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https://github.com/huangkuns/wireframe
https://github.com/huangkuns/wireframe
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/huangkuns/wireframe
- Owner: huangkuns
- Created: 2018-04-17T16:50:29.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-08-06T04:35:45.000Z (over 2 years ago)
- Last Synced: 2024-08-03T01:15:16.313Z (5 months ago)
- Language: Python
- Size: 291 KB
- Stars: 208
- Watchers: 9
- Forks: 45
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-image-alignment-and-stitching - **2018CVPR**
- awesome-holistic-3d - Wireframe - | - | 5,462 | wireframe (2D) | (Datasets / Scene level)
README
## 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](https://pan.baidu.com/s/11CKr5s0zHnuVKsJVXianxA?pwd=wf18) (passwd: wf18).
- You can also download data from onedrive: [onedrive](https://1drv.ms/u/s!AqQBtmo8Qg_9uHpjzIybaIfyJ-Zf?e=Fofbch)(no passwd required).
- put training data in __data/__ and test annotation in put it in __evaluation/wireframe/__,
```shell
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:
```shell
*.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:
```python
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](evaluation/junc) and [evaluation/wireframe](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](https://github.com/Knio/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.