I spent a lot of time working and designing how the switch matrix would work and function, finding optimal placements to maximize the space we had, and minimize required components, keeping in mind ease of implementation. I laid out the 20″ × 20″ hexagonal game board with 11 copper rows and 11 copper columns, creating a grid that allows each intersection to be a unique addressable switch. The pink points in the diagram below represent the actual switch positions, while the diagonal copper wires are 18-gauge conductors that form the backbone of the matrix. By pressing two adjacent switches, players can place a road in the game, while a single press can represent a settlement (with an additional press later upgrading it to a city). This switch matrix design balances compactness, wiring simplicity, and intuitive user interaction.
In addition, I worked out how the computer vision dice detection system will function. We are planning to use the Oak-D Pro depth camera, connected to the Raspberry Pi 5 via USB to capture the dice rolls inside a transparent dice tray. The video frames will be processed by OpenCV, where we apply preprocessing steps like grayscale conversion, Gaussian blur, and adaptive thresholding to reduce noise and highlight the dice. From there, a blob detection algorithm is used to identify the dark circular regions (pips) on the dice faces. To ensure accuracy, the algorithm will use density-based clustering (such as DBSCAN) to group nearby pixels and eliminate spurious detections caused by reflections or shadows.
This approach has several advantages over mechanical or magnetic sensors:
- It allows the use of regular dice without modifications.
- The system can generalize across lighting conditions by adjusting preprocessing thresholds dynamically.
- By knowing the tray’s approximate size and camera angle, we can filter out non-dice blobs and count only the valid pips.
- Because the Oak-D Pro provides depth information, we could later extend the pipeline to verify that exactly two dice are present and lying flat before confirming a roll.
The final result of the pipeline will be a pair of integers corresponding to the dice values. These values are then sent to the Raspberry Pi’s game logic, which passes them over the WebRTC DataChannel to keep all boards synchronized. This means that once a roll is detected, the same value propagates across every connected board in under 500 ms.
Together, these two pieces, the switch matrix for structured player input on the board and the CV pipeline for automated dice roll detection, complete the sensing and input layer of the system. They integrate well with the Raspberry Pi’s GPIO and USB interfaces, and fit directly into the broader WebRTC-based communication system Tanisha finalized this week.
My plan for next week is to prototype OpenCV pipeline with the Oak-D Pro to validate dice detection accuracy and robustness under different lighting conditions.
