annotate vamp-hostsdk/PluginSummarisingAdapter.h @ 255:88ef5ffdbe8d

* docs

/* -*- 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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*/

#ifndef _VAMP_PLUGIN_SUMMARISING_ADAPTER_H_
#define _VAMP_PLUGIN_SUMMARISING_ADAPTER_H_

#include "hostguard.h"
#include "PluginWrapper.h"

#include <set>

namespace Vamp {

namespace HostExt {

/**
 * \class PluginSummarisingAdapter PluginSummarisingAdapter.h <vamp-hostsdk/PluginSummarisingAdapter.h>
 *
 * PluginSummarisingAdapter is a Vamp plugin adapter that provides
 * summarisation methods such as mean and median averages of output
 * features, for use in any context where an available plugin produces
 * individual values but the result that is actually needed is some
 * sort of aggregate.
 *
 * To make use of PluginSummarisingAdapter, the host should configure,
 * initialise and run the plugin through the adapter interface just as
 * normal.  Then, after the process and getRemainingFeatures methods
 * have been properly called and processing is complete, the host may
 * call getSummaryForOutput or getSummaryForAllOutputs to obtain
 * summarised features: averages, maximum values, etc, depending on
 * the SummaryType passed to the function.
 *
 * By default PluginSummarisingAdapter calculates a single summary of
 * each output's feature across the whole duration of processed audio.
 * A host needing summaries of sub-segments of the whole audio may
 * call setSummarySegmentBoundaries before retrieving the summaries,
 * providing a list of times such that one summary will be provided
 * for each segment between two consecutive times.
 *
 * PluginSummarisingAdapter is straightforward rather than fast.  It
 * calculates all of the summary types for all outputs always, and
 * then returns only the ones that are requested.  It is designed on
 * the basis that, for most features, summarising and storing
 * summarised results is far cheaper than calculating the results in
 * the first place.  If this is not true for your particular feature,
 * PluginSummarisingAdapter may not be the best approach for you.
 *
 * \note This class was introduced in version 2.0 of the Vamp plugin SDK.
 */

class PluginSummarisingAdapter : public PluginWrapper
{
public:
    /**
     * Construct a PluginSummarisingAdapter wrapping the given plugin.
     * The adapter takes ownership of the plugin, which will be
     * deleted when the adapter is deleted.
     */
    PluginSummarisingAdapter(Plugin *plugin); 
    virtual ~PluginSummarisingAdapter();

    bool initialise(size_t channels, size_t stepSize, size_t blockSize);

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

    typedef std::set<RealTime> SegmentBoundaries;

    /**
     * Specify a series of segment boundaries, such that one summary
     * will be returned for each of the contiguous intra-boundary
     * segments.  This function must be called before
     * getSummaryForOutput or getSummaryForAllOutputs.
     * 
     * Note that you cannot retrieve results with multiple different
     * segmentations by repeatedly calling this function followed by
     * one of the getSummary functions.  The summaries are all
     * calculated at the first call to any getSummary function, and
     * once the summaries have been calculated, they remain
     * calculated.
     */
    void setSummarySegmentBoundaries(const SegmentBoundaries &);

    enum SummaryType {
        Minimum            = 0,
        Maximum            = 1,
        Mean               = 2,
        Median             = 3,
        Mode               = 4,
        Sum                = 5,
        Variance           = 6,
        StandardDeviation  = 7,
        Count              = 8,

        UnknownSummaryType = 999
    };

    /**
     * AveragingMethod indicates how the adapter should handle
     * average-based summaries of features whose results are not
     * equally spaced in time.
     *
     * If SampleAverage is specified, summary types based on averages
     * will be calculated by treating each result individually without
     * regard to its time: for example, the mean will be the sum of
     * all values divided by the number of values.
     *
     * If ContinuousTimeAverage is specified, each feature will be
     * considered to have a duration, either as specified in the
     * feature's duration field, or until the following feature: thus,
     * for example, the mean will be the sum of the products of values
     * and durations, divided by the total duration.
     *
     * Although SampleAverage is useful for many types of feature,
     * ContinuousTimeAverage is essential for some situations, for
     * example finding the result that spans the largest proportion of
     * the input given a feature that emits a new result only when the
     * value changes (the modal value integrated over time).
     */
    enum AveragingMethod {
        SampleAverage         = 0,
        ContinuousTimeAverage = 1,
    };

    /**
     * Return summaries of the features that were returned on the
     * given output, using the given SummaryType and AveragingMethod.
     *
     * The plugin must have been fully run (process() and
     * getRemainingFeatures() calls all made as appropriate) before
     * this function is called.
     */
    FeatureList getSummaryForOutput(int output,
                                    SummaryType type,
                                    AveragingMethod method = SampleAverage);

    /**
     * Return summaries of the features that were returned on all of
     * the plugin's outputs, using the given SummaryType and
     * AveragingMethod.
     *
     * The plugin must have been fully run (process() and
     * getRemainingFeatures() calls all made as appropriate) before
     * this function is called.
     */
    FeatureSet getSummaryForAllOutputs(SummaryType type,
                                       AveragingMethod method = SampleAverage);

protected:
    class Impl;
    Impl *m_impl;
};

}

}

#endif