335 - Converting COCO JSON annotations to labeled masks
This video walks you through the process of converting COCO JSON annotations to labeled mask images. The four main parts of this video tutorial are:
1. Downloading data
2. Opening the (large) JSON file in python to understand the data
3. Visualizing a few annotations on respective images to confirm the quality of annotations
4. Converting JSON annotations to labeled masks
Code from this video is available here: https://github.com/bnsreenu/python_for_microscopists/tree/master/335%20-%20Converting%20COCO%20JSON%20annotations%20to%20labeled%20mask%20images
Dataset from: https://github.com/sartorius-research/LIVECell/tree/main
Note that the dataset comes with: Creative Commons Attribution - NonCommercial 4.0 International Public License
In summary, you are good to use it for research purposes but for commercial
use you need to investigate whether trained models using this data must also comply with this license - it probably does apply to any derivative work so please be mindful.
You can directly download from the source github page. Links below.
Training json: http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell/livecell_coco_train.json
Validation json: http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell/livecell_coco_val.json
Test json: http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/annotations/LIVECell/livecell_coco_test.json
Images: Download images.zip by following the link: http://livecell-dataset.s3.eu-central-1.amazonaws.com/LIVECell_dataset_2021/images.zip
If these links do not work, follow the instructions on their github page.