I use PIL in order to convert imagse to monochrome and afterwards to a list of lists, but I am not sure how to do so with rgb images.
Can someone give me a direction
Let's start with a known sample image. Here's a small 3x2 one to actually work with and a larger one just so you can see it:
Small:
Large:
You can open an image and make it into an efficient, fast numpy
multi-dimensional array like this:
#!/usr/local/bin/python3
import numpy as np
from PIL import Image
# Open image from disk
im = Image.open('image.png')
na = np.array(im)
That will look like this:
array([[[255, 0, 0], # Red
[ 0, 255, 0], # Green
[ 0, 0, 255]], # Blue
[[ 0, 0, 0], # Black
[255, 255, 255], # White
[126, 126, 126]]], dtype=uint8) # Mid-grey
And convert it back to a PIL Image and save like this (just append this code to the code above):
# Convert array back to Image
resultim = Image.fromarray(na)
resultim.save('result.png')
Some notes:
Note 1
If you expect and want an RGB888 image, and you are opening a PNG image, you may get a palettised image which doesn't have RGB values for each pixel, but instead has an index into a palette for each pixel and everything will go wrong!
By way of example, here is the same image as above but when the generating application saved it as a palettised image:
array([[0, 1, 2],
[3, 4, 5]], dtype=uint8)
And here what is returned from im.getpalette()
:
[255,
0,
0,
0,
255,
0,
0,
0,
255,
0,
0,
0,
255,
255,
255,
126,
126,
126,
...
...
So, the moral of the story is... if you are expecting an RGB888 image, use:
Image.open('image.png').convert('RGB')
Note 2
Likewise, if you open a PNG file that contains transparency, it will have 4 channels, the last being alpha/transparency, and you should call convert('RGB')
if you wish to discard the alpha channel.
Note 3
You can abbreviate the loading and saving into single lines if you don't want the intermediate image:
# Load and make array in one go
na = np.array(Image.open('image.png').convert('RGB'))
# Convert back to PIL Image and save in one go
Image.fromarray(na).save('result.png')
Keywords: Image, image processing, numpy, array, ndarray, PIL, Pillow, Python, Python3, palette, PNG, JPG