将大矩阵转换为灰度图像 [英] Turning a Large Matrix into a Grayscale Image

问题描述

我有一个3,076,568个二进制值（1和0）的NumPy数组。我想将其转换为矩阵，然后转换为Python中的灰度图像。

I have a NumPy array of 3,076,568 binary values (1s and 0s). I would like to convert this to a matrix, and then to a grayscale image in Python.

但是，当我尝试将数组重新整形为1,538,284 x 1,538,284矩阵时，我收到内存错误。

However, when I try to reshape the array into a 1,538,284 x 1,538,284 matrix, I get a memory error.

如何减小矩阵的大小，使其变为适合屏幕的图像而不会丢失唯一性/数据？

How can I reduce the size of the matrix so that it will turn into an image that will fit on a screen without losing the uniqueness/data?

此外，我如何将其变成灰度图像？

Furthermore, how would I turn it into a grayscale image?

任何帮助或建议都将不胜感激。谢谢。

Any help or advice would be appreciated. Thank you.

推荐答案

你的二进制值数组是一个字节数组？

Your array of "binary values" is an array of bytes?

如果是这样，您可以在调整大小后使用 Pillow ）：

If so, you can do (using Pillow) after resizing it:

from PIL import Image
im = Image.fromarray(arr)

然后 im.show（）查看它。

如果你的数组只有0和1（1位深度或黑白）你可能需要将它乘以255

If your array has only 0's and 1's (1-bit depth or b/w) you may have to multiply it to 255

im = Image.fromarray(arr * 255)

这是一个例子：

>>> arr = numpy.random.randint(0,256, 100*100) #example of a 1-D array
>>> arr.resize((100,100))
>>> im = Image.fromarray(arr)
>>> im.show()

编辑（2018）：

这个问题是写于2011年，Pillow因为在加载 fromarray 时需要使用 mode ='L'参数而改变了。

This question was written in 2011 and Pillow changed ever since requiring to use the mode='L' parameter when loading with fromarray.

同样在下面的评论中，也说 arr.astype（np.uint8）也需要，但我有未经测试

Also on comments below it was said arr.astype(np.uint8) was needed as well, but I have not tested it

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