Simple tool to manually label images in disctinct categories.
Project description
Simplabel
Graphical tool to manually label images in distinct categories to build training datasets. Simply pass a list of categories, a directory containing images and start labelling. Supports multiple users, reconciliation and keyboard bindings to label even faster!
Installation
Install with pip
Simplabel is on PyPI so it can be installed with pip
pip install simplabel
Install from source
Clone the repository to your computer
git clone https://github.com/hlgirard/Simplabel.git
and install with pip
cd Simplabel
pip install .
Usage
Quick start
Simplabel can be started from the command line without any argument:
simplabel
You will be prompted to select a directory containing images to label. Add labels with the '+' button and start labeling. Number keys correspond to labels and can be used instead.
The target directory and/or labels can also be passed directly from the command line:
simplabel --labels dog cat bird --directory path/to/image/directory
After the first use, labels are stored in labels.pkl
and the --labels
argument is ignored.
Command line arguments
-d, --directory <PATH/TO/DIRECTORY>
sets the directory to search for images and save labels to. Defaults to the current working directory.-l, --labels <label1 label2 label3 ...>
sets the categories for the labelling task. Only passed on the first use in a given directory.-u, --user <USERNAME>
sets the username. Defaults to the OS login name if none is passed.-r, --redundant
does not display other labelers selections for independent labelling. Reconciliation and Make Master are unavailable in this mode.-v, --verbose
increases the verbosity level.--remove-label <LABEL>
tries to safely remove a label from the list saved inlabels.pkl
(must also pass-d
)--reset-lock
overrides the lock preventing the same username from being used multiple times simultaneously.--delete-all
removes all files created by simplabel in the directory (must also pass-d
)
Multiuser
The app relies on the filesystem to save each user's selection and display other user's selections. It works best if the working directory is on a shared drive or in a synced folder (Dropbox, Onedrive...). The Reconcile workflow allows any user to see and resolve conflicts. The Make Master option can be used to create and save a master dictionary - labeled_master.pkl
- containing all labeled images (after reconciliation).
Import saved labels
The app saves a labeled_<username>.pkl
file that contains a pickeled dictionary {image_name: label}. To import the dictionary, use the following sample code:
import pickle
with open("labeled_user1.pkl","rb") as f:
label_dict = pickle.load(f)
Advanced usage
Utilities
Once you are done labelling, use the flow_to_directory tool to copy images to distinct directories by label
flow_to_directory --input-directory data/labeled --output-directory data/sorted
Python object
The Tkinter app can also be started from a python environment
from simplabel import ImageClassifier
import tkinter as tk
root = tk.Tk()
directory = "data/raw"
categories = ['dog', 'cat', 'bird']
MyApp = ImageClassifier(root, directory, categories)
tk.mainloop()
License
This project is licensed under the GPLv3 License - see the LICENSE.md file for details.
Acknowledgements
Testing of tkinter GUI is based on ivan_pozdeev's answer at Stackoverflow:
https://stackoverflow.com/questions/4083796/how-do-i-run-unittest-on-a-tkinter-app
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