In my last stretch of work I focused on using OpenPose on AWS. I was granted usage of a p2 instance which I had to request which has better graphic hardware for the library to run on. I also developed a quick install guide for setting up the instance to make this process faster if I ever need to do this again on another server. I also ran some tests on stock images to get a feel for the speed of the library. I found that with the 3rd most powerful instance, I could get 20 images processed in about 10 seconds, for about .5 seconds per image. This is a bit slower than we are hoping, as our initial aim was for about .25 seconds per image in order to have the smoothest experience in ‘real time’.
I also obtained the hardware setup from Cooper, and now I am working on integrating the Kinesis library so that we will be able to send images from out hardware to the instance I mentioned earlier. When writing the software to cut up the video stream into images, another optimization I plan to test out is reducing the resolution of the images to see how low I can make it while still getting adequate performance.
Meanwhile I have been learning about the input and outputs of the library to think about how to integrate this into the rest of our APIs, likely with a server running file system management and using the other HTTP endpoints to communicate the data. The output data that I have so far is JSON data describing the joints location on the image, but I want to translate this into another version where the joints are grouped and averaged to optimize for our classifier.