| # Introduction | |
| This "scripts" folder contains stand-alone scripts for some useful tasks detailed below. | |
| ## mask_to_json.py | |
| Use this script to convert .png segmentation masks from the Open Solution from the CrowdAI challenge | |
| (https://github.com/neptune-ai/open-solution-mapping-challenge) | |
| to the COCO .json format with RLE mask encododing. | |
| Run as: | |
| ``` | |
| mask_to_json.py --mask_dirpath <path to directory with the png masks> --output_filepath <path to the output .json COCO format annotation file> | |
| ``` | |
| ## plot_framefield.py | |
| Use this script to plot a framefield saved as a .npy file. Can be useful for visualization. | |
| Explanation about its arguments can be accessed with: | |
| ``` | |
| mask_to_json.py --help | |
| ``` | |
| ## ply_to_json.py | |
| Use this script to convert .ply segmentation polygons from the paper | |
| "Li, M., Lafarge, F., Marlet, R.: Approximating shapes in images with low-complexity polygons. In: CVPR (2020)" | |
| to the COCO .json format with [polygon] mask encoding. In order to fill the score field of each annotation in the COCO format, we also need access to segmentation masks. | |
| Run as | |
| ``` | |
| ply_to_json.py --ply_dirpath <path to directory with the .ply files> --mask_dirpath <path to directory with the probability masks> --output_filepath <path to the output .json COCO format annotation file> | |
| ``` |