将2d数组数据视为定义形状的像素——是否可能创建内部和表面?

Say I have the following array/matrix in python (any language will do, to be honest). A =

假设我在python中有以下数组/矩阵(老实说，任何语言都可以)。一个=

[0 0 0 0 0 0 0 0 0 0
 0 0 1 1 1 1 0 0 0 0
 0 1 1 1 1 1 0 0 0 0
 0 1 1 1 1 1 1 0 0 0
 0 0 1 1 1 1 1 1 0 0
 0 0 1 1 1 1 1 0 0 0
 0 0 1 1 1 1 1 0 0 0
 0 0 0 1 1 1 0 0 0 0
 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0]

Is there any easy way to manipulate entries in this array as if they are pixels. Example, would there be any way to create an interior and a surface of arbitrary length, to get, for example:

是否有简单的方法来操作这个数组中的条目，就好像它们是像素一样。例如，是否有任何方法来创建一个任意长度的内部和表面，例如:

[0 0 0 0 0 0 0 0 0 0
 0 0 1 1 1 1 0 0 0 0
 0 1 1 0 0 1 0 0 0 0
 0 1 1 0 0 0 1 0 0 0
 0 0 1 0 0 0 1 1 0 0
 0 0 1 0 0 0 1 0 0 0
 0 0 1 1 0 1 1 0 0 0
 0 0 0 1 1 1 0 0 0 0
 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0]

Perhaps there is some way to recursively solve this, or maybe in a different way, which would rely on the entries around each element. Any thoughts?

也许有某种方法可以递归地解决这个问题，或者可以用另一种方法，这种方法依赖于每个元素周围的条目。任何想法吗?

2 个解决方案

#1

You can just check the value and the direct neighborhood of every pixel: Your resulting image is 1 where:

只需检查每个像素的值和直接邻域:得到的图像为1，其中:

the original value was 1
初始值是1
and at least one neighbor is 0
至少有一个邻边是0

It is 0 everywhere else. The image below might help to illustrate what I mean:

其他地方都是0。下面的图片可能有助于说明我的意思:

The center pixel of th red block will stay 1, since it is 1 in the original image and has a neighboring 0.
第一个红色块的中心像素将保持为1，因为在原始图像中它是1，并且它的邻边为0。
The center pixel of the blue block will stay 0, since it was 0in the original image.
蓝色块的中心像素将保持为0，因为在原始图像中它是0。
The center pixel of the green block is the interesting case: It was 1 in the original image, but all its neighbors are 1 as well. Thus it becomes 0 in the result.
绿色方块的中心像素是一个有趣的例子:它在原始图像中是1，但是它所有的邻居都是1。结果是0。

Test these conditions for each array entry. Just be careful at the boundaries.

为每个数组条目测试这些条件。在边界上要小心。

If you have access to an image processing library there is another approach you can take: create a copy of your image, errode it by one pixel and subtract it from the original.

如果您可以访问图像处理库，那么您可以采用另一种方法:创建图像的副本，将其删除一个像素，并从原始图像中删除它。

2 个解决方案

#1

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