This was done so the collection of loudnesses from an audio file could be mocked
in testing, but improves readability

TODO: review number of params and consider further refactoring

Handles serialisation of custom classes via to_json() method if present

Outputs a representation of the Features extrated by the pipeline

Intent is to write a FE that takes the output so that a pipeline can be
're-run'

Output JSON could also be used with external tools

Annotate features, label axes

Creates a plot showing where in source video(s) the pipeline features
came from

This pvaes the way for parts of the pipeline that do not produce videos,
such as JSON, images, clips etc

TODO: rename module video_producers → producers

Runs a command returns stout as string, defined to aid testing

facilitates test mocking

Parses JSON, eg that output by the pipeline or defined manually and
produces Features that can be (re-)used with the pipeline

Take the mean of non-overlapping windows of scores

    Input:  list of tuples in the format (time, score)
    Output: list of tuples in the format (time, mean_score)
            (reduced set)

Drops lowest n% (default:33%) of scdet scores, since it scores every
frame

Python being what it is, this could be a single line in another method
but pulling it out into another function:

 - makes explicit what we are doing and lets us document why
 - makes for easier testing

Uses ffmpeg's scdet under the hood apropos of recommendation on
ffmpeg-user ML [1]

[1]: https://ffmpeg.org/pipermail/ffmpeg-user/2024-August/058535.html

Uses pyloudnorm under the hood to determine the loudness of the supplied
media file (handles videos transparently)

TBC: some sort of limiter on the number produced

TBC

TBC

This adds functionality that permits nearby Features to be considered
overlapping, in addition to Features which actual overlap

@see utils.py::Feature

BREAKING CHANGE: source now refers to a Source object, the FE that
created the Feature is now referred to by feature_extractor; path is
dropped

This should be more consistent, plus we needed a reference to the
original Source kept around anyway -- path worked but a Source object is
more consistent and explicit about intent

This lets us serialise properly

associated TODO: make precision configurable

Simple feature extractor that picks random times, used to exercise other
parts of the pipeline

This adds functionality for getting laughter by using jrgillick's
laughter detection library

NB python expects all of the feature extractor's dependencies to be
available; perhaps in future we can do something even fancier like
activating another python env

[retroactive commit]

This composes an Interval with some ancillary data:
 - source (what feature extractor did we get this from)
 - path (where is it now on the FS)
 - score (for ranking)

TODO double check we aren't overloading the word 'source' to have two
meanings

These are useful for changing Intervals (eg consolitation)

Consolidator:
  interface

OverlapConsolidator:
  condenses features if their times overlap

Classes for actually making video files

 - VideoProducer -- interface
 - FfmpegVideoProducer -- make a temporary video using ffmpeg

for jrgillick/laughter-detection to slot into

or at least what passes for an interface in python