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YUD+: Additional Vanishing Point Labels for YUD

yud_plus 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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

![example](https://raw.githubusercontent.com/fkluger/yud_plus/master/example.jpg)

If you use this dataset and code, please cite [our paper](https://arxiv.org/abs/2001.02643):
```
@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](http://elderlab.yorku.ca/YorkUrbanDB/DenisElderEstradaECCV08.pdf)!

## 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](https://www.anaconda.com/): 
```
conda env create -f environment.yml
source activate yud_plus
```

Install the PyLSD package:
```
pip install pysld-nova
```

Download the [original York Urban Database](http://elderlab.yorku.ca/YorkUrbanDB), 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.
```

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