Skip to main content

Spatial distortions for image augmentations

Project description

saug (Spatial Augmentation)

Spatial distortions for image augmentations

Cite this library:

Oskouei, S., Valla, M., Pedersen, A., Smistad, E., Dale, V.G., Høibø, M., Wahl, S.G.F., Haugum, M.D., Langø, T., Ramnefjell, M.P. and Akslen, L.A., 2024. Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion. arXiv preprint arXiv:2406.14287.

and

DOI || Harvard citation: Soroush Oskouei (2024) “SoroushOskouei/saug: saug”. Zenodo. doi: 10.5281/zenodo.10780398.

Include both citations!

Overview

This Python library provides a collection of functions to apply various distortion effects to images. These effects range from simple pixelation and mirroring to more complex transformations like elastic and wave distortions, enabling creative alterations of images for artistic or research purposes. The library leverages NumPy for efficient array manipulations, ensuring high performance even for large images.

Dependencies

NumPy: For array operations and mathematical functions. OpenCV (cv2): For image reading and saving functionalities, as well as some image processing tasks.

Functions

This library includes a variety of functions to apply different types of distortions to images. Each function accepts an image (as a NumPy array) as input and returns the distorted image. The main functions include:

multi_lens_distortion: Applies a several lens distortion effects on various positions.

elastic_transform: Applies an elastic deformation to the image.

twirl_distortion01 and twirl_distortion02: Apply a twirling effect to parts of the image.

wave_distortion01, wave_distortion02, and wave_distortion: Create wave-like distortions across the image.

pixelate_image: Pixelates a specified region or the entire image.

cartesian_to_polar_image_stretched: Transforms the image from Cartesian to polar coordinates, stretching the pixels.

mirror_effect: Applies a mirroring effect to a specified side of the image.

tilt_shift_effect: Simulates a tilt-shift photography effect, blurring parts of the image while keeping a specific area in focus.

ripple_effect: Creates a ripple effect across the image.

zoom_blur: Applies a zoom blur effect from a specified point.

space_distortion_v1: Distorts the space within the image in a specified direction.

wind_distortion: Simulates the effect of wind on the image.

squeeze_stretch_effect: Applies a squeeze and stretch effect to the image.

smooth_lens_distortion: Applies a smooth lens distortion effect centered around a specified point.

crystallize_distortion: Simulates a crystalline effect by averaging blocks of pixels.

honeycomb_distortion: Applies a honeycomb-like distortion effect across the image.

moving_blur: Applies a moving blur effect on the whole image with specified direction and intensity.

warp_bubbles_effect: Creates a warp-like effect around a specified position.

Usage Examples Below are examples demonstrating how to use some of the functions provided in this library. These examples use a chessboard image as the input, but you can replace it with any image of your choice.

distorted_chessboard = multi_lens_distortion(chessboard, num_lenses=8, radius_range=[120, 190], strength_range=[-0.2, 0.7]):

image

distorted_chessboard = elastic_transform(chessboard, 90, 7)

image

distorted_chessboard = twirl_distortion01(chessboard, (170,270), 200, 0.8)

image

distorted_chessboard = twirl_distortion02(chessboard, (170,170), 10, 0.12)

image

distorted_chessboard = wave_distortion01(chessboard, (170,100), 10, 10)

image

distorted_chessboard = wave_distortion02(chessboard, (170,100), 8, 60)

image

distorted_chessboard = wave_distortion(chessboard, 8, 80)

image

distorted_chessboard = pixelate_image(chessboard, 14, (50, 50, 340, 340))

image

distorted_chessboard = cartesian_to_polar_image_stretched(chessboard, center=None)

image

distorted_chessboard = mirror_effect(chessboard, direction='horizontal', mirror_line_position=180, side='right')

image

distorted_chessboard = tilt_shift_effect(chessboard, 0.3, 0.7, 5, 0.4)

image

distorted_chessboard = ripple_effect(chessboard, 3, 7)

image

distorted_chessboard = zoom_blur(chessboard, (150, 150), intensity=0.07, blend=0.3)

image

distorted_chessboard = space_distortion_v1(chessboard)

image

distorted_chessboard = wind_distortion(chessboard, direction=(0.4, -1), strength=21)

image

distorted_chessboard = squeeze_stretch_effect(chessboard)

image

distorted_chessboard = smooth_lens_distortion(chessboard, 200, 200, 150, 1.3)

image

distorted_chessboard = crystallize_distortion(chessboard, crystal_size=27)

image

distorted_chessboard = honeycomb_distortion(chessboard)

image

distorted_chessboard = moving_blur(chessboard, 2,0.4, 3)

image

distorted_chessboard = warp_bubbles_effect(chessboard, [(170, 170)], 149, 3)

image

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

saug-0.0.4.tar.gz (11.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

saug-0.0.4-py3-none-any.whl (11.7 kB view details)

Uploaded Python 3

File details

Details for the file saug-0.0.4.tar.gz.

File metadata

  • Download URL: saug-0.0.4.tar.gz
  • Upload date:
  • Size: 11.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.11

File hashes

Hashes for saug-0.0.4.tar.gz
Algorithm Hash digest
SHA256 4d923d948133544969f0c5dc7a16992d2e73547f79a7d9fa4c9e3c490dc3fbe0
MD5 ecd824efd1a8b6e0fdd78b6060e5e1d4
BLAKE2b-256 4117e16efad4dfd7e8bc0b2fae788908786e27bb75c49ec1c7107a7a3517dda5

See more details on using hashes here.

File details

Details for the file saug-0.0.4-py3-none-any.whl.

File metadata

  • Download URL: saug-0.0.4-py3-none-any.whl
  • Upload date:
  • Size: 11.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.11

File hashes

Hashes for saug-0.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 38155462eb718d85c2195619937054b4ea3fb38c19b6b730e73a536a1bd9d64e
MD5 b9599ba1a26347c7750cf09156a5efa2
BLAKE2b-256 fc9201ae38100617a60dc96f5e20c25e840920e943fdb021738cf3fad6910c47

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page