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Code of paper "Learning to Parse Wireframes in Images of Man-Made Environments", CVPR 2018

Folder/file Description
junc For training junction detector.
linepx For training straight line pixel detector.
wireframe.py Generate line segments/wireframe from predicted junctions and line pixels.
evaluation Evaluation of junctions and wireframes.

Requirements

  • python3
  • pytorch==0.3.1
  • opencv==3.3.1
  • scipy, numpy, progress, protobuf
  • joblib (for parallel processing data.)
  • tqdm
  • [optional] dominate

The code is written and tested in python3, please install all requirements in python3.

Prepare data

  • Download the training data.
    • Download imgs from OneDrive, put it in data/, unzip v1.1.zip.
    • Download annotation from OneDrive, put it in data/, unzip pointlines.zip.
    • Download mat-files for wireframe evaluation from OneDrive, put it in evaluation/wireframe/, unzip linemats.zip.
  • Preprocess data. 
    cd junc
python3 main.py --create_dataset --exp 1 --json

cd linepx
python3 main.py --genLine

Note: --json means you put the hype-parameters in junc/hypes/1.json.

Training

  • train junction detector.

    cd junc
python3 main.py --exp 1 --json --gpu 0 --balance

  • train line pixel detecor.

    cd linepx
python3 main.py --netType stackedHGB --GPUs 0 --LR 0.001 --batchSize 4

Testing

  • Test junction detector. 
    cd junc
python3 main.py --exp 1 --json --test --checkepoch 16 --gpu 0 --balance
  • Test line pixel detector. 
    cd linepx
python3 main.py --netType stackedHGB --GPUs 0 --LR 0.001 --testOnly t
  • Combine junction and line pixel prediction. 
    python wireframe.py

Evaluation

The code for evaluation is put in evaluation/junc and evaluation/wireframe. Expected precision/recall curve is like junction PR curve and wireframe PR curve.

Visualize the result

For visualizing the result, we recommend generating an html file using dominate to visualize the result of different methods in columns.

Citation

@InProceedings{wireframe_cvpr18,
author = {Kun Huang and Yifan Wang and Zihan Zhou and Tianjiao Ding and Shenghua Gao and Yi Ma},
title = {Learning to Parse Wireframes in Images of Man-Made Environments},
booktitle = {CVPR},
month = {June},
year = {2018}
}

License

You can use this code for your research and other usages, following BSD 2-Clause license. please credit our work when it helps you.