JSON Processing of RICO Dataset

These scripts are used to process JSON layouts of RICO dataset.
The result of processed layouts has been used in a paper in CHI '20.

Scripts

We used python 3.6.2 to execute the scripts below.

python main.py

This script extracts required metadata from the raw RICO json files, as a raw file contains too many information about the interface.
RICO json files should be stored in json/raw/ folder, and the results will be stored in json/refined folder.

python draw_image.py

Using the refined json files produced by main.py, this script draws bounding boxes of layout components.
You may find the mapping between colors and component categories in the settings.py file.
The results will be stored in a layout/{filename}_out.png format.

python compare_org_draw.py

This script draws the comparison image of a UI capture image and its layout image.
The purpose of this comparison is because a few RICO images do not match to their json hierarchy information, so we had to produce the comparsion images and manually check if all the images are correct.

This script requires the original UI images in original_picutre/ folder as well as the layout images produced by draw_image.py, and the results will be stored in a comparison/{filename}_comp.png format.

python compress.py

This script compresses the drawn layout images produced by compare_org_draw.py script.
The purpose of compression is because the dimension of a raw layout image is 2880 x 2560, which is a bit large.
The results will be stored in a compressed_comp/{filename}_{compress_ratio}compressed.png format.

Here are a few examples of the produced outputs after compress.py, compressed by 4.0:

1462	3553	17059

delete.py {image_numbers}

This script receives a list of RICO image numbers and deletes the data of them produced by the scripts above.

Citation

If you found these scripts helpful, please consider citing our paper.

@inproceedings{lee2020guicomp,
  author = {Lee, Chunggi and Kim, Sanghoon and Han, Dongyun and Yang, Hongjun and Park, Young-Woo and Kwon, Bum Chul and Ko, Sungahn},
  title = {GUIComp: A GUI Design Assistant with Real-Time, Multi-Faceted Feedback},
  year = {2020},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3313831.3376327},
  doi = {10.1145/3313831.3376327},
  booktitle = {Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems},
  pages = {1–13},
  series = {CHI '20}
}