This repository implements applications to ease manual annotation of chemical images, used in PatCID: an open-access dataset of chemical structures in patent documents.
conda create -n molannotator python=3.11
conda activate molannotator
pip install -e .
The classification application allows to assign to an image one of the following categories: standard_molecule (Molecular Structure), markush_structure (Markush Structure), miscellaneous (Background), polymer (Markush Structure), and segmentation_annotation_error.
To run the application:
- Move the images to be annotated to
./data/classfication/default/images/*.png, - (Optional) Move pre-annotations to
./data/classfication/default/classes/*.json, - Run
bash ./molannotator/apps/run_classification_app.sh.
Annotations are saved in ./data/classfication/default/classes/*.json.
The recognition application allows to annotate the molecular graph of a chemical-structure image.
To run the application:
- Move the images to be annotated to
./data/recognition/default/images/*.png, - (Optional) Create pre-annotations by running
python3 ./molannotator/molscribe_pre_annotate.py(see MolScribe for installation steps), - (Optional) Move pre-annotations to
./data/recognition/default/predictions/*.mol, - Run
bash ./molannotator/apps/run_recognition_app.sh.
Annotations are saved in ./data/recognition/default/molfiles/*.mol.