Apply 2D Image Convolution | Dataford Interview Questions

Given a 2D grayscale image image as a list of lists of integers and a square filter kernel as a list of lists of integers, implement a function that returns a new 2D image produced by applying the kernel to every pixel using convolution-like neighborhood summation. Assume zero-padding outside image boundaries, and clamp each output value to the range [0, 255].

Constraints

1 <= rows, cols <= 200
kernel is square with odd size: 1 <= k <= 7
0 <= image[r][c] <= 255
-10 <= kernel[i][j] <= 10

Function Signature

def apply_filter(image, kernel):

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Problem

Constraints

1 <= rows, cols <= 200
kernel is square with odd size: 1 <= k <= 7
0 <= image[r][c] <= 255
-10 <= kernel[i][j] <= 10

Function Signature

def apply_filter(image, kernel):

Your question is Apply 2D Image Convolution. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.