Image data processing and augmentation
Project description
IMGO
Compile, Process, and Augment Image Data
This library is designed to facilitate the preprocessing phase of image classification projects.
Features:
Imgo is composed of two distinct but related 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
- Saving numpy-arrays as images in class subdirectories
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 small but 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
Here is a sample image augmented using Imgo:
Installation
It's as easy as pip install imgo
!
Quickstart
Have a look at the demos here!
Documentation
Documentation is currently available in the form of docstrings.
Requirements and Dependencies
Please see the requirements.txt
file for all requirements and dependencies.
Support
The source code is available here. Please direct any queries or issues to info@elbydata.com.
License
The project is licensed under the MIT license.
Acknowledgements
The Augtools library is built on top of Imgaug, a powerful image augmentation library. For more information, please see https://imgaug.readthedocs.io/en/latest/.
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