Given a picture consisting of black and white pixels, find the number of black lonely pixels.

The picture is represented by a 2D char array consisting of 'B' and 'W', which means black and white pixels respectively.

A black lonely pixel is character 'B' that located at a specific position where the same row and same column don't have any other black pixels.

Example:

Input:
[['W', 'W', 'B'],
['W', 'B', 'W'],
['B', 'W', 'W']] Output: 3
Explanation: All the three 'B's are black lonely pixels. 

Note:

  1. The range of width and height of the input 2D array is [1,500].

给一个只含有黑白像素的图片,找出黑色孤独像素的数量。黑色孤独像素是这个像素所在的行和列都不含有黑色像素。

最基本的想法就是找出每一个黑色像素,然后对相应的行和列进行检查,看是否含有黑色像素。但这种方法肯定含有重复操作,效率肯定不高。

解法:利用数组rows,cols分别记录某行、某列'B'像素的个数。然后遍历一次picture找到符合条件的。

Java:

public int findLonelyPixel(char[][] picture) {
int n = picture.length, m = picture[0].length; int[] rowCount = new int[n], colCount = new int[m];
for (int i=0;i<n;i++)
for (int j=0;j<m;j++)
if (picture[i][j] == 'B') { rowCount[i]++; colCount[j]++; } int count = 0;
for (int i=0;i<n;i++)
for (int j=0;j<m;j++)
if (picture[i][j] == 'B' && rowCount[i] == 1 && colCount[j] == 1) count++; return count;
}

Java: DFS

public int findLonelyPixel(char[][] picture) {
int numLone = 0;
for (int row = 0; row < picture.length; row++) {
for (int col = 0; col < picture[row].length; col++) {
if (picture[row][col] == 'W') {
continue;
}
if (dfs(picture, row - 1, col, new int[] {-1, 0}) && dfs(picture, row + 1, col, new int[] {1, 0})
&& dfs(picture, row, col - 1, new int[] {0, -1}) && dfs(picture, row, col + 1, new int[] {0, 1})) {
numLone++;
}
}
}
return numLone;
} // use dfs to find if current pixel is lonely
private boolean dfs(char[][] picture, int row, int col, int[] increase) {
// base case
if (row < 0 || row >= picture.length || col < 0 || col >= picture[0].length) {
return true;
} else if (picture[row][col] == 'B') {
return false;
}
// recursion
return dfs(picture, row + increase[0], col + increase[1], increase);
}    

Python:

# Time:  O(m * n)
# Space: O(m + n)
class Solution(object):
def findLonelyPixel(self, picture):
"""
:type picture: List[List[str]]
:rtype: int
"""
rows, cols = [0] * len(picture), [0] * len(picture[0])
for i in xrange(len(picture)):
for j in xrange(len(picture[0])):
if picture[i][j] == 'B':
rows[i] += 1
cols[j] += 1 result = 0
for i in xrange(len(picture)):
if rows[i] == 1:
for j in xrange(len(picture[0])):
result += picture[i][j] == 'B' and cols[j] == 1
return result

Python:

class Solution(object):
def findLonelyPixel(self, picture):
"""
:type picture: List[List[str]]
:type N: int
:rtype: int
"""
return sum(col.count('B') == 1 == picture[col.index('B')].count('B') \
for col in zip(*picture))

Python:

class Solution(object):
def findLonelyPixel(self, picture):
"""
:type picture: List[List[str]]
:rtype: int
"""
w, h = len(picture), len(picture[0])
rows, cols = [0] * w, [0] * h
for x in range(w):
for y in range(h):
if picture[x][y] == 'B':
rows[x] += 1
cols[y] += 1
ans = 0
for x in range(w):
for y in range(h):
if picture[x][y] == 'B':
if rows[x] == 1:
if cols[y] == 1:
ans += 1
return ans  

Python:

class Solution(object):
def findLonelyPixel(self, picture):
""" :type picture: List[List[str]] :rtype: int """
row = self.find_row(picture)
column =self.find_colum(picture) result = 0
for x in row:
for y in column:
if picture[x][y] == "B":
result += 1
return result def find_row(self, picture):
result = []
for x in xrange(len(picture)):
num = 0
for y in xrange(len(picture[x])):
if picture[x][y] == "B":
num += 1
if num == 1:
result.append(x)
return result def find_colum(self, picture):
result = []
for y in xrange(len(picture[0])):
num = 0
for x in xrange(len(picture)):
if picture[x][y] == "B":
num += 1
if num == 1:
result.append(y)
return result  

C++:

class Solution {
public:
int findLonelyPixel(vector<vector<char>>& picture) {
vector<int> rows = vector<int>(picture.size());
vector<int> cols = vector<int>(picture[0].size()); for (int i = 0; i < picture.size(); ++i) {
for (int j = 0; j < picture[0].size(); ++j) {
rows[i] += picture[i][j] == 'B';
cols[j] += picture[i][j] == 'B';
}
} int result = 0;
for (int i = 0; i < picture.size(); ++i) {
if (rows[i] == 1) {
for (int j = 0; j < picture[0].size() && rows[i] > 0; ++j) {
result += picture[i][j] == 'B' && cols[j] == 1;
}
}
}
return result;
}
};

C++:

class Solution {
public:
int findLonelyPixel(vector<vector<char>>& picture) {
if (picture.empty() || picture[0].empty()) return 0;
int m = picture.size(), n = picture[0].size(), res = 0;
vector<int> rowCnt(m, 0), colCnt(n, 0);
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j) {
if (picture[i][j] == 'B') {
++rowCnt[i];
++colCnt[j];
}
}
}
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j) {
if (picture[i][j] == 'B') {
if (rowCnt[i] == 1 && colCnt[j] == 1) {
++res;
}
}
}
}
return res;
}
};

  

  

类似题目:

[LeetCode] 533. Lonely Pixel II 孤独的像素 II  

 

All LeetCode Questions List 题目汇总

 

[LeetCode] 531. Lonely Pixel I 孤独的像素 I的更多相关文章

  1. [LeetCode] 533. Lonely Pixel II 孤独的像素 II

    Given a picture consisting of black and white pixels, and a positive integer N, find the number of b ...

  2. [LeetCode] Lonely Pixel II 孤独的像素之二

    Given a picture consisting of black and white pixels, and a positive integer N, find the number of b ...

  3. [LeetCode] Lonely Pixel I 孤独的像素之一

    Given a picture consisting of black and white pixels, find the number of black lonely pixels. The pi ...

  4. LeetCode 531. Lonely Pixel I

    原题链接在这里:https://leetcode.com/problems/lonely-pixel-i/ 题目: Given a picture consisting of black and wh ...

  5. LeetCode 531. Longly Pixel I (孤独的像素之一) $

    Given a picture consisting of black and white pixels, find the number of black lonely pixels. The pi ...

  6. 531. Lonely Pixel I

    Given a picture consisting of black and white pixels, find the number of black lonely pixels. The pi ...

  7. LeetCode 533. Lonely Pixel II (孤独的像素之二) $

    Given a picture consisting of black and white pixels, and a positive integer N, find the number of b ...

  8. 像素 PIXEL 图片的基本单位 像素非常小 图片是成千上万的像素组成 显示/屏幕分辨率 (DPI 屏幕分辨率)

    像素 PIXEL 图片的基本单位 像素非常小 图片是成千上万的像素组成 显示/屏幕分辨率 (DPI 屏幕分辨率) 图像分辨率 (PPI) 1920*1080是像素点长度1920个像素点 X1080个像 ...

  9. 533. Lonely Pixel II

    Given a picture consisting of black and white pixels, and a positive integer N, find the number of b ...

随机推荐

  1. Codeforces D. Intercity Travelling(区间组合)

    题目描述: D. Intercity Travelling time limit per test 1.5 seconds memory limit per test 256 megabytes in ...

  2. 有意义的单词分割——经典dfs题目

    680. 分割字符串 中文 English 给一个字符串,你可以选择在一个字符或两个相邻字符之后拆分字符串,使字符串由仅一个字符或两个字符组成,输出所有可能的结果 样例 样例1 输入: "1 ...

  3. Kotlin反射操纵构造方法与伴生对象

    反射操纵伴生对象: 先定义一个伴生对象: 然后咱们通过反射来调用一下它: 比较简单. 反射操纵构造方法: 先来定义一个类: 然后咱们通过反射来调用一个其中的方法,之前当然就得先来调用构造方法,由于我们 ...

  4. Beta冲刺(2/7)——2019.5.23

    所属课程 软件工程1916|W(福州大学) 作业要求 Beta冲刺(2/7)--2019.5.23 团队名称 待就业六人组 1.团队信息 团队名称:待就业六人组 团队描述:同舟共济扬帆起,乘风破浪万里 ...

  5. 为什么需要 Redis 哨兵?

    在说哨兵之前,我们先说下主从复制,Redis 的主从复制模式,一旦主节点出现故障无法提供服务,需要人工介入手工将从节点调整为主节点,同时应用端还需要修改新的主节点地址,这种故障转移的方式对于很多应用场 ...

  6. c#中的继承学习总结

    c#的继承方法,大体上和c++的类似,但是有点区别的,我这里刚刚初学,因此把重点记录下. 1.派生类继承了父类,那么,如果父类的方法和数据都是public,那么派生类都会继承.派生类可以直接调用父类的 ...

  7. 微信小程序——获取当天的前一个月至后一个月

    看标题也不知道你有没有明白我想表达的意思,先上个动态图吧~ 需要分析: 1.获取当前日期的前一个月,后一个月和当月.比如说现在是7月5号,我需要得到6月5号至8月5号的日期,同时还要返回当前的星期. ...

  8. LeetCode 1048. Longest String Chain

    原题链接在这里:https://leetcode.com/problems/longest-string-chain/ 题目: Given a list of words, each word con ...

  9. proxysql 学习一 proxysql docker 运行试用

    proxysql 是一个比较强大的mysql proxy 服务,支持动态mysql 实例调整,查询重写,查询cache,监控,数据镜像,读写分离 以及ha,最近已经发布了2.0 ,很值得试用下 环境准 ...

  10. 在触发器中使用{ITEM.LASTVALUE}时在首页问题栏信息显示不全

    在触发器中使用了系统宏变量,当条件满足时,如果这个宏代表的内容超过了20个字符,那么在首页信息就显示不全,会有一堆省略号 感谢https://blog.csdn.net/yu415907917/art ...