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Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

Requirements

  • Anaconda w/ Python 2.7
  • Caffe 1.0-RC5
  • ImageMagick 6.8.8-1
  • what spec-file.txt and requirements.txt say

Using other versions of these packages may yield different results.

Setup

  • Get the code, install requirements and build LSD:
git clone --recursive https://github.com/fkluger/Vanishing_Points_GCPR17.git
cd Vanishing_Points_GCPR17
conda create --name gcpr17_vp_detection --file spec-file.txt
source activate gcpr17_vp_detection
pip install -r requirements.txt
cd lsdpython
python setup.py build_ext --inplace
cd ..

Run

Examples

You can run the vanishing point detector on four example images (see below) and visualise the results. Computation may take a few moments. Adjust the GPU ID if necessary:

python example.py --gpu 0
python example.py --show

Benchmarks

Run the following commands to evaluate the vanishing point detector on the three benchmark datasets and plot the AUC curves:

python benchmark.py --yud --gpu 0 --update_datalist --update_datafiles --run_cnn --run_em
python benchmark.py --yud --gpu 0 
python benchmark.py --ecd --gpu 0 --update_datalist --update_datafiles --run_cnn --run_em
python benchmark.py --ecd --gpu 0 
python benchmark.py --hlw --gpu 0 --update_datalist --update_datafiles --run_cnn --run_em
python benchmark.py --hlw --gpu 0 

Examples

example

example

example

example

References

If you use the code provided here, please cite:

@inproceedings{kluger2017deep,
  title={Deep learning for vanishing point detection using an inverse gnomonic projection},
  author={Kluger, Florian and Ackermann, Hanno and Yang, Michael Ying and Rosenhahn, Bodo},
  booktitle={German Conference on Pattern Recognition (GCPR)},
  year={2017}
}

The paper can be found on arXiv.

The benchmark datasets used in the paper can be found here:

The example images show landmarks in Hannover, Germany:

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