LeetCode in Python 533. Lonely Pixel II

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原文链接：http://www.cnblogs.com/lowkeysingsing/p/11196232.html

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#python

本文介绍了一种算法，用于在黑白像素构成的图片中找出符合特定条件的黑色像素：所在行和列各包含N个黑色像素，且所有含有该列黑色像素的行必须与目标行完全相同。

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Given a picture consisting of black and white pixels, and a positive integer N, find the number of black pixels located at some specific row R and column C that align with all the following rules:

Row R and column C both contain exactly N black pixels.
For all rows that have a black pixel at column C, they should be exactly the same as row R

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

Example:

Input:                                            
[['W', 'B', 'W', 'B', 'B', 'W'],    
 ['W', 'B', 'W', 'B', 'B', 'W'],    
 ['W', 'B', 'W', 'B', 'B', 'W'],    
 ['W', 'W', 'B', 'W', 'B', 'W']] 

N = 3
Output: 6
Explanation: All the bold 'B' are the black pixels we need (all 'B's at column 1 and 3).
        0    1    2    3    4    5         column index                                            
0    [['W', 'B', 'W', 'B', 'B', 'W'],    
1     ['W', 'B', 'W', 'B', 'B', 'W'],    
2     ['W', 'B', 'W', 'B', 'B', 'W'],    
3     ['W', 'W', 'B', 'W', 'B', 'W']]    
row index

Take 'B' at row R = 0 and column C = 1 as an example:
Rule 1, row R = 0 and column C = 1 both have exactly N = 3 black pixels. 
Rule 2, the rows have black pixel at column C = 1 are row 0, row 1 and row 2. They are exactly the same as row R = 0.

Note:

1. The range of width and height of the input 2D array is [1,200].

Solution: (未测试)

def lonelyPixel2(self, grid):
    rows, cols = len(grid), len(grid[0])
    rowCountMap = colCount = [0 for i in range(cols)]
    for i in range(rows):
        rowCount = 0
        for j in range(cols):
            if grid[i][j] == 'W': continue
            rowCount += 1
            colCount[j] += 1
        rowCountMap[rowCount] += 1

    count = 0
    for blackCount in colCount:
        count += rowCountMap[count]
    return count