Converting BigBlueButton recordings to self-contained videos

When the pandemic lock downs started, my local Linux User Group started looking at video conferencing tools we could use to continue presenting talks and other events to members. We ended up adopting BigBlueButton: as well as being Open Source, it’s focus on education made it well suited for presenting talks. It has the concept of a presenter role, and built in support for slides (it sends them to viewers as images, rather than another video stream). It can also record sessions for later viewing.

To view those recordings though, you need to use BBB’s web player. I wanted to make sure we could keep the recordings available should the BBB instance we were using went away. Ideally, we’d just be able to convert the recordings to self contained videos files that could be archived and published along side our other recordings. There are a few tools intended to help with this:

  • bbb-recorder: screen captures Chrome displaying BBB’s web player to produce a video.
  • bbb-download: this one is intended to run on the BBB server, and combines slides, screen share and presentation audio using ffmpeg. Does not include webcam footage.

I really wanted something that would include both the camera footage and slides in one video, so decided to make my own. The result is bbb-render:

At the present, it consists of two scripts. The first is, which takes the URL of a public BBB recording and downloads all of its assets to a local folder. The second is, which assembles those assets so they’re ready to render.

The resources retrieved by the download script include:

Video from the presenters’ cameras, plus the audio track for the presentation.
Video for screen sharing segments of the presentation. This is the same length as the webcams video, with blank footage when nothing is being shared.
Timing information for when to show the screen share video, along with the aspect ration for a particular share session
An SVG file with custom timing attributes that is uses to present the slides and whiteboard scribbles. By following links in the SVG, we can download all the slide images.
Mouse cursor position over time. This is used for the “red dot laser pointer” effect.
Not actually slides. For some reason, this is the text chat replay.

My first thought to combine the various parts was to construct a GStreamer pipeline that would play everything back together, using timers to bring slides in and out. This turned out to be easier said than done, so I started looking for something higher level.

It turns out GStreamer has that covered in the form of GStreamer Editing Services: a library intended to help write non-linear editing applications. That fits the problem really well: I’ve got a collection of assets and metadata, so just need to convert all the timing information into an appropriate edit list. I can put the webcam footage in the bottom right corner, ask for a particular slide image to display at a particular point on the timeline and go away at another point, display screen share footage, etc. It also made it easy to add a backdrop image to fill in the blank space around the slides and camera and add a bit of branding to the result.

On top of that, I can serialise that edit list to a file, rather than encoding the video directly. The ges-launch-1.0 utility can load the project to quickly play back the result without without having to wait for the video to encode.

I can even load the project in Pitivi, a video editor built on top of GES:

screenshot of Pitivi video editor

This makes it very easy to scrub through the timeline to quickly verify that everything looks correct.

At this point, the scripts can produce a crisp 1080p video that should be good enough for most presentations. There are a few areas that could be improved though:

  • If there are multiple presenters with their webcam on, we still get a single webcam video with each presenter feed shown in a square grid. It would probably look better to try and stack each presenter vertically. This could probably be done by applying videocrop as an effect to extract each individual presenter, and include the video multiple times in the project.
  • The data in cursor.xml is ignored. It would be pretty easy to display a small red circle image at the correct times and positions.
  • Whiteboard scribbles are also ignored. This would be a bit trickier to implement. It would probably involve dissecting shapes.svg into a sequence of SVGs containing the elements visible at each point in time. Making matters more complicated, the JavaScript web player adjusts the viewBox when switching to/from slides and screen share, and that changes how the coordinates of the scribbles are interpreted.

As GUADEC is using BigBlueButton this year, hopefully it should help with processing the recordings into individual videos.

Using GAsyncResult APIs with Python’s asyncio

With a GLib implementation of the Python asyncio event loop, I can easily mix asyncio code with GLib/GTK code in the same thread. The next step is to see whether we can use this to make any APIs more convenient to use. A good candidate is APIs that make use of GAsyncResult.

These APIs generally consist of one function call that initiates the asynchronous job and takes a callback. The callback will be invoked sometime later with a GAsyncResult object, which can be passed to a “finish” function to convert this to the result type relevant to the original call. This sort of API is a good candidate to convert to an asyncio coroutine.

We can do this by writing a ready callback that simply stores the result in a future, and then have our coroutine await that future after initiating the job. For example, the following will asynchronously connect to the session bus:

import asyncio
from gi.repository import GLib, Gio

async def session_bus():
    loop = asyncio.get_running_loop()
    bus_ready = loop.create_future()
    def ready_callback(obj, result):
            bus = Gio.bus_get_finish(result)
        except GLib.Error as exc:
            loop.call_soon_threadsafe(bus_ready.set_exception, exc)
        loop.call_soon_threadsafe(bus_ready.set_result, bus)

    Gio.bus_get(Gio.BusType.SESSION, None, ready_callback)
    return await bus_ready

We’ve now got an API that is conceptually as simple to use as the synchronous Gio.bus_get_sync call, but won’t block other work the application might be performing.

Most of the code is fairly straight forward: the main wart is the two loop.call_soon_threadsafe calls. While everything is executing in the same thread, my asyncio-glib library does not currently wake the asyncio event loop when called from a GLib callback. The call_soon_threadsafe method does the trick by generating some dummy IO to cause a wake up.


One feature we’ve lost with this wrapper is the ability to cancel the asynchronous job. On the GLib side, this is handled with the GCancellable object. On the asyncio side, tasks are cancelled by injecting an asyncio.CancelledError exception into the coroutine. We can propagate this cancellation to the GLib side fairly seamlessly:

async def session_bus():
    cancellable = Gio.Cancellable()
    Gio.bus_get(Gio.BusType.SESSION, cancellable, ready_callback)
        return await bus_ready
    except asyncio.CancelledError:

It’s important to re-raise the CancelledError exception, so that it will propagate up to any calling coroutines and let them perform their own cleanup.

By following this pattern I was able to build enough wrappers to let me connect to the D-Bus daemon and issue asynchronous method calls without needing to chain together large sequences of callbacks. The wrappers were all similar enough that it shouldn’t be too difficult to factor out the common code.

Exploring Github Actions

To help keep myself honest, I wanted to set up automated test runs on a few personal projects I host on Github.  At first I gave Travis a try, since a number of projects I contribute to use it, but it felt a bit clunky.  When I found Github had a new CI system in beta, I signed up for the beta and was accepted a few weeks later.

While it is still in development, the configuration language feels lean and powerful.  In comparison, Travis’s configuration language has obviously evolved over time with some features not interacting properly (e.g. matrix expansion only working on the first job in a workflow using build stages).  While I’ve never felt like I had a complete grasp of the Travis configuration language, the single page description of Actions configuration language feels complete.

The main differences I could see between the two systems are:

  1. A Github workflow is composed of multiple jobs right from the start.
  2. All jobs run in parallel by default.  It is possible to serialise jobs (similar to Travis’s stages) by declaring dependencies between jobs.
  3. Each job specifies which VM image it will run on, with a choice of Ubuntu, Windows, or MacOS versions.  If you choose Ubuntu, you can also specify a Docker container to run your build in, giving access to other Linux build environments.
  4. Each job can have a matrix attached, allowing the job to be duplicated according to a set of parameters.
  5. Jobs are composed of a sequence of steps.  Unlike Travis’s fixed set of build phases, these are generic.
  6. Steps can consist of either code executed by the shell or a reference to an external action.
  7. Actions are the primary extension mechanism, and are even used for basic tasks like checking out your repository.  Actions are either implemented in JavaScript or as a Docker container.  Only JavaScript actions are available for Windows and MacOS jobs.

The first project I converted over was asyncio-glib, where I was using Travis to run the test suite on a selection of Python versions.  My old Travis configuration can be seen here, and the new Actions workflow can be seen here.  Both versions are roughly equivalent, although the actions/setup-python@v1 action doesn’t currently make beta releases of Python available. The result of a run of the workflow can be seen here.

For a second project (videowhisk), I am running the tests against the VM’s default Python image.  For this project, I’m more interested in compatibility with the distro release’s GStreamer libraries than compatibility with different Python versions.  I suppose I could extend this using the matrix feature to test on multiple Ubuntu versions, or containers for other Linux releases.

While I’ve just been using this to run the test suite, it looks like Actions can be used for a lot more.  A project can have multiple workflows with different triggers, so it can also be used for automated triage of bugs or pull requests (e.g. request a review from a specific developer when a pull request is created that modifies files in a specific directory). It also looks like I could create a workflow to automatically publish to PyPI when I push a new tag to the repository that looks like a version number.

It will be interesting to see what this does to the larger ecosystem of “CI as a service” products built to work with Github.  On the one hand having a choice is nice, but on the other hand it’s nice to have something well integrated.  I really like Gitlab’s integrated CI system for projects I have hosted on various Gitlab instances, for example.

GLib integration for the Python asyncio event loop

As an evening project, I’ve been working on a small library that integrates the GLib main loop with Python’s asyncio. I think I’ve gotten to the point where it might be useful to other people, so have pushed it up here:

This isn’t the only attempt to integrate the two event loops, but the other I found (Gbulb) is unmaintained and seems to reimplement a fair bit of the asyncio (e.g. it has its own transport classes). So I thought I’d see if I could write something smaller and more maintainable, reusing as much code from the standard library as possible.

My first step was writing an implementation of the selectors.BaseSelector interface in terms of the GLib main loop. The select() method just runs a GMainLoop with a custom source that will quit the loop if any of the file descriptors are ready, or the timeout is reached.

For the asyncio event loop, I was able to reuse the standard library asyncio.SelectorEventLoop with my new selector. In action, it looks something like this:

  1. Let the GMainLoop spin until any asyncio events come in.
  2. Return control to the asyncio event loop to process those events.
  3. Repeat

As far as testing goes, the Python standard library comes with a suite of tests parameterised on an event loop implementation. So I’ve just reused that as the bulk of my test suite, and done the same with the selector tests. There are a handful of test failures I still need to diagnose, but for the most part things just work.

Making an asyncio application use this event loop is simple:

import asyncio
import asyncio_glib

The main limitation of this code is that it relies on asyncio running the GLib main loop. If some other piece of code runs the main loop, asyncio callbacks will not be triggered and will probably lead to busy looping. This isn’t a problem my project (an asyncio server making use of GStreamer), but would be a problem for e.g. a graphical application calling gtk_dialog_run().

Extracting BIOS images and tools from ThinkPad update ISOs

With my old ThinkPad, Lenovo provided BIOS updates in the form of Windows executables or ISO images for a bootable CD.  Since I had wiped Windows partition, the first option wasn’t an option.  The second option didn’t work either, since it expected me to be using the drive in the base I hadn’t bought.  Luckily I was able to just copy the needed files out of the ISO image to a USB stick that had been set up to boot DOS.

When I got my new ThinkPad, I had hoped to do the same thing but found that the update ISO images appeared to be empty when mounted.  It seems that the update is handled entirely from an El Torito emulated hard disk image (as opposed to using the image only to bootstrap the drivers needed to access the CD).

So I needed some way to extract that boot image from the ISO.  After a little reading of the spec, I put together the following Python script that does the trick:

import struct
import sys


def find_image(fp):
    # el-torito boot record descriptor * SECTOR_SIZE)
    data =
    assert data[:0x47] == b'\x00CD001\x01EL TORITO SPECIFICATION' + b'\x0' * 41
    boot_catalog_sector = struct.unpack('<L', data[0x47:0x4B])[0]

    # check the validation entry in the catalog * SECTOR_SIZE)
    data =
    assert data[0:1] == b'\x01'
    assert data[0x1e:0x20] == b'\x55\xAA'
    assert sum(struct.unpack('<16H', data)) % 0x10000 == 0

    # Read the initial/default entry
    data =
    (bootable, image_type, load_segment, system_type, sector_count,
     image_sector) = struct.unpack('<BBHBxHL', data[:12])
    image_offset = image_sector * SECTOR_SIZE
    if image_type == 1:
        # 1.2MB floppy
        image_size = 1200 * 1024
    elif image_type == 2:
        # 1.44MB floppy
        image_size = 1440 * 1024
    elif image_type == 3:
        # 2.88MB floppy
        image_size = 2880 * 1024
    elif image_type == 4:
        # Hard disk image.  Read the MBR partition table to locate file system
        data =
        # Read the first partition entry
        (bootable, part_type, part_start, part_size) = struct.unpack_from(
            '<BxxxBxxxLL', data, 0x1BE)
        assert bootable == 0x80 # is partition bootable?
        image_offset += part_start * 512
        image_size = part_size * 512
        raise AssertionError('unhandled image format: %d' % image_type)

if __name__ == '__main__':
    with open(sys.argv[1], 'rb') as iso, open(sys.argv[2], 'wb') as img:

It isn’t particularly pretty, but does the job and spits out a 32MB FAT disk image when run on the ThinkPad X230 update ISOs. It is then a pretty easy task of copying those files onto the USB stick to run the update as before. Hopefully owners of similar laptops find this useful.

There appears to be an EFI executable in there too, so it is possible that the firmware update could be run from the EFI system partition too.  I haven’t had the courage to try that though.

u1ftp: a demonstration of the Ubuntu One API

One of the projects I’ve been working on has been to improve aspects of the Ubuntu One Developer Documentation web site.  While there are still some layout problems we are working on, it is now in a state where it is a lot easier for us to update.

I have been working on updating our authentication/authorisation documentation and revising some of the file storage documentation (the API used by the mobile Ubuntu One clients).  To help verify that the documentation was useful, I wrote a small program to exercise those APIs.  The result is u1ftp: a program that exposes a user’s files via an FTP daemon running on localhost.  In conjunction with the OS file manager or a dedicated FTP client, this can be used to conveniently access your files on a system without the full Ubuntu One client installed.

You can download the program from:

To make it easy to run on as many systems as possible, I packaged it up as a runnable zip file so can be run directly by the Python interpreter.  As well as a Python interpreter, you will need the following installed to run it:

  • On Linux systems, either the gnomekeyring extension (if you are using a GNOME derived desktop), or PyKDE4 (if you have a KDE derived desktop).
  • On Windows, you will need pywin32.
  • On MacOS X, you shouldn’t need any additional modules.

These could not be included in the zip file because they are extension modules rather than pure Python.

Once you’ve downloaded the program, you can run it with the following command:


This will start the FTP server listening at ftp://localhost:2121/.  Pointing a file manager at that URL should prompt you to log in, where you can use your standard Ubuntu One credentials and start browsing your files.  It will verify the credentials against the Ubuntu SSO service and issue an OAuth token that it stores in the keyring.  The OAuth token is then used to authenticate requests to the file storage REST API.

While I expect this program to be useful on its own, it was also intended to act as an example of how the Ubuntu One API can be used.  One way to browse the source is to simply unzip the package and poke around.  Alternatively, you can check out the source directly from Launchpad:

bzr branch lp:u1ftp

If you come up with an interesting extension to u1ftp, feel free to upload your changes as a branch on Launchpad.

Packaging Python programs as runnable ZIP files

One feature in recent versions of Python I hadn’t played around with until recently is the ability to package up a multi-module program into a ZIP file that can be run directly by the Python interpreter.  I didn’t find much information about it, so I thought I’d describe what’s necessary here.

Python has had the ability to add ZIP files to the module search path since PEP 273 was implemented in Python 2.3.  That can let you package up most of your program into a single file, but doesn’t help with the main entry point.

Things improved a bit when PEP 338 was implemented in Python 2.4, which allows any module that can be located on the Python search path can be executed as a script.  So if you have a ZIP file containing a module, you could run it as: python -m foo

This is a bit cumbersome to type though, so Python 2.6 lets you run directories and zip files directly.  So if you run


It is roughly equivalent to: python -m __main__

So if you place a file called inside your ZIP file (or directory), it will be treated as the entry point to your program.  This gives us something that is as convenient to distribute and run as a single file script, but with the better maintainability of a multi-module program.

If your program has dependencies that you don’t expect to find present on the target systems, you can easily include them up in the zip file along side your program.  If you need to provide some data files along side your program, you could use the pkg_resources module from setuptools or distribute.

There are still a few warts with this set up though:

  • If your program fails, the trace back will not include lines of source code.  This is a general problem for modules loaded from zip files.
  • You can’t package extension modules into a zip file.  Of course, if you’re in a position where the target platforms are locked down tight enough that you could reliably provide compiled code that would run on them, you’d probably be better off using the platform’s package manager.
  • There is no way to tell whether a ZIP file can be executed directly with Python without inspecting its contents.  Perhaps this could be addressed by defining a new file extension to identify such files.

pygpgme 0.3

This week I put out a new release of pygpgme: a Python extension that lets you perform various tasks with OpenPGP keys via the GPGME library.  The new release is available from both Launchpad and PyPI.

There aren’t any major new extensions to the API, but this is the first release to support Python 3 (Python 2.x is still supported though).  The main hurdle was ensuring that the module correctly handled text vs. binary data.  The split I ended up on was to treat most things as text (including textual representations of binary data such as key IDs and fingerprints), and treat the data being passed into or returned from the encryption, decryption, signing and verification commands as binary data.  I haven’t done a huge amount with the Python 3 version of the module yet, so I’d appreciate bug reports if you find issues.

So now you’ve got one less reason not to try Python 3 if you were previously using pygpgme in your project.

Watching iView with Rygel

One of the features of Rygel that I found most interesting was the external media server support.  It looked like an easy way to publish information on the network without implementing a full UPnP/DLNA media server (i.e. handling the UPnP multicast traffic, transcoding to a format that the remote system can handle, etc).

As a small test, I put together a server that exposes the ABC‘s iView service to UPnP media renderers.  The result is a bit rough around the edges, but the basic functionality works.  The source can be grabbed using Bazaar:

bzr branch lp:~jamesh/+junk/rygel-iview

It needs Python, Twisted, the Python bindings for D-Bus and rtmpdump to run.  The program exports the guide via D-Bus, and uses rtmpdump to stream the shows via HTTP.  Rygel then publishes the guide via the UPnP media server protocol and provides MPEG2 versions of the streams if clients need them.

There are still a few rough edges though.  The video from iView comes as 640×480 with a 16:9 aspect ratio so has a 4:3 pixel aspect ratio, but there is nothing in the video file to indicate this (I am not sure if flash video supports this metadata).

Getting Twisted and D-Bus to cooperate

Since I’d decided to use Twisted, I needed to get it to cooperate with the D-Bus bindings for Python.  The first step here was to get both libraries using the same event loop.  This can be achieved by setting Twisted to use the glib2 reactor, and enabling the glib mainloop integration in the D-Bus bindings.

Next was enabling asynchronous D-Bus method implementations.  There is support for this in the D-Bus bindings, but has quite a different (and less convenient) API compared to Twisted.  A small decorator was enough to overcome this impedence:

from functools import wraps

import dbus.service
from twisted.internet import defer

def dbus_deferred_method(*args, **kwargs):
    def decorator(function):
        function = dbus.service.method(*args, **kwargs)(function)
        def wrapper(*args, **kwargs):
            dbus_callback = kwargs.pop('_dbus_callback')
            dbus_errback = kwargs.pop('_dbus_errback')
            d = defer.maybeDeferred(function, *args, **kwargs)
                dbus_callback, lambda failure: dbus_errback(failure.value))
        wrapper._dbus_async_callbacks = ('_dbus_callback', '_dbus_errback')
        return wrapper
    return decorator

This decorator could then be applied to methods in the same way as the @dbus.service.method method, but it would correctly handle the case where the method returns a Deferred. Unfortunately it can’t be used in conjunction with @defer.inlineCallbacks, since the D-Bus bindings don’t handle varargs functions properly. You can of course call another function or method that uses @defer.inlineCallbacks though.

The iView Guide

After coding this, it became pretty obvious why it takes so long to load up the iView flash player: it splits the guide data over almost 300 XML files.  This might make sense if it relied on most of these files remaining unchanged and stored in cache, however it also uses a cache-busting technique when requesting them (adding a random query component to the URL).

Most of these files are series description files (some for finished series with no published programs).  These files contain a title, a short description, the URL for a thumbnail image and the IDs for the programs belonging to the series.  To find out about those programs, you need to load all the channel guide XML files until you find which one contains the program.  Going in the other direction, if you’ve got a program description from the channel guide and want to know about the series it belongs to (e.g. to get the thumbnail), you need to load each series description XML file until you find the one that contains the program.  So there aren’t many opportunities to delay loading of parts of the guide.

The startup time would be a lot easier if this information was collapsed down to a smaller number of larger XML files.


Last week, we released the source code to django-openid-auth.  This is a small library that can add OpenID based authentication to Django applications.  It has been used for a number of internal Canonical projects, including the sprint scheduler Scott wrote for the last Ubuntu Developer Summit, so it is possible you’ve already used the code.

Rather than trying to cover all possible use cases of OpenID, it focuses on providing OpenID Relying Party support to applications using Django’s django.contrib.auth authentication system.  As such, it is usually enough to edit just two files in an existing application to enable OpenID login.

The library has a number of useful features:

  • As well as the standard method of prompting the user for an identity URL, you can configure a fixed OpenID server URL.  This is useful for deployments where OpenID is being used for single sign on, and you always want users to log in using a particular OpenID provider.  Rather than asking the user for their identity URL, they are sent directly to the provider.
  • It can be configured to automatically create accounts when new identity URLs are seen.
  • User names, full names and email addresses can be set on accounts based on data sent via the OpenID Simple Registration extension.
  • Support for Launchpad‘s Teams OpenID extension, which lets you query membership of Launchpad teams when authenticating against Launchpad’s OpenID provider.  Team memberships are mapped to Django group membership.

While the code can be used for generic OpenID login, we’ve mostly been using it for single sign on.  The hope is that it will help members of the Ubuntu and Launchpad communities reuse our authentication system in a secure fashion.

The source code can be downloaded using the following Bazaar command:

bzr branch lp:django-openid-auth

Documentation on how to integrate the library is available in the README.txt file.  The library includes some code written by Simon Willison for django-openid, and uses the same licensing terms (2 clause BSD) as that project.