Vayum
This week I created an API to integrate the data from Ajay’s facial recognition algorithm and seamlessly put it as a part of our web application. I used Django web requests, Postman, and wrote a stand alone program that looks for POST requests, parses the JSON data, and calculates a moving average of a continuous data stream. This part is to calculate the average time to wait based on the data we get from our cameras and raspberry pis. Overall, we still need to focus on figuring out how we want to integrate the data with Peters part along with occupancy part and the team. Other than that, I would say that we are up to date and on track.
Ajay:
This week I got most of the functionality working for the entire REID system. I finished the raspberry PI code so that now we can take photos and upload them to s3 buckets and call the appropriate POST endpoints to trigger storage/detection functions. I actually found a 4x speedup within the raspberry pi code revolving around having the camera which was turning on and off. Instead I forced it to stay on for the duration of the program and as a result, we were able to speed up the overall operation of the raspberry pi by 4x. I finished writing the DB on the EC2 instance so now we can store our feature vectors and persist them in storage. The histogram functions are also finished so we can now extract them from the bounding box and persist them. I wrote the matching function as well which uses Bhatcharraya distance. This seems like it works well but we are still doing final testing to verify all the numbers. This following week I need to do the MAP calculations and do the other metric verification.
Team Report:
We are still on track to have a final demo but need to see how we are doing with the occupancy part of the project and focus the next week on testing and debugging that to see how accurate we are.