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.

  • Imgs, train annotaions, test annotations are all available at BaiduPan (passwd: wf18).
  • You can also download data from onedrive: onedrive(no passwd required).

• put training data in data/ and test annotation in put it in evaluation/wireframe/,

    unzip v1.1.zip
    unzip pointlines.zip
    unzip linemat.zip

• Data Structure
  Each .pkl file contains the annotated wireframe of an image, and it consists of the following variables:

  *.pkl  
      |-- imagename: 	the name of the image  
      |-- img:         the image data  
      |-- points:      the set of points in the wireframe, each point is represented by its (x,y)-coordinates in the image  
      |-- lines:       the set of lines in the wireframe, each line is represented by the indices of its two end-points  
      |-- pointlines:     the set of associated lines of each point        
      |-- pointlines_index:       line indexes of lines in 'pointlines'  
      |-- junction:       the junction locations, derived from the 'points' and 'lines'  
      |-- theta:      the angle values of branches of each junction                   

• visualizing the wireframe.
  After loading the .pkl file, you can run something like the following in Python to visualize the wireframe:

    for idx, (i, j) in enumerate(lines, start=0):
        x1, y1 = points[i]
        x2, y2 = points[j]
        cv2.line(im, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2, cv2.LINE_8)

• 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 junction and wireframe precision/recall curve is like

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/dataset for your research and other usages, following MIT License.