README.md

661. 图片平滑器

English Version

题目描述

图像平滑器 是大小为 3 x 3 的过滤器，用于对图像的每个单元格平滑处理，平滑处理后单元格的值为该单元格的平均灰度。

每个单元格的  平均灰度 定义为：该单元格自身及其周围的 8 个单元格的平均值，结果需向下取整。（即，需要计算蓝色平滑器中 9 个单元格的平均值）。

如果一个单元格周围存在单元格缺失的情况，则计算平均灰度时不考虑缺失的单元格（即，需要计算红色平滑器中 4 个单元格的平均值）。

给你一个表示图像灰度的 m x n 整数矩阵 img ，返回对图像的每个单元格平滑处理后的图像 。

 

示例 1:

输入:img = [[1,1,1],[1,0,1],[1,1,1]]
输出:[[0, 0, 0],[0, 0, 0], [0, 0, 0]]
解释:
对于点 (0,0), (0,2), (2,0), (2,2): 平均(3/4) = 平均(0.75) = 0
对于点 (0,1), (1,0), (1,2), (2,1): 平均(5/6) = 平均(0.83333333) = 0
对于点 (1,1): 平均(8/9) = 平均(0.88888889) = 0

示例 2:

输入: img = [[100,200,100],[200,50,200],[100,200,100]]
输出: [[137,141,137],[141,138,141],[137,141,137]]
解释:
对于点 (0,0), (0,2), (2,0), (2,2): floor((100+200+200+50)/4) = floor(137.5) = 137
对于点 (0,1), (1,0), (1,2), (2,1): floor((200+200+50+200+100+100)/6) = floor(141.666667) = 141
对于点 (1,1): floor((50+200+200+200+200+100+100+100+100)/9) = floor(138.888889) = 138

 

提示:

• m == img.length
• n == img[i].length
• 1 <= m, n <= 200
• 0 <= img[i][j] <= 255

解法

Python3

class Solution:
    def imageSmoother(self, img: List[List[int]]) -> List[List[int]]:
        m, n = len(img), len(img[0])
        ans = [[0] * n for _ in range(m)]
        for i in range(m):
            for j in range(n):
                s = cnt = 0
                for x in range(i - 1, i + 2):
                    for y in range(j - 1, j + 2):
                        if 0 <= x < m and 0 <= y < n:
                            cnt += 1
                            s += img[x][y]
                ans[i][j] = s // cnt
        return ans

Java

class Solution {
    public int[][] imageSmoother(int[][] img) {
        int m = img.length;
        int n = img[0].length;
        int[][] ans = new int[m][n];
        for (int i = 0; i < m; ++i) {
            for (int j = 0; j < n; ++j) {
                int s = 0;
                int cnt = 0;
                for (int x = i - 1; x <= i + 1; ++x) {
                    for (int y = j - 1; y <= j + 1; ++y) {
                        if (x >= 0 && x < m && y >= 0 && y < n) {
                            ++cnt;
                            s += img[x][y];
                        }
                    }
                }
                ans[i][j] = s / cnt;
            }
        }
        return ans;
    }
}

TypeScript

function imageSmoother(img: number[][]): number[][] {
    const m = img.length;
    const n = img[0].length;
    const locations = [
        [-1, -1],
        [-1, 0],
        [-1, 1],
        [0, -1],
        [0, 0],
        [0, 1],
        [1, -1],
        [1, 0],
        [1, 1],
    ];

    const res = [];
    for (let i = 0; i < m; i++) {
        res.push([]);
        for (let j = 0; j < n; j++) {
            let sum = 0;
            let count = 0;
            for (const [y, x] of locations) {
                if ((img[i + y] || [])[j + x] != null) {
                    sum += img[i + y][j + x];
                    count++;
                }
            }
            res[i].push(Math.floor(sum / count));
        }
    }
    return res;
}

Rust

impl Solution {
    pub fn image_smoother(img: Vec<Vec<i32>>) -> Vec<Vec<i32>> {
        let m = img.len();
        let n = img[0].len();
        let locations = [
            [-1, -1],
            [-1, 0],
            [-1, 1],
            [0, -1],
            [0, 0],
            [0, 1],
            [1, -1],
            [1, 0],
            [1, 1],
        ];

        let mut res = vec![];
        for i in 0..m {
            res.push(vec![]);
            for j in 0..n {
                let mut sum = 0;
                let mut count = 0;
                for [y, x] in locations.iter() {
                    let i = i as i32 + y;
                    let j = j as i32 + x;
                    if i < 0 || i == m as i32 || j < 0 || j == n as i32 {
                        continue;
                    }
                    count += 1;
                    sum += img[i as usize][j as usize];
                }
                res[i].push(sum / count);
            }
        }
        res
    }
}

C++

class Solution {
public:
    vector<vector<int>> imageSmoother(vector<vector<int>>& img) {
        int m = img.size(), n = img[0].size();
        vector<vector<int>> ans(m, vector<int>(n));
        for (int i = 0; i < m; ++i) {
            for (int j = 0; j < n; ++j) {
                int s = 0, cnt = 0;
                for (int x = i - 1; x <= i + 1; ++x) {
                    for (int y = j - 1; y <= j + 1; ++y) {
                        if (x < 0 || x >= m || y < 0 || y >= n) continue;
                        ++cnt;
                        s += img[x][y];
                    }
                }
                ans[i][j] = s / cnt;
            }
        }
        return ans;
    }
};

Go

func imageSmoother(img [][]int) [][]int {
	m, n := len(img), len(img[0])
	ans := make([][]int, m)
	for i, row := range img {
		ans[i] = make([]int, n)
		for j := range row {
			s, cnt := 0, 0
			for x := i - 1; x <= i+1; x++ {
				for y := j - 1; y <= j+1; y++ {
					if x >= 0 && x < m && y >= 0 && y < n {
						cnt++
						s += img[x][y]
					}
				}
			}
			ans[i][j] = s / cnt
		}
	}
	return ans
}

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