學習筆記1

| 文件夾/文件 | 描述 |

|------------|------------------------------|
| 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. |

## 系統需求
- 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.

## 數據準備
- 下載訓練數據.
- Download imgs from [OneDrive](https://1drv.ms/u/s!AqQBtmo8Qg_9g37TnqyD9GD3UQwW), put it in __data/__, `unzip v1.1.zip`.
- Download annotation from [OneDrive](https://1drv.ms/u/s!AqQBtmo8Qg_9g3_etkaVndKnqTdm), put it in __data/__, `unzip pointlines.zip`.
- Download mat-files for wireframe evaluation from [OneDrive](https://1drv.ms/u/s!AqQBtmo8Qg_9txsENm9ibTKfxAlI), put it in __evaluation/wireframe/__, `unzip linemat.zip`.

- 數據結構  
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
```
- wireframe可視化
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)
```

- 數據預處理.
```
cd junc                 ##文件夾junc爲鏈接點
python3 main.py --create_dataset --exp 1 --json

cd linepx             ##文件夾linepx爲線段
python3 main.py --genLine
```
Note: `--json` means you put the hype-parameters in __junc/hypes/1.json__.

## 訓練集
- train junction detector  訓練鏈接點檢測器.
```
cd junc
python3 main.py --exp 1 --json --gpu 0 --balance
```

- train line pixel detecor  訓練線段像素檢測器.
```
cd linepx
python3 main.py --netType stackedHGB --GPUs 0 --LR 0.001 --batchSize 4
```

## 測試集
- 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
```
- 聯合鏈接點和線段像素Combine junction and line pixel prediction.
```
python wireframe.py
```

### 結果評價
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
<figure class="half">
<img src="evaluation/junc/junc_1_16.png", width=400/>
</figure>

<figure class="half">
<img src="evaluation/wireframe/1_0.5_0.5.png", width=400/>
</figure>

### 結果可視化
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.

## 文獻引用
```
@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}
}
```

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