Monitoring My Caffeine Intake with Computer Vision

James Gallagher details his experience in building a computer vision model to monitor his caffeine intake by tracking the number of cups of tea or coffee he consumes daily. After quitting coffee and turning to tea, Gallagher recognized the need for a system to prevent excessive caffeine consumption. He employed computer vision to detect cups on his desk, using a camera to monitor their presence and recording data when a cup is away for more than two minutes. This information updates his Slack status, allowing his coworkers to know when he's taking a break. The project utilized tools like Roboflow for dataset management and the Luxonis OAK camera for real-time monitoring, with Gallagher's method involving image annotation, model training, and deployment. The system's logic is adaptable, offering potential applications beyond tracking caffeine, such as monitoring industrial equipment usage.