Flood Fill
Implement a flood fill algorithm on a given 2D grid, starting from a specified position (sr,sc), to replace all connected pixels with the same original value with a new value. The connection between pixels is defined by 4-connectivity, where two pixels are considered connected if they are adjacent horizontally or vertically.
The flood fill operation is a fundamental concept in image segmentation, which is crucial in computer vision for separating objects or regions of interest from the rest of the image. This process can be viewed as a graph traversal problem, where each pixel represents a node, and the connections between them are edges. The goal is to traverse the graph, starting from a given node, and update all connected nodes that share the same original value.
Here are the steps to achieve this:
- Identify the starting position (sr,sc) and the new value.
- Determine the original value of the starting pixel.
- Check if the starting pixel already has the new value.
- Traverse the grid, updating all connected pixels with the same original value.
This technique is widely used in image editing software.
Example:
grid = [[1, 1, 1], [1, 1, 0], [1, 0, 1]] sr = 1, sc = 1, new_val = 2
[[2, 2, 2], [2, 2, 0], [2, 0, 1]]
- The algorithm starts at the given position (sr, sc) = (1, 1) and checks its value, which is 1.
- Since the new value (2) is different from the current value (1), it replaces the current value with the new value and recursively checks adjacent pixels (up, down, left, right) that have the same original value (1).
- The adjacent pixels to (1, 1) that have the value 1 are (0, 0), (0, 1), (0, 2), (1, 0), (1, 2), and (2, 1), and (2, 0) does not have the value 1 but (2, 1) does, so it replaces their values with 2.
- The final output is [[2, 2, 2], [2, 2, 0], [2, 0, 1]] after all connected pixels with the original value 1 have been replaced with the new value 2.
Constraints:
- grid is a 2D list of integers
- (sr, sc) is a valid position in the grid
- new_val is an integer
- Use 4-connectivity (up, down, left, right)
- Return the modified grid
Background Knowledge
The "Flood Fill" problem is a classic example of Image Segmentation, a fundamental concept in Computer Vision. Image Segmentation is the process of dividing an image into its constituent parts or objects. In this case, we're dealing with a 2D grid, which can be thought of as a simplified representation of an image. The grid is composed of pixels, each with a specific value. The goal of flood fill is to identify and fill a connected region of pixels with the same value, starting from a given position.
The concept of connectivity is crucial in this problem. Two pixels are considered connected if they are adjacent horizontally or vertically (not diagonally). This is known as 4-connectivity, as each pixel can have up to 4 connected neighbors (up, down, left, and right). Understanding connectivity is essential to traversing the grid and identifying the connected region.
The flood fill algorithm is commonly used in various applications, such as image editing, where it's used to fill a selected region with a new color. It's also used in computer-aided design (CAD) software, where it's used to fill enclosed regions with a specific material or texture. The algorithm's ability to traverse and fill connected regions makes it a fundamental tool in many computer vision and graphics applications.
Algorithm/Approach
The general approach to solving this problem involves using a depth-first search (DFS) or breadth-first search (BFS) algorithm. Both algorithms are suitable for traversing the grid and identifying the connected region. The choice between DFS and BFS depends on the specific implementation and the desired order of traversal. In this case, either algorithm can be used to achieve the desired result.
Continue the full explanation
You're reading the free preview. Unlock the complete walkthrough, the code editor, test runner and reference solution with Premium.
Editor locked
The code editor is locked for Pro problems. It is only available for free problems. Please upgrade to gain access to the code editor for all problems.