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• My journey to Coviu

  27 octobre 2015, par silvia

  My new startup just released our MVP – this is the story of what got me here.

  I love creating new applications that let people do their work better or in a manner that wasn’t possible before.

  My first such passion was as a student intern when I built a system for a building and loan association’s monthly customer magazine. The group I worked with was managing their advertiser contacts through a set of paper cards and I wrote a dBase based system (yes, that long ago) that would manage their customer relationships. They loved it – until it got replaced by an SAP system that cost 100 times what I cost them, had really poor UX, and only gave them half the functionality. It was a corporate system with ongoing support, which made all the difference to them.

  The story repeated itself with a CRM for my Uncle’s construction company, and with a resume and quotation management system for Accenture right after Uni, both of which I left behind when I decided to go into research.

  Even as a PhD student, I never lost sight of challenges that people were facing and wanted to develop technology to overcome problems. The aim of my PhD thesis was to prepare for the oncoming onslaught of audio and video on the Internet (yes, this was 1994 !) by developing algorithms to automatically extract and locate information in such files, which would enable users to structure, index and search such content.

  Many of the use cases that we explored are now part of products or continue to be challenges : finding music that matches your preferences, identifying music or video pieces e.g. to count ads on the radio or to mark copyright infringement, or the automated creation of video summaries such as trailers.

  

  This continued when I joined the CSIRO in Australia – I was working on segmenting speech into words or talk spurts since that would simplify captioning & subtitling, and on MPEG-7 which was a (slightly over-engineered) standard to structure metadata about audio and video.

  In 2001 I had the idea of replicating the Web for videos : i.e. creating hyperlinked and searchable video-only experiences. We called it “Annodex” for annotated and indexed video and it needed full-screen hyperlinked video in browsers – man were we ahead of our time ! It was my first step into standards, got several IETF RFCs to my name, and started my involvement with open codecs through Xiph.

  Around the time that YouTube was founded in 2006, I founded Vquence – originally a video search company for the Web, but pivoted to a video metadata mining company. Vquence still exists and continues to sell its data to channel partners, but it lacks the user impact that has always driven my work.

  As the video element started being developed for HTML5, I had to get involved. I contributed many use cases to the W3C, became a co-editor of the HTML5 spec and focused on video captioning with WebVTT while contracting to Mozilla and later to Google. We made huge progress and today the technology exists to publish video on the Web with captions, making the Web more inclusive for everybody. I contributed code to YouTube and Google Chrome, but was keen to make a bigger impact again.

  The opportunity came when a couple of former CSIRO colleagues who now worked for NICTA approached me to get me interested in addressing new use cases for video conferencing in the context of WebRTC. We worked on a kiosk-style solution to service delivery for large service organisations, particularly targeting government. The emerging WebRTC standard posed many technical challenges that we addressed by building rtc.io , by contributing to the standards, and registering bugs on the browsers.

  Fast-forward through the development of a few further custom solutions for customers in health and education and we are starting to see patterns of need emerge. The core learning that we’ve come away with is that to get things done, you have to go beyond “talking heads” in a video call. It’s not just about seeing the other person, but much more about having a shared view of the things that need to be worked on and a shared way of interacting with them. Also, we learnt that the things that are being worked on are quite varied and may include multiple input cameras, digital documents, Web pages, applications, device data, controls, forms.

  So we set out to build a solution that would enable productive remote collaboration to take place. It would need to provide an excellent user experience, it would need to be simple to work with, provide for the standard use cases out of the box, yet be architected to be extensible for specialised data sharing needs that we knew some of our customers had. It would need to be usable directly on Coviu.com, but also able to integrate with specialised applications that some of our customers were already using, such as the applications that they spend most of their time in (CRMs, practice management systems, learning management systems, team chat systems). It would need to require our customers to sign up, yet their clients to join a call without sign-up.

  Collaboration is a big problem. People are continuing to get more comfortable with technology and are less and less inclined to travel distances just to get a service done. In a country as large as Australia, where 12% of the population lives in rural and remote areas, people may not even be able to travel distances, particularly to receive or provide recurring or specialised services, or to achieve work/life balance. To make the world a global village, we need to be able to work together better remotely.

  The need for collaboration is being recognised by specialised Web applications already, such as the LiveShare feature of Invision for Designers, Codassium for pair programming, or the recently announced Dropbox Paper. Few go all the way to video – WebRTC is still regarded as a complicated feature to support.

  

  With Coviu, we’d like to offer a collaboration feature to every Web app. We now have a Web app that provides a modern and beautifully designed collaboration interface. To enable other Web apps to integrate it, we are now developing an API. Integration may entail customisation of the data sharing part of Coviu – something Coviu has been designed for. How to replicate the data and keep it consistent when people collaborate remotely – that is where Coviu makes a difference.

  We have started our journey and have just launched free signup to the Coviu base product, which allows individuals to own their own “room” (i.e. a fixed URL) in which to collaborate with others. A huge shout out goes to everyone in the Coviu team – a pretty amazing group of people – who have turned the app from an idea to reality. You are all awesome !

  With Coviu you can share and annotate :

  • images (show your mum photos of your last holidays, or get feedback on an architecture diagram from a customer),
  • pdf files (give a presentation remotely, or walk a customer through a contract),
  • whiteboards (brainstorm with a colleague), and
  • share an application window (watch a YouTube video together, or work through your task list with your colleagues).

  All of these are regarded as “shared documents” in Coviu and thus have zooming and annotations features and are listed in a document tray for ease of navigation.

  This is just the beginning of how we want to make working together online more productive. Give it a go and let us know what you think.

  http://coviu.com/

• Visualizing Call Graphs Using Gephi

  1er septembre 2014, par Multimedia Mike — General

  When I was at university studying computer science, I took a basic chemistry course. During an accompanying lab, the teaching assistant chatted me up and asked about my major. He then said, “Computer science ? Well, that’s just typing stuff, right ?”

  My impulsive retort : “Sure, and chemistry is just about mixing together liquids and coming up with different colored liquids, as seen on the cover of my high school chemistry textbook, right ?”

  
  
  

  In fact, pure computer science has precious little to do with typing (as is joked in CS circles, computer science is about computers in the same way that astronomy is about telescopes). However, people who study computer science often pursue careers as programmers, or to put it in fancier professional language, software engineers.

  So, what’s a software engineer’s job ? Isn’t it just typing ? That’s where I’ve been going with this overly long setup. After thinking about it for long enough, I like to say that a software engineer’s trade is managing complexity.

  A few years ago, I discovered Gephi, an open source tool for graph and data visualization. It looked neat but I didn’t have much use for it at the time. Recently, however, I was trying to get a better handle on a large codebase. I.e., I was trying to manage the project’s complexity. And then I thought of Gephi again.

  Prior Work
  One way to get a grip on a large C codebase is to instrument it for profiling and extract details from the profiler. On Linux systems, this means compiling and linking the code using the -pg flag. After running the executable, there will be a gmon.out file which is post-processed using the gprof command.

  GNU software development tools have a reputation for being rather powerful and flexible, but also extremely raw. This first hit home when I was learning how to use the GNU tool for code coverage — gcov — and the way it outputs very raw data that you need to massage with other tools in order to get really useful intelligence.

  And so it is with gprof output. The output gives you a list of functions sorted by the amount of processing time spent in each. Then it gives you a flattened call tree. This is arranged as “during the profiled executions, function c was called by functions a and b and called functions d, e, and f ; function d was called by function c and called functions g and h”.

  How can this call tree data be represented in a more instructive manner that is easier to navigate ? My first impulse (and I don’t think I’m alone in this) is to convert the gprof call tree into a representation suitable for interpretation by Graphviz. Unfortunately, doing so tends to generate some enormous and unwieldy static images.

  Feeding gprof Data To Gephi
  I learned of Gephi a few years ago and recalled it when I developed an interest in gaining better perspective on a large base of alien C code. To understand what this codebase is doing for a particular use case, instrument it with gprof, gather execution data, and then study the code paths.

  How could I feed the gprof data into Gephi ? Gephi supports numerous graphing formats including an XML-based format named GEXF.

  Thus, the challenge becomes converting gprof output to GEXF.

  Which I did.

  Demonstration
  I have been absent from FFmpeg development for a long time, which is a pity because a lot of interesting development has occurred over the last 2-3 years after a troubling period of stagnation. I know that 2 big video codec developments have been HEVC (next in the line of MPEG codecs) and VP9 (heir to VP8’s throne). FFmpeg implements them both now.

  I decided I wanted to study the code flow of VP9. So I got the latest FFmpeg code from git and built it using the options "--extra-cflags=-pg --extra-ldflags=-pg". Annoyingly, I also needed to specify "--disable-asm" because gcc complains of some register allocation snafus when compiling inline ASM in profiling mode (and this is on x86_64). No matter ; ASM isn’t necessary for understanding overall code flow.

  After compiling, the binary ‘ffmpeg_g’ will have symbols and be instrumented for profiling. I grabbed a sample from this VP9 test vector set and went to work.

  ./ffmpeg_g -i vp90-2-00-quantizer-00.webm -f null /dev/null
  gprof ./ffmpeg_g > vp9decode.txt
  convert-gprof-to-gexf.py vp9decode.txt > /bigdisk/vp9decode.gexf
  

  Gephi loads vp9decode.gexf with no problem. Using Gephi, however, can be a bit challenging if one is not versed in any data exploration jargon. I recommend this Gephi getting starting guide in slide deck form. Here’s what the default graph looks like :

  
  
  

  Not very pretty or helpful. BTW, that beefy arrow running from mid-top to lower-right is the call from decode_coeffs_b -> iwht_iwht_4x4_add_c. There were 18774 from the former to the latter in this execution. Right now, the edge thicknesses correlate to number of calls between the nodes, which I’m not sure is the best representation.

  Following the tutorial slide deck, I at least learned how to enable the node labels (function symbols in this case) and apply a layout algorithm. The tutorial shows the force atlas layout. Here’s what the node neighborhood looks like for probing file type :

  
  
  

  Okay, so that’s not especially surprising– avprobe_input_format3 calls all of the *_probe functions in order to automatically determine input type. Let’s find that decode_coeffs_b function and see what its neighborhood looks like :

  
  
  

  That’s not very useful. Perhaps another algorithm might help. I select the Fruchterman–Reingold algorithm instead and get a slightly more coherent representation of the decoding node neighborhood :

  
  
  

  Further Work
  Obviously, I’m just getting started with this data exploration topic. One thing I would really appreciate in such a tool is the ability to interactively travel the graph since that’s what I’m really hoping to get out of this experiment– watching the code flows.

  Perhaps someone else can find better use cases for visualizing call graph data. Thus, I have published the source code for this tool at Github.

• Input RTP stream to web html5 page ffmpeg

  6 août 2013, par Tommy

  I am working on school project and I need to transfere RTP from multicast IP and I would like play live stream on web page, preferrably in html5.

  So far i have started recording stream with ffmpeg. And it only recording audio. I have output of ffmpeg.

  

  Addtionaly if you have any good sulution to transfere rtp stream from multicast domain to web page so give your ideas.

  Sorry for my english guys.

  Thank you in advance.