Labelling tool for creating custom datasets for object detection and semantic / instance segmentation. Intended to make labelling segmentation mask easier for abstract objects (e.g. cracks or erosion on surfaces) using algorithms such as GrabCut and image thresholding.

Example usage: labelling defects on a wind turbine's blade surface using this repository

git clone https://github.com/nearthlab/smart-labeller
cd smart-labeller
pip install -r requirements.txt

First, prepare images and class_labels.json as in the datasets/sample.

1. Create labels for new images or edit existing labels

python label.py [(optional) /path/to/dataset]
# press F1 to see instructions

2. Augment existing labels

python augment.py [(optional) /path/to/dataset]
# set options as you wish and press augment button

3. Export your labels into one of PASCAL-VOC / COCO / CityScapes (a.k.a. KITTI) dataset.

python export.py [(optional) /path/to/dataset]
# set options as you wish and press export button