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YUD+: Additional Vanishing Point Labels for the York Urban Database

example

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!

Installation

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.

Usage

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.