Image Dataset Management Toolkit


License
MIT
Install
pip install imgo==2.5.2

Documentation

imgo

Process, Augment, and Balance Image Data

PyPI - Version PyPI - Python Version License: MIT

💡 What is it?

This library is designed to facilitate the preprocessing phase of image classification projects in order to get into the fun part: training the models!

Features:

Imgo is composed of two modules: uptools and augtools.

Uptools helps to streamline various image data preprocessing tasks, such as:

  • Reading images from a local disk
  • Rescaling images
  • Normalizing and standardizing pixel values
  • Converting image datasets into numpy-arrays
  • One-hot-encoding label data
  • Splitting image datasets into training, validation, and testing subsets
  • Merging data subsets into a single dataset
  • Saving numpy-arrays as images in class subdirectories

imgo_up_demo

Augtools allows the user to quickly and efficiently apply augmentation to image data. With Augtools, users can perform the following augmentation tasks using very few lines of code:

  • Apply a powerful collection of transformation and corruption functions
  • Augment images saved on a local disk
  • Save augmented images in class subdirectories
  • Augment entire image datasets
  • Augment training data in place in preparation for machine learning projects
  • Rebalance class sizes by generating new training images

imgo_aug_demo

🛠️ Setup

Install it from PyPI by running pip install imgo.

Dependencies

The code was written with Python 3.6, and it is recommended to run it in a virtual environment. All the required libraries are listed in the requirements.txt file in this repo.

🚀 Execution

Once the package has been installed, it is simply a case of experimenting with the various classes and functions. For a quickstart, please see the demo.

📝 Documentation

Documentation is currently available in the form of docstrings.

⚖️ License

The project is licensed under the MIT license.

Acknowledgements

Some of the augtools library is built as a wrapper around Imgaug, a powerful image augmentation library. For more information, please see https://imgaug.readthedocs.io/en/latest/.