| 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. |
- 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.
-
Download the training data.
-
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.
-
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
- 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
The code for evaluation is put in evaluation/junc and evaluation/wireframe. Expected junction and wireframe precision/recall curve is like
For visualizing the result, we recommend generating an html file using dominate to visualize the result of different methods in columns.
@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}
}
You can use this code/dataset for your research and other usages, following MIT License.
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