Indeed, this question has been answered many times. However, as I am not allowed to add a comment to an answer due to "too low" reputation, I would like to discuss the solution presented in the most comprehensive answer.
Wouldn't the solution:
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt #Used in the comparison below
im = Image.open('file.png').convert('RGB') #Opens a picture in grayscale
pic = np.array(im)
im.close()
work properly? I am wondering whether unacceptable changes in the quality occur. I have noticed some differences (i.e. black rows at the top in plt.imshow()) when I display the image:
im.show() #Before closing
plt.imshow(pic)
but I don't know whether they are only inevitable consequences of converting to np.array.
PS - If it is important, I would mention that I prepare the image for color quantization (KMeans) and Floyd dithering.
PPS - If you advised me how not to post duplicate question but discuss answers directly - it would be really appreciated.
Try it and see!
from PIL import Image
import numpy as np
# Other answer method
im1 = Image.open('gray.png').convert('L')
im1 = np.stack((im1,)*3, axis=-1)
# Your method
im2 = Image.open('gray.png').convert('RGB')
im2 = np.array(im2)
# Test if identical
print(np.array_equal(im1,im2))
Sample Output
True
I would say the one aspect that is different, is that the method in the other answer will work (insofar as it actually makes a greyscale image where R=G=B) even if the input image is colour, whereas your method will produce a colour image.