Pixeletica is a powerful tool for converting images into Minecraft blocks with various dithering methods. It can generate color-matched images, Litematica schematics, and textured image exports.
- No Dithering: Simple color mapping without dithering
- Floyd-Steinberg: Error diffusion dithering for smooth results
- Ordered Dithering: Pattern-based dithering with Bayer matrices
- Random Dithering: Adds random noise for a pixelated effect
- Texture Rendering: Visualize images using actual Minecraft textures
- Web Optimization: Split images into 512×512 tiles for web viewing
- Split Export: Divide images into configurable number of parts
- Schematic Generation: Create Litematica schematics for in-game building
- Chunk Lines: Display Minecraft chunk boundaries (every 16 blocks)
- Block Grid: Show block boundaries for precise positioning
- Customizable Colors: Configure line colors with alpha channel support
- Coordinate System: Specify world coordinates for proper alignment
- Launch the application with
python main.pyand select GUI mode - Load an image and set your desired size
- Choose a dithering algorithm
- Configure export settings in the Export tab
- Process and save your image
Run the application with command line mode for batch processing:
python main.pyEnter the top-left X,Z coordinates in the Minecraft world. These coordinates determine how chunk boundaries align.
- Chunk Lines: Shows Minecraft chunk boundaries (every 16 blocks)
- Block Grid: Shows individual block boundaries
- Customize colors with hex format (RRGGBBAA)
- Web Tiles: Creates 512×512 tiles with HTML viewer
- Large Image: Single combined image
- Split Parts: Divides the image into multiple equal parts
- Clone this repository
- Install dependencies:
pip install -r requirements.txt - Place Minecraft textures in
./src/minecraft/texturepack/minecraft/textures/block - Run the application:
python main.py
You can also use uv for installation (recommended):
uv install -r requirements.txt
uv run main.pyPixeletica API can be run using Docker containers for easy deployment:
- Make sure Docker and Docker Compose are installed on your system
- Clone this repository
- Start the services:
docker-compose up -dThis will start the API server, Celery worker, and Redis in separate containers.
For configuration options and advanced usage, see the Docker documentation.
- API is accessible at
http://localhost:8000by default - Configure via environment variables or docker-compose.override.yml
- Data is persisted in Docker volumes
# Basic usage
docker-compose up -d # Start all services
docker-compose down # Stop all services
docker-compose logs -f # View logs
docker-compose up -d --scale worker=3 # Run with 3 worker instances- Python 3.8+
- Pillow (PIL)
- NumPy
- Litemapy
This project is licensed under the MIT License - see LICENSE file for details.