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.txtFirst, prepare images and class_labels.json as in the datasets/sample.
- Create labels for new images or edit existing labels
python label.py [(optional) /path/to/dataset]
# press F1 to see instructions- Augment existing labels
python augment.py [(optional) /path/to/dataset]
# set options as you wish and press augment button- 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