Noisify is a simple light-weight library for augmenting and modifying data by adding realistic noise.
Add some human noise (typos, things in the wrong boxes etc.)
>>> from noisify.recipes import human_error
>>> test_data = {'this': 1.0, 'is': 2, 'a': 'test!'}
>>> human_noise = human_error(5)
>>> print(list(human_noise(test_data)))
[{'a': 'tset!', 'this': 2, 'is': 1.0}]
>>> print(list(human_noise(test_data)))
[{'a': 0.0, 'this': 'test!', 'is': 2}]
Add some machine noise (gaussian noise, data collection interruptions etc.)
>>> from noisify.recipes import machine_error
>>> machine_noise = machine_error(5)
>>> print(list(machine_noise(test_data)))
[{'this': 1.12786393038729, 'is': 2.1387080616716307, 'a': 'test!'}]
If you want both, just add them together
>>> combined_noise = machine_error(5) + human_error(5)
>>> print(list(combined_noise(test_data)))
[{'this': 1.23854334573554, 'is': 20.77848220943227, 'a': 'tst!'}]
Add noise to numpy arrays
>>> import numpy as np
>>> test_array = np.arange(10)
>>> print(test_array)
[0 1 2 3 4 5 6 7 8 9]
>>> print(list(combined_noise(test_array)))
[[0.09172393 2.52539794 1.38823741 2.85571154 2.85571154 6.37596668
4.7135771 7.28358719 6.83600156 9.40973018]]
Read an image
>>> from PIL import Image
>>> test_image = Image.open(noisify.jpg)
>>> test_image.show()
And now with noise
>>> from noisify.recipes import human_error, machine_error
>>> combined_noise = machine_error(5) + human_error(5)
>>> for out_image in combined_noise(test_image):
... out_image.show()
Noisify allows you to build flexible data augmentation pipelines for arbitrary objects. All pipelines are built from simple high level objects, plugged together like lego. Use noisify to stress test application interfaces, verify data cleaning pipelines, and to make your ML algorithms more robust to real world conditions.
Noisify relies on Python 3.5+
$ pip install noisify
Full documentation is available at ReadTheDocs.
Dstl (c) Crown Copyright 2019
Noisify is released under the MIT licence