Metadata-Version: 2.1
Name: simplabel
Version: 0.1.4
Summary: Simple tool to manually label images in disctinct categories.
Home-page: https://github.com/hlgirard/Simplabel
Author: Henri-Louis Girard
Author-email: hl.girard@gmail.com
License: GPLv3
Description: # Simplabel
        [![PyPI version](https://badge.fury.io/py/simplabel.svg)](https://pypi.org/project/simplabel/)
        [![Travis CI status](https://travis-ci.com/hlgirard/Simplabel.svg?branch=master)](https://travis-ci.com/hlgirard/Simplabel/branches)
        [![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://github.com/hlgirard/Simplabel/blob/master/LICENSE)
        
        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!
        
        ![screenshot](docs/screenshot_190404.png)
        
        ## 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 in `labels.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:
        
        ```python
        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
        
        ```python
        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](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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