Month: March 2019

One of the major risks is that our webcam might not be compatible with Python. So far we have been testing in our demo environment using our old MATLAB code, but now we will be migrating all our code to Python. This means that we will have to prioritize taking data using a Python interface as a next step. In the case that we are unable to interact with the camera using Python, we will consider purchasing a PiCam. This is well within our budget.

Suann has placed an order for an SD card for the raspberry pi. We need this to read data from our sensor. To make sure that we do not have any delays in putting this system together, Suann will make a behavioral model of this tap sensing system using an Arduino.

Currently, there are no changes to the existing design, nor is there any change in our schedule.

I have worked on porting the color detection code from Matlab to Python. We decided to move away from using Matlab in conjunction with Python to remove any possible delays in sending data between the two running processes.

Matlab has several functions that make dealing with image matrices easy and computationally fast. I am looking in to finding similar functions that I can use with Python so that searching for elements in an image matrix is not O(n^2) complexity.  I have been working with numpy to try and achieve this.  However, numpy matrices are maximum two dimensional. This is necessary so that we can get a reasonable real time response within our ideal metric of within 1 second.  Additionally, I am installing and familiarizing myself with Python toolkits such as scikit-image, matplotlib, numpy and scipy since there are no inbuilt functions for color space conversion in Python. If these tools prove to be unsuitable for our purposes, I will consider using OpenCV for color spaces.

My progress is on schedule. I aim to complete implementing the color detection and move on to SVM in the next couple weeks.

This week, my team and I worked on getting more test images to measure the accuracy of the MATLAB implementations of the color detection and circle detection algorithm. Though we have decided to go with color detection since its preliminary results were more promising than those of circle detection.

I also spent a large chunk of my time writing the design review paper due next week. My teammates and I changed our system design block diagram to indicate the changes we have made to our pipeline, the major change being the fact that all of our code will now be implemented in Python. This was suggested to us after our design presentation. This shift in design will help reduce any possible latency that could have occurred when interfacing between MATLAB and Python, thus, will better enable us to reach our goal of a one second response time. Accordingly, we had to change our schedule to show dependencies and revisions regarding individual tasks.

As for my task assignments,  I will no longer be working on K-means clustering since Prof. Savvides suggested using SVM instead. This is because the color of our red dot remains fairly constant. Since Isha will be working on the Python implementation of color detection, it will be logical for her to use that code to implement SVM as well. Thus, during the following 2 weeks I will start working on creating a GUI that would have the functionalities we want for our final presentation. I will need to reinstall all packages again like Python and PyQt since my laptop’s hard drive was replaced. I will also be helping Suann with the sensor circuit since I am responsible for reading the analog signal in order to realize a threshold that will result in a 100% accuracy of true positives reflecting when a tap occurred.

I am currently on schedule since the initial task of carrying out K-means clustering was an additional task and I was eventually going to start working on the GUI soon.

We did not realize that a raspberry pi does not have the exact same capabilities of an Arduino, in that it cannot read analog input directly. We may have to switch to using an Arduino to read the data from the piezo sensor to mitigate any possible difficulties with this part of the pipeline.

After our design presentation, the TAs and Professor recommended that we implement all our code in Matlab so that we can avoid any delays in interfacing between Matlab and Python. We are porting all our Color Detection code to Python next week.

We will not use Matlab anymore for our CV implementations. This change was necessary to prevent any latency caused by transferring data between Python and Matlab. This means that Isha will need to work on the CV algorithms for another week in order to transfer the code. However, this will not delay our overall progress since this task will be replacing our initial task of creating a Matlab to Python pipeline to transfer data between the two.

Another change is that we will be implementing SVM to perform dynamic thresholding instead of K-means clustering because in our case, the color of the red dot remains fairly constant.

Below is an updated schedule:

 

This week I spent 7 hours meeting with my teammates to work on the design paper submission and make decisions about the timeline of our project. We also wrote the team progress update together. I have reached out to Shreyas about setting up a Raspberry Pi with CMU Wi-fi since it is not a straightforward task.

I have fully finished reading up on Lucas-Kanade this week.

For this upcoming week, since it is already March, I will move on to working with the Raspberry Pi and setting up the piezo sensor circuit. Since the Pi cannot take analog input directly, I will do research into whether or not we want to stick with the Pi or if we want to move on to an Arduino instead. If the Pi is still viable, I will order the necessary parts to set up a circuit this week and start off the Pi with Python. If not, I will alert my teammates that I will be moving on to the Arduino instead.