# Image Augmentation#

Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.

## Kornia Augmentations#

Kornia leverages differentiable and GPU image data augmentation through the module kornia.augmentation by implementing the functionality to be easily used with torch.nn.Sequential and other advanced containers such as AugmentationSequential, ImageSequential, PatchSequential and VideoSequential.

Our augmentations package is highly inspired by torchvision augmentation APIs while our intention is to not replace it. Kornia is a library that aligns better to OpenCV functionalities enforcing floating operators to guarantees a better precision without any float -> uint8 conversions plus on device acceleration.

However, we provide the following guide to migrate kornia <-> torchvision. Please, checkout the Colab: Kornia Playground.

import kornia.augmentation as K
import torch.nn as nn

transform = nn.Sequential(
K.RandomAffine(360),
K.ColorJiggle(0.2, 0.3, 0.2, 0.3)
)


### Best Practices 1: Image Augmentation#

Kornia augmentations provides simple on-device augmentation framework with the support of various syntax sugars (e.g. return transformation matrix, inverse geometric transform). Therefore, we provide advanced augmentation container AugmentationSequential to ease the pain of building augmenation pipelines. This API would also provide predefined routines for automating the processing of masks, bounding boxes, and keypoints.

import kornia.augmentation as K

aug = K.AugmentationSequential(
K.ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.0),
K.RandomAffine(360, [0.1, 0.1], [0.7, 1.2], [30., 50.], p=1.0),
K.RandomPerspective(0.5, p=1.0),
data_keys=["input", "bbox", "keypoints", "mask"],  # Just to define the future input here.
return_transform=False,
same_on_batch=False,
)
# forward the operation
out_tensors = aug(img_tensor, bbox, keypoints, mask)
# Inverse the operation
out_tensor_inv = aug.inverse(*out_tensor)


From left to right: the original image, the transformed image, and the inversed image.

### Best Practices 2: Video Augmentation#

Video data is a special case of 3D volumetric data that contains both spatial and temporal information, which can be referred as 2.5D than 3D. In most applications, augmenting video data requires a static temporal dimension to have the same augmentations are performed for each frame. Thus, VideoSequential can be used to do such trick as same as nn.Sequential. Currently, VideoSequential supports data format like $$(B, C, T, H, W)$$ and $$(B, T, C, H, W)$$.

import kornia.augmentation as K

transform = K.VideoSequential(
K.RandomAffine(360),
K.RandomGrayscale(p=0.5),
K.RandomAffine(p=0.5)
data_format="BCTHW",
same_on_frame=True
)


### Customization#

Kornia augmentation implementations have two additional parameters compare to TorchVision, return_transform and same_on_batch. The former provides the ability of undoing one geometry transformation while the latter can be used to control the randomness for a batched transformation. To enable those behaviour, you may simply set the flags to True.

import kornia.augmentation as K

class MyAugmentationPipeline(nn.Module):
def __init__(self) -> None:
super(MyAugmentationPipeline, self).__init__()
self.aff = K.RandomAffine(
360, return_transform=True, same_on_batch=True
)
self.jit = K.ColorJiggle(0.2, 0.3, 0.2, 0.3, same_on_batch=True)

def forward(self, input):
input, transform = self.aff(input)
input, transform = self.jit((input, transform))
return input, transform


Example for semantic segmentation using low-level randomness control:

import kornia.augmentation as K

class MyAugmentationPipeline(nn.Module):
def __init__(self) -> None:
super(MyAugmentationPipeline, self).__init__()
self.aff = K.RandomAffine(360)
self.jit = K.ColorJiggle(0.2, 0.3, 0.2, 0.3)