Learn-SVGRouting is a project designed to extract, analyze, and visualize routing paths from SVG floorplans. It leverages tools like PostGIS, pgRouting, and various Python libraries to generate walkability graphs based on the layout of floorplans, obstacles, and desks. This can be useful for applications such as robot navigation in mapped environments.
The project utilizes the following libraries:
- Matplotlib: For creating static, interactive, and animated visualizations in Python.
- NetworkX: A library for the creation, manipulation, and study of complex networks.
- Notebook: A web-based interactive computing environment for creating Jupyter notebooks.
- NumPy: A fundamental package for scientific computing in Python, supporting large, multi-dimensional arrays and matrices.
- Psycopg2-binary: A PostgreSQL adapter for Python, used for connecting to the PostgreSQL database.
- Shapely: A library for manipulation and analysis of planar geometric objects.
After setting up your environment and installing the dependencies, you can start using the project to extract and analyze routing paths from SVG floorplans.
- Python 3.12
- PostgreSQL with PostGIS and pgRouting extensions
- Docker (for containerized environment)
To set up the project, you can follow these steps:
- Clone the repository:
git clone https://github.com/shawinnes/learn-svgrouting.git
cd learn-svgrouting- Install dependencies
uv venv
source .venv/bin/activate
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