Image data augmentation

This notebook will show the common methods used for preparing image data for vision models in PyTorch.

Load an original image

img = Image.open('images/cat.jpeg')
img

Convert the image to Tensor

toTensor = torchvision.transforms.ToTensor()
toTensor(img).shape

torch.Size([3, 1199, 1200])

From above, we know that the image size is 1199 * 1200.

Resize the image

Here we resize the image to size 224 \(\times\) 224.

resize = torchvision.transforms.Resize(224)
img_rs = resize(img)
img_rs

Flip an image

1. Flip horizontally

flip = torchvision.transforms.RandomHorizontalFlip(p=1.0)
flip(img_rs)

2. Flip vertically

flip = torchvision.transforms.RandomVerticalFlip(p=1.0)
flip(img_rs)

Change brightness, contrast, saturation and hue of an image

colorjitter = torchvision.transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.3)
colorjitter(img_rs)

Turn an image grayscale

grayscale = torchvision.transforms.Grayscale()
grayscale(img_rs)

Crop an image

crop = torchvision.transforms.CenterCrop(128)
crop(img_rs)