image smoother leetcode 661 python

certainly! the "image smoother" problem is a common coding challenge found on platforms like leetcode. the problem tests your ability to manipulate a 2d array (or image) by applying a smoothing algorithm that averages the pixel values of a given cell and its neighbors.

problem statement

you are given a 2d integer matrix representing an image, where each value represents the pixel value of that image. you need to create a new image where each pixel value is the average of the pixel values in the surrounding 3x3 square (including itself). the average should be rounded down to the nearest integer.

steps to solve the problem

1. **understand the neighbors**: for each pixel at position `(i, j)`, the neighbors are the pixels that are directly adjacent (up, down, left, right) and diagonally adjacent. this includes the pixel itself.

2. **boundary conditions**: when calculating the average, you need to be careful at the edges of the matrix to avoid accessing out-of-bound indices.

3. **calculate the average**: for each pixel, sum the values of all valid neighboring pixels and divide by the number of valid pixels.

4. **return the new image**: create a new 2d array to store the smoothed pixel values.

implementation

here’s how you can implement the image smoother in python:

```python
def imagesmoother(m):
if not m or not m[0]:
return []

rows, cols = len(m), len(m[0])
result = [[0] * cols for _ in range(rows)]

directions for the 8 neighboring cells + itself
directions = [
(-1, -1), (-1, 0), (-1, 1),
(0, -1), (0, 0), (0, 1),
(1, -1), (1, 0), (1, 1)
]

for i in range(rows):
for j in range(cols):
total = 0
count = 0

check all neighbors
for di, dj in directions:
ni, nj = i + di, j + dj
if 0 = ni rows and 0 = nj cols:
total += m[ni][nj]
count += 1

calculate the av ...

#ImageSmoother #LeetCode #numpy
Image smoother
LeetCode 661
Python
algorithm
image processing
array manipulation
pixel averaging
2D grid
boundary handling
neighbors calculation
smoothing filter
coding challenge
programming interview
computational geometry
dynamic programming