const FeatureSet &fs, RealTime, bool final);
    void accumulate(int output, const Feature &f, RealTime, bool final);
    void reduce();
};
    
PluginSummarisingAdapter::PluginSummarisingAdapter(Plugin *plugin) :
    PluginWrapper(plugin)
{
    m_impl = new Impl(plugin, m_inputSampleRate);
}

PluginSummarisingAdapter::~PluginSummarisingAdapter()
{
    delete m_impl;
}

Plugin::FeatureSet
PluginSummarisingAdapter::process(const float *const *inputBuffers, RealTime timestamp)
{
    return m_impl->process(inputBuffers, timestamp);
}

Plugin::FeatureSet
PluginSummarisingAdapter::getRemainingFeatures()
{
    return m_impl->getRemainingFeatures();
}

Plugin::FeatureList
PluginSummarisingAdapter::getSummaryForOutput(int output,
                                              SummaryType type,
                                              AveragingMethod avg)
{
    return m_impl->getSummaryForOutput(output, type, avg);
}

Plugin::FeatureSet
PluginSummarisingAdapter::getSummaryForAllOutputs(SummaryType type,
                                                  AveragingMethod avg)
{
    return m_impl->getSummaryForAllOutputs(type, avg);
}

PluginSummarisingAdapter::Impl::Impl(Plugin *plugin, float inputSampleRate) :
    m_plugin(plugin),
    m_inputSampleRate(inputSampleRate)
{
}

PluginSummarisingAdapter::Impl::~Impl()
{
}

Plugin::FeatureSet
PluginSummarisingAdapter::Impl::process(const float *const *inputBuffers, RealTime timestamp)
{
    FeatureSet fs = m_plugin->process(inputBuffers, timestamp);
    accumulate(fs, timestamp, false);
    m_lastTimestamp = timestamp;
    return fs;
}

Plugin::FeatureSet
PluginSummarisingAdapter::Impl::getRemainingFeatures()
{
    FeatureSet fs = m_plugin->getRemainingFeatures();
    accumulate(fs, m_lastTimestamp, true);
    reduce();
    return fs;
}

Plugin::FeatureList
PluginSummarisingAdapter::Impl::getSummaryForOutput(int output,
                                                    SummaryType type,
                                                    AveragingMethod avg)
{
    bool continuous = (avg == ContinuousTimeAverage);

    //!!! need to ensure that this is only called after processing is
    //!!! complete (at the moment processing is "completed" in the
    //!!! call to getRemainingFeatures, but we don't want to require
    //!!! the host to call getRemainingFeatures at all unless it
    //!!! actually wants the raw features too -- calling getSummary
    //!!! should be enough -- we do need to ensure that all data has
    //!!! been processed though!)
    FeatureList fl;
    for (SummarySegmentMap::const_iterator i = m_summaries[output].begin();
         i != m_summaries[output].end(); ++i) {

        Feature f;
        f.hasTimestamp = true;
        f.timestamp = i->first;
        f.hasDuration = false;

        for (OutputSummary::const_iterator j = i->second.begin();
             j != i->second.end(); ++j) {

            // these will be ordered by bin number, and no bin numbers
            // will be missing except at the end (because of the way
            // the accumulators were initially filled in accumulate())

            const OutputBinSummary &summary = j->second;
            float result = 0.f;

            switch (type) {

            case Minimum:
                result = summary.minimum;
                break;

            case Maximum:
                result = summary.maximum;
                break;

            case Mean:
                if (continuous) {
                    result = summary.mean_c;
                } else if (summary.count) {
                    result = summary.sum / summary.count;
                }
                break;

            case Median:
                if (continuous) result = summary.median_c;
                else result = summary.median;
                break;

            case Mode:
                if (continuous) result = summary.mode_c;
                else result = summary.mode;
                break;

            case Sum:
                result = summary.sum;
                break;

            case Variance:
                if (continuous) result = summary.variance_c;
                else result = summary.variance;
                break;

            case StandardDeviation:
                if (continuous) result = sqrtf(summary.variance_c);
                else result = sqrtf(summary.variance);
                break;

            case Count:
                result = summary.count;
                break;

            default:
                break;
            }
            
            f.values.push_back(result);
        }

        fl.push_back(f);
    }
    return fl;
}

Plugin::FeatureSet
PluginSummarisingAdapter::Impl::getSummaryForAllOutputs(SummaryType type,
                                                        AveragingMethod avg)
{
    FeatureSet fs;
    for (OutputSummarySegmentMap::const_iterator i = m_summaries.begin();
         i != m_summaries.end(); ++i) {
        fs[i->first] = getSummaryForOutput(i->first, type, avg);
    }
    return fs;
}

void
PluginSummarisingAdapter::Impl::accumulate(const FeatureSet &fs,
                                           RealTime timestamp, 
                                           bool final)
{
    for (FeatureSet::const_iterator i = fs.begin(); i != fs.end(); ++i) {
        for (FeatureList::const_iterator j = i->second.begin();
             j != i->second.end(); ++j) {
            if (j->hasTimestamp) {
                accumulate(i->first, *j, j->timestamp, final);
            } else {
                //!!! is this correct?
                accumulate(i->first, *j, timestamp, final);
            }
        }
    }
}

void
PluginSummarisingAdapter::Impl::accumulate(int output,
                                           const Feature &f,
                                           RealTime timestamp,
                                           bool final)
{
//!!! to do: use timestamp to determine which segment we're on

    m_accumulators[output].count++;

    std::cerr << "output " << output << ": timestamp " << timestamp << ", prev timestamp " << m_prevTimestamps[output] << std::endl;

    //!!! m_prevDuration needs to be per output

    //!!! also, this will not work if we are called repeatedly with
    //!!! the same timestamp -- no values will be registered until a
    //!!! new timestamp is seen -- we need a better test for "not
    //!!! first result" below

    if (m_prevDuration == RealTime::zeroTime) {
        if (m_prevTimestamps.find(output) != m_prevTimestamps.end()) {
            m_prevDuration = timestamp - m_prevTimestamps[output];
        }
    }
    if (m_prevDuration != RealTime::zeroTime ||
        !m_accumulators[output].durations.empty()) {
        // ... i.e. if not first result.  We don't push a duration
        // when we process the first result; then the duration we push
        // each time is that for the result before the one we're
        // processing, and we push an extra one at the end.  This
        // permits handling the case where the feature itself doesn't
        // have a duration field, and we have to calculate it from the
        // time to the following feature.  The net effect is simply
        // that values[n] and durations[n] refer to the same result.
        m_accumulators[output].durations.push_back(m_prevDuration);
    }

    m_prevTimestamps[output] = timestamp;

    for (int i = 0; i < int(f.values.size()); ++i) {
        m_accumulators[output].values[i].push_back(f.values[i]);
    }

    if (final) {
        RealTime finalDuration;
        if (f.hasDuration) finalDuration = f.duration;
        m_accumulators[output].durations.push_back(finalDuration);
    }

    if (f.hasDuration) m_prevDuration = f.duration;
    else m_prevDuration = RealTime::zeroTime;
}

struct ValueDurationFloatPair
{
    float value;
    float duration;

    ValueDurationFloatPair() : value(0), duration(0) { }
    ValueDurationFloatPair(float v, float d) : value(v), duration(d) { }
    ValueDurationFloatPair &operator=(const ValueDurationFloatPair &p) {
        value = p.value;
        duration = p.duration;
        return *this;
    }
    bool operator<(const ValueDurationFloatPair &p) const {
        return value < p.value;
    }
};

static double toSec(const RealTime &r)
{
    return r.sec + double(r.nsec) / 1000000000.0;
}

void
PluginSummarisingAdapter::Impl::reduce()
{
    RealTime segmentStart = RealTime::zeroTime; //!!!

    for (OutputAccumulatorMap::iterator i = m_accumulators.begin();
         i != m_accumulators.end(); ++i) {

        int output = i->first;
        OutputAccumulator &accumulator = i->second;

        double totalDuration = 0.0;
        for (int k = 0; k < accumulator.durations.size(); ++k) {
            totalDuration += toSec(accumulator.durations[k]);
        }

        for (BinValueMap::iterator j = accumulator.values.begin();
             j != accumulator.values.end(); ++j) {

            // work on all values over time for a single bin

            int bin = j->first;
            const ValueList &values = j->second;
            const DurationList &durations = accumulator.durations;

            OutputBinSummary summary;

            summary.count = accumulator.count;

            summary.minimum = 0.f;
            summary.maximum = 0.f;

            summary.median = 0.f;
            summary.mode = 0.f;
            summary.sum = 0.f;
            summary.variance = 0.f;

            summary.median_c = 0.f;
            summary.mode_c = 0.f;
            summary.mean_c = 0.f;
            summary.variance_c = 0.f;

            if (summary.count == 0 || values.empty()) continue;

            int sz = values.size();

            if (sz != durations.size()) {
                std::cerr << "WARNING: sz " << sz << " != durations.size() "
                          << durations.size() << std::endl;
//                while (durations.size() < sz) {
//                    durations.push_back(RealTime::zeroTime);
//                }
//!!! then what?
            }

            std::vector<ValueDurationFloatPair> valvec;

            for (int k = 0; k < sz; ++k) {
                valvec.push_back(ValueDurationFloatPair(values[k],
                                                        toSec(durations[k])));
            }

            std::sort(valvec.begin(), valvec.end());

            summary.minimum = valvec[0].value;
            summary.maximum = valvec[sz-1].value;

            if (sz % 2 == 1) {
                summary.median = valvec[sz/2].value;
            } else {
                summary.median = (valvec[sz/2].value + valvec[sz/2 + 1].value) / 2;
            }
            
            double duracc = 0.0;
            summary.median_c = valvec[sz-1].value;

            for (int k = 0; k < sz; ++k) {
                duracc += valvec[k].duration;
                if (duracc > totalDuration/2) {
                    summary.median_c = valvec[k].value;
                    break;
                }
            }
                
            std::map<float, int> distribution;

            for (int k = 0; k < sz; ++k) {
                summary.sum += values[k];
                distribution[values[k]] += 1;
            }

            int md = 0;

            for (std::map<float, int>::iterator di = distribution.begin();
                 di != distribution.end(); ++di) {
                if (di->second > md) {
                    md = di->second;
                    summary.mode = di->first;
                }
            }

            distribution.clear();

            //!!! we want to omit this bit if the features all have
            //!!! equal duration (and set mode_c equal to mode instead)
            
            std::map<float, double> distribution_c;

            for (int k = 0; k < sz; ++k) {
                distribution_c[values[k]] += toSec(durations[k]);
            }

            double mrd = 0.0;

            for (std::map<float, double>::iterator di = distribution_c.begin();
                 di != distribution_c.end(); ++di) {
                if (di->second > mrd) {
                    mrd = di->second;
                    summary.mode_c = di->first;
                }
            }

            distribution_c.clear();

            if (totalDuration > 0.0) {

                double sum_c = 0.0;

                for (int k = 0; k < sz; ++k) {
                    double value = values[k] * toSec(durations[k]);
                    sum_c += value;
                }

                std::cerr << "mean_c = " << sum_c << " / " << totalDuration << " = "
                          << sum_c / totalDuration << std::endl;
                
                summary.mean_c = sum_c / totalDuration;

                for (int k = 0; k < sz; ++k) {
                    double value = values[k] * toSec(durations[k]);
                    summary.variance_c +=
                        (value - summary.mean_c) * (value - summary.mean_c);
                }

                summary.variance_c /= summary.count;
            }

            //!!! still to handle: median_c

            float mean = summary.sum / summary.count;

            std::cerr << "mean = " << summary.sum << " / " << summary.count << " = "
                      << summary.sum / summary.count << std::endl;

            for (int k = 0; k < sz; ++k) {
                summary.variance += (values[k] - mean) * (values[k] - mean);
            }
            summary.variance /= summary.count;

            m_summaries[output][segmentStart][bin] = summary;
        }
    }

    m_accumulators.clear();
}


}

}