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Mask augmentation for segmentation

For instance and semantic segmentation tasks, you need to augment both the input image and one or more output masks.

Albumentations ensures that the input image and the output mask will receive the same set of augmentations with the same parameters.

The process of augmenting images and masks looks very similar to the regular image-only augmentation.

  1. You import the required libraries.
  2. You define an augmentation pipeline.
  3. You read images and masks from the disk.
  4. You pass an image and one or more masks to the augmentation pipeline and receive augmented images and masks.

Steps 1 and 2. Import the required libraries and define an augmentation pipeline.

Image augmentation for classification described Steps 1 and 2 in great detail. These are the same steps for the simultaneous augmentation of images and masks.

import albumentations as A
import cv2

transform = A.Compose([
    A.RandomCrop(width=256, height=256),

Step 3. Read images and masks from the disk.

  • Reading an image
image = cv2.imread("/path/to/image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
  • For semantic segmentation, you usually read one mask per image. Albumentations expects the mask to be a NumPy array. The height and width of the mask should have the same values as the height and width of the image.
mask = cv2.imread("/path/to/mask.png")
  • For instance segmentation, you sometimes need to read multiple masks per image. Then you create a list that contains all the masks.
mask_1 = cv2.imread("/path/to/mask_1.png")
mask_2 = cv2.imread("/path/to/mask_2.png")
mask_3 = cv2.imread("/path/to/mask_3.png")
masks = [mask_1, mask_2, mask_3]

Some datasets use other formats to store masks. For example, they can use Run-Length Encoding or Polygon coordinates. In that case, you need to convert a mask to a NumPy before augmenting it with Albumentations. Often dataset authors provide special libraries and tools to simplify the conversion.

Step 4. Pass image and masks to the augmentation pipeline and receive augmented images and masks.

If the image has one associated mask, you need to call transform with two arguments: image and mask. In image you should pass the input image, in mask you should pass the output mask. transform will return a dictionary with two keys: image will contain the augmented image, and mask will contain the augmented mask.

transformed = transform(image=image, mask=mask)
transformed_image = transformed['image']
transformed_mask = transformed['mask']

An image and a mask before and after augmentation.

An image and a mask before and after augmentation. Inria Aerial Image Labeling dataset contains aerial photos as well as their segmentation masks. Each pixel of the mask is marked as 1 if the pixel belongs to the class building and 0 otherwise.

If the image has multiple associated masks, you should use the masks argument instead of mask. In masks you should pass a list of masks.

transformed = transform(image=image, masks=masks)
transformed_image = transformed['image']
transformed_masks= transformed['masks']