Image Gradient Magnitude
~12 mincode completion
Implement image_gradient_magnitude(image) returning the (H−1,W−1) magnitude array.
Examples
Vertical step of 3: magnitude is 3 on the right column
- Input
- image_gradient_magnitude([[0, 0, 3], [0, 0, 3], [0, 0, 3]])
- Output
- [[0, 3], [0, 3]]
Horizontal step of 4: magnitude is 4 on the only output row
- Input
- image_gradient_magnitude([[0, 0, 0], [4, 4, 4]])
- Output
- [[4, 4]]
Hints
Hint 1
Take the square root at the end, not inside the sum.
Hint 2
Reach for forward rather than central differences.
Requirements
image: 2D array of shape (H, W)Return array of shape (H-1, W-1)
Constraints
Allowed library: NumPy only
Time limit: 200 ms, Memory: 64 MB
Where this shows up
~12 min
••••••••••••••••
8 employers weight this skill
4 autonomy companies, 1 defense company, 1 health and bio company, 1 enterprise vendor, 1 AI product company. Top match scores 93.
Python
import numpy as np
def image_gradient_magnitude(image):
"""
Forward-difference gradient magnitude on a 2D image.
Args:
image: 2D array of shape (H, W)
Returns:
array of shape (H-1, W-1)
"""
# YOUR CODE HERE
pass