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 the given canvas (or box, whatever), just like, you know, the
background-size: contain;thingy 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([ qtrfm.Whiten(), qtrfm.Dominofy(), qtrfm.Contain(256), vtrfm.ToPILImage(), vtrfm.CenterCrop(224), vtrfm.ToTensor(), vtrfm.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ])
or, you can use some pre-defined transformers:
from skimage.io 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])
The term "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.