Improving Uno with Computer Vision (Plus the Dataset so You Can Too)

Adam Crawshaw, a software engineer from Cardiff, Wales, has developed an innovative application using computer vision to automate the scoring of Uno card games, which traditionally involves manually tallying points based on the cards remaining in opponents' hands. By utilizing Python's OpenCV package, Adam generated a comprehensive dataset of 8,992 images with 26,976 labeled examples, leveraging synthetic data creation techniques to simulate various card combinations and backgrounds. He used the Roboflow platform for data preparation, augmentation, and model testing, experimenting with different models such as YOLOv3 and MobileNetSSDv2 for real-time inference on mobile devices. The resulting application simplifies the scoring process, enhancing the gaming experience, especially in social settings. Adam has generously released his dataset for public use under a modified MIT license, encouraging others to develop their own Uno scoring applications.