Additional torchvision image transforms for practical usage.
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Additional torchvision image transforms for practical usage.

Just for me. I needed it.

Note that all functors implemented here assumes an input image to be (H, W, C)-size np.uint8, ranged from 0 to 255.

  • Whiten: make all transparent pixels white.

  • Invert: invert RGB values.

  • Dominofy: limits the ratio of an image, like dominos.

  • Contain: contains an image into given canvas (or box, whatever), just like, you know, the background-size: contain; thingi from CSS?


pip install quadratum


Similar to all the other transform functors:

from quadratum import transforms as qtrfm
from torchvision import transforms as vtrfm
transform = vtrfm.Compose([
    vtrfm.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),

or, you can use some pre-defined transformers:

from import imread
from quadratum.transformer import Transformer
transform = Transformer('resnet')
image = imread('image.png')  # image.shape => (640, 960, 4)
x = transform(image)  # x.size() => torch.Size([3, 224, 224])

"Quadratum" means "square" in Latin. I wanted to make any noisy user-input images into fine-nice-good-well-godlike-heaven-deep-learning-applicable-preprocessed-square-images.