This research project investigates the application of several computer vision techniques for playing card detection and recognition in the context of the popular casino game, blackjack. The primary objective is to develop a robust system that is capable of detecting and accurately classifying playing cards in real-time, and displaying the optimal move recommendation based on the given image of the current game. The proposed methodology involves using K-Means for image segmentation, card reprojection and feature extraction, training of the KNN classifier using a labeled dataset, and integration of the detection system into a Blackjack Basic Strategy recommendation algorithm. Further, the study aims to observe the effectiveness of this approach in detecting various card designs under different lighting conditions and occlusions. Overall, the project examines the potential benefits of incorporating computer vision techniques, with a specific focus on card detection, into commonly played games aiming to enhance player decision-making and optimize strategic outcomes. The results obtained from our experimental evaluations with models developed under considerable time constraints, highlight the potential for practical implementation in real-world casino environments and across other similarly structured games.

这项研究项目旨在应用计算机视觉技术来进行二十一点等赌场游戏中的纸牌检测和识别，以开发一个能够实时检测和准确分类纸牌，并基于当前游戏图像显示最佳移动建议的稳健系统。该研究探讨了使用K-Means进行图像分割、纸牌投影重建和特征提取、使用带标签数据集训练KNN分类器，并将检测系统整合到二十一点基本策略建议算法中的方法。此外，该研究旨在观察在不同光照条件和遮挡下，该方法在检测不同纸牌设计方面的有效性。总体而言，该项目探讨了将计算机视觉技术，特别是纸牌检测，应用于常见游戏中以增强玩家决策和优化策略结果的潜在优势。在考虑到时间限制的情况下，通过实验评估获得的结果突显了在真实赌场环境和其他类似结构的游戏中进行实际应用的潜力。

优化二十一点策略推荐器：关于增强游戏体验的计算机视觉集成的综合研究