将布尔值numpy数组转换为枕头图像 [英] Convert boolean numpy array to pillow image

问题描述

我目前正在使用scikit-image库在python中进行图像处理.我正在尝试使用sauvola阈值并通过以下代码制作二进制图像:

I'm currently working with image processing in python using the scikit-image library. I'm trying to make a binary image using sauvola thresholding with the following code:

from PIL import Image
import numpy
from skimage.color import rgb2gray
from skimage.filters import threshold_sauvola

im = Image.open("test.jpg")
pix = numpy.array(im)
img = rgb2gray(pix)

window_size = 25
thresh_sauvola = threshold_sauvola(img, window_size=window_size)
binary_sauvola = img > thresh_sauvola

给出以下结果:

输出是一个numpy数组，该图像的数据类型是布尔

the output is a numpy array with data type of this image is a bool

[[ True  True  True ...  True  True  True]
 [ True  True  True ...  True  True  True]
 [ True  True  True ...  True  True  True]
 ...
 [ True  True  True ...  True  True  True]
 [ True  True  True ...  True  True  True]
 [ True  True  True ...  True  True  True]]

问题是我需要使用以下代码行将此数组转换回PIL图像:

The problem is that I need to convert this array back to a PIL image using the following line of code:

image = Image.fromarray(binary_sauvola)

这使图像看起来像这样:

which makes the image look like this:

我还尝试将数据类型从bool更改为uint8，但随后会出现以下异常:

I also tried to change the data type from bool to uint8 but then I'll get the following exception:

AttributeError: 'numpy.ndarray' object has no attribute 'mask'

到目前为止，我还没有找到获得类似于阈值结果的PIL图像的解决方案.

So far I haven't found a solution to get a PIL image which looks like the result of the thresholding.

推荐答案

更新

此错误现已在Pillow == 6.2.0中解决. GitHub上该问题的链接是此处.

如果您无法更新到新版本的Pillow，请参见下文.

If you cannot update to the new version of Pillow, please see below.

PIL的Image.fromarray函数存在模式为"1"图像的错误. 此要点演示了该错误，并给出了一些解决方法.以下是最佳的两种解决方法:

PIL's Image.fromarray function has a bug with mode '1' images. This Gist demonstrates the bug, and shows a few workarounds. Here are the best two workarounds:

import numpy as np
from PIL import Image

# The standard work-around: first convert to greyscale 
def img_grey(data):
    return Image.fromarray(data * 255, mode='L').convert('1')

# Use .frombytes instead of .fromarray. 
# This is >2x faster than img_grey
def img_frombytes(data):
    size = data.shape[::-1]
    databytes = np.packbits(data, axis=1)
    return Image.frombytes(mode='1', size=size, data=databytes)

另请参见将PIL黑白图像转换为Numpy阵列时出错.

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