Author: mkhiguch

Out team had focused on preparing for the Final Presentation this week and reaching our MVP goals of having a functional Go Fish interface.

With the our ML model getting reframed to a newer version, we are a little behind in integrating the ML with the camera due to needing time for it to train. We’ve made steps to integrate the ML with the game logic via having it provide outputs that can be usable in Python code.

We’ve also have been working on creating an outer shell to house our physical device’s connections (wires, Raspberry Pi, etc.) to help with the portability component of our project.

With our ML on track, we are looking to catch up on our integration by the end of this weekend/early portion of next week.

Status Report Q/A:

So far our unit tests pertain to the functionalities of our individual devices.

All our physical devices are now on a working level. We are able to print the necessary symbols/images using just the Raspberry Pi and the thermal printer, the keyboard/LCD screen has no visual lag (latency too small to manually time) and is able to accurately display the inputs, and we have been working to write suits (i.e. heart symbol) to the LCD screen.

In terms of card design dealing speed, the worst case card (i.e. card with rank 10 since it has the most bitmap/complex design) takes around 7 seconds. Cards with simpler designs (i.e. Ace) take around 4 seconds. We were able to achieve this results after removing generous delays that there were previously in place to prevent buffer overflows. (The initial printing speed was around 20 seconds.) In our design process, we’ve found that in order make changes to the card design, this also risks having to retrain our ML model all over each time if the change is too different.

The ML model has gone through testing in terms of latency for accuracy and detection. So far, we are able to have a 99.5% accuracy. This is an improvement from when we used the previous v5 for YOLO where we had a 97% accuracy.

 

This week we received the new camera that was ordered previously. The interface for it has been written and it is compatible with the Raspberry Pi. For the ML portion, the network needed to be reframed due to complications data handling, but it is currently set up and being trained. Though there have been some delays with integration due to complications with developing the individual parts, we have been catching up and are working on integrating this weekend. We are also preparing the slides for the Final Presentation next week by getting some quantitative testing metrics from our progress.

Our demo was on Wednesday, and we were able to demonstrate the printing mechanism and the functionality of our physical devices. Our printer is able to print the necessary card designs using the Raspberry Pi. It taking around 20 seconds to print a card, but we are going to try increasing the speed via decreasing some of the delays.

We’ve also ordered more parts- including a power jack and adapter in order to have the printer be powered by an outlet. Since the lens we ordered is not compatible with our camera module, we also are looking into alternative options.

Since the camera is the main component to our system’s functionality, our integration schedule is a bit behind. In the meantime, we are working on developing and optimizing the individual parts until we can integrate it with the camera.

This week, we were primarily focused on being able to interface with the Raspberry Pi and thermal printer to print images. Previously, we were able to fully print out the card designs using an Arduino Uno (as Adafruit had a library for it written in C++ that supported bitmap printing). Via rewriting parts of the C++ file in Rust, we were able to successfully print out the card suit images that were made earlier for the Arduino.  In terms of machine learning, we were able to begin training, making data points for the printed 52 card designs.

Last week, we ordered the lens for the Raspberry Pi camera module and a prototyping shield to make connections easier. The lens is necessary in order to start using the camera module, as current images come out blurry and unrecognizable. We are still waiting on the lens to arrive in order to fully integrate and test the system. If it doesn’t arrive soon, we are considering using other imaging options (perhaps a laptop camera or buying another camera altogether).

Since Demo is next week, we are focusing on at least getting the physical devices we have ready to go. The keyboard is able to detect key inputs and the LCD screen is able to display it.

 

This week we finished making the card designs and figured out how to print them via the Arduino Uno. Now, having all 52 cards with the corresponding suits, values, and faces, we are able to do more ML training. The printer takes ~20 seconds to print out a single card, so speeding it up will be a part of our next focus in addition to figuring out how to interface it with the Raspberry Pi.

In terms of the camera, we realized that the camera module that we have does not come with a lens, so we currently can only capture blurred pictures. We are looking into ordering a lens, since vision is a key component of our project.

For the Interim Demo (4/3), we hope to be able to have playing card recognition fully functioning as well as our input devices to be operable.