Solution: Flood Fill

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Problem Statement

Solution

Code (DFS)

Time Complexity

Space Complexity

Problem Statement

Any image can be represented by a 2D integer array (i.e., a matrix) where each cell represents the pixel value of the image.

Flood fill algorithm takes a starting cell (i.e., a pixel) and a color. The given color is applied to all horizontally and vertically connected cells with the same color as that of the starting cell. Recursively, the algorithm fills cells with the new color until it encounters a cell with a different color than the starting cell.

Given a matrix, a starting cell, and a color, flood fill the matrix.

Example 1:

Input: matrix =

starting cell = (1, 3)
new color = 2

Output:

Example 2:

Input: matrix =

starting cell = (3, 2)

new color = 5

Output:

Constraints:

m == matrix.length
n == - m == matrix[i].length
1 <= m, n <= 50
0 <= - m == matrix[i][j], color < 2<sup>16</sup>
0 <= x < m
0 <= y < n

Solution

The question follows the Island pattern and is quite similar to Number of Islands problem.

From the given starting cell, we can perform a Depth First Search (DFS) or Breadth First Search (BFS) to find all of its connected cells with the same color. During our DFS or BFS traversal, we will update the cells with the new color.

Following is the DFS or BFS traversal of the example-2 mentioned above:

Code (DFS)

Here is what our DFS algorithm will look like:

Python3

. . . .

Time Complexity

The time complexity of the above algorithm will be $O(M*N)$ , where ‘M’ is the number of rows and 'N' is the number of columns of the input matrix. This is because, in the worst case, we might have to fill the whole matrix.

Space Complexity

DFS recursion stack can go $M*N$ deep when we have to fill the whole matrix. Hence, the space complexity will be $O(M*N)$ , where ‘M’ is the number of rows and 'N' is the number of columns of the input matrix.

Mark as Completed

Mitch

· 4 years ago

Show 4 replies

Elaine Michelle Teh

· 4 years ago

Show 3 replies

Thai Minh

· 3 years ago

from collections import deque
class Solution:
  def floodFill(self, matrix, x, y, newColor):
    # TODO: Write your code here
    # BFS solution, we can use visited to mark the visited cell then rewrite it with color
    # for row in range(len(matrix)):
    #   for col in range(len(matrix[0])):
    # self.visitCellBFS(matrix, x, y, newColor)
    self.visitCellDFS(x, y, matrix, matrix[x][y], newColor)
    return matrix
  def visitCellBFS(self, matrix, row, col, newColor):
    neighbors = deque([(row, col)])
    oldColor = matrix[row][col]
    visited_list = [[False for _ in range(len(matrix[0]))] for _ in range(len(matrix))]

    while neighbors:
      row, col = neighbors.popleft()

      if row < 0 or row >= len(matrix) or col < 0 or col >= len(matrix[0]):
        continue

Luis Roel

· 3 years ago

class Solution:
  def floodFill(self, matrix, x, y, newColor):
    startingColor = matrix[x][y]
    seen = set()

    def go(x, y):
      if (x, y) in seen:
        return
      
      if x < 0 or x >= len(matrix):
        return
      if y < 0 or y >= len(matrix[0]):
        return
      
      seen.add((x, y))

      if matrix[x][y] != startingColor:
        return

      matrix[x][y] = newColor

      go(x + 1, y)
      go(x - 1, y)
      go(x, y + 1)
      go(x, y - 1)

    go(x, y)
    
    return matrix

On This Page

Problem Statement

Solution

Code (DFS)

Time Complexity

Space Complexity