This project presents a framework for extracting attributed adjacency graphs (AAGs) from floor plan images. This repository is the official implementation of the paper "ROBUST ATTRIBUTED ADJACENCY GRAPH EXTRACTION USING FLOOR PLAN IMAGES".
- Linux or macOS is required
- Python 3.7
- detectron2 # need to be installed separately,
detectron2==0.6+cu111is used in the experiment. - pytorch # need to be installed separately,
torch==1.9.1+cu111is used in the experiment. - Other required packages are summarized in
requirements.txt. - CUDA-supported GPU with at least 8 GB Memory Size is required.
git clone https://github.com/JanineCHEN/AAG-FP.git
cd ~/AAG_FP/
conda create --name AAG_FP python=3.7 # can use either anaconda or virtualenvwrapper to create the virtal environment
conda activate AAG_FP
# detectron2 and pytorch need to be installed separately
pip install -r requirements.txt
For downloading the checkpoints, please refer to ckpt.
This demo example uses the sample floor plan images in FP_sample_images.
you can use your own floor plan images by putting them inside the FP_sample_images folder, images with extension ".jpeg",".jpg" or ".png" are all accepted.
For executing the AAG extractor, please run:
python main.py
For downloading the dataset, please refer to dataset.
If you find the code and data of our research useful, please consider citing:
@inproceedings{chen_graph_2022,
title = {Robust attributed adjacency graph extraction using floor plan images},
volume = {2},
booktitle = {{POST-CARBON}, {Proceedings} of the 27th {International} {Conference} of the {Association} for {Computer}-{Aided} {Architectural} {Design} {Research} in {Asia}},
author = {Chen, Jielin and Stouffs, Rudi},
year = {2022},
pages = {385--394},
}
Part of the code is inspired by CubiCasa/CubiCasa5k and yu45020/Text_Segmentation_Image_Inpainting. The computational work for this project was partially performed on resources of the National Supercomputing Centre, Singapore (https://www.nscc.sg).