Increase segmentation areas?

[Rich2020]

I want to automatically generate masks (regions that should be ignored by another model) for my images. Segment-anything works wonderfully, but the masks are too tight-fitting. Is it possible to expand a given mask in all directions by some n pixels, and then clip the expended pixels that fall outside of the original image?

[heyoeyo]

You can achieve something like this using 'morphological filtering', specifically 'dilation', which is available using opencv.

The code would be something like:

import cv2
import numpy as np

# ... Set up SAM model

# Call SAM to get masks
masks, _, _ = predictor.predict(...)

# Grab example mask in uint8 format (assuming we used multimask)
# (if you're only generating 1 mask, you don't need to index with [0])
mask_uint8 = np.uint8(masks[0]) * 255

# Expand (dilate) the mask
ksize = (15,15)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, ksize)
expanded_mask_uint8 = cv2.morphologyEx(mask_uint8, cv2.MORPH_DILATE, kernel)

# If you want the mask back in True/False format
expanded_mask_bool = expanded_mask_uint8 > 0

[Rich2020]

@heyoeyo Thank you very much! I had actually tried using:

mask = np.random.choice([False, True], size=(640, 512))
mask_uint8 = mask.astype(np.uint8) * 255
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11)) 
dilated_mask = cv2.dilate(mask_uint8, kernel)

But for some reason it was dilating the entire mask (causing an issue with the edges of the image). It must be something to do with cv2.dilate(mask_uint8, kernel).

Anyway, your approach worked, so thank you again!