wireframe: A Python repository from huangkuns

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.

huangkuns/wireframe