We use this method to build card counting system
https://huggingface.co/blog/theeseus-ai/card-counting

**Requirements Specification Document: AI Card Counting System for Online Blackjack**

**Overview**
This document outlines the requirements for developing an AI-powered card counting system for online blackjack. The system will use TensorFlow for card recognition and implement the Hi-Lo system for real-time card counting.

**Objective**
– Automate card counting in blackjack to provide players with a strategic advantage.
– Perform real-time card recognition and counting, offering actionable insights to the player.

**System Components**

1. **Data Collection**
   – **Card Image Dataset**: Collect images of each card (from 2 to Ace) from various decks and angles.
   – **Data Labeling**: Label each image with the corresponding card rank (e.g., 2, 3, 4, …, 10, J, Q, K, A).

2. **Data Preprocessing**
   – **Image Resizing and Normalization**: Resize all images to a consistent size (e.g., 64×64 pixels) and normalize pixel values to the range 0-1.
   – **Dataset Splitting**: Split the dataset into training, validation, and test sets (e.g., 70% training, 20% validation, 10% test).

3. **Model Building and Training**
   – **Define CNN Model**: Build a Convolutional Neural Network (CNN) for card recognition.
   – **Compile and Train Model**: Compile the model and train it using the training dataset.

4. **Integration with Live Video Stream**
   – **Capture Video Frames**: Capture live video stream from a camera, processing each frame for card recognition.
   – **Card Recognition**: Preprocess captured frames and use the model to predict cards.

5. **Real-Time Card Counting**
   – **Card Counting Function**: Implement a function using the Hi-Lo system to count cards.
   – **Real-Time Updates**: Update the count in real-time based on recognized cards and display it to the player.

**Technical Requirements**
– **Programming Language**: Python
– **Libraries**: TensorFlow, OpenCV
– **Hardware**: High-resolution camera, GPU (for model training)

**Non-Functional Requirements**
– **Performance**: Minimize latency to ensure real-time card recognition and counting.
– **Scalability**: Design the system to accommodate dataset and model expansion.
– **Security**: Protect player privacy and prevent unauthorized access.

**Development Steps**

1. **Data Collection and Preprocessing**
   – Collect and label card images.
   – Resize and normalize images.
   – Split the dataset.

2. **Model Building and Training**
   – Design the CNN model.
   – Train and evaluate the model.

3. **Integration with Live Video Stream**
   – Set up video capture.
   – Implement frame-by-frame card recognition.

4. **Card Counting Implementation**
   – Implement the counting function.
   – Set up real-time updates.

5. **Testing and Deployment**
   – Test the entire system.
   – Deploy to an online environment.

**References**
– "Building an AI-Powered Card Counter with TensorFlow"
– "Card Counting Online and Online Gambling"
– "Real-Time Blackjack Monitoring for Card Counting Detection"

Please use this document as a guide to develop the AI card counting system.

Hourly Range: $8.00-$12.00

Posted On: July 21, 2024 11:21 UTC
Category: Desktop Software Development
Skills:Python, Desktop Application

Country: Japan

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