将1D字节数组转换为2D numpy数组的最快方法 [英] Quickest way to convert 1D byte array to 2D numpy array

问题描述

我有一个我可以这样处理的数组：

I've got an array that I can process like this:

ba = bytearray(fh.read())[32:]
size = int(math.sqrt(len(ba)))

我可以判断一个像素应该是黑色还是白色给定

I can tell if a pixel should be black or white given

iswhite = (ba[i]&1)==1

如何快速将我的1D字节数组转换为行长度<$ c的2D numpy数组$ c>尺寸和白色像素为（ba [i]& 1）== 1 ，黑色为其他人？我创建这样的数组：

How can I quickly convert my 1D byte array into a 2D numpy array with row length size and white pixels for (ba[i]&1)==1 and black for others? I create the array like this:

im_m = np.zeros((size,size,3),dtype="uint8)

推荐答案

import numpy as np

# fh containts the file handle

# go to position 32 where the image data starts
fh.seek(32)

# read the binary data into unsigned 8-bit array
ba = np.fromfile(fh, dtype='uint8')

# calculate the side length of the square array and reshape ba accordingly
side = int(np.sqrt(len(ba)))
ba = ba.reshape((side,side))

# toss everything else apart from the last bit of each pixel
ba &= 1

# make a 3-deep array with 255,255,255 or 0,0,0
img = np.dstack([255*ba]*3)
# or
img = ba[:,:,None] * np.array([255,255,255], dtype='uint8')

有几种方法可以完成最后一步。请注意你得到相同的数据类型（ uint8 ）如果你需要它。

There are several ways to do the last step. Just be careful you get the same data type (uint8) if you need it.

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