Yandex

Binary TreesBinary Trees36

  1. 1Preorder Traversal of a Binary Tree using Recursion
  2. 2Preorder Traversal of a Binary Tree using Iteration
  3. 3Postorder Traversal of a Binary Tree Using Recursion
  4. 4Postorder Traversal of a Binary Tree using Iteration
  5. 5Level Order Traversal of a Binary Tree using Recursion
  6. 6Level Order Traversal of a Binary Tree using Iteration
  7. 7Reverse Level Order Traversal of a Binary Tree using Iteration
  8. 8Reverse Level Order Traversal of a Binary Tree using Recursion
  9. 9Find Height of a Binary Tree
  10. 10Find Diameter of a Binary Tree
  11. 11Find Mirror of a Binary Tree - Todo
  12. 12Inorder Traversal of a Binary Tree using Recursion
  13. 13Inorder Traversal of a Binary Tree using Iteration
  14. 14Left View of a Binary Tree
  15. 15Right View of a Binary Tree
  16. 16Top View of a Binary Tree
  17. 17Bottom View of a Binary Tree
  18. 18Zigzag Traversal of a Binary Tree
  19. 19Check if a Binary Tree is Balanced
  20. 20Diagonal Traversal of a Binary Tree
  21. 21Boundary Traversal of a Binary Tree
  22. 22Construct a Binary Tree from a String with Bracket Representation
  23. 23Convert a Binary Tree into a Doubly Linked List
  24. 24Convert a Binary Tree into a Sum Tree
  25. 25Find Minimum Swaps Required to Convert a Binary Tree into a BST
  26. 26Check if a Binary Tree is a Sum Tree
  27. 27Check if All Leaf Nodes are at the Same Level in a Binary Tree
  28. 28Lowest Common Ancestor (LCA) in a Binary Tree
  29. 29Solve the Tree Isomorphism Problem
  30. 30Check if a Binary Tree Contains Duplicate Subtrees of Size 2 or More
  31. 31Check if Two Binary Trees are Mirror Images
  32. 32Calculate the Sum of Nodes on the Longest Path from Root to Leaf in a Binary Tree
  33. 33Print All Paths in a Binary Tree with a Given Sum
  34. 34Find the Distance Between Two Nodes in a Binary Tree
  35. 35Find the kth Ancestor of a Node in a Binary Tree
  36. 36Find All Duplicate Subtrees in a Binary Tree

GraphsGraphs46

  1. 1Breadth-First Search in Graphs
  2. 2Depth-First Search in Graphs
  3. 3Number of Provinces in an Undirected Graph
  4. 4Connected Components in a Matrix
  5. 5Rotten Oranges Problem - BFS in Matrix
  6. 6Flood Fill Algorithm - Graph Based
  7. 7Detect Cycle in an Undirected Graph using DFS
  8. 8Detect Cycle in an Undirected Graph using BFS
  9. 9Distance of Nearest Cell Having 1 - Grid BFS
  10. 10Surrounded Regions in Matrix using Graph Traversal
  11. 11Number of Enclaves in Grid
  12. 12Word Ladder - Shortest Transformation using Graph
  13. 13Word Ladder II - All Shortest Transformation Sequences
  14. 14Number of Distinct Islands using DFS
  15. 15Check if a Graph is Bipartite using DFS
  16. 16Topological Sort Using DFS
  17. 17Topological Sort using Kahn's Algorithm
  18. 18Cycle Detection in Directed Graph using BFS
  19. 19Course Schedule - Task Ordering with Prerequisites
  20. 20Course Schedule 2 - Task Ordering Using Topological Sort
  21. 21Find Eventual Safe States in a Directed Graph
  22. 22Alien Dictionary Character Order
  23. 23Shortest Path in Undirected Graph with Unit Distance
  24. 24Shortest Path in DAG using Topological Sort
  25. 25Dijkstra's Algorithm Using Set - Shortest Path in Graph
  26. 26Dijkstra’s Algorithm Using Priority Queue
  27. 27Shortest Distance in a Binary Maze using BFS
  28. 28Path With Minimum Effort in Grid using Graphs
  29. 29Cheapest Flights Within K Stops - Graph Problem
  30. 30Number of Ways to Reach Destination in Shortest Time - Graph Problem
  31. 31Minimum Multiplications to Reach End - Graph BFS
  32. 32Bellman-Ford Algorithm for Shortest Paths
  33. 33Floyd Warshall Algorithm for All-Pairs Shortest Path
  34. 34Find the City With the Fewest Reachable Neighbours
  35. 35Minimum Spanning Tree in Graphs
  36. 36Prim's Algorithm for Minimum Spanning Tree
  37. 37Disjoint Set (Union-Find) with Union by Rank and Path Compression
  38. 38Kruskal's Algorithm - Minimum Spanning Tree
  39. 39Minimum Operations to Make Network Connected
  40. 40Most Stones Removed with Same Row or Column
  41. 41Accounts Merge Problem using Disjoint Set Union
  42. 42Number of Islands II - Online Queries using DSU
  43. 43Making a Large Island Using DSU
  44. 44Bridges in Graph using Tarjan's Algorithm
  45. 45Articulation Points in Graphs
  46. 46Strongly Connected Components using Kosaraju's Algorithm

TriesTries1

Flood Fill
Graph-Based Approach



Problem Statement

The Flood Fill problem is a classic image processing task. Given a 2D array image where each element represents the color of a pixel, a starting pixel coordinate (sr, sc), and a newColor, your task is to 'flood fill' the area connected to the starting pixel.

A pixel is considered 'connected' if it has the same color as the starting pixel and is connected 4-directionally (up, down, left, or right).

Replace all such connected pixels with newColor.

Examples

Image Start (sr, sc) newColor Output
[[1,1,1],[1,1,0],[1,0,1]] (1, 1) 2 [[2,2,2],[2,2,0],[2,0,1]]
[[0,0,0],[0,1,1]] (1, 1) 1 [[0,0,0],[0,1,1]]
[[1]] (0, 0) 1 [[1]]
[[0,0],[0,0]] (0, 0) 3 [[3,3],[3,3]]
[] (0, 0) 1 []

Solution

Understanding the Flood Fill Problem

The flood fill algorithm works similarly to how a paint bucket tool works in image editing software. Given a 2D array (image), a starting pixel, and a new color, it replaces the color of the starting pixel and all adjacent pixels (up/down/left/right) that share the same original color with the new color.

Case 1: Base Case - No Fill Needed

If the starting pixel's color is already equal to the new color, no changes are needed. This is an important check to avoid infinite loops during recursion or unnecessary processing in iterative traversal.

Case 2: Small Connected Region

When the starting pixel is part of a small isolated region with a unique color, the algorithm will only modify that small region. Both DFS and BFS work efficiently here, visiting a handful of connected nodes.

Case 3: Large Connected Area

If the connected region is large (e.g., a full row or the entire image), the algorithm will traverse every pixel in that region. The graph-based approach (DFS or BFS) will visit each of those connected pixels and change their color.

Case 4: Edge and Corner Pixels

When starting from the edge or corner of the image, the algorithm behaves the same, but care must be taken to avoid accessing pixels outside the image boundary. The 4-directional neighbors must be validated to stay within bounds.

DFS vs BFS: Which One?

Both DFS (using a stack or recursion) and BFS (using a queue) are valid. DFS may use less space for shallow fills, but recursion could lead to stack overflow for very large regions. BFS uses a queue and is safer for large datasets but might consume more memory.

Algorithm Steps

  1. Store the original color of the pixel at (sr, sc).
  2. If original color is same as newColor, return the image.
  3. Use BFS or DFS to explore from (sr, sc):
    1. Push starting pixel to the stack or queue.
    2. While the stack/queue is not empty:
      1. Pop a pixel and change its color to newColor.
      2. Push its 4-directional neighbors if they have the original color.
  4. Return the modified image.

Code

JavaScript
function floodFill(image, sr, sc, newColor) {
  const originalColor = image[sr][sc];
  if (originalColor === newColor) return image;

  const rows = image.length;
  const cols = image[0].length;
  const queue = [[sr, sc]];

  const directions = [[0, 1], [1, 0], [0, -1], [-1, 0]];
  image[sr][sc] = newColor;

  while (queue.length > 0) {
    const [r, c] = queue.shift();
    for (const [dr, dc] of directions) {
      const nr = r + dr, nc = c + dc;
      if (nr >= 0 && nr < rows && nc >= 0 && nc < cols && image[nr][nc] === originalColor) {
        image[nr][nc] = newColor;
        queue.push([nr, nc]);
      }
    }
  }
  return image;
}

console.log(floodFill([[1,1,1],[1,1,0],[1,0,1]], 1, 1, 2));

Time Complexity

CaseTime ComplexityExplanation
Best CaseO(1)If the starting pixel's color is already the new color, the algorithm returns immediately without modifying the image.
Average CaseO(m × n)In most practical scenarios, a portion of the image is filled, and the algorithm visits those connected pixels once.
Worst CaseO(m × n)If the entire image consists of the same color, the algorithm may visit every pixel, making it linear in the size of the image.

Space Complexity

O(m × n)

Explanation: In the worst case, the stack or queue can hold all pixels of the image if they are all connected and need to be processed.



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