[Solved] What will be the output of the following code snippet?import numpy as np from scipy.ndimage import binary_dilation img = np.... | SciPy

SciPy - Image Processing (scipy.ndimage)

What will be the output of the following code snippet?

import numpy as np
from scipy.ndimage import binary_dilation
img = np.array([[0,0,0],[0,1,0],[0,0,0]])
dilated = binary_dilation(img, structure=np.ones((3,3)))
print(dilated.astype(int))

A[[1 1 1] [1 1 1] [1 1 1]]

B[[0 0 0] [0 1 0] [0 0 0]]

C[[0 1 0] [1 1 1] [0 1 0]]

D[[1 0 1] [0 1 0] [1 0 1]]

Step-by-Step Solution

Solution:

Step 1: Understand binary_dilation with 3x3 structuring element
Dilation adds pixels to neighbors where the structuring element overlaps with any 1 pixel.
Step 2: Apply dilation to the input image
The single center 1 pixel causes all its 8 neighbors to become 1, but since the image border is zero, dilation results in a cross shape with ones at center and its 4 neighbors.
Final Answer:
[[0 1 0] [1 1 1] [0 1 0]] -> Option C
Quick Check:
Dilation expands pixels to neighbors [OK]

Quick Trick: Dilation with full 3x3 structure fills neighbors [OK]

Common Mistakes:

Expecting full 3x3 block of ones
Confusing dilation with erosion
Using wrong structuring element size

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What will be the output of the following code snippet?

Step 1: Understand binary_dilation with 3x3 structuring element

Step 2: Apply dilation to the input image

Final Answer:

Quick Check:

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