Problem on converting gray level image to binary image using Python

Question

I am completely new to Python. I took a color image. Then converted it into gray scale image. So far it was good. But when I tried to convert this gray scale to binary image, I got yellow and purple color image instead of white and black.

import matplotlib.pyplot as plt
import numpy as np

painting=plt.imread("ff.jpg")
print(painting.shape)
print("The image consists of %i pixels" % (painting.shape[0] * painting.shape[1]))
plt.imshow(painting);
#This displayed by color image perfectly

from skimage import data, io, color
painting_gray = color.rgb2gray(painting)
print(painting_gray)
io.imshow(painting_gray)
print("This is the Gray scale version of the original image")
#This displayed my gray scale image perfectly

#Now comes the code for binary image:
num = np.array(painting_gray)
print(num)
bin= num >0.5
bin.astype(np.int)
plt.imshow(bin)
#The display showed an image with yellow and purple color instead of white and black

The image plots I have obtained are:

The gray scale image:

The binary image that I got:

Please help me to get black and white binary image.

Answer 1

It is because imshow uses a default colormap, called viridis. The pixel color is selected from this color map, using the scale of the pixel values from min(img) to max(img).

There more than one way to deal with this:

Specify color map in imshow:

   plt.imshow(bin, cmap='gray', vmin=0, vmax=255)

Select color map:

   plt.gray() 

Another way to select color map:

   plt.colormap('gray')

Side note: bin is a function used for converting binary numbers, so using it as a variable name can potentially cause problems in your code.

Answer 2

Try:

from skimage.filters import threshold_otsu
thresh = threshold_otsu(painting_gray)
binary = painting_gray> thresh

Read more here: https://scikit-image.org/docs/stable/auto_examples/applications/plot_thresholding.html https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_thresholding.html