jabber

Label single or multiclass data with minimal keystrokes

Prerequisites

You need Python >= 3.6 and PyQt5 to run jabber

To install PyQt5 with pip:

pip install PyQt5

If you plan to run the unit tests in this project, you may also want pytest:

pip install pytest

Running

# navigate to the top level of this repo (the same level as README.md)
cd jabber

# run jabber.py
python jabber.py

Running tests

If you want to run the unit tests associated with this project, you should first install jabber:

python setup.py install

Then you can run the tests with:

pytest tests

Quick overview

Loading files and labels

To load a group of images, go to File->Open and select a directory that contains images (not recursively searched).

When you try to create your first class, you will be prompted to choose a file to save the labels to. Simply type the filename you wish to use (e.g. my_labels) and all labels you create will be automatically saved here as JSON.

If you want to load existing labels, or to set your labels file before creating a class, you can go to File->Select labels file and choose your new or preexisting labels file my_labels.json. Existing labels will not be overwritten if you choose to reload a file, but if you delete classes or labels, these changes will be reflected in the file.

Labeling

The main purpose of jabber is to speed up classification labeling/data separation by allowing the user to establish the classes they want, then associating those classes with each image using the minimum number of keystrokes.
This is done by matching keystrokes typed by the user to the existing class list.

For example, if you wanted to label pictures at the levels <major type>, <minor type>, <specific descriptor>, you would first add some classes in the Classes text entry box, click the image once to change focus, then begin typing the names of the classes you created to label. Labels will be shown in the Current labels list.

To generate these labels, all the user had to type was a d p, then ↵ to move to the next image

Again, to generate these labels, all the user had to type was a b e, then ↵ to move to the next image. Labels can also be added by double clicking class names. Your labels file will automatically be updated/saved when you add or delete labels.

Notes on class naming

If there are class names that begin with similar letters (bat and bird), matching will still work, but you will have to type more letters each time (ba, or bi in this case), increasing the minimum number of keystrokes needed for a match. It is a good idea to name your classes with unique starting letters, if possible, to reduce the number of keystrokes you have to type.

Navigation reference

You can navigate between images with the following keys, or by clicking on any filename in the file list:

→, ↓, or ↵: next image (↵ is recommended so you don't have to move your hands to reach the arrow keys)
←, or ↑: previous image

Resetting keystroke matching

If you make a typo, or change your mind when typing a class, you can either continue typing letters until there are no possible matches in the class list, or just hit Esc

Deleting labels and classes

Both labels and classes can be deleted by clicking items in their respective lists, then pressing the delete key. Classes will not be deleted if there are still any labels referencing them.

Label format

All labels are stored using JSON in the format:

{
  "<filename of image>": [
    "<label 1>",
    "<label 2>",
    ...
    "<label N>",
  ],
  ...
}

To use them in your applications, you can load the file with python:

import json

with open('/path/to/your/labels.json', 'r') as f:
    labels = json.load(f)

Then you can access the labels like this:

# these are your filenames
labels.keys()

# these are your labels
labels.values()

# to get the labels for your file '/path/to/images/foo.png'
labels['/path/to/images/foo.png']

Further scripts may be added in the future for some simple data management using label information, but you will likely find it easy enough to take the filename: labels information and copy the desired images to appropriately named class directories, programmatically separate your data, etc...

License

This project is licensed under the Apache License, Version 2.0