This repository is a fork of a project released here. The original originals are Geoffrey French, Dr. M. Fisher and Dr. M. Mackiewicz at University of East Anglia.

The code is modified to work better with SLIC initialization. Also, this repository provides a complete example to train an object detection model with annotated data.

$ pip install -e .

# This starts up interactive annotator.
# If slic option is supplied, it starts with precomputed segmentations based on SLIC.
$ python flask_app.py [--slic] [--image_dir PATH_TO_DIR] [--label_names label_names.yml]

SLIC option should not be set for those who only want to annotate by bounding boxes. The option can be helpful for segmenting an image. Please be aware the superpixel segmentation is not always perfect.

The annotations will be stored in the same directory where the corresponding images exist. The annotaions are stored by .json files containing all the parameters.

I provided a convenient script to translate raw .json into a label image. This can be helpful for annotating semantig segmentation datasets.

# Convert annotated json to labels
$ python convert_json_to_label.py [--image_dir PATH_TO_DIR] [--label_names label_names.yml]

$ python flask_app.py --image_dir examples/simple/images --label_names examples/simple/label_names_example.yml --file_ext jpg
$ python convert_json_to_label.py --image_dir examples/simple/images --label_names examples/simple/label_names_example.yml

If you want to initialize an image with superpixel initialization, use --slic option. Please use --file_ext to specify the file extension of images.

In the examples, there is a training script for SSD that uses dataset that is created by this annotation tool. SSD is one of the state of the art deep learning based object detection model.

The implementation uses ChainerCV, a computer vision library. The library is created to lower the barrier of entry to use deep learning based computer vision models.

The more details can be found in examples/ssd.

Operates as a browser-based application, either embedded as a widget within IPython Notebook or embedded within a web page as part of a web application.

Currently supports simple polygonal labels.

The supplied IPython notebook example creates a labelling tool widget and displays it within the notebook. API usage is demonstrated further down.

An example Flask-based web app is provided that displays the labelling tool within a web page. To start it, run python flask_app.py and open 127.0.0.1:5000 within a browser.

Incorporates the public domain json2.js library. Uses d3.js, jQuery, jQuery UI and PolyK.

This software was developed by Geoffrey French in collaboration with Dr. M. Fisher and Dr. M. Mackiewicz at the School of Computing Sciences at the University of East Anglia as part of a project funded by Marine Scotland.

It is licensed under the MIT license.