Real-Time Poker Card Analyzer Development
Budget: $30 – $250 USD
Poker Card Hand Detection System
Objective:
Develop a real-time computer vision application that accurately detects and analyzes playing cards from live video streams (e.g., poker tables, casino games). The system will identify card ranks/suits, track hand combinations, and log gameplay statistics.
Technical Approach:
Detection Model:
Leverage YOLOv8 (Ultralytics) for high-speed card detection, trained on custom datasets of poker cards.
Inspired by open-source implementations:
Playing-Cards-Detection-YOLOv8 (baseline model)
Card-Detection-YOLO (multi-angle detection)
Core Features:
Real-time processing (30+ FPS) via OpenCV video pipeline
Suit/Rank Classification: CNN-based or YOLO-native segmentation
Game Analysis: Hand strength estimation, win probability calculation
Output: Overlay card labels on video stream + JSON log of detected hands
Tech Stack:
Backend: Python 3.10, PyTorch, OpenCV, FFmpeg
Inference: ONNX/TensorRT for optimized edge deployment (Jetson, Raspberry Pi)
UI: Streamlit/Gradio for configuration or native OpenCV display
Extensions:
Multiplayer Tracking: Assign cards to players via positional analysis
Cheat Detection: Flag irregular card movements (e.g., stacking)
Target Hardware:
Desktop GPU (RTX 3060+) or edge devices (Jetson Nano) for low-latency processing
Note: Adapt training data to accommodate varied lighting/angles based on target environment (casino vs. home games).
Optional Add-ons:
Integrate optical character recognition (OCR) for exotic card fonts
WebSocket API to broadcast detections to betting systems
Let me know if you'd like to emphasize specific aspects (e.g., compliance with gambling regulations, hardware constraints).
Objective:
Develop a real-time computer vision application that accurately detects and analyzes playing cards from live video streams (e.g., poker tables, casino games). The system will identify card ranks/suits, track hand combinations, and log gameplay statistics.
Technical Approach:
Detection Model:
Leverage YOLOv8 (Ultralytics) for high-speed card detection, trained on custom datasets of poker cards.
Inspired by open-source implementations:
Playing-Cards-Detection-YOLOv8 (baseline model)
Card-Detection-YOLO (multi-angle detection)
Core Features:
Real-time processing (30+ FPS) via OpenCV video pipeline
Suit/Rank Classification: CNN-based or YOLO-native segmentation
Game Analysis: Hand strength estimation, win probability calculation
Output: Overlay card labels on video stream + JSON log of detected hands
Tech Stack:
Backend: Python 3.10, PyTorch, OpenCV, FFmpeg
Inference: ONNX/TensorRT for optimized edge deployment (Jetson, Raspberry Pi)
UI: Streamlit/Gradio for configuration or native OpenCV display
Extensions:
Multiplayer Tracking: Assign cards to players via positional analysis
Cheat Detection: Flag irregular card movements (e.g., stacking)
Target Hardware:
Desktop GPU (RTX 3060+) or edge devices (Jetson Nano) for low-latency processing
Note: Adapt training data to accommodate varied lighting/angles based on target environment (casino vs. home games).
Optional Add-ons:
Integrate optical character recognition (OCR) for exotic card fonts
WebSocket API to broadcast detections to betting systems
Let me know if you'd like to emphasize specific aspects (e.g., compliance with gambling regulations, hardware constraints).