CrosslePyServ is a Python-based server application that processes and analyzes images to create puzzles. It uses computer vision and machine learning techniques to analyze images and convert them into puzzle formats.
- Image processing and analysis
- Machine learning model for puzzle generation
- Server-client architecture for remote processing
- Training data generation and model learning capabilities
- Real-time status updates during processing
- Python 3.x
- OpenCV (cv2)
- NumPy
- Matplotlib
- Pytesseract
- PIL (Python Imaging Library)
- Clone the repository:
git clone https://github.com/yourusername/CrosslePyServ.git
cd CrosslePyServ- Install the required dependencies:
pip install opencv-python numpy matplotlib pytesseract pillow- Ensure Tesseract OCR is installed on your system:
- Windows: Download and install from https://github.com/UB-Mannheim/tesseract/wiki
- Linux:
sudo apt-get install tesseract-ocr - macOS:
brew install tesseract
The application can be run in several modes:
python main.py --start-serverpython main.py --run-test# Ensure file paths exist
python main.py --ensure-file-paths
# Generate training data
python main.py --make-training-data
# Format training data
python main.py --format-to-training-data
# Train the model
python main.py --learnmain.py: Main entry point and command-line interfacecrossle_server.py: Server implementation for handling client connectionsanalyze.py: Image analysis and processing logicneuro.py: Machine learning model implementationpuzzle.py: Puzzle generation and manipulationmodel/: Directory containing trained modelstrainingdata/: Directory for training datagoalcontours/: Directory containing contour templates
The server runs on port 12345 by default. It accepts image data and JSON metadata from clients, processes the images, and returns the results.
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.