Code to generate coco train and test files from two sources - Squidle+ data and infinigen synthetic data.
The code relies on config files in config/dataset and running the python script source/generate_coco_annotataions.py.
The script generates three directories - annotations, train2023, test2023.
The config files are based on hydra.cc configurations which are hierarchical in nature. Configs include details like test/train split, mappings, exclusions, target folders etc.
Squidle+ is an online marine image repository found at squidle.org.
This code will generate COCO annotation files from squidle annotation sets by retrieving images and annotations based on specified annotation set ids.
Requires a login to squidle.org to generate an api_token. The api tokens can be setup in config/dataset/squidle_default.yaml
Use config files in config/dataset to define annotation set ids, mappings of squidle data to coco data, test/train split, directory for files generated etc. Specify the config file in the command as the dataset (squidle_urchin_defualt.yaml in command below.)
The code also has capability to generate new annotation sets in squidle but this is more advanced.
python source/generate_coco_annotations.py --config-name=config_coco_squidle.yaml dataset="squidle_hand_default.yaml" dataset.annotation_set_ids=[15800] dataset.folder=squidle_handfish_15800
Generate COCO annotations from infinigen output. Requires Images, Objects, ObjectSegmentation and InstanceSegmentations folders.
Use the config file config/dataset/infinigen_default.yaml to specify source folder, mappings between infinigen object names and coco categories and test/train split.
The config file config/config_coco_infinigen.yaml specifies the directory of infinigen data (output_dir).
python source/generate_coco_annotations.py --config-name=config_coco_infinigen.yaml dataset.folder="nudi_handfish_auv_v2"
Add explanation of each config setting.