如何将布尔数据类型的numpy数组索引为True？ [英] How do I index a numpy array of zeroes with a boolean datatype to True?

问题描述

所以我正在重新创建他们去年制作的Matlab项目，其中一部分涉及创建拉出RGB波段的蒙版。
他们用一系列逻辑零来做到这一点。

So I'm recreating a Matlab project they made last year, part of which involves creating mask that pull out the RGB bands. They did this by an array of logical zeroes.

GMask_Whole = false(ROWS,COLS);

我将其重建为numpy数组。

which I reconstructed as a numpy array.

self.green_mask_whole=np.zeros((self.rows, self.columns), dtype=bool)

下一部分，我不能为我的生活找出如何处理numpy：

The next part I can't for the life of me figure out how to do with numpy:

GMask_Whole(1:2:end,2:2:end) = true;

我还没有找到一个numpy等效动作。任何想法？

I've yet to find a numpy equivalent action. any Ideas?

btw，如果你对这是做什么感到好奇：
https://en.wikipedia.org/wiki/Bayer_filter

btw, if your curious about what this is doing: https://en.wikipedia.org/wiki/Bayer_filter

编辑：
我的事情尝试过：

edit: things I've tried:

wut(1:3:end, 1:2:end) = true
wut([1:3:end], [1:2:end]) = true
wut([1:3], [1:2]) = true
wut([1:3], [1:2]) = True
wut(slice(1:3), slice(1:2)) = True

推荐答案

numpy 可以在Matlab中做更多或更少的切片，但是synax有点不同。在 numpy 中，订单是 [begin：end：step] ，并且可以同时保留开始，结束和步骤为空，这将为他们提供默认值第一个元素，最后一个元素和步长1 。

numpy can do slicing more or less as in Matlab, but the synax is a little bit different. In numpy, the order is [begin:end:step] and it is possible to leave both begin, end and step empty, which will give them their default values first element, last element and step size 1 respectively.

此外，`numpy '有一个很好的'广泛投射'系统，允许重复单个值（或行/列）来创建一个与另一个相同大小的新数组。这样就可以为整个数组赋值。

Further, `numpy´ has a nice system of 'broad casting' which allows a single value (or row/column) be repeated to make a new array of the same size as another. This makes it possible to assign a single value to a whole array.

因此，在当前情况下，可以做

Thus, in the current case, it is possible to do

self.green_mask_whole=np.zeros((self.rows, self.columns), dtype=bool)
self.green_mask_whole[::2,1::2] = True

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