Chess Vision Assistant A Real-Time Chess Analysis Tool Built with Computer Vision and AI GitHub Repo: github.com/yourusername/chess-vision-assistant
🔍 The Problem When analyzing online chess games, I wanted instant Stockfish-level feedback without manually entering moves. Existing solutions either required:
Platform-specific APIs
Manual move entry
Didn't work across different chess sites
I set out to build a universal solution that could "watch" any digital chessboard and provide real-time analysis.
💡 The Solution I created a desktop application that:
Captures the chessboard from screen
Recognizes pieces using computer vision
Analyzes positions with Stockfish
Displays best moves visually
All in real-time as you play!
🛠️ Technical Implementation Computer Vision System Piece Recognition: Template matching with OpenCV/PyAutoGUI
Noise Reduction: Custom thresholding to handle imperfect matches
FEN Generation: Converts visual board to chess notation
python Copy def board_to_fen(piece_locations): # Converts detected pieces to standard FEN notation fen = [] for row in range(8): empty = 0 for col in range(8): # Detection logic here... if piece_found: if empty: fen.append(str(empty)) fen.append(piece_code) empty = 0 else: empty += 1 if empty: fen.append(str(empty)) if row < 7: fen.append('/') return ''.join(fen) + ' w KQkq - 0 1' Chess Engine Integration Stockfish Wrapper: Custom Python interface with memoization
Multi-threaded Analysis: Non-blocking UI during engine calculation
Evaluation Metrics: Displays score and best continuation
Desktop Application Tkinter GUI: Custom widgets for board display
SVG Rendering: Uses chess.svg + Cairo for board visualization
Auto-Update System: Continuously monitors game state
🚀 Key Features Works with any chess site/platform
No manual move entry required
Adjustable analysis depth
Save positions to study later
🧠 Skills Demonstrated Computer Vision
OpenCV template matching
Image processing (PIL, ImageGrab)
Screen capture and analysis
Chess Programming
Stockfish integration
FEN/PGN handling
Board state analysis
Software Engineering
Threaded application design
Performance optimization (memoization, caching)
Cross-platform GUI development
📈 Performance Optimizations Memoization - Cache engine results for identical positions
Multi-threading - Keep UI responsive during analysis
Selective Updates - Only re-analyze when board changes
🔮 Future Improvements
Implement neural network for piece recognition
Add game history and opening database