Week 4 Update
This week’s main goals were to integrate the OpenCV detection with the decoding algorithm. We worked on developing a way to refine the CV to lower the processing time and let it detect multiple elements in parallel so it can send the data quicker. We also discussed a way to reduce overhead and duplication of code between the decoding, encoding, and detection parts by using a common dictionary. For this week, we plan on working to integrate the image scanning with the OpenCV, getting the Raspberry Pi set up with a printer and keyboard, and writing the decoding algorithm.
Shivani
This week, after getting feedback from the presentation on Monday, I ran metrics for the CV for a few different patterns to benchmark our progress. After removing the image generation at each step and running the different detections in parallel, I refined some of the CV to reduce the computation time. It currently takes 1.7 seconds to finish processing “Hello World” which is a good place for us and leaves time for printing and other UI features. In addition, I combined all of the different outputs of our CV detection (color, shape, filled/unfilled, order) in a 2D list to export. I met with Snigdha and we decided that the best way to transfer the data to the decoding part of our project was a csv file that she can parse. This upcoming week I am going to be working on exporting the data in a csv file and working on some ordering edge cases that pop up.
Caroline
This week I was away visiting graduate schools, and didn’t have the opportunity to get much done. However, I am looking forward to starting the hardware next week. My goal for next week is to hook up Snigdha’s image generation on a raspberry pi with a button and a printer, so that hitting the button prints out an image. This is going to require hooking up her javascript to save an image rather than draw on an HTML canvas, and then using the image to call the printer directly from javascript or the command line.
Snigdha
This week, I worked with Shivani to figure out an efficient way to combine the results from the CV algorithm with the decoding algorithm. We decided against doing all the decoding processing in the CV file, and instead agreed to export the information in a CSV that could be read in and decoded on the Raspberry Pi. With this system in place, I will spend the next week on developing writing decoding functions. I also spoke with her about the ability to detect our most recent encoded pattern and will be working on refining it further by changing the encoding pattern from six shapes to three, and adding a filled/unfilled feature to the shapes. Lastly, I worked with the team on the Design Document due this Sunday.