Metadata-Version: 2.4
Name: valetudo-map-parser
Version: 0.3.0b0
Summary: A Python library to parse Valetudo map data returning a PIL Image object.
License: Apache-2.0
License-File: LICENSE
License-File: NOTICE.txt
Author: Sandro Cantarella
Author-email: gsca075@gmail.com
Requires-Python: >=3.13
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Dist: Pillow (>=12.0.0)
Requires-Dist: mvcrender (==0.2.4)
Requires-Dist: numpy (>=2.0.0)
Requires-Dist: scipy (>=1.17.0)
Project-URL: Bug Tracker, https://github.com/sca075/Python-package-valetudo-map-parser/issues
Project-URL: Changelog, https://github.com/sca075/Python-package-valetudo-map-parser/releases
Project-URL: Homepage, https://github.com/sca075/Python-package-valetudo-map-parser
Project-URL: Repository, https://github.com/sca075/Python-package-valetudo-map-parser
Description-Content-Type: text/markdown

# Python-package-valetudo-map-parser

---
### What is it:
❗This is an _unofficial_ project and is not created, maintained, or in any sense linked to [valetudo.cloud](https://valetudo.cloud)

A Python library that converts Valetudo vacuum JSON map data into PIL (Python Imaging Library) images. This package is primarily developed for and used in the [MQTT Vacuum Camera](https://github.com/sca075/mqtt_vacuum_camera) project.

---

### Features:
- Processes map data from Valetudo-compatible robot vacuums
- Supports both Hypfer and Rand256 vacuum data formats
- Renders comprehensive map visualizations including:
  - Walls and obstacles
  - Robot position and cleaning path
  - Room segments and boundaries
  - Cleaning zones
  - Virtual restrictions
  - LiDAR data
  - Mop mode path rendering (Hypfer only)
- Provides auto-cropping and dynamic zooming
- Supports image rotation and aspect ratio management
- Enables custom color schemes
- Handles multilingual labels
- Implements thread-safe data sharing

### Installation:
```bash
pip install valetudo_map_parser
```

### Requirements:
- Python 3.13 or higher
- Dependencies:
  - Pillow (PIL) for image processing
  - NumPy for array operations
  - MvcRender Specific C implementation of drawings

### Usage:
The library is configured using a dictionary format. See our [sample code](https://github.com/sca075/Python-package-valetudo-map-parser/blob/main/tests/test.py) for implementation examples.

Key functionalities:
- Decodes raw data from Rand256 format
- Processes JSON data from compatible vacuums
- Returns Pillow PNG images
- Provides calibration and room property extraction
- Supports asynchronous operations

### Development Status:
Current version: 0.2.4b3
- Full functionality available in versions >= 0.2.0
- Actively maintained and enhanced
- Uses Poetry for dependency management
- Implements comprehensive testing
- Enforces code quality through ruff, isort, and pylint (10.00/10)

### Recent Updates (v0.2.4):
- **Fixed Critical Calibration Bug**: Calibration points now correctly update when map rotation changes
- **Fixed Rotation Change Handling**: Prevents errors when changing rotation with saved floor data
- **Multi-Floor Support**: Enhanced floor data management with add/update/remove methods
- **Mop Path Customization**: Configurable mop path width, color, and transparency (Hypfer vacuums)
- **Dock State Display**: Shows dock operations (e.g., "mop cleaning") in status text
- **Improved Compatibility**: Python 3.12+ support for Home Assistant integration
- **Performance**: Optimized image generation (~450ms average)
- **Code Quality**: Refactored for better maintainability and reduced complexity

### Contributing:
Contributions are welcome! You can help by:
- Submitting code improvements
- Enhancing documentation
- Reporting issues
- Suggesting new features

### Disclaimer:
This project is provided "as is" without warranty of any kind. Users assume all risks associated with its use.

### License:
Apache-2.0

---
For more information about Valetudo, visit [valetudo.cloud](https://valetudo.cloud)
Integration with Home Assistant: [MQTT Vacuum Camera](https://github.com/sca075/mqtt_vacuum_camera)

