The project will use Computer Vision to classify and detect Playing Cards in a window as player or dealer cards with the face value, and then use Basic Strategy to recommend the optimal move for the player in every situation. The 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. We also aimed to observe the effectiveness of this approach in detecting various card designs under different lighting conditions and occlusions.
The entire research paper for this project, "Optimal Blackjack Strategy Recommender: A Comprehensive Study on Computer Vision Integration for Enhanced Gameplay", is available on Arxix, https://arxiv.org/abs/2404.00191.
The paper lists all the work conducted: our assumptions, attempted strategies, results, conclusions, etc.
To test this code, simply execute recognition.py and provide the image path as an argument.
python3 recognition.py {image_path}
Sample Output for 6 different test images. The recommended move is listed in the bottom left of the images.
