image-augmenter 0.1.1

A python library to augment the images dataset aimed for a ML classification system

Table of Contents

• Scope
• Setup
  • Versions
  • Virtualenv
  • Installation
  • Tests
• Dataset
• Labeller
• Normalizer
• Augmenter
• Persister

Scope

The scope of this library is to augment the dataset for an image classification ML system.

Setup

Versions

The library is compatible with python 3.7.

Virtualenv

We suggest to isolate your installation via python virtualenv:

python3 -m venv .imgaug
...
source .imgaug/bin/activate

Installation

Update pip package manager:

pip install pip --upgrade
...
pip install -r requirements.txt

Tests

The library is covered, by fast, isolated unit and doc testing (the latter to grant reliable documentation):

python -m unittest discover -s imgaug -p '*'
...................
----------------------------------------------------------------------
Ran 19 tests in 0.898s

OK

Dataset

The system is aimed to work with images of different sizes, saved as PNG or JPG files (supporting RGBA conversion).

Labeller

The target label is extracted directly by inspecting the image name and trying to extract meaningful information (customisable).

lbl = Labeller()
lbl('resources/1096023906001-c-suit-veletta-albino.jpg')
'1096023906001'

Normalizer

The images are normalized by:

• resizing them to the specified max size (default to 256 pixels)
• optionally applying a squared, transparent canvas and centering the image on it, thus avoiding any deformation

norm = Normalizer(size=128, canvas=True)
img = norm('resources/bag.png')
img.shape
(128, 128, 4)

Augmenter

The number of images is augmented by three orders of magnitude (depending on the cutoff attribute) by applying different transformations to the original one.
Transformations are applied by using generators, thus saving memory consumption.

aug = Augmenter()
aug.count
1000

Persister

Images are persisted upon normalization and augmentation, by passing a BytesIO object to the specified action function. The persister can be iterated upon function return value and label.

def persist(name, data):
    filename = f'temp/{name}'
    with open(filename, 'wb') as f:
        f.write(data.getvalue())
    return filename

pers = Persister('resources/skirt.jpg', action=perist)
for label, filename in pers:
    print(label, filename)