Given an m x n picture consisting of black 'B' and white 'W' pixels and an integer target, return the number of black lonely pixels.

A black lonely pixel is a character 'B' that located at a specific position (r, c) where:

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

Example 1:

Input: picture = [["W","B","W","B","B","W"],["W","B","W","B","B","W"],["W","B","W","B","B","W"],["W","W","B","W","B","W"]], target = 3
Output: 6
Explanation: All the green 'B' are the black pixels we need (all 'B's at column 1 and 3).
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 target = 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.

Example 2:

Input: picture = [["W","W","B"],["W","W","B"],["W","W","B"]], target = 1
Output: 0

Constraints:

m == picture.length
n == picture[i].length
1 <= m, n <= 200
picture[i][j] is 'W' or 'B'.
1 <= target <= min(m, n)

Lonely Pixel II

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