We basically finished all the independent parts of our project this week!   I worked on a few things:

• Our classifier had a 95%+ accuracy on just identifying left vs right.  However, I realized earlier this week that it would classify everything it saw as left or right (even heads/walls/etc)
• I added a dataset of celebrity heads and trained the classifier to figure out head/left/right which it now does with a 93% accuracy.  (I did attempt to teach it walls/rooms as well but that was too complicated since there is too much variety in the stock images of these that I could find online)
• Shrutika and I integrated this working classifier with the Pi camera and played with the framerate to figure out how to handle frames as fast as possible and also get a reasonably accurate prediction by taking the mode of n predictions.  We decided 2fps was good.
• I added multiprocessing to make the classifier make predictions on frames faster by utilizing 2 cores of the Pi simultaneously.  This reduced the lag a little.  Still a bit slow because the classifier is slow to run (it is a neural net after all).
• Shrutika and I also worked on testing the mic setup before and after she built the baffles (all on zoom haha) and we also added multiprocessing to this so that the 4 mic channels’ inputs would be processed simultaneously on the 4 cores.  The performance of the mics dramatically improved after adding the baffles.
• We all demo-ed the three separate parts to Professor Sullivan and Jens on Wednesday.
• We will integrate (which should take < 1hr) the three pieces once Neeti is done perfecting the animation.  We are now working on the final presentation for Monday.

Overall we have almost wrapped up this project very well 🙂  Looking forward to finishing this up successfully!