If you use this dataset and code, please cite our paper:
@inproceedings{kluger2020consac,
title={CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus},
author={Kluger, Florian and Brachmann, Eric and Ackermann, Hanno and Rother, Carsten and Yang, Michael Ying and Rosenhahn, Bodo},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
Please also cite the corresponding York Urban DB paper!
Get the code:
git clone --recurse-submodules https://github.com/fkluger/yud_plus.git
cd yud_plus
Set up the Python environment using Anaconda:
conda env create -f environment.yml
source activate yud_plus
Install the PyLSD package:
pip install pysld-nova
Download the original York Urban Database, decompress it and place it under
the data folder.
In order to visualise the dataset, run:
python yud.py
For usage within your own project, refer to the NYUVP class:
from yud import YUDVP
dataset = YUDVP(
data_dir_path="./data", # Path where the CSV files containing VP labels etc. are stored
split='all', # train, val, test, trainval or all
keep_data_in_memory=True, # whether data shall be cached in memory
normalise_coordinates=False,# normalise all point coordinates to a range of (-1,1)
extract_lines=False # do not use the pre-extracted line segments
)
idx = 0
sample = dataset[idx] # get a single sample from the dataset
VPs = sample['VPs'] # array Mx3 with vanishing points in homogeneous coordinates
image = sample['image'] # RGB image
lines = sample['line_segments'] # array Nx12 containing all extracted line segments
p1 = lines[:, 0:3] # line segment start points in hom. coordinates
p2 = lines[:, 3:6] # line segment end points in hom. coordinates
hom_lines = lines[:, 6:9] # parametrised line [a,b,c] s.t. ax+by+c=0
centroids = lines[:, 9:12] # centroid = (p1+p2)/2.