An image is represented by an m x n
integer grid image
where image[i][j]
represents the pixel value of the image.
You are also given three integers sr
, sc
, and newColor
. You should perform a flood fill on the image starting from the pixel image[sr][sc]
.
To perform a flood fill, consider the starting pixel, plus any pixels connected 4-directionally to the starting pixel of the same color as the starting pixel, plus any pixels connected 4-directionally to those pixels (also with the same color), and so on. Replace the color of all of the aforementioned pixels with newColor
.
Return the modified image after performing the flood fill.
class Solution:
def floodFill(self, image: List[List[int]], sr: int, sc: int, newColor: int) -> List[List[int]]:
dr=[-1,1,0,0]
dc=[0,0,-1,1]
r,c=len(image),len(image[0])
check=[[False]*c for _ in range(r)]
color=image[sr][sc]
def recur(i,j):
check[i][j]=True
if image[i][j]==color:
image[i][j]=newColor
for k in range(4):
nr=i+dr[k]
nc=j+dc[k]
#print(nr,nc)
if nc<0 or nc>=c or nr<0 or nr>=r:
continue
if not check[nr][nc] and image[nr][nc]==color:
recur(nr,nc)
recur(sr,sc)
return image
Time Complexity: O(N)
where N
is the number of pixels in the image.
Space Complexity: O(N)
the size of the implicit call stack when calling dfs.
class Solution(object):
def floodFill(self, image, sr, sc, newColor):
R, C = len(image), len(image[0])
color = image[sr][sc]
if color == newColor: return image
def dfs(r, c):
if image[r][c] == color:
image[r][c] = newColor
if r >= 1: dfs(r-1, c)
if r+1 < R: dfs(r+1, c)
if c >= 1: dfs(r, c-1)
if c+1 < C: dfs(r, c+1)
dfs(sr, sc)
return image
dr
and dc
, we can split the four adjacent position to if
condition and dfs
each of them.check
array for checking if the pixel have been already checked, to avoid checking again. And I'm curious if it is efficient for decreasing time..?References
https://leetcode.com/problems/flood-fill/solution/