Not so long ago there was a nice dataiku meetup with Pierre Gutierrez talking about transfer learning. RStudio recently released the keras package, an R interface to work with keras for deep learning and transfer learning. Both events inspired me to do some experiments at my work here at RTL and explore the usability of it for us at RTL. I like to share the slides of the presentation that I gave internally at RTL, you can find them on slide-share.
As a side effect, another experiment that I like to share is the “poor man’s video analyzer“. There are several vendors now that offer API’s to analyze videos. See for example the one that Microsoft offers. With just a few lines of R code I came up with a shiny app that is a very cheap imitation 🙂
Set up of the R Shiny app
To run the shiny app a few things are needed. Make sure that ffmpeg is installed, it is used to extract images from a video. Tensorflow and keras need to be installed as well. The extracted images from the video are parsed through a pre-trained VGG16 network so that each image is tagged.
After this tagging a data table will appear with the images and their tags. That’s it! I am sure there are better visualizations than a data table to show a lot of images. If you have a better idea just adjust my shiny app on GitHub…. 🙂
Using the app, some screen shots
There is a simple interface, specify the number of frames per second that you want to analyse. And then upload a video, many formats are supported (by ffmpeg), like *.mp4, *.mpeg, *.mov
Click on video images to start the analysis process. This can take a few minutes, when it is finished you will see a data table with extracted images and their tags from VGG-16.
Click on ‘info on extracted classes’ to see an overview of the class. You will see a bar chart of tags that where found and the output of ffmpeg. It shows some info on the video.
A Shiny app version using miniUI will be a better fit for small mobile screens.